Development and Characterization of Improved Red
Fluorescent Protein VariantsbyPremashis Manna
M.Sc., Indian Institute of Technology Kanpur, 2012

A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillmentof the requirements for the degree ofDoctor of Philosophy
Department of Chemistry & Biochemistry
2017

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This thesis entitled:
Development and Characterization of Improved Red Fluorescent Protein Variants
written by Premashis Manna
has been approved for the Department of Chemistry & BiochemistryProf. Ralph Jimenez

Prof. Amy E. Palmer

Date

The final copy of this thesis has been examined by the signatories, and we find that both thecontent and the form meet acceptable presentation standards of scholarly work in the above
mentioned discipline.

iii
Manna, Premashis (Ph.D., Chemistry & Biochemistry)
Development and Characterization of Improved Red Fluorescent Protein Variants
Thesis directed by Prof. Ralph Jimenez

Aequorea victoria-based green fluorescent proteins and their blue, cyan and red counterparts offer unprecedented advantage as biological markers owing to their genetic encodability andstraightforward expression in different organisms. Fluorescent proteins are characterized with complex photo-kinetics due to the presence of light-induced non-fluorescent or dark states which areresponsible for their fluorescence intermittency or ‘blinking’. We developed time- and frequencydomain techniques for probing the kinetics involving dark state conversion (DSC) and ground state
recovery (GSR) in red fluorescent proteins (RFPs). Ensemble-level DSC and GSR kinetics in FPs
were presented in the context of their single molecule fluorescence behaviors. Although significant
advancements have been made towards improving the key photo-physical properties of the red
fluorescent proteins (RFPs), they continue to perform sub-optimally compared to their green or
cyan counterparts. We developed a high-throughput microfluidic screening and sorting platform togenerate improved RFP variants by engineering their excited state lifetimes. Using this microfluidic
system, mCherry and FusionRed mutants were developed with higher in-vivo brightness and fusion
efficiency. We employed a novel rational design for the enhancement of brightness in RFPs throughengineering of their radiative rates and extinction coefficients. This enabled us to produce brightmutants those out-performed the existing red fluorescent proteins.

Dedication

To Ma, Baba, Ritu&
all the beautiful corals and jellyfishes

v

Acknowledgements

In Bengali, there is a famous poem by Sunirmal Basu, titled as ‘I am a Learner of Nature’.
In that poem, the poet describes how the vastness of sky teaches him to be open-minded, thepowerful wind inspires him to be industrious etc. and how all of Nature helps him towards anall-round development. Similarly, at my graduate school, I have learned different qualities from adiverse range of friends, colleagues, teachers, advisors without whom I would not be what I am
today.
Special thanks to Prof. Amitava Bagchi for encouraging me to stay in the field of Chemistry. I
acknowledge the contributions of Nabakumar Bera, SS Riaz, Debasis Jana for developing my solidbackground in physical chemistry and specially introducing me to the Jablonski diagram which
helped me a lot to perform some of the work presented here. Thanks to Prof. Madhav Ranganathan
for teaching me to solve chemical and biological problems with advanced mathematics. I am deeply
indebted to Prof. Nisanth N. Nair, Prof. Pratik Sen, Prof. S. Manogaran for teaching me thefundamentals of quantum mechanics, fluorescence spectroscopy and NMR.
I am really fortunate to have Dr. Ralph Jimenez as my thesis advisor. I recognize his endlesssupport and encouragement in pursuing new ideas, grooming me for the scientific world and teachingme: “Science is not a sprint. It’s a marathon”. Thanks to Prof. Amy Palmer for guiding our RFP
development project in proper direction and answering my stupid questions regarding biology withpatience. I have been lucky to work with many talented post-doctoral researchers in my lab.
Kevin Dean helped me in my early days at the graduate school introducing me to the fluorescentproteins and the microfluidics. Special thanks to Prof. Lloyd Davis for educating me with laser

vialignment and optical trap. Felix Vietmeyer was very helpful building different electronics thosewere invaluable in my project. Sheng Ting Hung continues to be my friend, philosopher and guide.
Pia Friis tolerated our endless demands of protein purifications and library constructions. I am
thankful to Brett Fiedler for he patiently answered all of my technical questions and gave invaluabletips for the grad school. Special thanks to Sheng Ting Hung, Samantha Allen, Srijit Mukherjee,Richard Erickson, Emma Simmerman, Josh Slocum for helping me professionally and emotionallywith scientific inputs, proof-reading this thesis and saying ‘hi’, day in and day out. Thanks toDavid Simpson & Maria Lo for handling the mammalian cells, Gary Mo & Prof. Jin Zhang for a
fruitful collaboration. JILA is a wonderful place with all the people very cooperative and helping
each others. I recognize the contribution of the staffs working in the Electronic shop and Machine
shop at JILA in achieving my doctoral degree.
During my graduate research I got continuous mental and emotional supports from my father,mother and other family members. Special mention to my elder brother, Raj Kumar Manna, as
he was always ready to help me when needed, be it with mental guidance or LaTeX formatting. I
acknowledge the support of all of my friends, here at CU and in India, including Shirong, Nirmal,
Shanti, Abir who motivated me all the time with positive words. Thanks to Snehashis Da for endlessscientific and political discussions. Many thanks to Amitava Da & Manika Di for illuminating myweek-ends with card games and recreational chitchat.
No appreciation would be sufficient in describing Ritu’s role in my journey here at graduate
school. Being wife of a graduate student she faced all the challenges that I faced and supported me
all the way no matter how hard it was for her.

vii

Contents

Chapter
1 Introduction1

1.1

Fluorescent Proteins: A Historical Perspective. . . . . . . . . . . . . . . . . . . . .

1

1.2

Structural and Photo-physical Properties of Fluorescent Proteins . . . . . . . . . . .

3

1.3

Application of Fluorescent Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.4

Directed Evolution of Red Fluorescent Proteins . . . . . . . . . . . . . . . . . . . . . 15

1.5

Thesis Objectives & Outlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2 Dark State Conversion and Ground State Recovery in Red Fluorescent Proteins

24

2.1

Publication Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3

Experimental Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3.1

Time-Domain Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.3.2

Frequency-Domain Measurement . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.3.3

Sample Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.4

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.5

Kinetic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.1

Time-domain Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.5.2

Frequency-Domain Measurement . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.6

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.7

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

viii
3 Single Molecule Fluorescence & Dark State Dynamics in Red Fluorescent Proteins

49

3.1

Publication Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.3

Fluorescence fLuctuation Increase by Nanometer Contact (FLINC) . . . . . . . . . 51

3.4

Photo-physical Measurements of TagRFP-T & DpTT Constructs . . . . . . . . . . . 56

3.5

3.4.1

Cell culture and transfection . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.4.2

Instrumentation & Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.4.3

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Simulation of Single Molecule Fluorescence . . . . . . . . . . . . . . . . . . . . . . . 62
3.5.1

2 State Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.5.2

3 State Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.5.3

Modified 3 State Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.6

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.7

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4 Multi-parametric Microfluidic Platform for Directed Evolution of the Red Fluorescent Proteins

73

4.1

Publication Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.3

Microfluidic Design and Manifold Assembly . . . . . . . . . . . . . . . . . . . . . . . 75

4.4

Optical Set-up and Detection Electronics . . . . . . . . . . . . . . . . . . . . . . . . 76

4.5

Theory of Frequency-Domain Lifetime Measurements in Flow Cytometry . . . . . . 80

4.6

Cell Culture, Library Generation, and Sample Preparation . . . . . . . . . . . . . . . 83

4.7

Multi-parametric Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.8

Sorting of the Improved Red Fluorescent Protein Variants . . . . . . . . . . . . . . . 89

5 Directed Evolution of Photostability and Excited State Lifetime in mCherry
5.1

92

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

ix
5.2

Evolution of mCherry: C4 and C12 Mutants . . . . . . . . . . . . . . . . . . . . . . . 93
5.2.1

Targeted Mutagenesis: Kriek1 Library . . . . . . . . . . . . . . . . . . . . . . 94

5.2.2

Targeted Mutagenesis: Kriek2 Library . . . . . . . . . . . . . . . . . . . . . . 96

5.3

Selection of Mutants after 2nd Round of Mutagenesis. . . . . . . . . . . . . . . . . 98

5.4

Evolution of C4 mutant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.5

Evolution of C9 and C12 Mutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.6

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.7

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6 Engineering of Excited State Lifetime in FusionRed for Improved in-vivo Brightness

113

6.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

6.2

Error-prone FusionRed Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.3

Evolution of FR-13 Mutant: FRX Mutants . . . . . . . . . . . . . . . . . . . . . . . 116

6.4

In-vivo Oligomerization and Labeling Efficiency of the FRX Mutants . . . . . . . . . 121

6.5

FR-1 Mutant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.6

FusionRed Site-Directed Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

6.7

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

6.8

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

7 Engineering of Radiative Lifetime in Red Fluorescent Proteins

133

7.1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

7.2

Library Design for the Radiative Rate Engineering . . . . . . . . . . . . . . . . . . . 135

7.3

Screening of the Radiative Rate Libraries and the Mutants . . . . . . . . . . . . . . 140

7.4

In-vivo Brightness of the Radiative Rate mutants . . . . . . . . . . . . . . . . . . . 143

7.5

In-vitro Photo-physical Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 144

7.6

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

7.7

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

x
8 Future Directions

151

Bibliography

153

Appendix
A Matlab Codes

170

A.1 Phase advance Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
A.2 Single Molecule Fluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
B Setting up and Cleaning of the Microfluidic System for Screening and Sorting177

B.1 General Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
B.2 Setting up the Microfluidic for Screening and Sorting . . . . . . . . . . . . . . . . . . 178
B.3 Cleaning Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
C Protocol for Microfluidic Screening and Sorting & Operation of the LabView Software181

D Protocol for Site-directed & Random Mutagenesis

185

D.1 Library construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
D.2 Error-prone libraries: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
D.3 Site directed libraries: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
D.4 Electroporation:. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

D.5 Site-directed mutagenesis using Gblock: . . . . . . . . . . . . . . . . . . . . . . . . . 187
E Photo-physical Characterization of the Fluorescent Proteins

189

E.1 Extinction Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
E.2 Quantum Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
E.3 pKa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
F Electronic Circults

195

xi
G Sequence Alignment for FusionRed mutants

197

H Program for Analyzing OSER Structures

199

xii

List of Tables

Table
2.1

Fitting Parameters for Time-Domain GSR Data to a − be−t/τGSR . . . . . . . . . . . 33

2.2

Molar extinction coefficients and excited state lifetime of the RFPs used for thesimulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.3

Fitting Parameters for Frequency-Domain GSR Results. . . . . . . . . . . . . . . . . 42

3.1

Time-constants of different photo-physical processes for TT and DpTT. . . . . . . . 60

5.1

Targeted Mutagenesis in mCherry: Kriek1 Library . . . . . . . . . . . . . . . . . . . 95

5.2

Targeted Mutagenesis in mCherry: Kriek2 Library . . . . . . . . . . . . . . . . . . . 97

5.3

Sequences and excited state lifetime of the mutants picked from Kriek2 library.
mCherry had lifetime of 1.9 ns as measured by phase fluorometry. . . . . . . . . . . 98

5.4

Photo-physical properties of the C4PB, SDC and SLT mutants.

. . . . . . . . . . . 103

5.5

Mutations and the screening results of the SDC and SLT mutants. Lifetime andbrightnesses (in yeast) of the mutants were obtained from the screening in the microfluidic. The brightness was quantified as the average of Ch0Max signals fromdifferent mutants.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.1

Photophysical properties of the mutants derived from FusionRed. . . . . . . . . . . . 118

6.2

Photophysical properties of the mutants derived from FusionRed. The mutations
involved in FR-13 and other FusionRed mutants are presented in Appendix G. . . . 120

xiii
7.1

Targets for the radiative rate libraries and the rationales. Numbering is based on
mCherry crystal structure (PDB: 2H5Q). . . . . . . . . . . . . . . . . . . . . . . . . 138

7.2

Proposed residues for targeting the four radiative rate libraries.

. . . . . . . . . . . 139

7.3

Choice of codon for the charged residues. Amino acid residues in black are
the desired ones whereas the AAs in reds are the uncharged residues. V=A/C/G,
R=A/G, S=C/G, N=A/C/T/G. The number in the parentheses are the frequencyof the residue occurrence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

7.4

Amino acid mutations identified in the radiative rate mutants. The numbering of
the residues is based on a reported mCherry crystal structure (PBD: 2H5Q). . . . . 143

7.5

FACS screening results displaying mean green and red fluorescence intensity of themutants upon yeast and mammalian (HeLa) cell expressions. For each run, 10000
cells were screened to quantify their green and red fluorescence intensities.

7.6. . . . . 145

In-vitro photo-physical measurements of the radiative rate mutants. pKa of mCherry
and TagRFP-T were obtained from reference. . . . . . . . . . . . . . . . . . . . . . . 147

E.1 pH = 3-6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
E.2 pH = 6.5-8.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
E.3 pH = 9.0-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Chapter 1
Introduction“I’ve always been attracted to colors. Color helps make the work more interesting andendurable. It helps when things aren’t going well.” -Roger Tsien (Recipient of Nobel Prize in
Chemistry, 2008)

1.1

Fluorescent Proteins: A Historical Perspective
The story of fluorescent proteins started in 1962 when Osamu Shimomura identified andpurified green fluorescent proteins (GFP) from the jellyfish Aequorea Victoria (avGFP) [1]. He
was intrigued by the beautiful blue and green colors from jellyfish and was curious about theirchemical origin. Eventually, he was able to purify the proteins responsible for the blue color ofjellyfish, aequorin. GFP was a byproduct of the purification of aequorin. It was later foundthat upon binding to Ca2+ (abundant in sea water), aequorin emits blue light (λmax , 465 nm).
This blue light triggers the excitation in GFP and subsequently it fluoresces green [2]. In 1979,
Shimomura reported the optical properties and chemical structure of the GFP chromophore [3, 2].
At that time, it was conjectured that the post-translational modifications to form the chromophorein GFP required specific enzymatic machineries and therefore it was not suitable for bio-marker
applications. So, Shimomura stopped working on GFP and continued his work on bio-luminescence.
Fluorescent proteins came back to life in the early 90s, when Martin Chalfie and Douglas Prashercloned the GFP gene [4] and expressed it in E. coli and C. elegans, a roundworm [5]. To their

2
surprise, both of these organisms fluoresced green when excited with blue light. This proved thatfor the formation of functional GFP, no additional cofactors or exogenous substrates were necessary.
It required only molecular oxygen for the formation of the chromophore [6]. Expression of GFP
in C. elegans by Chalfie et al. [5] clearly revealed the touch receptor neurons and the localization
pattern agreed well with previous studies. This eventually opened the floodgate of research inunderstanding and using fluorescent proteins for myriad applications.
Following the initial expression of GFP in bacteria and worms, Roger Tsien’s lab at the
University of California at San Diego pioneered the engineering of fluorescent proteins. Tsien andco-workers were able to modify avGFP to produce a palette of colors [7, 8]. The first red fluorescentprotein, DsRed, was derived from a coral Discosoma sp. [9]. But it was characterized to be an
obligate tetramer and with very slow maturation [10]. By changing the chromophore-forming tripeptide and manipulating the immediate chromophore environment of DsRed, Tsien and coworkers
generated several monomeric, fast-maturing FP variants with emission maxima ranging from 537
to 610 nm [11, 12, 13]. Together with blue fluorescent proteins (BFP), cyan fluorescent proteins(CFP), green fluorescent proteins, yellow fluorescent proteins (YFP) and red fluorescent proteins(RFP), FP technology ushered in a new era in live-cell imaging. Researchers from all around theworld started using GFP and its homologue fluorescent proteins as markers to illuminate hidden
biological activities, sensing metal ions, temperature, pH etc [14]. Later, harnessing the ‘blinking’property of FPs, super-resolution technologies were developed to break the diffraction-limited spatial resolution in optical imaging [15, 16, 17]. Consequently, FPs became part and parcel for thebroader community of chemists, biologists and physicists. Figure 1.1 proves this point. It displays
how literature of the fluorescent proteins increased over the years. The figure has been generated bysearching the keyword “green fluorescent proteins” in Web of Science (www.webofknowledge.com).
For their contribution to the development of fluorescent protein technology, Shimomura, Chalfie
and Tsien were awarded Nobel prize for chemistry in 2008.

3
4000

CFP
3500

GFP
YFP

Number of Records

3000

RFP

2500

2000

1500
400

300

200

100

0
1992

1996

2000

2004

2008

2012

2016

Publication Year

Figure 1.1: Number of articles that mentioned cyan, green, yellow and red fluorescent proteinsover the years. The data were generated by searching Web of Science with the key word “Green
Fluorescent Protein” etc.

1.2

Structural and Photo-physical Properties of Fluorescent Proteins
Despite having large dissimilarity in genetic sequences, the basic structure of green fluorescentproteins and its homologues are very similar. It consists of a 11-stranded β-barrel or β-can. An αhelix spirals down into the interior of β barrel and holds the chromophore [18]. The top and bottom
portions of the structure are characterized with numerous short loops. Figure 1.2 represents thegeneric structure of a monomeric FP (mCherry, PDB 2H5Q). A typical monomeric FP contains∼ 230 amino acids with molecular weight in the range of 25-30 kDa [19]. Although the basic
structure is similar for all of the FPs, structural heterogeneity can also be found. For instance,
unlike many FP variants from avGFP family, a monomeric RFP from DsRed family, mCherry,
was characterized with large intra-strand dynamics between β-7 and β-10 strand that has been

4
implicated to correlate with higher photobleaching from mCherry [20]. Figure 1.3 [19] shows theheterogeneity in structure of the FP β-barrel and chromophore of 202 fluorescent proteins takenfrom the protein data bank. It is notable that most of the chromophores in the reported literature
are in cis form. The 11-stranded β-barrel plays a huge role by minimizing the non-radiative
pathways and thereby increasing fluorescence quantum yield (φ).

Figure 1.2: (Left) The generic structure of a monomeric fluorescent proteins (mCherry, PDB:2H5Q).
It consists of a 11 stranded β-barrel with an α-helix co-axially spiraling down into the barrel. The
chromophore is formed autocatalytically through post-translational modification of the tri-peptide
66-67-68 (mCherry numbering). The dimension of the barrel is ∼ 2 nm in diameter and ∼ 4 nm inlength. (Right) The top-down view of mCherry structure focusing the chromophore structure.

The post-translational modification of the tri-peptide (X66-Z67-G68, mCherry numbering; X65-Z66-G67, avGFP numbering) is responsible for formation of the FP chromophore. Of the
X-Z-G trio, G (glycine) is always conserved across the fluorescent proteins because it is required forthe cyclization of the chromophore. Z can be any amino acid with aromatic side-chain. Although
for wild-type GFP and DsRed, Z is tyrosine, it can be phenylalanine (mBlueberry), tryptophan(CFP) etc. As found in the literature, X can be serine (avGFP), methionine (dsRed, eqFP578,

5
mHoneydew), threonine (CFP), glycine (YFP, EGFP), lysine (ZFP528), histidine (Kaede) etc. In
spite of the diversity in sequences, there are some conserved residues in FPs that indicate theirimportance in proper folding of the β barrel or formation of the chromophore. For example, Arg96
and Glu222 are highly conserved across the different types of FP. Wood et al. [21] studied theimportance of R96 residue in GFP by mutating it to several other non-charged or charged residues.
From their studies it was revealed that the maturation of R96M mutant was severely slowed down(from hours to months) while R96K restored the maturation speed. This indicates the importanceof a charged residue at the position 96 for the fast formation of GFP-like chromophores.

Figure 1.3: Structure of 202 different fluorescent proteins from protein data bank. From thisanalysis, it is obvious that most of the reported fluorescent proteins prefer cis conformations. The
Figure has been taken from Dedecker et al. [19].

For the proper formation of the chromophore, folding into a 3-dimensional β barrel-likestructure is essential. An early study by Reid et al. showed that improper folding of avGFP

6arrested the formation of the characteristic chromophore [22]. After folding, the post-translation
modification in the above mentioned tri-peptide occurs to form a functional chromophore. This
process is called chromophore maturation. For constitutively fluorescent proteins, the posttranslational reactions can be categorized into two different mechanisms:• GFP-like chromophore: In GFP-like proteins (e.g. avGFP), the formation of the chromophore occur in the following sequences: cyclization-oxidation-dehydration [23, 24]. Figure 1.4 demonstrates the mechanism of GFP chromophore formations in detail. First,
cyclization happens by the nucleophilic attack by the N67 of the Gly67 residue on the C-O
π ∗ orbital on C65 . This process is reversible. Next the cyclized intermediate trapped via
oxidation and followed by dehydration to form a double bond between Cα66 and Cβ66
carbons. The functional GFP chromophore contains an imidazolinone group conjugatedto the side-chain of Tyr66 through a double bond. Kinetic isotopic effect revealed thatthe removal of hydrogen atom of Cβ66 did not happen in the oxidation step but in thedehydration step. This oxidation step was found to be rate-determining [22].
• DsRed-like chromophore: There had been a considerable amount of debate on the formation of chromophore in red fluorescent proteins e.g. DsRed [25, 26, 27, 28]. A functional
DsRed-like chromophore contains an additional N-acylimine conjugated to the GFP-likechromophore. This extended conjugation de-localizes the electrons and lowers the energy
gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecularorbital (LUMO) resulting in a bathochromic shift the spectra. Initial studies by Gross etal. [25] suggested that DsRed chromophore formation happens with additional oxidation of
a GFP-like intermediate chromophore. But this mechanism could not explain the residual
green fluorescence in DsRed and the presence of a blue-intermediate (∼ 408 nm) during theDsRed chromophore maturation process [26]. Hence, Verkhusha et al. [26] proposed thatthe formation of red chromophore happens through formation of a protonated GFP-likechromophore (blue intermediate) and subsequent oxidation. But this model unrealistically7

Figure 1.4: Chromophore formation in GFP-like FPs: First, nucleophilic attack by the lone pair of
N67 of Gly67 generate the cyclized product a. Subsequent enolization forms c which establishes ahydration-dehydration equilibrium between c & d. Next, oxidation happens by molecular oxygento generate cyclic imine formation either in hydrated (e) or dehydrated (f ) form and releases
hydrogen peroxide (H2 O2 ). Finally hydrogen is abstracted from the β carbon (tyrosine-derived)
and subsequent bond re-arrangements forms fluorescent chromophore either in its neutral (g) oranionic form (h). The Figure has been taken from Wachter et al. [24].

8assumes that de-protonation is strictly irreversible. A branched mechanism proposed by
Strack et al. [27] solved some of the previous inconsistencies of red chromophore formation
mechanism. According to this mechanism, after the initial cyclization process, the cyclizedintermediate formed an hydroxylated cyclic imine through oxidation by molecular oxygen.
This is in rapid equilibrium with cyclic imine and represents the branched point for the
GFP/RFP chromophore formation. From this point, a GFP chromophore can be formed
by dehydration and further protonation-deprotonation equilibrium. Alternatively, anotheroxidation could lead to the generation of a cationic blue intermediate where N-acylimine
and the imidazolinone ring is conjugated. Finally, dehydration from this blue intermediate and subsequent additional rearrangements lead to the formation of a functional RFP
chromophore.
In 2012, the computational and experimental studies of TagRFP and TagBFP by Bravaya
et al. further refined this branched pathway model [28]. In particular, this study argued that theblue intermediate formed in the way of forming red chromophore is anionic in nature, unlike themechanism by Strack et al. where it was described as cationic [27]. This anionic structure of blue
intermediate is supported by its computational absorption energy that agrees with the experiment(408 nm, 3.04 eV). The proposition of anionic blue intermediate required that dehydration precedesoxidation. This mechanism also explained the role of Lys70 and Arg95 residues in stabilizing theblue intermediate. Figure 1.5 elucidates the mechanism of red chromophore formation by Bravaya
et al. [28]. This mechanism suggests that at the branching point the propensity of the reaction
to form GFP/RFP chromophore is controlled by two different dehydration mechanisms. In firstmechanism, if the dehydaration happens due to removal of H+ and OH – from methylene bridge
and imidazolinone respectively, a GFP-like chromophore forms. On the other hand, if the H+ isabstracted from the peptide bond of Phe65 for dehydration, it leads to a formation of blue intermediate which eventually forms the RFP chromophore with an additional oxidation. According tothis study, the blue intermediate formed during the formation of DsRed-like chromophore forma-

9

Figure 1.5: The mechanism of chromophore formation in DsRed-like RFPs as outlined by Bravaya
et al. [28]. The initial cyclization due to nucleophilic attack by N atom of Gly is followed bya oxidation reaction to produce a cyclic imine. This forms the branching point to form either
GFP-like or RFP-like chromophore. GFP-like chromophore can be formed by dehydration throughproton abstraction from Cβ (derived) from Tyr67. On the other hand, if dehydration happens
through de-protonation of Phe65 peptide bond and subsequent rearrangement, an anionic blue
intermediate is formed. Further oxidation of this blue intermediate is required to form the functional
red chromophore. The Figure has been adopted from the reference [28].

tion, in fluorescent timer [29], TagBFP [30], PAmCherry [31] is probably similar in structure if not
identical.
To be an efficient bio-marker, fast chromophore maturation is necessary. In Chapter 5 the formation of immature mCherry chromophore and mutations associated with incomplete maturation
is described.
Upon excitation with light, the FPs show complex photo-physics. Some fraction of the excitedmolecule returns to the ground state in a radiative fashion, leading to fluorescence. The molecules,
once excited, can also become trapped in long-lived non-fluorescent or dark states (via Dark State

10
Conversion or DSC). At the single molecule level this is manifested in the form of fluorescentintermittency or ‘blinking’. Dickson et al. [32] first reported the on/off blinking behavior of GFPmutants at time scale of seconds with 488-nm excitation. According to their report, the ‘blinking’kinetics depended on the illumination intensity and the off state molecule could be reverted back totheir emissive states by 405-nm irradiation. We have shown that RFPs can spontaneously returnfrom the long-lived dark states to the ground state (via Ground State Recovery or GSR) in thetime-scale of sub-ms [33]. The ‘blinking’ or reversible switching properties of various fluorescentproteins have been studied by fluorescence correlation spectroscopy [34, 35, 36]. The chemical
nature of dark state can be context dependent. It can be associated with different isomeric formsof the chromophore (cis-trans) [37], protonation state or radical form [38] etc. In Chapter 2, timeand frequency-domain measurements techniques for measuring DSC and GSR have been discussed
in detail. DSC & GSR dynamics are presented in the context of single molecule ‘blinking’ of thefluorescent proteins in Chapter 3.
Other than constitutively fluorescent proteins, there are classes of FP that can switch between
dark and bright states or change the absorption/emission wavelength. These FPs can be classified
in three categories:• Reversibly photo-switchable FPs (RSFP): These proteins can be reversibly switched between ON (bright) or OFF (dark) states. Dronpa [39], rsTagRFP [40], asFP595 [41],Skylan-NS [42] falls into this category. The mechanism behind the ON/OFF switchingin RSFP is presumably cis-trans isomerization, protonation-deprotonation or both. The
photo-switching in Dronpa and its mutants have been widely studied by X-ray crystallography [37, 43, 44], fluorescence correlation spectroscopy (FCS) [34] and molecular dynamics
simulations [45]. Dronpa forms the bright state upon excitation with 408-nm light (cisstate) and it can be converted into a dark state by 488 nm illumination [37]. Crystal structure analysis of the dark and bright state of Dronpa reveals that once excited with 488-nm
light the chromophore adopts a trans configuration with increased non-planarity and a re-11
duced number of electrostatic interactions with neighboring residues. Consequently, thismakes the trans-configuration non-fluorescent by opening various non-radiative pathways[37]. Similarly, the first red version of RSFP, rsTagRFP, can be switched on/off by blue
and yellow light respectively. The switching properties of these proteins have been usedin different modalities of super-resolution microscopy, e.g. REversible Saturable OpticaL
Fluorescence Transition (RESOLFT) microscopy [17].
• Photo-activatable FPs (PAFPs): This class of proteins, e.g., PA-GFP [46], PAmCherry1[47] etc. show light-induced conversion from dark to bright states. Upon photo-activation
with 413 nm light, PA-GFP shows ∼ 100 fold enhancement in fluorescence when excitedwith 488 nm light. The mechanism of photoactivation, as studied with the fluorescent andnon-fluorescent states of PAmCherry1, involves decarboxylation-mediated configurationalchanges of the chromophore [31]. PAFPs have been extensively used in photoactivatedlocalization microscopy (PALM) for achieving diffraction-unlimited resolution in biologicalsamples [15, 48].
• Irreversibly photo-switchable FPs: Dendra2 [49], Kaede [50], EosFP [51] change the wavelength of fluorescence when excited with high-energy illumination. This change in color
has been employed in PALM, stochastic optical reconstruction microscopy (STORM) [52]for diffraction-unlimited imaging.
The gradual loss fluorescence due to illumination is known as ‘irreversible photobleaching’or photodegradation. The molecule, in this scenario, goes off the excitation-emission cycle. FPs
are characterized with higher photobleaching tendency than their small molecule counterparts e.g.
organic dyes. The exact mechanism of photo-degradation in fluorescent proteins is still unclear but
probably it varies in different FPs and depends on the illumination and environment conditions.
For instance, a structure-based study on mIrisFP [53] revealed that at low illumination intensity,
the FP became non-fluorescent by photo-induced oxidation of sulfur-containing residues while athigher illumination intensity the mechanism of photo-bleaching involves decarboxylation of con-

12
served Glu212 moiety. The photobleaching rates also depend on the modality of imaging. This isevidenced by comparing photostability of FusionRed [54] and mCherry [12]: with wide-field imaging mCherry and FusionRed have comparable photostability but with confocal imaging FusionRed
bleaches several fold faster than mCherry. This is probably due to the fact that FusionRed has ahigher tendency for dark state conversion and with higher illumination energy in confocal imaging
the FP is forced it to be trapped in those dark states and subsequently photobleach. Photobleaching may proceed via formation of radical ions, as observed in the KillerRed series of FPs [38]. We
have discovered that there is a correlation between the excited state lifetime of the chromophoreand its photodestruction [55]. This can be explained by the fact that with longer lifespan of the FPs
in the excited state, it enhances the probability of various excited state reactions e.g. dark state
conversion (DSC) [56]. Although molecular oxygen is necessary for the formation of a functional
chromophore in FP, oxygen can enhance the photobleaching by reacting with the chromophore.
This is supported by reduction of photobleaching of TagRFP-T and Kriek in anaerobic conditions[11, 57].

1.3

Application of Fluorescent Proteins
Fluorescent proteins have become an extremely useful tool in cellular biology for imaging

cell-cycle progression [58], cellular trafficking [59] and architecture [60, 61], to name a few. Each
year, the armada of FPs is finding novel and innovative ways to illuminate the inner workingsof Nature. FPs offer a genetically-encodable platform for targeted localization that is essential
for illuminating the cellular architecture at unprecedented level of spatial and temporal resolution.
Although organic fluorophores have better photostability and brightness, FPs are superb for specificlocalization. Again, as they are protein-based, most FPs impart minimal photo-toxicity which isalways advantageous for long-term live-cell imaging. As the red fluorescent proteins can be excitedwith longer wavelength lights, they are particularly useful for long-term imaging to suppress photoinduced toxicity [62]. Also, as the scattering of light by molecules is inversely proportional of its

13wavelength, live-cell imaging with RFPs suffers minimally from scattering. As, light with longer
wavelength are not significantly absorbed by water, lipid and hemoglobin, RFPs, far-red FPs andinfrared FPs (iRFP) show negligible auto-fluorescence making them excellent markers for deeptissue imaging. This drives the engineering of bright and photostable far-red and iRFPs [63].
Multi-photon microscopy, light-sheet microscopy partly ameliorate the photobleaching in FPs owing
to their intrinsic confocal effect or selective plane illumination and thereby removing out-of-focus
light contribution.
Thanks to the dark states in FPs, they can be used in various diffraction-unlimited microscopies, e.g., PALM [15] or RESOLFT where routinely 20-50 nm spatial resolution is achieved.
Although PALM, STED, RESOLFT and STORM microscopies give higher spatial resolution, structured illumination microscopy (SIM) or stochastic optical fluctuation imaging (SOFI) can givehigher temporal resolutions, albeit with lower spatial resolution ( only ∼ 2-3 fold improvementfrom diffraction-limited resolution). These techniques reveal biological architectures that were notseen previously with diffraction-limited imaging [64, 65]. For example, the high-resolution mappingof phosphoinositide (PI) had been possible using the photo-switchable FP, mEos2, in conjunction
with photo-activated localization microscopy [66]. By and large, FPs are playing a big role in the
success of super-resolution imaging. Although, in theory, these techniques can have unlimited resolution capability, practically the resolution is limited due to the photo-bleaching and photo-fatigueof the FPs. More photostable fluorescent proteins are urgently needed for further proliferation ofthis field.
The change in fluorescence of FPs due to variations in environmental conditions has led tothe development of sensors for quantification of ions [67, 68, 69], pH [70], temperature [71], cAMP[72] etc. Baird et al. reported the first FP-based ion indicators where circular permutation andinsertion of calmodulin or zinc finger domain in EYFP led to several fold change in fluorescenceupon binding of metal ions [67]. Subsequent improvement of Ca2+ sensors generated the so-called
GCaMP series, which is now routinely employed to monitor calcium ions and action potentials inneuronal cells [73, 74, 75]. Other FP-based ion sensors e.g. Zn2+ [76, 77], Mg2+ [78], Cd2+ [79]14
etc. have been developed for assessing the roles of those metal ions in cellular biology. The sensors
based on FPs can be classified in two categories: ratiometric or intensiometric. In ratiometricsensors the FRET efficiency from a blue-shifted donor to a red-shifted acceptor changes several
fold depending on the concentration of a particular metal ion. For example, the Zn2+ sensors
developed by Dittmer et al. [76] where CFP and YFP are connected with a zinc finger domain thatremains unstructured with low Zn2+ and therefore exhibits a minimal FRET response. But, uponincreasing the concentration of Zn2+ and its resultant binding of Zn2+ to the zinc finger domain,
CFP and YFP are brought into close proximity which results in several fold enhancement of FRET.
On the other hand, intensiometric sensors depend on the change in fluorescence of a single FP uponchanging the environment, resulting in chromophore re-arrangement or a shift in pH stability. In
the single FP based GaMP6m sensor, Ca2+ binding to CaM stabilizes the anionic state of the FP
chromophore through electrostatic interaction, thereby enhancing the fluorescence several fold [80].
Although FP-based calcium indicators have been tuned to report Ca2+ ions very efficiently, theother FP based sensors, e.g. Zn2+ or voltage sensors are still in sub-optimal stage and could standsignificant improvements in temporal resolution, improvement in dynamic range, pH resistance andoptimal expression in mammalian cells. Dedicated strategies to screen large FP-based Zn2+ sensor
libraries and selection based on improved performance are a right step in this direction [81].
Apart from conventional fluorescence imaging, nanoscopy or sensors, FPs can be used in
optogenetics where light dependent conformational changes lead to the control of specific proteinfunctions. In chromophore-assisted light inactivation (CALI), following excitation, the chromophore
produces reactive oxygen species which can diffuse and oxidize nearby proteins and thereby altering
their activities [82]. KillerRed [83] or its monomeric version SuperNova [84] produce superoxide
radical anion upon green light excitation, which is ideal for optogenetic controls. Optogenetic
control of protein activity has also been demonstrated using light-dependent dimerization of a
Dronpa mutant [85].

15

1.4

Directed Evolution of Red Fluorescent Proteins
Several properties of FPs have been engineered to make them more efficient as a bio-marker.

Monomericity, brightness, cyto-toxicity and pH-resistance are a few properties of FPs that havebeen heavily evolved to cater to the ever-growing need of biologists. Fluorescent proteins fromnatural sources are mostly oligomeric and suffer from slow maturation. The oligomeric structurescompromise the usefulness of FPs by perturbing the native function of the targeted proteins. In
spatially confined cellular compartments, e.g. the secretory pathway, proteins tend to mis-fold andaggregate presumably due to oxidative nature of those organelles [86]. The brightness of a fluorescent protein improves the signal-to-noise of the image and offers a clear insight into the relevant
biological processes. Bright FPs also require lower irradiation illumination to obtain acceptable
image quality. This lower illumination ultimately reduces the photo-toxicity of the biological specimen and photo-destruction of FPs. Also, RFPs and far-red FPs are excited with longer wavelength
light where the absorption of water, hemoglobin, deoxy-hemoglobin is minimal and hence such FPs
are well-suited for deep-tissue imaging [87]. Consequently, in the last decade, there has been an
incredible amount of effort to engineer bright, monomeric, photo-stable RFP mutants with lower
cyto-toxicity, and fast maturation kinetics [88, 89, 90]. In spite of this effort, RFPs routinely show
sub-optimal performance relative to their green, yellow or cyan counterparts. Hence, the biologicalcommunity is eagerly waiting for a drastic improvement in the photo-physics of RFPs.
A particular phenotype of a FP is largely controlled by few key residues and some additional
supporting residues. For directed evolution of a particular phenotype, conventionally, site-directedmutagenesis is performed based on some hypothesis, previous findings or less commonly somesimulation guided residues. Residues close to chromophore mainly control the key photo-physics of
FPs but distal mutations have also been reported to control maturation kinetics, barrel fluctuations
etc. After the initial site-directed mutagenesis, normally the FPs become completely non-fluorescentor weakly fluorescent. Additional mutagenesis is generally required to restore the brightness throughimproving folding/maturation or improvement of extinction coefficient () of the FPs. By and large,

16

Figure 1.6: Evolution of avGFP homologues of FP with 11 stranded β barrel and bacteriaphytochrome based FP over the years. The x-axis displays their wavelength emission wavelength.
It is notable that FP derived from A. victoria, Discosoma sp. and E. quadricolor have been heavily
mutagenized and evolved while Allophycocyanin and FPs from other species have not been largely
explored that might have untapped potential. The Figure has been adopted from Rodriguez et al.
[80].

this restoration process is performed using random mutagenesis. However, computation guided
approaches based on PHONEIX and FASTER [91] have also been reported [92]. Generally, invitro mutagenesis and subsequent expression in E. coli is the preferred cellular host platform fordirected evolution of the FPs. However, in-vivo mutagenesis e.g. viral mutagenesis [93], somatic

17
hypermutation [94, 95] etc. have also been used for evolution of FP. Although in vivo mutagenesis
might be a laborious process, it can be used to create library in mammalian cells which ultimately
promise efficient expression in the host organism and minimal cyto-toxicity. On the other hand,mutagenesis in E. coli or yeast is easy to perform but the evolved FPs might have expression issues
in mammalian cells. A number of good reviews account for the directed evolution of red fluorescentproteins and discussed pros and cons of different approaches [88, 89, 90].
After the initial round of mutagenesis, the libraries are subjected to selection of desired
phenotypes. Traditionally, fluorescence activated cell sorter (FACS) and subsequent expression inplates (for yeast or bacterial mutagenesis) is the common choice of selection. However selection
based on FACS is limited by fluorescence intensity and wavelength. Also, bacterial colony-based
screening restrict the library size and thereby mutational space of the FPs. Microfluidic-based
sorting is an alternate option which can incorporate additional selection parameters, e.g., excitedstate lifetime, photo-stability, polarization, anisotropy etc [96, 97, 98, 55, 57]. Also rapid screening
and sorting would allow study of larger combinatorial libraries, to explore the mutational space
efficiently. In Chapter 5, 6 and 7 of this thesis, the utilization of a multi-parametric microfluidic
sorter for directed evolution of FPs have been discussed in detail.
Historically, monomerization of RFPs has been undertaken as a first approach to optimize their functionality [13, 99]. DsRed, the first RFP from Anthozoa sp. was obligate tetramer
and suffered from slow maturation. In order to produce a monomeric variant, Campbell et al.
first disrupted the A/B interface with replacing the non-polar group by large positively charged
residues (Arg, Lys) [13]. This modification introduces electrostatic repulsion at the oligomeric interface and renders formation of higher-order oligomer thermodynamically unfavorable. Next, thedisruption of A/C interface was performed by substituting charged residues as done previously. It
was observed that Anthozoa FPs had higher tolerance of disruption of A/B interface (involving
non-poplar residues) than A/C interface which involves replacing polar residues [90]. However, thedisruption of dimeric interface ‘cripples’ the FP by slow maturation which then becomes nearlynon-fluorescent. With several round of targeted mutagenesis, brightness had been restored to pro-

18

Figure 1.7: Evolution of DsRed to form the mFruit series of RFPs and others.

19
duce the first monomeric RFP, named mRFP1 [13]. Further site-directed mutagenesis of mRFP1
produced the ‘mFruit’ series of mutants with diversification of colors [12]. Similarly, the formationof monomeric TagRFP [99] was produced by destabilizing oligomeric interface of TurboRFP, a
dimeric, fast maturing variant of eqFP578. Although, recently it was observed that TagRFP tends
to dimerize in some specific cellular environments e.g. in endoplasmic reticulum [100, 101]. Figure
1.7 demonstrates the evolution of DsRed.
As discussed earlier, Brightness in fluorescent proteins is a very desirable property. Although theoretically, brightness is related to extinction coefficient (∝ absorption cross-section) andfluorescence quantum yield (φ) (∝ excited state lifetime) of the FPs, practically, it depends on folding/maturation kinetics, completeness of maturation, expression efficiency in host organism and theoptics (wavelength of excitation, profiles of dichroics and detection filter). Evolution of bright mApple was achieved from several rounds of random and site-directed mutagenesis by the selection ofbrightness and maintaining higher photo-stability [11]. The higher brightness of mApple can be
attributed to its higher φ than mCherry (0.49 vs. 0.22). For generation of bright mRuby3 variants,
Bajar et al. executed multiple round of mutagenesis on mRuby2 and subsequent selection basedon brightness [102]. Although the authors claimed that the improved brightness in mRuby3 wasdue to tighter packing of chromophore and reduction of non-radiative channel, it was not verified
and most of the mutations were facing outward or in the loop region of the protein barrel. Veryfew studies have been reported in the literature that are based on a strict rational hypothesis. In a
recent study by Pandelieva et al. [103], the chromophore of mRojoA was sandwiched between two
Tyr residues creating a rigid chromophore environment. This eventually leads to an increase in φby ∼ 3 fold and increased rigidity of the chromophore was validated by its B-factor or temperaturefactor. As the excited state lifetime of FPs is proportional to the φ, the engineering of lifetime isa viable option for enhancing the brightness of red fluorescent proteins. This idea has been employed by our group to generate brighter versions of mCherry. Recently, Bindels et al. generated
a bright RFP, mScarlet, using a synthetic gene by selecting mutants with improved lifetime [104].
mScarlet was reported to have a 70% quantum yield which is the highest for a monomeric RFP

20
to date. Screening and selecting based on lifetime could also be used to generate mutants withnearly mono-exponential decays which is advantageous in fluorescence lifetime imaging microscopy(FLIM).
Engineering efforts to improve the photostability of RFPs have largely been performed
based on random mutagenesis owing to a lack of clear mechanistic understanding of photobleaching decay pathways. Photostable versions of TagRFP and mOrange have been generated by acohort of targeted and random mutagenesis followed by colony-based based screening with irradiation under the arc lamp and subsequent selection of mutants to generate TagRFP-T and mOrange2
respectively [11]. Interestingly, Liu and coworkers proposed an explanation for the improved photostability of TagRFP-T based on four available states of its chromophore: trans-protonated, transdeprotonated, cis-protonated, cis-deprotonated. According to their “circular restoration model”,
the light excitation drives efficient conversion between dim and bright states in a dynamic loop
and thereby decreasing photo-degradation [105]. As discussed previously, molecular oxygen is necessary for the maturation process of chromophore. But oxygen is also implicated in the process ofphotobleaching as evidenced by the fact that TagRFP-T and other proteins show a reduced rate of
bleaching in anaerobic conditions [11, 57]. Recently, Ren et al. produced a photostable version ofmKate2 by introducing cysteine residues in the vicinity of the chromophore [106]. mStable, which isreported to be ∼ 12 times more photo-stable than its precursor undergoes a photo-induced reaction
to produce sulfinic and sulfonic acids making it more photo-stable.
A significant amount of rational designing has been employed for tuning the spectral properties of RFPs [107, 108]. Essentially, the emission maximum of a FP depends on the energy gap
between the ground and excited chromophore electronic states. There have been several hypothesispresented in literature to reduce this gap in order to achieve a bathochromic shift in the spectra.
In the DsRed-like chromophore, GFP-like chromophore is further conjugated with a N-acylimine
moiety. From a number of theoretical studies it was shown that during the excitation process, electron density from the phenol-ring was transferred to the N-acylimine region of the chromophore.
So, introducing new H-bonding at the N-acylimine part of the RFP chromophore would potentially21
stabilize the excited state and thereby decreasing the HOMO-LUMO gap in a FP. Indeed it has
been shown that direct or water mediated H-bonding with the N-acylimine and Glu16 has been
crucial for longer wavelength emission of mNeptune, mPlum and mCardinal [108]. Introductionof Tyr above or below the chromophore facilitates π-stacking interactions that leads to red-shifted
emission. This is due to increased dipole moment of Tyr at the excited state and preferential stabilization of its excited state in a polar solvent [109]. In fact, the introduction of Tyr above thechromophore of GFP and mCherry leads to development of YFP and mRojoA those emit longer
wavelength than their precursors [109, 92]. Grigorenko et al. proposed that a triple-decker motif
where the traditional GFP chromophore is sandwiched between two Tyr residues would emit ata lower energy wavelength [110]. Another proposed mechanism for reducing the HOMO-LUMO
gap, was to introduce a bulky group at the phenolate-end of the RFP chromophore and thereby
de-stabilize the ground state. This has not been validated though. Hense et al. [107] described
how altering the fraction of cis-trans conformations could lead red-shifting of eqFP611.
Chromophore maturation in RFPs is slower than GFPs and often it leads to incomplete
maturation by forming both green and red chromophores [95]. As both GFP and RFP chromophore
formation occurs through a branched pathway, a detailed understanding of roles of different aminoacids need to be investigated in order to differentially slowing down or accelerating certain reactions[111]. Engineering of FPs that withstand extreme conditions e.g. oxidizing conditions have beenachieved [86]. The cytotoxicity and dimerization tendency of mKate2 have been alleviated bygenerating the FusionRed variant. FusionRed is discussed in detail in Chapter 6.
It has been revealed that the key photo-physical parameters of FPs are largely inversely
correlated. For instance, with increasing the excited state lifetime of FPs, φ is increased but
at the cost of reduced photostability. To improve φ, rigidity of the chromophore is paramount,which can be achieved by increased interaction of the chromophore with the surrounding residues.
However, flexibility around the chromophore-forming tri-peptide is essential for fast maturation
process. Also, increase in emission wavelength requires longer conjugation within the chromophorethat is associated with enhanced vibrational motions and non-radiative decays. This is detrimental

22
for fluorescence quantum yields of the FPs. Innovative and creative approaches are essential for
bypassing these fundamental limitations. For example, directed mutagenesis to achieve a nonnative chromophore within the β-barrel with a higher number of double bonds could be attempted.
Engineering radiative lifetime of the chromophore is a viable strategy for simultaneous improvement
in quantum yield and photostability of the FPs as discussed in Chapter 7.

1.5

Thesis Objectives & Outlines
Even two decades after the introduction of RFPs, their performance is not comparable to thatof their green, cyan and yellow counterparts. Several laboratories around the globe are engaged indeveloping the key photo-physical properties of RFPs, e.g., brightness, maturation, photo-stability,
quantum yield etc. The complex photo-physics have yet to be discerned at a molecular level sothat FPs with any desired phenotype could be produced at will. This thesis describes different
photo-physical events with a molecular focus so that in the near future we can proceed into that
direction. For simultaneous enhancement of different photo-physical properties of RFPs, the use of amulti-parametric microfluidic platform was established presented along with some novel alternative
approaches for library design and selections. This thesis is a repository of large number of RFP
mutants produced by the evolution pressure to engineer the excited state properties of the RFPs.
These mutants could significantly advance the optical imaging with RFPs and provide a bedrock
to unravel some key questions in this field.
Chapter 2 describes the measurement of dark state conversion and ground state recoveryof the RFPs. Time and frequency-domain methods for DSC/GSR measurements for mCherry,
TagRFP-T and Kriek are discussed in this chapter.

The connection between ensemble level

DSC/GSR and single molecule level ‘blinking’ is elucidated in Chapter 3. This chapter also deals
with the increased blinking of Dronpa-linker-TagRFP-T (DpTT) compared to wild-type TagRFPT. The methods and instrumentation for microfluidic screening and sorting are discussed in Chapter
4. Chapter 5 describes the evolution of mCherry from a RFP with low quantum yield (∼ 20 %)23
to a mutant with 71 % quantum yield. In this chapter, I discuss how systematic engineering of
excited state lifetime of fluorescent proteins could lead to improvement in quantum yield. The
evolution of brighter variants of FusionRed is discussed in Chapter 6. This represents the resultof directed evolution in fluorescence lifetime and maturation of FusionRed. Chapter 7 describes
how engineering of radiative lifetime of the FPs could be an alternative and novel approach forenhancing the fluorescence quantum yield. The final Chapter 8 explores the future developmentsof the RFP engineering project.

Chapter 2
Dark State Conversion and Ground State Recovery in Red Fluorescent Proteins

2.1

Publication Note
This Chapter has been adopted from Manna et al., Time and frequency-domain measurementof ground-state recovery times in red fluorescent proteins, The Journal of Physical Chemistry B,
119 (15):4944-4954, 2015 [33] and the corresponding Supplementary Information of the article.

2.2

Introduction
The discovery of green fluorescent proteins (GFP) from the jellyfish Aequorea victoria and its

subsequent expression in living cells without exogenous substrates or cofactors had a revolutionary
impact on the field of in vivo bioimaging, biosensors, and super-resolution microscopy [5]. The
genetic encodability of the GFPs and their mutated homologous forms, blue, cyan, and yellow FPs,
makes them particularly advantageous in biosensing and live cell imaging [14, 19]. Red fluorescentproteins (RFPs) are particularly useful because they require low energy excitation (λ > 550 nm) to fluoresce and therefore impart less phototoxicity and reduce fluorescence from endogenous
fluorophores. Despite these advantages, low brightness and low photostability compromise theusefulness of existing RFPs, and the continued development of monomeric RFPs with increased
brightness, complete chromophore maturation, long lifetime, photostability, and pH-stability is an
ongoing challenge [11, 99, 90].

25
The low brightness and quantum yield of the FPs are generally attributed to various nonradiative pathways including reversible and irreversible photobleaching mechanisms. Experimental
evidence on green and yellow FPs revealed the existence of long-lived non-fluorescent or darkstates (lifetimes ranging from µs to s) responsible for the reversible photobleaching [32, 112, 113].
RFP molecules are trapped in these dark states via dark state conversion (DSC), which reduces
the fluorescence quantum yield. In addition, permanent photobleaching or photodestruction may
take place from these dark states. These rich photo-dynamics have motivated various in vitro
and in vivo analyses characterizing these dark states and elucidating the mechanism involved inphotobleaching using X-ray crystallography, fluorescence correlation spectroscopy (FCS), single
molecule spectroscopy (SMS), two-photon laser scanning microscopy (TPLSM), NMR, and othertechniques [114, 115, 116, 38, 117, 118]. These dark states may be triplet states, different geometricisomers, or different protonated forms of the chromophore [37, 119]. For example, a crystallographic
study of mKate (mutant of eqFP578) revealed that in an acidic environment the chromophore of
the FP is in a non-fluorescent, trans configuration, whereas at high pH it is in a fluorescent, cisconfiguration [117]. In general the ground-state recovery (GSR) times for FPs vary from millisecond
to sub-millisecond time scales. Knowledge of the GSR time can be used to inform the optimal choiceof FP for a particular imaging method, which may have an associated excitation intensity, scanrate, and duty cycle. For example, by employing pulsed excitation with pulse separation times
longer than the GSR time, Donnert et al. observed considerable increase in fluorescence output
from GFP and the dye Atto532 [119]. Beyond this report, however, there are few instances inwhich pulsed excitation has been used to mitigate the contribution of dark states to reduction of
the fluorescence output.
Super-resolution microscopy has taken in vivo imaging to new heights by surpassing thediffraction-limited spatial resolution of traditional optical microscopy [120, 121]. Several of thesetechniques rely on manipulating the DSC process. In particular, single molecule localization-based
super-resolution techniques such as photoactivated-localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and direct STORM (dSTORM) employ the spontaneous

26
blinking or photo-switching properties of fluorescent proteins and organic dyes [122]. The resulting
demand for well-characterized blinking, photo-activatable, photo-convertable, and photoswitchable
FPs has led to the generation of multiple FPs covering a wide range of excitation and emission
windows [47, 123, 40, 124]. Measurement of the switching rates and elucidating the underlying
mechanism of blinking is helpful in engineering these FPs for system-specific applications. Flowcytometry is a promising technique for high-throughput screening and sorting of FP libraries generated by random or site-directed mutagenesis. Recent efforts have been made to incorporate
photostability and fluorescence lifetime as sorting parameters for developing new FPs [125, 98, 97].
New methods to measure the GSR times or switching rates of the RFPs would provide anotherdimension to these cytometry-based selection procedures and would therefore promote broader
application of super-resolution microscopies.
Permanent photobleaching is one of the main drawbacks of FPs. The mechanism of photobleaching is still poorly characterized and seems to vary among FPs, yet a detailed mechanistic
understanding along with methods to quantify the rate of these processes would be useful for generating improved fluorophores. The DSC process is closely related to the permanent photobleaching,which can occur from dark states as well as from the initially excited state and may act to increaseor decrease the photostability of an FP [56]. Although several reports in the literature described thenature of dark states, to the best of our knowledge few detailed studies of DSC and GSR kinetics
and analysis of the measurement techniques have been reported [32, 112, 113, 114, 36, 126].
Here we examine approaches for the measurement of GSR times of RFPs based on timeand frequency-domain techniques. In the time-domain technique, the sample is excited with 2
ms pulses at a varying inter-pulse delay, to populate the dark states. The time constant of thesubsequent fluorescence recovery directly quantifies the relaxation rate. The frequency-domain
technique is based on phase fluorimetry [127, 128]. In conventional frequency-domain fluorescence
lifetime measurements, the sample is excited with a sinusoidally modulated laser beam with amodulation frequency comparable to the reciprocal of the excited-state lifetime. The intensity offluorescence from this sample oscillates at the same frequency but the signal is time-delayed if the27molecules spend a finite amount of time in the excited state [129]. The oscillating emission signal
shows a negative phase-shift (i.e., phase lag) and a modulation amplitude lower than that of theexcitation waveform. For a two-state system, this phase shift (φ) and reduction in modulation
amplitude (m, modulation) are directly related to the lifetime of the sample as,

φ = arctan(ωτ )

(2.1)

1
1 + ω2τ 2

(2.2)

m =

√

where, ω and τ are the modulation frequency and the excited-state lifetime, respectively.
Recent experiments and calculations by Zhu and co-workers [127, 128] show that this phase shift
can be positive if the chromophore has a dark state (i.e., for a three-state or higher state system)
and if the modulation frequency employed in the measurement is comparable to the inverse of
the GSR time. Under these conditions, the emission waveform seems to precede the excitation
waveform. This “fluorescence anomalous phase advance” (FAPA) can be explained in the following
way: After excitation to S1 (Figure 2.6a), a fraction of the excited-state population is trappedin the dark state. When the modulation frequency is very low compared to the GSR rate, thefluorescence closely follows the excitation, giving φ ∼ 0. At high frequency compared to the GSR
rate, the dark state is unresponsive and φ < 0. But if the two frequencies are comparable, therising half of the oscillation populates a fraction of the molecule in the dark state. But due to thelong lifetime of the dark states, the falling half of the waveform will excite a smaller population
resulting in decreased fluorescence. This effect makes the waveform asymmetric (Figure 2.1) andcauses the apparent phase advance [128].

28

Figure 2.1: Schematic diagram displaying phase advance. When molecules having dark or nonfluorescent states are excited with sinusoidally modulated laser (green) and the frequency of themodulation is comparable to its dark-state dynamics (DSC/GSR), distortion of the fluorescence(black) signal causes anomalous phase advance.

2.3

Experimental Methods

2.3.1

Time-Domain Measurement
For the time-domain method the sample was excited with laser pulses from a 532-nm diode-pumped solid-state laser (Laser Quantum, Opus-2.0W). The laser pulses had a rectangular time
profile with a 2 ms exposure and varying inter-pulse dark times (zero laser intensity) ranging
from 5 µs to 50 ms. Some samples, especially TagRFP-T showed a kindling effect upon first
exposure to light, resulting unusually high fluorescence for a few microseconds (data not shown).
To remove this artifact, all the samples were pre-illuminated at very low laser intensity (<500
W/cm2 ) for 2 ms duration before the measurement pulse-sequence. An acousto-optic modulator(AOM, Gooch & Housego, 35210-BR) was used to obtain this pulse profile. The RF input to theAOM driver (Gooch & Housego, R31210-6ADS) was delivered by a function-generator (Agilent,
33250A) controlled by custom-made software. The modulated light enters a custom-built inverted

29
microscope and is focused on the sample with a 100X 0.8 NA air-immersion objective (Olympus).
The measured FWHM of the beam profile was 3 µm. The average power at the focal point of thelens was measured with a power meter. Scattered light from the sample was focused onto a CMOS
camera (ThorLabs, DCC1545M) for visualization of the sample. A 532-nm long-pass filter wasused to separate the excitation from emission. The epifluorescence was collected using an Avalanche
Photo Diode (APD, Advanced Photonics) or Photo-Multiplier Tube (PMT, Hamamatsu Photonics,
R9880U-20). The PMT photocurrent was converted to voltage with a home-built trans-impedance
operational amplifier to improve the signal to noise ratio. The signal from the PMT was digitizedat a sampling frequency of 1 MHz with a data acquisition (DAQ) card (National Instruments).
Typical fluorescence signals obtained from the RFPs are shown in Figure 2.2. The raw fluorescencedata were spline/exponential fitted to get smooth traces. The ground state recovery or percent
recovery (PR) for first three pulses were calculated from the fluorescence profiles as follows:

PR =FR − FB
× 100
FL − FB(2.3)

where FL, FB and FR are initial fluorescence, fluorescence after exposure and recovered
fluorescence respectively as explained in Figure 2.2. The average percent recoveries from thesethree pulses were fitted against the dark times with a single-exponential function to extract the
GSR time constant.
To extract the irreversible photobleaching time-constants in the time-domain measurements,
we locate the peak intensities of the pulses and fit those intensities with a single exponential function(Figure 2.3). We have specifically chosen the time-domain data set having inter-pulse delay time as
10 ms between two successive 2 ms pulses. This allows most of the population trapped in the darkstate to relax back in the ground state. Hence, these time-constants reflect the combined bleachingfrom bright and dark states (Figure 2.6a, kS1B and kDB ).

30

Figure 2.2: (left)Typical fluorescence signal obtained from RFPs in time-domain GSR experiment.
The sample is excited with a pulse having constant exposure time (t0 ), but the time between two
pulses (t) varied from 5 µs to 50 ms. This figure shows the fluorescence intensity profile of mCherry
at 25 kW/cm2 for exposure and dark time 2 and 10 ms respectively. (right) Percent Recovery (PR)of the first 3 pulses and the average of the PR for mCherry at 25 kW/cm2 . Average PR is fitted
against the dark time to extract the GSR time-constant.

Figure 2.3: Peak intensities of the pulsed fluorescence traces are identified and fitted with a single
exponential function to extract the pulsed photo-bleaching time-constants of the FPs. Fluorescentproteins were excited with pulses of 2 ms exposure time and 10 ms of inter-pulse delay.

31

2.3.2

Frequency-Domain Measurement
For frequency-domain measurements, purified proteins were excited with the same 532-nm

laser as in time-domain measurements but with a light beam sinusodially modulated by the AOM
driven by a function generator. The incoming beam from the AOM is split with a 50:50 beam
splitter. Half of the modulated beam was sent to a photodiode (DET36A, ThorLabs) to provide areference signal for a high-frequency lock-in amplifier (Zurich Instruments, UHF). The remainderof the beam used for sample excitation went through the same custom-built inverted microscope
setup described as before. The fluorescence signal was collected by a PMT and further processedby the lock-in amplifier to obtain the phase-shift induced by the FPs. To eliminate any effectof varying photoelectron transit time on the measured phase, the PMT was operated with samegain (or voltage) for different sets of phase measurements. We use home-built software (Labview)for interfacing the laser, function generator, and lock-in amplifier. The sample was scanned frommodulation frequency of 1 kHz to 200 kHz with an average 4 s of exposure and a 80 ms lock-intime constant. The last 1000 phase points from the lock-in output were averaged to obtain thephase shift. Phase values at each modulation frequency were obtained by focusing the laser at
different spots on the coverslip to avoid any photo-bleaching. Phase shift values from the scatteredsignal by a blank coverslip were measured to provide an absolute phase shift value which accounts
for time-delays induced by cables, optics, and electronics in the experimental setup. This absolutephase shift is subtracted from the sample phase shift to obtain the actual phase shift [129]. To
ensure that phase measurements are not impacted by the free diffusion of the proteins, we verified
that the same results were obtained from phase spectra on localized proteins expressed in E. coli
and FPs measured in solution.

2.3.3

Sample Preparation
RFPs used in these experiments were either purified proteins (frequency-domain experiments)

or expressed in E. coli (time-domain experiments). For the expression in E. coli, we used standard

32
molecular biology techniques. The competent cells (Top10) were transfected with plasmid DNA(pBAD vector) and cultured overnight on agar plates containing Lysogeny Broth (LB) media andampicillin at 37◦ C. After the FPs were well-expressed in these bacteria, we picked a single colony
and transferred it to 500 µL of Ca2+ and PO4 3 – free HEPES-buffered Hank’s balanced salt
solution (HHBSS, pH 7.4). Measurements were performed on a 20 µL volume of the buffer solution
containing the bacteria placed on a coverslip with 20 µL of poly-L-lysine for cell immobilization.
Purified RFPs were dissolved in 3-(N-morpholino)propanesulfonic acid buffer (15 mM MOPS, 100
mM KCl, pH 7.4), and measurements were performed on 25 µL volumes.

2.4

Results
Figure 2.4 shows the results of time-domain GSR measurement of RFPs expressed in E.

coli excited at 4, 8, and 25 kW/cm2 . The fluorescence decay profiles were fit with multiexponential functions to extract the weighted photobleaching time constants of mCherry, TagRFP-T, andKriek which are 3.5, 2.1, and 7.6 ms, respectively (Figure 2.4a). For mCherry and Kriek, the decay
had been fitted with biexponential functions whereas a triexponential function was needed to fit
the TagRFP-T profile. At 25 kW/cm2 laser intensity, TagRFP-T bleaches ∼1.7 times faster thanmCherry during the course of a 2 ms exposure, in agreement with previous findings [125, 56]. The
Kriek FP is a higher photostability variant of mCherry selected from a mCherry-based mutagenesis
library after multiple rounds of microfluidic-based sorting based on photostability [57]. Accordingly,
Kriek bleaches ∼ 2 times slower than mCherry. In this 2 ms window, this decay of the fluorescencesignal is the result of both irreversible and reversible processes. To isolate the irreversible photobleaching from the total photobleaching, we use a pulse sequence of 2 ms exposure and varying
dark time as explained in Experimental Methods. We specifically used a 2 ms pulse train as it wasobserved (Figure 2.2a) that in this regime, a significant fraction of the population is bleached andtherefore the dark state is significantly populated.
Figure 2.4 presents the percent recoveries (PR; calculated from 2.3) of the RFPs as a function

33
Table 2.1: Fitting Parameters for Time-Domain GSR Data to a − be−t/τGSR
RFP
TagRFP-T
TagRFP-T
TagRFP-T
mCherrymCherry
Kriek

Intensity (kW/cm2 )
25
8
4
25
8
25

τBl (ms)
5.0
14
17
28
250
49

τGSR (ms)
0.22
0.20
0.26
0.99
1.2
2.8

a
44
35
41
58
73
65

b
43
35
38
54
74
62

P Rlim (observed) = (a + b)/2
44
33
40
56
74
64

of dark time. The fitted GSR times are collected in Table 2.1. The value of PR does not reach
100 % even if the dark time is allowed to be sufficiently large to reach an asymptotic value. As
shown in the kinetic modeling section (below), the limiting value falls below 100 % because someof the population is bleached during the 2 ms irradiation period. As expected, it is observedthat the laser intensity does not have a pronounced effect on the GSR time scale. The recovery
of mCherry at 4 kW/cm2 and Kriek at 4 kW/cm2 and 8 kW/cm2 are not determined at these
intensities, as there is not enough dark-state population to produce a reliable recovery curve (datanot shown). The irreversible photobleaching or photodestruction time constants (τBl ), measured
by fitting the peak intensities of the pulses with single-exponential functions (Figure 2.3), show
strong intensity dependency (Table 2.1), indicating that the processes involved in photodestruction
are light-induced rather than spontaneous. At 25 kW/cm2 , the irreversible photobleaching occurs
from mCherry with a time constant (τBl ) of 28 ms, but at 8 kW/cm2 , it is almost ∼ 9 times more
photostable with τB l of 250 ms. This photobleaching reflects the total photodestruction of the FPs
out of both excited and dark states (Figure 2.6a). The relative order of irreversible photobleachingtime constants of the three FPs studied here is Kriek > mCherry > TagRFP-T, in agreement withprevious reports [56, 57].
Figure 2.5 depicts the results of frequency-domain GSR measurements for the three FPs
in vitro, over the range from 1 kHz to 175 kHz. In this frequency window we observe positive
phase shift or phase advance rather than a phase lag. Our results reveal (Figure 2.5) the existenceof phase advance up to 2◦ at 25 kW/cm2 in this frequency region. The maximum values of the

34

Figure 2.4: Fluorescence profiles of TagRFP-T (red), mCherry (blue), and Kriek (green) expressedin E. coli at 25 kW/cm2 . The solid black lines represent the biexponential (mCherry, Kriek) andtriexponential (TagRFP-T) fit of the fluorescence decay. The weighted decay constants based on
the fit are 2.1, 3.5, and 7.6 ms for TagRFP-T, mCherry, and Kriek, respectively. Plots b-d show
the percent recovery of each FP as a function of dark time between the two pulses at different
intensities.

phase advance observed by Min et al. [127] (∼10◦ ) were significantly larger due to the higher
laser intensities employed in that study (600 kW/cm2 ) as well as the higher rate of DSC for thechromophores (Flavin adenine dinucleotide and fluorescein isothiocyanate). As explained by our35
numerical simulations (below) the magnitude of the phase advance also decreases if the GSR rate ishigher than or comparable to the DSC rate. Figure 2.5d presents the intensity dependence of phaseadvance of mCherry at 1 kHz modulation frequency. It is clear that with the increase of intensity,
the phase advance also increases, which supports the fact that FAPA is an intensity-dependent
phenomenon. At higher excitation intensity, a larger fraction of the population is trapped in thesedark states, increasing the phase advance. As described in Introduction of this chapter, this phaseadvance is inherently related to the dark-state relaxation, and we modeled these data to extract
the DSC time-constants (kS1D , kDS1 ).

2.5

Kinetic Modeling

2.5.1

Time-domain Measurement
The observed time-domain and frequency-domain results can be explained with a three-statekinetic model (Figure 2.6a) with S0 , S1 , and D as the ground electronic state, first excited state,
and non-fluorescent (dark state) state, respectively. The population photo-excited to S1 can either
photobleach irreversibly or it can be transferred to the dark state and subsequently relax to theground state. There is also a possibility of photobleaching from the dark state. The kinetic
equations describing the populations of these states are:d[S0 ]
dt
d[S1 ]dt
d[D]dt= −kex [S0 ] + kem [S1 ] + kGSR [D](2.4)= kex [S0 ] − kem [S1 ] − (kS1D + kS1B )[S1 ] + kDS1 [D](2.5)= kS1D [S1 ] − (kDS1 + kGSR + kDB )[D]

(2.6)

kex =

Iσλ
hc

(2.7)

The excitation rate is calculated with Eqn. 2.7, where, I, σ, and λ are laser intensity, absorption cross-section, and excitation wavelength, respectively, and h and c are Planck’s constant36

Figure 2.5: (a-c) Frequency-domain results for TagRFP-T, mCherry, and Kriek measured in vitro.
Green, red, and blue dots represent the phase shift at 4 kW/cm2 , 8 kW/cm2 , and 25 kW/cm2
laser intensities, respectively. (d) The phase shift of mCherry at a modulation frequency of 1 kHz
increases with the laser intensities.

and the speed of light. I and λ (532 nm) are known quantities in this experiment. The absorption cross-section at any arbitrary wavelength λ, σλ , has been calculated using the known molar
extinction coefficient of the FPs using the following equations:

σλ = 2.303 ×

λ
× 103
NA

(2.8)

where, σλ is the molar extinction coefficient at λ in M-1 cm-1 , NA is the Avogadro’s constant.

37
Table 2.2: Molar extinction coefficients and excited state lifetime of the RFPs used for the simulations.
RFP
TagRFP-T
mCherryKriekmax (mM-1 cm-1 )
81
72
48

532 (mM-1 cm-1 )
39
29
18

Lifetime (τ , ns)
2.71
1.87
1.00

The molar extinction coefficient at 532 nm of the three RFPs used in the calculations has been
given in Table 2.2.
In time-domain experiments, the sample was excited with rectangular pulses of duration t0 (2ms) with a variable inter-pulse delay time (t) (Figure 2.2a). After the exposure t0 , the populationsof ground and dark states are denoted as [S0 ]t0 and [D]t0 , respectively. As the fluorescence is
proportional to the ground-state population, F L = 1 and F B = [S0 ]t0 . Now assuming that in
the dark time period, molecules trapped in the dark state primarily relax to the ground state, thepopulation of D after a dark time of t is given by [D]t0 e−kGSR t . Therefore, during this dark time,
the population that returns to the ground state is [D]t0 − [D]t0 e−kGSR t = [D]t0 (1 − e−kGSR t ). So,
F R = [S0]t0 + [D]t0 (1 − e−kGSR t ). Incorporating these expressions for the fluorescence signals into
the expression for PR gives:

PR =
=
=

FR − FBFL − FB[S0 ]t0 + [D]t0 (1 − e−kGSR t ) − [S0 ]t0× 100
1 − [S0 ]t0[D]t0 (1 − e−kGSR t )1 − [S0 ]t0= P Rlim (1 − e−t/τGSR )(2.9)(2.10)(2.11)(2.12)

1
where, τGSR = kGSRandP Rlim =

[D]t0
× 100
1 − [S0 ]t0(2.13)P Rlim reflects the percent recovery of the sample at very long dark time when all populationfrom the dark state has relaxed to the ground state. As Eqn. 2.13 shows, to obtain the limiting

38
percent recovery (P Rlim ), [S0 ]t0 and [D]t0 need to be calculated by solving the set of coupled
differential equations (Eqn. 2.5-Eqn. 2.6), which cannot be done analytically for sinusoidally
modulated excitation. The solutions are very complicated and difficult to interpret even for aconstant exposure, so we solved the equations numerically. ODE45 solver in Matlab had been used
to solve those coupled differential equations.
Figure 2.6b and 2.6c shows the dependence of P Rlim on the various kinetic parameters of thethree-level system. As shown in Figure 2.6b, P Rlim decreases monotonically with the increase of thebleaching rate constant (kS1B ) whereas percent recoveries increase with the increase of kS1D . This
trend can be explained by the fact that, with an increase of bleaching rate constant (kS1B ), a larger
fraction of the molecules are permanently bleached and therefore removed from excitation-emission
cycle, thus lowering the limiting percent recovery. On the other hand, with an increase in the DSC
rate constant (kS1D ), a larger fraction of the molecules are trapped in the photo-protective darkstate (kDB = 0Hz), leading the molecules to recover slowly and accordingly increasing the P Rlim .
Figure 2.6c shows that, with increasing GSR rate constant (kGSR ), P Rlim reaches a maximum
at intermediate GSR rate constant (∼1 kHz) but subsequently decreases as kGSR continues toincrease. This can also be explained on same line of argument as Figure 2.6b: For small values ofkGSR , the molecules are photo-protected in the dark state, increasing the percent recovery, but witha continuing increase in kGSR , this effect becomes less pronounced and causes high photobleaching
due to a nonzero kS1B , resulting a decrease in P Rlim . Percent recoveries presented in these figures
have been calculated using exposure and dark time as 2 and 5 ms, respectively.
Figure 2.6d shows the simulations of our time-domain experiments. Percent recovery (PR)was calculated using numerical simulations based on the three-state model system with dark-state
lifetimes ranging from 5 µs to 50 ms and 2 ms light exposure time. The simulated percent recovery
vs time profiles were fitted with single exponential functions to extract the values of kGSR . As
expected, the kGSR from fitting results (kGSR fitting) match the actual kGSR used in the simulations.
Notably, with increasing kGSR the limiting percent recovery decreases, which supports simulation
results in Figure 2.6c and the experimental results as discussed later. As the above analytical and39

Figure 2.6: (a) Three-state photophysical model with ground state (S0 ), excited state (S1 ), darkstate (D), and the relevant photokinetic parameters. (b) Numerical simulations based on the threestate model show the dependency of P Rlim on kS1D and kS1B rate constants. Parameters used inthis study are, kex = 7.4 MHz (∼ 25 kW/cm2 ), kem = 530 MHz (τ ∼1.87 ns), kS1D = 10-250 kHz,kDS1 = 0 kHz, kGSR = 2 kHz, kS1B = 0-10 kHz and kDB = 0 kHz. The limiting percent recovery(P Rlim ) is calculated using exposure and dark time as 2 and 5 ms, respectively. Magnitudes ofpercent recoveries are given in the color bar. (c) Numerical results displaying the dependence of
P Rlim on dark-state conversion rate constant (kS1D ) for and ground state recovery rate constants(kGSR ). Parameters used in this calculations were kex =7.4 MHz (∼ 25 kW/cm2 ), kem = 530 MHz(τ ∼ 1.87 ns), kS1D =12-100 kHz, kDS1 = 0 kHz, kGSR = 0-10 kHz, kS1B = 1 kHz, and kDB = 0 kHz.
The exposure and dark time used in these calculations were 2 and 5 ms, respectively. (d) Simulated
percent recovery (dots) and single exponential fitting (lines) at different dark times ranging from 5µs to 50 ms for different kGSR . The exposure time used was 2 ms. The kGSR extracted from single
exponential fitting (kGSR fitting) agrees well with the actual kGSR used in the simulations (kGSR
input).

numerical analysis of percent recovery suggests that PR can be modeled as a single exponential

40

Figure 2.7: . (a) Results of time domain GSR experiments for different RFPs at 25 kW/cm2 laser
intensity. (b-d) Single exponential fitting (red lines) of the percent recovery (blue dot) measured
at 25 kW/cm2 in time-domain experiments. The plots are fitted with a function of a − be−t/τGSR
to extract the GSR time constants. The fit parameters are presented in Table 2.1. The order ofthe observed GSR time constant is Kriek > mCherry >TagRFP-T.

function of the dark time (Eqn 2.12), we fit the observed PR with a functional form of (a−be−t/τGSR )to extract GSR time constants. As shown in Eqn. 2.12, the parameters a or b can give theapproximate P Rlim . Hence, we define the observed P Rlim as the average of a and b: (a + b)/2.
Figure 2.7 shows the fitting of the percent recovery of the FPs at 25 kW/cm2 . Table 2.1 gives thefitted parameters, showing that TagRFP-T has the fastest and Kriek has the slowest ground-state41
recovery.

2.5.2

Frequency-Domain Measurement
To extract the photokinetic parameters from our frequency domain measurements, numericalsimulations were performed using the three-state model system (Figure 2.6a). We calculated thephase shift numerically by a home-built software that simulates the operation of a lock-in amplifier.
We solved the three-state model kinetics numerically for a system excited with a sinusoidally
varying intensity. In this analysis, the excited-state population S1 (t), which is proportional to thefluorescence signal, was mixed with the modulated reference signal sin(ωt) and integrated to obtain
the in-phase (I) component. Similarly, the in-quadrature (Q) component was obtained by mixing
S1 (t) with cos(ωt) and subsequent numerical integration. Finally, since I and Q are proportional
to cos(φ) and sin(φ), respectively, for a sinusoidal excitation, the phase shift (φ) was calculatedusing, tan−1 (Q/I). Technical details of the simulation and the Matlab code have been presented
in Appendix A.
As direct least-squared fitting of the model parameters from the coupled differential equations
is impractical, we systematically investigated the effect of various parameters on the phase shift
in the frequency range in which the individual RFPs show phase advances and fit the parameters
thereafter. From our simulations, it was found that the phase shift is insensitive to the bleachingrates from the excited state (kS1B ) or dark state (kDB ). Hence, we conclude that the frequencydomain measurement cannot be used effectively to resolve the rate of irreversible photobleaching.
On the other hand, the simulations showed that the phase shift is sensitive to changes in kS1D
and kDS1 , which is unsurprising since these rate constants are directly responsible for equilibrationof the excited state with the dark state, which in turn governs the phase advance. These twoparameters were varied to obtain phase spectra with over a range of kS1D and kDS1 values. Figure
2.8b and 2.8c demonstrates the effect of dark-state conversion rates (kS1D , kDS1 ) on phase shift formCherry at 25 kW/cm2 . As expected, increasing the value of kS1D results in an increasing positive
phase shift because it corresponds to increasing population transfer to the dark state, which is42
Table 2.3: Fitting Parameters for Frequency-Domain GSR Results.
RFP
TagRFP-T
TagRFP-T
TagRFP-T
mCherrymCherry
Kriek

Intensity (kW/cm2 )
25
8
4
25
8
25

τGSR (ms)
0.22
0.20
0.26
0.99
1.2
2.8

τS1D (µs)
15
14
15
36
40
43

τDS1 (ms)
0.017
0.031
0.058
0.14
0.50
0.40

positively correlated to phase advance. Conversely, Figure 2.8c shows the phase shift decreases
with increasing kDS1 as it corresponds to decreased dark-state population. By examining the effectof each parameter on the value of phase advance, it was possible to narrow down the initial rangeof kinetic parameters to permit fitting of the spectra.
The excitation rates (kex ) were calculated using Eqn 2.7 and emission rates (kem ) were approximated as the reciprocal of the excited-state lifetime of the RFPs found in literature [56, 57].
The dark-state recovery times (kGSR ) were taken from the time-domain GSR rate constants. The
best fit of the phase spectra was obtained after a few rounds of iterating the parameters kS1D
and kDS1 . Figure 2.8d shows the result for the best set of values for the mCherry phase spectra
at 25 kW/cm2 with kS1D = 28 kHz and kDS1 = 7 kHz. Inability of the fit to perfectly capture
both the low and high frequency range of the data may be due to the presence of multiple DSC
pathways. Table 2.3 displays the best fitting results for other FPs at various laser intensities. The
time constants, τ , for the different process in Table 2.3 are the reciprocals of the corresponding
rate constants.

2.6

Discussion
The time-domain measurements were done on RFPs in E. coli, whereas the frequency-domain

measurements were done on solutions with purified proteins. Time-domain measurements of thepercent recovery require the proteins to be immobilized on the longest time-scale of the pulse sequence which is ∼ 260 ms. To mitigate the effects of diffusion, we tried to perform time-domain

43

Figure 2.8: (a) Frequency-domain results comparing the region of phase advance for mCherry,
TagRFP-T, and Kriek at 25 kW/cm2 . The solid black line represents the zero phase shift. (b)Simulated phase spectra showing the dependence of kS1D at a fixed value of kGSR . The valueof kS1D was varied from 10 kHz to 50 kHz with an interval of 10 kHz. Other parameters usedwere kex = 7.4 MHz (∼ 25 kW/cm2 ), kem = 530 MHz (τ ∼ 1.87 ns), kDS1 = 6 kHz, kGSR =1011 Hz, kS1B = 100 Hz and, kDB = 0 Hz. (c) Simulated phase spectra demonstrating that phaseshift increases with decreasing of kDS1 .The values of the parameters were kex = 7.4 MHz (∼ 25
kW/cm2 ), kem = 530 MHz (τ ∼ 1.87 ns), kDS1 = 2-10 kHz, kS1D = 25 kHz, kGSR = 1011 Hz, kS1B
= 100 Hz and, kDB = 0 Hz. (d) Best fit obtained for mCherry at 25 kW/cm2 by systematic studyof various relevant parameters gives τS1D = 36 µs and τDS1 = 0.14 ms.

measurement of RFPs in 50 µm microdroplets prepared by mixing 1-octanol with the buffer containing purified RFPs in a 9:1 ratio according to a previously reported method [130]. As seen in44
Figure 2.9, using the microdroplets, RFPs did not show the monotonic decay due to photobleaching(Figure 2.3), but instead it shows a rise in fluorescence intensity associated with diffusion of FPs
into the excitation volume. Figure 2.2a shows that by using RFPs expressed in immobilized E.
coli, this diffusion artifact can be avoided.

Figure 2.9: Time-domain fluorescence trace of mCherry in microdroplet showing that the decay
is not monotonic due to diffusion-related artefacts. The exposure and dark time used in thisexperiment was 2 ms and 1 ms respectively.

On the other hand, the frequency-domain measurements in bacteria are tricky to perform
because of two reasons: 1. In bacteria, due to low protein content, signal intensity was low resulting
in noisy phase-shift and 2. Due to less ground state recovery times for the RFPs, it requires longer
exposure that assists the FPs to bleach completely before the phase is measured. So, RFPs in
solution is perfect for the frequency-domain measurements. To see whether the phase shift is
influenced by the diffusion of the proteins in solutions, we compare the phase shift of purified
mCherry in solution and mCherry expressed in E. coli attached to the cover-slip using poly-Llysine. It was shown that phase-shift using purified proteins and immobilized bacteria did not
differ by much [33] and within error limit. So it proves that the frequency-domain method does

45
not have the diffusional artifact in it which is also supported by the fact that phase shift is theproperty of bulk solution.
The experimental and simulation results show that the time- and frequency-domain measurements are complementary. The time-domain experiments give accurate measurements of photobleaching and GSR rate, whereas the frequency-domain experiments are more sensitive to thedark-state conversion rates (kS1D , kDS1 ). The intensity-dependent, time-domain measurements
reveal that the kGSR is nearly constant in the intensity range from 4 kW/cm2 to 25 kW/cm2 . In
these measurements, a correlation between the limiting percent recovery and kGSR is also notable
as displayed in Table 2.1. TagRFP-T has the highest GSR rate and the lowest P Rlim (∼44%) while
Kriek has the slowest GSR rate but highest limiting percent recovery (∼64%) at 25 kW/cm2 laser
intensity. mCherry shows intermediate GSR rates and P Rlim (∼56%). This decrease in percent
recovery with increase of GSR rate is also supported by our simulation results of time-domain
experiments (Figure 2.6d).
The frequency-domain results are consistent with the expected intensity dependence of thephase advance [128, 127] (Figure 2.5d), which increases with intensity. Although the modulation
frequencies at the maxima of the phase advances (∼1 kHz) are not widely different for the three
RFPs, the shapes of the spectra are different at a particular laser intensity as shown by Figure 2.8a,
where the solid black line represents the zero phase shift. It can be clearly observed that, at thesame laser intensity, the crossover point from positive to negative phase shift happens in the RFPs
in the order from lowest to highest frequency: Kriek, mCherry, and TagRFP-T, demonstratingthat the region of phase advance in frequency space is larger for TagRFP-T than for mCherry andKriek.
Our experiments and simulations of time- and frequency-domain measurements suggest thatthe rate constants of DSC and GSR are correlated. As it is demonstrated in Table 2.3, at 25
kW/cm2 , TagRFP-T has both fastest GSR and DSC time constants as 0.22 ms and 15 µs, respectively. On the other hand, Kriek has both slowest GSR/DSC rates (2.8 ms and 43 µs, respectively).
This correlation between DSC and GSR time constant supports our previous findings [56]. We found46
that the relative rate of the GSR/DSC of the three RFPs are as follows: TagRFP-T > mCherry >Kriek. The largest DSC and GSR rate constants of TagRFP-T among the three RFPs studied heresuggests that it is more susceptible to blinking or has higher kS1D and kDS1 compared to mCherry
and Kriek, which is also in agreement with our previous photobleaching and DSC measurements of
RFPs [56].
Interestingly, from our measurements of GSR/DSC and previously reported photobleachingtendencies of the FPs, a correlation between the photostability and DSC or GSR rates can benoted. From our time-domain irreversible photobleaching study we observe that, at 25 kW/cm2 ,TagRFP-T (τBl , 5 ms) has almost 6 times higher photodestruction rate compared to mCherry (τBl ,28 ms), whereas Kriek (τBl , 49 ms) bleaches ∼2 times slower than mCherry. Our previous studyof closely related RFPs indeed shows a weak correlation between percent dark-state conversion
and irreversible photobleaching [56]. Although this trend needs to be examined more broadly, theavailable data suggest that GSR/DSC measurements may serve as a proxy for susceptibility tophotobleaching. Therefore, selection of FPs based on their DSC/GSR kinetics can be performed togenerate photostable FP variants. The correlation between DSC/GSR and photostability indicatesthat dark state involved in the FP studied here is photo-protective. However, if the dark state is
photo-destructive and susceptible to higher order excitation, this trend of DSC/GSR and photostability may not exist. To employ a selection strategy based on DSC/GSR, intensity-dependence of
DSC/GSR kinetics should also be considered. A fluorescent protein, selected based on DSC/GSR
using high illumination intensity may posses different dark state dynamics at low irradiation.
For the three RFPs examined here, the 10−3 to 10−4 s GSR time scales and the 10−5 s DSC
time scales fall within the range of “flickering” time scales observed by FCS and single molecule
fluorescence techniques [112, 115, 36, 35]. For example, an FCS study of yellow FPs by Schwille etal. revealed conversion to a dark state populated on the ms to µs time scale, with a flickering ratethat varies linearly with laser intensity [36]. The current results indicate the values of kGSR andkS1D are nearly constant at intensities from 4 kW/cm2 to 25 kW/cm2 , whereas the rate from darkstate to excited state (kDS1 ) increases with intensity. This variation in kDS1 explains the intensity

47
dependence of the flickering observed in FCS. Similar intensity-dependent sub-ms flickering was
obtained in FCS measurements of dsRed and mCitrine by Heikal et al. [112] and in a FCS studyof solution-phase and immobilized eqFP611 and drFP583 by Schenck et al. [35]. The similarity ofthese time scales suggests that although some tuning occurs due to the specific structural context ofa particular FP, the flexibility of the chromophore environment is maintained across a wide range ofmonomeric, dimeric, or tetrameric FPs, regardless of whether they are derived from GFP, DsRed,or other wild-type species.
For photo-switchable FPs, dark-state structural changes, including formation of radicals or
triplet states, cis-trans isomerization, different protonation states of the chromophore, and changes
in the chromophore hydrogen bonding, may serve as modulators of biochemical activity [38, 37, 131,132]. For example, crystal structures of the photo-switchable FPs Dronpa and Padron reveal thatswitching is due to cis-trans chromophore isomerization [37, 133, 44]. An NMR study on Dronpa
switching relates the loss of fluorescence in the trans chromophore isomer to the disruptions ofhydrogen bonds and β-barrel flexibility [134]. Isomerization can be coupled to the exterior of
the barrel and the solvent, as demonstrated by the viscosity-dependent photo-switching kinetics
of Dronpa and the Dronpa-3 variant [135] or it can be coupled to the interaction between βbarrel subunits, as exemplified by tetrameric Dronpa K145N mutant, which shows photo-switchingrates slower than that of monomeric Dronpa [43]. Apparently, oligomerization decreases β-barrel
flexibility hence slowing the isomerization. A molecular dynamics simulation study of Dronpa
mutants supports the notion of a positive correlation between flexibility and increased cis-transisomerization rates [45]. Isomerization is not the only important structural change in the darkstate. In particular, studies of IrisFP suggest that the formation of the dark-state chromophorewith a distorted structure is due to the protonation of the chromophore methylene bridge byneighboring side chain of residue Arg66 [136, 137]. In addition, the photo-switchable FP Dreiklangshows a unique blinking mechanism in which dark-state formation is due to hydration-dehydration
reactions [124]. Hence, it is clear that the formation of the dark state can be due to the flexibilityof the β-barrel interior to accommodate a range of ms and sub-ms time scale structural dynamics.

48
The measurement techniques presented here could be useful for high-throughput screening andsorting of FP clones with a diverse range of switching mechanisms and rates, which could be
pivotal in the generation of improved photo-switching FPs with useful bio-photonic functionality
for sensing and actuating biological processes. For this purpose, the frequency-domain technique ismost suited because it only involves measurement of phase advance from a lock-in amplifier output
using sinusoidally modulated excitation. One drawback of this technique is that it might need highlaser intensity to observe an appreciable amount of positive phase shift which might be detrimental
to viability in cell-based library screening. Time-domain GSR measurement can be helpful foroff-chip GSR time analysis of various FPs.

2.7

Conclusion
In this chapter, we report two methods of measuring the ground state recovery and dark-state

conversion rates of fluorescent proteins and compare the results we obtained. We found that theswitching rates of the three RFPs (mCherry, TagRFP-T, and Kriek) fall in the range of ms to µs
time-scale, which is consistent with previous observations on similar FPs. It has been observedthat the time- and frequency-domain methods described here are complementary to each other.
While accurate GSR times can be obtained using the time-domain technique, phase-shift-basedfrequency-domain method is most-suited for measurements of the rates of DSC processes. We also
found a correlation between GSR and DSC time constants of the RFPs studied here. Our results
show that the GSR rate and rate from excited state to the dark state (kS1D ) are almost constantfor 4 kW/cm2 to 25 kW/cm2 , although rate from dark state to excited state (kDS1 ) shows slightintensity dependence in this intensity range. This type of measurement can shed light into thevarious photophysical processes in FPs and rates associated with them. Based on the speed ofthe measurement and the direct experimental readout of the phase advance, the frequency-domain
method is more suitable for the implementation of high-throughput screening and sorting on thebasis of ground-state recovery times and dark-state conversion rates.

Chapter 3
Single Molecule Fluorescence & Dark State Dynamics in Red FluorescentProteins

3.1

Publication Note
A portion of this Chapter has been adopted from Mo et al., Genetically encoded biosensors

for visualizing live-cell biochemical activity at super-resolution, Nature Methods, 14(4):427-434,
2017 [65] and the corresponding Supplementary Information of the article.

3.2

Introduction
Using single molecule measurements, a variety of photo-physical dynamics have been investi-

gated in the previous decades. Protein folding-unfolding, spectral diffusion, triplet state blinking,multichromophoric assemblies etc. are few notable examples of that [138, 139, 140, 141]. Various
dynamical features in a system are obscured in the ensemble-level measurements through statistical
averaging of its molecular properties. On the other hand, single molecule-based techniques generate
the distribution of certain photo-physical parameters, such as fluorescence off and on-times, thus
revealing the heterogeneity of the system. This provides unprecedented insights into the dynamics
of the system under investigation which are otherwise not possible with bulk-scale measurements.
Single molecule techniques can shed light on very rare processes in the molecules which might beovershadowed by more frequent or abundant processes [138]. Two main requirements of this tech-

50
nique are: 1) use of very dilute sample so that single molecules are under investigation and 2) ahigh signal to noise ratio.
The first single molecule study of fluorescent proteins was carried out by Dickson et al. [32].
The authors observed “blinking” in GFP mutants and revealed the intensity dependence of thefluorescence fluctuation. This study found that GFP-mutants could be trapped in very long-lived
dark states with 488-nm illumination and could be recovered back to the bright states with 405-nm
excitation. After this initial finding, several other groups contributed to unraveling the hidden
dynamics of FPs at single molecule level [142, 143, 144, 145, 146, 147]. Single molecule studies
by Garcia-Parajo et al. and others revealed the intra-molecular energy transfer process in DsRed
between different monomer subunits [144, 114]. The sequential bleaching mechanism of DsRed wasobserved by Lounis et al. using single molecule spectroscopy [114]. Intensity and pH-dependent
single molecule experiments on GFP mutants by Peterman et al. revealed that the “blinking” iscorrelated with the time-scale of cis-trans isomerization of the chromophore in those FPs and was
not associated with protonation-deprotonation of the chromophore [143]. Recently, the fluorescence
fluctuation and photo-activation properties of FPs have been harnessed to generate a large pool ofsuper-resolution microscopy techniques [15, 17, 122]. The photostability of the related FPs needs
to be enhanced in order to improve the performance of these diffraction-unlimited microscopies. A
detailed understanding of the single-molecule dynamics of the FPs in the context of its immediate
chromophore environment, pH, illumination intensity is still missing. A mechanistic understandingof the fluorescence dynamics in FP is required for the suppression of fluctuation to create biomarkers for regular fluorescence-based approaches or to induce efficient “blinking” imparting highimage contrast and the generation of super-resolution probes with reduced photobleaching andphoto-fatigue [42, 148]. A holistic view of single molecule properties and their ensemble-level
manifestation is of utter importance for achieving this goal.
In this Chapter, we explore the connection between single-molecule fluctuation of red fluorescent proteins and their dark state conversion (DSC)/ ground state recovery (GSR) processes
measured at the bulk scale. In particular, we studied the Fluorescence fLuctuation Increase by

51
Nanometer Contact (FLINC) phenomenon discovered by our collaborators [65] . We have measured GSR/DSC and excited state lifetime of these FLINC constructs as a function of irradiance.
Finally, Monte-Carlo based simulation were performed to explain the dark state dynamics of those
constructs with a 3-state model system consisting of ground, excited and dark state.

3.3

Fluorescence f Luctuation Increase by Nanometer Contact (FLINC)
Dronpa [39], a photo-switchable GFP, is routinely used in various super-resolution based

techniques including PALM, STORM, RESOLFT etc. It can be switched between on and off
states with 405-nm and 488-nm laser illuminations, respectively [37]. On the other hand, TagRFPT [99, 11], is an RFP derived from eqFP578 used as a bio-marker in fluorescence microscopy.
TagRFP-T has been reported to show “blinking” or fluorescence fluctuation that was used to
implement Super-resolution Optical Fluctuation Imaging (SOFI) [149]. Recently, the study by Mo
et al. reported that the “blinking” behavior of TagRFP-T varies in a distance-dependent manner
when fused to Dronpa with a short flexible linker (comprising of 8 amino acid) and expressedat the plasma-membrane [65]. This proximity-based effect resembles to Fluorescence Resonance
Energy Transfer (FRET) in which a blue-shifted donor transfers its energy to a red-shifted acceptor
in a non-radiative manner. Here, in the vicinity of another FP (Dronpa), the the amplitudeof fluorescence fluctuations of TagRFP-T are increased several fold. This phenomenon had beentermed Fluorescence fLuctuation Increase by Nanometer Contact (FLINC) which has been coupledwith SOFI to study enzymatic activities in live-cells at a ∼ 3 fold higher spatial resolution thanthe diffraction-limit.
Figure 3.1a shows the representative fluorescence images of TagRFP-T (TT), co-targeted
Dronpa/TagRFP-T (Dp+TT), Dronpa-linker-TagRFP-T (DpTT) and EGFP-linker-TagRFP-T expressed in plasma-membrane of HeLa cells via a myristoylatedpalmitoylated sequence (lyn). Upon561-nm illumination, the DpTT construct showed ∼10 fold decrease in mean fluorescence intensityand ∼ 5 fold increase in the amplitude of fluctuation (see Figure 3.1c & d) compared to wild-type

52

a

c

b

d

Figure 3.1: (a) Representative fluorescence images of TagRFP-T (TT), co-targeted Dronpa andTagRFP-T (Dp+TT), Dronpa-linker-TagRFP-T (DpTT), EGFP-linker-TagRFP-T (EGTT) expressed in plasma-membrane of HeLa cells. The images have been acquired by 561-nm illumination and 35 ms exposure time. Scale bar: 10 µm. (b) Representative images of Dronpa
S142D-linker-TagRFP-T (S142D), Dronpa C62G Y63G -linker-TagRFP-T (GGG), Dronpa N102Ilinker-TagRFP-T(N102I) and Dronpa N102I R149E-linker-TagRFP-T (NIRE) expressed in plasmamembrane of HeLa cells. 561-nm illumination and 35 ms exposure had been used to acquire theimages. Scale bar: 10 µm. (c) Normalized skewness values of TagRFP-T (TT), WT (DpTT),S142D and other constructs as quantified from the time-series Total Internal Reflection Fluorescence (TIRF) images (Eqn 3.1). In S142D & GGG constructs Dronpa was non-fluorescent andfluctuation was still observed as observed from their relatively high normalized skewness. In N102I
and NIRE constructs mutations were introduced in outer-side of the barrel and this suppressed
the fluctuations observed in DpTT construct. (d) Mean fluorescence intensities of the constructs.
DpTT (WT) had ∼ 10 fold reduction in the mean fluorescence intensity compared to TagRFP-T.
Pair-wise t-test results are marked where data were compared with the construct marked “ref”; nnumbers are marked in the corresponding bars. n.s.: not-significant; *: p<0.05; **:p<0.01; ***:
p<0.001 where applicable.

TagRFP-T.

53
It was assumed that fluorescence fluctuations arise from the reversible transition of the
TagRFP-T chromophore between an emissive (bright) and non-emissive (dark) state and denoted
the lifetime of these two states as τon and τof f , respectively [150]. In observing N identical but fluctuating fluorophores, the probability that emission from n fluorophores will be detected at any given
ontime is given by a binomial distribution with probability of success pon = τonτ+τ
(bright-stateof fprobability) and number of trials n. In principle, the distribution of the fluorescence recorded in eachdetector pixel over time could be used to estimate the degree of fluorescence fluctuations. We considered two statistical parameters as being appropriate: skewness (defined below) and normalized
2
variance σnorm. From a series of Monte Carlo simulation, it was found that skewness approximatedthe fluctuations of the fluorophore well and displayed a close correspondence with pon . A decreasein pon (i.e., more fluctuations) unambiguously corresponds to an increased skewness. In contrast,
normalized variance is strongly dependent on imaging conditions and is non-monotonic, making
it unsuitable. Skewness is also only marginally sensitive to changes in fluorophore brightness andessentially independent of fluorophore diffusion. We therefore considered skewness to be a good
metric for estimating the degree of fluorescence fluctuations. The normalized skewness (g) of asample is defined as:
1 Pj
3
i=1 (xi − x̄)jg = 1 Pj
( j i=1 (xi − x̄)2 )3/2

(3.1)

where, x̄ is the sample mean and j is the number of the sample. This calculation was performedon the time series from each pixel within a cell. To account for possible photobleaching of thefluorescent probe, the average skewness of each time series (840 to 1700 in size) in intervals of 50,
100, and 170 images was measured. The mean skewness over different interval sizes is displayed in
Figure 3.1c.
Figure 3.2a shows the distance-dependence of the FLINC phenomenon. A series of Dronpalinker-TagRFP-T were generated by inserting rigid helical linkers (H1, H2 etc.) of known lengths[151]. It is obvious from the Figure 3.2a that normalized skewnesses of the constructs approach the

54value of the skewness of that of Dp+TT after H5. This indicated that FLINC is functional in the
5-6 nm range. In the Dp+TT construct, where Dronpa and TagRFP-T were expressed together
in plasma-membrane, the fluctuation presumably reduced due to larger distance between themcompared to the DpTT construct.
It was shown that the FLINC phenomenon was induced by the β-barrel of Dronpa andnot by its chromophore. The S142D & “GGG” (C62G/Y63G) variants of Dronpa which show
no fluorescence due to absence of a functional chromophore were used to create DpTT-like constructs. This constructs showed identical fluorescence fluctuation as the DpTT constructs. Further
random-mutagenesis on Dronpa revealed mutations involved in the interaction between Dronpa andTagRFP-T. It was reported that a combination of N102I & R149E mutations that point outside ofthe β-barrel were enough to restore the brightness of TagRFP-T and showed reduced fluctuation(see Figure 3.1). This proves that the FLINC is induced by a charge-charge interaction between thebarrels of TagRFP-T and Dronpa. In fact the electrostatic interactions between the those FPs had
been corroborated by the basic pocket and acidic patch in the surface of Dronpa and TagRFP-T respectively (Figure 3.2d). Size-exclusion chromatography shows the presence of a small hetero-dimer
peak of Dronpa and TagRFP-T [65].
The in-vitro spectral analysis of DpTT constructs showed the presence of immature TagRFPT. This indicates that the charge-charge interactions between the barrel of TagRFP-T and Dronpa
might divert the maturation process of the TagRFP-T to produce immature proteins. Presumably
the over-expression of TagRFP-T in the plasma-membrane obscure the “blinking”. Due to thegeneration of a fraction of immature proteins, the effective label-concentration might be reduced forthe DpTT construct, thereby enhancing the fluorescence fluctuation. The generation of immature
proteins also explained the ∼ 10 fold decrease in brightness in DpTT constructs compared to TT.
Figure 3.2c displays the results of the single molecule experiments with purified TagRFP-T& DpTT in vitro at 561-nm with 24 W/cm2 irradiance. Values of τon and τof f measured from thesingle molecule traces showed that DpTT had 21 % faster τon compared to wild-type TagRFP-T(111.3 ms vs 141.3 ms respectively). The values of τof f were similar for both of the proteins (∼ 80

55

a

b

c

d

Figure 3.2: (a) The distance dependence of FLINC shown by introducing rigid helical linker withknown distance between Dronpa and TagRFP-T. Fluorescence fluctuations (quantified by normalized skewness) of constructs practically reduced to wild-type TagRFP-T after H5. (b) Schematic of
the fluorescence trace obtained in a single molecule measurement. The dwell time of the moleculesin the ON/fluorescent states (ton ) and OFF/non-fluorescent states (tof f ) are collected from the fluorescence trace. From the distribution of ton and tof f , the value of the ON and OFF time-constants,i.e., τon and τof f are obtained. (c) Single molecule fluorescence traces of TagRFP-T and Dronpalinker-TagRFP-T (DpTT) revealed that the later had faster τon (141.3 ms in TagRFP-T; 111.3 ms
in DpTT) while τof f was similar for both of the constructs (∼ 80 ms). (d) Electrostatic surfaces ofDronpa and TagRFP-T calculated by the Adaptive Poisson-Boltzmann Solver (APBS) plug-in in
PyMOL. The basic pocket in Dronpa and the acidic patch around the chromophore in TagRFP-T,
both features believed to be important for FLINC, have been highlighted. Pair-wise t-test results
are marked where data were compared with the construct marked “ref”; n.s.: not-significant; *:p<0.05; **:p<0.01; ***: p<0.001 where applicable.

ms). Now, it is known that τon and τof f due to single molecule blinking are related to the trappingof the molecule in a long-lived dark states [139]. In particular, τon is related to the conversion ratefrom bright state to the dark state (i.e. dark state conversion, DSC) and τof f is related to the56transfer rate of dark state population in the bright state (i.e. ground state recovery) (see Figure
3.10a). In a 3 state model consisting of two bright states (S0 & S1 ) and a dark state (D), τon andτof f are related in the following way [139]:

1
τon
1
τof f

= kex

kdsc
kr + kic + kgsr

= kgsr

(3.2)
(3.3)

where, kex , kr , kic , kdsc and kgsr are rate-constants involving excitation, radiation (emission), internal conversion, DSC and GSR respectively (Figure 3.10a). The derivation of the above equations
assumes that kex is very small compare to the combined rate of radiative, DSC and internal conversion processes, i.e., kex << (kr +kic + kdsc ). This assumption will be explained later in detail(Section 3.5.2).
We carried out experiments to quantify the kinetics of DSC and GSR processes of TagRFP-T
and DpTT. We also measured the excited state lifetime of those constructs.

3.4

Photo-physical Measurements of TagRFP-T & DpTT Constructs

3.4.1

Cell culture and transfection
For DSC and GSR measurements HeLa cells expressing nuclear-localized TagRFP-T and

DpTT were used. HeLa cells were cultured in DMEM medium with FBS (Fetal Bovine Serum),
Penicillin/Streptomycin to ∼100% confluency in 100 mm dish. Next they were split cells into 60
mm imaging dishes 24 hrs prior to transfection. Typically 40-70% of confluency was targeted attime of transfection, which generally translates to a seeding density of 1.5-2 ×105 cells per dish.
Cells were transfected using Lipofectamine 3000 reagent, following a standard protocol using OptiMEM media for diluting reagents and DNA. 1.5 µg of plasmid DNA was used per transfection.
DNA/Lipofectamine mixture was incubated for 20 min at room-temperature. The mixture was

57
added drop-wise to cells in the imaging dish and the dish was rocked to mix. Cells were incubatedat 37◦ C & with 5% CO2 for 48 hours prior to measuring DSC/GSR kinetics. Purified proteins
were used for excited state lifetime measurements. For intensity dependence of GSR time-constants,
TagRFP-T expressed in yeast was used.

3.4.2

Instrumentation & Analysis
HeLa cell expressing TagRFP-T/DpTT were irradiated with a 561-nm solid-state diode-pumped laser (Genesis MX, Coherent) through a custom-built inverted microscope at an irradiance of ∼ 8-10 kW/cm2 . A 629/56-nm band-pass filter (Semrock) was used to remove excitationfrom emission. Epifluorescence from the cells was collected using a PMT (Hamamatsu Photonics)
and subsequently the PMT photo-current was converted to voltage using a custom-built transimpedance operational amplifier for a better signal to noise ratio. Fluorescence signal was digitizedusing a data acquisition card (DAQ, National Instruments) at a rate of 1 MHz. Pulsed illumination
was achieved using an acousto-optic modulator (AOM, Gooch & Housego, 35210-BR). For DSC
measurement, cells expressing FPs were irradiated continuously for 0.1 s and fluorescence traces
were collected for further analysis.
The raw fluorescence data collected for DSC measurements were background-corrected andspline-fitted for smoothing. The fluorescence traces had three distinct features: a rapid decay up
to 100 µs time-scale, a flat region in 100-1000 µs region and finally a slow decay on a ms timescale. Hence, the fluorescence traces were fitted with a 3-exponential function using equi-distanttime-points in log scale. The fastest time-constant and its corresponding weight from the fitting
results were taken as the DSC time-constant and percent DSC respectively [56].
To measure the GSR time-constants, FPs were excited with pulse-trains having 2 ms exposure
time and varying inter-pulse delays (dark time) ranging from 5 µs to 10 ms. Typical fluorescencetraces for this measurement are displayed in Figure 3.3. Fluorescence traces collected using thepulsed excitations were used to compute percent recoveries without further processing using the

58

Figure 3.3: Typical pulsed fluorescence trace in Ground State Recovery rate measurements. This
figure shows fluorescence trace of TagRFP-T with 5 ms dark time and 2 ms exposure time at ∼ 10
kW/cm2 .

following equation:

PR =FR − FB
× 100
FL − FB(3.4)

where, PR is the percent recovery; FL, FB, FR are initial fluorescence intensity, fluorescence intensity after 2 ms exposure and fluorescence recovered between pulses, respectively (Figure 3.3).
Home-built software (Matlab) was used to compute percent recoveries of the fluorescence and finally percent recovery vs. dark time plots were fitted with single exponential to extract GSR
time-constants [33].
Excited state lifetimes of mCherry, TagRFP-T, DpTT were measured with a commercial
TCSPC system (Fluro Time 100, PicoQuant) using 560-nm laser excitation with a repetition rateof 2.5 MHz. Lifetime measurements were performed with purified proteins and PBS buffer (pH=7.4)
was used for necessary dilutions. The fluorescence transients of the FPs were fitted with iterative
reconvolution with a bi-exponential function and using the measured instrument function (IRF) of
the system.

59

Figure 3.4: Fluorescence traces of TagRFP-T (red dot) and DpTT (black square) with their corresponding fits (red and black lines respectively) measured at ∼ 8 kW/cm2 illumination intensity.
DpTT shows higher dark state population and faster DSC time-scale than TagRFP-T.

3.4.3

Results
Fluorescence decays of TagRFP-T and DpTT with their fits are depicted in Figure 3.4. These

decays are the average of traces from 5 individual HeLa cells.
Table 3.1 gives DSC time-constants and percent DSC of TagRFP-T and DpTT in a “mean± standard deviation” format. The results show that DpTT has 25% faster DSC time-constant
than TagRFP-T whereas percent DSC (or dark state population) of DpTT is 26% higher thanTagRFP-T.
Figure 3.5 depicts percent recovery vs. dark time plot for the constructs. Single exponential
fitting of percent recovery gives GSR time-constants of TagRFP-T and DpTT as 50 µs and 51 µs
respectively. We therefore conclude that TagRFP-T and DpTT have similar GSR kinetics.
The intensity-dependence of the GSR time-constants is demonstrated in Fig 3.6. The measurements were done with TagRFP-T expressed in yeast cells. The GSR time-constants extracted
from the corresponding dark time and percent recovery are revealed as 150 µs and 96 µs at 2
kW/cm2 and 5 kW/cm2 illumination irradiances respectively. The results clearly show that ground60
Table 3.1: Time-constants of different photo-physical processes for TT and DpTT.
Constructs
TagRFP-T
DpTT

Percent DSC (%)
19 (± 4.0)
24 (± 1.8)

τDSC (µs)
24 (± 5.8)
18 (± 1.4)

τGSR (µs)
5051Lifetime (ns)
2.45
2.40

Figure 3.5: Percent recovery vs. dark time plot for TagRFP-T and DpTT constructs measured
at 10 kW/cm2 . Single exponential fitting of the percent recoveries give GSR time-constants of
TagRFP-T and DpTT as 50 µs and 51 µs respectively.

state recovery process is an intensity-dependent phenomenon. At higher illumination intensity, therecovery process is faster. The intensity dependence might originate from higher order absorptionof the dark states and subsequent relaxation. Alternatively, a light-driven transition can happen
from dark state to bright state in a non-radiative fashion, thus accelerating the recovery processes.
These different mechanisms are discussed in the Simulation section (Section 3.5) of this Chapter.
The intensity-weighted average excited state lifetime of mCherry, TagRFP-T and DpTT were
measured as 1.70 ns, 2.45 ns and 2.40 ns respectively. This shows that TagRFP-T and DpTT havealmost same excited-state lifetimes and they have similar fluorescence dynamics in ns time-scale.
Figure 3.7 displays the fluorescence decays of the FPs and instrument response function (IRF).

61

Figure 3.6: The intensity-dependence of GSR time-constants for TagRFP-T expressed in yeast.
It is obvious that with higher illumination intensity, τGSR decreases, indicating that the recovery
process is associated with excitation of dark state population or transition from dark state to brightstate via non-radiative process.

Figure 3.7: Fluorescence decays of the FPs and instrument response function (IRF). Iterative
reconvolution fits give average excited state lifetime of of mCherry, TagRFP-T and DpTT as 1.70
ns, 2.45 ns and 2.40 ns respectively. The measurement were performed on purified proteins.

62

3.5

Simulation of Single Molecule Fluorescence
To investigate the single molecule “blinking” and dark state dynamics more closely, we per-formed Monte Carlo based simulation using different model systems. First, we used a simple 2 statemodel system consisting of bright and dark state to understand the underlying influence of different
rate-constants into the ON and OFF dwell-times of the system. Next we moved to a 3 state modelthat consist of ground state (S0 ), excited state (S1 ) and a dark state (D0 ). This is a more realistic
model system to describe the FP dynamics. With this model, the intensity-dependence of τon canbe nicely explained as reported by various authors [32, 143, 142]. However, this model fails tojustify the intensity-dependence of GSR and hence associated τof f constants. So, we proposed amodified 3 state model system that accounts for the light-driven recovery of dark state population
into the ground state.

3.5.1

2 State ModelIn this 2 state model, the molecule can be either in fluorescent/bright state (ON, A) or non-fluorescent/dark state (OFF, B). The rates of their inter-conversions are given by k1 and k2 . k1 is
the forward reaction rate from A (ON) to B (OFF), whereas k2 is the backward reaction rate fromB (OFF) to A (ON).
A

k1
k2

B

Now for a time-window of ∆t, the probability of the molecule of leaving the state A is given by:p(A) = 1 − e−k1 ∆t(3.5)

similarly, the probability of leaving the state B is given by:p(B) = 1 − e−k2 ∆t

(3.6)

63

Figure 3.8: Algorithm to generate single molecule fluorescence trace from a 2 state model system
consisting of ON and OFF states.

For a sufficiently small ∆t, p(A) and p(B) can be approximated as k1 ∆t and k2 ∆t respectively.
From the above-mentioned probabilities, one can compute the average dwell time of the system in
ON and OFF states and subsequently fluorescence traces can be calculated. The algorithm isdepicted in Figure 3.8.
Figure 3.2b shows the representative single molecule fluorescence traces obtained by using
this algorithm. The dwell times for ON and OFF states are collected from these traces to generate
a series of ton and tof f . The histograms for ton and tof f are shown in Figure 3.9. Finally the
distribution of ton and tof f are fitted with a single exponential function to extract τon and τof f
respectively.
In this simulation, time-step (∆t), k1 and k2 of 0.01 s, 5 s−1 3 s−1 respectively were used.
The simulation was run to generate the fluorescence signal for 200 s. As Figure 3.9 displays, τonand τof f calculated from these histograms were 0.19 s and 0.30 s respectively which is almost equal
to the reciprocal of the forward and backward reaction rates (5 s−1 and 3 s−1 ).

64Figure 3.9: Distribution of ton and tof f and single exponential fitting (red lines) to extract τon andτof f .

Additional simulations were performed varying the backward and forward reaction rates andsubsequently computing the τon and τof f from the single molecule fluorescence traces. It was foundthat τon and τof f were always the reciprocal of k1 and k2 . So, it can be concluded that for a simple
two state model, τon and τof f are related with k1 and k2 in the following ways:

τon =
τof f

=

1
k1
1
k2

(3.7)
(3.8)

This result makes sense as k1 is the rate-constant for bright state to dark state and hence dictates
dwell time in ON states or τon . On the other hand, k2 is the rate constant for dark to bright state
conversion and consequently it is related to τof f . This initial idea gained in 2 state model system
was propagated into a 3 state model system to obtain τon and τof f consisting of multiple brightand dark states. For such a complex system, the “blinking” dynamics can be computed if various
model parameters (kgsr , kdsc , kr , kex etc.) are known.

65

3.5.2

3 State Model
Now we use a 3 state model system (Figure 3.10a) to explain the single molecule kineticsof TagRFP-T and DpTT constructs. In this model, the molecule once excited into S1 can emit
fluorescence by radiative transition into ground state (S0 ) or become trapped in a long-lived darkstate (D). The molecule can return to the ground state from dark state through non-radiative
decay. The necessary parameters for this model e.g. kdsc (1/τDSC ), kgsr (1/τGSR ) and excitedstate lifetime of those constructs were measured and displayed in Table 3.1.
Assuming the excitation rate-constant is very small compared to (kr + kic + kdsc ), thepopulation in S1 ([S1 ]) is very small and constant at a given time. Therefore, a steady-state
approximation can be applied on [S1 ]:d[S1 ]= kex [S0 ] − (kr + kic + kdsc )[S1 ] = 0
dt

(3.9)

The above equation can be re-arranged to give,[S1 ] =kex
[S0 ]kr + kic + kdsc

(3.10)

Now in analogy to the 2 state model, for a 3 state model τon and τof f can be given by:

kon =

1
kex
kdsc
=
τon
kr + kic + kdsc

(3.11)&

1= kgsrτof f(3.12)

1=τ
kr + kic + kdsc

(3.13)

kof f =
Again,

where, τ is excited state lifetime. Substituting Eqn. 3.13 into Eqn. 3.11 we find that,1= kex kdsc τ
τon(3.14)

66
kex in the above equation can be obtained by the following way,

kex =

Iσλ
hc

(3.15)

where, I, σ, λ are illumination irradiance (∼ 24 W/cm2 ), absorption cross-section of the FP andwavelength of the excitation (561 nm). σ can be calculated from the extinction coefficient of theFPs:

σλ = 2.303 ×

λ
× 103
NA

(3.16)

where, λ is the molar extinction coefficient at λ (71 M-1 cm-1 ), NA is the Avogadro’s constant.
Using the τDSC , τGSR and excited state lifetime of TagRFP-T and DpTT (from Table 3.1) into Eqn
3.14 and Eqn 3.12, we can calculate values of τon and τof f . The values of τon calculated from thismodel for TagRFP-T and DpTT are 532 ms and 408 ms respectively. The value of τof f for both ofthe constructs was ∼ 50 µs. So this model predicts that DpTT has ∼ 23% faster τon than TagRFPT which agrees well with the single molecule experiment where we observed that DpTT showed 21%
faster τon relative to TagRFP-T (141.3 ms, TagRFP-T; 111.3 ms, DpTT). However, it is notablethat although these analytical expressions predict the relative τon for the constructs qualititatively,
the absolute values are far off. Also, τof f calculated from these analytical expressions was ∼1000
fold faster than the experimentally observed τof f . Hence, in order to justify these discrepancies, we
performed Monte Carlo based numerical simulations using a modified 3 state model system (Figure
3.10b).

3.5.3

Modified 3 State ModelIn this modified 3 state model (Figure 3.10b), following the light-induced transition fromthe ground state (S0 ) to the excited state (S1 ), molecules can enter into the dark state (D). The
rate of this transition from bright state (S1 ) to the non-fluorescent or dark state (D) is governedby the DSC rate constant, kdsc . From D, the molecule can relax to the ground state via ground67
state recovery. This model assumes that recovery can be thermally-driven or induced by light withcorresponding rate constants kgsr and Φg ∗ kex . Here, kex is the excitation rate which depends onlaser intensity (Eqn 3.15) and Φg is an adjustable constant.

Figure 3.10: (a) 3 State model consisting of ground state (S0 ), excited state (S1 ) and dark state(D). Upon laser illumination, the molecules can be excited to S1 . From S1 , the molecules candecay in a radiative manner or can get trapped into a dark state (D, via dark state conversion)
with subsequent radiationless transition into ground state (S0 , via ground state recovery). Thismodel can explain the intensity-dependence of τon observed in single molecule experiments but fails
to account for intensity-dependence of τof f . (b) In this modified 3 state model, the ground state
recovery process is considered to be both thermally and light-driven process with the corresponding
rate constants of kgsr and Φg ∗ kex respectively. This is the minimal model to explain the intensitydependence of τon and τof f .

This model is justified by the observation that the recoveries of the dark state populations inmany FPs and organic dyes were found to be intensity-dependent as revealed by several FCS andmodulation-based fluorescence studies [36, 112, 35, 152]. For instance, Schwille et al. [36] measured
intensity dependent “flickering rate” of YFP mutants in the intensity range of 50 kW/cm2 to few
W/cm2 . Similar intensity-dependent flickering was observed by the FCS studies in DsRed [112].
Schenk et al. [35] observed that the “flickering rate” and dark fraction in eqFP611 and drFP583
depends on excitation rate and explained the observation invoking a light-dependent as well as
spontaneous recovery from the dark states. More recently, Mahoney et al. [152] used modulation

68
based techniques to enhance fluorescence intensity in cyanine dyes by relying on light-dependent
recovery of the molecules from the dark states. Based on these observations, we modeled DSC asa thermally driven process from excited state S1 to D but GSR as both a spontaneous and lightdriven phenomenon. Intensity-dependence of the recovery process can be explained by excitationof dark state (D) into an excited state and relaxation to the ground state thereafter. This modelis basically a 4 state system comprising of 2 bright state and 2 dark state as given by Schwille etal. [36]. Alternatively, the light-dependent recovery can happen through a stimulated transition ofdark state population into the ground state in a non-radiative fashion as described by Schenk et al.
[35]. Our model (Figure 3.10b) closely resembles the model given by Schenk et al. [35]. We prefer
this model, as this is the minimal model to explain all the experimental observations.
Based on this 3 state model (Figure 3.10b), the molecule can switch between bright (S0 , S1 )and dark state (D). The probability of leaving a particular state depends on the reaction rates outof that state and the time-step. For example, the probability of leaving the state S1 is given by,p(S1 ) = 1 − e−(kdsc +kr +kic )∗∆t(3.17)

where, ∆t is the time-step of the simulation. The probabilities of leaving the other states can beobtained in similar way. Now the probabilities of changing the conformational states (i.e from S0
to S1 or vice verse) relies on the weightage of the process leading to the conformational change
compared to other processes. For instance, probability of changing the state S1 to S0 can be
expressed as,cP rob(S1 ) =kr + kic
kdsc + kr + kic(3.18)All these probabilities can be used to estimate the duration of time the molecule spends in aparticular state. Considering that the molecule would fluoresce (signal=1) only when it resides
in state S0 or S1 (bright states), we can generate the fluorescence traces of the system. Finallyfrom these fluorescence traces, τon and τof f can be extracted by fitting ON and OFF dwell-time

69
respectively. Each simulation was run for 100,000 ms of time with a step-size of 1 µs. The algorithm
for these simulations and the corresponding Matlab code is presented in Appendix A.

Figure 3.11: Histograms displaying distributions of ton and tof f . Single exponential fits of thehistograms are shown by the solid red lines.

Figure 3.11 shows the histograms of ton and tof f and the single exponential fittings. τonof TagRFP-T and DpTT are given by 584 ms and 434 ms respectively which is similar to thatobtained from the analytical expression (Eqn 3.14). τof f of TagRFP-T and DpTT are found to be
70 ms and 77 ms respectively. The relative τon and τof f values of TagRFP-T and DpTT are ingood agreement with the experimental values. In the single molecule experiments, we observe 21%
faster ON time of DpTT compared to TagRFP-T whereas in the simulation we find 26% faster ON
time of DpTT. Also in the experiments, no significant changes was observed in experimental τof f

70values for the two constructs. The simulated τof f times show a similar trend.

3.6

Discussion
The measurements of dark state dynamics of TagRFP-T and DpTT constructs reveal thatthey have similar GSR time-constants but DSC time-constants of DpTT constructs was 25% shorter
than TagRFP-T (τDSC 24 µs in TagRFP-T and 18 µs in DpTT). This faster dark state conversion
in DpTT results a higher fraction of dark state in this construct. Indeed we observe 26% higher
dark state fraction in DpTT compared to wild-type TagRFP-T (Table 3.1).
The ensemble level measurement of GSR and DSC of TagRFP-T and DpTT can explain the
single molecule kinetics measured in vitro. τon and τof f measured from individual fluorescence traces
showed 21% faster ON time for DpTT while OFF time was similar for both of those constructs.
With the input from DSC/GSR, excited state lifetime measurements and using numerical simulation
based on a 3 state model, we were able to show that the faster DSC kinetics in DpTT was responsiblefor its shorter τon .
The 3 State model used in the simulations assumes an intensity-dependence of GSR which
is supported by the previous studies [35, 112, 147]. From our GSR measurements of TagRFP-T
at different intensities (Figure 3.6), we have observed that τGSR tends to be shorter at higher
intensities. In a previous study (Chapter 2, Figure 2.6a) [33], we presented a kinetic model for
RFP, where population from dark state (D) can relaxes back to the ground state (S0 ) or can go
the excited state (S1 ). This later process could be termed as “reverse DSC” and we have found thecorresponding rate constant (kDS1 ) to be slightly intensity-dependent. So, that model is equivalent
to the model presented here (Figure 3.10b).
It is worth noting that although numerical simulation was able to account for the relative
τon of TagRFP-T and DpTT, the absolute value of τon measured from simulation was far off. In
fact, the τon values obtained from simulation were higher than the experimental ones. While theexperimentally observed τon for TagRFP-T and DpTT was 141.3 and 111.3 ms respectively, the71numerical simulations results produced τon of TagRFP-T and DpTT as 584 ms and 434 ms. This
discrepancy could be due to the fact that photo-bleaching from excited (S1 ) and dark states (D)have not been taken into account in our model. With additional bleaching from S1 and D, τon
calculated from the model system would be lower. Using a more refined model, better agreementwith the experiments could be achieved.
From the photo-physical and single molecule measurements, it is clear that Dronpa-linkerTagRFP-T showed ∼ 25% faster blinking and higher dark state conversion. But, by measuring theskewness of the image, ∼ 5 fold higher fluorescence fluctuation was observed in DpTT construct.
So, dark state conversion alone can not account for the visibly different fluctuations in DpTT
compared to TagRFP-T. It has been discussed earlier that the purification of DpTT constructrevealed a fraction of immature TagRFP-T. It is possible that due to charge-charge interactions
between Dronpa and TagRFP-T, the maturation pathway in TagRFP-T was affected resulting in
the formation of non-functional chromophores. Thus the effective TagRFP-T concentration would
be reduced when expressed in plasma membrane and as a consequence the fluctuations in TagRFPT would be more prominent which is otherwise obscured by high FP-concentration. This could also
explain ∼ 10 fold reduction in brightness for DpTT construct. In essence, it can be concluded thatthe increased fluctuations in DpTT is the result of increased dark state conversion and inefficientmaturation pathway of TagRFP-T leading to a lower label-concentration.
The charge-charge interactions in Dronpa and TagRFP-T is supported by the presence ofbasic and acidic patches on the surface of the β-barrel. These interactions can introduce barrelflexibility that in turn can change the photo-physical behavior in FPs e.g. non-radiative decay orphoto-switching kinetics [153, 135]. Kao et al. reported that the viscosity of the medium can exerthydrodynamic effect to slow down the photo-switching kinetics in Dronpa [135]. So, it is possiblethat the interactions between basic and acidic patches of Dronpa/TagRFP-T produce the similarprotein-flexibility mediated coupling altering dark state conversion process.
Currently, FLINC is limited to only Dronpa and TagRFP-T. Generations of EGFP-linkerTagRFP-T or Dronpa-linker-mCherry constructs did not show the characteristics fluctuations ob-

72
served in DpTT. During our engineering of improved red fluorescent proteins, we generated amCherry mutant (C4PB-12) showing higher dark state fraction upon 561 nm illumination. We
used this RFP to create DpTT-like construct. Although the resulting constructs showed high fluorescence fluctuations, due to the its fast photo-bleaching, the construct could not be used as a
PKA activity sensor. A complete mechanistic and theoretical understanding of the FLINC process
is lacking for the future development of FLINC-based sensors.

3.7

Conclusion
In this Chapter, we explored the dynamics of dark state conversion (DSC) and ground state

recovery (GSR) processes of the FPs and present them in the context of their impact on single
molecule characteristics e.g. τon and τof f . We discussed a novel class of fluorescent-based bio-sensor
which works by altering the fluorescence fluctuation of an RFP in a distance-dependent manner.
When Dronpa is within 5-6 nm of TagRFP-T, the fluorescence fluctuations in red fluorescencesignal of TagRFP-T increases dramatically enabling it as a smart reporter of in-vivo enzymatic
activities at super-resolution. Characterization of DSC, GSR kinetics of nuclear-localized TagRFPT and DpTT (Dronpa-linker-TagRFP-T) in HeLa cells reveals a 25% increased rate of dark state
conversion in DpTT. This increase can explain the kinetics that partly accounts for the enhanced
fluctuations. Monte Carlo based numerical simulations were performed in different model systems
to quantify the τon and τof f of the constructs. The intensity-dependence of τon and τof f was
explained by this model.

Chapter 4
Multi-parametric Microfluidic Platform for Directed Evolution of the Red
Fluorescent Proteins

4.1

Publication Note
Part of this Chapter has been adopted from Dean et al., High-speed multiparameter pho-

tophysical analyses offluorophore libraries, Analytical Chemistry, 87(10):5026-5030, 2015 [55] andthe corresponding Supplementary Information of the article.

4.2

Introduction
The key photo-physical properties of FPs, e.g. brightness, photostability, spectral shift etc.,

are largely inter-connected. For this reason, the engineering of a particular property of an FP often
leads to inevitable changes in its other important photo-physical parameters. In many cases, thesechanges are undesirable. For example, with a selection based on excited state lifetime and using
a synthetic RFP gene template, Bindels et al. [104] developed mScarlet which has a long lifetime(3.9 ns) and high quantum yield (70 %). However, this mutant lacks photostability compared
to mCherry [12], the most commonly used RFP. mScarlet also shows slower maturation which ismanifested in its lower than predicted in-vivo brightness based on its high extinction coefficient() and quantum yield (φ). In fact, mScarlet-I, the faster-maturing version of mScarlet, displays
higher brightness when expressed in mammalian cells although its theoretical brightness ( ∗ φ)is lower than mScarlet. In the process of generating mScarlet-H, the more photostable version of

74
mScarlet, the brightness was compromised. Similarly, far-red FPs with lower photo-toxicity andgreater contrast over cellular auto-fluorescence tend to have lower QY and thus reduced brightness[95, 154]. Hence, for all-around improvement of FPs, the simultaneous monitoring of multiple
photo-physical parameters is important. A multi-parametric selection of FP library is a viableoption to achieve this goal.
Development of most FPs has been achieved by a combination of targeting the chromophoreforming residues, its immediate environment [12, 11] or dimeric interfaces [54] and subsequent
random mutagenesis. However, further attempts to tune the photo-physical properties of FPs, particularly RFPs, have usually not been satisfactory. For example, to engineer mCherry, mutations in
the residues surrounding the chromophore led to mutants with more pronounced dark state conversion (and therefore enhanced photobleaching) or immature chromophore as described in Chapter
5. This indicates that all the “low lying fruits” may have been procured through conventional RFP
engineering and selection methods. Therefore, unconventional, inclusive and advanced engineering
strategies need to be taken to overcome this deadlock. There is evidence in the literature that
mutations in the distal part of the β-barrel can also modulate the photo-physical properties of theFPs in unique ways, e.g. through barrelfluctuations [135, 155]. These residues could be targeted
to enhance the diversity and improve the FP photo-physics.
Also, some mutations are known to be context-dependent or to co-evolve with other residues in
an FP. Inclusion of these new target residues could eventually increase the library size exponentially.
To explore libraries of a large number of genetically distinct mutants requires high-throughput
screening and sorting platforms. Traditionally, FPs are selected by plate-based techniques [11] orthroughfluorescence activated cell sorting (FACS) or combinations of both. However, plate-based
screening is slow and therefore incapable of covering large mutational space. Furthermore, although
FACS is suitable for fast sorting and screening, FACS based selection is limited to only fluorescenceintensity and wavelengths.
To enable simultaneous selection for evolution of the FP photo-physics and production of
FPs with novel functionalities from a large library, we designed a multi-parametric microfluidic-

75
based platform [55]. Using phasefluorimetry and photobleaching through multiple laser beams, thismicrofluidic platform is capable of characterizingfluorescence intensity, excited state lifetime andphotostability of the RFP mutants. This Chapter discusses the methodologies, instrumentation andoperation of this microfluidic sorter. The use of this multi-parametric sorter leads to the generationof various RFP variants that are presented in Chapter 5, 6, 7.

4.3

Microfluidic Design and Manifold Assembly
To employ the multi-parametric sorting on RFP libraries, we adopted a microfluidic designfrom our previous work [98, 125]. The details of the microfluidic design are depicted in Figure
4.1a. The microfluidic is entirely made of borosilicate glass that can withstand the high intensity
laser power used for optical sorting. The microfluidic chip consists of three inlets (sheath, sample,
sheath) and two outlets (collection and waste). Cell suspensions are flowed through the middle
channel. Two sheath channels hydrodynamically focus the middle channel to narrow the stream
and thereby improve the coefficient of variation (CV) of the optical measurements. CV is measured
as the ratio of mean and standard deviation of the measured values.
The microfluidic is sealed by a manifold through o-rings (Figure 4.1b). The manifold is madeof polytetrafluoroethylene (PTFE) that minimize the non-specific adsorption of cells [125]. Three
inlets of the manifold are connected with and regulated by three pressure-controllers (Pneutronics,
OEM, EPS10-5-0-2) for independent control of theflow of each channel. The microfluidic chip isfitted in the middle of the manifold, allowing the delivery of light for epi- and trans-illumination.
The inlet reservoirs of the manifold can be filled with up to 150 µl of sample or sheath buffers.
Following the addition of buffer/sample into the inlets, the reservoirs are sealed with stoppers anda metal plate. The two outlets (collection and waste) are left open.

76

a

45 mm

15 mm

Collection
Reservoir

Waste
Reservoir
Sample Channel

Sheath Channel

b
20 mm

Manifold
InletsPressure
InletsOutlets
Microfluidics

Figure 4.1: Microfluidic Design and Manifold Assembly: (a) Design of the microfluidic chip.
The chip is custom fabricated by Micronit Microtechnologies B.V., The Netherlands. The length
and width of different portion of the channels are: A [20 mm, 100 µm], B [15 mm, 130 µm], C[5 mm, 130 µm], D [0.5 mm, 150 µm], E [27 mm, 125 µm], F [27 mm, 125 µm]. The height of
all channels is wet-etched to 25 µm. Solutions with cells are flowed in the middle channel (A).
Sheath channels (B) hydrodynamically focus the sample channel to produce a narrow stream that
improves the coefficient of variation (CV) of the measurements. (D) is the interrogation region
where multiple laser beams are focused. (E) and (F) are collection and waste channels respectively.
(b) Microfluidic and manifold (made of ploytetrafluoroethylene, PTFE) assembly consisting of three
inlets (sheath, sample, sheath) and two outlets (waste and collection). The pressure of each channel
is independently controlled by pressure controllers (Pneutronics, OEM, EPS10-5-0-2) within 0-2
psi range. The microfluidic chip is entirely made of borosilicate glass and fits in the middle of the
manifold allowing delivery of light for epi- and trans-illuminations [98, 125].

4.4

Optical Set-up and Detection Electronics
The optical set-up for the microfluidic sorter is presented in Figure 4.2. In previous work,

for in-flow photobleaching measurement of RFPs, a microfluidic platform with multiple beams was

77
used [98]. The multiple beams with appropriate spacing would allow us to achieve higher resolution
in photobleaching and eliminate the reversible bleaching contributions of the mutants originating

seath

from dark state conversion [33].
Lifetime/Pre-bleach Beam
Microfluidic Device
cell
seath

Objective
Post-bleach
Beam

M

M
BS

Dichroic
FilterM

M: Mirrors
BS: 50:50 Beam Splitter
λ/2 : Half-wave Plate

BS

Cylindrical
Lens

M

Fluorescence

M
70:30
BS
Beam Splitter

Tube Lens
λ/2

M

532/561- nm
Laser

M
Lenses

EOM

M

M
M

Lifetime
PMT

Polarizers

Slit
Slit

Photobleaching
PMT

FilterSide port
Prism

Figure 4.2: Schematic of the Optical Layout: A beam from 532-nm or 561-nm laser is passed
through multiple lenses, mirrors, beam splitters and focused onto the microfluidic chip. The epifluorescence from the RFP-expressing cells upon their passage through the beams are collected using
two PMTs. The details can be found in the text.

A 532-nm (Coherent Verdi, 5W) or 561-nm laser (Genesis MX, Coherent, 1W) laser beamis split with a 70:30 beam splitter. 30% of the beam power is directed through an electro-optic
modulator (EOM, ThorLabs, EO-AM-NR-C4) that amplitude-modulates the beam at a frequencyof 29.5 MHz. Before the beam enters the EOM, it is focused with a lens to fit into the EOM
aperture. Two polarizers (Newport) and a half-wave plate (Newport) are introduced after the
EOM to control the power of the lifetime beam. The remaining 70% of the beam is split into 9

78
beams with the help of multiple mirrors and 50:50 beam splitters (Figure 4.2). All these beams
together with the modulated lifetime beam are passed through a plano-convex cylindrical lens (f= 150 mm), transforming the circular beams into elliptical ones. This minimizes the alignment
sensitivity of the laser beams and reduces thefluctuations offluorescence signals that arises due tovariations of the cell trajectories perpendicular to the direction of flow in the microfluidic channel[125]. The elliptical beams from the cylindrical lens enter the side-port of a commercial inverted
microscope (Olympus IX71) and are reflected through a dichroic mirror (Semrock, FF573-Di0125x36, for 561-nm laser). The reflected beams are finally focused into the microfluidic chip throughan air-objective (Olympus, 20x, NA 0.45). The FWHM of the lifetime beam is measured to be
9 µmand 56 µm in the minor and major axes of the elliptical beam, respectively. The otherbeams have similar dimension as the lifetime beam. Epifluorescence from cells expressing RFPs isseparated from the excitation beams using a band-pass filter (Semrock, FF01-629/56-25, for 561 nm
laser). Subsequently, the lifetime and post-bleach beams are spatially separated from each otherand other intervening beams using mirrors and slits and collected by two red-wavelength sensitive
photo-multiplier tubes (PMT, Hamamatsu R9880U-20) as illustrated in Figure 4.2.
The electronic components for detection, amplification and processing of the fluorescence signals are schematically illustrated in Figure 4.2. A function generator (Agilent, 33520B) is used toprovide a sinusoidally modulated electrical signal with 10 V peak-to-peak at 29.5 MHz to drive theEOM, and also send a 1 V peak-to-peak reference signal to the lock-in amplifier. Thefluorescencesignal obtained from the lifetime PMT is split into two components (high and low frequencies)
using a biased-tee. The high frequency component (> 1 MHz) is directed to a custom-made amplifier and then sent to a lock-in amplifier (Zurich Instruments, UHF) for frequency-domain lifetime
measurements. The lock-in amplifier is controlled by a web-based LabOne user interface. The
low frequency components (< 1 MHz) from the biased-tee (pre-bleach), along with the signal frompost-bleach beam are further amplified with a home-built trans-impedance amplifier for improving
the signal-to-noise ratios. The pre- and post-bleach signals are further smoothed with a custombuilt 5 kHz low-pass filter. Electronic amplification of the pre-bleach beam is achieved by either

79
a linear or logarithmic trans-impedance amplifier. The logarithmic amplifier has higher dynamic
range and thus helps to resolve the brightness of a mixture of mutants better than the linear amplifier. Finally, the pre-bleach, post-bleach, in-phase and quadrature-phase signals are digitizedat 125 kHz, 16-bit resolution by a data acquisition card (DAC, National Instruments BNC-209a,
PCI-6251 with NI-SCX). The DAC board communicates with a Target computer that runs the LabView RealTime module to analyze the digitized data streams, identify the isolated non-overlapping
bursts of peaks due to the passage of cells through the laser beams, and make selection decisions
based on user-defined thresholds of lifetime,fluorescence intensity, transit time of the cells throughthe microfluidics or photostability of the mutants. The Target computer is connected to a Host
computer that controls the operation of the sorter and used for real-time data display. The designof the biased-tee, trans-impedance amplifiers and filters is provided in the Appendix.
Various methods for cell sorting have been reported [156, 157, 158, 159], including electroosmosis, generation of explosive vapor bubbles [157], optical force [158, 160], and acoustic wavebased [156, 161] sorting. We employ optical force gradient based sorting that does not require a highNA objective and is compatible with live-cells and the microfluidic chip we use. Our implementationof this technique is schematically shown in Figure 4.4 and has been described in detail in reference[98].
The 1064-nm laser can deliver up to 20W power but only 30-40% of it (6 - 8 W) is required
to generate force for the deflection of the cells. Although higher trap-laser intensity increases theselection efficiency, it reduces the viability of the sorted cells [98]. Higher intensity of the trap-laser
also increases the risk of damage to the microfluidic chip.

80
Sine wave
29.5 MHz, 10V
for driving EOM

Photobleaching
PMT

Lifetime
PMT

Function Generator

BiasedTeeHigh frequency
component

Sine wave
29.5 MHz, 1V
Reference

Low frequency
component

Lock-in amplifier

In-phase signal (VI)

Trans-impedence
Amplifier

In-Quadrature signal (VQ)
5 kHz Low-pass
FilterNI DAQ Board

Host Computer

Target Computer

Figure 4.3: Schematic of the Electronics and Signal Processing: Following the collectionsof red fluorescence from lifetime and post-bleach beams by two red-sensitive PMTs (Hamamatsu
R9880U-20), further processing of the signals is conducted by multiple electronics components as
described in the text.

4.5

Theory of Frequency-Domain Lifetime Measurements in Flow
Cytometry
The intensity (I(x, y)) of a Gaussian laser beam focused to an elliptical spot with 1/e2

intensity radii σx and σy is given as [55]I = I0 e

2

2

σx

σy

(− 2x2 − 2y2 )(4.1)

where I0 is the intensity at the center of the Gaussian beam.
In flow-cytometry, the fluorescence signal generated due to the passage of cells through the laser

81

Figure 4.4: Schematic of the Components for Optical Force Switching: A 1064-nm laser(IPG Photonics Laser, YLR-20-1064-LP, 20W) beam is expanded 3.5 times using a [L1 (Planoconcave, f = -50 mm, ThorLabs LC1715-C) & L2 (Plano-convex, f = 175 mm, ThorLabs LA1229C)] lens assembly and focused on a piezo-tilt mirror (PM, Physik Intrumente, S-315). Laser reflectedfrom the PM is further directed through [L3 (Plano-convex, f =1000 mm, ThorLabs LA1464-C)& L4 (Plano-convex, f = 300 mm, ThorLabs LA1484-C)] and focused into the microfluidic chipfrom top using an aspheric lens (f = 11 mm, ThorLabs A397TM-C). PM is used to translate thefocus laser spot with a scan range of 88 µm. It is driven by a 3-channel 100V amplifier (Physik
Intrumente, E-663 LVPZT amplifier) controlled by 0-10 V input signals. (inset) The Huygens
point-spread function of the laser beam focus. The Figure has been adopted from reference [98].

beam has a Gaussian profile in temporal coordinate. The peak fluorescence intensity (F (t)) of acell with radius r and velocity vx across the laser beam is given by2

F (t) = F0 e
where, σt =√ 2

ωx +r22vx(− t 2 )σt(4.2)

and the flow is along the x-direction. The fluorescence intensity, F0 , is pro-portional to the excitation intensity (I(x, y)). Using σx as 9 µm, r as 3 µm and vx as 10 mm/s, σt
is found to be 0.5 ms. In our measurements, the passage time of the cells in each beam is in therange of 200 µs -1 ms.
For frequency-domain lifetime measurements, the sample is excited with an amplitude modulated
sinusoidal excitation with a period typically on the order of its excited state lifetime. The fluorescence signal emitted by the sample has the same frequency but is phase delayed due to a
finite excited state lifetime. So, if the excitation has the form of Asin(ωt), then the corresponding

82
fluorescence signal would be [129]:A ∗ m ∗ sin(ωt − φ)(4.3)

where A is the amplitude of the signal, m is the modulation, φ is the phase shift, and ω is theangular frequency of the excitation (ω = 2πν), ν is the modulation frequency). It can be shownthat and m are related to the excited state lifetime (τ ) and angular frequency of the system in thefollowing way [129]:τ=1tan(φ)
ω(4.4)

1
1 + ω2τ 2

(4.5)

m= √

In frequency-domain flow cytometry, the fluorescence signal is the a product of Eqn 4.2 and 4.3.
i.e. ∝ sin(ωt − φ)e

−t2
2σt2

. Now, the fluorescence signal is further delayed due to the optics and theelectronics used to amplify the signal. The detected signal can be represented as:−t2
2

F (t) ∝ sin(ωt − φ − δ)e 2σt(4.6)

where, δ is the phase shift induced by optics and electronics [162].
Before the fluorescence signal reaches the lock-in amplifier, the low frequency component
(e

−t2
2σt2

∼kHz) is separated from the high frequency component (sin(ωt − π − δ), ∼ MHz) using a

biased-tee. In the lock-in amplifier, the fluorescence signal is demodulated by multiplying with thereference signal, sin(ωt) and cos(ωt) , to give VI and VQ , respectively. The in-phase signal is thengiven by:VI

∝ sin(ωt − φ − δ)sin(ωt)∝

11
cos(2ωt − φ − δ) − cos(φ + δ)
2
2

(4.7)
(4.8)

83
The first part of the voltage contribution in the above equation with high frequency can be eliminated using a low-pass filter to give,

VI = Bcos(φ + δ) + VBI(4.9)

where B is a constant and VBI is the background signal for the detection of VI . Similarly, it canbe shown that the in-quadrature phase signal (VQ ) would be:

VQ = Bsin(φ + δ) + VBQ(4.10)

Eqn. 4.9 and 4.10 can be rearranged to give,φ = tan−1 (VQ − VBQ)−δ
VI − VBI(4.11)

Using Eqn. 4.4 and Eqn. 4.11 we get,τ=VQ − VBQ
1tan(tan−1 () − δ)
ωVI − VBI(4.12)

Further simplification of the above equations givesτ=

1 V¯Q cos(δ) − V¯I sin(δ)
2πν V¯I cos(δ) + V¯Q sin(δ)(4.13)

where V¯I and V¯Q are background corrected in-phase and quadrature-phase signal (V¯I = VI − VBI ,V¯Q = VQ − VBQ ). Eqn. 4.13 is used for the in-flow quantification of the excited state lifetime of thesystem. V¯I and V¯Q are directly obtained from the lock-in amplifier. The modulation frequency, ν,
is 29.5 MHz. The parameter δ is obtained by using a FP with known fluorescence lifetime. We use
mCherry (τ = 1.87 ns) as a reference for the adjustment of δ.

4.6

Cell Culture, Library Generation, and Sample Preparation
For the RFP engineering project, we use yeast (Saccharomyces cerevisiae) as the host or-

ganism. Conventionally, bacteria (E. coli ) or mammalian cell-lines are preferred choices of host84
organisms for library generation. However, we choose yeast rather than bacteria or mammaliancell-lines for the following reasons. (1) Using yeast as a host organism makes the library generation,screening, sorting and the recovery of cells after sorting much faster, since the doubling time is only
90 minutes for yeast cells comparing to 20 hrs for mammalian cells. Also, mammalian cell-lines
require more maintenance than yeast cells which are more robust to the change in pH or temperature. (2) Unlike bacteria, yeast cells are eukaryotic and have a proper nucleus. In this respect, they
are more similar to the mammalian cell-lines where eventually the engineered proteins would betargeted. So, ideally, the phenotypes shown in the yeast cells are expected to be more comparable
to the phenotype shown in mammalian cells. However, as will be discussed in Chapter 5, 6 and 7,
the fluorescence intensity of some RFPs in yeast is not comparable to the mammalian cells.
The engineering of RFP mutants involves incorporation of point mutations within the protein
and library generation. To generate point mutations, a particular amino acid in FP is changed toa different one. The accumulation of many protein variants with different mutations is what leads
to the creation of library. To create mutations, we use either Quick Change or g-Block protocols as
described in Appendix D. Library generation is associated with changing a defined (site-directedmutagenesis) or a random (random mutagenesis) amino acid residue with multiple other aminoacids or nucleotides (for synonymous mutations). For the microfluidic screening of yeast cells
containing RFPs, the corresponding library or the culture is freshly grown from a stored stock andexpressed transiently. A 0.5 ml volume of stored culture media is added to 10 ml solution of growth
media (yeast nitrogen base, ammonium sulphate, dextrose) and grown for 8 hrs. in a 50 ml tube.
Next, 0.5 ml of this freshly grown cell culture is added to 10 ml solution of induction media (yeast
nitrogen base, ammonium sulphate, galactose, raffinose) in an Erlenmeyer flask. Cells are screenedor sorted 17-20 hrs post-induction. During growth and expression, the cultures are incubated at
30◦ C and constantly shaken at 250 rpm. For the expression of the FPs, oxygen is necessary. Hence,care is taken so that the flasks containing yeast cultures are sufficiently exposed to the air. The
stock yeast culture is stored in a 4◦ C fridge and passaged twice a month. For long-term use of theyeast cultures or libraries, a glycerol-stock is prepared and stored in a -80◦ C freezer.

85
For screening in the microfluidics, the expressed yeast cells are diluted (10-20 fold) with theblank media (yeast nitrogen base, ammonium sulphate) containing 14% OptiPrep (60% weight/volume iodixanol in water), and subsequently filtered using a 40 µm filter to discard the cell debris.
OptiPrep increases the specific density of the media making the cells buoyant inside the microfluidic
channel. Also, OptiPrep modifies the refractive index of the solution, which is advantageous for
tracking the flow of the sample channel and alignment of the trap-laser beam.

4.7

Multi-parametric Screening
Microfluidic screening reveals the fluorescence intensity, excited state lifetime and photosta-

bility profiles of the individual mutants or RFP libraries. Based on the initial screening results ofa library, decision is made to sort some sub-population of the library for further enrichment andselection. Screening is also carried out to quantify the properties of individual mutants with large
number of cells (∼10,000). The detailed, step-by-step protocol for using the microfluidic for thescreening and sorting purpose are presented in Appendix B and C. In brief, 150 µl of ultra-purewater (milli-Q water filtered with a 2 µm filter) and the cell media (blank solution with 14% OptiPrep) is added in the two sheath and sample channels of the microfluidic respectively and run
for 10 minutes at high pressure (10 psi). This step eliminates the dirt or cell-debris that is present
in the microfluidic channels from the previous sorting/screening. As the sample channel and thesheath channels are running two different media with different refractive index, theflow originatingfrom each channel can be visually distinguished and ideally the flow focus would be symmetric.
Next, a blank solution is run in all of the three channels for 30 minutes at 2 psi. This step stabilizes
the flow and eliminates any bubbles present in the channels. Finally, if the flow is stable, thesample is loaded in the sample reservoir (the middle inlet) and blank solution is loaded into thetwo side-inlets for hydrodynamic focusing of the sample.
At different stages of the project, different combinations of the optical set-up (Figure 4.2)
were used. However, the basic configuration remained the same. For the development of C4LT

b

Lifetime PMT
Signaltime (ms)Ch0MaxCh1MaxPost-bleach(photobleaching)

Photostability =Pre-bleach(lifetime)

Photobleaching
PMT Signal
Ch1MaxdceFigure 4.5: Multi-parametric Microfluidic Screening: (a) Cells expressing RFPs are passed through the laser beams focusedon the microfluidic chip for rapid in-flow determination of brightness, photostability and lifetime. (b) The first beam is amplitude
modulated at 29.5 MHz. PMT signal from this beam is used to determine the excited state lifetime and also produce pre-bleachfluorescence intensity (Ch0Max). Ratio of the fluorescence intensity from last beam (Ch1Max) and first beam (Ch0Max) is definedas the photostability of the mutants. Transit time between first and last beam is ∼ 8 -10 ms. (c) Screening results of a mixture ofcells expressing mCherry and TagRFP-T at 5 kW/cm2 illumination intensity (lifetime beam). (d, e) Distribution of excited state
lifetime and photostability, showing clearly-resolved peaks for mCherry and TagRFP-T.

Ch0Maxa86

87
and C4PB mutants that will be described in Chapter 5, a 532-nm laser (Coherent Verdi, 5W) wasused with all the lifetime and photobleaching beams (i.e. 1 lifetime beam + 8 photobleaching
beams). Here, simultaneous evolution of excited state lifetime and photostability was carried out
using mCherry as a template. On the other hand, for the development of SDC, SLT (Chapter5), FusionRed mutants (Chapter 6) and Radiative Rate mutants (Chapter 7), the photostability
parameter was dropped. In these cases, evolution pressure was applied for the generation of mutants
with bright mutants having high quantum yield. Here, a 561-nm laser (Genesis MX, Coherent, 1W)
was used and the 8 photobleaching beams were blocked. Only the lifetime beam (which measures
the brightness and excited state lifetime of the mutants) and one timing beam was used. The
timing beam was required for the computation of cell velocity in the microfluidic channel that isrequired for the efficient sorting.
When the photostability parameter is included, as a cell passes through different laser beams,
the FPs photobleach and the fluorescence signals from the lifetime and post-bleach beams aredetected in two different PMTs. After processing by several electronics components, the digitizeddata from the PMTs are analyzed by a custom-made LabView software. The software quantifies theexcited state lifetime of the mutants from the in-phase (VI ) and in-quadrature phase (VQ ) values
provided by the lock-in amplifier using the Eqn. 4.13. The LabView software also pair-matches thebursts of signals arises due to the passage of the cells based on their transit time between lifetime andpost-bleach beams (Figure 4.5a). This pair-matching recognizes the fluorescence signals from thelifetime and photobleaching beams which are Gaussian in nature (see Figure 4.5b). The maximumof the lifetime and photobleaching beams are denoted as Ch0Max and Ch1Max respectively. The
photostability of is quantified as:

P hotostability =Ch1M ax
Ch0M ax(4.14)

Figure 4.5c demonstrates typical screening results obtained by mixing yeast cells expressing mCherry
and TagRFP-T. The x and y-axis of the plot displays the photostability and the lifetime of the

88mutants. Figure 4.5d and e show the distribution of lifetime and photostability of these two RFP
mutants. The distributions clearly demonstrate two peaks due to the presence of two mutants inthe sample.

Figure 4.6: Microfluidic Screening with Linear and Logarithmic Amplifier: (a) Microfluidic
screening displaying the profile of fluorescence intensity (Ch0Max) and the excited state lifetime of asample containing mCherry, TagRFP-T and SDC-1. Linear amplifier was used for the amplificationof lifetime PMT signal. (b) Screening results of the same sample using logarithmic amplifier. (c& d) Histogram of Ch0Max of the sample containing mCherry, TagRFP-T and SDC-1 using linear
and logarithmic amplifier respectively. ∼ 30,000 cells were screened for each run.

It should also be noted that the lifetime measured from the frequency-domain flow cytometry
is approximate in nature. The lifetime results are accurate when the fluorescence lifetime decaysof the FPs are purely mono-exponential. For the FPs that display multi-exponential fluorescencedecays, only the major component is captured by the frequency-domain lifetime measurements. For

89instance, fluorescence decay of mCherry (in purified proteins) when measured in a Time Correlated
Single Photon Counting (TCSPC) system shows a bi-exponential decay with a major (73%) 1.74
ns component and a minor (27%) 1.05 ns component. However, in the microfluidic screening ofmCherry (in yeast) using the frequency-domain technique, it shows a lifetime of 1.87 ns. Thisvalue is close to the value of the major component (1.74 ns) as measured by TCSPC. The slight
discrepancy of the values may be attributed to the different environment of the mCherry in two
different measurement systems (mCherry in yeast for microfluidic and mCherry in purified proteinsfor TCSPC).
For assessing the brightness of the mutants, thefluorescence intensity from the lifetime beam
(Ch0Max) was used. During the microfluidic screening, either a linear or logarithmic amplifier
was used for the amplification of lifetime PMT signal. Figure 4.6 a & b present the lifetime vs.
Ch0Max plots of a sample containing three RFPs: mCherry, TagRFP-T and SDC-1 with linear
and logarithmic amplifier respectively. Although, both amplifiers clearly resolve the excited state
lifetime of the three mutants, owing to its higher dynamic range, only the logarithmic amplifiercaptures the peak of thefluorescence intensity. This is obvious from the Figure 4.6 c & d.

4.8

Sorting of the Improved Red Fluorescent Protein Variants
Once the lifetime, brightness and photostability profiles of a RFP library are revealed throughthe microfluidic screening, the logical next step is to sort the mutants with desired phenotypes. As
described previously, the sorting of cells is done with optical force using a 1064-nm pulsed laser
operated by a custom-made LabView software.
The sorting process is illustrated in Figure 4.7. For the sorting purpose, the flow of thesample is biased to direct the cells into the waste channel. The trap-laser is focused to a placeclose the junction of collection and waste channels and slightly below the sample stream. The
LabView software measures the distance between A and B when the pixel positions of lifetime andpost-bleach beams are entered. From the transit time of cells from A to B (TAB ) and the physicalSeath90
Lifetime/Pre-bleach
Beam

Post-bleach Beam

Trajectory of the
Optical trap

Sorted cell

Seath

SampleCollection

TBC

TAB

1

Cell

TCD
C

B

A

D

Trash

Optical trap
Cell

2

3

Collection
Cell
Waste

Figure 4.7: Sorting of Improved RFP Variants with Optical Gradient Force Switching:(above) The flow in the microfluidic is biased to direct the cells to the waste channel. Once a mutant
shows improved phenotype based on some pre-defined thresholds, the optical sorter is turned on
to sort the corresponding cell. Based on the physical distance between lifetime and post-bleachbeams and the transit time associated with it (TAB ), the cell velocity is computed. From the cell
velocity, the time delay from position B to C (TBC ), and the transit time from C to D (TCD ) for
sweeping velocity for the firing of trap laser are determined so that the trajectory of the opticaltrap intercepts with the cell to deflect the cell into the collection channel. (below) A portion of
the microfluidic is zoomed to display a set of images showing a yeast cell being captured into thecollection channel.

distance between them, the velocity of the cells in the microfluidic channel is computed. Fromthe measured cell velocity and the distance from B to C and C to D, the transit time of the cellto travel BC (TBC ) and CD (TCD ) is measured. TBC and TCD determine the delay time and thesweeping velocity of the trap laser so that the trap laser intercepts the cell and deflects it to thecollection channel. However, the speed of the cells is not uniform in the microfluidic channel and91
tends to decrease in the sorting junction. To account for this effect, another adjustable parameter(Extra Delay) was added in the software. The value of the Extra Delay parameter is optimized toincrease the sorting efficiency through visual inspection of the cell trajectory in the camera. Oncethe sorting efficiency is optimized, mutants with improved photo-physical properties can be sorted
based on user-defined thresholds.
A detailed step-by-step sorting protocol and the instructions for operation of the LabView
software are provided in the Appendix C.

Chapter 5
Directed Evolution of Photostability and Excited State Lifetime in mCherry

5.1

Introduction
The brightness of the red fluorescent proteins (RFPs) is relatively lower than their green or

cyan counterparts. The main reason behind the significantly low brightness of RFPs is their lower
quantum yield (φ) compared to GFPs or CFPs. For instance, mTurquoise2 [155] and mNeonGreen[163] are two bright green and cyan fluorescent proteins with 93% and 80% quantum yield respectively. On the other hand, mCherry [12], the most widely-used RFP, has only a 22% quantum yield.
The mKate2 RFP [164] has a higher quantum yield (40%) than mCherry but still significantly lower
than other green and cyan FPs. TagRFP-T is another bright and photostable RFP derived from
TagRFP with high φ (41%) and extinction coefficient ( max , 81,000 M-1 cm-1 ) [11]. Unfortunately,
fusion-constructs of TagRFP-T are reported to aggregate in the cellular environment [101, 100].
Similarly, mRuby2 [165] and mRuby3 [102], RFPs derived from eqFP611 aggregate when expressedin mammalian cells [104]. Recently, Bindels et al. reported mScarlet, a bright red fluorescent protein developed through excited state lifetime-based screening [104]. Based on its in-vitro properties,
mScarlet is the brightest, monomeric RFP so far with its 70% fluorescence quantum yield and highmax value (100,000 M-1 cm-1 ). However, it photobleaches faster than mCherry and matures more
slowly. In fact, due to its slower maturation kinetics, mScarlet shows lower in-vivo brightness thanits mScarlet-I variant which has significantly lower QY (54%) but matures ∼ 5-fold faster thanmScarlet. We have also observed that mScarlet tends to mis-localize when expressed in Golgi e.g.

93with GalT-mScarlet constructs (see Chapter 6).
In order to develop RFP mutants with higher brightness and reduced photobleaching, westarted with mCherry as a template. mCherry displays high photostability, excellent pH-resistance,
fast maturation and a moderate extinction coefficient. However, mCherry is significantly dimmer
than other more recently developed RFPs, e.g. mRuby3 or mScarlet, due to its lower φ. Hence, we
set out to improve the brightness of mCherry by improving its quantum yield. As the φ of a chromophore is directly proportional to its excited state lifetime (τ ), enhancing the lifetime holds thepromise of improving its quantum yield. Based on this concept, several site-directed and randommutagenesis libraries were created and screened for higher excited state lifetime and photostability. In this Chapter, the directed evolution of mCherry is discussed. Several mCherry mutants
with improved photo-physical properties were selected and characterized. Figure 5.1 describes thegenealogy of the mCherry mutants generated in the course of this work. Targeted mutagenesis
of mCherry and subsequent photostability and excited state lifetime-based selections resulted in
the generation of C4, C9 and C12 mutants. The C4 mutant was further evolved through multiple rounds of error-prone and FACS-based selection to produce C4PB and C4LT mutants. SDC
and SLT mutants were generated through site-directed and random mutagenesis of C9 and C12
following the enrichment of populations with higher fluorescence lifetimes. The multi-parametricmicrofluidic platform presented in Chapter 4 was used for the screening and sorting of the FP
libraries.

5.2

Evolution of mCherry: C4 and C12 Mutants
In order to achieve simultaneous improvement in brightness and photostability of mCherry,first we took a targeted mutagenesis approach. We decided the positions of the residues based onstudies from our lab and from the literature.

94

mCherry
Targeted at V16, M66,
W143, I161 & Q163

Kriek1 Library
Selected for higher
photostable mutants

Targeted at F99, Y173,
A175, I197 & K70

Kriek2 Library

Selected for higher lifetime
mutants

C4

C9, C12Targeted at W143,
I161 & Q163

Error-prone mutagenesis
for improving brightness

Selection for higher
lifetime

EPC4 LibraryMultiple rounds of FACSenrichments & selection

C4PB-12
C4PB-6

SDC Library

SDC-1
SDC-5

SLT Library

SLT-1
SLT-11

Error-prone
mutagenesis &selection for higher
lifetime

C4LT-6
C4LT-2

Figure 5.1: Genealogy of the mutants derived from mCherry through photostability and excitedstate lifetime-based selections.

5.2.1

Targeted Mutagenesis: Kriek1 Library
In first round of targeted mutagenesis, we tried to improve the photostability of mCherry.

Although molecular oxygen (O2 ) is essential for the auto-catalytic formation of chromophore in
FPs [27, 28], it can also enhance the rate of irreversible photobleaching by reacting with the excitedstate chromophore [11, 57]. Chapagain et al. performed MD simulations on mCherry and Citrine(a yellow fluorescent protein) and suggested that the inferior photostability of mCherry might bedue to its increased inter-strand dynamics between β-7 and β-10 [20]. According to this study, in
Citrine, entry of molecular oxygen from the bulk solvent to the interior of the FP is energeticallyunfavored. On the other hand, the dynamics of mCherry are such that oxygen can easily enter into
the inside of FP barrel to enhance its photo-degradation. Their MD simulations also predicted that

95
Table 5.1: Targeted Mutagenesis in mCherry: Kriek1 Library
Residues
V16
M66
W143
I161
Q163

Amino acids targeted
E/D/A/K/N/T/V
K/E/Q/V/M/L/N/D/H/Y/I/F
All 20
L/M/V/I/F
All 20

Rationalecommon target
common targetMD
common targetMD

the W143K, Q163R, and R164E mutations could improve the photostability of mCherry by closing
the major low-energy pathways for oxygen entry. We found that incorporation of these mutations
into mCherry resulted in a non-fluorescent variant, presumably due to poor folding. In order torestore the brightness, we looked for commonly mutated residues in the mFruit series of FPs [12]and identified V16, M66, W143, I161, Q163, I197, and A217 as candidates for mutagenesis. Twoof these residues (W143 and Q163) were also identified as highly dynamic in the MD simulations.
Based on further literature analysis, we selected the following residues to target in our first round ofsite-directed mutagenesis: V16, M66, W143, I161, Q163. The resulting library was called Kriek1.
Residue R164 was not included as it was reported that the presence of charged side-chains at thisposition disrupt dimer formation in mFruit RFPs [13], and it would be undesirable to compromise
the monomeric character. Positions I197 and A217 were excluded in the first round to limit thesize of the library. Table 5.1 displays the residues targeted in Round 1 site-directed library and therationale for choosing those positions. All the numbering of the residues used in this Chapter andChapter 7 was based on the numbering used in a reported mCherry crystal structure (PDB 2H5Q)[166] . The size of Kriek1 library was: 7 × 12 × 20 × 5 × 20 = 168000 members. Fluorescentcolony screening of the library in E. coli showed that ∼10% of the library was fluorescent.
Figure 5.2 displays the screening results of Kriek1 with its precursor mCherry. Both mCherry
and Kriek1 were screened for 15,000 cells. It was evident from the screening results that Kriek1
contained a population of higher photo-stability. We sorted the library to enrich the population
with higher photostability with sort criteria set according to the pink box in Figure 5.2b. This
sorter-enriched population served as the template for our next round of targeted mutagenesis.

96

Figure 5.2: Screening results showing the excited state lifetime and photo-stability of mCherry (a),Kriek1 library before (b) and after (c) the microfluidic sorting. ∼15,000 cells were screened foreach panel. Kriek1 library displayed some mutants with higher photostability (population in thepink box; b) those were further enriched by the microfluidic sorter.

5.2.2

Targeted Mutagenesis: Kriek2 Library
After the first round of targeted mutagenesis and subsequent enrichment of the photostability,

we produced a population of lower photobleaching tendency (Figure 5.2c). Unfortunately, theexcited state lifetime of these mutants was significantly shorter than the parent mCherry, andtherefore we suspected they would be of lower quantum yield. To improve the lifetime of those
populations, we targeted the I197 residue, whose side-chain is reported to be capable of a ππ stacking interaction with the chromophore [109]. For example, in mRojoA, sandwiching thechromophore between two aromatic residues at 197 and 63, along with a few other mutations,
resulted in a 3-fold improvement of quantum yield over mCherry and a 22 nm red-shift of theemission spectrum [103, 92]. Mutation at this position in GFP also red-shifted its spectra to produce
YFP [109]. Hence, we speculated that introducing a stacking interaction with the residue at 197
might rigidify the chromophore and thereby reduce rate of non-radiative processes. Also, it wasreported that mutations at position 70 are correlated with those at 197 (i.e. K70 “co-evolved” 197)
[99]. We therefore chose I197 and K70 as target residues for 2nd round of mutagenesis. Additional
residues targeted in this phase of library generation were suggested by the MD simulations ofmCherry by Regmi et al. [167] who explored the oxygen diffusion pathways into the β-barrel andidentified F99, Y173, A175 as ‘gateway’ residues. We hypothesized that rigidifying the barrel by

97
Table 5.2: Targeted Mutagenesis in mCherry: Kriek2 Library
Residues
F99
Y173
A175
I197
K70

Amino acids targeted
All 20All 20All 20All 20
K/R

Rationalegatekeeper
surrounding gatekeeper
surrounding gatekeeper
stacking residue
co-evolved with stacking residue

reducing the motions of these dynamic residues would have a beneficial effect on the fluorescencequantum yield.
Table 5.2 summarizes the positions targeted in this round of library generation. The total
size of this library, which was named Kriek2, is 20 × 20 × 20 × 20 × 2 ∼ 320, 000 members. Figure
5.3 shows the results of screening Kriek2. We observed mutants with increased lifetime (populationin the pink box), whose population we enriched with the microfluidic sorter.

Figure 5.3: (a) Microfluidic screening results of Kriek2 library. In this library, we observed someclusters with higher excited state lifetime (population in the pink box) which were selected to
identify Kriek2 mutants: C4, C9, C12 etc. (b) Screening results demonstrating the lifetime andphotostability of a mixture containing mCherry, TagRFP-T, C4, C9 and C12 at 6 kW/cm2 . Allof the mutants displayed improved lifetime relative to mCherry. The C4 mutant showed higher
photostability and longer lifetime than mCherry.

98

5.3

Selection of Mutants after 2nd Round of Mutagenesis
To isolate the mutants with higher lifetime, we grew our lifetime-enriched library on galactose-

containing (for inducing expression in yeast) plates. Resulting yeast colonies were fluorescent andthe lifetime for each colony was measured in a fashion similar to the in-flow phase fluorimetry, as
described below. To pick colonies according to their lifetimes, all beams except for the modulated
lifetime beam were blocked (see Chapter 4). This excitation beam was focused onto single colonies
at a low excitation intensity to avoid saturation of the PMT. The PMT signal was demodulated by
the lock-in amplifier (Zurich Instruments, UHF). The resulting in-phase and in-quadrature outputs
were digitized and processed to extract fluorescence lifetime values. To verify the performance of
this technique, mCherry and TagRFP-T colonies were used as references.
Table 5.3: Sequences and excited state lifetime of the mutants picked from Kriek2 library. mCherry
had lifetime of 1.9 ns as measured by phase fluorometry.
Clone
C4
C9
C11
C12
C15
K2C

LT (ns)
2.5
3.0
2.4
3.4
2.6
∼1

V16
T
A
A

H17
R
R
R

K70
R

T

R

R

F99
S

W143

I161
M

Q163
M

Y173
L

L

L

V

L

I

M
M

L
V

S

A175
W
T
S
W

I197
R
R
H
R
R

We picked 16 colonies from the sorted Kriek2 library, and DNA sequencing showed that only
5 of them were genetically distinct clones. Table 5.3 displays the sequence differences among the
mutants, which were named C4, C9, C12, C11 and C15. It was found that mutations at position
16 were linked to a H17R (pointing outwards of β-barrel) mutation. This was not an intentionalpart of the Kriek2 library and was due to an error in the primer design. Clones C4 and C15
only differed by a single mutation at 163 (M/L). Figure 5.3b shows the lifetime and photostability
screening results of the mutants measured individually, together with mCherry and TagRFP-T,
plotted all together in one dot plot. The C4 mutant showed improved excited state lifetime andphotostability. It was found to have a Kriek-like I161M mutation [57]. The C9 and C12 clones had

99significantly longer lifetime than mCherry but lower photostability. It is interesting to note that
C12 is only a single mutation (I197R) away from mCherry but has an 82% improvement in lifetime(1.87 ns vs. 3.4 ns). The L173 residue found in C9 also appeared in mRuby2 [165], TagRFP-T [11],FusionRed [54], mKate, Katuschka, mNeptune, mCardinal [154] and eqFP578. An R70 residue isfound in TagRFP-T, mRuby2 and FusionRed.

5.4

Evolution of C4 mutant
The C4 mutant was promising as it showed improved excited state lifetime and photostability

in the microfluidic screening (Figure 5.3b). However, as Figure 5.4a demonstrates, this clone wasdimmer (lower Ch0Max) than the parent mCherry. This dimness may be due to its lower extinction
coefficient () or slower chromophore maturation in the cellular environment. Owing to the lack of amechanistic understanding of the residues involved in chromophore maturation or EC enhancement,
we opted for multiple rounds of random (i.e. “error-prone” PCR) mutagenesis to increase thebrightness of the C4 mutant.
Figure 5.4 shows the evolution of the lifetime and brightness of the C4 libraries tracked
for each round of FACS and error-prone PCR mutagenesis. The initial error-prone PCR librarygenerated from C4 (called EPC4) contained few mutants with higher brightness (Figure 5.4b). Twoand three rounds of FACS-based selection and subsequent random mutagenesis on EPC4 resulted
in EPC4-FACS-R2 and EPC4-FACS-R3 respectively, which are brighter than the initial C4 clone(Figure 5.4c).
Multiple mutants were selected from EPC4-FACS-R2 and EPC4-FACS-R3. Figure 5 shows
the lifetime vs. brightness dotplots of these two libraries. The major population in these libraries
had slightly higher photostability and lifetime ∼ 2.3 ns. However, there was also a minor population
with a lifetime with ∼ 3 ns. Individual mutants were isolated from these two libraries by picking
colonies on plates according to their fluorescence lifetimes, as described in the previous section.
The subpopulations selected with ∼ 2.3 ns and ∼ 3 ns lifetimes were named C4PB and C4LT

100

Figure 5.4: Evolution of C4 mutant: (a) Although C4 mutant displayed higher excited state
lifetime and photostability, the brightness (Ch0Max) was lower than the parent mCherry. (b, c)Three rounds of error-prone mutagenesis and subsequent FACS-based selection were carried out toenhance the brightness of C4 mutant.

respectively. The “C4” indicates their parental origin. The “PB” and “LT” stands for PhotoBleaching and LifeTime, respectively.
The photo-physical properties of the individual C4PB and C4LT clones expressed in yeast
were characterized by screening in the microfluidic and by FACS. The results revealed that although the lifetimes of all the C4PB mutants were similar, the following mutants showed improved
brightness relative to mCherry: C4PB-3, 5, 6 and 12. Sequence analyses of all the C4PB mutants
showed the presence of K166R mutation indicating its involvement in improving the brightness ofthe mutants. C4LT-2 and 6 showed improved brightness and photostability respectively.
Figure 5.6 shows microfluidic screening results for C4PB-12 along with mCherry [12], Fu-

101

Figure 5.5: C4PB and C4LT mutants: Microfluidic screening results displaying lifetime andphotostability profiles of 2nd (EPC4-FACS-R2) (a) and 3rd round (EPC4-FACS-R3) (b) of FACSenriched error-prone C4 libraries. C4PB mutants were selected randomly from EPC4-FACS-R2 andEPC4-FACS-R3 libraries. C4LT was selected from the small populations in those libraries having
longer excited state lifetime.

sionRed [54], mRuby2 [165] and TagRFP-T [11] expressed in yeast at different illumination intensities. At lower intensity (2.5 kW/cm2), all the RFPs show similar photostability. However, at
higher intensities (5-10 kW/cm2 ), C4PB-12 displays significant increase in photostability. Fromthese results, we hypothesize that C4PB-12 would perform better in confocal imaging conditions
where higher irradiances are employed.
The C4PB mutants were expressed in E. coli and purified and detailed in-vitro and in-vivo
characterizations were carried out. Table 5.4 displays their photo-physical properties. All thein-vitro measurements were performed using PBS 10x (pH=7.4 buffer). Lifetime measurements
were performed in a TCSPC set-up (Pico-quant) and the fluorescence traces were fitted with a biexponential function. For measurement of the quantum yield, Cresyl Violet (φ=0.54 in methanol)
was used as a reference. Extinction coefficients of the purified proteins were measured using alkali
denaturation protocol described in reference [12]. Quantification of in-vivo brightness in yeast was
performed in a FACS set-up using 561-nm laser excitation. Photostability measurements werecarried out with 532 nm laser excitation in the DSC/GSR set-up described in Chapter 2. Detailed

102

Figure 5.6: The lifetime and photostability profiles of yeast cells expressing mCherry, FusionRed,mRuby2, TagRFP-T and C4PB-12 at (a) 2.5 kW/cm2 (b) 5 kW/cm2 (c) 7.5 kW/cm2 and (d)10 kW/cm2 illumination intensities. All the RFP mutants tend to show similar photostability
at 2.5 kW/cm2 (a) but at higher illumination intensities, C4PB-12 performs significantly better
photostability than rest of the RFPs. With increasing illumination intensities, lifetime of themutants tends to be lower due to ground state depletion as described in the reference [55].

procedures for the in-vitro characterization of the RFPs are presented in the Appendix.
As Table 5.4 shows, all the C4PB mutants were blue-shifted in absorption and emission compared to mCherry. The excited state lifetimes of all the mutants were longer than that of theparent mCherry. This was expected as the mutants were selected for higher lifetime. However, it isinteresting to note, even after significant improvement in lifetime, the enhancement of the quantum
yield was only marginal for C4-derived mutants. For instance, C4PB-12 had 29 % (2.22 ns vs. 1.72ns) increase in lifetime relative to mCherry but only 8 % (0.27 vs. 0.25) improvement in φ. We
found that this apparent discrepancy was due to the reduction of radiative rate constants for the
C4PB mutants as discussed below.

103
Table 5.4: Photo-physical properties of the C4PB, SDC and SLT mutants.
Mutants
mCherrymScarlet
C4PB-12
C4PB-6
C4PB-5
C4PB-3
SDC-1
SDC-5
SLT-1
SLT-11

λabs
(nm)
587
569
572
573
572
573
570
560
562
559

λems
(nm)
610
594
608
606
604
604
599
592
592
588

τ
(ns)
1.7
3.9
2.2
2.3
2.2
3.6
3.6
3.8
3.8

max (M-1
cm-1 )
0.25 74,000
0.70 96,000
0.27 55,000
0.27 57,000
64,0000.22 61,000
0.55 0.50 0.68 0.71 56,000
φBrightness(max ∗ φ)
100
402
80
83
76
238

Brightness(in yeast)
100
135
117
105
79
-

Photostability(in yeast)
100
103
89
88
86
-

The radiative rate constant (kr ) of a fluorophore can be obtained from the following equation:

φ
τ

kr =

(5.1)

where, φ and τ are fluorescence quantum yield and the excited state lifetime of the fluorophore.
Using the observed φ and τ of C4PB-12, we found that it had 17% reduction in radiative rate
constant compared to mCherry. To investigate further the origin of this decrease in kr , we computed
the contributions of the emission spectra and the extinction coefficient to the radiative rate constantusing the Strickler-Berg equation [168]:

8 × 2303πn2
A21 = kr =< νf−3 >−1c2Zν dlnν(5.2)

where, c, n, ν and ν are speed of light, refractive index of media, frequency and extinction coefficientat the frequency ν. ν. < νf−3 > is given by:< νf−3 >=Rν −3 I(ν)dν
R
I(ν)dν

where, I(ν) is the fluorescence intensity spectrum.

(5.3)

104
Our analysis of the different components of the Strickler-Berg equation (Eqn. 5.2) showedthat although the contribution due to the emission spectra (< νf−3 >) was similar for mCherry
R
and C4PB-12, there was 16% decrease in the EC component ( ν dlnν) of the C4PB-12. Thisaccounted for the 17% decrease in radiative rate of this mutant. Therefore, it can be concluded
that the decrease in  reduced the radiative rate of the mutants which eventually resulted in adecreased φ.
The decrease in max of the C4PB mutants resulted in a reduction of the molecular brightness
which is quantified as the product of max and φ. The slight increase in brightness measured in-vivo
was due to 561-nm laser excitation in the FACS screening that resulted in more effective absorptionof the light by the C4PB mutants compared to mCherry due to the hypsochromic shift of theirabsorption spectra.

Figure 5.7: Photobleaching decays of mCherry and C4PB-12: (a) Normalized photobleaching decays of mCherry and C4PB-12 upon continuous illumination from a 532-nm laser at 10
kW/cm2 . C4PB-12 displayed faster photobleaching compared to mCherry. (b) Fluorescence decays of mCherry and C4PB-12 when excited with pulsed-laser beams with Gaussian profile (0.20
ms FWHM, 0.65 ms inter-pulse delay) similar to as experienced by the cells in the microfluidic.

In-vivo photostability of the mutants in yeast cells was measured by continuous 532-nm
laser irradiation. The area-under the fluorescence curve was then computed from the normalized
fluorescence decay of the mutants to quantify the photobleaching. Figure 5.7a showed the fluores-

105
cence traces of mCherry and C4PB-12 at 10 kW/cm2 532-nm illumination. Although the mutants
showed improved photostability in the microfluidic screening, we found that the photostabilities of
the C4PB mutants were similar to or lower than that of mCherry. To investigate the discrepancy
between the photostability measured in microfluidic vs. off-chip, we excited the mutants with similar illumination pattern as the mutants experienced in the microfluidic. The yeast cells expressing
the mutants were excited with Gaussian pulses having 0.2 ms FWHM and 0.65 ms inter-pulse delays with 10 kW/cm2 peak intensity. The resulting normalized fluorescence traces for mCherry and
C4PB-12 are shown in Figure 5.7b. It clearly showed that C4PB-12 mutants bleached slower thanmCherry only in the 4-20 ms time-window. This was within the interrogation time-regime in the
microfluidic for measuring the photostability of the mutants by the ratio of the fluorescence signals
from 9th beam and the first beam. It appeared that the improved photostability of the C4PB mutants in the microfluidic was due to their unique fluorescence decay behavior upon high-intensity
pulsed illuminations. Presumably, at high illumination intensity, a significant portion of the C4PB
mutants is trapped in photo-protective dark states reducing the photobleaching only in 4-20 ms
time window. DSC measurements of the C4PB mutants indeed revealed a higher percentage of
DSC compared to mCherry (23% in mCherry vs. 64% in C4PB-12; Figure 5.8a and b). The C4LT
mutants were also found to have a high propensity for DSC and poor off-chip photostability (Figure
5.8c and d).
As the mutants derived from C4 did not show significant improvement in brightness or photostability, we focused attention on C9 and C12 mutants, aiming to enhance their brightness.

5.5

Evolution of C9 and C12 Mutants
The C9 and C12 mutants were found to have longer lifetimes than mCherry or C4 mutants.

However, their brightnesses were lower than that of mCherry. In order to restore the brightness
while maintaining their increased lifetimes, at first, we performed random mutagenesis. However,
the random mutagenesis on C9 and C12 did not produce any bright mutants. Hence, we performed

106

Figure 5.8: Fluorescence traces (black dots) of mCherry (a) and C4PB-12 (b) displaying highpercent dark state conversion (DSC) of C4PB-12 compared to mCherry. The fluorescence traces
were fitted with three exponential functions (red lines) and DSC time-constants and percent DSC
were extracted as described in reference [56] and Chapter 3. Percent DSC of mCherry and C4PB-12
were found to be 23% and 64% respectively. DCS time-constants of mCherry and C4PB-12 were 59µs and 138 µs respectively. Yeast cells expressing the RFPs were irradiated with 561-nm laser at ∼5 kW/cm2 to obtain the fluorescence traces. (c) Fluorescence decays showing similar photostabilityof mCherry and C4LT-6 when expressed in yeast and excited with 532-nm laser at 10 kW/cm2 . (d)C4LT-6 displayed rapid drop of fluorescence upon 532-nm laser irradiation indicating higher DSC
compared to mCherry.

targeted mutagenesis on those mutants. We specifically targeted W143, I161 and Q163 (Figure5.9a) positions because of their sequence diversity in mFruit series of FPs [12].
Figure 5.9 b, c and d showed the lifetime, brightness and photostability profiles of C9, C12
and site-directed library of C12 generated by full saturation mutagenesis at the above-mentioned
residues. Both the site-directed C9 and C12 libraries contained mutants with high lifetime andbrightness. With the microfluidic platform, we sorted the mutants to enhance the populations with

107

a

b

I197
I161
Q163

Chromophore
W143

c

d

Figure 5.9: Evolution of C9 and C12 mutants: (a) Site-directed libraries were generated
targeting W143, I161 and Q163 positions of C9 and C12. (b, c, d) Lifetime, Ch0Max (brightness)and photostability profiles of the resulting C12 SD (site-directed) library along with mCherry, C9
and C12 mutants. The mutants with higher lifetimes (population in the pink box) were enrichedin the microfluidic sorter to generate SDC and SLT libraries. Subsequent selection from SDC andSLT libraries produced SDC and SLT mutants respectively.

lifetime higher than ∼ 3.5 ns. This library was called Site-Directed C9 and C12 library or simply
SDC. We selected a few mutants from this library and called them SDC mutants. Two further
error-prone mutagenesis and subsequent selection for higher lifetime mutants (with selection gate,
lifetime> 3.7 ns) were carried out to generate the SLT library. Mutants selected from the SLTlibrary were called SLT mutants.
Table 5.5 shows the mutations in the SDC and SLT mutants and the microfluidic screening
results. All of the mutants had I197R which indicated that C12 (mCherry I197R) mutant was
the precursor of the SDC and SLT mutants. As expected, the SLT mutants were found to have

108
Table 5.5: Mutations and the screening results of the SDC and SLT mutants. Lifetime and brightnesses (in yeast) of the mutants were obtained from the screening in the microfluidic. The brightness
was quantified as the average of Ch0Max signals from different mutants.
Mutants

143 161 163 197 Error-prone mutations

mCherry
SDC-1
SDC-5
SLT-1
SLT-11a

W
M
L
L
S

I
V
T
V
V

Q
L
C
I
Y

I
R
R
R
R

E10G
N98S,
Q137L

R125H,

F129L,

τ
(ns)
1.87
3.58
3.57
3.68
3.74

Brightness(in yeast)
100
115
145
82
83

slightly longer lifetimes than SDC mutants, and contained few mutations that appeared during
the error-prone mutagenesis. The brightnesses of those mutants was quantified as the averageof lifetime-PMT signal (Ch0Max, see Chapter 4) obtained from the microfluidic screening andnormalized with the Ch0Max for mCherry. SDC-5 was the brightest mutant while SLT-11a had
the longest lifetime.
All the SDC and SLT mutants were expressed and purified for detailed in-vitro photo-physicalinvestigation. However, attempts to purify SLT-11a failed in E. coli even after prolonged incubation at 28C. As SLT-11a did not show higher brightness even with its long lifetime, we performed
further mutagenesis to enhance its brightness. The N98S, F129L, Q137L mutations were sequentially reverted back to the mCherry-like residues with the QuickChange protocol discussed in the
Appendix. It was observed that a combination of S98N and L129F was necessary for the successful
expression of SLT-11a mutant in E. coli leading to its purification. Position 125 was fully saturated
and mutants were selected with improved red fluorescence upon expression on galactose-containing
plates. This led to the generation of the SLT-11 mutant with the following mutations relative tomCherry: W143S, I161V, Q163Y, I197R and H125K.
Figure 5.10 displays the absorption spectra of the SDC and SLT mutants. Similar to C4PB
mutants, these mutants also showed blue-shifted spectra relative to mCherry. Along with RFP
absorption peak around ∼ 560 nm, SDC-1, SDC-5 and SLT-1 mutants were also found to have anadditional peak at ∼ 490 nm (green peak). This extra absorption peak indicates the chromophore

109

Figure 5.10: Absorption spectra revealed that all the mutants were blue-shifted relative to mCherry.
SDC-1, SDC-5 and SLT-1 were characterized with green peak with maxima at ∼ 490 nm whichcorresponds to GFP-like immature chromophores. SLT-11 had negligible amounts of immaturepeak indicating a complete maturation in that mutant.

maturation process in these mutants was not complete and that some fraction of the population
forms a GFP-like chromophore. This is a serious drawback for these mutants as the immaturepeak restricts their use in multi-color fluorescence imaging with multiple green and red fluorescentproteins. Attempts were made to assist complete maturation by introducing new mutations but
without any success (described later). On the other hand, absorption spectra of SLT-11 showed no‘green peak’ with a small shoulder at ∼ 490 nm.
Table 5.4 displays the photo-physical measurements of the SDC and SLT-1 mutants measured
in purified proteins. All the mutants showed significant improvement in their quantum yield.
Fluorescence quantum yield of SLT-11 was very high (71%) and similar to mScarlet (φ, 70%). Dueto the presence of immature peak in the absorption spectra of SDC-1, 5 and SLT-1, measurementof  was not possible in those mutants. max of SLT-11 was measured as 56,000 M-1 cm-1 which wassignificantly lower than that of mCherry (76,000 M-1 cm-1 ).

110
The in-vitro brightness (max ∗ φ) of mCherry, mScarlet and SLT-11 are presented in Table
5.4. The brightness of SLT-11 was higher than mCherry due to its high quantum yield but it
was lower than mScarlet because of its lower extinction coefficient. mCherry, mScarlet, SLT-11
were also expressed in nuclear-localized mammalian cells (HeLa) and the in-vivo brightness wasmeasured through FACS screening. The screening results were also in qualitative agreement with
the in-vitro brightness measurements: STL-11 displayed higher brightness than mCherry but thebrightness was lower than mScarlet.

5.6

Discussion
Engineering of mCherry with brightness and photostability as selection pressures resulted

in a large number of mutants with interesting photo-physical properties. In order to achieve thebrighter mutants from C4 and C12 mutants, 532-nm and 561-nm laser were used for the excitations.
As a result, all spectra of the resulting mutants were blue-shifted. For instance, the C12 mutant
was characterized with hypsochromic shift compared to mCherry (572 nm vs. 587 nm). Further
selection based on brightness with 561-nm laser excitation resulted in brighter SDC-5 and SLT-1
mutants with 560 and 559 nm absorption peak respectively. These absorptions are very close to561 nm and excited very efficiently at this laser line thus resulting in bright fluorescence.
The improvement in lifetime of the C4PB mutants did not ensure the enhancement of quantum yield. As our analysis results showed, this is due to the reduction in the radiative rate constantsof these mutants. Further analysis of radiative rate constants with the Strickler-Berg equation (Eqn.
5.2) suggested that reduction in extinction coefficient of those mutants was responsible for theirlower radiative rate constants. While this result proves that enhancement of excited state lifetime
does not always ensure the increase in φ, it indicates that the radiative rate constants of the FPs
can also be modified through mutations. These findings created the bedrock of our next librarygeneration for engineering the radiative rate constants of the RFPs as discussed in Chapter 7.
In the microfluidic screening, C4PB mutants showed lower photobleaching tendency relative

111
to mCherry. Intensity-dependent screening results (Figure 5.6) demonstrated that C4PB-12 hadhigher photostability than mCherry, FusionRed, mRuby2 and TagRFP-T, particularly at higher
illumination intensities. However, C4PB-12 photobleached faster in wide-field and confocal imaging
conditions. We speculated that this discrepancy was due to the enhanced dark state conversion of
the C4PB mutants and the presence of photo-protective dark states. This outcome indicated thatphotostability screening of the mutants should be performed employing exact imaging conditions forconsistency of chip and off-chip photobleaching measurements. For instance, LED-based screening
with illuminating the entire path of the cells through microfluidic channel at low irradiation intensity
can be implemented to generate mutants with higher photostability under wide-field illumination
conditions. Complex photobleaching kinetics of the mutants indicates that screening based on therate or quantum yield of the photobleaching could be more effective than the simple ratio-metricmeasurement of photobleaching to isolate mutants with improved photostability. In our future
work, we intend to implement these advanced screening techniques for the directed evolution of thephotostability of the mutants.
A fraction of the SDC and SLT mutants showed a ‘green peak’ at ∼ 490 nm indicating
incomplete maturation of the chromophore. This peak suggests that amino acid residues at 143,
161 and 163 play significant roles in the formation of a functional RFP chromophore. Indeed,
M163Q mutation eliminated the green peak (at ∼ 510 nm) observed in RFP1.4 and other DsRed
mutants [12]. mRojoB, a RFP derived from mCherry, showed similar green peak at ∼ 500 nm
which contained Q163M mutation [92]. As the 163 position is also responsible for high quantum
yield and the residues at this position were selected through multiple round of sorting, we did
not saturate this position to attain complete maturation of the mutants. Instead we searched foralternative pathways to assist the maturation in our SDC/SLT mutants. The mutational analysis
of mRojoA, mRojoB and mRouge indicated that residues at 177, 195 and 217 positions could also
improve maturation of the chromophore. However, library generation with saturation mutagenesis
at these residues failed to eliminate the ‘green peak’ observed in SDC/SLT mutants. Generating
multiple single mutants by replacing residues at 161 with non-polar residues (Ala, Ile, Leu) reduced

112
the ‘green peak’ but at the cost of lower brightness and shorter lifetime of the mutants.
The unsuccessful attempts to eliminate the green peak in SDC-1, SDC-5 and SLT-1 indicatedthat it might be a dead-end for the evolution of these mutants. We therefore focused on SLT-11
which had excellent quantum yield and complete chromophore maturation. However, the brightnessof this mutant was compromised due to lower extinction coefficient. In our next level of librarygeneration, we attempted to improve the extinction coefficient and radiative rate-constant of thismutant for improved brightness. This effort is discussed in Chapter 7.

5.7

Conclusion
In this Chapter, directed evolution of mCherry was discussed. Employing photostability andexcited state lifetime as selection pressures, multiple mCherry mutants were generated with improved photo-physical properties. Although C4PB mutants displayed improved photostability inthe microfluidic screening presumably due to their pronounced dark state conversion, the mutants
showed faster photobleaching compared to mCherry in the confocal and wide-field imaging platforms. The C4PB mutants were characterized with reduced radiative rate constants. Consequently,
the improvement in their excited state lifetime resulted in only marginal increase of quantum yield.
On the other hand, mutants derived from C9 and C12 displayed higher fluorescence quantum
yield and brightness relative to mCherry. Few SDC and SLT mutants were characterized within-complete chromophore formations resulting in immature peaks in their absorption spectra. The
SLT-11 mutant, owing to its higher quantum yield (71%) and complete chromophore maturation,
was selected as a template for the next round of library generation to achieve further improvement
in its brightness.

Chapter 6
Engineering of Excited State Lifetime in FusionRed for Improved in-vivo
Brightness

6.1

Introduction
High monomericity and low cytotoxicity upon cellular expression are important propertiesof fluorescent proteins (FPs) for live-cell imaging. Although some assays those involving cell-typeidentification, do not require monomeric FPs, monomericity is of utter importance in experiments
involving the fusion of FPs in sub-cellular compartments. Absence of dimerization in the FPs
is essential for reliable interpretations of the underlying biological phenomena. FRET efficiency
between GFP and YFP was found to have artifacts due to the weak dimerization tendencies of
the related FPs [169]. Oligomerization of FPs was also reported to cause non-specific protein
interaction or reorganization of cell organelle structure [170]. On the other hand, cytotoxicity of
the FPs is a drawback for long-term imaging experiments [54]. Significant efforts have been madeto generate monomeric variants of RFPs, e.g. mRFP1 [13], mCherry [12], TagRFP [99], mKate2[164] etc., by disrupting the oligomeric interfaces of their natural ancestors. Although these RFPs
were successfully employed in a myriad of applications, they were found to perform sub-optimallycompared to GFPs [54, 104]. For instance, TagRFP was reported to cause structural reorganizationof endoplasmic reticulum (ER) when expressed in a protein located at the cytoplasmic-end of the
ER (CytERM) [100] and mCherry was found to be functionally perturbing for in-vivo imaging offission yeast [171].

114
To address the dimerization and cytotoxicity issues in RFPs, FusionRed was developed [54].
The precursor of FusionRed, mKate2, was characterized as having low cytotoxicity in transgenic
Xenopus laevis embryos [164]. However, mKate2 displayed monomer-dimer equilibrium in highpressure liquid chromatography (HPLC) due to some residual dimerization tendency. By targeting
the residues involving dimeric interfaces and replacing the hydrophobic C-terminal with a flexible
glycine-rich linker, the dimer formation in mKate2 was reduced. These changes were followed by
few more mutations to produce FusionRed, which displays better fusion efficiency than mCherry.
FusionRed was also characterized with low cytotoxicity, as shown by the degree of optic fissure
closure when expressed in the eyes of Xenopus laevis embryos. Recently, the photo-switching
behavior of FusionRed was employed to obtain super-resolution images [149]. FusionRed was also
used to develop a genetically encodable voltage sensor [172] and fluorescent proteins with yellow(535 nm)/orange (565 nm) emission [173].
With its reduced dimerization tendency [101], improved fusion efficiency and low cytotoxicity[54], FusionRed has potential to be an excellent bio-marker for live-cell imaging. However, we foundthat in-vivo brightness is the ‘Achilles heel’ of FusionRed. Theoretically, the in-vitro brightnessof FusionRed, which is reported to have a 19% quantum yield (φ) and high extinction coefficient(max = 94,500 M-1 cm-1 ) is similar to mCherry (φ, 22%; max =72,000 M-1 cm-1 ) [12]. However, weobserved decreased brightness of FusionRed compared to mCherry upon expression in mammaliancell-lines (HeLa). Hence, we focused on developing a new version of FusionRed with improved
in-vivo brightness in particular by improving its low fluorescence quantum yield. As improvement
in excited state lifetime (τ ) holds promise to improve quantum yield in FPs [155], we opted for
improving the lifetime of FusionRed. This Chapter discusses the excited state lifetime engineeringof FusionRed to enhance the brightness while retaining its good fusion properties. Figure 6.1
displays the genealogy of the mutants we generated in the course of these studies. FusionRed
was first subjected to error-prone mutagenesis. The resulting error-prone library was sorted in
the microfluidic system to produce two mutants with longer lifetimes: FR-1, FR-13. The invivo brightness of FR-13 was increased by multiple rounds of random mutagenesis and subsequent

115
selection in the microfluidic. This process resulted in the FRX mutants. In a separate evolutionary
route, a site-directed mutagenesis library of FusionRed was generated by targeting residues known
to have significant effects in the mCherry mutants. Two rounds of FACS and multiple rounds of
selection based on lifetime generated FSD-9 and FSD-11 mutants. The photo-physical propertiesof the FusionRed mutants are also presented in this Chapter.

FusionRed
Error-prone mutagenesis

Targeted at 165, 167, 201
and 203

FusionRed
Error-prone Library

FusionRed
Site-directed Library

Microfluidic sortingfor longer lifetime
mutants

FR-1

Two rounds of
FACS-enrichment

FR-13

Error-prone mutagenesis
for improving brightness

Selection for
shorter lifetime

Microfluidic sortingfor brighter mutants

FEP Library
Error-prone mutagenesis
for improving brightness

FSD-11

Microfluidic sortingfor brighter mutants

FR-F
FR-G
FR-H

Selection for longer
lifetime

FRX
M4V mutation mutants

FSD-9

XF
XG
XH

Figure 6.1: Genealogy of the FusionRed mutants.

6.2

Error-prone FusionRed Library
In order to improve the brightness of FusionRed, first we generated a random mutagenesis

library (EP-PCR library). Figure 6.2 demonstrates the microfluidic screening results of FusionRed(wild-type) and the EP-PCR library expressed in yeast. The library was found to have a small

116
population with longer excited state lifetime than FusionRed. Multiple rounds of microfluidic
sorting were performed to enrich this population (Figure 6.2b; population in the pink box) withlifetime greater than 2.2 ns. Following the microfluidic enrichment, the library was expressed on
galactose-containing plates. Several mutants of FusionRed were selected from these plates, guided
by the excited state lifetime of the colonies. The resulting mutants are called FR mutants. Out ofseveral FR mutants, the excited state lifetime of FR-1 and FR-13 were characterized to be 2.48 ns
and 2.82 ns (measured in microfluidic screening) respectively, which was significantly higher thanthe lifetime of FusionRed (τ ; 2.05 ns). As the lifetime of FR-13 was longer than FR-1, we chose
this mutant as a template for further engineering.

Figure 6.2: Microfluidic screening dotplots displaying lifetime and brightness (Ch0Max) profilesof FusionRed and the FusionRed EP-PCR library generated by random mutagenesis. The library
contained a population with longer lifetime (enclosed in the pink box) which was enriched to produce
the FR mutants.

6.3

Evolution of FR-13 Mutant: FRX Mutants
With the promising improvement in lifetime, FR-13 was expressed in and purified from E. coli

and photophysical measurements were carried out. Table 6.1 displays the in-vitro photo-physicalproperties of FR-13 and FusionRed. The brightness calculated from the values of max and φ of

117
this mutant is ∼ 2.6 fold higher than FusionRed wild-type. However, the in-vivo brightness of
FR-13 measured by microfluidic screening in yeast showed that it was dimmer than FusionRed(Figure 6.3b & c). We speculated that the sub-optimal in-vivo brightness of FR-13 might be due
to its slow maturation as was observed during its purification in E. coli. Both site-directed andrandom mutagenesis approaches were therefore undertaken to improve the in-vivo brightness of
this mutant.

a

b
A224

F83

ChromophorecdFigure 6.3: (a) FR-13 was found to have the following mutations: H25Y, V47I, F83Y and A224T(avGFP numbering). Position of A224 displayed in the mKate (precursor of FusionRed) crystal
structure (3BXB). (b, c, d) Microfluidic screening results displaying lifetime and brightness of
FusionRed, FR13 and FR13 T224A mutants. The T224A mutation improved the brightness of
FR-13 (Ch0Max 0.53 V → 0.80 V) while reducing the lifetime (2.72 ns → 2.02 ns).

The DNA sequence of the FR-13 mutant revealed the following mutations relative to FusionRed: H25Y, V47I, F83Y and A224T. The amino acid residues were numbered by aligning the118
sequence of the FR mutants with avGFP sequence as done in the original FusionRed article [54].
The sequence alignment is presented in the Appendix G.
Table 6.1: Photophysical properties of the mutants derived from FusionRed.
Mutants
FusionRed
mCherry
FR-13
FR-F
FR-G
FR-H
FR-1
FR L177M
FR-1 C165V

λabs
(nm)
575
587
571
571
572
572
569
571
569

λems
(nm)
596
611
591
591
591
592
594
594
589

τ (ns)

φ

1.79
1.72
2.89
2.36
2.73
2.73
2.25
2.14
2.55

0.26
0.22
0.48
0.36
0.42
0.42
0.34
0.34
0.39

max
(M −1 cm−1 )87,30072,000123,500
104,300
104,900
90,10084,90071,10091,100Brightness(max ∗ φ)
100
70
262
167
192
165
127
107
157

pKa
4.5
4.4
4.7
4.6
4.7
-

To improve the brightness of FR-13, we first investigated the roles of each mutation thatappeared in the mutant. Of the four mutations observed in FR-13, only position 224 was internal(the residue facing inward of the β-barrel) while others were located either in the α-helix (F83) or
facing outward of the barrel (H25 and V47). Four mutants were generated from FR13 by revertingback the mutations to FusionRed-like residues one by one: FR-13 Y25H, FR-13 I47V, FR-13 Y83F
and FR-13 T224A. From the microfluidic screening of yeast cells expressing these mutants, weobserved that position 224 controlled both lifetime and brightness in FR-13. As it is evident fromthe Figure 6.3b, c and d, introduction of T224A mutation in FR-13 resulted in improved brightness
with reduction of excited state lifetime. From these analyses, we concluded that the longer lifetimeand lower brightness of FR-13 originated from the same mutation: A224T.
These photo-physical studies on FR-13 mutants led us to target position 224 with full saturated mutagenesis, in the hope of finding FR-13 mutants with similar or higher lifetime andimproved brightness. However, the site-directed libraries generated by targeting only A224 or in
combination with H25, V47 and F83 did not produce variants with higher brightness or longer
lifetime. As several attempts of site-directed mutagenesis failed to improve the brightness of FR13,
we took the random mutagenesis approach.

119

Figure 6.4: Evolution of FusionRed through random mutagenesis: FR-13 was generated
from the error-prone mutagenesis library of FusionRed and subsequent selection based on excitedstate lifetime of the mutants. Two rounds of random mutagenesis on FR-13 and selection basedon brightness produced FR-F, G and H mutants with improved brightness and lifetime. Individual
mutants were screened with same number of cells and the results were overlaid with different color
maps to generate this plot.

Figure 6.4 displays the evolution of FR-13 through several random mutagenesis libraries.
A first round of EP-PCR mutagenesis and subsequent enrichment of brighter mutants throughmicrofluidic sorting generated the FEP library. One of the mutants selected from the FEP library,FR-13 G160R, showed improved brightness relative to FR-13 and brightness similar to that of
FusionRed. Further random mutagenesis on top of the FEP library generated multiple mutants
brighter than FusionRed wild-type: FR-F, FR-G and FR-H. The in-vitro photophysical propertiesof these mutants are given in Table 6.1. The mutants were found to have improved max and φ,resulting in higher in-vitro brightness. The pKa values of the mutants were similar to FusionRed.
Sequencing of these mutants revealed the following mutations relative to FR-13: FR-F (V4M,
Q115H, L142M); FR-G (V4M, G160R, T230S); FR-H (V4M, G160R, T230R).

120
Table 6.2: Photophysical properties of the mutants derived from FusionRed. The mutations involved in FR-13 and other FusionRed mutants are presented in Appendix G.
Mutants
HeLa
mCherry
FusionRed
FR-F
FR-G
FR-H
XF (FR-F M4V)
XG (FR-G M4V)
XH (FR-H M4V)

Fl. Intensity(Trial 1)
56
448
175
163
184
208
252
285
415

Fl. Intensity(Trial 2)
461
223
200
192
340
272
340
375

Fl. Intensity(Mean)
56
455
199
182
188
274
262
313
395

Relative
Brightness
229
100
91
94
138
131
157
198

Although FR-F, G and H mutants showed higher brightness in yeast, when expressed in
mammalian cells they displayed poor brightness relative to FusionRed. The sequence analyses
showed the presence of a V4M mutation in all of the mutants. During the evolution of mKate2
from mKate, the M4V mutation was introduced to create an optimal Kozak sequence and efficient
expression in the mammalian cells [164]. Another study based on FACS-seq indicated that multiple
initiation sites, as observed in these mutants, could be detrimental for the effective expression of theproteins in the mammalian cells [174]. From these analyses, we speculated that reversing the V4M
mutations in these mutants could improve their expression efficiency and brightness in mammaliancells. Hence, we generated the FRX mutants: XF (FR-F, M4V); XG (FR-G, M4V); XH (FR-H,
M4V).
Figure 6.5 and Table 6.2 displayed the FACS screening results of the nuclear-localized FRX
mutants and their precursors FR-F, G & H upon transient expression in HeLa cells. FRX mutants showed higher brightness than FusionRed and their precursors. For instance, XG showed abrightness of 157 which is significantly higher than FusionRed (100) or FR-G (94). However, it isnotable that the brightness of these mutants is still lower than mCherry, which is inconsistent withexpectations based on the in-vitro brightness (Table 6.1). Although these FRX mutants showed
improved brightness compared to FusionRed upon transient expression in HeLa cells, when the mu-

121

a

b

Figure 6.5: FACS screening results of the FusionRed mutants: (a) Distribution of thebrightness (FL4 Log:FL4) of nuclear-localized FusionRed (FR-WT), FR-F, FR-G and FR-H upontransient expression in the mammalian cells (HeLa). Brightness was quantified by the mean offluorescence intensity of cells within R2 region. FusionRed mutants displayed similar brightness
relative to the wild-type FusionRed in mammalian cells. (b) FRX mutants (XF, XG and XH)
showed improved brightness in mammalian cells.

tants were expressed stably in the MCF10A cell-line, their brightnesses were lower than FusionRed.
This inconsistency in brightness results suggest that the slow maturation of FusionRed continues
to play a role in reducing the in-vivo brightness.

6.4

In-vivo Oligomerization and Labeling Efficiency of the FRX
Mutants
Next, we moved to investigate the in-vivo dimerization tendency and labeling efficiency ofthese FRX mutants. For the quantification of in-vivo dimerization propensity of the RFP mutants,
the organized smooth endoplasmic reticulum (OSER) assay [100] was used. FPs were fused to thecytoplasmic-end of endoplasmic reticulum (ER) signal anchor membrane protein (CytERM) and122
expressed in HeLa cells. The FPs having in-vivo oligomeric tendencies, tend to interact with eachother, driving the restructuring of the reticular architecture of ER. This leads to the formationof OSER, which is manifested by small, bright puncta or whorls in fluorescence imaging. (Figure
6.6b). The OSER score, defined as the percentage of cells expressing CytERM-RFP constructs
displaying no whorl, can be used to quantify the in-vivo dimerization tendency, i.e. an OSER scoreof 100 or 0 refers to a completely monomeric or oligomeric FP, respectively.

acb

CytERM-FusionRed

b

whorl

CytERM-TagRFP-T

Figure 6.6: OSER assay of the FusionRed mutants: (a, b) HeLa cells expressing CytERMFusionRed and CytERM-TagRFP-T constructs. Most of the cells expressing CytERM-FusionRed
constructs displayed proper localization with reticular-like structures while TagRFP-T, owing to its
in-vivo oligomerization tendencies, showed small (1-7 µm), bright puncta (whorl), upon fusion to
CytERM. (c) OSER score of FRX mutants indicating their low in-vivo oligomerization tendencies.

The identification of whorls from a large number of cells is required to obtain a statisticallyrobust OSER score. This process is tedious and subject to human bias and errors. To account123
for these issues, we developed a custom-based image analysis program based on CellProfiler [175].
The functioning of this program has been explained in detail in Appendix H. Briefly, first, froma given image, the program identifies the cells expressing CytERM-RFP based on their size andfluorescence intensity. Next, the bright structures or whorls inside the cells are identified using
thresholds based on size and fluorescence intensity. Shape-based filtering is done to remove thefalse-positive whorls. Finally, the whorls and cells are related based on their position and an OSER
score is computed. An OSER score, analyzing thousands of cells can be obtained within an hourin an objective manner.
Figure 6.6c displays the OSER score of different mutants as a function of number of cells
analyzed. From Figure 6.6c, it is evident that analysis of ∼ 400 cells was sufficient to obtain a stable
OSER score. Different parameters and thresholds of the image analysis program were adjusted withthe known OSER score of TagRFP-T (positive control) and FusionRed (negative control) [101]. As
shown in Figure 6.6, TagRFP-T and FusionRed showed an OSER score of 43 and 86 respectively,
which is close to the value obtained by Cranfill et al. by analyzing ∼ 10,000 cells for each FP (41.2
and 91.5 for TagRFP-T and FusionRed respectively) [101]. In our hands, mCherry showed a low
OSER score which contradicts previous results [104, 101]. mScarlet, FusionRed and FRX mutants
showed high OSER score indicating low in-vivo oligomerization tendencies.
To investigate the fusion efficiency of the mutants, FusionRed, mCherry, mScarlet and the
FRX mutants were fused to Connexin-43 (Cx43, gap junction), Rab5a (endosome) & GalT (Golgi
complex) and transiently expressed in mammalian cells (HeLa). Cells expressing these constructs
were imaged on a commercial spinning disc microscope (Nikon) 48 hrs post transfection. Although
localization of Cx43 was optimal for all the FPs, mCherry and mScarlet showed several instances of
mis-localization with GalT constructs. Unlike FusionRed and FRX mutants, a significant fractionof the cells expressing GalT-mCherry and GalT-mScarlet constructs showed mobile vesicles that did
not resemble the stack of perinuclear membranes characteristic of the Golgi complex (Figure 6.7d,e). Similar vesicles were observed by Kaberniuk et al. with GalT-Dendra2 constructs [176]. We
quantified the fusion efficiency of mutants expressing GalT-RFP constructs with ∼150-250 number

124

a

XH-Cx43
mis-localization

proper localization

XH-GalTdc

XH-Rab5fe
mis-localization

mCherry-GalTbmScarlet-GalT

proper localization

mScarlet-GalT

Figure 6.7: Localization efficiency of the FusionRed mutants: (a, b, c) Representative images
displaying localization of XH in Cx43, GalT and Rab5. (d, e, f) mCherry and mScarlet showedpoor performance when fused with GalT.

of cells. It was observed that the GalT fusion efficiencies of mCherry, mScarlet and XG were 17%,57% and 87% respectively. This result is consistent with the high fusion efficiency of FusionRed
relative to mCherry, as reported in the original FusionRed article [54].
FRX mutants showed improved in-vitro brightness and upon expression in yeast cells. Thesemutants displayed higher OSER score than mCherry, indicating lower in-vivo oligomerization tendencies. FRX mutants were characterized with excellent localization efficiency. However, transientand stable expression in the mammalian cells did not produce the anticipated high brightness presumably due to their slow maturation. The in-vivo brightness of these mutants was low compared
to mScarlet and mCherry. On the other hand, FR-1 showed faster maturation when expressed in125E.coli and also had a longer lifetime than FusionRed. Hence, we focused on further mutagenesis of
this variant to develop a brighter FusionRed mutant.

6.5

FR-1 Mutant
As discussed previously in section 6.2, FR-1 was generated by random mutagenesis on Fu-

sionRed and subsequent selection of longer lifetime mutants. Indeed, FR-1 was characterized withlonger τ and higher φ compared to FusionRed as displayed in Table 6.1. Although the in-vitro
brightness of FR-1 was found to be only ∼ 30% relative to FusionRed, when expressed in yeast
it showed ∼ 3.5-fold and ∼1.7-fold higher brightness than FusionRed and mCherry respectively(Figure 6.8b & 6.9b). As the in-vitro brightness of FR-1 is ∼1.8 fold higher than mCherry (Table
6.1) which is consistent with the in-vivo measurements (∼ 1.7-fold higher brightness), we speculatethat the slow maturation in FusionRed is remedied in FR-1. The reported maturation half-time
for mCherry and FusionRed are 15 minutes and 130 minutes, respectively [12, 54].

ba
Chromophore
L52
L177

V22

Figure 6.8: (a) FR-1 was found to have V22I, L52P & L177M mutations (avGFP numbering).
Positions of these residues are displayed in mKate (3BXB) crystal structure. (b) Microfluidic
screening results showed improved lifetime and brightness of the FR-1 mutant relative to FusionRed.

126
The FR-1 mutant was characterized with the following mutations relative to FusionRed:
V22I, L52P and L177M. L177 pointed inside the β-barrel, close to the chromophore. On the other
hand, L52 & V22 were in the loop region (Figure 6.8a). From our previous experience with FR-13
mutants, we speculated that the residue at position 177 might be crucial in modifying the photophysical properties in FR-1. To confirm this notion, we generated FusionRed L177M (abbreviatedas FR L177M). Table 6.1 compares the photo-physical properties of FusionRed, FR-1 and FR
L177M. Although the φ of FR-1 and FR L177M mutants were same, the max of FR L177M wasslightly lower. However, we observed faster maturation in FR L177M upon bacterial expression.
Hence, we concluded the L177M mutation improved the φ and maturation speed in FR1, while
V22I and L52P mutations helped to maintain the high max of FusionRed.

6.6

FusionRed Site-Directed Library
As described in the previous Chapter, by using mCherry as a template we generated several

mutants with high quantum yield and brightness through site-directed mutagenesis. For instance,
SLT-11, an mCherry mutant, was generated by targeting 143, 161, 163 & 197 residues (numbering
based on mCherry crystal structure, PBD 2H5Q), showed a φ of 71%. Inspired by these results,
we targeted the analogous residues in FusionRed to generate a site-directed library: 165, 167, 201& 203 (avGFP numbering) which correspond to 161, 163, 195 & 197 in mCherry-based numbering.
Figure 6.9a displays the brightness (Ch0Max) and lifetime profiles of the resulting site-directed
library which contained a significant population with shorter lifetime (∼1.2 ns) and lower brightnessof a higher lifetime population. We enriched the bright population from this library (population
enclosed in the pink box) through FACS-based selection. After several rounds of microfluidic and
FACS-based sorting, we selected a few mutants from this library and called them FusionRed SiteDirected mutants or simply FSD mutants.
Figure 6.9b demonstrates the screening results of the FSD mutants along with FusionRed,
FR1, SDC-5 and mCherry. The x- and y-axes represents the red and green fluorescence intensity

127

Figure 6.9: (a) Microfluidic screening results displaying the brightness (Ch0Max) and lifetime
profile of FusionRed site-directed library generated by targeting residues at 165, 167, 201 and203. The population with improved brightness (within the pink box) was enriched using FACS.
Multiple rounds of subsequent microfluidic and FACS-based sorting generated FusionRed SiteDirected (FSD) mutants. (b) FACS screening results displaying mean red (561-nm excitation) andgreen (488-nm excitation) fluorescence intensities of the FSD mutants along with mCherry, SDC-5,
FusionRed and FR-1.

of the mutants obtained by the excitation of 561-nm and 488-nm laser respectively. As evident
from the Figure, several mutants showed higher brightness than FusionRed. For example, FSD-9 &FSD-11 show a single mutation from the wild-type FusionRed and were brighter or comparable tomCherry. The FR-1 variant, as discussed previously, showed higher brightness than both mCherry
and FusionRed. All the mutants displayed lower green fluorescence than SDC-5, indicating the
likely absence of GFP-like chromophore and therefore complete maturation.
The sequence analysis of FSD-9 and FSD-11 revealed the C165V and C165L mutations,
respectively. Figure 6.10a shows that position 165 is very close to the phenolate-end of the chromophore and therefore it is unsurprising that modifications at this position modulate the photophysical properties. Figure 6.10b shows the fluorescence decays of FusionRed, FSD-9 and FSD-11
measured using yeast cell-lysate in TCSPC with tail-fitting. It is interesting to note that substitution of cysteine with valine at this position leads to significant increase in lifetime (1.67 ns in
FusionRed vs. 2.03 ns in FSD-9) while leucine at this position reduced the fluorescence lifetime(1.21 ns in FSD-11).

128

b

a
Chromophore
165

c

d

Figure 6.10: (a) Position 165 (161 in mCherry numbering) is displayed in mKate crystal structure (PBD 3X3B). Mutations at this positons controls the excited state lifetime, photobleachingand photoswitching properties of FusionRed (wild-type) FSD-9 and FSD-11. (b) FSD-9 & FSD-11
showed longer and shorter excited state lifetimes compared to FusionRed respectively. (c) Photobleaching decays of FusionRed mutants with 560-nm LED illumination at ∼ 4 W/cm2 . Excitation
intensities were not normalized according to the extinction coefficient of each mutant for these photobleaching experiments. (d) Photoswitching behavior of the FusionRed, FSD-9 & FSD-11 withalternate 560-nm and 438-nm LED illumination.

Figure 6.10c demonstrates the photobleaching decays of the mutants expressed in yeast upon
560-nm LED illumination from a commercial inverted microscope (Olympus) at ∼ 4 W/cm2 . Both
FusionRed and FSD-11 display a rapid drop in fluorescence in a short time-scale (2-4 s) indicatinga significant amount of dark state conversion (DSC). This rapid decay of fluorescence in a short
time-scale is absent in FSD-9 mutant and the photobleaching decay was nearly mono-exponential.
This suggests that FSD-9 has reduced dark state conversion tendency compared to FusionRed.
FSD-11 shows photoactivation followed by photobleaching upon 560-nm LED illumination. Please129
note that excitation intensity was not normalized to give the same excitation rate while performing
these bleaching experiments. All the mutants were irradiated with uniform ∼4 W/cm2 LED light.
The photoswitching properties of these mutants have been captured in Figure 6.10d. Yeast
cells expressing the FSD mutants were excited with 560-nm LED light for 2 s followed by a 2 s
438-nm LED illumination (with 561-nm LED off) and the whole cycle was repeated for 80 times.
Along with inducing fluorescence, 560-nm illumination drives some population into photo-induced
dark or OFF states. High-energy 438-nm illumination converts the population from the OFF stateback to the ON state. Thus, alternative 560-nm and 438-nm illumination in FPs can reveal their
ON-OFF state dynamics or photoswitching. Both FusionRed and FSD-11 show photoswitching
while FSD-11 displays photoactivation similar to that observed in photobleaching decay (Figure
6.10c). Unlike FusionRed and FSD-11, photoswitching is nearly absent in FSD-9.

6.7

Discussion
Random mutagenesis on FusionRed generated FR-13 and FR-1 mutants. Photo-physicalanalyses of FR-13 mutants revealed that the A224T mutation was responsible for its improved
lifetime/quantum yield as well as its reduced in-vivo brightness. Reduction in brightness of FR13 upon cellular expression may be explained by its slow maturation observed in the bacterial
expressions. We hypothesize that a polar threonine residue at 224 improved the rigidity of thechromophore through electrostatic or H-bonding interactions improving the lifetime and quantum
yield but interfered with the chromophore formation process by slowing it down.
Multiple rounds of error-prone mutagenesis improved the brightness of FR-13 in yeast but
the brightness was sub-optimal in mammalian cells. Although the M4V mutation improved thebrightness in HeLa cells transiently expressing the mutants, the brightness was still lower thanits promised max and φ. For instance, from the in-vitro analyses, XH should be ∼ 2.4 times
brighter than mCherry but as Table 6.2 shows, XH is slightly dimmer than mCherry. When the
FRX mutants were stably expressed in MCF10A cell-line, the brightness was even lower than the

130wild-type FusionRed. We interpret these inconsistencies in brightness as FRX mutants sufferingfrom slow chromophore maturation as observed during their bacterial expression.
A custom-made image analysis program was used to quantify results of the OSER assay,
which in turn was used to quantify the in-vivo dimerization efficiency. This technique enabled us
to obtain the OSER score free of human subjectivity and in a high-throughput manner. As the
Figure 6.6c indicates, to obtain a statistically reliable OSER score, ∼ 400 cells need to be analyzed.
The original OSER article [100] and Bindels et al. [104] used ∼ 50 and ∼ 200 cells for OSER
analyses which might be insufficient to get a robust score. On the other hand, Cranfill et al. used
∼ 10,000 cells to obtain the OSER score which we think was over-kill. The image analysis program
developed by us could provide an objective OSER score for the FPs, eliminating large fluctuationsin the score found in the literature [104, 101].
A single mutation (L177M) was able to improve the lifetime and quantum yield in FR-1. The
L177M mutation was beneficial for the fast chromophore maturation in this mutant as observedby its fast expression in E.coli. Probably due to the faster maturation of FR-1, its in-vitro and invivo brightness were consistently higher than mCherry mutant by ∼1.8 and ∼ 1.7-fold respectively.
To determine if improvement of brightness in FR-1 is due to the aggregation or dimerization,
we measured fluorescence anisotropy decays of purified proteins and observed the rotational timeconstant of FR-1 and FusionRed. The rotational time constants of FusionRed and FR-1 were foundto be 15 ns and 17 ns, respectively. The in-vivo oligomerization tendency can be investigated by
the OSER assay as described in Section 6.4. As the two other mutational positions of FR-1 (V22I &L52P) are in the loop region and the nature of mutations are similar i.e. non-polar group changed
to non-polar, we anticipate that the good labeling efficiency of FusionRed would be retained in
FR-1 upon mammalian cell expression.
Mutants derived from the FusionRed site-directed library showed interesting photo-physics.
A single mutation in FSD-9 (i.e. C165V), reduced its dark state conversion (DSC) and increased
the excited state lifetime. On the other hand, C165L mutation in FSD-11 reduced its lifetime
and induced photoactivation. These results can shed light into the DSC and other non-radiative

131
pathways in FusionRed and its derivatives. The 165 position in FusionRed is equivalent to the 161
position in rsCherry. The authors in the rsCherry article [177] argued that by replacing a bulky
isoleucine with a smaller serine residue at position 161, the chromophore is allowed to undergoa cis-trans isomerization enabling the photoswitching in rsCherry. This argument may explain
replacing a cysteine with a bulkier valine at position 165 resulted in decreased DSC, which can beinterpreted as decreased cis-trans isomerization of the chromophore in FSD-9. A reduced cis-transisomerization in FSD-9 can decrease the possible non-radiative processes which is in agreementwith its longer lifetime. However, replacing cysteine with a leucine at position 165 in FusionRed
to form FSD-11 does not eliminate the reversible photoswitching property. Perhaps the bulkier
leucine at 165 reduces the planarity of the chromophore, enhancing the non-radiative rate andthereby decreasing the fluorescence lifetime. A detailed investigation is necessary to explain thesephoto-physical behaviors of the FSD mutants.
Intrigued by the beneficial mutations at 177 (L177M in FR1) and 165 (C165V in FSD-9),
we generated the mutant FR-1 C165V that contained both L177M and C165V mutations. FR-1
C165V showed higher quantum yield and extinction coefficient than FR-1 resulting a high in-vitro
brightness (Table 6.1). However, the mutant was characterized by reduced bacterial expression.
These results indicate that targeting 177 and 165 positions and subsequent selection of mutants
with longer lifetime and brightness can generate improved version of FusionRed. Our future work
will include these library generations.

6.8

Conclusion
Evolution of brighter FusionRed mutants through engineering of excited state lifetime wasdiscussed in this Chapter. Several rounds of random mutagenesis on FusionRed generated FR13 and FRX mutants which displayed higher in-vitro brightness, low oligomerization tendency andsuperb fusion efficiency. However, the brightness in mammalian cell-lines of those mutants remained
sub-optimal, which we hypothesize is due to their slower chromophore maturation. On the other132
hand, the FR-1 mutant showed improved expression in bacteria which we interpret as improved
chromophore maturation. With its improved quantum yield, lifetime and robust expression, FR-1
is an improvement over FusionRed. Site-directed mutagenesis libraries of FusionRed, produced
mutants with interesting photo-physics. Dark state conversion was nearly eliminated in FSD-9,
resulting in mono-exponential fluorescence decay and reduced photoswitching. The FSD-11 mutant
was characterized with photoactivation. These mutants can serve the templates for future librarygeneration to improve the photostability or photoswitching properties of FusionRed or related FPs.

Chapter 7
Engineering of Radiative Lifetime in Red Fluorescent Proteins

7.1

Introduction
In our previous attempts to improve the fluorescence quantum yield (φ) in red fluorescentprotein (RFP) mutants, we opted for increasing their excited state lifetime (τ ). The rationale forthis approach is that φ and τ are proportional to each other according to the equation below.

φ = kr ∗ τ

(7.1)

1
kr + knr

(7.2)

τ=

where, kr and knr are the rate-constants for radiative and non-radiative decays, respectively. Therefore, an increase in τ ensures an improvement in φ, as long as the radiative rate remains constantfor a FP, resulting in a brighter FP variant. Based on this concept, several FusionRed and mCherrymutants were developed with fluorescence quantum yields up to 71%. However, as we discovered,
the radiative rate may change with mutagenesis. For instance, C4PB-12 (described in Chapter 5)
had a 29% improvement in lifetime compared to mCherry but that resulted in a marginal (8%)
increase in φ compared to its precursor. Further analyses revealed that the reason behind thisapparent inconsistency was a decline in radiative rates in C4PB mutants (see Chapter 5). Thissuggested that radiative rate is not an intrinsic property of the FP chromophore and is subject tochange upon mutations.

134
As the Eqn. 7.1 and 7.2 show, enhancement of kr leads to an increase in φ and a corresponding
decrease in τ if the non-radiative rate remains unchanged. Although the engineering of molecular
structure to increase radiative rate has not yet been reported, the effect of the metal surface onradiative rates of the fluorophores and the subsequent effects on their lifetimes and quantum yields
have been described in the literature [178]. It was shown that fluorescence intensity of Eu3+
ions increased ∼5 -fold in the presence of a Ag surface with a concomitant 100-fold decrease influorescence lifetime [179]. These observations were explained by the enhancement of the radiative
rates in Eu3+ due to its electromagnetic coupling with Ag. Similar effects were also observed inerythrosin and rhodamine dyes on surface [180, 181].
Improvement in the radiative rate of a fluorophore is generally associated with an increase
in its extinction coefficient (). As the Strickler-Berg equation shows, the radiative rate of achromophore is inherently related to its  as follows [168]:

8 × 2303πn2
A21 = kr =< νf−3 >−1c2Zν dlnν(7.3)

where, c, n, ν and ν are the speed of light, refractive index of the media, frequency of light andextinction coefficient at the frequency ν, respectively. ν. < νf−3 > is given by:< νf−3 >=Rν −3 I(ν)dν
R
I(ν)dν

(7.4)

where, I(ν) is the fluorescence intensity spectrum. According to Eqn. 7.3, an improvement in kr
can be achieved by increasing  or vice versa. This inter-relation of extinction coefficient and theradiative rate-constant is illustrated by a number of FP engineering efforts. For instance, C4PB-12
had 17% reduction in kr resulting in 26% decrease in max compared to mCherry (Chapter 5). In
another example, our analyses revealed that mRuby3 [102] had 13% increase in its radiative rateconstant (1.51 × 108 s-1 vs. 1.71 × 108 s-1 ) which is associated with 13% enhancement (113,000
M-1 cm-1 vs. 128 M-1 cm-1 ) in max relative to its precursor mRuby2 [165]. Similarly, mScarlet [104]was found to have 14% increase in max relative to mCherry (88,000 M-1 cm-1 vs. 100,000 M-1 cm-1 )135with a concomitant 18% increase in kr (1.53 × 108 s-1 vs. 1.80 × 108 s-1 ).
Based on these observations and analyses, we hypothesized that improving radiative rates
of fluorescent proteins holds promise for simultaneous enhancement in extinction coefficients andquantum yields. This would improve overall theoretical brightness ( ∗ φ) of the FPs. Although
improvement in brightness of the FPs by increasing the excited state lifetime or via error-prone
mutagenesis are reported in the literature [155, 104, 102], to the best of our knowledge, rational
approach to enhance the extinction coefficient of the FPs have not been achieved so far [88].
In Chapter 5, we developed SLT-11 through the engineering of its excited state lifetime.
The SLT-11 mutant was found to have a high quantum yield (71%) and complete chromophore
formation. However, due to its reduced , both in-vitro and in-vivo brightness of SLT-11 wereobserved to be lower than mScarlet (φ, 70%) [104]. Therefore, we attempted to improve thebrightness of this mutant by explicitly engineering its radiative rate. Several large libraries (librarysize ∼ 800,000) were generated from SLT-11 by targeting the outward pointing residues. Multiple
bright mutants were selected from the FACS-enriched libraries.

7.2

Library Design for the Radiative Rate Engineering
For improving extinction coefficient or radiative rate in a FP, we set out to modulate thetransition dipole moment (µ12 ) of the chromophore. This concept is based on the following set of
equations:

Z
B12 ∝

ν dlnνB12 ∝hΨ1 |M̂ |Ψ2 i

∝ µ12

(7.5)
2

(7.6)
(7.7)

where, B12 is the probability of a transition from state 1 to state 2, ν is the extinction coefficientat the frequency ν, µ12 is the transition dipole moment, Ψ1 and Ψ2 are the electronic state wave-

136
functions of state 1 and state 2, respectively. M̂ is the dipole moment operator (M̂ =Pi eri , whereri is the vector joining the electron i to the origin of a coordinate system linked to the molecule).
From Eqn. 7.6 and 7.7 it is obvious that increasing transition dipole moment can enhance
the transition probability from state 1 to state 2 (B12 ) which in turn can improve the extinction
coefficient of the molecule. We propose that µ12 can be modulated by tuning the electric field in
the three-dimensional space where the chromophore of the FP is located i.e. inside the β-barrel.
This in essence, can change the intrinsic radiative rate of a chromophore [178]. We argue that theelectric field of the space inside the β-barrel of a FP can be modulated with charged amino acids.
Amino acids with charged side-chains can increase the electric field of the space, thereby increasing
the effective excitation intensity, absorption cross-section or radiative rates of the chromophore[178].
Ideally, to change the radiative rate of FPs, we want to target the residues which do not
influence the non-radiative rates, folding, maturations, monomericity etc. of FPs. In this regard, we
hypothesized that mutations that point outwards of the β-barrel could effectively position charges
in a such way as to influence the transition dipole, without adversely affecting properties such as
chromophore maturation and folding. We further hypothesized that the residues located on theplane of transition dipole moment (µ12 ) vector would be most effective in tuning the amplitude ofµ12 . Hence, we propose that outward pointing residues on the plane of transition dipole moment
vector can be targeted with charged residues to enhance the radiative rate of FPs.
The analysis of mutations involved in the evolution of mRuby3, mScarlet and C4PB-12
support our hypothesis. For instance, during enhancement of the brightness of C4 through randommutagenesis, N98K and K166R mutations appeared in some of the mutants (e.g. C4PB-12). Boththe residues are located at the phenolate-end of the chromophore and lie around the plane of theµ12 vector (Figure 7.1a, b and Figure 7.4). In fact, all C4PB mutants generated through randommutagenesis contain the K166R mutation. The K166R mutation appeared during the evolutionof mScarlet in the course of enhancement of brightness through random mutagenesis. We believe
this outward-pointing mutation may be involved in enhancing the amplitude of µ12 and thereby

137
increasing the max or brightness of the related FPs. We found similar mutations in mRuby3. For
example, the residue at position 114 is located at the imidazolinone-end of the chromophore pointing
outward was changed from a polar cysteine to a negatively charged glutamic acid (C114E) during
the evolution of mRuby3 from mRuby2 [102]. Positively charged residues were also introduced at
the phenolate-end of the barrel (S171H) during evolution of mRuby3.

a

b

162K

145A

98N

198K
142G
172H
166K

c

d
166K
17H
119I

123K
121K

108T

172H
121K
98N

114Q

162K

114Q
92K

92K

Figure 7.1: Residues targeted to generate radiative rate libraries: Site-directed mutagenesis with charged amino acid residues (arginine, lysine, aspartic acid and glutamic acid) at thephenolate-end (a, b) and imidazolinone-end (c) of the chromophore produced Ph libraries and Im
library respectively. (d) A combined radiative rate library (Combined library) was generated bytargeting both phenolate- and imidazolinone-end of the chromophore. Residues located at thephenolate- and imidazolinone-end of the chromophore are shown with blue and green sphere respectively. Targets are shown in a crystal structure of mCherry (PDB: 2H5Q).

Our analyses suggest that outward-pointing mutations in the vicinity of the phenolateand imidazolinone-end of the chromophore might be effective at tuning the radiative rate of

138
the chromophore. Therefore, we designed two libraries targeting either the phenolate-end or theimidazolinone-end of the chromophore. We also recognized that the transition dipole moment is acomplex function of charge re-distribution between the ground state and excited state of the chromophore. Therefore, we generated another library targeting both ends of the chromophore. We
called these libraries as “Ph-library”, “Im-library” and “Combined library”, respectively. However,as the residues designed for Ph-library were very close to each other, we had to split the targets
into two separate libraries for effective library construction: Ph-1 and Ph-2 library.
Table 7.1 demonstrates the residues we targeted and the rationale behind choosing them.
Table 7.1: Targets for the radiative rate libraries and the rationales. Numbering is based on
mCherry crystal structure (PDB: 2H5Q).
Residues
166

Position
Phenolate-end

172

Phenolate-end

162
98

Phenolate-end
Phenolate-end

145
142
198
114

Phenolate-end
Phenolate-end
Phenolate-end
Imidazolinone-end

121
92

Imidazolinone-end
Imidazolinone-end

108
17
119
123

Imidazolinone-end
Imidazolinone-end
Imidazolinone-end
Imidazolinone-end

Rationale
appeared in mScarlet, C4PB clones which improves the brightness.
Preferred mutation
K166R
appeared in mScarlet and mRuby3.
H in
mCherry, mRuby3. R in mScarlet.
appeared in mRuby3.
appeared in C4PB mutants. Improves brightness.
Non-polar, close to phenolate-end.
Non-polar, close to phenolate-end.
Similar position as K166, close to phenolate-end.
appeared in mRuby3 (C114E), close toimidazolinone-end.
appeared in mRuby3 (H121N).
appeared in mApple (K92R), which is brighter
than mCherry.
Close to imidazolinone-end.
appeared in C4PB clones.
Non-polar, close to imidazolinone-end.
Close to imidazolinone-end.

For selection of residues we considered the following criteria:• Residues close to either end of the chromophore and previously reported to improve thebrightness. For example, in our C4PB mutants and in mScarlet we found the mutation

139
K166R to improve the brightness. Similarly, 114Q in mRuby3 and 92K in mApple reported
to enhance the brightness.
• Residues close to either phenolate- or imidazolinone-end of the chromophore that are nonpolar.
Table 7.2 shows the residues we targeted to generate radiative-rate libraries.
Table 7.2: Proposed residues for targeting the four radiative rate libraries.
Library
Ph-1 Library
Ph-2 Library
Im Library
Combined Library

Targets
166, 172, 162, 198
98, 145, 142
114, 121, 92, 108, 17, 119, 123
114, 121, 92, 166, 172, 162, 98

Ideally, we wanted to saturate the phenolate- and imidazolinone-end of the chromophore withcharged residues (K, H, R, E, D). After analyzing the codons that encode these amino acids, itappeared that we could not saturate our targets only with charged residues without adding someuncharged ones. Two possible codons and the resulting amino acids are shown in Table 7.3.
Table 7.3: Choice of codon for the charged residues. Amino acid residues in black are thedesired ones whereas the AAs in reds are the uncharged residues. V=A/C/G, R=A/G, S=C/G,
N=A/C/T/G. The number in the parentheses are the frequency of the residue occurrence.
Codon
VRS
RRN

Amino acid residues
K(1 ), H(1 ), R(3 ), E(1 ), D(1 ), S(1 ), N(1 ), Q(1 ), G(2 )K(1 ), R(1 ), E(1 ), D(1 ), S(1 ), N(1 ), G(2 )Unlike lysine (K) and arginine (R), histidine is positively charged only when the pH is below
6, so we can effectively target the amino acids with just K, R, E and D. As the Table 7.3 suggests
using the RRN codon we can have K, R, E and D along with fewer uncharged residues (S, N, G)
and with more uniform frequencies of occurrences of the amino acids. We therefore chose to employ
the RRN codon. For the Combined and Im libraries, we targeted 7 residues (Table 7.2) with 7
amino acids using RRN codon (Table 7.3). So, the library size was 77 ∼ 800,000. The size of Ph-1
and Ph-2 libraries were 2401 (74 ) and 343 (73 ), respectively.

140

a

b

Figure 7.2: Distribution of the red fluorescence intensities (FL4Log::FL4) of mCherry/SLT-11 (a)and Im/Combined (b) libraries upon expression in yeast. The mean fluorescence intensities ofmCherry and SLT-11 were measured as 38 and 66 respectively. > 106 cells expressing the libraries
were screened through the FACS. The brightest 3% and 0.3% of the screened populations wereselected from the Im and Combined libraries, respectively.

7.3

Screening of the Radiative Rate Libraries and the Mutants
Due to the large size of the radiative rate libraries, FACS-sorting was carried out to select

only the fluorescent mutants. Figure 7.2 shows the FACS screening results of mCherry, SLT-11, Im
and Combined libraries. Although the sizes of Im and Combined libraries were similar, we observeda larger number of fluorescent mutants in the Im library compared to the Combined library. Using
FACS, we sorted the brightest 3% and 0.3% of the populations from Im and Combined libraries,
respectively and subsequently grew them up.
Following the initial FACS-enrichment, the libraries were screened in the microfluidic system.
Microfluidic screening revealed very low brightness of the mutants and consequently another FACSenrichment was performed. Figure 7.3 displays the brightness and lifetime profiles of the libraries
measured in microfluidic after two rounds of FASC-enrichment . Screening results of the libraries
revealed two main populations: one with ∼ 2 ns lifetime (mCherry-like) and another with ∼4 ns
lifetime (SLT-11-like).

141

Figure 7.3: Screening results of radiative rate libraries after two rounds of FACS-enrichment eliminating the dim mutants. (a) Screening results of mCherry and SLT-11 mutant. SLT-11 is theimmediate precursor of the radiative rate libraries (b, c, d).

These FACS-enriched libraries were expressed on galactose-containing plates and several mutants were selected based on their brightness and approximate lifetimes. Mutants selected from the
Combined library with ∼4 ns lifetime were called RCB mutants (R=Radiative, C=Combined, B=
selected based on Brightness). Similarly, mutants selected from Im library were called RIB. Ph1 andPh2 mutants were selected from Ph1 and Ph2 libraries, respectively. DNA sequences of mutants
with ∼2 ns lifetimes revealed unwanted R197I mutations that occurred inadvertently during library
constructions. I197R mutation was introduced in the mCherry mutants at the very beginning of
the directed evolution. This mutation was observed to play beneficial roles in the enhancement

142
of excited state lifetime and fluorescence quantum yield of the mutants. Therefore, the mutants
having ∼ 2 ns lifetime where the reversal of I197R mutation occurred, were not pursued further.

114

121
92

Chromophore

198

98
166
162
172
Figure 7.4: Mutations that appeared in the variants derived from the radiative rate libraries.
Residues at positions 98, 162, 166, 172 and 198 are located at the phenolate-end of the chromophore
while residues at positions 92, 121 and 114 are in the imidazolinone-end of the chromophore.
The pink arrow shows the approximate direction of the transition dipole moment in a DsRed-likechromophore [182].

Table 7.4 depicts the mutations identified after screening the radiative rate libraries andselecting mutants with improved brightness compared to the precursor, SLT-11. There was no
definitive pattern of charge placement for these mutations. However, we found a re-distribution of
the charged residues in the phenolate- and imidazolinone-ends of the chromophore. We also foundseveral mutations that were also observed in mRuby3, mScarlet or mApple FPs. For instance,
K166R appeared in RCB-6, and was also found during the evolution of C4PB and mScarlet [104].

143
The K92R mutation was found to enhance the brightness of mApple [11], and was identified in
RCB-2. During the evolution from mRuby2 to mRuby3 [102], C114E (i.e. polar to negatively
charged) mutation was observed. However, in both RCB-2 and RCB-6, glutamines (polar) at thisposition were substituted with positively charged lysine or arginine, respectively.
Table 7.4: Amino acid mutations identified in the radiative rate mutants. The numbering of theresidues is based on a reported mCherry crystal structure (PBD: 2H5Q).
RFPs
mCherry
SLT-11
RCB-2
RCB-6
Ph1-3
mScarletmRuby2mRuby3FusionRed

7.4

92
K
R
S

T
T
T

114 121 98
Q
K
N
S
K
E
R
S
S
S
E
C
H
R
E
N
R
N
T

162 166 172 198
K
K
H
K

D

H
D
D

N
R
N
R

K
R
K
R

S
R
R
R

In-vivo Brightness of the Radiative Rate mutants
FACS screening results displaying the in-vivo brightness of the radiative rate mutants upon

expression in yeast and mammalian (HeLa) cells are presented in Figure 7.5. To obtain the red
and green fluorescence, 561-nm (610/20-nm emission filter) and 488-nm (530/30-nm emission filter)
excitations were used, respectively. Mean red and green fluorescence intensities of the mutants were
measured from the FACS screening and described in Table 7.5. All of the radiative rate variants
showed significant improvement in brightness relative to their precursor SLT-11 upon expression inyeast cells. In particular, RCB-2 and 6 displayed ∼ 3.5-fold and ∼ 5-fold higher brightness relative
to mCherry and were brighter than previously published improved RFPs: mScarlet and mRuby3.
All the mutants were characterized with low green fluorescence intensities compared to SDC-5,
indicating likely complete chromophore formation. Indeed, the absorption spectra of the mutants
did not have prominent ‘green peak’ (discussed below). A slight increase in green fluorescence in
RCB-2 and 6 relative to SLT-11 might be due to bleed-through from the red fluorescence channel

144
into the green fluorescence channel.

Figure 7.5: (a) Screening results of the radiative rate mutants along with mCherry, mScarlet, SDC5, mRuby3 and SLT-11 displaying their red and green fluorescence intensities upon expression inyeast. RCB-6 and RCB-2 show increased red fluorescence compared to mScarlet and mRuby3. (b)FACS screening results of the radiative rate mutants upon expression in HeLa cells as a fusion tohistone 2B, which restricts fluorescence to the nucleus . Mutants showed reduced red fluorescenceintensity presumably due to their slow maturation in mammalian cells.

Although the radiative rate mutants showed enhanced brightness in yeast cells, upon expression in HeLa cells, the brightness of all mutants was lower than SLT-11 and mScarlet. Thisis probably due to their slow maturation or inefficient expression in the mammalian cells. The
inefficient expression in the mammalian cells might be due to the incorporation of yeast-preferredcodons in the mutants during their evolution in yeast. Currently, we are in the process of codonoptimization of the mutants for their efficient expression in mammalian cell-lines.

7.5

In-vitro Photo-physical Measurements
Inspired by the increased brightness in yeast cells, a few promising radiative rate mutants

were expressed in E.coli for purification and subsequent photo-physical investigations. In particular,
RCB-2 & RCB-6 were selected for their improved brightness relative to mScarlet and mRuby2.
Although Ph1-3 was characterized with lower brightness than mScarlet/mRuby3, cell-lysate and145
Table 7.5: FACS screening results displaying mean green and red fluorescence intensity of themutants upon yeast and mammalian (HeLa) cell expressions. For each run, 10000 cells werescreened to quantify their green and red fluorescence intensities.
Mutants
mCherrymScarletmRuby3
SDC-5
SLT-11
Ph1-3
RCB-2
RCB-6

Green Fl.
(in yeast)
100
257
95
1000
141
123
489
197

Red Fl. (inyeast)
100
286
283
346
130
249
498
368

Green Fl.
(in HeLa)
100
184
201
123
187
150

Red Fl. (in
HeLa)
100
214
129
34
103
67

microfluidic-based measurements revealed a significant decrease in its excited state lifetime. As adecrease in lifetime might be associated with increased radiative rate in the FPs, we also selected
the Ph1-3 mutant for further characterization. However, the expression of these mutants was
sub-optimal in E.coli, thus requiring longer growth time to show the characteristic color of the
RFPs. Consequently, yield of the expressed proteins from E.coli cultures was low. Measurementsof absorption spectra of these mutants showed unusually high background at wavelength <400 nm(Figure 7.6a), indicating the presence of impurities such as non-functional FPs. Due to this unusual
background at shorter wavelengths, measurements requiring quantitative absorption spectra, i.e.,
extinction coefficient and quantum yield could not be measured reliably. However, the emissionbased in-vitro measurements (pKa, fluorescence lifetime, rotational time-constant) of those mutants
were performed.
Figure 7.6 and Table 7.6 display in-vitro measurements of the radiative rate mutants. The
absorption spectra revealed that the absorption maxima of the mutants remained unchanged compared to the precursor SLT-11. However, the shoulder at ∼ 490 nm was slightly increased in RCB-2
and it was totally absent in Ph1-3 (Figure 7.6a). Interestingly, pKa values of the mutants weresignificantly higher than SLT-11 indicating a decrease in pH resistance. The pKa of RCB-2 wasclose to that of mScarlet (5.3 in RCB-2 vs. 5.4 in mScarlet). The excited state lifetimes of all

146

Figure 7.6: In-vitro photophysical measurements displaying (a) absorption spectra, (b) pHdependence of fluorescence (c) excited state lifetimes and (d) anisotropy decays of the radiative
rate mutants.

the mutants, particularly Ph1-3, were shorter compared to the precursor SLT-11 (Table 7.6). Thismight be a signature of increase in radiative rates in these mutants. The fluorescence transientsof the mutants were fitted with iterative reconvolution with a bi-exponential function using themeasured instrument response function (IRF) of the system. The rotational time-constant (τr ) of
the mutants was obtained by fitting their fluorescence anisotropy decays with a single exponentialfunction. As Table 7.6 describes, all the radiative rate mutants were found to have similar τr as
mCherry, suggesting they likely retained in-vitro monomeric character. TagRFP-T was characterized with slower τr , relative to the other mutants. This is consistent with its reported pronounced

147
in-vivo oligomerization tendencies [100, 101].
Table 7.6: In-vitro photo-physical measurements of the radiative rate mutants. pKa of mCherry
and TagRFP-T were obtained from reference.
Mutants
mCherry
TagRFP-T
SLT-11
Ph1-3
RCB-6
RCB-2

7.6

λmax (nm)
587
555
558
561
559
559

λmax (nm)
611
584
588
591
588
591

pKa
< 4.5
4.6
4.8
6.0
5.8
5.4

τ (ns)
1.55
2.31
3.82
3.57
3.72
3.77

τr (ns)
16
27
16
17
17
17

Discussion
We have employed a novel rational design for the directed evolution of brightness in red

fluorescent proteins. Based on fundamental principles, we hypothesized that the transition dipole
moment (µ12 ) involved in the chromophore excitation or emission can be effectively tuned to enhance extinction coefficient or radiative rates of the FPs, respectively, leading to their improved
brightness. We argued that this tuning of µ12 can be achieved by the rearrangement of the charged
residues along the direction of the transition dipole moment vector i.e. the residues located at thephenolate- and imidazolinone-end of the chromophore. Rearrangement of the charge residues atthese locations can potentially modify the electric field of the three-dimensional space around thechromophore, thereby altering the electromagnetic coupling between its ground state and excitedstate. We opted for the outward-pointing residues as it was known from our previous investigations
and others [12, 11] that the internal residues at these locations were associated with the proper
folding and maturation of the chromophore etc.
Based on this hypothesis, we generated 4 site-directed libraries using SLT-11 as a template.
Subsequent enrichments and selections of bright mutants resulted in three improved RFPs: Ph1-3,
RCB-2 and RCB-6. While all of the mutants were characterized with higher brightness relative totheir precursor (SLT-11), RCB-2 and RCB-6 also displayed higher red fluorescence intensity com-148
pared to two bright RFPs, mRuby3 and mScarlet, upon expression in yeast. However, brightnessof these mutants was sub-optimal upon expression in bacteria and mammalian cell-lines. Further
modifications of the radiative rate mutants are underway for their efficient expressions in theseorganisms.
It is interesting to observe that the outward-pointing residues in the radiative rate mutantscause subtle changes in their absorption spectra (Figure 7.6a). While the spectra of RCB-6 wassimilar to the precursor SLT-11, the shoulder at ∼490 nm was slightly increased and totally absent
in RCB-2 and Ph1-3, respectively. The radiative rate mutants were characterized by lower pH
resistance or higher pKa than SLT-11. A similar reduction of the ‘green peak’ and change in pKa
were observed in the generation of mGarnet2, an mRuby2 variant [183]. With the introduction of
S171H and V121L mutations, pKa of mGarnet2 reduced to 6.8 from 7.4 with concomitant decreasein the peak at ∼480 nm. The residue at 171 (174 in mCherry numbering) points outward of the
β-barrel and close to the phenolate-end of the chromophore.
All the radiative rate mutants showed shorter excited state lifetimes compared to their precursor, SLT-11 (Table 7.6). This might be a signature of their improvement in radiative rate-constants(Eqn. 7.2). Measurements of φ and max of the mutants are required to support this claim unequivocally. The rotational time-constants of the radiative rate, measured from their anisotropy
decays, were similar to mCherry. This might indicate that the increased brightness of the mutantsis less likely due to aggregation or dimerization. However, in-vivo dimerization assay (i.e. OSER
assay, Chapter 6) is required to quantify the dimerization tendencies of the mutants in cellularenvironments. Currently, we believe that the improvement in brightness of these mutants is due totheir enhanced radiative rates or increased max or the combination of both. However, improved
folding or maturation efficiency of the radiative rate mutants leading to a larger amount of protein
production in yeast cells, could also be a viable reason for the observed increase in brightness associated with mutants expressed in yeast cells. More in-vitro measurements (φ, max , maturation
rate etc.) and theoretical calculations e.g. mapping of the electric field inside the β-barrel, areneeded for the complete understanding of the photo-physical behaviors of these mutants.

149
Recently, a quantum mechanics/molecular dynamics study by Park et al. described theimprovement of fluorescence quantum yield for GFP variants through the re-distribution of theelectric field inside the chromophore [184]. The authors of the article argued that improvementin φ of the GFP mutants was due to the hindrance in twisting (non-radiative) pathway of thechromophore induced by the electric field of the protein β-barrel. Their simulations predicted that
increased electric field in the phenolate-end and decreased field in the imidazolinone-end of thechromophore was beneficial for improved φ in the GFP mutants. The authors also found that theresidues in the phenolate-end were more influential in controlling the electrostatic potential of theFPs. This is in line with our observations of the mutations appeared in the radiative rate mutants(Figure 7.4) and also from the analyses of mScarlet, mRuby3, and C4PB mutations. However,there was no clear pattern of positively and negatively charged residues in the context of theirrespective locations at phenolate- and imidazolinone-end of the chromophore. Instead we found a
re-distribution of the charged residues at the different ends of the chromophore (Table 7.4).
Improving the fluorescence quantum yield of FPs by decreasing the non-radiative pathways
has a deleterious effect in their photostabilities. In previous work, we observed the fluorescence
lifetimes and photostabilities of the RFP mutants were inversely-correlated [55]. This can be
explained by the fact that, upon increasing the lifetime, the molecule spends a longer time inthe excited states, which can increase the likelihood of various chemical changes (e.g. dark sateconversions, forming of radical species etc.) which ultimately leads to higher photobleaching. On
the other hand, increasing radiative rates of the FPs can lead to an improvement in φ (beneficial
for brightness) and reduction in lifetime (beneficial for photostability). Therefore, simultaneous
improvement in brightness and photostability can be achieved by enhancing the radiative rates of
the FPs. Suppression of photobleaching was observed in GFP and mCherry by increasing theirradiative rates in nanofibers or nanostructures [185, 186].
Throughout our engineering effort of the radiative rate mutants and the FusionRed mutants(FR-13 and FRX mutants) described in Chapter 6, a resounding inconsistency of brightness ofthe FPs in yeast and mammalian cells was observed. Radiative rate mutants, similar to the FRX

150
mutants, were evolved in yeast and indeed the brightness in yeast was superb. However, this higher
brightness of the mutants was not reproducible in the mammalian cell lines. While this indicatesthe complexity of the expression of FPs in different organisms, it is a clear message of “you getwhat you select”. At this point, it is not clear why the FPs express differentially in yeast andmammalian cell-lines. The possible explanation would be different pH- & temperature-resistance,
choice of codons in yeast and mammalian cells or combination of these factors. Detailed and careful
investigations need to be carried out to find the answer.

7.7

Conclusion
In this Chapter, enhancement of brightness in RFPs based on a novel approach was discussed.

We hypothesized that amino acid residues that point outward of the β-barrel and located near thephenolate- and imidazolinone-end of the chromophore can be substituted with charged residues tomodulate the transition dipole moments of the chromophore involving its excitation or emission.
This can be harnessed to increase the radiative rate or oscillator strength of the transition leading
to enhancement of fluorescence quantum yield or extinction coefficient of the FPs. Based on thishypothesis, four site-directed libraries were generated and multiple bright mutants were selected.
Some of the mutants displayed significant improvement in brightness upon expression in yeast relative to the previously published bright RFPs e.g. mRuby3 or mScarlet. However, expression
efficiency of these mutants was sub-optimal in bacteria and mammalian cell-lines presumably due
to slow maturation in these organisms. Current effort involves further mutagenesis of these mutantsfor efficient expression in mammalian cells. Detailed in-vitro measurements and theoretical investigations are needed for in-depth understanding of the improvement in brightness of these mutants.
We speculate that the strategy described in this Chapter can be readily employed in improving thebrightness of other sub-optimal FPs, e.g., infra-red FPs.

Chapter 8
Future Directions

With site-directed mutagenesis of C12 targeting 143, 161 and 163 residues, several of mutants
were produced with lifetime ranging from ∼1 ns to ∼4 ns. The measurement of their fluorescencequantum yield revealed a set mutants having fluorescence quantum yields ranging from 10% to71%. The analyses of lifetime and fluorescence quantum yield of the mutants revealed a linear trend
indicating similar radiative rate-constants of the mutants. Detailed in-vitro measurements of theseclosely related mutants can reveal the inter-relation of different photo-physical parameters withenhancement of φ. From these mutants, underlying mechanism(s) behind the improvement in thequantum yield of the RFPs can be studied in a systematic manner. The contribution of planarity,electric field, electrostatic interactions etc. can be investigated in the context of improvement in φ.
It is well-known in the literature that chromophore formation in RFPs occurs through abranched pathway. After the initial cyclization of the chromophore-forming residues, a fractionof the FP can form a GFP-like chromophore (via GFP pathway), while rest of the fraction forms
an RFP-like chromophore through a blue-emitting intermediate (via RFP pathway). Some RFPs,e.g. mCherry, show a single peak around ∼ 560-580 nm in their absorption spectra indicating
the chromophore formation pathway is heavily biased towards the formation of red-emitting chromophore. On the other hand, absorption spectra of some of the mCherry mutants, e.g., SLT-1,
were characterized with two peaks ∼490 nm (green peak) and ∼560 nm (red peak). This suggeststhat these mutants have propensities to go either GFP or RFP pathway forming a mixture of
red and green-emitting FPs. Currently it is not known what molecular environments around the152chromophore-forming tri-peptide drives some reactions to GFP or RFP pathways. In our course of
mutagenesis, we have generated multiple mCherry mutants with varying degree of ‘green peak’ intensities. Mutational analyses, time-resolved absorption-emission spectra etc. coupled with molecular dynamics simulations involving these mutants can reveal the molecular origin of the branching
pathways in the formation of RFP chromophores. This can help the rational designing of the FPs
with complete chromophore maturation.
In the course of evolving FusionRed, we generated FR-1, FR-13, FSD-9 and FSD-11 mutants which show interesting and contrasting photophysical properties. In FR-1, a single mutation
L177M improves its maturation speed. On the other hand, FR-13 was characterized with very
slow maturation. Further analyses involving FR-1 and FR-13 can reveal the molecular origin of
the diverse maturation kinetics in these mutants. While photoswitching in FSD-9 is eliminatedwith a single mutation (C165V), photoactivation is induced in FSD-11 with C165L mutation upon
561-nm illumination. Detailed photo-physical and structural investigations including DSC, GSR,
single molecule fluorescence trajectories of these mutants can elucidate the underlying mechanismof the light-induced behaviors in these mutants. These understandings can be used to generate
photostable, photoswitching and photoactivatable version of FusionRed.

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Appendix A
Matlab Codes

A.1

Phase advance Computation
The Matlab code used to compute phase shifts of the system described by a 3 state model(2.6a) that was described in Chapter 2:
1

f u n c t i o n P h a s e F r e q 3 S t a t e % phase c a l c u l a t i o n based on a 3 s t a t e model .

2t i c ; % c l o c k f o r measuring t h e computation time3

clc ; clear ;4

dataName = i n p u t ( ’ Save data a s . . ? :’ , ’s ’);56

f u n c t i o n dy=f r e q 3 S t a t e ( t , y , nu )78

% photo p h y s i c a l Parameters

910

h =6.626∗ e 3 4 ; c =2.998∗ e8 ; NA=6.022 e23 ;% u n i v e r s a l c o n s t a n t s

11

I 0 =25e7 ; % L a s e r I n t e n s i t y ; W/mˆ2

12e p s i l o n =72e3 ; % Molar e x c t i n c t i o n c o e f f i c i e n t i n Mˆ 1 cm ˆ 1 ;13

sigma =2.303∗ e p s i l o n ∗1 e3 ∗1 e 4 / NA; % A b s o r p t i o n c r o s s s e c t i o n

14lambda=532∗e 9 ; % e x c i t a t i o n wavelength ; 532 nm

15

kEx0=I 0 ∗ sigma ∗ lambda / ( h∗ c ) ; % peak e x c i t a t i o n r a t e

171
16

kEx=kEx0 ∗ ( s i n ( 2 ∗ p i ∗nu∗ t ) +1) ; % e x c i t a t i o n r a t e

17

tau = 1 . 8 7 ; % e x c i t e d s t a t e l i f e t i m e ; ns18

QY= 0 . 2 2 ; % quantum y i e l d

19kEm=(1 e9 ∗QY) / tau ; % e m i s s i o n r a t e c o n s t a n t

20

kS1B=100; % b l e a c h i n g r a t e c o n s t a n t from b r i g h t s t a t e , S121

kDB=0; % b l e a c h i n g r a t e c o n s t a n t from dark s t a t e , D

22

kS1D=10e3 ; % DSC r a t e c o n s t a n t

23

kGSR=2e3 ; % GSR r a t e c o n s t a n t

24

25% kinetics

26

dy=z e r o s ( 3 , 1 ) ;27

dy ( 1 ) = kEx∗y ( 1 )+kEm∗y ( 2 )+kGSR∗y ( 3 ) ;28

dy ( 2 )=kEx∗y ( 1 ) ( kEm+kS1B+kS1D ) ∗y ( 2 ) ;29

dy ( 3 )=kS1D∗y ( 2 ) ( kGSR+kDB) ∗y ( 3 ) ;

30

end

31

32

nu=1e3 : 5 e3 : 5 1 e3 ; % Frequency Range

33

p h i=z e r o s ( s i z e ( nu ) ) ;34

p e r i o d =10; % number o f f u l l p e r i o d used f o r phase c a l c u l a t i o n s

35

36

f o r i n d =1: l e n g t h ( nu ) ;37

38

t i m e s t e p =1e 8 ; % time s t e p ; 10 ns

39

Tspan =0: t i m e s t e p : p e r i o d /nu ( i n d ) ; % time span t o i n t e g r a t e

40[ T,Y]= ode45 (@( t , y ) f r e q 3 S t a t e ( t , y , nu ( i n d ) ) , Tspan , [ 1 , 0 , 0 ] ) ; % S o l v e
the k i n e t i c s

41

P=t r a n s p o s e ( I 0 ∗ s i n ( 2 ∗ p i ∗nu ( i n d ) ∗T) ) ;

172
42

Q=t r a n s p o s e ( I 0 ∗ s i n ( 2 ∗ p i ∗nu ( i n d ) ∗T+( p i / 2 ) ) ) ;43

x=sum (P∗Y( 1 : l e n g t h ( Tspan ) , 2 ) ) ; % in phase component ( I )44

y=sum (Q∗Y( 1 : l e n g t h ( Tspan ) , 2 ) ) ; % in q u a d r a t u r e component (Q)45p h i ( i n d )=atan ( y/x ) ∗180/ p i ; % phase i n d e g r e e

46

47

end

48

t o c ; % computation done !

49

s a v e ( dataName , ’ nu ’ , ’ p h i ’ , ’ I 0 ’ , ’ tau ’ , ’ kS1B ’ , ’ kS1D ’ , ’kDB ’ , ’ t i m e s t e p ’ , ’
p e r i o d ’ ) ; % s a v e t h e data

50

end

A.2

Single Molecule Fluorescence
Matlab code that generates single molecule fluorescence traces based on a modified 3 statemodel (Figure 3.10b)described in the Chapter 3:
1

clc ; clear

2

3

s t e p s i z e =1e 6 ; %s t e p s i z e o f t h e s i m u l a t i o n ; micro s e c o n d

4time =100000; % t o t a l d u r a t i o n o f t h e s i m u l a t i o n i n s e c o n d ;5

N=round ( time / s t e p s i z e , 0 ) ; % t o t a l number o f s t e p s

6t u r n o v e r =300; % t o t a l number o f t u r n o v e r from on t o o f f s t a t e s

78%photo p h y s i c a l p a r a m e t e r s

910kGSR=14; %kb , GSR r a t e c o n s t a n t

11

kDSC=42e3 ; %ka2 , DSC r a t e c o n s t a n t

12

ka1 =0; % no t r a n s i t i o n t o dark s t a t e from S0

173
13

QY= 0 . 4 1 ; % quanutm y i e l d14

tau =2.45 e 9 ; %e x c i t e d s t a t e l i f e t i m e

15

kEm=QY/ tau ; % ga2 , r a d i a t i v e e m i s s i o n r a t e c o n s t a n t

16

k i c =(kEm/QY) kEm kDSC ; % i n t e r n a lconversion rate constant17

18

h =6.626∗ e 3 4 ; c =2.998∗ e8 ; NA=6.022 e23 ;% u n i v e r s a l c o n s t a n t s

19

I 0 =24e4 ; % L a s e r I n t e n s i t y i n W/mˆ2

20

e p s i l o n =71e3 ; % Molar e x c t i n c t i o n c o e f f i c i e n t i n Mˆ 1 cm ˆ 1 a t lambda ;21

sigma =2.303∗ e p s i l o n ∗1 e3 ∗1 e 4 / NA; % A b s o r p t i o n c r o s s s e c t i o n

22lambda=561∗e 9 ; % e x c i t a t i o n wavelength23

kEx=( I 0 ∗ sigma ∗ lambda ) / ( h∗ c ) ; % ga1 , e x c i t a t i o n r a t e

2425

p11=(0+0)∗ s t e p s i z e ; p21=(0+0)∗ s t e p s i z e ;26p12=(ka1+kEx ) ∗ s t e p s i z e ; p22 = 1 exp ( ( kDSC+kEm+k i c ) ∗ s t e p s i z e ) ;27

prob =[ p11 p12 ; p21 p22 ] ; % p r o b a l i t y o f l e a v i n g s t a t e B and A;

28

prob B=kGSR∗ s t e p s i z e ; % p r o b a l i t y o f l e a v i n g B s t a t e29

c11 =0; c21 =0;30

c12=kEx / ( kEx+ka1 ) ; c22 =(kEm+k i c ) / (kEm+kDSC+k i c ) ;31

cProb =[ c11 c12 ; c21 c22 ] ;32

33

s i g n a l =1;

34

t =1;

35

j =1;

36

k=1;

37

38

t r a c e=z e r o s (N, 1 ) ;39

on=z e r o s ( l e n g t h ( t u r n o v e r ) , 1 ) ;

174
40

o f f=z e r o s ( l e n g t h ( t u r n o v e r ) , 1 ) ;

41

42

c =1;

43

c t =1;

44

m=1;

45

n=1;

46

47

c o n f o r m a t i o n=z e r o s (N, 1 ) ;48

c1=z e r o s ( l e n g t h (N) , 1 ) ;49

c2=z e r o s ( l e n g t h (N) , 1 ) ;

50

51

52

f o r i =1:N;

53

54

t r a c e ( i )=s i g n a l ;55

c o n f o r m a t i o n ( i )=c ;56

i f s i g n a l ==1;

57

i f rand> prob ( c , s i g n a l +1)

58

t=t +1; % c o n t i n u e with t h e p r e s e n t s t a t e59

c t=c t +1;

60

e l s e i f rand>=cProb ( c , s i g n a l +1)61

c t=c t +1; % change t h e f l u o r e s c e n t s t a t e

62

on ( j )=t ∗ s t e p s i z e ;63

j=j +1;64s i g n a l=rem ( s i g n a l +1 ,2) ;

65

t =1;

66

else

t=t +1; % change t h e c o n f o r m a t i o n

175
if

67

c==1;

68

c1 (m)=c t ;69

m=m+1;
e l s e c2 ( n )=c t ;70

n=n+1;

71

end

72

73

c=rem ( c , 2 ) +1;

74

c t =1;
end

75

end

76

else

77

i f rand>prob B ; % m o l e c u l e i n t h e dark s t a t e

78

t=t +1; c t=c t +1;

79

e l s e o f f ( k )=t ∗ s t e p s i z e ;80

k=k+1;

81

s i g n a l=rem ( s i g n a l +1 ,2) ;

82

t =1;
end

83

end

84

85

end

86

tim =0: s t e p s i z e : ( l e n g t h ( t r a c e ) 1 ) ∗ s t e p s i z e ;8788

s a v e ( ’ TagT 3Smodified2 ’ , ’ tim ’ , ’ t r a c e ’ , ’ kEx ’ , ’kEm ’ , ’kGSR ’ , ’kDSC ’ , ’s t e p s i z e ’ ) % s a v e data

176

Figure A.1: Algorithm to generate single molecule fluorescence traces with multiple bright anddark states.

Appendix B
Setting up and Cleaning of the Microfluidic System for Screening and Sorting

B.1

General Rules• No microfluidics or any part of the device holder can leave the optics table tent without
being sealed in a jar. They can never be exposed to ambient air.
• Never touch any microfluidics or any parts of the device manifold (white manifold, “o”
rings, and orange rubber stoppers) with anything other than tweezers.
• Make sure that the water level of the sonicator is never higher than the lids of the containers
since they are not water-proof jars. However, keep the water at the minimum level indicatedon the sonicator tank.
• Try to never use high pressure (>20 psi). With high pressure more debris will likely toappear.
• Every liquid (except cells) goes through 0.2 µm filter.
• Ultrapure water does not need to be filtered for cleaning purposes but does need to be
filtered if being pumped through the microfluidic.
• Refrigerate any solutions other than bleach and water.
• Use a new syringe and filter each day.

178
• Never store anything other than glass chips in bleach solutions.
• If you need to use new tubes or want to clean the tubes, cut them to the required lengthand sonicate them in ultrapure water for > 40 minutes in a sealed jug. Then, attach themto the 20 psi air (on microscope) and run air through them to dry them out for > 20
minutes.
• Fill reservoirs with 150 µl of solution.
• Fill and empty reservoirs using micropipettes.

B.2

Setting up the Microfluidic for Screening and Sorting• Sonicate the device in the same ∼10% bleach solution that it was stored in overnight for
60 minutes.
• Simultaneously sonicate the jar of “o” rings and orange rubber stoppers with adding asmall amount of detergent (alconox). The manifold does not need to be sonicated.
• Rinse white manifold three times with ultrapure water. Leave third rinse water in container
to store manifold in when experiments are over.
• Rinse the “o” rings and stoppers three times with ultrapure water and then leave them inthird aliquot of water.
• Remove the microfluidic from the jar with tweezers and hold it over the waste bucket torinse with the ultrapure water.
• Put microfluidic into bottom of metal device holder.
• Place “o” rings on manifold with tweezers.
• Place manifold on top of microfluidic.

179• Screw manifold retaining ring on.
• Fill side reservoirs with 150 µl with filtered (with 2 µm filter) ultrapure water. In the
middle reservoir add cell media (blank media ( (yeast nitrogen base, ammonium sulphate))+ 14% OptiPrep). The OptiPrep help to distinguish the sheath channels and the sample
channel.
• Place stoppers into holes with tweezers.
• Attach metal lid with two screws (can be done without tweezers).
• Pump water through the device at 10 psi for 10 minutes. This would clear up any dirt orcell-debris that is present from the previous run. The flow would be symmetric. If not,there might be some clogging upstream of the channel. Try to flow the media with inserting
the pressure tubes into the two outlets (back-pressure). This usually clear the clogging andmakes the flow symmetric again.
• Run the microfluidic with adding 150 µl blank media through all of the three channels at
2 psi. This step eliminate any bubble/air that is present in the channel and stabilizes thestream.
• Now the microfluidic is ready run for screening or sorting. Add blank media into the two
sheath channel and filtered sample containing cells into the middle/sample channel. It isvery important to filter the sample with 40 µm filter. Otherwise, the cell-debris clogs thechannel.
• Use the pressure controller to control the flow in the microlfuidic.
• If you want to recover the cells after the experiment, load the collection reservoir with someamount of ( ∼150 µl) blank media.

180

B.3

Cleaning Procedure• Empty all the reservoirs.
• Run ∼ 20 % bleach through all the reservoirs.
• Empty inlets and outlets.
• Disassemble device holder and all parts of manifold except stainless steel quick connects• Put microfluidic into its conical tube with new 20% bleach solution (filtered bleach andultrapure water, neither of which need to be re-filtered at this point).
• Sonicate for 40 minutes (can put on timer and leave overnight in sonication bath).
• Everything else is stored in water overnight with possible addition of alconox. Bleach willcause rust to metal and degrade rubber so never leave these parts in bleach for any lengthof time longer than 10 min.

Appendix C
Protocol for Microfluidic Screening and Sorting & Operation of the LabView
SoftwareAfter the flow is stable and symmetric in the microfluidic, screening & sorting can be performed. Here is the detailed step-by-step protocol for screening and sorting with the operation of
the custom-made LabView Software.
• For screening, load the buffer (blank media) in the sheath reservoirs and filtered sample
in the middle reservoir. Run the microfluidic in low pressure so that sample stream goes
through the middle of the channel and transit through the middle of the elliptical beams
focused on the chip. However, for sorting, before turning on the pressure make sure theflow is biased towards to the waste channel otherwise there would be contamination.
• For sorting purpose, flow speed of all the channels should be below 1 psi.
• Mark the positions where the cells are passing through the beams (only lifetime beams andphotobleaching beams). This will help you to monitor if there is any fluctuation of flow
during the screening. It will give the coordinates of the lifetime and photobleaching beams
that are required to compute the speed of the cells through the channel. Speed of the cells
will be used for setting-up the timing for sorting.
• Turn on the PMT with ∼-750 V. This voltage may vary depending on the brightness ofthe sample. I have used the voltage (gain) from -700 V to -800 V.

182• Turn on the Lock-in amplifier (Zurich Instruments) and open the associate LabOne software. Choose the modulation frequency (29.5 MHz), filters and the gain of the I & Q
channels.
• Open the LabView software. There are three components of the software: Target Program(Target-41.vi; real-time control of the sorter etc.), Host Program (Host-41.vi; control ofsort and data representation) and Test program (Test AO and DO.vi; for setting-up thetrap laser).
• First, turn on the Target.vi and then Host.vi.
• From the Host.vi, adjust the threshold for Channel 0 (Ch0; signal from lifetime PMT) and
Channel 1 (Ch1; signal from photobleaching PMT), so that you get equal count on bothchannels. You can adjust the threshold from ‘Pulse’ panel of the program.
• Adjust the ‘transit time’ from the panel ‘Matching’ in the Host.vi program.
• Calibrate the lifetime of the sample adjusting ‘N-Delta’ control from the ‘PMT1’ panelin the Host.vi program. I used mCherry for this calibration process (τ = 1.87 ns in thephase-fluorimetry).
• Now, the microfluidic is ready for screening. Re-start the Host.vi program and continue toobtain data with your desired counts. For screening single mutants, I screen ∼ 7,000 - 10,000
counts. For the counts I refer to the number of cells that is pair-matched (‘MatchedToCh0’;
Panel ‘Run’; Host.vi program).
• If you plan to sort, now stop both the Target.vi & Host.vi programs.
• Turn on the trap laser with 30 % power.
• Add a neutral density (ND) filter (OD > 2.0 ?) in front of the entrance of the trap-laser
box.

183
• Open ‘Test AO and DO.vi’ program and run it. It will show the trajectory of the trap laser
beam in the camera. Place the trajectory at little upstream of the junction for the waste
and collection channel (see Chapter 4 Figure 4.7).
• Focus the trap laser on the microfluidic. This is very crucial part. This will determine
whether you would effectively sort or burn the chip.
• Take out the ND filter in front of the trap laser box.
• Stop the ‘Test AO and DO.vi’ program. Run the Target.vi and Host.vi, in that sequence.
• Next step is the setting-up of the timing for efficient sorting. In principle, we need toenter the Switch factor (SwitchF), Delay factor (DelayF) and adjust the Extra Delay inthe program for efficient sorting (Figure C.1) [98]. In a previous avatar of this sorter, we
had multiple beams to measure these parameters (SwitchF & DelayF) and the measured
parameters were entered into the program. However, I follow these protocols for setting-up
the timing:∗ Enter the position of lifetime and PostBleach beams into the ‘Switch’ panel of the
Host.vi program.
∗ Use a previously found SwitchF (∼ 0.7) and DelayF (∼ 0.3).
∗ Adjust the ExtraDelay parameter in combination with visual inspection of trap-laser
firing and consequent sorting of the cells.
∗ Adjust the ExtraDelay in a systematic way until you get the desired efficiency, .
• Once you have good sorting efficiency (> 50 %), you can select a population you want tosort in the ‘Select’ panel of the Host.vi program.
• Sort the population to obtain the desired number of the cells.

184

Selection
Gate

Camera Position

Trajectory ofOf the trap laser

Timing Set-up

Figure C.1: Sorting protocol: (above) Selection gate shown in the LabView program. (below)
The parameters required to add for the timing set-up during sorting: lifetime and post-bleach beam
positions, SwitchF, DelayF, Extra.

Appendix D
Protocol for Site-directed & Random Mutagenesis

D.1

Library construction
The original DNA templates for mCherry and other FPs were amplified with gene-specificprimers and cloned into pDonr221 using Gateway recombination system (Life Technologies) andsequenced. The forward primer for each FP included a recombination recognition sequence (attB1),
a Shine-Dalgarno sequence for prokaryotic expression, a BamH1 restriction endonuclease site, aKozak sequence for mammalian expression and an ∼ 30 nucleotide sequence complementary to the
FP. The reverse primer for each FP included ∼ 30 nucleotide complementary to the FP, a stopcodon, an EcoR1 restriction endonuclease site and an attB2 recombination recognition sequence.
Still using the Gateway recombination system an LR reaction was performed to clone the FP
into pYestDest52 vector (Life technologies). After sequence was confirmed the FP/pYestDest52
plasmids were used as template for library construction.

D.2

Error-prone libraries:
GeneMorph II Random Mutagenesis kit (Agilent Cat No. 200550) was used to create theerror-prone libraries. The kit protocol was followed with different amount of template and cycles
depending on the error rate. T7 and V5 universal primers (both located on pYesDest52 vector) wereused for the amplification. After first round of PCR, the PCR product was run on a gel and gel-

186
extracted. The gel-extracted PCR product was used for a second round of PCR to create enoughDNA for homologous recombination. After PCR purification the library DNA was isopropanolprecipitated and eluted in a few µl of water.

D.3

Site directed libraries:
QuikChange site-directed mutagenesis method was used to make point mutations or switch

single amino acids using PfuTurboDNA polymerase and a Thermo cycler. PfuTurboDNA polymerase replicates both plasmid strands with high fidelity and without displacing the mutant oligonucleotide primers. The basic procedure utilizes a supercoiled double-stranded DNA (dsDNA) vector
with the FP of interest and two synthetic oligonucleotide primers containing the desired mutation.
The oligonucleotide primers, each complementary to opposite strands of the vector, are extended
during temperature cycling by PfuTurboDNA polymerase. Incorporation of the oligonucleotide
primers generates a mutated plasmid containing staggered nicks. Following temperature cycling,
the product is treated with DpnI. The DpnI endonuclease digest the parental DNA template andmakes it possible to select for mutation-containing synthesized DNA. The nicked vector DNA containing the desired mutations is then transformed into E.coli (Top10).
Libraries with multiple site-directed targets were created using SOE reaction (Splicing overlap
extension). Primers were designed to introduce the desired mutations and the initial PCR s generate
overlapping gene segments that are used as template DNA for another PCR to create a full-length
product. The internal primers generate overlapping, complementary 30ends on the intermediatesegments and introduce the desired nucleotide changes for site-directed mutagenesis. Overlapping
strands of these intermediate products hybridize at this 30region in a subsequent PCR and areextended to generate the full-length product amplified by flanking primers. The full length productis gel-extracted and isopropanol-precipitated and eluted in a few µl water.

187

D.4

Electroporation:Fresh Competent yeast cells (Saccaromyces cerevisiae BY4741) were prepared prior to elec-

troporation.
Cells, DNA and cut pYestDest52 vector were combined and left on ice for 5 min. Electroporation
conditions (Bio-Rad Gene Pulser Xcell): C = 25 µF , PC = 200 ohm, V = 1.5 kV (in 0.2 cm
cuvettes).
Cells were passed twice prior to expression.
Interesting mutants were transferred to pBad-His vector for expression/Ni-Nta protein purification.

D.5

Site-directed mutagenesis using Gblock:• Design the desired open reading frame (ORF) of the mutants in silico.
∗ Include entire ORF (from ‘start’ to ‘stop’ codon)∗ Add restriction enzyme cloning sites to the 50and 30ends (50-BamHI, 30-NotI)∗ Add single bp ‘A’ just downstream of 50-BamHI site to keep things in frame• Digest Gblock(s) w/ BamHI-HF/NotI (∼ 2hrs at 37◦ C) and perform reaction clean up(Qiagen MinElute kit - Cat no. 28004).
• Ligate BamHI/NotI-cut Gblock into BamHI/NotI-cut PiggyBac-H2B vector.
∗ ∼ 60ng vector∗ ∼ 25ng Gblock∗ 1µl T4 ligase (from NEB)
∗ 15 µl reaction volume total∗ room temperature/∼2hrs• Transform competent E.coli (Stellar cells from Takara Bio) w/ 2.5 µl of ligation mixture.

188• Pick single colonies, grow up in LB/Amp100 (37◦ C O/N), and miniprep (Omega Biotek
kit - Cat no. D6942).
• Sequence over inserted Gblock (including cloning sites) to ensure fidelity.

Appendix E
Photo-physical Characterization of the Fluorescent Proteins

E.1

Extinction Coefficient

Figure E.1: Absorption spectra of mCherry and FusionRed in Tris-HCl buffer (pH=7.4) and NaOH
solution (pH=13 or pH=14).

The extinction coefficient of mCherry, FusionRed and their mutants are calculated using thealkali-denaturation method described in the literature [54]. We measured absorption spectrumof the fresh RFP in Tris-HCl buffer (pH=7.4), and repeated the same measurement with same190
amount of fresh RFP in NaOH solution with same volume as previously used Tris-HCl buffer inthe same quartz cuvette. The NaOH solution had pH=13 for mCherry and its mutants and pH=14
for FusionRed and its mutants. The spectra were obtained using Cary 500 Scan UV-Vis-NIR
spectrometer.
The absorption spectra of mCherry and FusionRed were normalized according to the absorption peak of each RFP in Tris-HCl buffer and plotted in Figure E.1. The extinction coefficients of
the alkali-denatured chromophore near 380 nm (380nm ) and 450 nm (450nm ) are 70,000 M-1 cm-1
and 44,000 M-1 cm-1 , respectively [54]. Using these extinction coefficient values, the extinction
coefficient of RFPs (RF P ) can be calculated:
RF P =ARF P
A380nm /380nm + A450nm /450nm(E.1)

where  is the extinction coefficient and A is the absorbance.

E.2

Quantum Yield
For the measurement of fluorescence quantum yield, we use the following protocol:• A series of samples containing purified fluorescent proteins (in Tris-HCl buffer, pH=7.4) is
prepared with 4-5 different concentrations. Maximum OD of the most concentrated sample
should be around 0.2-0.3, measured in a cuvette of 4 cm path length.
• Absorption spectra of the samples are measured in a spectrophotometer. Baseline corrections are performed for the spectra. For measuring the absorption spectra, we use a cuvetteof 4 cm path length rather than the usual 1 cm path length cuvette. This gives more robustand error-free absorption spectra.
• The emission spectra of the same samples are measured in a fluorimeter. Baseline corrections are performed for the spectra. Cuvette with 1 cm path length is fine for thesemeasurements.

191• Integrated fluorescence of the samples are quantified by measuring the area under theemission spectra.
• Integrated fluorescence of the spectra are plotted against the OD at the wavelength thesamples are excited the to obtain the emission spectra. Usually we excite the samples at
520 nm to obtain the emission spectra of the RFPs. Figure E.2 displays the Integrated
fluorescence vs. OD@520 nm of some FPs.

Figure E.2: Integrated fluorescence vs. OD at 520 for few red fluorescent proteins.

• Integrated fluorescence vs. OD plot can be fitted with a straight line of the form: y =slope ∗ x, where y= integrated fluorescence and x = OD. Please note that the fitted line
should go through (x=0, y=0) point. If the fitted line deviates from the (0,0) point, thisindicates some errors in the measurements.

192• Fluorescence quantum yield of the sampel (φsample ) is then computed as:
φsample =2
φref ∗ slopesample ∗ ηsample

(E.2)

2
sloperef ∗ ηrefwhere ηsample and ηref are the refractive index of the sample and the reference, respectively.
• mCherry (φ, 0.22 in Tris-HCl, pH=7.4) or Cresyl violet (φ, 0.54 in EtOH) is used as areference.
• Tris-HCl buffer (pH=7.4) is used for all the dilutions.

E.3

pKa
For the measurement of pKa, we follow the method described by Cranfill et al. [101].
• A set of buffers with pH ranging from 2-10 is prepared. The composition of the buffers
are displayed in Table E.1, E.2 and E.3. The buffer with pH 2.2 (Buffer A) is prepared bydiluting 2.5 ml of 0.2M glycine-HCl to 10 ml.
Table E.1: pH = 3-6
Buffers
B
C
D
E
F
G
H

Calculated
pH
3.0
3.6
4.0
4.6
5.0
5.4
6.0

0.1 M citric acid
(ml)
4.7
3.7
3.3
2.6
2.1
1.6
0.95

0.1 M Na-citrate
(ml)
0.3
1.3
1.7
2.4
2.9
3.4
4.0

Diluted
(ml)
10
10
10
10
10
10
10

to

Diluted(ml)-

toTable E.2: pH = 6.5-8.0
Buffers
I
J
K

Calculated
pH
6.5
7.0
8.0

0.2 M KH2 PO4
(ml)
6.9
3.9
0.5

0.2 M Na2 HPO4
(ml)
3.1
6.1
9.5

193
Table E.3: pH = 9.0-10
BuffersL
M

Calculated
pH
9.0
10.0

0.2M
(ml)
2.5
2.5

Glycine0.2M NaOH (ml)
0.44
2.3

Diluted
(ml)
10
10

to

Figure E.3: pH-resistance of the FusionRed mutants. pKa is defined as the pH where the fluorescence is 50% of the maximum value.

• The actual pH of the buffers are measured using a pH-meter.
• Same amount of purified FP is added in all the buffers and left at room temperature foran hour.
• The emission spectra of the buffer solutions containing FPs are measured using a platereader.
• Fluorescence of the samples are quantified by the maxima of the emission peaks or the area
under the emission spectra. Both method gives similar results.
• Fluorescence of the sample and the corresponding measured pH values are plotted. Figure

194
E.3 displays a typical pH vs. Fluorescence for a set of fluorescent proteins.
• pKa of the FP is defined as the pH where the fluorescence is half of the maximum fluorescence.

Appendix F
Electronic Circults

DC OUT
86 kHz680

680 pF

680

680

RF OUT
29.5 MHz

680

21k

INPUT
29.5 MHz

Figure F.1: Circuit diagram of the bias tee used in the microfluidic sorter.

196

DC OUT

47 pF

V-

ddV

51

20
450k
2N3905220 nF
20k

V+

V+ ddVddV

0.1 μF0.1 μFV-AC OUT

V+

51

100
V+

0.1 μFV-

PMT IN

0.1 μF

Vdd

V-

Vdd

V-

2k

2200 pF

Figure F.2: Circuit diagram of the log amplifier used for the signal amplification.

Appendix G
Sequence Alignment for FusionRed mutants

Sequence alignment of FusionRed mutants:Residue numbering is based on alignment
with avGFP sequence. Residues highlighting blue and yellow are associated with folding andmonomerization of FusionRed clones. Mutations accumulated in first, second and third round of
EP-PCR mutagenesis libraries are highlighted in orange, magenta and cyan respectively. Internal
residues are highlighted in grey.

198

Appendix H
Program for Analyzing OSER Structures

Here are the details of the analysis program that we have developed for an objective quantification of OSER score (see Chapter 6 ). The analysis pipeline can be summarized as follows:

Raw Images⇓
Identification of cells based on a global
thresholding & size⇓
Identification of bright structures (whorls)
inside cells based on intensity & size⇓
Filtering the bright structures to
identify whorls based on shape ofthe structures⇓
Relate cells & whorls based on
location⇓

200
Analysis
For the identification of cells, a binary image was generated by a global thresholding that
discriminate fluorescence from the image background (Figure H.1b). In the next step, the cells
were identified by:• using the previously generated binary image as a guide &• size of the identified objects.
10-50 µm window was used as a size criterion for the cells. Median size of the cells was ∼ 20 µm.
The program correctly identified cells (green outlines) and rejected the tiny dots (pink outlines)
based on size (Figure H.1c).
The intensity distribution of the identified cells were measured. Another binary image was
generated with using a threshold of: µcell + n ∗ σcell , where, µcell and σcell are the mean andstandard deviation of the intensity distribution of the cells. n is an adjustable parameter. n= 1-2
works well in our purpose for the identification of bright structures in the cells. n=1.5 was used
to generate the binary image 2 (Figure H.1d). Bright structures in the cells were identified using
the binary image generated in the previous pipeline & based on size criterion. ∼1-7 µm was used
as a size criterion for the OSER structures. The program correctly identifies the bright structures(green outlines) inside the cells H.1e.
In the original OSER article [100], part of the nucleus having “karmellae” was not considered
as whorls. These are nuclear-associated paired membranes produced by over-expression of the
enzymes. As “karmellae” s have different shapes than the whorls, they can be easily filtered out
using either eccentricity the form factor (f ) of the identified objects. Form factor is defined asfollows:

f = 4π ∗ A/p2

(H.1)

where, A and p are the area and perimeter of the structure. A “karmellae” from the cells havebeen filtered out (shape with pink outline) using a criterion of form factor > 0.25 (Figure H.1f).

201

a

Original imageb

Binary imagec

Identification of
cells

d

Binary image 2

eIdentification of
whorlsf

Filtering ofwhorlsg

Parent cellshChildren whorls

Figure H.1: The analysis of the OSER structures. Detailed description is given in the text.

202
Structures with roundish shape have been identified as whorls (green outlines). Filtering of whorl
based on form factor also eliminates some false positive structures e.g. nice reticular network.
Finally, identified whorls & cells are related based on their location. In Figure H.1g and H.1h cells
were color-labeled with the whorls associated with them.