Spectroscopic Analysis of Red Fluorescent Proteins andDevelopment of a Microﬂuidic Cell Sorter for the
Generation of Improved VariantsbyJennifer L. Lubbeck
B.S., Wake Forest University, 2007

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
College of Arts and Sciences Department of Chemistry and Biochemistry2013

UMI Number: 3562002

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This thesis entitled:
Spectroscopic Analysis of Red Fluorescent Proteins and Development of a Microﬂuidic Cell Sorterfor the Generation of Improved Variants
written by Jennifer L. Lubbeckhas been approved for the College of Arts and Sciences Department of Chemistry andBiochemistryProf. 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
Lubbeck, Jennifer L. (Ph.D., Physical Chemistry)
Spectroscopic Analysis of Red Fluorescent Proteins and Development of a Microﬂuidic Cell Sorterfor the Generation of Improved Variants
Thesis directed by Prof. Ralph Jimenez

The discovery of the green ﬂuorescent protein (GFP) launched the development of a wide
variety of ﬂuorescent protein (FP) mutants whose spectral and photophysical diversity revolutionized in vivo imaging. The excitation and emission spectra of red ﬂuorescent proteins (RFPs), in
particular, have been ideally tuned to a window optically favorable for in vivo work. However,their quantum yields, photostabilities and ﬂuorescence intermittency properties require improvement if they are to be broadly employed for low-copy or single-molecule measurements. Attempts
to engineer improved RFPs often result in optimization of one photophysical property at the expense of others. We developed a microﬂuidic-based cytometer for screening HeLa cell-based genetic
RFP-libraries simultaneously on the basis of ﬂuorescence lifetime (a proxy for quantum yield), photostability, and brightness. Ten 532 nm excitation beams interrogate each cell in ﬂow. The ﬁrst iselectro-optically modulated (30 MHz) to enable lifetime measurement with phase ﬂuorimetry. The
remaining beams act as a pulse sequence for isolating the irreversible photobleaching time constant.
Optical-force switching is employed to sort cells based on any combination of the photophysicalparameters. Screening with this instrument enables identiﬁcation of regions of the structure thatsynergistically aﬀect quantum yield and photostability and the sorting capability provides a newtool for accelerating the development of next generation RFPs.

Dedication

To all the individuals who have helped shape me into the person I am today.

v

Acknowledgements

I would like to acknowledge past and present friends, family, colleagues, and advisors without
whom, I would not be where I am today. I would like to ﬁrst thank my parents who instilled in me
the value of knowledge and education from a very young age. Their support allowed me to always
put my education ﬁrst and obtain my undergraduate degree. They have always put me ﬁrst andhelped me pursue my dreams, even halfway across the country. My sister, Kristin, has given me
unwavering encouragement and help, for which I am truly thankful.
A great deal of thanks is due to my past and present advisors who have shaped my career,mind, and future. I want to thank my high school chemistry teacher, Mrs. Jordan, for providing mea solid foundation in chemistry. Paramount to my early research-education was my undergraduate
research advisor, Prof. Christa Colyer, who helped me learn the successes and failures of conducting
research as well as helping me to navigate my way though a challenging undergraduate curriculum
and applying to graduate school. I thank Prof. Bruce King for letting me try my hand at organic
synthesis in his lab and introducing me to the wider chemistry community at my ﬁrst national
conference. I thank Prof. Tahei Tahara for allowing me to round out my knowledge of ultrafast
spectroscopy in his lab and introducing me to the scientiﬁc community at the world-scale. I
would also like to acknowledge my undergraduate and graduate professors who provided me witha thorough, superior knowledge of chemistry, physics, math, and biology. I have yet to feel limitedby my core education and they always had extra time in their oﬃce hours to answer every last oneof my questions. I acknowledge my lab, the Jiminez lab, and the Palmer lab for their support andhelp throughout the years and, lastly, I would like to thank my current advisors Ralph Jimenez

vi
and Amy Palmer for supporting my work on such an amazing project. It is rare that a student
ﬁnds a project and an advisor that are truly outstanding, and in this case, my project was headedby two outstanding individuals. I thank Amy for her willingness to act as my advisor whenever
the occasion presented itself. I thank Ralph for providing me with the optimal graduate career inwhich to ﬂourish. In his lab, as his advise, my only limitations were my own. I am grateful for
his advice, guidance, and his willingness to inform and instruct as well as to let me make my own
mistakes and choose my own path. I am grateful that Ralph and Amy have always had or madetime for me and this project.
My friend and partner throughout this roller coaster is Kevin Dean. I could not have asked
for a better collaborator. I hold him in great esteem and am grateful for our successful team andeverything I have learned from him. I hope that many more shop-talks over beer lie ahead ofus, even if late-night data sessions do not. I thank Geoﬀ Bee for his support and encouragement
throughout the good and bad times. Lastly, I thank my friends who helped me keep perspective
throughout these past several years and celebrated the ups and downs of the pursuit of a Ph.D.

vii

Contents

Chapter
1 Introduction1

1.1

Discovery and Development of Fluorescent Proteins . . . . . . . . . . . . . . . . . . .

1

1.2

Spectroscopy of Fluorescent Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

1.2.1

The Links Between Spectroscopy and Structure . . . . . . . . . . . . . . . . .

7

1.2.2

Dark State Conversion and Photobleaching . . . . . . . . . . . . . . . . . . .

8

1.2.3

Quantum Yield and Fluorescence Lifetime . . . . . . . . . . . . . . . . . . . . 10

1.3

1.4

Fluorescent Protein Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.1

Library Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.3.2

Selection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Analysis of Red-Fluorescent Proteins Provides Insight into Dark-State Conversion and Photodegredation

15

2.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2

Publication Status and Author Contributions . . . . . . . . . . . . . . . . . . . . . . 15

2.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4

Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1

in vitro Fluorescent Protein Characterization . . . . . . . . . . . . . . . . . . 17

2.4.2

Mammalian Cell Culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

viii

2.5

2.6

2.4.3

in vivo Photobleaching Measurements . . . . . . . . . . . . . . . . . . . . . . 18

2.4.4

Photobleaching Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4.5

in vitro Fluorescence Lifetime Measurements . . . . . . . . . . . . . . . . . . 20

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.1

Spectral Changes Associated with Mutations . . . . . . . . . . . . . . . . . . 20

2.5.2

Ensemble Photobleaching: Diﬀerentiating DSC and Irreversible Photobleaching 21

2.5.3

mCherry Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.5.4

Comparison of Irreversible Photobleaching in FP Variants . . . . . . . . . . . 32

2.5.5

Comparison of Dark-State Conversion in FP variants . . . . . . . . . . . . . . 33

2.5.6

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

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3 Microﬂuidic Flow Cytometer for Quantifying Photobleaching of Fluorescent Proteins in Cells 45
3.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.2

Publication Status and Author Contributions . . . . . . . . . . . . . . . . . . . . . . 46

3.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.4

Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.5

3.6

3.4.1

Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.4.2

Microﬂuidics and Optical Layout . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.4.3

Data Acquisition and Processing . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.4.4

Sample Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5.1

Instrument Validation and Theoretical Considerations . . . . . . . . . . . . . 59

3.5.2

Single-RFP Population Photobleaching . . . . . . . . . . . . . . . . . . . . . 61

3.5.3

Resolving Fluorescent Protein Populations . . . . . . . . . . . . . . . . . . . . 61

3.5.4

Photokinetic Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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

ix
4 Microﬂuidic Cell Sorter for use in Developing Red Fluorescent Proteins with Improved Photostability

73

4.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.2

Publication Status and Author Contributions . . . . . . . . . . . . . . . . . . . . . . 74

4.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.4

Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.5

4.6

4.4.1

Microﬂuidic Chip, Microscope, and Detection . . . . . . . . . . . . . . . . . . 79

4.4.2

Optical Design of Gradient Force Switching . . . . . . . . . . . . . . . . . . . 80

4.4.3

Timing Set-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.4.4

Real-Time Data Analysis for Cell Selection . . . . . . . . . . . . . . . . . . . 84

4.4.5

Solution and Flow Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.5.1

Instrument Operating Conditions . . . . . . . . . . . . . . . . . . . . . . . . . 92

4.5.2

Cell Selection Eﬃciency and Viability After Selection . . . . . . . . . . . . . 94

4.5.3

Cell Selection Based on Irreversible Photobleaching . . . . . . . . . . . . . . . 95

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5 Using the Microﬂuidic Cell-Sorter to Sort Red Fluorescent Protein Libraries

102

5.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.2

Publication Status and Author Contributions . . . . . . . . . . . . . . . . . . . . . . 102

5.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.4

Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.5

5.4.1

Sorting Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.4.2

Library Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.4.3

Cell Maintenance and Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . 106

Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.5.1

Screened Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

x
5.5.2
5.6

Kriek Library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Conclusions and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6 Preliminary Frequency Domain Lifetime Measurements in Flow

130

6.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

6.2

Publication Status and Author Contributions . . . . . . . . . . . . . . . . . . . . . . 130

6.3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

6.4

Experimental Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.5

6.4.1

Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.4.2

Measurements on Texas Red Labeled Beads . . . . . . . . . . . . . . . . . . . 135

6.4.3

Measurements on Cells Expressing Red Fluorescent Proteins . . . . . . . . . 137

Conclusions and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Bibliography

142

Appendix
A Mathematica Code for Designing Microﬂuidic Geometries

153

B Microﬂuidic Cleaning and Setup Procedure

157

B.1 General Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
B.2 Pre-Run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
B.3 Post-Run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
C Microﬂuidic Fabrication Procedure

161

C.1 General Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
C.2 Anodic Bonding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

C.3 Etching Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
C.4 Drilling Port Holes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

xi
D Electrical Diagrams

167

D.1 Custom Operational Ampliﬁer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
D.2 Custom I/Q Demodulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

xii

TablesTable
2.1

in vitro Spectral Properties of Fluorescent Proteins . . . . . . . . . . . . . . . . . . . 22

2.2

Percent DSC, Percent Recovery, and Irreversible Photobleaching Time-Constants
Obtained under Pulsed and Continuous Illumination . . . . . . . . . . . . . . . . . . 27

3.1

Fluoresence Coeﬃcient of Variation for Each Cell Line . . . . . . . . . . . . . . . . . 58

3.2

Photophysical Properties for the Four RFPs Studied . . . . . . . . . . . . . . . . . . 64

3.3

Percent Fluorescence Recovery During Pulsed Excitation . . . . . . . . . . . . . . . . 65

3.4

Summary of Reactions and Equations Used in Photokinetic Model . . . . . . . . . . 65

4.1

Flow Diagram of Target Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5.1

Novel K2 and K4 Mutations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.2

Spectral Properties of Novel Mutants . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

xiii

FiguresFigure1.1

mCherry, a Red Fluorescent Protein . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

1.2

The Red Fluorescent Protein Chromophore . . . . . . . . . . . . . . . . . . . . . . .

4

1.3

mFruit Family Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

1.4

TagRFP Family Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.5

Jablonski Diagram Illustrating Quantum Yield . . . . . . . . . . . . . . . . . . . . . 11

2.1

Structural and Spectral Changes Associated with FPs . . . . . . . . . . . . . . . . . 23

2.2

FP Nuclear Localization and Extent of Photobleaching Observed in HeLa Cells . . . 24

2.3

TagRFP-T Mutant and mFruit Photobleaching Spectra . . . . . . . . . . . . . . . . 25

2.4

Photobleaching Decay for TagRFP R67K N143S S158T F174L . . . . . . . . . . . . 28

2.5

Comparison of Irreversible Photobleaching Parameters of the Diﬀerent FPs . . . . . 34

2.6

Dark-State Conversion Correlation Plots . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.7

Numerical Simulations of Proposed Four-State Model

3.1

Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.2

Microﬂuidic and Manifold Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.3

3-Beam Microﬂuidic Cytometer Experimental Design . . . . . . . . . . . . . . . . . . 56

3.4

Bead Calibration of Cytometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.5Single Cell Photobleaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.6

Resolving RFPs Based on Photobleaching in a Microﬂuidic Cytometer . . . . . . . . 66

. . . . . . . . . . . . . . . . . 39

xiv
3.7

Kinetic Modeling Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.8

Pulsed Photobleaching Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1

Cells Flowing Through the Microﬂuidic . . . . . . . . . . . . . . . . . . . . . . . . . 78

4.2

Schematic of the Experimental System . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.3

Layout of Optical Components for Optical Force Switching . . . . . . . . . . . . . . 83

4.4

Camera Image of Microﬂuidic Device with Overlay of Laser Beam Positions andTrajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.5

Screen Shot of Host PC Interface Program . . . . . . . . . . . . . . . . . . . . . . . . 90

4.6

Series of Images from a Movie Demonstrating Cell Selection . . . . . . . . . . . . . . 93

4.7

Cell Selection Eﬃciency vs. 1064-nm Laser Power . . . . . . . . . . . . . . . . . . . . 96

4.8

The Fluorescence Signal Voltage from Beam 4 vs. Beam 1 . . . . . . . . . . . . . . . 98

4.9

Ratiometric Criteria for Cell Enrichment Calculation . . . . . . . . . . . . . . . . . . 99

4.10 Histogram of Ratiometric Excitation . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.11 Image of Collected Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.1

Histogram of Photostability for Error-Prone TagRFP-T Library . . . . . . . . . . . . 109

5.2

Morello and mCherry Photostability . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.3

Morello Library Residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5.4

Sequence Alignment for Selected Red mFruits . . . . . . . . . . . . . . . . . . . . . . 113

5.5

Gap Between β-strands 7 and 10 of mCherry and Citrine . . . . . . . . . . . . . . . 114

5.6

Residues on mCherry Identiﬁed for Potential Library Construction . . . . . . . . . . 115

5.7

mCherry Photostability Compared to Four Sorting Rounds of Kriek . . . . . . . . . 117

5.8

Histogram of Fluorescence Intensity from Beam 1 for Each Cell Population . . . . . 118

5.9Photostability of K2C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.10 Lifetime of K2C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.11 Residues Randomized for K2.1 Library . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.12 Residues Randomized for K2.2 Library . . . . . . . . . . . . . . . . . . . . . . . . . . 125

xv
5.13 Histogram of Photostability of K2.1 Relative to K2 . . . . . . . . . . . . . . . . . . . 126
5.14 Histogram of Photostability of K2.2 Relative to K2 . . . . . . . . . . . . . . . . . . . 127
6.1

Modulated Excitation and Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

6.2

Multiparametric Optics Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

6.3

Electronics of the Lifetime and Photobleaching Setup . . . . . . . . . . . . . . . . . . 136

6.4

Texas Red Arbitrary Fluorescence Phase Shift . . . . . . . . . . . . . . . . . . . . . . 137

6.5

mOrange2 Arbitrary Fluorescence Phase Shift . . . . . . . . . . . . . . . . . . . . . . 138

6.6

Arbirtrary Fluorescence Phase Shifts of a Mixture of Three Red Fluorescent Proteins 140

C.1 Anodic Bonding Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
C.2 Anodic Bonding Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
C.3 Anodic Bonding Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

Chapter 1
Introduction

1.1

Discovery and Development of Fluorescent Proteins
The use of the green ﬂuorescent protein (GFP) from the jellyﬁsh, Aequorea victoria [1, 2],revolutionized in vivo imaging [3] when it was demonstrated that the GFP gene contained all of theinformation necessary to express and fold the ﬂuorescent protein (FP) into a functional state in otherorganisms, thus making it a ﬂuorescent reporter of unparalleled biological compatibility [1, 3, 4].
Subsequent discovery of new FPs from marine organisms other than A. victoria launched
the evolution of FPs with a wide range of excitation-emission wavelengths and photophysical properties [5–7]. Additionally, this new ﬂuorescent toolbox has enabled researchers to explore cellular
dynamics with unprecedented spatiotemporal resolution [5–8] by genetically fusing FPs to a protein
of interest and targeting them to speciﬁc regions within cells. Furthermore, genetically-encodable
probes based on FPs permit the investigation of diverse analytes and enzymatic processes in living
cells [8–10]. This increasing importance of new FPs has been accentuated by tremendous advances
in ﬂuorescence imaging including in vivo and super-resolution techniques [11–13].
The chromophores of all known FPs form autocatalytically during protein folding and consistof three contiguous amino acids which cyclize and oxidize to form the conjugated chromophore.
This chromophore is attached to an α-helix which is threaded through a β-barrel (Figures 1.1 and
1.2). The β-barrel is a cylindrical structure formed from 11 β-sheets or strands that shields thechromophore from the solvent, as evidenced by the non-ﬂuorescence of the isolated chromophorein solution [14]. The β-barrel also tunes the chromophore’s photophysical properties, as will be2discussed later in detail. All FPs, with the notable exception of A. victoria GFP (AvGFP) and
AvGFP variants, are naturally occurring dimers or tetramers, which necessitated extensive mutagenesis eﬀorts to disrupt the oligomeric interfaces, jeopardizing the barrel integrity, to createmonomers. The two main RFP families (derived from diﬀerent marine organisms), the mFruits
and the TagRFP-variants, originated from a tetramer (DsRed) and a dimer (eqFP578) respectively. As each oligomeric interface was broken, quantum yields and/or extinction coeﬃcientsdecreased as summarized in Figures 1.3 and 1.4. The development of this photophysically-diverse
but structurally-related FP family has provided researchers with a basis set to study the eﬀects ofstructural changes on spectroscopic properties.

3

Figure 1.1: mCherry, a red ﬂuorescent protein. The β-barrel and α-helix are depicted in grey andthe chromophore in red. (PDB 2H5Q)

Figure 1.2: Chromophore environment of mCherry. The β-barrel and α-helix are depicted in grey and the chromophore in color wherecarbon is green, nitrogen is blue, sulfur is yellow, and oxygen is red. Example inward facing residues, which possibly tune the photophysics,
are also depicted in red. (PDB 2H5Q)

4

breakdimer2
Ɛ = 69 mM -1 cm -1
fl = 0.69
t bleach = N.D.

break
A/C
interfacemRFP1(monomer)Ɛ = 50 mM -1 cm -1
fl = 0.25
t bleach = 6.5 s

mPlum(monomer)Ɛ = 41 mM -1 cm -1
fl = 0.1
t bleach = 53 s

mCherry(monomer)Ɛ = 72 mM -1 cm -1
fl = 0.22
t bleach = 98 s

Figure 1.3: mFruit family tree. Bleaching half-lives (tbleach ) are reported for wide ﬁeld illumination. All information is taken fromreferences [6, 15].

DsRed
A/B
interface(tetramer)Ɛ = 75 mM -1 cm -1
fl = 0.79
t bleach = N.D.

new colors
improve brightness

red mFruits

5breakdimer
interface

TagRFP(monomer)Ɛ = 100 mM -1 cm -1
fl = 0.48
t bleach = 260 s

mKateƐ = 45 mM -1 cm -1
fl = 0.33
t bleach = 1,260 s

TagRFP-T(TagRFP S158T)
Ɛ = 81 mM -1 cm -1
fl = 0.41
t bleach = 337 s

Figure 1.4: TagRFP family tree. Bleaching half-lives (tbleach ) are reported for wide ﬁeld illumination. All information is taken fromreferences [16–18]

improve
eqFP578
TurboRFP
maturation(dimer)(dimer)Ɛ = 102 mM -1 cm -1
Ɛ = 92 mM -1 cm -1
fl = 0.67
fl = 0.54
t bleach = N.D.
t bleach = N.D.

new colors
improve brightness

TagRFP variants

6

7

1.2

Spectroscopy of Fluorescent Proteins

1.2.1

The Links Between Spectroscopy and Structure

Due to the chromophore being buried inside the barrel, the protein structure plays a crucialrole in tuning the chromophore’s photophysical properties. Early FP-engineering eﬀorts resulted insigniﬁcant improvements to the biochemical and optical properties of AvGFP (reviewed in [2, 19]).
However, most early engineering eﬀorts were targeted at improving maturation and folding at 37◦
C. Later it was established that mutations directly to, or around, the chromophore narrow theexcitation spectrum, increase the extinction coeﬃcient [20], diversify FP wavelengths (blue, cyan,
and yellow) [21, 22], and reduce halide and pH sensitivity [23].
Beyond the ﬁrst few solvation shells of the chromophore, the β-barrel continues to play a
key role in the photophysics because it acts as a barrier between the chromophore and solvent.
Protein engineering eﬀorts to disturb the oligomeric interfaces have lead to large decreases inphotostability, extinction coeﬃcient, and quantum yield. These properties were partially restoredby subsequent engineering [24, 25] and illustrate the photophysical inﬂuences of the barrel. Morerecently, this point has been further illustrated by directed evolution eﬀorts of the AvGFP family by
Nguyen and Daugherty. Improved versions of cyan ﬂuorescent protein (CFP) and yellow ﬂuorescentprotein (YFP) (named CyPet and YPet) were generated using error-prone PCR and gene assembly
mutagenesis [26]. Six of 7 mutations in CFP and 7 of the 11 mutations in YFP were amino acids
on the outer surface of the barrel or in the loop regions that cap the barrel.
Given these unique ﬂuorescent attributes, FPs have been the subject of intense theoreticaland experimental analysis. For example, spectroscopic work on FPs has revealed an intricate chromophore environment, exhibiting excited-state proton transfer (ESPT) [27], a β-barrel mediateddynamic Stokes shift [28], and a quadratic Stark eﬀect that shifts the absorption wavlengths of
the red mFruits [29]. Despite this wealth of information, it remains poorly understood why FPs
routinely emit 10-100-fold fewer photons than small-molecule ﬂuorescent dyes [17, 30]. RFPs are of
special interest as their excitation-emission maxima lie in an optically favorable spectral window of8
reduced scattering and exclusive of water absorption and cellular autoﬂuorescence. Unfortunately,
RFPs especially suﬀer from decreased brightness and both accelerated irreversible and reversible
photobleaching [8, 17, 30]. These properties limit the use of RFPs in many imaging applications
that require prolonged imaging times, high excitation intensities, or low expression levels [17, 30]
and are discussed in further detail below.

1.2.2

Dark State Conversion and Photobleaching

The two main mechanisms that contribute to diminished photon output are photobleachingand dark-state conversion (DSC). When single FP molecules are observed, DSC is manifested as
blinking of the ﬂuorescence intensity due to formation of a long-lived (10’s of μs to minutes) darkstate that temporarily removes the FP from the excitation-emission cycle [31–33]. On the ensemble
level, DSC (also known as blinking or reversible photobleaching-these terms will be used interchangeably) results in decreased brightness. Reversible photobleaching is distinct from irreversible
photobleaching (typically referred to as simply photobleaching), which is photodestruction of thechromophore, resulting in complete and sudden loss of ﬂuorescence at the single molecule level andexponential decrease of ﬂuorescence with time at the ensemble level.
In GFP and its yellow-emitting variants, two DSC processes that occur on the 0.01-1 mstimescale have been identiﬁed. One, which is pH-dependent, is attributed to protonation of the
p-hydroxybenzylidene moiety and the other, which is pH-independent, is attributed to a conformational change of the chromophore and/or its environment into a non-radiative conﬁguration [32, 34, 35]. This latter result is supported by more recent studies which reveal that blinking
can be altered by changes to the immediate chromophore environment, particularly those aﬀecting chromophore crowding (Figure 1.2). In particular, studies which examined point mutations
on three positions in AvGFP revealed that increased space around the chromophore positively
correlates with the magnitude of internal conversion and mutations which are designed to rigidify
the chromophore impede internal conversion as determined by absorption spectroscopy, as well
as steady-state and time-resolved ﬂuorescence spectroscopy [36]. Additional crystallography and9single-molecule ﬂuorescence studies on photoconvertable FPs show that molecular crowding andhydrogen-bonding networks inﬂuence the probability of isomerization based on the ﬂickering ratesof single molecules and the observed isomerization in the crystal structure of these proteins upon
excitation [37–39].
A separate sub-μs process has been identiﬁed in GFP, likely involving intersystem crossing to
the triplet state. With 100-picosecond one-photon, or 100-femtosecond two-photon pulsed excitation of GFP, an increase of the inter-pulse interval from ≈10 ns to 2 μs, to allow relaxation between
pulses, leads to decreased irreversible photobleaching and an increase in the total photon yield bya factor of 5-25 [40]. In RFPs, DSC can be both pH-sensitive [37,41] and pH-insensitive [32,42,43],
with photophysical processes taking place on the μs (e.g., the triplet state) and 0.1 ms (conformational dynamics) timescales [44].
There are multiple mechanisms by which photobleaching can occur. However a prominentmechanism in many FPs likely involves reaction with oxygen as singlet oxygen has been detected
upon irradiation of GFP [44], and photobleaching is typically minimized by treatment of the sample
with singlet oxygen scavengers such as sodium azide [45]. Although, as previously mentioned, theβ-barrel shields the chromophore from the solvent, several studies indicate that shielding eﬀects
of the barrel are sub-optimal, especially in RFPs. For example, when EGFP was embedded in apolyelectrolyte matrix, its photostability was doubled [46]. This observation suggests that ionicscreening eﬀects decrease photobleaching by excluding diﬀusible ions from the chromophore. Furthermore, as mentioned above, mutations to the barrel of AvGFP and DsRed-derived proteins leadto increases in photostability, indicating that they are not optimally-shielding the chromophore [26].
Lastly, recent molecular dynamics simulations show a larger, more dynamic gap between β-strands
7 and 10 (denoted the ”β-bulge”) of a DsRed-derived protein (mCherry) relative to the otherstrands. These studies also compared the gap between mCherry’s strands 7 and 10 with that ofits GFP-homologue (citrine), and found it to be larger and more dynamic in mCherry than incitrine [47, 48].
The pathways and kinetics for DSC and photobleaching are poorly understood but in general

10
are linked. The linking of the kinetics arises because irreversible photobleaching often results fromabsorption of light from an excited state, such as a triplet state or a dark state. Conversion to adark state is also found to accentuate irreversible photobleaching, but by an amount that varies
for diﬀerent RFPs [49]. The magnitudes and time-constants of both reversible and irreversible
photobleaching depend on the ﬂuorophore, excitation intensity, and excitation wavelength [17, 33,
49, 50].

1.2.3

Quantum Yield and Fluorescence Lifetime

Quantum yield (φf l ) is deﬁned as the fraction of photons radiatively emitted relative tothe number of photons absorbed (Figure 1.5). The ﬂuorescence lifetime (τ ) of a ﬂuorophore is
the average time a ﬂuorophore spends in the excited state (Sn>0 ) before relaxing back to theground state. The ﬂuorescence lifetime is directly related to the quantum yield because the lifetime
determines how much time the excited-state ﬂuorophore has to interact with its environment.
Mathematically, the quantum yield and lifetime are deﬁned as:

krad
krad + knr(1.1)= (krad + knr )−1(1.2)

φf l =
τWhere krad is the emissive rate of the ﬂuorophore and knr is the sum of the rates of all
non-radiative relaxation pathways. These equations, when combined, linearly relate the quantum
yield and lifetime by:

φf l = τ × krad

(1.3)

Small molecule dyes such as rhodamines have quantum yields approaching 1, and have thebrightest emissions, where brightness is deﬁned as the product of the quantum yield and extinction
coeﬃcient [51].

11

Figure 1.5: Jablonski diagram illustrating the main processes aﬀecting quantum yield. Quantum
yield is the ratio of photons radiatively emitted versus photons absorbed. Fluorescence lifetime isthe average time the chromophore spends in the emissive-excited states. In addition to intersystemcrossing to triplet states, other processes, such as conformational transitions or excited state protontransfer can lead to dark-state formation and, therefore, decreased quantum yield.

12
Although ﬂuorescence lifetime linearly correlates with quantum yield, the longer a ﬂuorophore
remains in the excited state, the more time is available for it to undergo excited state reactions(possibly resulting in photobleaching) or collisional quenching. Consider molecular oxygen which
is a known collisional quencher and photobleaching agent (as discussed above). The diﬀusion
coeﬃcient (D) of oxygen in water at 25◦ C is 2.5 × 10−5 cm2 /s. The height and diameter of
the GFP barrel are approximately 40 Å and 30 Å respectively [52]. This means that the oxygen
molecule can diﬀuse across the average of the barrel axes (Δ x = 35 Å) in 2.5 ns (t) according to
the Einstein equation [51]:Δx2 = 2Dt

(1.4)

The ﬂuorescence lifetimes of TagRFP and mCherry are 2.42 ns and 1.87 ns respectively [49]meaning that any increase in ﬂuorescence lifetime (or quantum yield) would leave these proteinseven more vulnerable to photobleaching or ﬂuorescence quenching. This example illustrates why
improving the photostability and quantum yield are synergistic as they both rely heavily on thepermeability of the β-barrel. Furthermore, it illustrates why optimization of one parameter can
result in decreases in the other parameter due to the implications of a longer or shorter lifetime.

1.3

Fluorescent Protein Development

1.3.1

Library Development

The procedures by which improved FPs are being developed have recently been reviewed[53]. A common workﬂow involves rational design of amino-acid replacements at key positions in
the protein to inﬂuence a spectral phenotype followed by directed evolution to ﬁnd combinationsof residues at supporting positions to tune the photophysical properties. Usually this process
is achieved by in vitro random mutagenesis coupled with bacterial expression and screening toisolate high-performing mutants. Alternatively, site-directed mutagenesis coupled with expression
in mammalian cells automatically selects proteins readily expressible by the eukaryotic machinery

13and should thus provide advantages for developing mutants with low cytotoxicity that are optimized
for expression and stability within these cells [53]. Several successive rounds of mutagenesis andselection are generally required and hence selected cells must be capable of proliferation.
Our library design closely resembles that described above. We designed hypothesis-driven,targeted libraries of RFPs and expressed these libraries in mammalian cells to ensure that engineered proteins fold and mature in vivo at 37◦ C. However, previous selection strategies to improvemFruit proteins were restricted to manual screening (as discussed below) of 104 clones [15, 24].
To accelerate FP photostability improvements, larger library sizes are necessary. In this work, we
present the development of an instrument capable of sorting hypothesis-driven libraries on the orderof 105 clones. As an example, saturated mutagenesis on 4 residues results in 204 =1.6×105 clones.
However, saturated mutagenesis at just one more residue results in a library size of 3.2×106 clones.
Therefore, some amino acid positions are chosen to be more restricted in diversity to maintainmanageable library sizes. We also recognize that longer-range or synergistic eﬀects of amino acids
are diﬃcult to predict and so error-prone libraries and additional rounds of mutagenesis between
sorts are also incorporated.

1.3.2

Selection Methods

Currently, the major high-throughput screening approach in use for directed evolution issorting of bacterial libraries using a ﬂuorescence activated cell sorter (FACS). FACS instrumentation is limited to detection of three parameters: ﬂuorescence intensity, wavelength, and lightscatter. Suggestions for new modalities of FACS that would be useful in developing new RFPs
include ﬂuorescence emission spectrum, brightness with two-photon excitation, ﬂuorescence polarization anisotropy, and ﬂuorescence lifetime [53]. Further to these suggestions, the microﬂuidic
sorter development and operation, which is presented in this thesis, provides a diﬀerent and newcapability that directly addresses an essential requirement for applying selection pressure to improve photostability in advanced RFPs - namely sorting of cells on the basis of reduced irreversible
photobleaching, measured while minimizing the concomitant decrease in ﬂuorescence signal from14
reversible DSC.
In previous work by Tsien and coworkers, directed evolution of orange and red ﬂuorescentproteins with increased photostability was achieved by manual selection of colonies of bacterial cells
that maintained ﬂuorescence following prolonged (10-120 min) wide-ﬁeld exposure to illumination[17]. Similarly, manual sorting of E. coli colonies in a petri dish following ﬂuorescence lifetime
measurements has been used to screen a structure-guided library of the Cerulean FP, which has
a quantum yield of ≈49%, to develop a cyan ﬂuorescent protein with an almost 2-fold gain in
ﬂuorescent quantum eﬃciency [54]. While the decreasing of photobleaching could potentially yield afar larger gain in total photon signal, technology for sorting individual cells based on photobleachinghas not been previously available.
It is known that mutations which improve one photophysical property of FPs often diminish
another [15, 55]. Therefore, in addition to sorting cells on the basis of photostability, eﬀorts topursue multi-parametric sorts (which sort mutants on the basis of photobleaching and quantum
yield) are also presented herein.

1.4

Conclusion
FPs have become invaluable tools in modern Biophysics and, as such, have enabled revolu-

tionary microscopy and imaging techniques. However, to facilitate further advancements, new FPs
with higher photostability and brightness must be developed. The work presented in this thesis
provides us with important new tools and insight to further this goal. Throughout this project,
an eﬀort has been made to not only develop new methods and technologies to meet the challenges
detailed above, but also to analyze and interpret experimental results to guide future FP and instrumentation development. In this way, the technology, as well as the proteins, is subject to its
own form of directed evolution. While many more rounds of evolution are still required to achieve
the next generation of ﬂuorescent probes, this work outlines the foundation of one novel approachand discuses its strengths, weaknesses, and future directions.

Chapter 2
Analysis of Red-Fluorescent Proteins Provides Insight into Dark-State
Conversion and Photodegredation

2.1

Abstract
Fluorescent proteins (FPs) are powerful tools that permit real-time visualization of cellular

processes. The utility of a given FP for a speciﬁc experiment depends strongly on its eﬀective
brightness and overall photostability. However, the brightness of FPs is limited by DSC and irreversible photobleaching, which occur on diﬀerent timescales. Here, we develop in vivo ensemble
assays for measuring DSC and irreversible photobleaching under continuous and pulsed illumination. Analysis of closely related red-FPs reveals that DSC and irreversible photobleaching are notalways connected by the same mechanistic pathway. DSC occurs out of the ﬁrst-excited singlet
state, and its magnitude depends predominantly on the kinetics for recovery out of the dark-state.
The experimental results can be replicated through kinetic simulations of a four-state model of theelectronic states. The methodology presented allows light-driven dynamics to be studied at theensemble level over six orders of magnitude in time (μs − s timescales).

2.2

Publication Status and Author Contributions
Dean, K.M., Lubbeck, J.L., Binder, J.K., Schwall, L.R., Jimenez R, and Palmer, A.E. Analy-

sis of Red-Fluorescent Proteins Provides Insight into Dark-State Conversion and Photodegradation.
Biophys. J. 2011. Aug 17; 101(4)961-9.
K.M.D., J.L.L., R.J, and A.E.P. designed the research. K.M.D. and J.L.L. performed re-16
search. K.M.D. performed data analysis and numerical simulations. J.K.B. and L.R.S. provided
reagents and assisted in research. K.M.D., R.J., and A.E.P wrote the paper.

2.3

Introduction
Despite work on a variety of RFPs, published models are in disagreement and provide little

insight into irreversible photobleaching. As a result, it is not clear if irreversible photobleachingoccurs out of transient and/or long-lived dark-states. In this chapter we analyze multiple closely
related proteins to explore how diverse photophysical properties coevolve with one another. Our
goal is to provide a model that combines DSC and irreversible photobleaching in the context ofadditional photophysical properties (quantum yield, extinction coeﬃcient, etc.), and to shed lighton how these properties change upon mutation of the protein structure.
TagRFP, and a closely-related variant, mKate, are ideal candidates for evaluating how photophysical properties vary within a series of FPs [16]. mKate was derived from TagRFP by directed
evolution with selection pressure for red-shifted emission and is characterized by incorporation of 4
mutations (R67K, N143S, F174L, H197R) [16, 18]. Subsequent studies identiﬁed a single mutationin TagRFP, S158T, that improved its photostability 9-fold and is referred to here as TagRFP-T [17].
In mKate, an S158A mutation, hereafter referred to as mKate2, improved the brightness (deﬁned
as the product of the extinction coeﬃcient and quantum yield) 2.8-fold [17, 56]. Given the limited
number of mutations necessary to evaluate this pathway, its diverse phenotypes, and the sensitivity to modest structural perturbations (e.g., S158T), we characterized the photophysics of thissystem and introduce a pulsed photoexcitation method that separately resolves the magnitudes ofirreversible photobleaching and DSC.

17

2.4

Materials and Methods

2.4.1

in vitro Fluorescent Protein Characterization

TagRFP-T was cloned into pBAD and mutations were introduced using overlap extension
mutagenesis. Upon commercial DNA sequencing, mutants were transformed into Top10 E. coli,
induced with 0.02% arabinose for 24 hours at room temperature, puriﬁed using His-tag/Ni-NTA
chromatography, and buﬀer exchanged into 15 mM MOPS, 100 mM KCl, pH=7.0. UV-Visible
spectroscopy was performed in a dual-beam reference mode spectrometer with baseline correction.
Excitation and emission spectra were collected for dilute ﬂuorescent protein solutions (≈ 1 μM) on a
ﬂuorimeter and corrected for temporal and spectral lamp intensity ﬂuctuations with a beam splitterand photodiode prior to the sample cuvette. The observed emission intensity was corrected for the
wavelength dependent PMT quantum eﬃciency. Extinction coeﬃcients were determined using the
dynamic alkaline denaturation method which permits one to distinguish between red and immature(i.e., blue, teal, and green) chromophores [57]. In all cases, an isosbestic point was observed between
the red chromophore and the alkaline denatured green chromophore (λabs =447 nm,  = 44, 000
M−1 cm−1 ), and the average change in absorption was used to determine the extinction coeﬃcient of
the red chromophore [58]. Quantum yields were measured in two steps. First, the optical density
was determined within a 40 mm cuvette to improve instrument accuracy for weakly absorbing
solutions. The resulting solution, which had an optical density well below 0.1 per 10 mm of
cuvette pathlength to minimize secondary absorption artifacts, was transferred into a clean 10mm ﬂuorescence cuvette and the integrated ﬂuorescence intensity was immediately measured. All
calculations included corrections for the refractive indices of solvents and were cross-calibrated using
rhodamine 101 in absolute ethanol [59], and cresyl violet in absolute methanol [60], as reference
standards. Experimental uncertainty was assessed from the cross-calibration of rhodamine 101 andcresyl violet, and the error was kept below 10 percent.

18
2.4.2

Mammalian Cell Culture

HeLa cells were cultured in minimum essential medium supplemented with Earles salts, 10%
fetal bovine serum (FBS), and 1% penicillin-streptomycin. FPs were localized to the nucleus by fusing a nuclear localization signal peptide (KPKKKRKVEDA) to the C-terminus of the FP. Nuclear
localized FPs in a pcDNA3 mammalian expression vector were transiently transfected using commercially available reagents 48 hours prior to imaging, and the cells were placed in HEPES-Buﬀered(4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) Hanks Balanced Salt Solution (HHBSS, pH
7.4) prior to all imaging experiments.

2.4.3

in vivo Photobleaching Measurements

To eliminate diﬀusion artifacts, all photobleaching measurements were performed on freely
diﬀusing FPs within the nucleus of living adherent HeLa cells. Laser based photobleaching measurements on the microsecond timescale were performed by directing a 532 nm continuous wave (CW)
laser through an acousto-optic modulator (AOM). Undesired light scattering originating from AOM
optical heterogeneities were eliminated with a spatial ﬁlter, and the resulting ﬁrst-order diﬀracted
light was directed into an inverted microscope, reﬂected oﬀ of a dichroic mirror, and focused onto
the adherent cell by a 10x, 0.40 Numerical Aperture (NA) air-objective. At the focus, the laser
beam had a Gaussian proﬁle with a full-width half-max (FWHM) of 9.1 μm, thereby completely
illuminating the nucleus of the cell (diameter ≈ 5 μm). Due to the Gaussian beam waist proﬁle, theexcitation intensity at the periphery of the nucleus was 19% less than the intensity at the center ofthe nucleus. The resulting ﬂuorescence emission was collected in epi-mode through the same objective, separated from the excitation light with a dichroic mirror and a long-pass ﬁlter, and detectedby a red-optimized photomultiplier tube (PMT). The resulting photocurrent was converted into a
voltage without any additional signal processing, and recorded using custom software. To avoid
PMT saturation eﬀects, i.e., PMT blinding [61], the applied PMT voltage was decreased, enabling
operation in a low-gain mode. Furthermore, the observed photophysical responses of cells did not19change with cellular brightness, and no ﬂuorescence recovery was observed for analogous photobleaching assays performed on commercially available ﬂuorescent beads of comparable brightness(data not shown).
Wide-ﬁeld photobleaching measurements were performed on a commercial epi-ﬂuorescencemicroscope equipped with a Xe arc lamp, a 540/25 excitation ﬁlter, 590 nm dichroic, and 630/60
emission ﬁlter, CCD camera, and 40x 1.3 N.A. oil-immersion objective. Wide-ﬁeld measurements
were performed without neutral density ﬁlters, resulting in an excitation intensity of ≈100 W/cm2 .

2.4.4

Photobleaching Data Analysis

To compare FPs with diﬀerent excitation proﬁles and molar absorptivities, all photobleaching
spectra were normalized for the rate of excitation. For laser-based measurements, the rate of
excitation was calculated by multiplying the absorption cross-section at 532 nm with the laser
intensity, deﬁned throughout as the average laser intensity at FWHM. For wide-ﬁeld photobleachingmeasurements, the rate of excitation was determined by integrating the product of the lamp spectral
output, excitation ﬁlter transmission, dichroic reﬂectivity, FP excitation proﬁle, and light intensity
at the objective focus.
Laser-based photobleaching measurements at intensities of 25.0 kW/cm2 and 2.5 kW/cm2
were collected with 1 and 10 μs temporal resolutions, respectively, and each data set spanned 6orders of magnitude in time. To weight the data equally and avoid a bias in subsequent exponential
ﬁts, a smoothing spline was applied to the data and values were interpolated at equally spaced logtime intervals. For each data set, a minimum number of exponentials were used to ﬁt the data,
and an optimal global ﬁt was found after iteratively optimizing the exponential decays for the fastmonoexponential (< 3 ms) and slow biexponential (> 10 ms) phases independently. For each ﬁt
the conﬁdence intervals, coeﬃcient of determination (R2 ), and residuals were evaluated, and manyﬁts showed unavoidable and minor oscillations (< 3%) in the residuals. Analysis of the conﬁdence
intervals for the multiexponential ﬁt suggested accurate time constants, yet unsatisfactory preexponential amplitudes for direct comparison of the fast and slow photobleaching phases. As a20
result, the amplitude of the initial decrease in ﬂuorescence intensity was determined by ﬁtting theinitial phase to a single-exponential decay with a constant oﬀset. Subtraction of the oﬀset fromthe normalized ﬂuorescence intensity provided the amplitude of the fast process. mApple andmKate2 were excluded from analysis given their complicated photobleaching kinetics, i.e., rapid
DSC followed by photoactivation and subsequent photobleaching. All data analysis was performed
using MATLAB.

2.4.5

in vitro Fluorescence Lifetime Measurements

The experimental setup used here has been described elsewhere [62]. Brieﬂy, dilute solutions(≈ 100 nM) of puriﬁed protein were excited with 1 kW/cm2 of light originating from a diodepumped solid-state 532 nm laser. Fluorescence was collected in the epi direction and separated
from the excitation light using a dichroic mirror, focused through a 50 μm pinhole, detected withavalanche photodiodes, and binned into 1064 channels using a commercial time-correlated photon
counter. All signals were background corrected, and the resulting ﬂuorescence decay was subjected
to a single or biexponential ﬁt, and the initial 1 ns of the decay was omitted to avoid artifacts
originating from deconvolution.

2.5

Results

2.5.1

Spectral Changes Associated with Mutations

To characterize the photophysical properties of a series of closely related FPs, we generated variants of TagRFP with combinations of the 4 mutations that convert TagRFP into mKate(R67K, N143S, F174L, H197R). These mutations are illustrated in Figure 2.1, which depicts thechromophore environment of TagRFP and mKate. We also incorporated additional mutations(S158A/C/T and H197I/Y) to explore the inﬂuence of these amino acid substitutions on photostability and red shift, respectively [65]. 27 proteins were generated, puriﬁed and compared to
six mFruits: mApple [17], mCherry [15], mOrange [15], mOrange2 [17], mStrawberry [15], and21
mPlum [66]. Table 2.1 lists the excitation and emission wavelength, extinction coeﬃcient, quantum
yield, and ﬂuorescence lifetime measured for each protein. Not surprisingly, these parameters varied widely across the proteins and the inﬂuence of each individual mutation on the photophysicalproperties was strongly context dependent, (i.e. dependent on the other mutations present). For
example, incorporation of F174L into TagRFP R67K S158T caused a dramatic reduction in thequantum yield from 0.36 to 0.04, but the same mutation introduced into TagRFP N143S S158T
caused a slight increase in the quantum yield from 0.25 to 0.40.
It was previously shown that TagRFP and mKate crystal structures reveal trans and cis chromophore conﬁgurations, respectively (Figure 2.1) [56, 67], consistent with spectroscopic and electronic structure calculations that suggested trans-to-cis isomerization of the p-hydroxybenzylidene
moiety into a negatively charged electrostatic environment would cause a red-shift in the excitation [68,69]. For the mutant proteins examined here, the maximum excitation wavelength generally
clustered in two groups, perhaps corresponding to proteins with chromophores in either the trans or
cis conﬁguration (Figure 2.1c). However, there were proteins whose excitation spectrum exhibiteda bimodal distribution (i.e., Figure 2.1d, TagRFP N143S S158T), suggesting that a mixture of two
chromophore conﬁgurations exist throughout the transformation of TagRFP-T to mKate2. These
results suggest that the change in free energy between the cis and trans ground-state conﬁgurations
may be relatively small and acutely sensitive to the mutational context in TagRFP-T variants.

2.5.2

Ensemble Photobleaching: Diﬀerentiating DSC and Irreversible Photobleaching

Photobleaching, i.e. the gradual decay of ﬂuorescence upon exposure to light, signiﬁcantly
limits the photon output of FPs, yet the mechanisms of ﬂuorescence decay remain poorly characterized. To examine photobleaching for a panel of FPs in vivo, freely diﬀusing, nuclear localized FPs
were expressed in HeLa cells and continuously illuminated using a Xe arc lamp or continuous-wave
laser. Figure 2.2 provides a representative image of FP localization and the extent of photobleaching observed. TagRFP-T mutants were selected from the previous section to include well-maturing

22

Table 2.1: in vitro Spectral Properties of Fluorescent Proteins
Fluorescent Protein
TagRFP
TagRFP S158T (TagRFP-T)
TagRFP S158C
TagRFP S158ATagRFP R67K S158T
TagRFP N143S S158T
TagRFP S158T F174L
TagRFP S158T H197R
TagRFP R67K N143S S158T
TagRFP R67K N143S S158ATagRFP R67K N143S S158C
TagRFP R67K N143S
TagRFP R67K S158T F174L
TagRFP R67K S158T H197R
TagRFP N143S S158T F174L
TagRFP N143S S158T H197R
TagRFP S158T F174L H197R
TagRFP R67K N143S S158T
H197Y
TagRFP R67K N143S S158T
F174L
TagRFP R67K S158T F174L
H197R
TagRFP R67K N143S S158T
H197R
TagRFP N143S S158T F174L
H197R
TagRFP R67K N143S S158T
F174L H197R
TagRFP R67K N143S F174L
H197R (mKate)
TagRFP R67K N143S S158C
F174L H197R
TagRFP R67K N143S S158A
F174L H197R (mKate2)
TagRFP R67K N143S S158T
F174L H197Y
mApplemCherry
mOrangemOrange2
mPlum
mStrawberry

555/579555/580
570/589556/589
550/579569/590584/590548/559580/602578/607580/608
576/596577/593556/580
582/594578/609
N.D.

Extinction
Coeﬃcient(M−1 cm−1 )95,000104,000
77,00070,000105,000
110,000
89,000N.D.
100,000
120,000
100,000
115,000
93,000N.D.
100,000
N.D.
N.D.

589/623

72,0000.11±.04

578/601

93,0000.34±.12

576/616

N.D.

N.D.

578/617N.D.

0.20±.05

580/610

N.D.

N.D.

566/62177,0000.25±.06

586/619

105,000

0.25±.02

583/615

115,000

0.22±.03

585/618

89,0000.38±.16

588/618

100,000

0.08±.01

569/590586/606
547/562550/564583/633
575/594

88,00097,000100,000
110,000
75,00098,0000.49±N.D.
0.16±.02
0.67±N.D.
0.55±.04
0.08±.02
0.35±.04

Ex/Em
(nm)

Quantum
Yield
0.48±.04
0.47±.08
0.24±.01
0.1±. 01
0.36±.09
0.25±.06
0.05±.01
0.45±.10
0.41±.120.30±.12
0.39±.16
0.30±.10
0.04±.01
N.D.
0.40±.12
0.17±.04
N.D.

Fluorescence
Lifetime
(ns)
2.42
2.71

2.36

2.79

3.11
1.87
3.62
3.28
2.04

N.D. = Not Determined. Accurate extinction coeﬃcient and quantum yield could not be
obtained due to protein misfolding and/or lack of red chromophore formation.

23

Figure 2.1: Crystal structure of chromophore pocket for TagRFP (a) and mKate (b). Crystal
structures show TagRFP (PDB 3M22) and mKate (PDB 3BXB) in the trans and cis conﬁgurations,
respectively, due to rotation around the bond marked by the arrow. Mutations explored in thisstudy include R67K, N143S, S158T, S158A, F174L, and H197R. (c) A bimodal distribution of
excitation wavelengths, likely indicative of a mixture of the trans and cis conﬁgurations of thechromophore throughout the transformation of TagRFP to mKate. Table 2.1 summarizes themeasured extinction coeﬃcients, quantum yields, ﬂuorescence lifetimes, and excitation and emission
wavelengths for these mutant FPs. (d) Excitation spectra of single mutants in the TagRFP-T
background: TagRFP-T (open circle), TagRFP-T R67K (open square), TagRFP-T N143S (open
diamond), and TagRFP-T F174L (black circle). Note the broadened excitation spectra for TagRFPT F174L, likely indicative of two ground-state conﬁgurations, both of which absorb.

24

Figure 2.2: Overlay of mOrange ﬂuorescence (red) and DIC (Diﬀerential Interference Contrast)
images before (a) and after (b) photobleaching. Cells were continuously illuminated with arc-lamp
illumination and the ﬁnal ﬂuorescence intensity was ≈12% of the initial ﬂuorescence intensity. The
circular shape of ﬂuorescence image is characteristic of nuclear localization, and was observed for
all FPs studied here.

variants (i.e., predominantly red-absorbing) with diverse spectral properties, thereby allowing assessment of how photobleaching correlates with diﬀerent photophysical attributes such as quantum
yield and ﬂuorescence lifetime.
Figure 2.3 demonstrates that the FPs exhibit a wide range of photobleaching behavior andkinetics upon exposure to either wide-ﬁeld or laser (2.5 kW/cm2 and 25 kW/cm2 ) illumination.
Observed responses included monoexponential decay, multi-exponential decay, photoactivation, andrapid decreases in ﬂuorescence intensity followed by a transient increase and subsequent decay. As
expected, increasing the illumination intensity led to faster photobleaching. However, there werealso unexpected responses suggesting that FPs may exhibit diﬀerent mechanisms of photobleaching
upon wide-ﬁeld vs. laser illumination or at diﬀerent intensities of laser illumination. For example, TagRFP-T undergoes photoactivation with wide-ﬁeld illumination (Figure 2.3a). Yet whenilluminated at 25 kW/cm2 , the same protein undergoes a rapid decrease in ﬂuorescence intensity
followed by multiexponential decay with negligible photoactivation (Figure 2.3c). Figure 2.3c, inset
highlights that at 25 kW/cm2 , photobleaching is characterized by a rapid decrease in ﬂuorescenceintensity during the ﬁrst ﬁve milliseconds of illumination, followed by a slower decay.

Figure 2.3: Continuous photobleaching curves were collected under (a) wideﬁeld illumination conditions (≈100 W/cm2 ), (b) 2.5 kW/cm2
and (c) 25 kW/cm2 532 nm laser illumination. (c) Inset provides closer inspection of ﬁrst 50 ms for the photobleaching decay. Each line
is representative of the photobleaching observed for a single cell.

25

26
The most commonly observed behavior involved rapid decay followed by a slower decrease.
Because ﬂuorescence decay occurred over a wide range of timescales, decay curves were interpolated
and converted to time points equally-spaced over six orders of magnitude in log-time. Figure 2.4ashows a typical FP ﬂuorescence decay curve in log-time. Decay is characterized by three separate
kinetic phases: an initial monoexponential decay (< 800 μs), followed by a steady-state phase (800μs - 5 ms) where ﬂuorescence intensity remains constant, and ﬁnally a gradual biexponential phase(>5 ms).
To gain insight into this complex behavior, photobleaching upon continuous illumination
was compared to photobleaching using a train of 2 ms 25 kW/cm2 pulses separated by 8 ms dark
periods. Hereafter, we refer to this excitation scheme as pulsed. Figure 2.4b shows a characteristic
photobleaching curve using pulsed excitation. The inset of Figure 2.4b demonstrates that theinitial monoexponential decay phase (i.e., < 800 μs) observed during continuous illumination was
replicated in each excitation pulse, and was largely reversible. Accordingly, we hypothesized that
this rapid decay corresponds to conversion to a transient dark state. In this context, ”dark-state”
means a state that is non-ﬂuorescent, less ﬂuorescent, less absorbing, or non-absorbing at the
wavelength used (e.g., the protonated chromophore, or the triplet state).
To quantify the extent of ﬂuorescence recovery, we deﬁned the percent recovery as (FRFB)/(F0-FB), where F0 is the ﬂuorescence intensity immediately after exposure to the excitation
beam, FB is the ﬁnal ﬂuorescence intensity after 2 ms, and FR is the ﬂuorescence intensity after 8
ms of recovery in the dark (Figure 2.4b, inset) [17]. The percent recovery values for each protein
are listed in Table 2.2 and vary from 55 to 100%. Fluorescence recovery appeared to be complete
within 8 ms, as prolonged durations in the dark (up to 10 s) did not lead to statistically signiﬁcant
increases in percent ﬂuorescence recovery (P < 0.05, ANOVA, TagRFP, mOrange2, mCherry).
However, in some cases (e.g., mKate2), the percent recovery changed depending upon the numberof pulse exposures, presumably due to residual dark-state accumulation.
To quantify the photobleaching of diﬀerent FPs and to diﬀerentiate irreversible photobleaching from DSC, the data were ﬁt to a sum of exponentials. The ﬁtted rate-constants enable evalu-

27
Table 2.2: Percent DSC, Percent Recovery, and Irreversible Photobleaching Time-Constants Obtained under Pulsed and Continuous Illumination.

FluorescentProtein
Laser Intensity
TagRFP S158T
TagRFP
TagRFP R67K
S158T
TagRFP R67K
N143S S158T
TagRFP N143S
S158T F174L
TagRFP R67K
N143S S158T
F174L
TagRFP R67K
N143S S158ATagRFP R67K
N143S
TagRFP R67K
N143S S158A
F174L H197R(mKate2)mApplemCherry
mOrangemOrange2
mStrawberry

Irreversible
Percent
DSC TimePhotobleachPercent
RecovConstant
ing
DSC
ery
(μs)
Time-Constant(ms)
25 kW/cm2
59±11
25±5
12.0±0.5
29±4 (n=3)(n=3) (n=3)
(n=3)
83±5
69±7
10.8±0.473±17(n=3) (n=3)
(n=3)
(n=3)
72±11
29±2
80.9±0.948±4 (n=3)(n=3) (n=3)
(n=3)
82±11
45±3
40.0±1.0
118±22(n=3) (n=3)
(n=3)
(n=3)
83±4
68±3
14.8±0.9173±3(n=3) (n=3)
(n=3)
(n=3)
77±4
(n=3)

46±3
(n=3)

16.0±2.0
(n=3)

85±1
(n=3)
85±3
(n=2)

50±2
(n=3)
34±4
(n=2)

34.0±3.8
(n=3)

102±2
(n=3)

79±1
(n=3)

55±5
(n=3)
88±5
(n=3)
90±1
(n=3)
80±5
(n=3)
88±4
(n=3)

77±3
(n=3)
18±3
(n=3)
70±10
(n=3)
74±2
(n=3)
30±1
(n=3)

50±13 (n=3)

99±3 (n=3)
132±11
(n=3)
101±8
(n=3)

Pulsed Photobleaching
TimeConstant(ms)
74±7 (n=3)
113±12(n=2)
470±150(n=2)
82±17 (n=2)

Irreversible
Photobleaching
Time-Constant(ms)
2.5 kW/cm2
550±100(n=3)
385±80 (n=3)
1700±110(n=3)
936±160(n=2)

98±9 (n=2)

225±4 (n=2)

81±9 (n=2)

801±27 (n=3)

98±31 (n=2)
253±45(n=2)

654±192(n=X)
1167±150
(n=2)

N.D.

187±29(n=3)

236±72
(n=3)

N.D.

N.D.

N.D.

N.D.

N.D.

57.0±4.6(n=3)
1.27±2.0(n=3)
0.96±0.2(n=3)
9.68±0.4
(n=3)

73±29
(n=3)
147±89(n=3)
180±14
(n=3)
79±34
(n=3)

744±35(n=2)

3457±670(n=3)
229±170(n=2)

183±9 (n=2)
212±13(n=2)
296±25(n=3)

262±74 (n=2)
719±400(n=3)

Reported error is the standard deviation, and n represents the number of measurements
performed for each FP. DSC was measured at 25 kW/cm2 and was determined by ﬁtting theinitial ﬂuorescence decay to an exponential decay with a y-oﬀset. Irreversible photobleaching wasdetermined after ﬁtting the entire decay to a triexponential ﬁt, and is reported as the weighted
average of the two slow components. Pulsed photobleaching was found by locating the peak
ﬂuorescence intensity of each excitation pulse and ﬁtting these to an exponential decay. N.D. =Not Determined. Rapid decrease and subsequent transient increase in ﬂuorescence intensity
prohibited accurate ﬁtting by a sum of three exponentials.

Figure 2.4: (a) Photobleaching kinetics and respective triexponential ﬁt (red) in log-time. Initial decay representative of DSC (i.e., < 1ms), steady-state plateau (i.e., ≈1 ms), and irreversible photobleaching phase (i.e., > 5 ms) (a) inset, photobleaching kinetics plotted inlinear-time. (b) Pulsed photobleaching kinetics, shown as a red line, is deﬁned as the exponential decay of the peak ﬂuorescence intensityof each pulse under pulsed excitation. (b) inset, percent recovery after 2 ms excitation and 8 ms in the dark. Percent ﬂuorescence recovery
is deﬁned as (FR-FB)/(FL-FB), where FL and FR are deﬁned as the ﬂuorescence intensity of the initial and second pulse, respectively;
and FB is the ﬁnal ﬂuorescence intensity after 2 ms of excitation.

28

29
ation of the time constants (deﬁned as the reciprocal of the rate constant) for the diﬀerent phasesof ﬂuorescence decay. At 25 kW/cm2 , continuous photobleaching data were ﬁt to a sum of three
exponential decays, allowing the kinetics of the fast and the weighted average of the slow biexponential phase to be independently determined. A representative ﬁt is shown in Figure 2.4a. Because
our pulsed excitation suggested the initial fast decay was largely reversible, this phase is referred
to as DSC. To evaluate the eﬀect of the cellular environment on the observed photophysics, wemeasured the kinetics and percent DSC for a representative FP (mCherry) in vitro. Interestingly,
relative to our in vivo data, DSC increased 7% in vitro, presumably due to environmental factors.
Furthermore, we compared the kinetics and percent DSC with published results from ﬂuorescencecorrelation spectroscopy (FCS) on the same protein [37], and further discrepancies were attributed
to diﬃculties in diﬀerentiating reversible and irreversible ﬂuorescence ﬂuctuations in FCS (For adetailed discussion, see mCherry Analysis, below). Conversely, the second slower phase appeared
to be irreversible and hence is referred to as irreversible photobleaching. Table 2.2 summarizes
parameters obtained from the ﬁts of 14 diﬀerent proteins including the amplitude of DSC (deﬁned
as % of the total decay attributable to DSC), as well as the time constants for DSC and irreversible
photobleaching for FPs exposed to 25 kW/cm2 . Besides a small decrease in ﬂuorescence intensity(≈1-4%) at 10 ms, no convincing evidence of triplet state dynamics was observed. At 2.5 kW/cm2 ,the steady-state plateau was less pronounced, and thus the more gradual part of the photobleaching
was ﬁt to a biexponential decay (weighted time constant presented in Table 2.2). Under wide-ﬁeld
conditions, the three phases were not broadly identiﬁable and consequently, these data were not ﬁtusing this approach.
To quantitatively assess whether photobleaching occurs out of transient dark-states, photobleaching assays were also performed under pulsed excitation conditions. For each FP, the time
constant of irreversible photobleaching was determined for pulsed excitation conditions by locating
the maximum ﬂuorescence intensity (i.e., FR) for each excitation pulse and ﬁtting the decrease inpeak ﬂuorescence intensity to a monoexponential decay (i.e., red curve, Figure 2.4b, and inset).
Hereafter, the results from this kinetic analysis are referred to as the pulsed irreversible photobleach-30
ing time-constant, and the values are presented in Table 2.2. For all FPs studied, photobleachingunder pulsed excitation is slower than photobleaching under continuous illumination. The extreme
cases are mCherry, which exhibits a 13-fold gain in the photobleaching time-constant under pulsed
illumination, and TagRFP R67K N143S S158T which only undergoes a 2-fold gain. This result
suggests that for some FPs, irreversible photobleaching from dark-states is minimized by pulsed
excitation, when the pulse separation is suﬃcient for these states to depopulate between excitation
pulses.

2.5.3

mCherry Analysis

Using the rapid-photobleaching methodology discussed in the above, we measured 18% DSC
for mCherry in vivo using 532 nm CW illumination at 25 kW/cm2 . This is in contrast to measurements performed by Hendrix et al., where they found a 46% DSC for mCherry in vitro, using
543 nm CW illumination at 24 kW/cm2 [37]. Prompted by the apparent discrepancy between ourmCherry data and that by Hendrix et al., we performed rapid photobleaching and DSC assays
on puriﬁed mCherry in vitro. To facilitate comparison, we prepared fresh protein under identical buﬀer conditions as Hendrix et al. (Phosphate Buﬀered Saline, pH=7.4, 50:50 v/v glycerol),and localized freely diﬀusing protein within aqueous microdroplets inside of an insoluble octanolorganic phase [70, 71]. To test if glycerol changed the observed DSC, samples were prepared withand without glycerol. Importantly, no statistically signiﬁcant diﬀerence (two-tailed t-test, P=0.83)
was observed between samples that contained glycerol (25.4% DSC, n=11) and those that did not(25.1%, n=10). Interestingly, the percent DSC did change from 18% (n=3) in vivo to 25% (n=21)
in vitro (two-tailed t-test, P=0.0003), perhaps owing to changes the local environment.
Nevertheless, the 7% increase in DSC upon going from an in vivo to an in vitro environment
could not entirely describe the observed percent DSC reported by Hendrix et al. Furthermore, thechange in excitation intensity and excitation wavelength seems unlikely to account for the remainderof the discrepancy. Consequently, we attribute the remainder of this to artifacts arising from FCS
analysis [72–74]. More speciﬁcally, FCS does not diﬀerentiate between ﬂuorescence ﬂuctuations

31
due to reversible or irreversible processes, unless they occur on largely disparate timescales.
To test this hypothesis quantitatively, we calculated the expected irreversible photobleachingthat could occur during the transit time through the excitation beam in FCS. For example, Hendrix
et al., report that an apparent diﬀusion coeﬃcient of 56 μm2 s−1 , which according to the EinsteinStokes relation (Equation 2.10), predicts a hydrodynamic radius of 3.89 nm for mCherry. HereKb is the Boltzmann constant, T is the temperature, η is the solvent viscosity (1.002 mPa s forwater at 20◦ C, and 8.4 mPa s for 50:50 v/v water glycerol mixture at 20◦ C), D is the diﬀusion
coeﬃcient, and r is the hydrodynamic radius.

r=

Kb T
6πηD

(2.1)

Assuming diﬀraction limited excitation and collection volume for FCS, the lateral (Equation
2.11) and axial dimensions (Equation 2.12) of the excitation volume at full width and half maximum(FWHM) are approximately 200 and 500 nm, respectively [11]. Here, λ is the wavelength of light,
n is the refractive index of the immersion liquid (water = 1.33), and α is the angle for which theobjective can collect light (66◦ for a 1.2 numerical aperture objective)

dlateral =daxial =λ2n sin(α)λ
n sin2 (α)

(2.2)
(2.3)

Under these conditions, the transit time for a single FP through the excitation volume canbe estimated using Brownian diﬀusion (Equation 2.13).

t=

x2
2D

(2.4)

Using the aforementioned hydrodynamic radius of mCherry (3.89 nm), and the viscosity of a
50:50 v/v glycerol water solution (8.4 mPa s), this equation dictates that it takes, on average, 3.7
and 19.2 ms to travel through the excitation volume in the lateral and axial directions, respectively.

32
Using our measured irreversible photobleaching time-constant for mCherry of 57 ms, we predictthat 6.3% of the molecules traversing the beam in the lateral direction will undergo irreversible
photobleaching, and 28.6% for those traversing the axial direction. This observed photobleachingis even more problematic for FPs with accelerated photobleaching, including mStrawberry (9.77%
and 41.34%), and mOrange (97.5% and 100%).
Indeed, this is consistent with our observation that mCherry undergoes irreversible photobleaching within the ﬁrst 2 ms of pulsed excitation (See Table 2.2, and Figure 2.4b). Combining
these values with the observed increase in DSC upon going from in vivo to in vitro conditions (18to 25%), in addition to subtle excitation wavelength eﬀects, we infer that the remaining 20% of
DSC observed by Hendrix et al. results from irreversible photobleaching. Lastly, this is also consistent with the observation that Hendrix et al. observed relaxation longer time-constants (98.6 ms)
than ours (73 ms) as this likely represents a weighted average of the time-constants for irreversible
photobleaching dark-state relaxation.

2.5.4

Comparison of Irreversible Photobleaching in FP Variants

Measurement of photobleaching in a panel of FPs under diﬀerent illumination conditions
allowed us to identify general trends and hence common themes in ﬂuorescence decay. Figure 2.5acompares the irreversible photobleaching time-constant under continuous illumination at 2.5 and
25 kW/cm2 . Overall, time constants at 2.5 kW/cm2 were signiﬁcantly greater than at 25 kW/cm2 ,indicating a slower rate of ﬂuorescence decay at lower intensity illumination. In general, the time
constants were correlated so that FPs that were more susceptible to photobleaching at 2.5 kW/cm2
were also more susceptible at 25 kW/cm2 (see mOrange and mOrange2). However, some FPs
showed heightened sensitivity to increases in excitation intensity. For example, mCherry was 30
percent less photostable than TagRFP R67K S158T at 25 kW/cm2 but 2-fold more photostable
at 2.5 kW/cm2 . This observation points to a need for understanding photostability in termsof photexcitation rates relative to timescales of excited-state population transfer. Figure 2.5bcompares the photobleaching time constant for pulsed vs. continuous illumination at 25 kW/cm2 .

33Here, the two parameters are poorly correlated amongst the proteins tested, suggesting that some
proteins undergo gains in photostability when subjected to pulsed excitation whereas others do not.
For example, although mCherry is less photostable than TagRFP R67K S158T under continuous
illumination at 25 kW/cm2 , it becomes more photostable when subjected to pulsed excitation at
the same intensity.

2.5.5

Comparison of Dark-State Conversion in FP variants

Figure 2.6a presents a comparison of the percent DSC (i.e., the fast reversible phase ofphotobleaching under continuous illumination) vs. irreversible photobleaching time constant (i.e.,the slow and irreversible phase under continuous illumination) at 25 kW/cm2 . For these FPs,
as the percent DSC increases the photobleaching time constant decreases (correlation provided
with dashed line), suggesting that as DSC increases, the propensity to photobleach also tends
to increase (as observed for mOrange, mOrange2, TagRFP, etc.). However there are signiﬁcant
exceptions. For example, a single mutation in TagRFP, S158T, solely aﬀects DSC (Figure 2.6a,
Arrow 1), whereas incorporation of R67K into TagRFP S158T, exclusively modulates irreversible
photobleaching (Figure 2.6a, Arrow 2). Conversely, some mutations simultaneously modulate both
DSC and irreversible photobleaching rates (Figure 2.6a, Arrow 3).
To examine the eﬀect of excited state lifetime on DSC, the ﬂuorescence lifetimes of puriﬁedproteins (Table 2.1) were compared to the observed DSC kinetics for FPs in cells. Figure 2.6b reveals
a correlation between increasing ﬂuorescence lifetime and increased percent DSC. At 25 kW/cm2 ,depending upon the absorption cross-section, on average one photon is absorbed every 40-80 ns(see Materials and Methods), suggesting that transient absorption out of the ﬁrst excited singlet
state is unlikely. Furthermore, the radiative rate, as estimated from the ﬂuorescence quantum yieldand lifetime (φf l = krad × τf l ), was found to be independent of the mutational context. These
results suggest that DSC is competitive with radiative decay from the ﬁrst excited singlet state.
Consequently, FPs exhibiting the longest ﬂuorescence lifetimes, and hence those with the greatest
percent DSC and propensity to undergo irreversible photobleaching, also tended to have the largest

34

Figure 2.5: (a) Correlation between irreversible photobleaching time constants at 2.5 kW/cm2 and25 kW/cm2 laser illumination shows that diﬀerent proteins have diﬀerent sensitivities to heightened
excitation rates, and the rank-order of photostability changes with intensity. Dashed-line shows
anticipated correlation for a simple 3-state system (e.g., ground, excited, and bleached) where 10fold increases in excitation intensity result in 10-fold decrease in the photobleaching time-constant.
(b) Comparison of the irreversible photobleaching time constant obtained for continuous vs pulsed
illumination. Under pulsed illumination, FPs with photoreactive dark-states become more photostable, whereas FPs with photoprotective dark-states do not.

35
quantum yields (see Relationship between quantum yield and DSC in Materials and Methods fordetailed discussion).
As mentioned previously, both the amplitude and kinetics of DSC varied substantially fordiﬀerent FP variants. Figure 2.6c compares the time constant versus percent DSC for diﬀerent
FPs. Interestingly, this comparison revealed that RFPs with a slower rate of DSC, hence thosethat reach the steady-state phase of photobleaching more slowly, had a greater percent DSC. Thisobservation will be explained by the kinetic modeling (below).

2.5.6

Kinetic Modeling

Careful examination of the RFP photobleaching behavior at 25 kW/cm2 revealed clear trends
between the percent DSC, the measured rate of DSC, the ﬂuorescence lifetime, and the measured
rate of irreversible photobleaching. Given these observations, we sought to expand upon existingmodels for DSC, to see if we could quantitatively replicate the trends observed, and if so, gain
additional insight into the mechanisms of DSC and irreversible photobleaching. Previously, Dickson
et al. proposed a four-state model consisting of two anionic and two neutral chromophore states
to describe the blinking of yellow-emitting GFP variants at the single-molecule level [31]. Here, we
perform numerical simulations on an analogous 4-state system consisting of two bright-states (S0and S1 ) and two dark (or less-ﬂuorescent) states (D0 and D1 ). In the context of RFPs, dark-states
likely represent a mixture of neutral (non-absorbing at 532 nm) and/or isomerized (absorbing at
532 nm) chromophore states. A schematic of this model is presented in Figure 2.7a. The rates
input into the kinetic simulations are referred to as microscopic, the rates measured by ﬁtting theresults from the numerical simulations are referred to as the simulated time-constant. In contrast,
kinetic analysis from experimental data is referred to as measured.
All simulations discussed here were performed with a commercially available kinetic analysis
software package. Simulations involving continuous laser illumination were modeled using RungeKutta analysis [63]. All numerical simulations were treated with mass-action kinetics, and can besummarized with the following coupled diﬀerential equations:36

Figure 2.6: (a) Single mutations perturb the percent DSC (arrow 1), irreversible photobleachingtime-constant (arrow 2), or both parameters simultaneously (arrow 3). A weak correlation (dashedline) suggests that increases in the percent DSC are accompanied by decreased photostability. (b)Comparison of the percent DSC versus ﬂuorescence lifetime suggests that DSC is competitive withemission from the ﬁrst excited singlet state. (c) Percent DSC versus the time-constant of DSC.
Linear correlation reveals that the percent DSC increases in proteins with slower rates, or larger
time-constants, of DSC.

37

d[S0 ]
dt
d[S1 ]
dt
d[D1 ]
dt
d[D1 ]
dt= −kS0ex [S0 ] + kem [S1 ] + kdsr [D0 ](2.5)= kS0ex [S0 ] − kS1B [S1 ] − kem [S1 ] − kS1 D1 [S1 ] + kD1 S1 [D1 ](2.6)= kS1 D1 [S1 ] − kD1 S1 [D1 ] − kD1B [D1 ] − kic [D1 ] + kD0ex [D0 ](2.7)= −kdsr [D0 ] + kic [D1] − kD0ex(2.8)

All calculations for the excitation rate (kex ) were obtained from the Beer-Lambert law. Here,light intensity, I, decreases in magnitude with respect to distance x as it passes through a solution
containing n molecules per cm3 with an absorption cross-section in cm2 [51].

dI= −Iσndx(2.9)

Using the conditions I=I0 , and x=0 cm, integration results in Equation 2.6, the Beer-Lambertlaw, where D is the path length for light absorption in centimeters:

ln

I0
I


= σnD

(2.10)

Equation 2.7 is another form of the Beer-Lambert law where  is the decadic molar extinction
coeﬃcient (M−1 cm−1 ), C is the molar (mol/L) concentration, and D is the path length for lightabsorption.

log

I0
I


= CD

(2.11)

Combining Equation 2.6 with Equation 2.7 allows one to relate the experimentally measured
decadic molar extinction coeﬃcient to the absorption cross-section for a single-molecule in terms of
cm2 , where the factor of 2.303 results from the log/natural log conversion, and the factor of 1,000
accounts for the conversion between milliliters (cm3 ) and liters.

σ = 2.303 × 1000

N

(2.12)

38
Multiplication of the absorption cross-section in cm2 with the laser intensity in W/cm2 gives
the total joules per second absorbed, which is converted into photons absorbed per second (kex )using the relationship between Plancks constant (h = 6.626068 × 10−34 Js) and the speed (c) andwavelength (λ) of light.

σIλ
kex =
= 2.303 × 1000
hc



Iλ
N hc


(2.13)For the FPs studied here ( ≈ 100,000 M−1 cm−1 ), this gives a maximum excitation rateof 2.5 × 107 s−1 , or on average 1 photon every 40 nanoseconds. Due to oﬀ-peak excitation, theactual excitation rates are likely to be ≈ 4-fold slower. Rates for emission (kem ), conversion from
S1 to D1 (kS1 D1 ), conversion from D1 to S1 (kD1 S1 ), D1 internal conversion (kic ), and ground-state
recovery (kdsr ) were, 2.5×108 - 1×1010 s−1 , 5×105 s−1 , 0 - 5×105 s−1 , 1×109 - 1×1012 s−1 , and 1×10
- 1×106 s−1 , respectively. Photobleaching out of S1 (kS1B ), and D1 (kD1B ), were varied between
10 and 108 s−1 . Pulsed illumination was analyzed using a suite of nonlinear and diﬀerential andalgebraic solvers (SUNDIALS) [64]. Here, the rate of excitation was provided with a Fourier series,(3 × 107 + 2.5 × 107 × (4/π) × Σ(1/n) × sin([n × π × t]/L), where t is time, L is the duration of thepulse in seconds and n is number of odd integer harmonics. To compare the eﬀects of pulsed versus
continuous illumination, numeric modeling was performed in the presence (two-way) or absence(one-way) of dark-state excitation. For two-way simulations, kD0ex was arbitrarily set to equal
kS0ex , as was kS1 D1 and kD1 S1 . For one-way simulations, kD0ex and kD1 S1 were set equal to zero.
In this four-state model, absorption of a photon (i.e. electronic transition from S0 to S1 ) isfollowed by depopulation through emission of a photon (kem ), non-radiative internal conversion, ora low quantum-eﬃciency (φdsc =1×10−3 , kS1 D1 ≈ 5×105 s−1 ) [42] conversion to the weakly or nonradiative D1 state. D1 can decay to D0 (kic ), which can subsequently be converted back to S0 (kdsr ).
In accordance with Dickson et al., recovery from D0 to S0 was assumed to be rate limiting (26).
As shown in Figure 2.7b, variation of the microscopic time constant for dark state recovery, τdsr
from 1-500 μs altered the populations of the S0 ground state and D0 dark state (i.e. the percent

39

Figure 2.7: (a) Four state model includes the rate of excitation (kS0ex ), ﬂuorescence emission(kem ), conversion from S1 to D1 (kS1 D1 ), internal conversion from D1 to D0 (kic ), and darkstate recovery (kdsr ). Photobleaching was incorporated out of both S1 (kS1B ) and D1 (kD1B ).
In some FPs, dark-state excitation (kD0ex ) is included, and D1 may be weakly ﬂuorescent. (b)The inﬂuence of dark-state recovery kinetics on S0 depletion at 25 kW/cm2 . As the microscopic
time-constant for dark-state recovery (τdsr ) increases, the S0 state is depleted and a signiﬁcant
increase in the D0 population is observed. (c) Microscopic time-constant of dark-state recovery vs
the time-constant obtained by ﬁtting the simulated DSC. At fast time-scales, the measured timeconstant of DSC accurately reﬂects the time-constant of ground-state recovery, with increasing
deviations observed for FPs with particularly slow DSC kinetics (long time-constants). (d) Percent
DSC vs time-constant of DSC. Kinetics of DSC were determined by ﬁtting the results from thenumerical simulation. The model predicts that the percent DSC linearly increases with the timeconstant of DSC. (e) Numerical modeling of photobleaching out of S1 and D1 . Kinetic modeling
was performed in the presence (Two-Way) and absence (One-Way) of dark-state excitation for both
continuous (CW) and pulsed illumination. The x-axis represents the microscopic photobleachingrate for S1 or D1 , and the y-axis represents the photobleaching kinetics obtained by ﬁtting thenumerical simulations. For example, CW 1-Way D1 Bleach represents a continuously illuminatednumerical simulation where the dark-state does not absorb (i.e., 1-way), and the D1 bleachingrate is iteratively adjusted while the S1 bleaching rate is held constant. Modeling demonstrates
that when D0 does not absorb, the observed photobleaching rate is independent of bleaching out
of D1 and linearly correlated with bleaching out of S1 . Under two-way conditions, D1 bleaching
becomes signiﬁcant for continuous illumination, but is minimized upon stroboscopic illumination.
(f) Four-state model explains complex photophysical behavior observed for mKate2 and mApple
under continuous photobleaching at 25 kW/cm2 . In cases where D1 may be weakly ﬂuorescent, due
to changes in the ﬂuorescence quantum yield of excitation rate, the rapid decrease and subsequenttransient increase in ﬂuorescence intensity represents population transfer from a bright-state to adim-state prior to the onset of irreversible photobleaching.

40DSC). This result suggested that the measured variation in the percent DSC (from 18 to 87%,
Table 2.2) reﬂects changes in the kinetics of recovery from the dark state (τdsr ). To test whether
the DSC time constant obtained by ﬁtting the fast phase of ﬂuorescence decay reﬂects the rateof ground state recovery (i.e. transition from D0 to S0 ), we varied the microscopic time constantfor dark state recovery (τdsr ) and determined the corresponding time constant for DSC from thenumerical simulations. At fast time scales (i.e., < 100 μs), the time constant of DSC is correlatedwith the microscopic dark-state recovery kinetics (Figure 2.7c); while at longer time-scales (i.e., >100 μs) the parameters are uncorrelated. For some of the FPs (7 out of 13), the measured DSC
time constant is within the linear range of the simulated parameters, suggesting that for these FPs,
the observed ﬂuorescence decay directly reﬂects the kinetics of ground state recovery.
The four-state model also explains the correlation between the fastest timescale of ﬂuorescencedecay and the magnitude of DSC. Here, FPs with slower rates of ground state recovery will havean increased population build-up in D0 . In agreement with our experimental results, simulations
predict that the percent DSC is linearly proportional to the DSC time constant (Figure 2.7d).
Additionally, in agreement with our experimental data (Figure 2.5b), modeling conﬁrmed that
DSC increases linearly with the lifetime of S1 (τf l = 0.5 - 4.0 ns) with small changes (≈ 2-fold) inthe simulated kinetics of DSC (results not shown).
Kinetic pathways for photodegradation out of both S1 and D1 (Figure 2.7a, kS1B and kD1B ,respectively) were incorporated into the four-state model. Simulations of both continuous andpulsed illumination experiments were performed, as a function of both microscopic photobleachingrates (Figure 2.7e). In cases where excitation from D0 to D1 does not occur (One-Way in Figure2.7e), the microscopic rate kS1B was found to correlate linearly with the observed photobleachingrate for both pulsed and continuous illumination. In this case, photobleaching from the dark-state
was negligible, and required kD1B rates at 1000-fold greater than kS1B to signiﬁcantly alter theobserved photobleaching kinetics. This result indicates that non-absorbing or weakly absorbing
dark-states are photoprotective. Interestingly, in the case where the excitation from D0 to D1
does occur, (i.e., Two-Way in Figure 2.7e) large diﬀerences were observed in the photobleaching41
rate between continuous and pulsed illumination. For continuous illumination, the photobleachingrate was no longer linearly correlated with changes in kS1B , and changes in the observed photostability became signiﬁcant for kD1B at rates comparable to kS1B (i.e., > 103 s−1 ). Pulsed
illumination however, minimizes the contribution of kD1B to the observed photobleaching kinetics.
These simulations suggest that comparison of photobleaching under pulsed and continuous illumination provides insight into whether the dark-state is photoprotective (bleaching does not occur
from D1 ) or photoreactive (bleaching does occur from D1 ). For example, FPs with photoprotective
dark-states, i.e., TagRFP R67K S158T, likely do not absorb at the given excitation wavelength(i.e. transition from D0 to D1 is insigniﬁcant).

2.6

Discussion
Rapid irreversible photobleaching and DSC remain major obstacles in the utilization of FPs

for single molecule applications, low-copy gene expression, and particle tracking in vivo. A better
understanding of DSC, and new methods for measuring it, permit a detailed characterization of
diﬀerent FPs and may provide insight into which FP is most suited for a particular application. For
example, our results (Figure 2.3a), as well as those of Shaner et al. [17], suggest that TagRFP-T may
be the most photostable FP for low excitation intensity imaging (e.g., wide-ﬁeld arc-lamp, totalinternal reﬂection ﬂuorescence (TIRF), and live-cell laser scanning confocal microscopy). However,in single-molecule applications (single particle tracking, FCS, etc.), where excitation intensities
exceed 1 kW/cm2 , mCherry and TagRFP R67K S158T appear substantially better than TagRFP-T(Figure 2.3b and 2.3c). These observations, and those on wavelength dependence (see reference [17]and Materials and Methods), illustrate that extensive data on excitation intensity and wavelength
dependence data on each FP are necessary to select the optimal FP for a particular imaging
application.
Numerical simulations demonstrate that a simple four-state model can explain how the magnitude of DSC varies with the kinetics of DSC and ﬂuorescence lifetime, and how DSC contributes
to irreversible photobleaching. The modeling also suggests that the measured kinetics of the initial42
decrease in ﬂuorescence (i.e., kinetics of DSC) reﬂects the rate of dark-state recovery (τdsr ), thatthe percent DSC is sensitive to changes in τdsr , and that comparison of CW and pulsed excitation
measurements provides insight into coupling of the dark-state to photodegradation. In addition to
replicating general trends, the four-state model can also explain complex behavior of select FPs suchas mKate2 and mApple. These FPs undergo a rapid decrease in ﬂuorescence intensity followed bya transient rise in ﬂuorescence intensity prior to irreversible photobleaching (Figure 2.3b and 2.3c).
This behavior can be explained by considering population transfer from the initially excited brightstate to a less ﬂuorescent state (i.e., decreased quantum yield φD1F l and/or absorption rate kD0ex ,)rather than a strictly dark state. Consequently, a transient rise in ﬂuorescence intensity represents
population build-up in the less-ﬂuorescent state prior to the onset of irreversible photobleaching(Figure 2.7f).
Kinetic analysis of photobleaching 25.0 kW/cm2 revealed a clear trend in mutations that act
synergistically or antagonistically to impact irreversible photobleaching. For example, introductionof R67K into TagRFP S158T resulted in a 6.4-fold increase in the irreversible photobleaching timeconstant without signiﬁcantly altering the percent DSC (Table 2.2). Likewise, single mutations in
the context of TagRFP R67K S158T, including N143S, T158S, or T158A, all exhibited excellent
photostability, although to a lesser extent than TagRFP R67K S158T alone. Interestingly, inall tested cases, the presence of F174L decreased the photostability (Table 2.2), rendering the FP
similar or worse in performance to the parent TagRFP S158T. These results suggest that mutations
act in concert with regard to irreversible photostability, and that signiﬁcant gains in photostability
may be possible within the correct mutational context.
The kinetic simulations suggest that the rate of D0 excitation dictates the extent to which
D1 is populated and hence whether D1 is a signiﬁcant precursor along a photodegradative pathway.
For example, at 25 kW/cm2 mCherry is markedly more photostable under pulsed vs. continuous
illumination, suggesting that mCherrys dark-state is photoreactive. Alternatively, TagRFP R67K
S158T does not undergo as large of a gain in photostability under pulsed conditions, suggesting thatits dark-state is more photoprotective than photoreactive. For TagRFP R67K S158T, our numerical43
modeling simulations suggest that this behavior could potentially result from a decreased darkstate excitation rate (e.g., temporary alterations in the chemical structure of the chromophore, or
protonation of the p-hydroxybenzylidene moiety upon a change in the local chemical environment).
The variability of photobleaching out of dark-states also provides some explanation of the contrasting behavior of mCherry and TagRFP R67K S158T at 25 kW/cm2 compared to 2.5 kW/cm2 .
At moderate intensities (tens of kW/cm2 ), DSC rates scale linearly with excitation intensity [32]and consequently, FPs with more photoreactive dark-states will show heightened sensitivity to increases in excitation intensity relative to those with less reactive dark-states. Our experimental
results and kinetic simulations also provide some context for published results that demonstrate
the excitation wavelength dependence of FP DSC and photostability [17, 42, 43]. This wavelength
dependence may be explained by changes in the excitation rate of D0 relative to S0 , which varies
in accordance with their corresponding excited-state absorption spectra. In cases where dark tobright-state conversion occurs, the ﬁnal steady-state distribution of dark-state will depend on therate of kD0ex . For irreversible photobleaching, the relative rates of kD0ex and kS0ex dictate whether
D1 or S1 is the dominant pathway for photobleaching.
The key reason for the value of our pulsed excitation method on FPs is that DSC and photobleaching occur on widely varying timescales, which are probed by the microsecond time-resolved
ﬂuorescence transients repetitively observed during the millisecond excitation/dark intervals. The
method therefore resolves kinetics over six orders of magnitude in time, in particular extending
measurements beyond the ms timescale. Experiments with a narrower experimental time-window,
such as FCS, do not directly resolve both timescales, and therefore require a rigorous, separate analysis of irreversible photobleaching for the DSC time constants to be accurately determined [75–78].
Another key advantage in the broad time-window of our method is that it can be employed over
the three orders of magnitude in excitation intensities encompassed by many commonly-used imaging techniques, thus potentially providing one set of measurements for quantitative comparisons ofsignal intensities.
In conclusion, examination of several closely-related RFPs with pulsed excitation provides

44
evidence for dark-states of varying reactivity and highlights the role of these states in irreversible
photobleaching. This chapter introduces a spectroscopic method for independently measuring DSC
and irreversible photobleaching at the ensemble level. In comparison with DSC kinetics measured on
single-molecules [32,34,37,41,42], our methodology provides additional insight into slow events suchas irreversible photobleaching, without requiring surface immobilization, and permits measurements
inside living mammalian cells.

Chapter 3
Microﬂuidic Flow Cytometer for Quantifying Photobleaching of FluorescentProteins in Cells

3.1

Abstract
Traditional ﬂow cytometers are capable of rapid cellular assays on the basis of ﬂuorescenceintensity and light scatter. Microﬂuidic ﬂow cytometers have largely followed the same path of technological development as their traditional counterparts; however, the signiﬁcantly smaller transport distance and resulting lower cell speeds in microchannels provides for the opportunity to detect
novel spectroscopic signatures based on multiple, nontemporally coincident excitation beams. Here,we characterize the design and operation of a cytometer with a three-beam, probe/bleach/probe
geometry, employing HeLa suspension cells expressing FPs. The data collection rate exceeds 20
cells/s under a range of beam intensities (5 kW to 179 kW/cm2 ). The measured percent photobleaching (ratio of ﬂuorescence intensities excited by the ﬁrst and third beams: Sbeam3 /Sbeam1 )partially resolves a mixture of four RFPs in mixed samples. Photokinetic simulations are presented
and demonstrate that the percent photobleaching reﬂects a combination of the reversible and irreversible photobleaching kinetics. By introducing a photobleaching optical signature, which complements traditional ﬂuorescence intensity-based detection, this method adds another dimension tomultichannel ﬂuorescence cytometry and provides a means for ﬂow-cytometry-based screening of
directed libraries of FP photobleaching.

46

3.2

Publication Status and Author Contributions
Lubbeck, J.L., Dean, K.M., Ma, H., Palmer, A.E., Jimenez, R. Microﬂuidic Flow Cytome-

ter for Quantifying Photobleaching of Fluorescent Proteins in Cells. Anal. Chem. 2012. May
7;9(5):1425-34.
J.L.L., K.M.D., A.E.P, and R.J. designed the research. J.L.L. and K.M.D. performed research. H.M. performed optical calculations. J.L.L. performed data analysis and photobleaching
numerical simulations. J.L.L., A.E.P., and R.J wrote the paper.

3.3

Introduction
Traditional ﬂow cytometers employ light-scattering and ﬂuorescence-based detection to assess

spectral diversity [79–81], cell size [82], ﬂuorescence brightness [83–85], ﬂuorescence lifetime [86,87],
and analyte concentration [88] on individual cells ﬂowing through one or more tightly focused excitation beams at speeds of ≈1-10 m/s. In this operating regime, the time window for opticalexcitation and detection is approximately a few microseconds per beam and hundreds of microseconds between beams. In contrast, the short transport dimensions and conﬁning properties of
microﬂuidic channels enable highly stable ﬂows at cell speeds of 10−6 to 10−3 m/s. We exploitedthese properties to develop the ability to screen with optical or photophysical properties that are
manifested at longer timescales (tens of milliseconds or slower) by implementing multipoint ﬂuorescence excitation measurements in a microﬂuidic ﬂow cell. We speciﬁcally investigate the probing ofphotobleaching in ﬂow. Although it is likely to be ubiquitous in ﬂow cytometry, few studies haveinvestigated photobleaching in detail, and there are no reports of ﬂuorophore screening or sorting
based on photobleaching. Previous reports by van den Engh et al. and Doornbos et al. focused onunderstanding photobleaching and photon saturation in DNA stains, ﬂuorescein conjugates, phycoerythrin, and allophycocyanin, via pulse shape and power-dependence measurements, primarily
with the goal of optimizing the magnitude of ﬂuorescence signals [89,90]. The excitation conditionsin those studies accessed time windows of 2 μs to 2 ms, at excitation intensities of 5-3200 kW/cm2 .

47
Here, we report the design of a cytometer for assessing photobleaching of genetically encodable
FPs, at excitation intensities comparable to those used for confocal imaging and single molecule
spectroscopy (10-100 kW/cm2 ).
FPs exhibit complex excited-state dynamics, including processes such as DSC and photobleaching which limit their photon output. The magnitudes and time-constants of both DSC andirreversible photobleaching depend on the ﬂuorophore, excitation intensity, and excitation wavelength [17, 33, 49, 50]. Although these factors make the composite photobleaching process tricky
to quantify, there is a clear potential for using it in ﬂow-cytometric screening for the developmentof new FPs. This approach would be signiﬁcantly faster than microplate or colony-based screening. Due to the longer time scale photophysics in FPs compared to small molecule ﬂuorophores,
a cytometry-based screening system will require that a correspondingly longer time-window beaccessible to the measurement.
In Chapter 2, the use of millisecond pulse sequences to dissect the photobleaching process of
FPs in individual HeLa cells was reported [49]. Here, to measure photobleaching of cells in ﬂow, we
implemented a design that quantiﬁes photobleaching on the millisecond time scale, independent of
ﬂuorophore concentration, ﬂuorescence quantum yield or extinction coeﬃcient. We employ threespatially separated beams: a low-intensity probe beam (5 kW/cm2 ) to measure initial ﬂuorescence,
followed by a high-intensity bleach beam (5-179 kW/cm2 ) to initiate photodestruction of the ﬂuorophores, followed by a second low-intensity probe beam, of equal intensity to the ﬁrst, to assess
the extent of photobleaching. This approach simpliﬁes data acquisition compared to direct measurement of a time-resolved ﬂuorescence decay because ratios of peak signal intensities are easier
to ﬁt and deﬁne than the multiexponential decays which characterize the photobleaching process.
Here, we combine microﬂuidics and spectroscopy techniques in a ﬂow cytometer for measuring
the combined eﬀect of reversible and irreversible photobleaching at a rate of >20 cells/s. To ourknowledge, this capability has not previously been reported. We present calculations guiding theoptical design of a multibeam cytometer describing how to optimize measurement precision andalignment tolerance in a simple 2D hydrofocusing geometry. The technique is demonstrated on

48
four diﬀerent RFPs, under a variety of excitation conditions, which uncovered a diverse rangeof reversible and irreversible photobleaching on the millisecond time scale. Lastly, we present
kinetic simulations to examine the eﬀects of reversible and irreversible photobleaching rates on thecapability of our method to discriminate populations.

3.4

Experimental
The cytometer consists of four general components: (1) a microﬂuidic hydrofocusing chipand manifold which holds the sample and drives ﬂuid ﬂow, (2) an inverted microscope which serves
as an optical platform for the cytometer, (3) optics to shape and align the three beams into themicroscope, (4) the data acquisition and processing electronics.

3.4.1

Design Considerations

One of the design goals was to ensure that the distribution of measured ﬂuorescence intensities
for a population accurately reﬂects cellular RFP expression heterogeneity rather than instrument
resolution. Even for a single-beam ﬂuorescence measurement of each cell in ﬂow, the excitation
intensity and therefore the observed emission signal is strongly dependent on the trajectory ofeach cell as it traverses through the 3-D laser focus in the microchannel. Although, in principle,
3-D hydrofocusing geometries would more precisely deﬁne these cellular trajectories, we show that
simpler-to-fabricate 2D hydrofocusing devices can provide suﬃcient precision for properly designed
multibeam excitation/ detection geometries. In 2-D hydrofocusing, cells ﬂowing past a crossjunction with two channels of sheath ﬂow at higher pressures are laterally conﬁned by the ﬂow todimensions signiﬁcantly narrower than the channel width (Figure 3.1a) [91]. Nevertheless, there
will always be lateral and axial variation (relative to the optical axis of the microscope objective:
see Figure 3.1) in trajectories from cell to cell. We quantify the eﬀects of this variation on the
ﬂuorescence signal, along with the eﬀects of slight misalignment of the two probe beams relative
to the ﬂow axis of the microﬂuidic. We minimized the alignment sensitivity by introducing acylindrical lens (f = 150 mm) positioned ≈190 mm before the objective lens, to shape the Gaussian

49
beam into an elliptical proﬁle in the microchannel, in which the focused beam size perpendicular
to the ﬂow direction (y-axis) is much larger than along the ﬂow direction (x-axis).
To quantify the impact of cell transit variation on the ﬂuorescence signal, we ﬁrst calculated
the astigmatic transformation of a Gaussian beam through a cylindrical lens and objective opticalsystem with a number of experimental constraints. We assumed that each cell was 14 μm in
diameter (the mean cell size observed for HeLa-S cells on a wide-ﬁeld microscope), had a uniform
RFP concentration (i.e., an RFP containing sphere) and traveled between two 4 μm diameter(FWHM) laser beams (Figure 3.1a and b). This beam size matched our measurements of the
focused probe beam sizes produced with a 20x, 0.45 N.A. air-objective. The observed ﬂuorescencesignal, S (Equation 3.1), is the convolution of the RFP density, s(x, y, z), and the three-dimensional
Gaussian intensity proﬁle at the laser focus, B(x, y, z):  S(x◦ , y◦ , z◦ ) =x,y,zs(x − x◦ , y − y◦ , z − z◦ ) × B(x, y, z)dxdydz(3.1)

The intensity function of a radially symmetrical Gaussian beam may be written as:
B[x, y, z] =2P
πw2 [z]exp−2(x2 + y 2 )w2 [z]

(3.2)

Where P is the total power of the beam and w[z] is the beam radius where the intensity drops
to 1/e2 of its peak value. The evolution of the Gaussian beam along the propagation direction,deﬁned as the z axis here, is written as follows:
wi [z] = w◦

1+

z2
2
zRi

(3.3)

Where zR = πw◦2 /λ is known as the Rayleigh range, at z = zR the radius of beam is√2times larger than its waist value wc◦ , or the beam area doubles. For a Gaussian beam propagating
through a cascade of optical elements in space, its wave function is modiﬁed by the optical elements(e.g. lenses) it passes through. Due to the unique transform characteristics of the Gaussian beam,
its propagation can be treated analogous to geometric optics following a general ABCD matrix

M

ΔZ = 12.5 μm

a
M 2μ

m

50

X 5 mm s -1

=
ΔY

c

Z-axis position(μm)

Z-axis position(μm)

b

Y-axis position (μm)

Y-axis position (μm)

eZ-axis position(μm)

Z-axis position(μm)

d

Y-axis position (μm)

Y-axis position (μm)

g
Signal 2

Signal Dispersion

f

Cells

Signal 1

Figure 3.1: (a) Schematic of hydrodynamically-focused cells traveling through two circularly shaped
Gaussian laser beams where ΔZ and ΔY refer to the cell axial and lateral displacement from thecenter of the channel. (b-e) Contour plots showing peak ﬂuorescence signal as a function of thecell displacement (ΔZ and ΔY ) as it ﬂows through circularly (b-c) and elliptically (d-e) shaped
Gaussian beams. (b and d) Represent the ﬂuorescence signal if the two beams are perfectly centeredin the channel, whereas (c and e) represent the ﬂuorescence signal if the second beam is displaced
0.5 μm along the y-axis relative to the center of the channel. (f) For the misaligned beams, ascatter plot of the ratio of peak ﬂuorescence signals (I1 /I2 ) from 500 cells randomly displaced fromthe center of the channel (within ΔY = ±2 mm and ΔZ= ±5.5 mm) in signal from ellipticallyshaped beams (red) is 35-fold smaller than that of circularly shaped beams (black).

51
method [92], by deﬁning a complex radius of curvature,

q̃

−1

=R

−1


−i

λ
πw2 [z](3.4)

where Rz is the radius of curvature of the wave at position z.
In a case of astigmatic transformation, for example, the propagation of a Gaussian beamthrough a cylindrical lens, the evolution of the beam in the x and y directions diﬀer and theevolution in each orthogonal direction can be treated independently.
In our experiment, a Gaussian beam from the laser ﬁrst passes through a cylindrical lens(fy =150 mm), then is focused by an objective lens (20x, NA=0.45) onto the sample. The sample
is placed at the x-axis focal point of the objective lens, where the beam has passed the y-axis focal
point and is therefore expanded across the microﬂuidics channel. We ﬁrst deduced the analyticalpropagation equation of the Gaussian beam in this astigmatic optical system, then calculated theemission signal intensity of the ﬂuorescent cells as they traverse through the beams. Here forsimplicity we treated the objective lens as a simple lens with a speciﬁed focal length of 9 mm, as
it is neither practical nor necessary to trace the beam propagation through each one of the dozens
or more individual optical elements in a modern objective lens. In the x direction in which thecylindrical lens does not focus, the beam transfer matrix may be written as:⎡⎤z◦⎢ 1 − fobj

Mx = ⎣

1− fobj

z◦ ⎥
⎦
1

(3.5)

where z0 is the distance between the objective lens and the sample, which is placed at thex-axis waist position, and fobj is the focal length of the objective lens. The transfer matrix in they direction can be calculated in a similar way, except that the cylindrical lens needs to be includedin the transfer matrix.
The deduced analytical form of the Gaussian beam after the objective can be written as:

 
2P
2x2
2y 2
B(x, y, z) =× exp −+
πwx [z]wy [z]wx2 [z] wy2 [z]

(3.6)

52
where P is the total power of the beam, wx [z] and wy [z] are respectively the x-axis and y-axis
beam radius where the intensity drops to 1/e2 of its peak value. The evolution of the Gaussian
beam along the propagation direction, deﬁned as the z-axis here, is:wi [z] = w◦

z2
1+ 2
zRi


(3.7)

2 /λ is known as the Rayleigh range. At
where i denotes x or y-direction, and zRi = 2πw◦i√z = zRi the beam radius is 2 times larger than its waist value w◦ , and the beam area doubles.

The calculated beam radius at the sample, which is positioned at the x-axis waist position,is wx [z]|z=z◦ = 2 μm and wy [z]|z=z◦ = 56 μm, which is consistent with the measured beam radiusof wx = 3 μm and wy = 67 μm. The distribution of peak signal intensities from a cell traversing a
spherically and cylindrically focused beam centered on the hydrodynamic ﬂow are shown in Figure
3.1b and d, respectively. These contour plots reveal how the observed ﬂuorescence signal varies asa function of the cell axial and lateral position with respect to the center of the channel (ΔZ =ΔY=0). Figures 1c and 1e are similar, but correspond to the case in which the beam is displaced
laterally (ΔY=0.5 μm) with respect to the center of the channel. This displacement represents
the precision of experimental alignment. Due to the relatively low NA of the optical system, thesignal intensity is insensitive to cell axial positioning (±5.5 μm is the maximum range for a 14 μm
diameter cell in a channel of 25 μm height) for both spherical and cylindrical focusing. However, inthe lateral direction, a comparison of spherical vs. cylindrical beam shaping reveals very diﬀerent
sensitivity to cell position and beam alignment. In particular, if the beams are misaligned or the cell
drifts by ΔY=0.5 μm, the diﬀerence in the peak ﬂuorescence intensity of the second beam relative
to the ﬁrst beam is signiﬁcantly smaller with the cylindrical focus (Figure 3.1d-e). Consequently,
we consider cells transiting only along the X-axis between the two probe beams at randomly chosen
axial and lateral positions in the range ΔY = ±2 μm and ΔZ= ±5.5 μm. If the two beams are
perfectly aligned, then for both cases, the ratio of ﬂuorescence intensities (beam 1 / beam 2 )= 1. However, for the misaligned geometry, this signal ratio depends on the cell position in the

53channel. Figure 3.1g shows that for cylindrically focused beams, it shows a 35-fold lower dispersion
in the signal intensities compared to the spherically focused beams. This result indicates thatpairs of cylindrically focused beams will lead to substantially smaller variability in ﬂuorescencemeasurements.

3.4.2

Microﬂuidics and Optical Layout

Microﬂuidic devices were built by anodically bonding a 25 μm thick 2 in. diameter siliconwafer to a 1.7 mm thick glass-slide. Silicon was etched down to the glass in the pattern of thechannels using standard photolithography and plasma etching techniques [93]. This method resultsin optically transparent channels of 25 μm height × 150 μm width × 1 mm length for the central
interrogation channel. Sample ports of 1 mm diameter were drilled in a second, identical glass slide
before bonding to the silicon.
The microﬂuidic was compression-ﬁt with O rings against a manifold constructed from polytetraﬂuoroethylene (PTFE; to minimize nonspeciﬁc adsorption of cells) with 200 μL sample reservoirs(Figure 3.2. The microﬂuidic device and combined manifold assembly were mounted onto the stageof a commercial inverted microscope. Flow was driven using three closed-loop air-pressure controllers connected by PTFE tubing to the sample ports. By independently varying the pressureson all three inlets, the hydrodynamic focal width was kept constant at 15 μm as measured byimaging the ﬂuorescence from a dye ﬂowing in only the center cell channel [91]. The cell speed
in the interrogation region was varied from 1 to 15 mm/s to control exposure time to the bleachbeam. The speed was calculated from measurements of cell transit times between probe beams
1 and 2 using ﬂuorescence signals, and measurements of spatial separation of the beams (Figure
3.3; typically 240 ± 3 μm). The cell speed distribution typically had a standard deviation of 1%.
The ﬂow was visualized with a CMOS camera, and wide-ﬁeld transillumination was provided by ahome-built condenser.
The three-beam geometry consisted of two equal intensity probe beams measuring the peak
ﬂuorescence from a cell before and after a higher-intensity photobleaching beam. To implement this

54

20 mm

Outlets

InletsMicrofluidic

Pressure Inlets

Figure 3.2: Microﬂuidic and manifold assembly.

55
experimental geometry, a 2 W 532 nm continuous wave laser was split into three beams by a seriesof beam splitters (30:70 and 50:50) and waveplate-polarizer pairs, thereby allowing independent
control of each beams excitation intensity. After splitting, all beams were shaped by a cylindricallens (150 mm focal length), directed into the microscope, reﬂected from a 532 nm dichroic mirror,
and focused inside of the microﬂuidic channels by a 20×, 0.45 NA air-objective (Figure 3.3a).
Shaping the beams with the cylindrical lens results in elliptical beams (75 μm length and 3.5 μm
width, FWHM, as measured by imaging light scattered from the sample focal plane onto a CMOS
camera) that stretch the entire width of the hydrofocus. The beams were distributed over a 240 μm
distance with the bleach beam located midway between the two probe beams (Figure 3.3b). The
probe beam intensity was 5 kW/cm2 and the bleach beam was 170 kW/cm2 (calculated from the
FWHM of the beam dimensions and laser power measured at the sample plane (±5%)). The probe
beams were matched in intensity before each experiment. Fluorescence was collected through thesame objective and separated from excitation light by the 532 nm dichroic mirror and a 545 nm longpass ﬁlter. The emission was detected by a red-sensitive photomultiplier tube (PMT, Hamamatsu,
R9880U- 20) on the primary imaging port of the microscope. At this port, the ﬂuorescence signals
from the three beams are spatially resolved, which allowed for placement of a mask at the focal
plane which blocks the photobleaching beam. A lens is used to refocus ﬂuorescence from the twoprobe beams onto the PMT.

3.4.3

Data Acquisition and Processing

The PMT photocurrent was processed by a custom-built AC-coupled transimpedance operational ampliﬁer, which improves the signal-to-noise ratio by removing high and low frequency noise
components outside the band-pass of 0.16 - 106 kHz. The resulting voltage levels were digitizedat 250 kHz with a PC-based data acquisition board (16 bit ADC) and custom software (LabView,
National Instruments). After ﬁtting each peak to a Gaussian, the peak ﬂuorescence signals forthe ﬁrst and second probe beams (Sbeam1 and Sbeam3 ) were recorded. Typical ﬂuorophore transit
times through each beam varied from 0.2 to 3.5 ms depending on the cell velocity (1-15 mm/s) and56a

Objective

532 nm Laser

λ/2
P

BS1
λ/2

BS2

P

DM
LPF

CL
PMT

b

b

ng
si el
cu nn
Fo ha
C

B

240 μmCell
Channel

150 μmProbe-1

BleachProbe-2

Figure 3.3: (a) Schematic of the optical setup. Relevant components include: 20× 0.45 N.A. air
immersion objective (Obj.); 532 nm dichroic mirror (DM); 545 nm long-pass ﬁlter (LPF); redenhanced photomultiplier tube (PMT); 150 mm focal length, cylindrical lens (CL, placed 19 cm
from back aperture of objective); half-wave plate (λ/2); Glan-Thompson polarizer (P); 70:30 beam
splitter (BS1); 50:50 beam splitter (BS2). (b) Schematic of the microﬂuidic channel geometry at theinterrogation region. Cells were hydrodynamically focused to a width of 15 μm before encountering
the elliptical bleach and probe beams (FWHM 3 × 75 μm).

57
neglecting ﬂuorophore diﬀusion within the cell.

3.4.4

Sample Preparation

HeLa suspension (HeLa-S, mean diameter = 14.4 μm, coeﬃcient of variation (CV) = 21.8%, asmeasured from images taken on a wideﬁeld microscope) cells were maintained in spinner ﬂasks at 37◦
C in a 5% CO2 atmosphere using spinner-modiﬁed Dulbeccos Modiﬁed Eagles Medium, 10% FBS,
and 1% penicillin-streptomycin. HeLa-S cells were virally transduced according to manufacturers
protocols with an FP either under a constitutive cytomegalovirus promoter (TagRFP, and TagRFPT in pCLNCX) or an inducible tet-responsive promoter (mCherry, mOrange2, pCL-TRE). Brieﬂy,virus was generated by transfecting the appropriate combination of DNA (pCLNCX-FP, pCLTREFP, pCL-Ampho, pCL-TetOn, pVSV-G) into HEK293FT cells. After two days, the viral containing
supernatant was collected, passed through a 0.45 μm cellulose acetate ﬁlter, and added to HeLa
suspension cells with 12 μg/ml polybrene, and if appropriate, expression was induced with 1 μg/ml
doxycycline. Two days after the retroviral transduction of HeLa suspension cells, ﬂuorescenceactivated cell sorting (FACS) with a Dako Cytomation Mo-Flo cell sorter was used to enrich the
population of successfully infected cells (RFP ﬂuorescence positive). Fluorescent cells were enrichedusing a commercial ﬂow-cytometer at a concentration of approximately 1 million cells per mL in
Ca2+ and Mg2+ free HHBSS, pH=7.4. Excitation was performed with a 568 nm krypton laser,
forward scatter was used to trigger acquisition, and the ﬂuorescence emission was separated from theexcitation scatter by use of a 630/30 band-pass ﬁlter. The PMT was set at 450 volts, and forward
scatter, side-scatter, and ﬂuorescence were all operated in logarithmic modes. The ﬂow-cytometer
was maintained at around 2,000 events per second. Suﬃcient optical alignment was conﬁrmed bya narrow (1.3%) coeﬃcient of variation using Beckman Coulter Flow-Check Fluorospheres (partnumber: 6605359, diameter = 10 μm). Uninfected, and thus non-ﬂuorescent, HeLa suspension
cells were used to set the threshold (or gate) and so the sort attempted to select all cells withﬂuorescence greater than cellular autoﬂuorescence. To avoid biasing the ﬂuorescent populations
evaluated in our experiments, FACS was only performed initially for each individual cell-line, andTable 3.1: Fluoresence Coeﬃcient of Variation and Expression Vector for Each Cell Line
Protein Expressed in Cells
TagRFP-T
TagRFP
mCherry
mOrange2

VectorpCLNCXpCLNCX
pCL-Tet-On
pCL-Tet-On

58

Fluorescence CV
131.52%158.26%250.16%145.77%

G418 (working concentration of 100 mg/mL) was applied to select against potential loss of retroviralgene insertion. The ﬂuorescence observed for virally transduced cells varied from cell-line to cellline according to Table 3.1.
Aliquots of cells were concentrated via swinging-bucket centrifugation at 1000 rpm for 5 min.
To prevent clumping and settling within the microﬂuidic reservoirs, cell pellets were resuspended ina density-matched medium using a commercially available density matching solution and HEPESbuﬀered Hanks balanced salt solution (HHBSS), pH 7.4 solution supplemented with 1% bovine
serum albumin. Experiments involving beads utilized 6 μm diameter ﬂuorescently labeled beads
from a Invitrogen LinearFlow Deep Red Flow Cytometry Intensity Calibration Kit, suspended ina density matched 20% (v/v) glycerol in water solution. The microﬂuidic channels were passivatedwith a 1% solution of bovine serum albumin prior to each run. Cell suspensions were loaded into
the center reservoir in 150 μL aliquots at a concentration of ≈5×105 cells/mL. The side channels
were ﬁlled with 150 mL aliquots of HHBSS, pH = 7.4 for the sheath ﬂow.

3.5

Results and Discussion
The measured quantity in all experiments described below is the %bleach, which is deﬁned

in terms of the measured peak ﬂuorescence signal for the ﬁrst and third beams (Sbeam1 and Sbeam3 ,respectively), as:%bleach = 1 −Sbeam3
Sbeam1
×
W ith Bleach Beam

Sbeam3
Sbeam1(3.8)
N o Bleach Beam

To correct for small diﬀerences in beam intensity, and lateral misalignments of probe beams,

59
the %bleach is deﬁned as the function of reference measurements taken in the absence of thebleach beam. Note that %bleach may be composed of a combination of reversible and irreversible
photobleaching, as dictated by the excited-state dynamics of the ﬂuorophores. To shed light on
the molecular origins of the measured %bleach in terms of the rate constants for reversible andirreversible photobleaching, we present and discuss numerical simulations of the signals in terms ofa four-state model of RFP photophysics.

3.5.1

Instrument Validation and Theoretical Considerations

We ﬁrst performed multibeam ﬂuorescence measurements on ﬂuorescently labeled beads toverify the precision of the measurements matched predictions from the design considerations discussed above. Data from one probe beam yields a ﬂuorescence intensity CV ranging from 6 to 16%,depending on the ﬂuorescence intensity of the bead type (higher intensity beads yielded lower CV,
Figure 3.4b). The CV value averaged over all three populations of beads, was within 10% of theaverage value obtained on a Dako Cytomation Mo-Flo FACS (Figure 3.4). This variability is lower
than many other 2D-focusing microﬂuidic cytometers (CV of 25-30%) [94, 95] and comparable to
3D-focusing microﬂuidic cytometers (CV of 1-9%) [96, 97] but remains larger than state-of-the-art
ﬂow cytometers (CV < 3%, BD Biosciences). For a two probe beam measurement with a mixtureof beads, a plot of Sbeam3 vs. Sbeam1 was linear, with a coeﬃcient of determination, R2 =0.99 with
a 7% standard deviation in the ratio of Sbeam1 vs. Sbeam3 for greater than 3,000 events (Figure3.4a). Additionally, our microﬂuidic platform detected all but the most weakly ﬂuorescent beads,
those which have intensities comparable to cellular autoﬂuorescence on a standard ﬂow cytometer.
Furthermore, the ﬂexibility of our microﬂuidic platform permitted accurate measurement of ﬂuorescence intensities spanning three orders of magnitude (0.1-10.0 V). Our signal to noise was limitedby the noise-ﬂoor of our transimpedance ampliﬁer and saturation of our PMT or analog-to-digitalconversion hardware, enabling the user to tune the cytometer for maximum utility within a speciﬁcrange of ﬂuorescence intensities.

60

a

10

S Beam 3 (V)2

R = 0.997

8

Slope = 0.93

6
4
2
0
0

2

4

6

8

10

S Beam 1 (V)

b

800
16%

Counts

600

11%

400

200
6.8%
0

0

2

4

6

8

10

Fluorescence Intensity (V)Figure 3.4: Commercially available intensity calibration beads (Invitrogen LinearFlow Deep Red
Flow Cytometry Intensity Calibration Kit), suspended in a density matched 20% (v/v) glycerol inwater solution) were used to characterize the microﬂuidic cytometer. (a) Using a mixture of beads
with diﬀerent ﬂuorescence intensities, the correlation between the two probe beams was determined
to be linear. The data collected in this run resulted in a slope of 0.93 with a CV of 7%. (b) With asingle excitation beam (10 kW/cm2 ), mixtures of polystyrene beads with three diﬀerent ﬂuorophoredensities were resolved and yielded ﬂuorescence intensity coeﬃcients of variation (CV, listed as %in the above ﬁgure) similar (less than 10% diﬀerent on average) to those measured individually on
a FACS in a linear ﬂuorescence mode (from left to right: 12.1%, 11.3%, and 7.8%).

61
3.5.2

Single-RFP Population PhotobleachingTwo-beam measurements (without a bleach beam) on HeLa-S cells expressing TagRFP-T
were ﬁt to a line with a coeﬃcient of determination of 0.99 and an 11% CV in the ratio of Sbeam3
vs Sbeam1 (Figure 3.5). In principle, for two probe beams of identical intensity, and in the absenceof photobleaching, we observed slopes of slightly less than 1 for both the beads(0.93, CV=7%, 3.4)
and TagRFP-T-expressing cells (0.94, CV=11%) which were statistically the same by an unpairedt test (T=1.42). Under these probe beam conditions, we expect photobleaching of the beads,
and even the less photostable RFPs, to be negligible. For example, using photobleaching kinetics
parameters measured for TagRFP-T at 532 nm in immobilized HeLa cells at similar intensities [49],we estimate 0.4% photobleaching occurs. It seems likely that the nonunity slope occurs primarily
due to a slight mismatch in the probe beam power transmitted through the objective, which weobserve to be highly sensitive to alignment into the microscope. In our deﬁnition of %bleach,
we account for this mismatch in probe beam intensities, to provide for a corrected measure of
the bleaching magnitude. For TagRFP-T cells, with a bleach beam intensity of 170 kW/cm2
and ﬂow speed of 27.9 mm/s (exposure time of 125 μs), the slope decreases to 0.52 (Figure 3.5),
which indicates a signiﬁcant amount of bleaching (%bleach = 51%, from Equation 3.8). The same
performance for all measurements were reproduced with the beams intersecting the hydrofocusedstream anywhere along the 1 mm length from immediately after the hydrofocus to the end of theinterrogation channel.

3.5.3

Resolving Fluorescent Protein Populations

To evaluate the ability to resolve subpopulations on the basis of photobleaching, three-beammeasurements were performed on a mixture of HeLa-S cells expressing TagRFP [16], TagRFPT [17], mOrange-2 [17], and mCherry [15]. Photophysical properties of these FPs are summarizedin Table 3.2. With a bleach beam intensity of 170 kW/cm2 and ﬂow speed of 12.9 mm/s (exposure
time of 270 μs), the beam spacing resulted in an 8 ms average cell transit time between beams.

6210
R = 0.997

8

SBeam3 (V)Non-Bleached

6
Bleached4

2
125 μs Exposure2

170 kW/cm Intensity

0
0

2

4

6

8

10

12

SBeam1 (V)Figure 3.5: HeLa-S cells virally transduced with TagRFP-T interrogated with the two probe beams
indicate that the cytometers response is linear with respect to ﬂuorescence intensity and the signals from each probe beam are highly correlated (CV =11%, R2 =0.997, Non-Bleached Slope =0.94, Bleached Slope = 0.52). Upon addition of the bleach beam, the slope decreases, indicating
photobleaching. Here, each point represents a measurement performed on a single cell.

63This time scale permits complete recovery from higher-ordered excited states and dark states [49](Table 3.3). Under these conditions, four populations of cells were apparent in the plots of Sbeam3
vs Sbeam1 (Figure 3.6a). Each RFP-expressing cell population was identiﬁed by measurements on
the individual cell types under identical ﬂow and intensity conditions. A histogram of %bleach forthe ≈1891 cells in this sample also reveals four subpopulations, corresponding to the four RFPs
(Figure 3.6b). The rank order of average ﬂuorescence intensities for the cell lines measured in the
microﬂuidic cytometer agreed with those measured by FACS (TagRFP-T = TagRFP > mOrange2> mCherry). The diﬀerences in ﬂuorescence brightness for diﬀerent RFP-expressing cell lines may
result from diﬀerences in the relative absorption cross section at 532 nm (Table 3.1) or from differences in cellular RFP concentrations, which in turn may result from incomplete chromophorematuration and diﬀerences in the transcription promoter strength. As stated previously, the cells
assayed in the cytometer were not prescreened or enriched for brightness; therefore, a large range(CV > 130%) of intensities were screened. Tuning the PMT gain to optimize detection of weakly
ﬂuorescent cells would permit improved resolution of the photobleaching response in these cells [51].
In Figure 3.6c, we plot the measured %bleach vs prebleach ﬂuorescence intensity for the cell mixture. These data show a resolution of the mixture into four populations and demonstrate that themeasured %bleach depends on the RFP but is largely independent of the ﬂuorophore concentration(as given by the prebleach emission level). The ability of %bleach to resolve the mixture of four
RFPs may be quantiﬁed by ﬁtting the histogram (Figure 3.6b) to a sum of four Gaussians. Theﬁt parameters (Figure 3.5 caption) reveal that the mean %bleach values for mOrange2, mCherry,
and TagRFP were separated by at least 1σ, whereas the TagRFP-T population was separated fromthe others by at least 2σ. The percentages of cells that could be uniquely assigned to one population with at least 99.9% conﬁdence were obtained by determining the conﬁdence interval of theGaussian ﬁt for a given cell population which has less than 0.1% overlap from the Gaussian ﬁts forthe other cell populations. This conﬁdence interval deﬁnes the percentage of cells in a populationthat can be assigned to a given Gaussian ﬁt with 99.9% conﬁdence. The percentages resolved bythis criterion are as follows: 1% of the mOrange 2 cells were resolved from the mCherry cells, 43%

64
Table 3.2: Reported photostabilities are from Shaner et al. [17]. Experimental details for themeasurement of these photophysical properties can be found in [49].

RFP

Ex/Em
Max (nm)mOrange2
mCherry
TagRFP
TagRFP-T

550/563586/609554/579555/580

Abs. CrossSection @532 nm(cm2 )
1.36×10−16
1.45×10−16
2.55×10−16
2.93×10−16

Quantum
Yield

Chromophore

Reported
Photostability[17] t1/2 (s)0.55 ± 0.04
0.16 ± 0.02
0.48 ± 0.04
0.47 ± 0.08

Tricylic [98]Cis [98]
Trans
Trans

2,900
1,800
550
6,900

of the mCherry cells from the mOrange2 and TagRFP cells, 10% of the TagRFP cells from the
mCherry and TagRFP-T cells, and 100% of the TagRFP-T cells from the others.

3.5.4

Photokinetic Simulations

To understand the connection between microﬂuidic photobleaching measurements and ﬂuorophore photophysics, we performed numerical simulations based on a four state system consistingof the ground state (S0 ), the ﬁrst excited state or bright state (S1 ), and two dark or weakly ﬂuorescent states (D0 and D1 ) (Figure 3.7a). Table 3.4 contains a summary of the reactions andkinetic parameters. Photobleaching was permitted out of S1 and D1 . Recently we used this fourstate model to describe the excited-state dynamics of RFPs in immobilized single cells exposed
to a series of millisecond timescale excitation pulses, and demonstrated that this model faithfully
captured trends for reversible and irreversible photobleaching for a panel of 13 FPs [49]. A similar
model has been used to examine reversible photobleaching (i.e. blinking) of GFP [31]. We modiﬁed
our previous simulations by approximating the excitation proﬁle as a sum of Gaussian pulses that
replicate the durations and excitation rates experienced by the ﬂuorophores as they ﬂow throughthe three beams in the cytometer.
Previous investigations of photodynamics in ﬂow cytometry primarily focused on photobleaching and photon saturation [89, 90], which are the dominant processes operative at the ≈103

65

Table 3.3: Percent recoveries ± the standard deviation for the four RFPs after a 2 ms, 100 kW/cm2
pulse of illumination from a 532 nm CW laser followed by either 8 ms, 1 s, and 10 s in the dark
prior to the next 2 ms pulse. Experimental details are reported in [49].
RFPmOrange2
mCherry
TagRFP
TagRFP-T

Percent
Recovery 8 ms
70 ± 3 (n=3)
73 ± 6 (n=2)
54 ± 1 (n=3)
20 ± 4 (n=3)

Percent
Recovery 1s
75 ± 2 (n=3)
74 ± 7 (n=3)
62 ± 7 (n=3)
32 ± 5 (n=3)

Percent
Recovery 10s
72 ± 2 (n=2)
81 ± 6 (n=2)
63 ± 7 (n=2)
26 ± 5 (n=2)

ANOVA
P-Value
0.12
0.49
0.14
0.059

Table 3.4: Summary of Reactions and Equations Used in Photokinetic Model
Process
Emission and
Non-Radiative Decay
Reversible
Photobleaching
Dark-State Decay
Ground-State
RecoveryS1 Bleach
D1 Bleach
Excitation

Reaction

Reaction Rate

Rate Constant Value (s−1 )S1 →S0[S1 ] kEm

kEm = 1×109

S1 →D1[S1 ] kRev

kRev = Varied = 0 - 1×106

D1 →D0[D1 ] kDSD

kDSD = 1×109

D0 →S0[D0 ] kGSR

kGSR = 1×104

S1 →Null
D1 →Null
S0 →S1[S1 ]kS1Bleach[D1 ] kD1Bleach[S0 ] kEx

kS1Bleach = Varied = 0 - 5×106
kD1Bleach = Varied = 0 - 5×1010
kEx = Gaussian

66

a

5
mCherry4

SBeam3 (V)mOrange2

TagRFP

3
2
1

TagRFP-T
0

0

2

4
6
SBeam1 (V)

b 250

8

10

TagRFP-T

Cell Counts

200
150
TagRFP

100

mOrange2 mCherry
50
0
0

20
40
60
80
100
Percent Photobleaching

120

Percent
Photobleaching

c 100
90

80
70
60
50
40
30
20
10
0
-100

1

2

3

4 5 6
SBeam1 (V)

7

8

9 10

Figure 3.6: (a) A mixture of cells expressing one of four RFPs was resolved on the basis of photobleaching. Each point represents an individually assayed cell and the slope of the Sbeam1 versus
Sbeam3 plot yields the %bleach for each RFP. (b) The mean %bleach for each RFP-expressing cellline (upon measurement of 200-300 cells) was 4.4 (CV = 145%) %bleach for mOrange2, 26.8 (CV= 49%) %bleach for mCherry, 52.0 (CV = 9%) %bleach for TagRFP, and 77.3 (CV = 4%) %bleachfor TagRFP-T as determined using a ﬁtting program which ﬁt a sum of four Gaussian functions
to the histogramed data. (c) Plot of %bleach vs. pre-bleach intensity, showing the resolving power
provided by bleaching measurements.

67

a

b 100
BleachBleachS1 Bleach

Rev

S0

D1
DS Decay

Emission/Non-Radiative
Decay

Excitation

S1

Percent Photobleaching

80

60

40

Rev

20

0

GS
ery
Recov

D0

-20

0

1

2

3
4
6 -1
k ( x10 s )5

6

Figure 3.7: (a) Jablonski diagram depicting four-state model used for photokinetic modeling results.
(b) Simulated percent photobleaching as a function of kS1Bleach or kRev . kS1Bleach was held constant
while kRev was varied and the percent photobleaching was calculated (). kRev was held constant
while kS1Bleach was varied and the percent photobleaching was calculated (). Increasing rates ofirreversible photobleaching out of the ﬁrst excited singlet state and reversible photobleaching have
opposite eﬀects on observed photobleaching.

68
kW/cm2 intensities and microsecond timescales considered in those investigations. Photon saturation occurs when the average time between excitation-photon absorption approaches the time theﬂuorescent molecule spends in the excited state. We estimate that the average arrival time between
excitation photon for our highest intensity beam (170 kW/cm2 ) beam was 1.7 photons/msec. Since
the excited-state lifetime of the RFPs are in the range of 2-3 ns [49], photon saturation is negligible
under the conditions employed here. We therefore focused on photobleaching and DSC processes.
The excitation rate was calculated using the measured, average-excitation intensity to ﬁnd
the peak-excitation intensity, which was then used to calculate the maximum rate of excitation for arepresentative RFP (TagRFP-T, which was chosen because its photophysical properties represent
the median of the four proteins assayed) using its extinction coeﬃcient at 532 nm (52000 M−1
cm−1 ) and the Beer-Lambert law:

σIλ
kex =
= 2.303 × 1000
hc



Iλ
N hc


(3.9)

where σ is the absorption cross-section, I is the light intensity, λ is the wavelength, h is
Plancks constant, c is the speed of light,  is the decadic molar extinction coeﬃcient, and N is thenumber of molecules.
The ﬂuorophores ﬁrst experience an excitation rate corresponding to the ﬁrst probe beam.
The rate of excitation increases and then decreases in a Gaussian proﬁle in time from zero up to thepeak rate of excitation (8×104 s−1 ) and then back to zero over the course of 0.54 ms. The excitation
rate remains at zero for 20 ms (as mentioned previously, ground state recovery is complete after
8 ms) before experiencing the excitation of the bleach beam (maximum rate of 1.6×106 s−1 ) and,lastly, the third probe beam. In accord with the calculations and measurements on the cylindricalbeam shaping, the excitation proﬁle was assumed to be constant in the direction perpendicular
to the cells travel. The FWHM of the laser spatial proﬁle was transformed into time coordinatesassuming an average cell velocity of 6 mm/s which is approximately the mid-point of the range of
speeds used in these experiments. The peak of the time-dependent ﬂuorescence proﬁles from the

69
ﬁrst and third excitation beams was then used to calculate the %bleach.
The values of the rate constants for each step in the four-state model were taken from Chapter
2 (Table 3.4) [49]. In particular, three parameters were varied individually and the magnitude ofphotobleaching was calculated for each simulation. First, the rate of bleaching out of the higherenergy dark-state (kD1Bleach ) was varied while kRev and kS1Bleach were held at 5×105 s−1 . A
negligible increase in %bleach was observed for all but extremely large rate constants (1×1010
s−1 ) indicating that, in this model, the dark state acts photoprotectively, i.e. the ﬂuorophore
does not bleach out of the dark state. Next, the rate of bleaching out of the ﬁrst excited state(kS1Bleach ) was increased from 0 - 5×106 s−1 while kRev was held at 5×105 s−1 and kD1Bleach
was held at 0. This perturbation resulted in an expected increase in %bleach since the increased
rate of bleaching allowed the bleaching process to compete more successfully with the other S1
depopulation pathways. Lastly, the rate of reversible photobleaching (kRev ) was increased overthe same range while kS1Bleach was held at 5×105 s−1 and kD1Bleach was held at 0, leading to adecrease in %bleach. This trend shows that reversible and irreversible photobleaching are competing
processes since increases in the rate of either process leads to opposite impacts on the observed%bleach. These trends are plotted together in Figure 3.7b. Although the results of our model
indicate that the kS1Bleach has a greater eﬀect than kRev on the observed values of %bleach, notethat in general, the rates of both processes are known to change with excitation intensity, pulsed
vs. continuous wave illumination, and excitation wavelength [17, 33, 49].
For the four RFPs investigated here, the rates of reversible and irreversible photobleaching vary over 1-2 orders of magnitude across the range of excitation intensities characteristic of
wideﬁeld and confocal microscopies (10 W/cm2 - 1 kW/cm2 ) (Table 3.2) [17]. Therefore, for completeness, these calculations were performed using a range of rate values (Table 3.4), covering bothexperimental and modeling estimates [31, 33, 37, 43, 49, 90]. Our kinetic simulations indicate it islikely that the rates of both reversible and irreversible photobleaching are in the range from 1×105
to 1×106 s−1 since experimental and modeling results for the observed %bleach are in agreementin this range. Furthermore, these simulations predict that, for the current set of measurements,

70
the magnitudes of reversible and irreversible photobleaching are anti-correlated (Figure 3.7b). The
RFPs examined have rate constants that vary by an order of magnitude or less and our modeling
results indicate that, over this small range, the eﬀects of reversible photobleaching signiﬁcantly
inﬂuence the observed extent of irreversible photobleaching.
The four proteins examined in this study represent closely related ﬂuorophores. In particular,
TagRFP and TagRFP-T diﬀer by only one point mutation and have similar ﬂuorescence spectra andquantum yields (Table 3.2). However, as emphasized by the resolution of the populations in Figure
3.6, the FPs diﬀer greatly in their photostabilities and propensities for reversible photobleaching.
Our photokinetic simulations show that although the four cell populations are diﬀerentiated withour 3-beam geometry, they are not resolved solely on the basis of reversible or irreversible photobleaching but rather, by a combination of both processes. For this reason, additional resolving
power will be necessary if the two processes are to be separated. For example, according to pulsed
photobleaching experiments performed on stationary, individual cells (Figure 3.8 and Tables 3.2
and 3.3), mCherry is less prone to both irreversible and reversible photobleaching than mOrange2.
However, the signiﬁcant contribution of reversible photobleaching for mOrange2 causes it to appear very similar to mCherry, if only %bleach is considered. Building on the multi-beam geometry
described here, other excitation schemes may be devised to separately measure the rates of bothprocesses.

71

a

3.5

mOrange2

Fo

3

b

4

mCherry

3.5
5
3

2.5

2.5

Fr

2

Fluorescence Intensity (V)2
1.5%REC =
1

Fr-Fbl
Fo-Fbl

1.5
1

Fbl

0.5

0.5

0

0

c

5

d

2
TagRFP

TagRFP-T

4

1.5

3
1
2
0.5

1

0

0
0

0.5

1

1.5

2 10 10.5 11 11.5 12

0

0.5

1

1.5

2 10 10.5 11 11.5

12Time (ms)Figure 3.8: Measurements were conducted with a 2 ms, 25 kW/cm2 pulse of illumination from a
532 nm CW laser. The cells were then kept in the dark for 8 ms allowing for ﬂuorescence recovery
before the next 2 ms pulse. This duty cycle was designed to resemble cytometry experimental
parameters. However, due to experimental limitations, the intensity was reduced to an eighth ofthe cytometry bleach-beam (a) mOrange2 ﬂuorescence recovery of 80 ± 5% (n=3). (b) mCherry
ﬂuorescence recovery of 92 ± 6% (n=3). (c) TagRFP ﬂuorescence recovery of 76 ± 7% (n=3). (d)TagRFP-T ﬂuorescence recovery of 58 ± 18% (n=3).

72

3.6

Conclusion
To our knowledge, this is the ﬁrst cytometer designed to quantify photobleaching in mixed

populations. This multibeam ﬂow cytometer capitalizes on the spatiotemporal properties of thecells in microﬂuidic ﬂow to measure photobleaching with a ratiometric approach, which inherently
diﬀers from previous experiments with one or two beams. We demonstrated the capability tocharacterize the individual cells within a mixed population and note that our experiment resolves
two RFPs (Tag-RFP [16] and Tag-RFP-T [17]) that cannot be resolved by previously available
spectral signatures (e.g., ﬂuorescence lifetime, excitation/emission spectra, absorption).
As revealed in these experiments and simulations, this three- beam pulse sequence probes
both irreversible and reversible photobleaching. These two processes are highly interdependent andmust be considered in tandem. To discriminate reversible from irreversible photobleaching, theexcitation pulse sequence would need to operate on time scales that create a steady-state darkstate population. Generally, by controlling the cell velocity, excitation intensity, and dimensionsof the beam and ﬂuidic channel and by employing time-resolved ﬂuorescence detection, it will
be possible to implement speciﬁc probes of other photophysical dynamics, at high throughput.
Furthermore, building on a recent suggestion in the literature [53], the method reported here
can be integrated with measurements of ﬂuorescence lifetime [86, 87] and microﬂuidic cell-sorting
techniques [99, 100] to enable the screening of genetic libraries of FPs on the basis of photostability
and ﬂuorescence quantum yield. Further evolution of the instrumentation towards this goal, toenable the development of more photostable FPs, is described in the next chapter.

Chapter 4
Microﬂuidic Cell Sorter for use in Developing Red Fluorescent Proteins with
Improved Photostability

4.1

Abstract
Here we present a novel microﬂuidic cytometer for mammalian cells that rapidly measures

the irreversible photobleaching of RFPs expressed within each cell and achieves high purity (>99%)
selection of individual cells based on these measurements. The selection is achieved by using submillisecond timed control of a piezo-tilt mirror to steer a focused 1064-nm laser spot for opticalgradient force switching following analysis of the ﬂuorescence signals from passage of the cell througha series of 532-nm laser beams. In transit through each beam, the FPs within the cell undergo
conversion to dark states, but the microﬂuidic chip enables the cell to pass suﬃciently slowly thatrecovery from reversible dark states occurs between beams, thereby enabling irreversible photobleaching to be quantiﬁed separately from the reversible DSC. The microﬂuidic platform achievessorting of samples down to sub-mL volumes with minimal loss, wherein collected cells remain alive
and can subsequently proliferate. The instrument provides a unique ﬁrst tool for rapid selection ofindividual mammalian cells on the merits of photostability and is likely to form the basis of subsequent lab-on-a-chip platforms that combine photobleaching with other spectroscopic measurementsfor on-going research to develop advanced RFPs by screening of genetic libraries.

74

4.2

Publication Status and Author Contributions
Davis, L.M., Lubbeck, J.L., Dean, K.M., Palmer, A.E., Jimenez, R. Microﬂuidic cell sorterfor use in developing red ﬂuorescent proteins with improved photostability. Submitted to Lab Chip.
J.L.L., K.M.D., A.E.P., and R.J. designed research. L.M.D. designed and built the opticaltrap, and also performed real-time programming. J.L.L. and K.M.D. performed research. L.M.D.,
A.E.P., and R.J. wrote the paper.

4.3

Introduction
In the previous chapter, we presented a microﬂuidic sorter capable of screening cells on thebasis of the composite photobleaching process which is the concatenation of irreversible photobleaching and DSC. The microﬂuidic sorter described in this chapter oﬀers the innovative capability of selecting RFPs with reduced irreversible photobleaching in mammalian cells. Irreversible
photobleaching is a particularly important parameter to isolate, because it ultimately limits thenumber of ﬂuorescence photons that can be collected from a single protein, which is typically 10
to 100 times fewer than that obtained from a ﬂuorescent dye molecule [30, 49]. While DSC may be
advantageous for a particular bioimaging format, irreversible photobleaching is always detrimental
as it terminates single-molecule observation and limits super-resolution precision. The pathways
and kinetics for DSC and photobleaching are poorly understood but in general are linked. Whereas
photobleaching is caused by breaking of chemical bonds in the ﬂuorophore, the mechanisms for
DSC may include pH dependent protonation of part of the ﬂuorophore moiety, or a conformationalchange of the ﬂuorophore or its immediate environment, each with a relatively slow recovery (≈101000 μs), and intersystem crossing to the triplet state with a faster recovery (≈1 μs). The linkingof the kinetics arises because irreversible photobleaching often results from absorption of light froman excited state, such as a triplet state or a dark state.
For wide-ﬁeld modulated excitation of cells expressing RFP with laser pulses obtained using
a continuous 532-nm laser and an AOM (cycles of 2 ms at 25 kW/cm2 followed by 8 ms in the75dark), the ﬂuorescence signal from various RFPs characteristically quickly decreases during eachpulse due to conversion to a dark state and mostly recovers between pulses due to recovery from thedark state. Moreover, there is also a slower decrease of the maximum ﬂuorescence signal from one
pulse to the next, which provides a measure of irreversible photobleaching that is largely isolated
from the faster ﬂuorescence decrease arising from conversion into dark states [49]. To isolate andmeasure irreversible photobleaching in the microﬂuidic cell sorter, a similar modulated excitation
as that formed by the AOM is achieved by allowing cells to ﬂow at an appropriate speed through aseries of laser beams upstream from the sorting region, as shown in Figure 4.1. In contrast to the
traditional FACS, which employs ﬂow speeds of 1-10 m/s, the microﬂuidic device enables a muchslower ﬂow, with transit times of up to ≈2 ms and with up to ≈8 ms intervals between beams,
which are separated by ≈30 μm.
While the number of beams required to diﬀerentiate RFPs with substantially diﬀerent photobleaching can be as few as two or three [101], a larger number of beams provides greater photobleaching and better resolution of RFPs with high photostability, and hence the instrument may
be conﬁgured to use as many as 8 beams of 532-nm light to characterize photobleaching, althoughoftentimes only 3 or 4 are suﬃcient. The ratio of the maximum ﬂuorescence signal from the ﬁnal
beam to that from the initial beam is independent of the level of expression of RFP within thecell and yields a simple and reliable measure of the irreversible photobleaching. In Chapter 3, thismeasure has been shown to be capable of distinguishing individual cells within a mixed populationeven when they cannot be resolved by other spectroscopic signatures, such as ﬂuorescence lifetime,
or excitation or emission spectra [101].
The microﬂuidic instrument described in this chapter is unique in that it combines this
rapid measurement of irreversible photobleaching with immediate analysis for real-time sorting oflive mammalian cells. Several methods can be used for sorting of cells within microﬂuidic devices.
Early devices used switching of electro-osmotically driven ﬂow [102,103], but are mostly impracticalfor manipulation of fragile mammalian cells [104]. Other possible cell sorting techniques include
use of acoustic waves [105, 106], and ﬂuidic displacement induced by pulsed laser cavitation bubble

76
formation [107]. Use of optical forces for sorting cells, which is based on early work on opticaltrapping of cells [108], is particularly suited for use within microﬂuidic devices. Optical sorting ofcells in a microﬂuidic device has been demonstrated by switching on a tightly focused line from a
980-nm diode bar laser so cells ﬂow along the line [109] and also by pushing a cell by the opticalscattering force along the laser direction into a separate ﬂuidic channel in a multilayer device [110].
Computer controlled holographic optical trapping with a high NA microscope objective to provide
tight focusing has been reported for manipulating and sorting of human embryonic stem cells [111].
In this work, sorting using optical gradient force switching was chosen because it is compatible
with a single layer microﬂuidic device, it does not require high NA optics and it is suitable for live
mammalian cells [99]. As depicted in Figure 4.1, a single focused 1064-nm laser spot is turnedon and moved at an angle across the microﬂuidic channel, to deﬂect a cell by the optical gradient
force so that it follows the trajectory of the spot towards a separate exit channel. As the speed oftranslation of the laser spot is matched to the ﬂow velocity, the optical force does not have to bestrong enough to overcome the ﬂow, hence strong axial trapping is not needed and only a low NAlens is required for focusing the laser beam. The technique has previously been demonstrated tomaintain the viability of HeLa cells with a 1064-nm laser power of >10 W and exposure of several
milliseconds, which provides suﬃcient optical forces for eﬃcient cell selection [99]. Other authors
have used a similar approach for sorting of mouse macrophage cells infected by a ﬂuorescently
labelled pathogen [112].
In each of these earlier works, the ﬂuorescence signal from transit of a cell through a single
laser beam was passed to a hardwired circuit with a leading-edge discriminator, which triggeredcustom electronics [99] or a programmed function generator [112] to drive an AOM after a pre-set
delay to deﬂect the beam and translate the laser spot. In contrast, in the present work, analysisof the multi-beam photobleaching signature is performed in software on a separate computer using
the Real-Time module of LabView (National Instruments) to achieve deterministic (about ±0.005ms), low-latency (< 3 ms), adjustably timed control of the sweep of the laser spot using a piezo-tilt
mirror.

77
The instrument reported in this chapter is an application of miniaturization and automationthat presents marked advantages over existing manual techniques used in developing new RFPs.
Previous sorting techniques have relied on manual selection of bacterial colonies [17, 54], which is
time-intensive, or ﬂuorescence activated cell sorters (FACS), which are primarily limited to detection of ﬂuorescence intensity, wavelength, and light scatter. In contrast to these previously usedtechniques, the present instrument enables high-speed automated selection of individual mammalian cells. It uses photobleaching irradiance levels similar to those used in many ﬂuorescenceimaging experiments (≈2-25 kW/cm2 [113,114]) and it achieves high purity (>99%) in the selected
population, as demonstrated by experiments with known mixtures of cells. It hence provides animportant new tool for eﬀorts to develop novel RFPs with enhanced photostability and low toxicity
in mammalian cells. It is likely to lead the lab-on-a-chip community to develop subsequent cell
sorters that combine photobleaching with other spectroscopic measurements [115].

78

Figure 4.1: Schematic of a cell ﬂowing through a series of line-focused 532-nm laser beams to yielda measure of irreversible photobleaching, which is used to decide its possible selection, made byturning on and translating a focused 1064-nm laser beam for optical gradient force switching.

79

4.4

Experimental

4.4.1

Microﬂuidic Chip, Microscope, and Detection

Figure 4.2 shows a schematic of the entire experimental system, with speciﬁc details for key
components given in the ﬁgure or ﬁgure caption. An expanded diagram of the microﬂuidic chip is
at the top left in the ﬁgure. The cell sorting uses up to 20 W of 1064-nm laser light, so to avoid
absorption of this light by the device and possible burning or damage, the microﬂuidic chip (custom
fabricated by Micronit, Netherlands) is made entirely from borosilicate glass. The ports of the chipare sealed to a polytetraﬂuoroethylene (PTFE) manifold using compression ﬁt o-rings. For each of
the 5 inlet/outlet ports, the manifold contains a reservoir connected at the top to an access hole,sealed by a cap screw, which is removed for loading and recovering samples with a micropipette,
and also to a side line for delivery of compressed air to enable the headspace of the reservoir to bepressurized. The outlet reservoirs (channels E and F) are left open to atmospheric pressure andthree adjustable, electronic pressure controls (Pneutronics, OEM EPS10-5-0-2) with a range of 0-2
psi above atmospheric pressure are used to regulate the inlet ﬂow rates of the sample (in channel
A, Figure 4.2) and two buﬀer streams (in channels B) to achieve hydrodynamic focusing of thesample stream (in channel D), and adjust its path within the microﬂuidic device so that it exits
into the side channel (channel F).
As shown in Figure 4.2, the microﬂuidic chip (1) and manifold are mounted on an inverted
microscope that uses a dry objective (2) for epi-illumination and collection of ﬂuorescence. The
beam from a 532-nm laser is split into 9 beams (only 5 are shown) of adjustable powers with a seriesof beamsplitters and neutral density ﬁlters. The beams pass through a cylindrical lens (3), enter
the side port of the microscope and reﬂect from a dichroic mirror (4) to form line-focused beams
within the microﬂuidic device (1) (8 of the beams in channel D, as depicted in Figure 4.1, and the9-th monitor beam in channel E, for counting of selected cells, as discussed below). Any of thebeams may be blocked, including the monitor beam; oftentimes only 4 or 5 beams are used. Red
ﬂuorescence from cells passing through these beams is isolated using ﬁlters (5, 6) (pass-band 545-

80
690 nm) and detected with a photomultiplier (operated at -650 V). The photomultiplier signal isconditioned by a custom-built trans-impedance ampliﬁer (which gives 10V output for a 100 μA input
and 0.16-10.6 kHz band-pass) and digitized at 125 kHz, 16-bit resolution by a multifunction dataacquisition (DAQ) card (National Instruments PCI-6251 with NI-SCXI). This card also provides adigital output for switching the 1064-nm laser on/oﬀ and two 0-10 V analogue outputs for controlof two actuators of the piezo-tilt mirror (7) through a low-voltage piezo ampliﬁer. The DAQ cardis operated in a separate computer (Target PC) controlled by the main computer (Host PC). For
sorting of cells, the piezo-tilt mirror steers the beam from a 1064-nm laser, which is reﬂected from adielectric mirror (8) and focused in through the top of the microﬂuidic chip by an aspheric lens (9).
In order to visualize cells during operation of the sorter, trans-illumination of the chip is provided a
Krypton bulb (10) and a long-pass ﬁlter (>750 nm) (11) and scattered light from cells is separated
by a dichroic ﬁlter (12) for viewing with a CMOS camera.

4.4.2

Optical Design of Gradient Force Switching

The 1064-nm optical deﬂection beam is focused through the top cover-plate of the microﬂuidic
device using an aspheric lens (9) with focal length f=11 mm. The lens is antireﬂection coated andprovides high transmission (>99%) and low loss compared to a microscope objective. Although thelens is designed for use at a wavelength of 670 nm and for focusing through a laser diode window of
0.275 mm thickness with NA 0.3, Zemax optical design software indicates that when the Gaussian1064-nm laser beam is focused by the lens through the 1.1 mm thick glass top of the microﬂuidic
device at a NA of 0.25, the focused spot size, as seen in the inset of Figure 4.3, is close to thediﬀraction limited size of the Airy disk over the entire trajectory. As the depth of the microﬂuidic
channel is only 25 μm compared to 50 μm in earlier work [99], a higher NA was chosen (0.25 rather
than 0.2) to produce a sharper axial optical ﬁeld gradient (the spot area doubles over the Rayleighrange of ±4.1 μm rather than ±6.3 μm).
The piezo-tilt mirror used to translate the focused spot within the microﬂuidic channel has
an equilateral tripod mount that gives up to ±600 μrad of tilt in total when two of the three

81

Figure 4.2: (1)=the microﬂuidic chip, shown within the system and at top left: Channel lengths(mm), widths (μm) are: A 20,100; B 15, 130; C 5, 130; D 0.5, 15, E 27, 125; F 27, 125. All channels
are wet etched to 25 μm depth into a 0.7 mm thick borosilicate glass substrate, which is bonded toa 1.1 mm thick glass cover-plate that has powder-blasted vias of 0.6-1.7 mm (bottom-top) diameter(≈1.2 μL volume). Key system components are: (2)=Olympus IX71, inverted microscope with
Olympus LUC Plan FLN 20× UIS2 NA0.45/∞/cc0-2 mm objective; (3)=Thorlabs LJ1996L1-A,
plano-convex cylindrical lens, focal length f=300 mm; (4)=Chroma z532 rdc, dichroic ﬁlter (reﬂects
532 nm, transmits >550 nm); (5)=Semrock FF01-720/SP-25, short-pass ﬁlter (transmits 320-690
nm, blocks >720 nm with optical density OD>7); (6)=Chroma HQ545LP, dichroic ﬁlter (transmits>545 nm, blocks <540 nm OD>5); (7)=Physik Instrumente S-315, piezo-tilt mirror, (8)=Newport
10QM20HM.15, dielectric mirror (reﬂects 1064 nm, transmits <900 nm); (9)=Thorlabs A397TM-C,
aspheric lens, f=11 mm; (10)=Maglite, Krypton bulb; (11)=Semrock FF01-736/LP-25, long-passﬁlter (transmits >750 nm, blocks <736 nm OD>3); (12)=Semrock FF720-SDi01-25×36 dichroicﬁlter (transmits 400-700 nm, reﬂects 720-890 nm).

82
actuators are oppositely extended over their full range. The full angular tilt in a beam reﬂectedfrom the mirror is two times that value, or 2400 μrad in total. If this tilt is applied at the entrance
pupil of the 11 mm focal length aspheric lens, the scan distance of the spot would be 11 mm ×
2.4 × 10-3 = 26.4 μm. To increase the scan distance, as shown in the optical layout in Figure
4.3, the piezo-tilt mirror (PM) is imaged onto the entrance pupil of the aspheric lens (L5) with amagniﬁcation of 3.33, using a pair of lenses (L3 and L4), which are spaced so that the collimatedbeam from the piezo-tilt mirror is re-collimated at the aspheric focusing lens. With adjustment of
two of the three actuators, the laser spot can be scanned in the microﬂuidic channel over an areawith longest diagonal of 88 μm. The tilt mirror is driven by a 3-channel 100 V ampliﬁer controlledby 0-10 V analogue input signals. The minimum time taken to scan the full range is approximately
2 ms. Manipulation of beads has been demonstrated with the same piezo-tilt mirror [116].
To achieve the desired numerical aperture in focusing, the 1064-nm laser beam is expanded
×3.5 using a pair of plano-concave and plano-convex singlet lenses (L1 and L2) and the beam path
is folded to form a more compact assembly for enclosure within a box, as seen in Figure 4.3.

4.4.3

Timing Set-Up

In setting up for sorting, the trajectory over which the 1064-nm laser spot scans is adjusted
downstream from the photobleaching beams, as shown in Figure 4.4. The orientation and voltage
limits of the piezo-tilt mirror are set to make a triangular trajectory, 1-2-3, shown by the dashed
red line in Figure 4.4, where the 1064-nm laser turns on over path 1-2 and turns oﬀ over path 3-1.
When a cell passes through the photobleaching beams (green lines at left in Figure 4.4), the signals
are analyzed and if the cell is selected, the 1064-nm spot must turn on with correct start time andscan with a speed matched to that of the cell so that it intercepts the cell.
To accomplish this, the camera image is used to measure the displacements shown in Figure
4.4: A, between the ﬁrst and last green photobleaching beams; B, between the last photobleaching
beam and point 2 of the 1064-nm spot trajectory; C, the down-channel component between points
1 and 2 of the trajectory; and D, between the last photobleaching beam and the monitor beam,

Figure 4.3: M=Mirror, PM=Piezo-tilt mirror (rays are shown for the extreme tilts), L=Lens (Thorlabs part #), L1=Plano-concave f=50 mm (LC1715-C), L2=Plano-convex f=175 mm (LA1229-C), L3=Plano-convex f=1000 mm (LA1464-C), L4=Plano-convex f=300 mm(LA1484-C), L5=convex aspheric f=11 mm (A397TM-C). Separations between component vertices are: L1-L2=125.2 mm; PM-L3=998.5
mm; L3-L4=1289.0 mm; L4-L5= 289.4 mm. L1 orientation is reversed to balance spherical aberration. The inset shows the Huygens
point-spread function of the laser beam focus.

83

84
which is positioned in the sort channel (channel E of Figure 4.2 as a means of counting cells thatpass into that channel. The program on the Target PC measures TA the time of passage of a cell
between the 1st and last photobleaching beams, which depends on the setting of the pressures thatdrive the ﬂow. (Due to the pressure-driven parabolic ﬂow proﬁle, the passage time also depends
on the exact depth of the cell within the channel, but all cells are carried close to the centre depth
where the ﬂow speed is greatest, so the variation is found to be only ± 9 % at most.)
The start time of the trajectory of the 1064-nm spot is adjusted so that the time delay TB
from the moment that a cell passes the last photobleaching beam to the time when the 1064-nm
spot passes point 2 in its trajectory is TB = (B/A) TA . The speed of the trajectory along path
2-3 is adjusted so that TC = (C/A) TA . Finally, the delay for recognition of a cell passing throughthe monitor beam is TD = F (D/A) TD , where a factor of F ≈ 1.67 is used to account for aslow-down in the ﬂow speed beyond the channel junction, as the microﬂuidic device is designedwith a main channel of width 150 μm and two exit channels each of width 125 μm, so that the
net ﬂow speed slows by a factor of 0.6 (=1/1.67). If the monitor beam is used, a peak that follows
one from a cell passing the last photobleaching beam within a delay of (1±δ) TD is categorized as
due to that same cell passing through the monitor beam, where δ ≈ 0.1 is a tolerance parameter.
However, such a peak could also be due to a new cell entering beam 1 with the required delay.
Thus the monitor beam gives a useful indication of successful cell selection for optimizing eﬃciency,
but the categorization of peaks on the basis of their timing leads to over-counting of sorted cells,
particularly when the incoming rate of cells is high, and hence it provides only a low-limit estimate
for the purity of the selected cells. As discussed below, the purity of the selected cells is better
determined by subsequent spectroscopic analysis of the selected population of cells.

4.4.4

Real-Time Data Analysis for Cell Selection

A program operating on the Target PC is used to analyse the digitized data stream fromthe photomultiplier, recognize isolated bursts composed of sets of peaks due to the passage of cells
through the of 532-nm photobleaching beams, make selection decisions based on photobleaching,

85

Figure 4.4: The 8 green lines at left represent line-focused 532-nm ﬂuorescence excitation beams.
The red dashed triangle is the trajectory of the 1064-nm optical deﬂection laser spot. The dashedyellow lines envelope the trajectories of cells as they pass into the exit channel at lower right. The
image in this camera frame has captured a cell that was deﬂected along the light blue line to point
3, is now within the light blue circle, and will subsequently pass into the sort channel at upper
right, to be counted as it passes through the 9-th 532-nm laser line at far right.

86
program the DAQ card outputs using the calculated timing for cell selection, and count cells thatpass through the monitor beam if this is used. With low priority, this program also transmits
analysis results to the Host PC and adjusts its operating parameters upon command from theHost. The Host runs a separate interactive program that graphically presents analysis resultsreceived from the Target and allows parameters to be transmitted to the Target for update.

4.4.4.1

Target PC Program

Table 4.1 gives a ﬂow diagram of the main parts of the algorithm of the Target PC program,which ﬁnds peaks, counts them into bursts, and categorizes and counts bursts of diﬀerent types. In
the main loop, at line 9 the program awaits a block of data (typically 625 points in 5 ms), then atline 10 it ﬁnds the locations and amplitudes of peaks in the data stream using the LabView libraryroutine Peak Detector, which is a point-by-point routine that retains needed data from prior calls.
This ﬁnds peaks above an adjustable level (set at the current average plus an adjustable thresholdso as to recognize a wide dynamic range but yet distinguish partially overlapping peaks) by ﬁtting
a quadratic of given width to the data stream (so that peaks of small width due to noise are notfound). In the for-loop of lines 1137, the timings of peaks are used to count up peaks into bursts.
The count, i, is reset to the beginning value (i = 0;) designating the ﬁrst peak of a burst whenever
the time since the last peak is not within the set limits (between Min and Max at lines 17 or 24,
or for the monitor between (1-δ) TD and (1+δ) TD at line 33), or whenever the count equals thenumber of laser beams used (at lines 27 or 36). If this ﬁrst peak follows the previous peak too
closely, the burst that follows will be ﬂagged as overlapping (line 40 or 41). An array (A[ ]) is used
to store the amplitude of the ﬁrst peak and the normalized amplitudes of subsequent peaks in aburst. After a cell has passed through all photobleaching laser beams, if all the amplitudes are
between the lower limits and upper limits (which are deﬁned in the arrays LL[ ] and UL[ ], i.e.,
LL[j]<A[j]<UL[j] for j=0,1, , N) and the burst is not overlapping with a prior one (O=false at line21), the time and speed for the motion of the 1064-nm laser spot is calculated (line 22, Calculate
timing) using the procedure discussed in section 4.4.3, taking into account the positions of the peaks

87in the data stream and the amount of time that has elapsed during the computation, as determinedfrom readings of the Target PC clock. If the piezo-tilt mirror has completed its previous trajectoryand if there is suﬃcient time to intercept the cell, a subprogram (line 22, Initiate selection), which
will send the calculated voltages from the DAQ card with the calculated timing, is started; at thistime the ﬂag for Selected is set true.
After the peaks are counted for a cell passing through all photobleaching beams, the next
peak will be reset to the beginning of a new burst if the monitor beam is not used (line 27) or will
be tested to see if it corresponds to a monitor peak (line 30), in which case the next peak will bereset to the beginning of a new burst (line 36). With completion of a burst in either of the twocases of these possible resets after all photobleaching beams are counted, the type of the burst isdetermined and a counter for the particular burst type is incremented. Note that (in line 21) a
burst may be selected only if it does not overlap with the prior burst. However, whether selected
or not, it will be designated as overlapping if it overlaps with either the prior or the following burst(lines 27, 28 or 33, 34, or 36, 37).

4.4.4.2

Host PC Program

Figure 4.5 shows a screen shot of the Host PC program, captured during the execution of anexperiment. The program, which runs on a PC that uses the Windows 7 operating system, receives
the time and amplitude of each detected ﬂuorescence peak over the network from the Target PC. It
groups these peaks into bursts of peaks using the same algorithm that runs on the Target PC (asdescribed above), but with its own (Host) values of adjustable parameters, to determine graphicalrepresentations of the passage of individual cells, and also the would-be sorting statistics displayedin the top right yellow box, i.e., those that would be obtained on the Target PC if that had used
the same parameters. Thus at the start of an experiment, the parameters on the Host PC canbe adjusted to optimize the grouping of peaks into bursts. These revised parameters can thenbe downloaded over the network to replace the parameters used by the Target PC, or otherwise
discarded and replaced by values uploaded from the Target PC. The Target PC also collects its

Table 4.1: Flow Diagram of Target Algorithm
1
2
3
4
5
6
7

N = 2, 3, 4, 5, 6, 7, or 8 = number of photobleaching beams used
M = 0, or 1 = if monitor beam is blocked, or used
i = N + M; // initialize count of peaks in the present burst
S = false; // initialize ﬂag S to indicate if the burst is selected
O = false; // initialize ﬂag O to indicate if the burst overlaps with the prior one
sou = 0; nou = 0; som = 0; nom = 0; sgu = 0; ngu = 0; sgm = 0; ngm = 0;// initialize counters of bursts, s/n = selected/not selected, o/g = overlapping/good,
m/u = monitored/unmonitored

8
9 Read block of data points, ﬁnd the Average
10 Call Peak Detector (Threshold + Average, Width )11 For each peak found {12
Find Δt = time since last peak, A = peak amplitude
13
if ( i = 0, 1, , N-2 ) { // peak that precedes last photobleaching beam14
if ( Min < Δt < Max ) {15
i++; A[i] = A / A[0]; }16else {
17
Reset; } }18else if ( i = N-1 ) { // peak may be due to last photobleaching beam19
if ( Min < Δt < Max ) {20
i++; A[i] = A / A[0]; S = false;21
if ( LL[ ] < A[ ] < UL[ ] and O = false ) {22
Calculate timing; Initiate selection; S = true; }}23else {
24
Reset; } }25else if ( i = N ) { // peak that follows that from last photobleaching beam26
if ( M = 0 ) { // if no monitor beam is used27
O2 = O; Reset;28
if ( O or O2 ) { if ( S ) { sou++; } else {nou++; } } else { if ( S ) { sgu++; }else {ngu++; } } }29else { // if a monitor beam is used, i.e., M = 1
30
if ((1-δ) TD < Δt < (1+δ) TD ) {31
i ++; }
32
else {
33
O2 = O; Reset2;
if ( O or O2 ) { if ( S ) { sou++; } else {nou++; } } else {34
if ( S ) } sgu++; }else { ngu++; } } } }35else if ( i = N+1 ) { // peak that follows that from monitor beam
36
O2 = O; Reset;37
if ( O or O2 ) { if ( S ) { som++; } else {nom++; } } else { if ( S ) { sgm++; }else { ngm++; } } } }38 Go to line 939
40 Reset: i = 0; A[0] = A; if (Δt < Max ) { O = true; } else {O = false; } Return;
41 Reset2: i = 0; A[0] = A; if (Δt < (1+δ) TD ) { O = true; } else {O = false; } Return;a Adjustable parameters are underlined; ++ means increment the counter by 1.

88

89
own (true) statistics, which are sent to the Host PC and displayed in the light blue region near top
right.
The adjustable parameters (indicated by pink numbered arrows) are: (1) Threshold, (2)Width, which together are used by the LabView point-by-point library routine Peak Detector toﬁnd the locations and amplitudes of peaks in the data stream; (3) Minimum (time delay), and (4)Maximum (time delay), which are used together with counting of the peaks in a burst to piece
together the peaks into bursts of up to 8 peaks and to determine whether a burst is overlappingwith a previous burst (designated as Overlapping) or not overlapping (designated as Good); (5) aset of Switch Parameters, which deﬁne the voltages at the 3 vertices of the piezo-mirror triangular
trajectory(A0, A1, B0, B1, C0, C1), the time for motion along the legs of the trajectory, 12(designated as T AB), 23 (designated as T BC and readjusted to be T C ), and 34 (designated as
T CA), the ratios B/A (Switch-Time Factor), C/A (Delay Factor), and F (D/A) (Check Delay
Factor) needed to calculate the timings, the tolerance parameter δ (Tolerance) for designating apeak as due to a cell passing through the monitor beam, and an additional parameter (Extra Delay)
to provide an empirically determined oﬀset to T B to account for slow-down of the ﬂow near thejunction and latency of the switch; (6) a set of values for peak amplitude ratios (listed within
the green box), set by using the mouse to position cursors on the graph labelled Normalized Peak
Amplitudes. These values select the photobleaching characteristics of cells that we wish to switch
to the upper exit channel.
Note however, that the switch will only be attempted if the burst of ﬂuorescence peaks fromthe cell is designated as Good (not Overlapping). The switch will also not be attempted if thecalculated time to wait until the start of the switch cycle (shown as usToWait at lower left in
Figure 4.5) is negative, in which case the cell is counted as Too Fast (in blue box at top right
Figure 4.5); or if the piezo-mirror trajectory from the previous selection will not be completed intime, which is counted as Busy (in blue box at top right Figure 4.5). After the peak of a burst
as a cell passes the last photobleaching beam, whether the switch of the cell is selected (S) or notselected (NS), a peak that follows within a delay of (1±δ)T D is categorized as due to that same

90

Figure 4.5: During this particular experiment, the 532-nm laser power was 0.98 W, with ≈ 0.1 W
in each of the 9 beams, while the 1064-nm laser power was 14 W. The ﬂow speed was 8 mm/s (240μm/30 ms) and the time for the 1064-nm spot to move from 2 to 3 in Figure 4.4 was ≈ 15 ms.

91
cell passing the monitor beam and the burst is designated as having 9 peaks, but this may also
be due to a new cell passing into beam 1. Thus the monitor beam provides an indication of a
successful cell selection useful for setting up the instrument (and is visually indicated by extending
the line in the graph of Times of Peaks in Bursts, as highlighted within the pink diamond shapein Figure 4.5), but as discussed earlier, it over-counts the cells that pass into the sort channel (thebursts that have 9 peaks), particularly those cells that were not selected. While the success ofa sort must ultimately be determined by further analysis of the sorted population of cells (as isreported in section 4.5.3), the monitor beam provides data from which a lower limit of the purityof the sort can be determined. For the data displayed in Figure 4.5, this is: Relative purity >9GS/(9GS+9GNS) = 138/(138+35) = 0.8.

4.4.5

Solution and Flow Conditions

In typical operating conditions, the speed of HeLa cells through the 532-nm beams is ≈6-8
mm/s. It is possible to operate about 3 times faster, which still gives enough time for dark-state
relaxation between beams, although the camera frame rate then becomes too slow to follow cellselection. A faster ﬂow rate not only speeds sorting, but it helps prevent cells sticking to the wallsof the microﬂuidic device, particularly at the apex of the junction.
To reduce the HeLa cells sticking or settling in the reservoirs, OptiPrepT M (60% weight/volume
iodixanol in water) is added to the buﬀer for the cells. The iodixanol increases the speciﬁc density
to make the cells buoyant, but the refractive index of the solution also increases. Hence to maintaina diﬀerence between the refractive index of the medium and the cell, as needed to generate opticalforces, the concentration of iodixanol is kept at ≈8% weight/volume, similar to that used in earlier
work [99], resulting in a refractive index of 1.3479 (≈1.36 for cells) and density of 1.049 g/ cm3(1.0357 g/cm3 for HeLa cells [117].

92

4.5

Results and Discussion

4.5.1

Instrument Operating Conditions

Figure 4.6 shows a series of images captured by the camera of a HeLa cell passing throughthe 8 photobleaching beams (1, 2), being selected by the 1064-nm beam (3, 4), and then passing
towards the selection channel (5, 6) and through the monitor beam (7).
The program only attempts to select cells that pass through the series of beams alone, without
overlap between preceding or following cells. A greater number of 532-nm beams may provide better
resolution of diﬀerences in irreversible photobleaching. However, there is an advantage to using
fewer beams that span a smaller length of the microﬂuidic channel and to blocking the extra beamthat is used to monitor cells passing into the sort channel–namely, the use of a smaller length of the
ﬂow channel reduces the overlapping of bursts from successive cells, which increases the eﬀective
rate at which cells may enter the sorter and be non-overlapping. In the approximation that cells
enter at random times at a rate R, the time between cells is exponentially distributed with mean
1/R, so the probability that a cell does not overlap with the prior or following ones is e−2RT , where
T is the passage time from the ﬁrst to the last beam. The maximum rate of non-overlapping cells(labeled Good in Figure 4.5) is then RG = 1/(2Te ) when R = 1/(2T ). Both the rate of cells
and their speed through the microﬂuidic device may be controlled by adjusting the pressures thatdrive the ﬂow of the sample and the buﬀer streams. For the experiment of Figure 4.5, the time ofpassage from beam 1 to beam 8 (240 μm) is 30 ms, and from beam 1 to beam 9 (400 μm) is ≈113
ms, hence the maximum rate at which cells may be selected is RG ≈ 1.6 s−1 , or if the monitor
beam and the check for successful switching are turned oﬀ, RG ≈ 6.1 s−1 . The actual selection
rate will be RG × the fraction of cells in the sample that have the desired photostability. As an
indication of the fastest practical selection rate that can be expected, when the number of beams
is reduced to 4 spread over ≈100 μm and the speed is increased by a factor of 3, so that T = 4
ms, the maximum rate of selectable cells is RG = 46 s−1 , which enables samples of ≈ 105 cells withenhanced photostability to be obtained within a few hours.

93

Figure 4.6: Series of images from a movie demonstrating cell selection. A HeLa cell passes throughthe 8 photobleaching beams (1, 2), is selected by the 1064-nm beam (3, 4), and then passes towards
the selection channel (5, 6) and through the monitor beam (7).

94
4.5.2

Cell Selection Eﬃciency and Viability After Selection

To optimize operating conditions, experiments evaluating the selection eﬃciency and subsequent cell viability as a function of buﬀer conditions and 1064-nm laser power were undertakenusing HeLa-S cells, which are suitable for use in experiments for developing new RFPs. To promote
cell viability, especially during sorting experiments lasting longer than an hour, cells were suspended in a solution of phenol-red-free Minimum Essential Medium (Invitrogen, 41061-029), with
10% FBS (Invitrogen), 14% Optiprep (Sigma-Aldrich), and 1% Penicillin Streptomycin (Invitrogen,
Pen Strep). This medium was chosen instead of a low-nutrient buﬀer (e.g., HEPES-buﬀered Hanks
Balanced Salt Solution), as it was found to give increased cell viability over time. Also, a medium
free of phenol-red pH indicator was chosen to decrease background ﬂuorescence.
Selection experiments using four photobleaching beams and the monitor beam for assessing
success were performed for various 1064-nm laser powers using either (i) the cell suspension medium
described above, or (ii) a buﬀer solution composed of calcium-, magnesium-, phenol-red-free Hanks
Balanced Salt Solution (Invitrogen, 14175) with 1% Bovine Serum Albumin (Sigma-Aldrich, BSA)
in the side channels (B of Figure 4.2) used for hydrodynamic focusing. In these experiments, thespeed of cells through the photobleaching beams was kept constant at 6 mm/sec. The sample cellconcentration was diluted to ≈250,000 cells/ml to decrease the rate R of cells entering the sorterand thereby decrease the probability of overlapping bursts and also increase the ﬁdelity of themonitor beam for counting of successful selection events. For each power and ﬂuid condition, 100
cells were assayed. As shown in Figure 4.7, the selection eﬃciency was found to be increased for thesame 1064-nm laser power by using buﬀer in the side channels instead of media (possibly because
the buﬀer contains no Optiprep and so leads to greater refractive index contrast and optical forces).
The viability for each 1064-nm laser power was then assessed for both solution conditions. In
earlier work [99], mammalian cell viability was quantiﬁed by a trypan blue exclusion assay about 1
hour after exposure to the 1064-nm laser beam. However, this assay only accounts for immediate
deterioration of the plasma membrane, and cannot identify cells in earlier stages of necrosis or95
apoptosis. To account for slower forms of cellular death, the cell viability was measured 24 hours
after microﬂuidic analysis for both sorted and non-sorted cells using a Calcein-AM based assay.
Calcein-AM (Invitrogen) was added directly to the cells (ﬁnal concentration of 1 mM) within a
96-well plate and allowed to incubate at room temperature for 30 minutes. Calcein-AM becomes
ﬂuorescent upon hydrolysis of the acetoxymethyl moiety by intracellular esterases. In contrast,
dead cells remain non-ﬂuorescent, or very weakly ﬂuorescent, and can be identiﬁed by comparisonof ﬂuorescence and diﬀerential interference contrast (DIC) images. Using this method, for higher
laser exposure conditions, cell viability was found to be decreased after 24 hours relative to thesame assay when performed immediately after microﬂuidic sorting. Also, the viability was lowered
when buﬀer was used in the side channels. However, the viability for each 1064-nm laser power
was found to be identical (≈80%) when the selected cells were delivered into an on-chip reservoir
pre-loaded with 750 μL of the running media to minimize the cells time spent in buﬀer to ≈0.5 sec(approximate transit time from interrogation region to outlet). Furthermore, cells were capable ofcontinued replication over the course of several weeks.

4.5.3

Cell Selection Based on Irreversible Photobleaching

To demonstrate the eﬀectiveness of the instrument for selecting cells that have RFPs withlower irreversible photobleaching, experiments were performed using samples containing known
mixtures of HeLa-S cells expressing either mCherry (25% of cells) or mOrange2 (75% of cells). In
these tests, the instrument was conﬁgured to use 4 photobleaching beams and a monitor beam,with 0.09 W of power in each photobleaching beam (peak irradiance ≈ 2 × 104 W cm−2 ). The ﬂow
speed of cells through the beams was 6.0 mm s−1 . As shown in Figure 4.8, the ratios of the peak
amplitudes (beam 4/beam 1) are ≈0.63 for cells with mCherry, and ≈0.40 for cells with mOrange2,
whereas in a separate experiment using ﬂuorescently labelled beads (Invitrogen, F-8858 ﬂuospheres,
4 μm, 580/605) the ratio is ≈0.98. The operating parameters were adjusted to select mCherry cellswith lower irreversible photobleaching from the mixture using a 1064-nm laser power of ≈12 W.
Over a duration of ≈2.5 hours, 4000-5000 cells were selected. The purity of the selected sample

96

Figure 4.7: Cell selection eﬃciency vs. 1064-nm laser power for: (i) cell suspension medium(lower curve, •), or (ii) buﬀer without Optiprep (upper curve, ), in the side channels used forhydrodynamic focusing.

97
was then determined by imaging it using a wide-ﬁeld ﬂuorescence microscope in which mCherry
and mOrange2 cells are spectrally distinguished by use of a set of appropriately chosen bandpass excitation ﬁlters (540/25 and 577/20). Figure 4.9 shows the spectra for these ﬁlters together
with the excitation spectra of mCherry and mOrange2. As seen in Figure 4.10, the ratio of theﬂuorescence signals seen with each of these ﬁlters enables cells expressing mCherry or mOrange2
to be unequivocally distinguished. The purity of the selected sample was thus determined bycounting the cells of either type in the image. As seen in Figure 4.11, cells expressing RFPs withlower irreversible photobleaching (mCherry) are selected from the mixture with greater than 99%
purity, which is considerably higher than that typically achieved in FACS and other prior reported
microﬂuidic cell sorters [99].

98

Figure 4.8: The non-bleach assay is obtained using ﬂuorescent beads labelled with small moleculedyes that demonstrate negligible photobleaching in the cytometer (Invitrogen, F-8858 ﬂuospheres,
4 μm, 580/605). The mCherry and mOrange2 curves are obtained from a mixture of HeLa-S cells
expressing either mCherry or mOrange2. Note that mCherry is more photostable than mOrange2
under these excitation conditions.

99

Figure 4.9: The ﬂuorescence excitation (left) and emission (right) spectra of mOrange2 (solid
purple curves) and mCherry (solid red curves) and the transmission (dashed black curves) of thetwo ﬂuorescence excitation ﬁlters, 527-553 nm (left) and 567-587 nm (right). The identity of imaged
cells is determined from the ratio of the ﬂuorescence signals obtained from each of the excitation
bands.

Figure 4.10: Histogram of Ratiometric Excitation (i.e., ﬂuorescence for 567-587 nm excitation /ﬂuorescence for 527-553 nm excitation) showing clear resolution of mCherry cells (right peak) frommOrange2 cells (left peak).

100

Figure 4.11: Blue/pink pseudo color represents cells expressing mCherry; green pseudo color represents mOrange2. Only one cell expressing mOrange2 is visible. Larger diameter objects are clustersof cells in close enough proximity that their edges are not resolvable.

101

4.6

Conclusion
This chapter reports a novel microﬂuidic cell sorter for live mammalian cells expressing RFPs

that enables selection of cells with proteins that have enhanced photostability. The instrument uses
the Real-Time module of LabView for analysis of multi-beam laser-induced ﬂuorescence signals todetermine irreversible photobleaching and to achieve sub-millisecond timing of the sweep of a 1064nm laser spot to deﬂect individual selected cells by the optical gradient force to a separate collection
channel. Suitable cell suspension media and laser operating conditions have been determinedfor eﬃciently selecting HeLa cells while maintaining their long-term viability and capability to
proliferate. The eﬀectiveness of the instrument has been demonstrated by selecting cells expressing
a FP with higher photostability (lesser irreversible photobleaching) at a rate exceeding 0.5 s−1 froma sample containing a known mixture of cells expressing either mCherry (25%) or mOrange2 (75%).
Subsequent analysis of the collected cells on the basis of diﬀerences in the ﬂuorescence excitationand emission spectra of mCherry and mOrange2 found that >99% of the selected cells express
mCherry, indicating a remarkably high purity in the selection. In the next chapter, this instrument
is employed to develop new FPs with improved photostability.

Chapter 5
Using the Microﬂuidic Cell-Sorter to Sort Red Fluorescent Protein Libraries

5.1

Abstract
Here we present the successes and limitations of our microﬂuidic cell-sorter when used to sortand screen genetic libraries of RFPs expressed in HeLa cells. The results of screens were analyzed todetermine if the library showed suﬃcient diversity to be sorted. A diverse library was identiﬁed andsubjected to three rounds of sorting. The results demonstrate a population shift towards greater
photostability after one round of sorting. The remaining mutants were analyzed and a novel mutant
with ≈ 3× the photostability of mCherry was discovered. However, this photostability increase wasat the expense of a decreased quantum yield. The mutants with increased photostability were thensubjected to additional mutagenesis in order to further increase photostability in future roundsof sorting. The successes of these ﬁrst attempts at directed evolution highlight the power of this
instrument while illuminating how to properly exploit them. Furthermore, the shortcomings ofthese attempts guide future library designs, sorting methodologies and instrument improvements.

5.2

Publication Status and Author Contributions
The work presented here is unpublished.
K.M.D., J.L.L., A.E.P., and R.J. designed research. K.M.D. designed ﬂuorescent protein

libraries. K.M.D. and J.L.L. performed research. K.M.D. performed spectroscopic measurements
and data analysis.

103

5.3

Introduction
Currently, most new RFPs are developed by sorting libraries by automated colony pickers,

manual selection of colonies, or FACS [17,18,53]. These automated approaches are higher throughput and therefore less time-intensive, however, they are typically limited to detection of ﬂuorescenceintensity, wavelength, and light scatter. Manual techniques provide more ﬂexibility of the selection
criteria but are very low throughput, limiting library sizes to 10-1,000s, or limiting the assayedpopulation of the library [16, 18]. This has resulted in most RFPs being optimized on the basis ofbrightness, maturation, or excitation/emission wavelengths [16–18, 20].
Irreversible photobleaching is a particularly important parameter to isolate, because it ultimately limits the image acquisition time and the number of emitted photons that can be collected from a single protein (which remains orders of magnitude fewer than a ﬂuorescent dye
molecule [30, 49]) [51, 114]. In the previous chapter, we reported an instrument with marked advantages over the aforementioned, existing sorting techniques used to develop new RFPs. This
instrument is capable of high-speed automated selection of individual RFP-expressing mammaliancells on the basis of photostability. The photobleaching irradiance levels are similar to those used inmany ﬂuorescence imaging experiments (≈2-25 kW/cm2 [113,114]) and it achieves high sort purity(>99%). For these reasons it is an important tool for eﬀorts to develop a new RFP.
We develop, screen, and sort hypothesis-driven, targeted libraries of RFPs which are expressed
in HeLa cells to ensure proper folding and maturation for in vivo imaging conditions. Our librarysizes are on the order of 105 clones, and we are able to screen and sort them with > 300% coverage.
We recognize that longer-range or synergistic mutations are diﬃcult to predict and so error-prone
mutagenesis is also incorporated. Furthermore, we present initial attempts at directed evolution
techniques.
Additionally, in this chapter we explore the utility of this microﬂuidic cell-sorter as a RFPlibrary screener/sorter to select an RFP with greater photostability. Furthermore, library designand construction are considered and analyzed in the context of the advantages and limitations of

104this tool as well as in the context of increasing the photostability of proteins. In this way, we takethe approach of optimizing library development and instrument running procedures synergistically.
Ultimately, this approach results in development of a mutant with ≈ 3× greater photostability thanmCherry. However, photostability is increased at the expense of quantum yield. The successes andlimitations of this approach, presented herein, inform future attempts at library development andindicate opportunities to further develop this cell-sorter into a more powerful tool.

5.4

Experimental
A detailed description of the experimental setup and performance can be found in Chapter 4.

The routine sorting methods and day-to-day operating procedure for sorting libraries is described
below.

5.4.1

Sorting Methodologies

The microﬂuidic and manifold was cleaned and assembled before and after each run as described in Appendix B. The 532 nm excitation/photobleaching laser was set to 1 W resulting in
0.1 W in each of the 9 photobleaching and sort-checking beams. This resulted in a peak irradiance of ≈2×104 W cm−2 . The 1064 nm sorting beam was set to 8.3 W (50% current) and thecells were hydrofocused with buﬀer but suspended in phenol-free media as determined optimal in
Chapter 4. The last four of the eight photobleaching beams were deemed unnecessary for optimal
photostability-resolution in the libraries evaluated here and were therefore blocked prior to entering
the microscope. Each of the remaining ﬁve 532 nm beams, the infrared (IR) sorting-beam, andthe microﬂuidic channels were aligned to a pre-determined position speciﬁed by a pixel coordinate
to maintain consistent timing parameters (Detailed in Chapter 4). As described in Chapter 4,
extended interaction between the IR beam and the microﬂuidic setup and solutions can lead toboiling or burning in the channels. To avoid this, the IR beam was turned on and aligned at only
5% diode current, which is just above the lasing threshold.
The pressures on the reservoir of each channel were approximated prior to cell injection but

105
optimized upon addition of the cells to achieve a target cell velocity of approximately 6 mm/s
which resulted in pressures of 0.5 ± 0.1 psi and 0.8 ± 0.1 psi on the respective lower and upperhydrofocusing channels (B, Figure 4.2) and 0.5 ± 0.1 psi on the center cell channel. The target
velocities and hydrodynamic widths were monitored on the velocity histogram of the data acquisition program described in Chapter 4 to maintain day-to-day consistent ﬂow parameters. The
desired 20 μm hydrodynamic focus resulted in a 16% standard deviation in the velocity. The twohydrofocusing channel pressures were selected such that the hydrofocused channel was directed into
the unsorted-outlet channel but was traveling close to the junction of the two outlets (Figure 4.4).
The PMT voltage was set to -700 V before the addition of cells but was increased (± 100V) to achieve a median peak intensity of 1 V. This signal level was monitored on the Times andAmplitudes of First Peaks graph (Figure 4.5). The PMT voltage along with the Maximum Time
Between Peaks, Minimum Time Between Peaks, and Width of Peaks (Figure 4.5) parameters were
optimized to achieve a 50% (0.5) or greater value of Peaks Put Together. This observation indicatedthat the maximum number of cells was being analyzed because the ﬂuorescence signal from each
was being detected at each beam with minimum cell-overlap. This value typically increased 5-10%
when the ﬁfth sort-checking beam was blocked (see below).
After the aforementioned parameters were optimized, ≈100 cells were detected and the sorting
threshold of the fourth photobleaching beam was selected based upon this representative sample(discussed in further detail below). At this point, the IR sort beam was set to 50% current tobegin sorting cells. The sort-checking beam was on and detected by the program at the beginningof each sort. To turn the detection of the check beam on or oﬀ the Check Delay Factor (Figure4.5) is set to the median Actual value or 0 respectively. Once it was veriﬁed that ≈70% of thesorted cells were detected by the check-beam (Figure 4.4), the check-beam was blocked (before
entering the microscope) and the detection of the beam was turned oﬀ. This was done to allow
the program to run at maximum speed (since upon detecting the photobleaching beams, it did nothave to wait for the check-beam) and therefore minimize the number of overlapping cells. At thispoint, the outlet reservoirs were emptied via pipette and the sorted-outlet was preloaded with 750

106
μL DMEM supplemented with 30% FBS and 1% penicillin- streptomycin, cell media (BSA-free,phenol-containing). The sorting program was then restarted to achieve accurate statistics.

5.4.2

Library Construction

The original DNA templates for the parent proteins were ampliﬁed with gene- speciﬁc primers
and cloned into pDonr221 using the Gateway recombination system (Life Technologies). In eachcase, a single clone was isolated and submitted to commercial DNA sequencing prior to mutagenesis.
To introduce mutations in a site-speciﬁc manner, primers were designed with codon wildcards at
the appropriate positions. Fragments of the gene were ampliﬁed, gel-puriﬁed, and subsequently
reassembled using site-overlap extension.
After reassembly of the gene, an LR reaction with the modiﬁed pCLNCX vector was carriedout for ≈18 hours at 25◦ C, followed by proteinase-K treatment at 37◦ C for ≈15 minutes, andsubsequent ethanol precipitation and electroporation of E. coli (ElectroMax DH10B, Life Technologies). The library was allowed to grow overnight supplemented with ampicillin. After ≈15
hours of growth, 1 mL of the overnight culture was inoculated for further growth and plasmid DNA
isolation (Midi-Prep Kit, Qiagen).
For generation of virus, HEK-293 FT cells were cotransfected with pCL-Ampho, pVSV-G, andthe library using reduced serum media (Opti-MEM, Life Technologies) and standard transfection
reagents (TransIT- LT1, Mirus Bio LLC). The virus containing supernatant was titrated onto
HeLa cells and the cells were prepared for enrichment of the red-ﬂuorescent clones using a Dako
Cytomation Mo-Flo cytometer. In each case, the multiplicity of infection was kept below 10% todecrease the likelihood of multiple viral integrations per cell.

5.4.3

Cell Maintenance and Recovery

Prior to sorting, HeLa cells were cultured in Dulbeccos modiﬁed essential medium (DMEM)
supplemented with 10% FBS and 1% penicillin-streptomycin, as previously described (Chapter 2).
For microﬂuidics-based sorting, the library containing HeLa suspension cells were placed into a solu-

107tion of phenol-free MEM-Alpha (Gibco) with the addition of 10% FBS, 1% penicillin-streptomycin,
and 14% Opti-Prep. After the sorts, the cells were placed into a 96-well plate for maintenance at
37◦ C and 5% CO2. After several weeks of expansion, a population of the cells were frozen downin the presence of 10% DMSO, and the remainder of the cells were subjected to additional roundsof selection.
After several rounds of sorting, mRNA was isolated from ≈300,000 HeLa S cells (RNeasy
Micro Kit, Qiagen). In each case, enough individual clones (≈ 20 were isolated, and submitted forcommercial sequencing, to identify whether the population had converged.

5.5

Results and Discussion

5.5.1

Screened Libraries

The following libraries were screened but, due to their narrow photostability (as a measure of
ﬂuorescence intensity from beam 4 divided by that of beam 1) distribution, they were not sorted.

5.5.1.1

Error-Prone TagRFP-T

From studies performed in Chapter 2, TagRFP-T R67K N143S was known to be spectrally
similar to mCherry but >2-fold brighter. It was therefore chosen as the starting template for alibrary using error-prone mutagenesis with >9 mutations per kilobase pair (kb) which resulted in
1,000,000 members of which 7% (70,000) were ﬂuorescent (by FACS). Twenty clones were sequenced,
55% of which were found to be the parent sequence. Of the remaining non-parent population, 47%of the mutations were located on the outside of the barrel or in the disordered loops between thestrands of the barrel and 41% of the mutations were already observed in the TagRFP to mKate
evolutionary trajectory. Therefore, 12% of the mutations were novel and could potentially aﬀect thephotophysics of the protein. Due to the low diversity of this library, no attempts were made to sortit. However, it was screened and the coeﬃcient of variation (CV) of the photostability distribution
for this library was 100% (Figure 5.1) which is similar to a distribution of a monoclonal population

108(mCherry, Figure 5.2).

5.5.1.2

Morello LibraryThis library (Morello-0) was created using mCherry as the parent and randomizing K70, S146,
V177, and I197 to all 20 amino acid options coupled with error-prone PCR (>6 mutations/kb).
These are the four mutations used to evolve TagRFP into mKate, whose emission at wavelengths
longer than 650 nm is ≈200% brighter than that of TagRFP [18]. It was hypothesized that thesesame mutations may play analogous roles in improving mCherry. These mutations are depicted in
Figure 5.3. This library had a photostability CV of 72% and had an average photostability (0.994)
slightly higher than that of mCherry (0.959) which had a CV of 100% (Figure 5.2).

5.5.2

Kriek Library

Recent molecular dynamics (MD) simulations performed by Chapagain et al. [47, 48] on
mCherry have indicated that the gap between β-strands 7 and 10 is larger relative to citrine (Figure5.5), which was chosen for comparison because it is a naturally occurring monomer and a GFP
homologue of mCherry. Side-by-side comparison of MD simulations on both these proteins show
this region to be more dynamic in mCherry as well. It has long been hypothesized that this gap
allows increased oxygen permeability and therefore, increased photobleaching. This study identiﬁes
three point mutations (W143K, Q163R, and R164E) which, in MD simulations, drastically decrease
the gap between strands 7 and 10 and minimize the dynamic motions of strand 7. The gap was
decreased and stabilized because key residues were exchanged for carefully chosen charged residues
which attracted oppositely charged residues on the parallel strands . During a 10 ns MD simulation,
this mCherry mutant remained stably folded, however, incorporating these exact mutations intomCherry resulted in a non-ﬂuorescent protein. It is possible that the monomeric protein was unable
to properly fold due to the addition of the positive inward facing residue, Lys143, as all other Lys
residues on the barrel are outward facing due to the hydrophobic protein interior. Furthermore,
it is possible that the protein folded properly, but the chromophore was unable to mature due toFigure 5.1: Histogram of photostability for error-prone TagRFP-T library.

109

Figure 5.2: Morello is more photostable than mCherry (mean = 0.994, vs. 0.959) but is not diverse in photostability (CV = 72%, vs.
100%).

110

111

Figure 5.3: Morello library residues. The barrel is depicted in red, chromophore in blue, andmutated residues in green.

112
decreased oxygen permeability, thereby limiting the necessary oxidation reaction.
A thorough literature review revealed positions that had been frequently cited in the literature as having contributed to the development of an improved FP. Positions which had converged
on a single amino acid were then eliminated from consideration. Hypothetically, the remaining
indentiﬁed-positions had high mutational potential. Interestingly, two of the mutations identiﬁedin the MD simulations (W143 and Q163) fell into this category but R164 was revealed to be a
critical mutation for monomerization [24]. The remaining positions identiﬁed in this search were
V16, Q66, I161, I197, and A217 (Figures 5.4 and 5.6). Positions I197 and A217 were omitted due
to diﬃculties in the site-overlap extension reaction.
The Kriek library (K0) was constructed using mCherry as the parent due to it being themost photostable RFP under laser excitation as compared to other mFruits (Chapter 2 [49]). V16
was randomized to E, D, A, K, N, or T. Q66 was randomized to K, E, Q, M, V, L, N, D, H, Y,
I, or F. I161 was randomized to L, M, V, I, or F. W143 and K163 were randomized to all twenty
amino acid options. The ﬁnal library size was 144,000 members. Non-ﬂuorescent members were
removed by an initial brightness screen via FACS, and <10% were found to be ﬂuorescent.

5.5.2.1

Initial Rounds of Sorting

The Kriek library was sorted three times (K1, K2, and K3). At each sort, the number ofcells analyzed was greater than three times the estimated number of unique clones. For example,<10% of unique mutants remained after the FACS sort for ﬂuorescent mutants. Therefore > 3× (10% of 144,000) = 43,200 cells were analyzed during the ﬁrst round of sorting. For the ﬁrst
two rounds of sorting, cells in the top 25% of the photostability distribution were selected andrecovered. As described earlier, the threshold for the selection was set after initially screening 100
cells. After each round of sorting, a shift towards greater photostability was observed (Figure 5.7).
However, for the third sort, only the 10% most photostable cells were selected. Due to the decreased
selected population, contamination from unselected cells accidently traveling into the sorted channel
overwhelmed the selected population, and a population-shift towards decreased photostability was

113

20

mOrange1
mOrange2
K2C
mCherrymAppleConsensus

MV S K G E E NNM
MV S K G E E DNM
MV S K G E E DNM
MV S K G E E DNM
MV S K G E E NNM
MV S K G E E DNM

A I I K E FMR F K
A I I K E FMR F K
A I I K E FMR F K
A I I K E FMR F K
A I I K E FMR F K
A I I K E FMR F K

40

V RME G S VNGHV RME G S VNGHVHME G S VNGHVHME G S VNGHVHME G S VNGHVHME G S VNGHE F E I EGEGEGE F E I EGEGEGE F E I EGEGEGE F E I EGEGEGE F E I EGEGEGE F E I EGEGEG60

R P Y E G F QT A K
R P Y E G F QT A K
R P Y E G TQT A K
R P Y E G TQT A K
R P Y E A F QT A K
R P Y E G F QT A K

L K V T K GG P L P
L K V T K GG P L P
L K V T K GG P L P
L K V T K GG P L P
L K V T K GG P L P
L K V T K GG P L P

60
60
60
60
60

100%
%

Conservation
0%
%

80

mOrange1
mOrange2
K2C
mCherrymAppleConsensus

F AWD I L S P Q FF AWD I L S P H F
F AWD I L S P Q FF AWD I L S P Q FF AWD I L S P Q FF AWD I L S P Q F

T Y G S K A Y V KH
T Y G S K A Y V KH
MY G S K A Y V KH
MY G S K A Y V KH
MY G S K V Y I KH
MY G S K A Y V KH

100

P AD I P DY F K L
P AD I P DY F K L
P AD I P DY L K L
P AD I P DY L K L
P AD I P DY F K L
P AD I P DY F K L

S F P E G F KWE R
S F P E G F KWE R
S F P E G F KWE R
S F P E G F KWE R
S F P E G F RWE R
S F P E G F KWE R

120

VMN F E DGG V V
VMN Y E DGG V V
VMN F E DGG V V
VMN F E DGG V V
VMN F E DGG I I
VMN F E DGG V V

T V TQD S S L QD 120
T V TQD S S L QD 120
T V TQD S S L QD 120
T V TQD S S L QD 120
HVNQD S S L QD 120
T V TQD S S L QD

100%%

Conservation
0%
%

140

mOrange1
mOrange2
K2C
mCherrymAppleConsensus

GE F I YKVK L R
GE F I YKVK L R
GE F I YKVK L R
GE F I YKVK L R
GV F I YKVK L R
GE F I YKVK L R

G T N F P S DG P V
G T N F P S DG P V
G T N F P S DG P V
G T N F P S DG P V
G T N F P S DG P V
G T N F P S DG P V

160

MQK K TMGWE A
MQK K TMGWE A
MQK K TMG I E A
MQK K TMGWE A
MQK K TMGWE A
MQK K TMGWE AS S E RMY P E DG
S S E RMY P E DG
S S E RMY P E DG
S S E RMY P E DG
S E E RMY P E DG
S S E RMY P E DG

180

A L K G E I KMR L
A L K G K I KMR L
A L K G EMK V R L
A L K G E I KQR L
A L K S E I KKR L
A L K G E I KMR L

K L KDGGH Y T S 180
K L KDGGH Y T S 180
K L KDGGH YDA 180
K L KDGGH YDA 180
K L KDGGH Y AA 180
K L KDGGHY X A

R A E G RH S T GG
R A E G RH S T GG
R A E G RH S T GG
R A E G RH S T GG
R A E G RH S T GG
R A E G RH S T GG

MD E L Y K 236
MD E L Y K 236
MD E L Y K 236
MD E L Y K 236
MD E L Y K 236
MD E L Y K

100%%Conservation
0%
%

200

mOrange1
mOrange2
K2C
mCherrymAppleConsensus

E VKT T YKAKKE VKT T YKAKKE VKT T YKAKKE VKT T YKAKKE VKT T YKAKKE VKT T YKAKKP VQ L P GA Y I V
P VQ L P GA Y I V
P VQ L P GA Y NV
P VQ L P GA Y NV
P VQ L P GA Y I V
P VQ L P GA Y I V

220

G I K L D I T S HN
D I K L D I T S HNN I K L D I T S HNN I K L D I T S HN
D I K L D I V S HN
X I K L D I T S HN

E DY T I V E QY E
E DY T I V E QY E
E DY T I V E QY E
E DY T I V E QY E
E DY T I V E QY E
E DY T I V E QY E

100%%

Conservation
0%%

Figure 5.4: Sequence alignment for selected red mFruits showing conservation of each residue. Each
residue is color coded for clarity.

Figure 5.5: Gap between β-strands 7 and 10 of mCherry. The gap between strands 7 and 10 of citrine was found to have a relatively
constant separation distance of ≈ 5 Åwhereas the gap between strands 7 and 10 of mCherry was found to ﬂuctuate between ≈ 5-9 Å [48].
This gap is evident in this ﬁgure where the amino acids of strands 7, 8, and 10 are depicted as spheres sized accordingly to the atoms they
represent (green is carbon, blue is nitrogen, and red is carbon).
114

115

Figure 5.6: Residues on mCherry identiﬁed for potential library construction are depicted in green,
the barrel in red, and the chromophore in blue.

116
observed (Figure 5.7). After each round of sorting, the sorted cells were recovered and maintained
as described above. Once they had recovered and suﬃciently multiplied, an aliquot was frozen
and preserved for future reference or use. Due to day-to-day variabilities in the beam intensities(discussed in Chapter 3), data for Figures 5.7 and 5.8 was taken on the same day under identicalconditions using these frozen samples to allow direct comparisons.
We observed that each round of sorting and subsequent shift towards increased photostabilityof the population resulted in a shift towards decreased brightness (as measured by the ﬂuorescencesignal from beam 1, Figure 5.8). This decreased brightness is most likely because photostability
was increased by decreasing the ﬂuorescence lifetime (discussed in Chapter 1). This hypothesis is
supported by ﬂuorescence lifetime data taken on some of the K2 mutants and is discussed later infurther detail. In an eﬀort to rescue the ﬂuorescence brightness K2 was resorted with the added
selection pressure of selecting mutants that were in the top 30% of brightness as well as in the
top 20% of photostability. This sort resulted in fewer than 5% of the cells being selected. This
population was named K4 and was used in future attempts at directed evolution detailed later.

5.5.2.2

Successful Mutants and Their Spectroscopic Characteristics

Sequencing was carried out on several mutants from the K2 and K4 populations. mCherry
was present in many of the results, however, the novel mutants shared many of the same mutations(detailed in Table 5.1). Puriﬁed protein samples were created for many of these mutants and theirquantum yields and absorption, excitation and emission spectra were measured and summarized inTable 5.2. The mutant K2C is red shifted relative to mCherry and the other K2 mutants. Examining
the K2, K4, and mCherry mutants revealed that K2C, mCherry I161M Q163V, and mCherry W143I
I161M were the most red shifted relative to the other mutants and they all contained the I161M
point mutation. A literature search revealed that the mutation I161M was found in two screens
designed to shift the ﬂuorescence wavelength further into the red region [66, 118].
The mutants’ photostabilities were then evaluated oﬀ-chip. First, puriﬁed protein samples ofthe mutants were sonicated in octanol to form emulsions and then examined under 561 nm CW laser-Figure 5.7: All data in this graph was taken on the same day on the cytometer in screening mode. Each cell population was ﬂushed outof the cytometer with phenol-free media between each run for >5 min to eliminate cross-contamination.

117

Figure 5.8: After each sort for photostability, the cell population shifted towards decreased brightness.

118

119
Table 5.1: Mutations observed for K2 and K4. In mutants with V16A mutations, an unintended
mutation, likely arising from primer synthesis, also incorporated H17R.
RFP
K2A
K2B
K2C
K2D
K2E
K2F
K4A
K4B
K4C
K4D
K4F
K4H
K4I
K4J
K4K
K4L
K4M
K4P
K4Q
K4S
K4T

V16

M66

A
A
A

Q
Q
Q

Q

W143
A
C
I
M
S
M
A
S
S
A
A
S
A
S

I161
L
V
M
V
V
V
L
V
V
L
L
V
L
V

Q163
I
T
V
L
L
W
I
L
L
I
I
L
I
L

A
A
A
A

L
L

I
I

L

I

A

L

I

Table 5.2: Spectral properties of novel mutants.
RFP
K2A
K2B
K2C
K2D
K2E
K2F
mCherrymCherry W143I
mCherry I161M
mCherry Q163V
mCherry W143I I161M
mCherry W143I Q163V
mCherry I161M Q163V

λabs (nm)
588
585
592
588
583
587
587
583
588
585
589
587
588

λex (nm)
510
586
593
588
583
588
591
583
592
586
592
588
590

N.D. = Not Determined

λem (nm)
610
607
613
607
603
606
608
611
617
614
618
618
612

φf l
0.12
0.12
0.08
N.D.
N.D.
N.D.
0.23
N.D.
N.D.
N.D.
N.D.
N.D.
N.D.

120
svaning confocal conditions. K2C was the most photostable mutant, with other mutants showing
modest improvements over mCherry. Given this result, the other mutants were no longer pursued.
K2C was then evaluated relative to mCherry by nuclear localizing both proteins within adherent
HeLa cells. Photostabilty was measured under several in vivo conditions as photostability is known
to be non-linear with excitation intensity [49]. Photobleaching measurements were conducted with
100% and 10% laser powers in a 561 nm CW laser-scanning confocal mode and in a wide ﬁeld
epiﬂuorescence mode (Figure 5.9). For laser illumination, both K2C and mCherry were ﬁt witha biexponential and the weighted lifetime was used for comparison. At 100% laser power, K2C
was ≈50% more photostable than mCherry (58 s vs 37 s, Figure 5.9, bottom panel). At 10% laser
power, K2C was ≈180% more photostable than mCherry (1700 s vs 600 s, Figure 5.9, center panel).
Under wideﬁeld CW conditions, K2C is ≈150% more photostable than mCherry (284 s vs 111 s,
Figure 5.9, top panel).
K2C, while more photostable than mCherry, had a quantum yield ≈3-fold lower than mCherry(Table 5.2). As discussed in Chapter 1, quantum yield is known to scale linearly with ﬂuorescence
lifetime. To ascertain if K2C’s ﬂuorescence lifetime decreased, resulting in greater photostability
but decreased quantum yield, time-correlated single photon measurements were preformed. K2C’s
ﬂuorescence lifetime was determined to be ≈2-fold lower than that of mCherry (Figure 5.10, indicating that a decreased ﬂuorescence lifetime most likely contributed to the improved photostability
and decreased quantum yield of K2C.

5.5.2.3

Additional Mutagenesis and Sorting on Kriek-2 and Kriek-4 Successful
Mutants

The DNA of the successful mutants from K2 and K4 (Detailed in 5.5.2.2) were combined
equally and subjected to two diﬀerent-further rounds of mutagenesis to create two new libraries:
Kriek2.1 (K2.1) and Kriek2.2 (K2.2). K2.1 consisted of the K2 mutants randomized at positions
59, 62, and 199 to all 20 amino acid options (resulting in 900,000 mutants, 12% of which were
ﬂuorescent by FACS, Figure 5.11). These positions were chosen due to their proximity to the121

Figure 5.9: Photobleaching curves for K2C and mCherry. Under wide ﬁeld photobleaching conditions, K2C was 150% more photostable than mCherry (Top Panel). For high-power confocal
photobleaching (100% laser power), K2C was 50% more photostable than mCherry (Middle Panel).
For low-power confocal photobleaching (100% laser power), K2C was 180% more photostable thanmCherry (Bottom Panel).

122

Figure 5.10: Fluorescence lifetime of K2C (≈1.0 ns) and mCherry (≈1.9 ns).

123
mutations present in K2 and K4 as they could potentially act synergistically with these mutations.
K2.2 consisted of the K2 and K4 mutants randomized at positions 70, 197, and 215 to all 20
amino acid options. These positions were selected based on their proximity to Glu215, an aminoacid known to undergo decarboxylation and thought to be involved in irreversible photobleaching(Figure 5.12) [71, 119–124]. K70 is located above the chromophore and reported as important to
the photstability of the TagRFP variants [49]. I197 is the position analogous to the Π-stacking
T203Y mutation in Citrine and Venus [23, 125].
Both K2.1 and K2.2 showed a decrease in average photostability and similar photostability
distributions (CV = 160% and 95% respectively) relative to the parent (K2, CV = 140%) and were
therefore not sorted (Figure 5.13 and 5.14).

124

Figure 5.11: Residues randomized for K2.1 library are depicted in green, the barrel in red, and thechromophore in blue.

125

Figure 5.12: Residues randomized for K2.2 library are depicted in green, the barrel in red, and thechromophore in blue.

Figure 5.13: Histogram of photostability of K2.1 relative to K2.

126

Figure 5.14: Histogram of photostability of K2.2 relative to K2.

127

128

5.6

Conclusions and Future Directions
This chapter details our initial attempts at library construction, screening, and sorting. The

results from the libraries screened thus far indicate that greater diversity in the initial libraryis needed. It also indicates that residue choice and not necessarily library size plays a criticalrole in achieving diversity in photostability. Furthermore, since many libraries are not diverse
enough to merit sorting, it would be useful to screen libraries for photostability before they are
expressed in HeLa cells, which is the rate-limiting step. Preliminary results (data not shown) showthat screening E. coli in the microﬂuidic platform gives excellent signal-to-noise and hydrofocusing
characteristics but that ﬁtting the ﬂuorescent peaks from the E. coli is diﬃcult due to cell division.
E. coli divide such that the two cells (resulting from the division of one) remain fused along thelong axis of the cell. When they are in this conﬁguration, they give rise to doublet peaks whichpose extreme diﬃculties for data acquisition and processing. Therefore, additional eﬀorts need tobe made to eliminate these fused cells and thus accelerate library development. This would also aid
in identifying key residues which drastically aﬀect the photostability. It is much less labor intensive
to screen a library as opposed to sorting it, and so it would be more eﬃcient to drastically mutate
areas of the protein with saturated mutagenesis, screen the library in bacteria, and determine if that
region of the protein signiﬁcantly altered the photostability as determined by the photostability
distribution. Regions of sequence that do signiﬁcantly alter the photostability would merit more
attention during library development. In this way, our eﬀorts would beneﬁt from this additional
information and be guided by failures and successes, instead of existing, published successes.
Sorting of the Kriek library demonstrated that a library can converge with only two roundsof sorting. However, the sorted cells are far less healthy than unsorted cells (due to interaction
with the sorting laser). This leaves the sorted population vulnerable to contamination by unsorted
cells. These factors indicate that an optimal sorting protocol would consist of selecting ≈20% of
the most photostable cells each sort and sorting fewer times.
Observing the diversity of all of the screened and sorted libraries, it is clear that additional

129
mutagenesis should be applied between sorts (i.e. directed evolution) to achieve drastically improved photostabilities. Currently, the microﬂuidic sorter is limited to library sizes of 100,000s(assuming 10% ﬂuorescence by FACS) due to the slower sort-rate relative to FACS (Chapter 4).
Creating libraries of this size and ﬁnding a drastically improved mutant has a very low probability
as evidenced by the extensive mutagenic eﬀorts detailed in the literature and our results in thischapter. However, creating an extremely large library and choosing the most photostable mutants
would be prohibitively ineﬃcient. Therefore, directed-evolution is the best solution.
Our ﬁrst attempts at directed-evolution are outlined in this chapter but additional eﬀorts areneeded to guide this course of action. However, these eﬀorts would be better informed by successful
mutants that show simultaneous improvements in photostability and brightness. Unfortunately, as
detailed above, shifts towards increased photostability resulted in decreased brightness. This result
is not surprising. As discussed in Chapter 1, selecting on the basis of one criterion often optimizes
that criterion at the expense of others. To counter this, both criteria need to be optimized simultaneously. In this chapter, an attempt to this end was made by sorting on the basis of photostability
and brightness of beam 1. However, the brightness of beam 1 is an extensive measurement of theconcatenation of many parameters including cell cycle, RFP quantum yield, cell size, and RFP
maturation. These considerations indicate that the selections should employ an intensive metric of
RFP brightness (i.e. quantum yield), which will be discussed in Chapter 6.

Chapter 6
Preliminary Frequency Domain Lifetime Measurements in Flow

6.1

Abstract
Development of the next generation of ﬂuorescent probes hinges on multiparametric sorting

methodologies. Our ability to exploit the power of sorting a single population on the basis of multiple photophysical parameters is currently limited by sorting technology which, to date, has not been
extensively developed to meet this need. Here we present the development of a microﬂuidic-based
cell sorter which can screen cells on the basis of ﬂuorescence lifetime (a proxy for quantum yield).
This instrumentation, coupled with the sorting and photostability-assesing capabilities presented in
the preceding chapters provides the foundation of a multiparametric sorter. This new instrument
is a unique tool for high-throughput, rapid analysis and selection of individual mammalian cells
which, with further development, can be used to generate advanced RFPs. Furthermore, throughanalysis of successful RFP mutants, key and, possibly, synergistic mutations can be identiﬁed tohelp elucidate the complex relationship between the structure and photophysics of these FPs.

6.2

Publication Status and Author Contributions
The work presented here is unpublished.
K.M.D., J.L.L., A.E.P., and R.J. designed research. J.L.L. built intensity modulation setup.

K.M.D. and J.L.L. performed research. J.L.L. performed data analysis.

131

6.3

Introduction
Photostability is an important parameter to improve in FPs as it limits the number of photons

that can be collected from a single protein and it limits imaging acquisition time [51,114]. However,
it is known that mutations which improve one photophysical property often diminishes another[15, 55]. In the previous chapter we demonstrated a microﬂuidic-cell sorter capable of sorting
libraries of RFPs on the basis of photostability. A new mutant with increased photostability
was discovered, but the photostability was optimized at the expense of the quantum yield. Theseexperimental results have illustrated the need for multiparametric sorts as recently discussed in [53].
To address this need, we present progress towards a multiparametric cell-sorter capable of sorting
cells on the basis of photostability and quantum yield.
Currently, the predominant methods for improving the quantum yield of FPs rely on ﬂuorescence activated cell sorting (FACS) to sort libraries on the basis of the ﬂuorescence intensity
elicited from a continuous wave (CW) laser beam [15, 23, 24, 126, 127]. Unfortunately, this measurement is the concatenation of many parameters including quantum yield, extinction coeﬃcient,
excitation/detection wavelengths, protein maturation, DSC, cell size and genome integration site.
For this reason, it is a qualitative measurement of quantum yield and therefore could lead to inaccurately including or excluding library members during a sort. For library enrichments which
require multiple rounds of sorting, an intensive measurement of quantum yield is desired to decrease
variability throughout these many selection rounds.
The ﬂuorescence lifetime (τ ) is linearly related to the the quantum yield (φf l ) by:

φf l = τ × krad

(6.1)

Where krad is the emissive rate of the ﬂuorophore. Therefore, measurements of the ﬂuorescence lifetime provide an intensive measurement of quantum yield. Recently, ﬂuorescence lifetime
was used as a metric to manually select colonies for increased quantum yield [54] and resulted in a
50% increase in quantum yield.

132

Figure 6.1: Emission is delayed in time relative to the 30 MHz modulated excitation by the phaseangle (φ) which is linearly related to the ﬂuorescence lifetime τ .

Measuring ﬂuorescence lifetime in ﬂow requires accurately determining the lifetime during the
1-2 ms the cell spends in the interrogation beam, indicating that frequency domain measurements
are best suited for this task. In frequency domain lifetime measurements, the excitation light is
rapidly intensity-modulated at a rate similar to the ﬂuorescence lifetime (25-30 MHz in the caseof FPs). The emission is delayed relative to the excitation due to the time lag between absorption
and emission (Figure 6.1). The delay is measured as a phase shift (φ) between the excitation andemission. The lifetime is linearly related to the phase shift by the excitation modulation frequency
in radians/s (ω = 2π× Hz) [51].

tan(φ) = ωτ

(6.2)

However, extra delays between the excitation and emission light can result from delays in theelectronics or optics and so careful calibration (such as that detailed in [128]) is necessary.
There is precedence for ﬂuorescence lifetime detection on a high-throughput instrument whichtakes measurements on cells or beads in ﬂow [86,87,129] but implementing this parameter in tandemwith our photobleaching metric has necessitated careful electronics and optics design. Additionally,

133
these studies have not comprehensively measured lifetimes of FPs or FPs with multi-exponential
ﬂuorescence lifetimes or long-lived dark states. Therefore, preliminary data is presented in thischapter, but further experiments and data analysis are required before multiparametric sorts are
implemented.

6.4

Experimental Results and Discussion
Here we present preliminary results for ﬂuorescence lifetime detection and multi parametricscreening. Further data analysis and experimentation is required before a comprehensive analyses
can be made. Detailed day-to-day running procedures can be found in Chapter 5.

6.4.1

Experimental Setup

The experimental setup has been described in detail in Chapter 4. However, for ﬂuorescence
lifetime detection, an extra 1 W/cm2 532 nm beam was added prior to photobleaching beams
and was modulated at 29.5 MHz by an electro-optic modulator (EOM). The experimental setup is
depicted in Figure 6.2.
The resulting modulated ﬂuorescence from the lifetime beam was collected on the same
PMT as the photobleaching signals. A schematic of the electronics is provided in Figure 6.3. The
current from the PMT was sent to a custom-built radio frequency ampliﬁer which ampliﬁed thesignal and separated the low frequency components of the signal (< 1 MHz) and sent them to acustom-built operational ampliﬁer (Appendix D) which turned the current into a voltage, further
ampliﬁed the signal (to a total gain of 10 fold), and AC coupled the signal. The AC coupled signal
contained the photobleaching information (low frequency ﬂuorescence intensities) and was sent to
the data acquisition card for further processing on the computer. The remaining radio-frequency(RF) components of the signal were sent to a 20 dB ampliﬁer and then to the custom-built I/Q
demodulator. Before the signal was processed on the I/Q demodulator, it was input to an automatic
gain control (AGC) circuit on-board of the demodulator which controlled the gain of the input signal
to keep it at a constant root mean square (RMS) value of 100 mV. This signal was then sent to the134

Figure 6.2: Optical setup for ﬂuorescence lifetime and photostability detection where LA are columnizing optics, L’s are focal-length matched mirrors, SF is a spatial ﬁlter, PD is a photodiode, BS are
50:50 beam splitters, CL is a cylindrical lens, CCD is a camera, DM2 is a notch 532 nm dichroic,
LPF2 is a 760 nm long pass ﬁlter, PM is a piezo-tilt mirror, AL2 is a 523 and 1064 nm notch ﬁlter,DM3 is a 400-700 nm transmitting, 720-890 nm reﬂecting dichroic, DM is a 532 nm notch dichroic
mirror, WLS is a wight light source, AL1 is a high numerical aperture aspheric lens, and LFP is a
540 nm long pass ﬁlter.

135
I/Q demodulation chip which output the in phase (I) and quadrature (Q) components (Appendix
D).
An I/Q demodulator compares the 29.5 MHz sine wave from the arbitrary wave form generator (AWG, which is the same signal used to modulate the EOM) with the resulting ﬂuorescencesignal. First, the reference signal from the wave form generator is split along two paths. One path
maintains the phase of the sine wave and the other shifts it 90◦ . Each reference signal is then mixed(frequency multiplied) with the RF ﬂuorescence signal. Fluorescence signal which was mixed with
the 90◦ -shifted reference signal carries the Q component and the ﬂuorescence signal which was
mixed with the 0◦ -shifted reference signal carries the I component. The I and Q peaks were thenfound using a Gaussian-ﬁtting routine on LabView and the arbitrary phase shift (φarb ) was thencalculated as:

arctan(Q/I) = φarb

(6.3)

The I/Q demodulator can impose an additional phase shift to the signal in steps of 22.5◦ .
This parameter was adjusted to maintain I≈Q to ensure an ≈45◦ arbitrary phase shift for maximum
signal to noise levels. All phase shifts are reported as arbitrary as the electronics add additional
phase shifts to the phase shift resulting from the ﬂuorescence lifetime. These extra phase shifts
arise from the distance the photons travel in the setup, additional lengths of cables between theelectronics, and delays caused by the electronics themselves [51, 128]. However, diﬀerences in PMT
voltage settings were not found to appreciably change the detected arbitrary phase shift unlike
those reported in [129]. Additionally, phase shifts caused by the setup add linearly to the lifetime
phase shift [128].

6.4.2

Measurements on Texas Red Labeled Beads

Fluorescent beads labeled with Texas Red dye (Bangs Laboratories) were assayed in the
lifetime cytometer. Texas Red had a reported lifetime of 4.15 ns at pH=7 [130]. This lifetime
measurement was made on puriﬁed dye in buﬀer and so the lifetime of the dye when conjugated to

136

Figure 6.3: Electronics of the lifetime and photobleaching setup.

137

Figure 6.4: Arbitrary phase shift histogram of Texas Red beads. The mean was 0.9297 and thestandard deviation was 0.0196, resulting in a coeﬃcient of variation (CV) of 2%.

the beads suspended in a 20% (w/v) glycerol solution should be determined experimentally. The
beads showed a narrow standard deviation of 0.0196 and an average phase shift of 0.9297 (arbitrary
units) resulting in a CV of 2% (Figure 6.4) which is approaching that reported in literature (1%[129]).

6.4.3

Measurements on Cells Expressing Red Fluorescent Proteins

Given the low CV of the beads assayed, cells expressing mOrange2 were next assayed. mOrange2 has a lifetime of 3.28 ns (measured on puriﬁed protein [49]) and also showed a very narrow
standard deviation of 0.0171 and a mean of 0.7234 (arbitrary units) resulting in a CV of 2.4%
(Figure 6.5).
Three populations of cells, each expressing either mOrange2, mCherry, or TagRFP-T, werethen assayed individually to determine their arbitrary phase shifts and then mixed together totest the instrument’s resolving power (Figure 6.6). mCherry showed a larger arbitrary phase shift(0.9721 arbitrary units) than TagRFP-T (0.8403 arbitrary units) which was larger than mOrange2(0.7233 arbitrary units). These phase shifts are in opposite order of the proteins’ lifetimes which

138

Figure 6.5: Arbitrary phase shift histogram of HeLa cells expressing mOrange2. The mean was
0.7234 and the standard deviation was 0.0171, resulting in a CV of 2.4%.

139
were 1.87 ns, 2.71 ns, and 3.28 ns for mCherry, TagRFP-T and mOrange2 respectively [49]. It
remains unclear whether the arbitrary phase shift caused by the electronics added to the phaseshift resulting from the ﬂuorescence lifetime could result in this ordering. Texas Red, which has areported lifetime longer than that of mOrange2, showed an arbitrary phase shift intermediate to thatof mCherry and TagRFP-T. Therefore, while the proteins were resolved, the correlation between the
ﬂuorescence lifetime and arbitrary phase shift does not appear to be linear. However, as discussed
earlier, the reported lifetime of Texas Red was not measured under the exact conditions that thedye was assayed on our cytometer and so this must be veriﬁed to conclude that the relationshipbetween the ﬂuorecence lifetime and arbitrary phase shift is indeed non-linear. Additionally, DSC
has been shown to have a non-negligible contribution to ﬂuorescence lifetime measurements whenthe excitation frequency approaches that of the dark-state lifetime [131]. While our excitation
frequency is much faster than the dark-state lifetimes of these FPs, calculations should be done to
fully understand their contribution.
Detection of the lifetimes of the cells also proved diﬃcult. While the ﬂuorecence signal
from each cell was of a typical magnitude (0.2-10 V), The signal to noise value for the I and Q
data was prohibitively low, frequently approaching 1, which meant most peaks on these channels
were not detected. It is interesting to note that, I and Q data peak magnitudes correlate withﬂuorescence intensity (theoretically and experimentally) but the phase shifts from the lifetimes donot (theoretically and experimentally). For these cells, I and Q peaks were only detected for thebrightest cells (ﬂuorescence signal > 9 V). It is also interesting to note that cells and beads which
had longer lifetimes (which would theoretically result in larger phase shifts) were the best detected
due to large signal to noise values (> 2) on the I and Q data (data not shown).

140

Figure 6.6: Arbirtrary ﬂuorescence phase shifts of a mixture of TagFRP-T, mOrange2, andmCherry. Standard deviations are not reported due to low sample numbers.

141

6.5

Conclusions and Future Directions
We have presented the completed experimental setup for a multiparametric cell-sorter capableof diﬀerentiating RFPs on the basis of photostability and ﬂuorescence lifetime. Preliminary data
indicates that the instrument is capable of resolving RFPs on the basis of their ﬂuorescence lifetimes
with narrow CVs. However, the correlation between ﬂuorescence lifetimes and arbitrary phaseshift must be further examined to reliably sort cells for increased quantum yield. To solve thisproblem, the lifetime of the Texas Red beads should be measured under our experimental conditions.
Additionally, computational studies (such as those presented in [131]) should be carried out tounderstand the contribution of dark states in FPs to lifetime measurements. Lastly the phaseshift induced by our electronics should be carefully investigated (such as that detailed in [128]) to
determine if it could lead to an inverse-linear correlation between ﬂuorescence lifetime and arbitrary
phase shift.
Detection of cells must also be made more reliable as only the brightest cells or cells withthe largest ﬂuorescence lifetimes (phase shifts) are currently detected. Solutions to this problem
require a better understanding of the phase shift caused by the electronics. With this knowledge, it
is possible that the electronics could be reconﬁgured to oﬀer better signal to noise or that a simple
ampliﬁer which AC couples the I and Q data (such as that used for the photobleaching signal in
Figure 6.3) could be added after the I/Q demodulator. Once these issues are solved, libraries will
be enriched on the basis of quantum yield and photostability to fully demonstrate the capabilitiesof this new instrument.

Bibliography[1] M. Chalﬁe, Y. Tu, G. Euskirchen, W. W. Ward, D. C. Prasher, et al., “Green ﬂuorescentprotein as a marker for gene expression,” Trends in Genetics, vol. 10, no. 5, pp. 151–151,
1994.
[2] R. Y. Tsien, “The green ﬂuorescent protein,” Annual Review of Biochemistry, vol. 67,
pp. 509–544, Jun 1998.
[3] D. C. Prasher, V. K. Eckenrode, W. W. Ward, F. G. Prendergast, M. J. Cormier, et al.,“Primary structure of the aequorea victoria green-ﬂuorescent protein,” Gene, vol. 111, no. 2,
p. 229, 1992.
[4] S. Inouye and F. I. Tsuji, “¡ i¿ aequorea¡/i¿ green ﬂuorescent protein: Expression of the geneand ﬂuorescence characteristics of the recombinant protein,” Febs Letters, vol. 341, no. 2,
pp. 277–280, 1994.
[5] A. Miyawaki, A. Sawano, T. Kogure, et al., “Lighting up cells: labelling proteins with ﬂuorophores.,” Nature Cell Biology, p. S1, 2003.
[6] N. C. Shaner, P. A. Steinbach, and R. Y. Tsien, “A guide to choosing ﬂuorescent proteins,”
Nature methods, vol. 2, no. 12, pp. 905–909, 2005.
[7] V. V. Verkhusha and K. A. Lukyanov, “The molecular properties and applications of anthozoa
ﬂuorescent proteins and chromoproteins,” Nature biotechnology, vol. 22, no. 3, pp. 289–296,
2004.
[8] B. N. Giepmans, S. R. Adams, M. H. Ellisman, and R. Y. Tsien, “The ﬂuorescent toolbox forassessing protein location and function,” Science Signalling, vol. 312, no. 5771, p. 217, 2006.
[9] J. Zhang, R. E. Campbell, A. Y. Ting, R. Y. Tsien, et al., “Creating new ﬂuorescent probes
for cell biology,” Nature Reviews Molecular Cell Biology, vol. 3, no. 12, pp. 906–918, 2002.
[10] R. B. Robey, O. Ruiz, A. V. Santos, J. Ma, F. Kear, L.-J. Wang, C.-J. Li, A. A. Bernardo,
and J. A. Arruda, “ph-dependent ﬂuorescence of a heterologously expressed aequorea green
ﬂuorescent protein mutant: in situ spectral characteristics and applicability to intracellular
ph estimation,” Biochemistry, vol. 37, no. 28, pp. 9894–9901, 1998.
[11] S. W. Hell, “Far-ﬁeld optical nanoscopy,” Science, vol. 316, pp. 1153–1158, May 2007.
[12] D. J. Stephens and V. J. Allan, “Light microscopy techniques for live cell imaging,” Science
Signalling, vol. 300, no. 5616, p. 82, 2003.

143[13] T. Ha and P. Tinnefeld, “Photophysics of ﬂuorescent probes for single-molecule biophysics andsuper-resolution imaging,” Annual Review of Physical Chemistry, vol. 63, no. 1, pp. 595–617,
2012. PMID: 22404588.
[14] H. Niwa, S. Inouye, T. Hirano, T. Matsuno, S. Kojima, M. Kubota, M. Ohashi, and F. I. Tsuji,“Chemical nature of the light emitter of the aequorea green ﬂuorescent protein,” Proceedingsof the National Academy of Sciences, vol. 93, no. 24, pp. 13617–13622, 1996.
[15] N. C. Shaner, R. E. Campbell, P. A. Steinbach, B. N. G. Giepmans, A. E. Palmer, and
R. Y. Tsien, “Improved monomeric red, orange and yellow ﬂuorescent proteins derived fromdiscosoma sp. red ﬂuorescent protein,” Nature Biotechnology, vol. 22, pp. 1567–1572, Nov
2004.
[16] E. M. Merzlyak, J. Goedhart, D. Shcherbo, M. E. Bulina, A. S. Shcheglov, A. F. Fradkov, A. Gaintzeva, K. A. Lukyanov, S. Lukyanov, T. W. J. Gadella, and et al., “Brightmonomeric red ﬂuorescent protein with an extended ﬂuorescence lifetime,” Nature Methods,
vol. 4, pp. 555–557, Jun 2007.
[17] N. C. Shaner, M. Z. Lin, M. R. McKeown, P. A. Steinbach, K. L. Hazelwood, M. W. Davidson, and R. Y. Tsien, “Improving the photostability of bright monomeric orange and red
ﬂuorescent proteins,” Nature Methods, vol. 5, pp. 545–551, May 2008.
[18] D. Shcherbo, E. M. Merzlyak, T. V. Chepurnykh, A. F. Fradkov, G. V. Ermakova, E. A.
Solovieva, K. A. Lukyanov, E. A. Bogdanova, A. G. Zaraisky, S. Lukyanov, and et al., “Bright
far-red ﬂuorescent protein for whole-body imaging,” Nature Methods, vol. 4, pp. 741–746,
Aug 2007.
[19] D. A. Zacharias and R. Y. Tsien, “Molecular biology and mutation of green ﬂuorescent protein,” Methods of Biochemical Analysis, Green Fluorescent Protein: Properties, Applications
and Protocols, vol. 47, p. 83, 2005.
[20] R. Heim, A. B. Cubitt, and R. Y. Tsien, “Improved green ﬂuorescence,” Nature, vol. 373,
1995.
[21] R. Heim, D. C. Prasher, and R. Y. Tsien, “Wavelength mutations and posttranslational
autoxidation of green ﬂuorescent protein,” Proceedings of the National Academy of Sciences,vol. 91, no. 26, pp. 12501–12504, 1994.
[22] R. Heim, R. Y. Tsien, et al., “Engineering green ﬂuorescent protein for improved brightness,longer wavelengths and ﬂuorescence resonance energy transfer,” Current Biology, vol. 6, no. 2,
pp. 178–182, 1996.
[23] O. Griesbeck, G. S. Baird, R. E. Campbell, D. A. Zacharias, and R. Y. Tsien, “Reducing
the environmental sensitivity of yellow ﬂuorescent protein,” Journal of biological chemistry,
vol. 276, no. 31, pp. 29188–29194, 2001.
[24] R. E. Campbell, O. Tour, A. E. Palmer, P. A. Steinbach, G. S. Baird, D. A. Zacharias, and
R. Y. Tsien, “A monomeric red ﬂuorescent protein,” Proceedings of the National Academyof Sciences, vol. 99, no. 12, pp. 7877–7882, 2002.

144[25] H.-w. Ai, J. N. Henderson, S. J. Remington, and R. E. Campbell, “Directed evolution of amonomeric, bright and photostable version of clavularia cyan ﬂuorescent protein: structuralcharacterization and applications in ﬂuorescence imaging,” Biochemical Journal, vol. 400,
no. Pt 3, p. 531, 2006.
[26] A. W. Nguyen and P. S. Daugherty, “Evolutionary optimization of ﬂuorescent proteins for
intracellular fret,” Nature biotechnology, vol. 23, no. 3, pp. 355–360, 2005.
[27] M. Chattoraj, B. A. King, G. U. Bublitz, and S. G. Boxer, “Ultra-fast excited state dynamics
in green ﬂuorescent protein: multiple states and proton transfer.,” Proceedings of the National
Academy of Sciences, vol. 93, pp. 8362–8367, Aug 1996.
[28] P. Abbyad, W. Childs, X. Shi, and S. G. Boxer, “Dynamic stokes shift in green ﬂuorescentprotein variants,” Proceedings of the National Academy of Sciences, vol. 104, pp. 20189–
20194, Dec 2007.
[29] M. Drobizhev, S. Tillo, N. S. Makarov, T. E. Hughes, and A. Rebane, “Color hues in red ﬂuorescent proteins are due to internal quadratic stark eﬀect,” The Journal of Physical Chemistry
B, vol. 113, pp. 12860–12864, Oct 2009.
[30] G. S. Harms, L. Cognet, P. H. Lommerse, G. A. Blab, and T. Schmidt, “Autoﬂuorescent proteins in single-molecule research: Applications to live cell imaging microscopy,” BiophysicalJournal, vol. 80, pp. 2396–2408, May 2001.
[31] R. M. Dickson, A. B. Cubitt, R. Y. Tsien, and W. E. Moerner, “on/oﬀ blinking and switching
behaviour of single molecules of green ﬂuorescent protein,” Nature, vol. 388, July 1997.
[32] A. A. Heikal, S. T. Hess, G. S. Baird, R. Y. Tsien, and W. W. Webb, “Molecular spectroscopy
and dynamics of intrinsically ﬂuorescent proteins: Coral red (dsred) and yellow (citrine),”Proceedings of the National Academy of Sciences, vol. 97, pp. 11996–12001, Oct 2000.
[33] D. Sinnecker, P. Voigt, N. Hellwig, and M. Schaefer, “Reversible photobleaching of enhanced
green ﬂuorescent proteins,” Biochemistry, vol. 44, no. 18, pp. 7085–7094, 2005. 924EU Times
Cited:28 Cited References Count:33.
[34] P. Schwille, S. Kummer, A. A. Heikal, W. E. Moerner, and W. W. Webb, “Fluorescence correlation spectroscopy reveals fast optical excitation-driven intramolecular dynamics of yellow
ﬂuorescent proteins,” Proceedings of the National Academy of Sciences, vol. 97, pp. 151–156,
Jan 2000.
[35] U. Haupts, S. Maiti, P. Schwille, and W. W. Webb, “Dynamics of ﬂuorescence ﬂuctuations
in green ﬂuorescent protein observed by ﬂuorescence correlation spectroscopy,” Proceedingsof the National Academy of Sciences, vol. 95, pp. 13573–13578, Nov 1998.
[36] G. Jung, J. Wiehler, and A. Zumbusch, “The photophysics of green ﬂuorescent protein:inﬂuence of the key amino acids at positions 65, 203, and 222,” Biophysical journal, vol. 88,
no. 3, pp. 1932–1947, 2005.
[37] J. Hendrix, C. Flors, P. Dedecker, J. Hofkens, and Y. Engelborghs, “Dark states in monomeric
red ﬂuorescent proteins studied by ﬂuorescence correlation and single molecule spectroscopy,”
Biophysical Journal, vol. 94, pp. 4103–4113, May 2008.

145[38] J. N. Henderson, H. W. Ai, R. E. Campbell, and S. J. Remington, “Structural basis forreversible photobleaching of a green ﬂuorescent protein homologue,” Proceedings of the
National Academy of Sciences of the United States of America, vol. 104, no. 16, pp. 6672–
6677, 2007. 159ME Times Cited:32 Cited References Count:44.
[39] K. A. Lukyanov, D. M. Chudakov, S. Lukyanov, and V. V. Verkhusha, “Photoactivatable
ﬂuorescent proteins,” Nature Reviews Molecular Cell Biology, vol. 6, no. 11, pp. 885–890,
2005.
[40] G. Donnert, C. Eggeling, and S. W. Hell, “Major signal increase in ﬂuorescence microscopy
through dark-state relaxation,” Nature Methods, vol. 4, pp. 81–86, Dec 2007.
[41] K. Mudalige, S. Habuchi, P. M. Goodwin, R. K. Pai, F. De Schryver, and M. Cotlet, “Photophysics of the red chromophore of hcred: Evidence for cistrans isomerization and protonationstate changes,” The Journal of Physical Chemistry B, vol. 114, pp. 4678–4685, Apr 2010.
[42] F. Malvezzi-Campeggi, M. Jahnz, K. G. Heinze, P. Dittrich, and P. Schwille, “Light-induced
ﬂickering of dsred provides evidence for distinct and interconvertible ﬂuorescent states,”
Biophysical Journal, vol. 81, pp. 1776–1785, Sep 2001.
[43] A. Schenk, S. Ivanchenko, C. Röcker, J. Wiedenmann, and G. Nienhaus, “Photodynamics of red ﬂuorescent proteins studied by ﬂuorescence correlation spectroscopy,” BiophysicalJournal, vol. 86, Jan 2004.
[44] X. Ragàs, L. P. Cooper, J. H. White, S. Nonell, and C. Flors, “Quantiﬁcation of photosensitized singlet oxygen production by a ﬂuorescent protein,” ChemPhysChem, vol. 12, pp. 161–
165, Jan 2011.
[45] L. Greenbaum, C. Rothmann, R. Lavie, and Z. Malik, “Green ﬂuorescent protein photobleaching: a model for protein damage by endogenous and exogenous singlet oxygen,” Biologicalchemistry, vol. 381, no. 12, pp. 1251–1258, 2000.
[46] A. Diaspro, S. Krol, B. Campanini, F. Cannone, and G. Chirico, “Enhanced green ﬂuorescentprotein (gfp) ﬂuorescence after polyelectrolyte caging,” Opt. Express, vol. 14, pp. 9815–9824,
2006.
[47] C. K. Regmi, Y. R. Bhandari, B. S. Gerstman, and P. P. Chapagain, “Exploring the diﬀusionof molecular oxygen in the red ﬂuorescent protein mcherry using explicit oxygen molecular
dynamics simulations,” The Journal of Physical Chemistry B, 2013.
[48] P. P. Chapagain, C. K. Regmi, and W. Castillo, “Fluorescent protein barrel ﬂuctuations andoxygen diﬀusion pathways in mcherry,” The Journal of chemical physics, vol. 135, no. 23,
pp. 235101–235101, 2011.
[49] K. M. Dean, J. L. Lubbeck, J. K. Binder, L. R. Schwall, R. Jimenez, and A. Palmer, “Analysisof red-ﬂuorescent proteins provides insight into dark-state conversion and photodegradation,”
Biophysical Journal, vol. 101, no. 4, pp. 961–969, 2011. 810CS Times Cited:0 Cited References
Count:30.
[50] N. C. Shaner, G. H. Patterson, and M. W. Davidson, “Advances in ﬂuorescent protein technology,” Journal of Cell Science, vol. 120, no. 24, pp. 4247–4260, 2007. 249QG Times Cited:192
Cited References Count:79.

146[51] J. R. Lakowicz, Principles of ﬂuorescence spectroscopy. New York: Springer, 2006.
[52] M. Ormo, A. B. Cubitt, K. Kallio, L. A. Gross, R. Y. Tsien, S. J. Remington, et al., “Crystal
structure of the aequorea victoria green ﬂuorescent protein,” SCIENCE-NEW YORK THEN
WASHINGTON-, pp. 1392–1395, 1996.
[53] F. V. Subach, K. D. Piatkevich, and V. V. Verkhusha, “Directed molecular evolution to design
advanced red ﬂuorescent proteins,” Nature Methods, vol. 8, pp. 1019–1026, Nov 2011.
[54] J. Goedhart, L. Van Weeren, M. A. Hink, N. O. Vischer, K. Jalink, and T. W. Gadella,“Bright cyan ﬂuorescent protein variants identiﬁed by ﬂuorescence lifetime screening,” Nature
methods, vol. 7, no. 2, pp. 137–139, 2010.
[55] T. Nagai, K. Ibata, E. S. Park, M. Kubota, K. Mikoshiba, and A. Miyawaki, “A variant ofyellow ﬂuorescent protein with fast and eﬃcient maturation for cell-biological applications,”
Nature biotechnology, vol. 20, no. 1, pp. 87–90, 2002.
[56] S. Pletnev, D. Shcherbo, D. M. Chudakov, N. Pletneva, E. M. Merzlyak, A. Wlodawer,
Z. Dauter, and V. Pletnev, “A crystallographic study of bright far-red ﬂuorescent protein
mkate reveals ph-induced cis-trans isomerization of the chromophore,” Journal of Biological
Chemistry, vol. 283, pp. 28980–28987, Aug 2008.
[57] S. Kredel, K. Nienhaus, F. Oswald, M. Wolﬀ, S. Ivanchenko, F. Cymer, A. Jeromin, F. J.
Michel, K.-D. Spindler, R. Heilker, and et al., “Optimized and far-red-emitting variants of
ﬂuorescent protein eqfp611,” Chemistry and Biology, vol. 15, pp. 224–233, Mar 2008.
[58] M. Chalﬁe and K. Kobs, Green ﬂuorescent protein: properties, applications and protocols.
Hoboken, N. J.: Wiley-Interscience, 2006.
[59] T. Karstens and K. Kobs, “Rhodamine b and rhodamine 101 as reference substances for ﬂuorescence quantum yield measurements,” The Journal of Physical Chemistry, vol. 84, pp. 1871–
1872, Jul 1980.
[60] D. Magde, J. H. Brannon, T. L. Cremers, and J. Olmsted, “Absolute luminescence yield ofcresyl violet. a standard for the red,” The Journal of Physical Chemistry, vol. 83, pp. 696–699,
Mar 1979.
[61] F. Mueller, P. Wach, and J. G. McNally, “Evidence for a common mode of transcription
factor interaction with chromatin as revealed by improved quantitative ﬂuorescence recovery
after photobleaching,” Biophysical Journal, vol. 94, pp. 3323–3339, Apr 2008.
[62] J. L. Fiore, J. H. Hodak, O. Piestert, C. D. Downey, and D. J. Nesbitt, “Monovalent anddivalent promoted gaaa tetraloop-receptor tertiary interactions from freely diﬀusing singlemolecule studies,” Biophysical Journal, vol. 95, pp. 3892–3905, Oct 2008.
[63] J. C. Butcher, Numerical methods for ordinary diﬀerential equations. Chichester, England;
Hoboken, NJ: Wiley, 2008.
[64] A. C. Hindmarsh, P. N. Brown, K. E. Grant, S. L. Lee, R. Servan, D. E. Shumaker, and C. S.
Woodward, SUNDIALS: Suite of nonlinear and diﬀerential/algebraic equation solvers. Acm
T Math Software, 2005.

147[65] M. Z. Lin, M. R. McKeown, H.-L. Ng, T. A. Aguilera, N. C. Shaner, R. E. Campbell, S. R.
Adams, L. A. Gross, W. Ma, T. Alber, and et al., “Autoﬂuorescent proteins with excitationin the optical window for intravital imaging in mammals,” Chemistry and Biology, vol. 16,
pp. 1169–1179, Nov 2009.
[66] L. Wang, W. C. Jackson, P. A. Steinbach, and R. Y. Tsien, “Evolution of new nonantibody proteins via iterative somatic hypermutation,” Proceedings of the National Academyof Sciences, vol. 101, pp. 16745–16749, Nov 2004.
[67] O. M. Subach, V. N. Malashkevich, W. D. Zencheck, K. S. Morozova, K. D. Piatkevich,
S. C. Almo, and V. V. Verkhusha, “Structural characterization of acylimine-containing blue
and red chromophores in mtagbfp and tagrfp ﬂuorescent proteins,” Chemistry and Biology,
vol. 17, pp. 333–341, Apr 2010.
[68] K. Nienhaus, H. Nar, R. Heilker, J. Wiedenmann, and G. U. Nienhaus, “Transcis isomerization is responsible for the red-shifted ﬂuorescence in variants of the red ﬂuorescent protein
eqfp611,” Journal of the American Chemical Society, vol. 130, pp. 12578–12579, Sep 2008.
[69] W. Yan, D. Xie, and J. Zeng, “The 559-to-600 nm shift observed in red ﬂuorescent protein
eqfp611 is attributed to cis–trans isomerization of the chromophore in an anionic protein
pocket,” Physical Chemistry Chemical Physics, vol. 11, no. 29, p. 6042, 2009.
[70] G. Patterson, S. Knobel, W. Sharif, S. Kain, and D. Piston, “Use of the green ﬂuorescentprotein and its mutants in quantitative ﬂuorescence microscopy,” Biophysical Journal, vol. 73,
pp. 2782–2790, Nov 1997.
[71] G.-J. Kremers, K. L. Hazelwood, C. S. Murphy, M. W. Davidson, and D. W. Piston, “Photoconversion in orange and red ﬂuorescent proteins,” Nature Methods, vol. 6, pp. 355–358,
Apr 2009.
[72] Z. Petrášek and P. Schwille, “Photobleaching in two-photon scanning ﬂuorescence correlation
spectroscopy,” ChemPhysChem, vol. 9, pp. 147–158, Jan 2008.
[73] G. Nishimura and M. Kinjo, “Systematic error in ﬂuorescence correlation measurements identiﬁed by a simple saturation model of ﬂuorescence,” Analytical Chemistry, vol. 76, pp. 1963–
1970, Apr 2004.
[74] I. Gregor, D. Patra, and J. Enderlein, “Optical saturation in ﬂuorescence correlation spectroscopy under continuous-wave and pulsed excitation,” ChemPhysChem, vol. 6, pp. 164–170,
Jan 2005.
[75] S. Veettil, N. Budisa, and G. Jung, “Photostability of green and yellow ﬂuorescent proteins with ﬂuorinated chromophores, investigated by ﬂuorescence correlation spectroscopy,”
Biophysical Chemistry, vol. 136, pp. 38–43, Jul 2008.
[76] B. Hinkeldey, A. Schmitt, and G. Jung, “Comparative photostability studies of bodipy andﬂuorescein dyes by using ﬂuorescence correlation spectroscopy,” ChemPhysChem, vol. 9,
pp. 2019–2027, Oct 2008.
[77] C. Eggeling, J. Widengren, L. Brand, J. Schaﬀer, S. Felekyan, and C. A. M. Seidel, “Analysisof photobleaching in single-molecule multicolor excitation and frster resonance energy transfer

148
measurementsä,” The Journal of Physical Chemistry A, vol. 110, no. 9, pp. 2979–2995, 2006.
PMID: 16509620.
[78] C. Eggeling, A. Volkmer, and C. A. M. Seidel, “Molecular photobleaching kinetics
of rhodamine 6g by one- and two-photon induced confocal ﬂuorescence microscopy,”
ChemPhysChem, vol. 6, pp. 791–804, May 2005.
[79] H. M. Shapiro, “Multistation multiparameter ﬂow-cytometry - a critical-review and rationale,” Cytometry, vol. 3, no. 4, pp. 227–243, 1983. Pw418 Times Cited:104 Cited References
Count:200.
[80] C. G. Wade, R. H. Rhyne, W. H. Woodruﬀ, D. P. Bloch, and J. C. Bartholomew, “Spectra of cells in ﬂow cytometry using a vidicon detector,” Journal of Histochemistry andCytochemistry, vol. 27, no. 6, pp. 1049–1052, 1979. Hb751 Times Cited:14 Cited References
Count:16.
[81] G. Goddard, J. C. Martin, M. Naivar, P. M. Goodwin, S. W. Graves, R. Habbersett, J. P.
Nolan, and J. H. Jett, “Single particle high resolution spectral analysis ﬂow cytometry,”
Cytometry Part A, vol. 69A, no. 8, pp. 842–851, 2006. 089WE Times Cited:16 Cited References Count:53.
[82] P. F. Mullaney, M. A. Vandilla, J. R. Coulter, and P. N. Dean, “Cell sizing - a light scattering
photometer for rapid volume determination,” Review of Scientiﬁc Instruments, vol. 40, no. 8,
p. 1029, 1969. D8859 Times Cited:136 Cited References Count:10.
[83] H. R. Hulett, W. A. Bonner, J. Barrett, and Herzenbe.La, “Cell sorting - automated separation of mammalian cells as a function of intracellular ﬂuorescence,” Science, vol. 166, no. 3906,
p. 747, 1969. E5938 Times Cited:128 Cited References Count:7.
[84] W. A. Bonner, R. G. Sweet, H. R. Hulett, and Herzenbe.La, “Fluorescence activated cellsorting,” Review of Scientiﬁc Instruments, vol. 43, no. 3, p. 404, 1972. L8456 Times Cited:303
Cited References Count:8.
[85] H. R. Hulett, W. A. Bonner, R. G. Sweet, and Herzenbe.La, “Development and applicationof a rapid cell sorter,” Clinical Chemistry, vol. 19, no. 8, pp. 813–816, 1973. Q3301 Times
Cited:124 Cited References Count:5.
[86] J. A. Steinkamp, T. M. Yoshida, and J. C. Martin, “Flow cytometer for resolving signals
from heterogeneous ﬂuorescence emissions and quantifying lifetime in ﬂuorochrome-labeledcells particles by phase-sensitive detection,” Review of Scientiﬁc Instruments, vol. 64, no. 12,
pp. 3440–3450, 1993. Mm177 Times Cited:31 Cited References Count:35.
[87] J. P. Houston, M. A. Naivar, and J. P. Freyer, “Digital analysis and sorting of ﬂuorescence
lifetime by ﬂow cytometry,” Cytometry Part A, vol. 77A, no. 9, pp. 861–872, 2010. 646AU
Times Cited:0 Cited References Count:18.
[88] H. H. Engelhard, J. L. Krupka, and K. D. Bauer, “Simultaneous quantiﬁcation of c-myc
oncoprotein, total cellular protein, and dna content using multiparameter ﬂow-cytometry,”
Cytometry, vol. 12, no. 1, pp. 68–76, 1991. En249 Times Cited:24 Cited References Count:43.

149[89] G. Vandenengh and C. Farmer, “Photobleaching and photon saturation in ﬂow-cytometry,”
Cytometry, vol. 13, no. 7, pp. 669–677, 1992. Jn954 Times Cited:30 Cited References
Count:18.
[90] R. M. P. Doornbos, B. G. deGrooth, and J. Greve, “Experimental and model investigationsof bleaching and saturation of ﬂuorescence in ﬂow cytometry,” Cytometry, vol. 29, no. 3,
pp. 204–214, 1997. Ye060 Times Cited:7 Cited References Count:44.
[91] J. B. Knight, A. Vishwanath, J. P. Brody, and R. H. Austin, “Hydrodynamic focusing ona silicon chip: Mixing nanoliters in microseconds,” Physical Review Letters, vol. 80, no. 17,
pp. 3863–3866, 1998. Zj826 Times Cited:389 Cited References Count:17.
[92] J. T. Verdeyen, Laser electronics. Prentice Hall series in solid state physical electronics,
Englewood Cliﬀs, N.J.: Prentice Hall, 3rd ed., 1994. 93002184 Joseph T. Verdeyen. ill. ; 25
cm. Includes bibliographical references and index.
[93] D. Li, Encyclopedia of Microﬂuidics and Nanoﬂuidics. Boston, MA: Springer-Verlag, 2008.
[electronic resource] / edited by Dongqing Li. Access is available to the Yale community.
Springer eBooks. Yale Internet Resource None Online resource.
[94] Z. Wang, J. El-Ali, M. Engelund, T. Gotsaed, I. R. Perch-Nielsen, K. B. Mogensen, D. Snakenborg, J. P. Kutter, and A. Wolﬀ, “Measurements of scattered light on a microchip ﬂow
cytometer with integrated polymer based optical elements,” Lab on a Chip, vol. 4, no. 4,
pp. 372–377, 2004. 839VY Times Cited:102 Cited References Count:46.
[95] D. P. Schrum, C. T. Culbertson, S. C. Jacobson, and J. M. Ramsey, “Microchip ﬂow cytometry using electrokinetic focusing,” Analytical Chemistry, vol. 71, no. 19, pp. 4173–4177, 1999.
241RM Times Cited:150 Cited References Count:29.
[96] Y. Morimoto, W. H. Tan, and S. Takeuchi, “Three-dimensional axisymmetric ﬂow-focusing
device using stereolithography,” Biomedical Microdevices, vol. 11, no. 2, pp. 369–377, 2009.
417QR Times Cited:8 Cited References Count:27.
[97] X. L. Mao, S. C. S. Lin, C. Dong, and T. J. Huang, “Single-layer planar on-chip ﬂow cytometerusing microﬂuidic drifting based three-dimensional (3d) hydrodynamic focusing,” Lab on a
Chip, vol. 9, no. 11, pp. 1583–1589, 2009. 448NN Times Cited:37 Cited References Count:43.
[98] X. Shu, N. C. Shaner, C. A. Yarbrough, R. Y. Tsien, and S. J. Remington, “Novel chromophores and buried charges control color in mfruits,” Biochemistry, vol. 45, no. 32,
pp. 9639–47, 2006. GM72033/GM/NIGMS NIH HHS/United States NS27177/NS/NINDS
NIH HHS/United States Journal Article Research Support, N.I.H., Extramural Research
Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. United States.
[99] M. M. Wang, E. Tu, D. E. Raymond, J. M. Yang, H. C. Zhang, N. Hagen, B. Dees, E. M.
Mercer, A. H. Forster, I. Kariv, P. J. Marchand, and W. F. Butler, “Microﬂuidic sorting of
mammalian cells by optical force switching,” Nature Biotechnology, vol. 23, no. 1, pp. 83–87,
2005. 885ZK Times Cited:122 Cited References Count:34.
[100] C. H. Chen, S. H. Cho, F. Tsai, A. Erten, and Y. H. Lo, “Microﬂuidic cell sorter withintegrated piezoelectric actuator,” Biomedical Microdevices, vol. 11, no. 6, pp. 1223–1231,
2009. 518UT Times Cited:12 Cited References Count:23.

150[101] J. L. Lubbeck, K. M. Dean, H. Ma, A. E. Palmer, and R. Jimenez, “Microﬂuidic ﬂow cytometer for quantifying photobleaching of ﬂuorescent proteins in cells,” Analytical Chemistry,
vol. 84, no. 9, pp. 3929–3937, 2012.
[102] P. C. H. Li and D. J. Harrison, “Transport, manipulation, and reaction of biological cells
on-chip using electrokinetic eﬀects,” Analytical Chemistry, vol. 69, no. 8, pp. 1564–1568,
1997.
[103] A. Y. Fu, C. Spence, A. Scherer, F. H. Arnold, S. R. Quake, et al., “A microfabricatedﬂuorescence-activated cell sorter,” Nature biotechnology, vol. 17, no. 11, pp. 1109–1111, 1999.
[104] V. Kiermer, “Facs-on-a-chip,” Nature Methods, vol. 2, no. 2, pp. 91–91, 2005.
[105] T. Franke, S. Braunmüller, L. Schmid, A. Wixforth, and D. Weitz, “Surface acoustic wave
actuated cell sorting (sawacs),” Lab on a Chip, vol. 10, no. 6, pp. 789–794, 2010.
[106] M. V. Patel, A. R. Tovar, and A. P. Lee, “Lateral cavity acoustic transducer as an on-chipcell/particle microﬂuidic switch,” Lab on a Chip, vol. 12, no. 1, pp. 139–145, 2012.
[107] T.-H. Wu, Y. Chen, S.-Y. Park, J. Hong, T. Teslaa, J. F. Zhong, D. Di Carlo, M. A. Teitell,and P.-Y. Chiou, “Pulsed laser triggered high speed microﬂuidic ﬂuorescence activated cell
sorter,” Lab on a Chip, vol. 12, no. 7, pp. 1378–1383, 2012.
[108] A. Ashkin, J. Dziedzic, and T. Yamane, “Optical trapping and manipulation of single cells
using infrared laser beams,” Nature, vol. 330, no. 6150, pp. 769–771, 1987.
[109] R. Applegate Jr, J. Squier, T. Vestad, J. Oakey, D. Marr, et al., “Optical trapping, manipulation, and sorting of cells and colloids in microﬂuidic systems with diode laser bars,” Optics
express, vol. 12, no. 19, pp. 4390–4398, 2004.
[110] S. K. Hoi, C. Udalagama, C. H. sow, F. Watt, and A. A. Bettiol, “Microﬂuidic sorting system
based on optical force switching,” Applied Physics B-Lasers and Optics, vol. 97, pp. 859–865,
2009.
[111] X. Wang, S. Chen, M. Kong, Z. Wang, K. D. Costa, R. A. Li, and D. Sun, “Enhanced cellsorting and manipulation with combined optical tweezer and microﬂuidic chip technologies,”
Lab on a Chip, vol. 11, no. 21, pp. 3656–3662, 2011.
[112] T. D. Perroud, J. N. Kaiser, J. C. Sy, T. W. Lane, C. S. Branda, A. K. Singh, and K. D.
Patel, “Microﬂuidic-based cell sorting of francisella tularensis infected macrophages using
optical forces,” Analytical chemistry, vol. 80, no. 16, pp. 6365–6372, 2008.
[113] C. Eggeling, J. Fries, L. Brand, R. Günther, and C. Seidel, “Monitoring conformational
dynamics of a single molecule by selective ﬂuorescence spectroscopy,” Proceedings of the
National Academy of Sciences, vol. 95, no. 4, pp. 1556–1561, 1998.
[114] J. Pawley, Handbook of biological confocal microscopy. Springer, 2006.
[115] J. L. Lubbeck, K. M. Dean, L. M. Davis, A. E. Palmer, and R. Jimenez, “Microﬂuidic
cytometer for simultaneous high-throughput screening of ﬂuorescent proteins on the basis of
ﬂuorescence lifetime, photostability, and brightness,” Biophysical Journal, vol. 102, p. 208,
2012.

151
[116] C. Mio, T. Gong, A. Terray, and D. Marr, “Design of a scanning laser optical trap for
multiparticle manipulation,” Review of Scientiﬁc Instruments, vol. 71, no. 5, pp. 2196–2200,
2000.
[117] G. Czerlinski, D. Reid, A. Apostol, K. Bauer, and D. Scarpelli, “Determination of the densityof cells from sedimentation studies at 1g,” Journal of Biological Physics, vol. 15, no. 2, pp. 29–
32, 1987.
[118] R. A. Chica, M. M. Moore, B. D. Allen, and S. L. Mayo, “Generation of longer emission
wavelength red ﬂuorescent proteins using computationally designed libraries,” Proceedings of
the National Academy of Sciences, vol. 107, no. 47, pp. 20257–20262, 2010.
[119] J. J. van Thor, T. Gensch, K. J. Hellingwerf, and L. N. Johnson, “Phototransformation ofgreen ﬂuorescent protein with uv and visible light leads to decarboxylation of glutamate 222,”
Nature Structural & Molecular Biology, vol. 9, no. 1, pp. 37–41, 2001.
[120] F. V. Subach, G. H. Patterson, S. Manley, J. M. Gillette, J. Lippincott-Schwartz, and V. V.
Verkhusha, “Photoactivatable mcherry for high-resolution two-color ﬂuorescence microscopy,”
Nature methods, vol. 6, no. 2, pp. 153–159, 2009.
[121] S. Habuchi, M. Cotlet, T. Gensch, T. Bednarz, S. Haber-Pohlmeier, J. Rozenski, G. Dirix,
J. Michiels, J. Vanderleyden, J. Heberle, et al., “Evidence for the isomerization and decarboxylation in the photoconversion of the red ﬂuorescent protein dsred,” Journal of theAmerican Chemical Society, vol. 127, no. 25, pp. 8977–8984, 2005.
[122] J. N. Henderson, R. Gepshtein, J. R. Heenan, K. Kallio, D. Huppert, and S. J. Remington,“Structure and mechanism of the photoactivatable green ﬂuorescent protein,” Journal of theAmerican Chemical Society, vol. 131, no. 12, pp. 4176–4177, 2009.
[123] V. Adam, P. Carpentier, S. Violot, M. Lelimousin, C. Darnault, G. U. Nienhaus, and D. Bourgeois, “Structural basis of x-ray-induced transient photobleaching in a photoactivatable green
ﬂuorescent protein,” Journal of the American Chemical Society, vol. 131, no. 50, pp. 18063–
18065, 2009.
[124] A. F. Bell, D. Stoner-Ma, R. M. Wachter, and P. J. Tonge, “Light-driven decarboxylationof wild-type green ﬂuorescent protein,” Journal of the American Chemical Society, vol. 125,
no. 23, pp. 6919–6926, 2003.
[125] R. M. Wachter, M.-A. Elsliger, K. Kallio, G. T. Hanson, and S. J. Remington, “Structural
basis of spectral shifts in the yellow-emission variants of green ﬂuorescent protein,” Structure,
vol. 6, no. 10, pp. 1267–1277, 1998.
[126] B. J. Bevis and B. S. Glick, “Rapidly maturing variants of the discosoma red ﬂuorescentprotein (dsred),” Nature biotechnology, vol. 20, no. 1, pp. 83–87, 2002.
[127] B. P. Cormack, R. H. Valdivia, S. Falkow, et al., “Facs-optimized mutants of the green
ﬂuorescent protein (gfp).,” Gene, vol. 173, no. 1 Spec No, p. 33, 1996.
[128] M. Booth and T. Wilson, “Low-cost, frequency-domain, ﬂuorescence lifetime confocal microscopy,” Journal of microscopy, vol. 214, no. 1, pp. 36–42, 2004.

152[129] B. G. Pinsky, J. J. Ladasky, J. R. Lakowicz, K. Berndt, and R. A. Hoﬀman, “Phase-resolved
ﬂuorescence lifetime measurements for ﬂow cytometry,” Cytometry, vol. 14, no. 2, pp. 123–
135, 2005.
[130] H. Brismar, O. Trepte, and B. Ulfhake, “Spectra and ﬂuorescence lifetimes of lissamine rhodamine, tetramethylrhodamine isothiocyanate, texas red, and cyanine 3.18 ﬂuorophores: inﬂuences of some environmental factors recorded with a confocal laser scanning microscope.,”Journal of Histochemistry & Cytochemistry, vol. 43, no. 7, pp. 699–707, 1995.
[131] X. Zhu and W. Min, “Frequency-domain phase ﬂuorometry in the presence of dark states: A
numerical study,” Chemical Physics Letters, vol. 516, no. 1, pp. 40–44, 2011.
[132] J. P. Brody, P. Yager, R. E. Goldstein, and R. H. Austin, “Biotechnology at low reynolds
numbers,” Biophysical Journal, vol. 71, no. 6, pp. 3430–3441, 1996.

Appendix A
Mathematica Code for Designing Microﬂuidic Geometries

The following code was created in mathematica from the equations found in the paper”Biotechnology at low Reynolds Numbers” by Brody et al. [132]. It is used by iteratively changing
the channel geometries until the target running pressure (≈1-2 psi) is achieved for the given cell
velocity (5 mm/s, in our case).

ü

2 D device
Resistance:

R = Resistance (in units of N s m-5 )h = Viscosity (in units of N s m-2 M
x = Channel Length (in units of m )w = Channel Width (in units of m )h = Channel Height (in units of m )Q = Flow Rate
Vmax = Max Velocity
P = Pressure (in units of Psi )NB: Widths are assumed to be independent of x. In cases where this is not true (channels are tappered) the average of the channel width is used and this is denoted by a subscript of "2".
R@x_, w_, h_D := 12 h x ê Hh ^ 3 wL
h = 0.0089
0.0089
Resistance of center input channel :h1 = 25 μ 10 ^ - 6 ;xc = 19.286 μ 10 ^ - 3;
xc2 = 0;wc = 100 μ 10 ^ - 6;
wc2 = 0;Rc = N@R@xc, wc, h1DD
1.318236672 μ 1015
Resistance of side input channels :xs = 26.328 μ 10 ^ - 3;
xs2 = 2.9995 * 10 ^ - 3;
ws = 150 μ 10 ^ - 6;
ws2 = N@Hws + H100 * 10 ^ - 6LL ê 2D;
Rs = N@R@xs, ws, h1D + R@xs2, ws2, h1DD
1.3637317632 μ 1015
Total input resistance :Rinput = 1 ê H1 ê Rc + 2 ê RsL
4.4940726108911956 μ 1014

2

UltimateMicrofluidicDesign.nb

Resistance of mid - section

xm = 1.032 μ 10 ^ - 3;
wm = 150 μ 10 ^ - 6;
Rm = N@R@xm, wm, h1DD
4.702617600000001 μ 1013
Resistance of outputs :xo = 25.9497 μ 10 ^ - 3;
xo2 = 2.1213 * 10 ^ - 3;
wo = 200 μ 10 ^ - 6;
wo2 = Hwo + H100 * 10 ^ - 6LL ê 2;
Ro = N@R@xo, wo, h1D + R@xo2, wo2, h1DD
9.835203456 μ 1014
Total Output resistance :Routput = Ro ê 2
4.917601728 μ 1014
Total Device Resistance :TotalDeviceResistance = N@Rinput + Rm + RoutputD
9.881936098891195 μ 1014
Necessary Pressure for velocity of (input below) :Vmax = 0.005;

In Hele - Shaw limit :Q@h_, w_, v_D := 2 h w v ê 3
FlowRate = N@Q@h1, wm, VmaxDD
1.2499999999999997 μ 10-11

UltimateMicrofluidicDesign.nb

P = FlowRate * TotalDeviceResistance ê 6894.7
1.7915819576796657

3

157

Appendix B
Microﬂuidic Cleaning and Setup Procedure

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 level at minimum level indicated onsonicator tank.
•If you need to change the liquids in the reservoirs, use tweezers and put the orange stoppers
into their water jar. The metal lid and screws do not go into the water jar.
•Try to never use high pressure (>20 PSI). With high pressure more debris will likely appear.
•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 beﬁltered if being pumped through the microﬂuidic.
•Refrigerate any solutions other than bleach and water.
•Use a new syringe and ﬁlter each day.
•Never store anything other than glass chips in bleach solutions.
•If liquid backs up into the tubes, disconnect them from the device but leave them connectedto the pressure controllers and run air through them at high pressure until dry.

158
•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 them to the
20 PSI air (on microscope) and run air through them to dry them out for >20 min.
•Fill reservoirs with ¡ 150 μL of solution.
•Fill and empty reservoirs using micropipettes.

B.2

Pre-Run•Sonicate the device in the same ≈10% bleach solution that it was stored in overnight for 40

minutes.
•Simultaneously sonicate the jar of ”o” rings and orange rubber stoppers (could add a small
amount of alconox before).
•Hold the jar with the manifold and ultra pure water (could add a small amount of alconox before) in the sonicator for a few minutes (do not want it to change temperature though,temperature swings will distort the plastic (Kel-F) manifold).
•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 oﬀ 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.
•Screw manifold retaining ring on.
•Fill reservoirs with ≤150 μL 0.2 μm ﬁltered ultrapure water.
•Place stoppers into holes with tweezers.
•Attach metal lid with two screws (can be done without tweezers).

159•Clean outsides of device interrogation region (top and bottom) with pink objective cleaner
and/or MeOH with lens tissue (after it is mounted so that ports are covered!) so that lasers to not
scatter from possible dirt.
•Pump water through device at 10-20 PSI for 5-10 minutes.
•Replace water with BSA-containing (if running cells) buﬀer solution (at this point, bubbles
should not have appeared and so the device is primed and you should not use or need to use >2
PSI for buﬀer solutions or any solutions with proteins/cells in them) and pump buﬀer solution at
2 PSI for >30 min. Throughout this procedure ”buﬀer” refers to the desired running buﬀer chosen
according to the sample one is running (e.g. cells or beads). If running beads, the buﬀer solution
is 20% w/v glycerol/ultrapure water and should NOT contain BSA.
•Empty outlets.
•Read to run! (remember to ﬁlter cells with 40 μm ﬁlter and buﬀers with 0.2 μm ﬁlter).
•If you want to recover the cells after the experiment, load the outlet reservoir (”keep”
reservoir) with 75 μL phenol-containing cell media.

B.3

Post-Run•Remove cells.
•Run buﬀer through device for 5-10 minutes to try and get rid of as many cells as possible

at 2 PSI.
•Empty outlets.
•Replace buﬀer with water and run for ≈10 min at 10-20 PSI.
•Empty outlets.
•Replace water with 100% ﬁltered bleach from bottle (use a diﬀerent pipette tip each time
you dip into bleach bottle) and run ≈10 minutes at 10-20 PSI.
•Empty inlets and outlets.
•Disassemble device holder and all parts of manifold except stainless steel quick connects.

160•Put microﬂuidic into its conical tube with new ≈10-20% 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 length of time
longer than ≈10 min.

161

Appendix C
Microﬂuidic Fabrication Procedure

C.1

General Procedure
The microﬂuidic devices are made by ﬁrst anodically bonding 2 in diameter silicon wafers to

2 in diameter pyrex glass wafers (≈ 1.78 mm thick). These pieces are then sent to a commercial
polishing facility which polishes the unit down to 1.78 mm plus 25 μm (the ﬁnal thickness of thesilicon. The silicon is then masked and etched to create the channels. The holes for the channel
ports are drilled in the top piece of glass (which may have the silicon channels or not depending on
the desired ﬁnal conﬁguration of detection). The unit is then anodically bonded to a second pyrex
wafer of desired thickness.

C.2

Anodic Bonding•Soak pyrex and silicon wafers in Piranha etch solution at 65◦ C for >30 min in clean room.
•Rinse pyrex and silicon wafers in ultra pure water in clean room.
•Rinse pyrex and silicon wafers in ethanol in clean room.
•Dry wafers with nitrogen air stream in clean room.
•Place glass wafer on top of silicon wafer in clean room.
•Keep wafers in contact and bring to anodic bonding setup and align as shown in Figures

C.1, C.2, and C.3.
•Raise temperature on brass slab to >270◦ C and then begin raising the voltage 100 V at atime (making sure that the current stays < 1 mA) until it is at 1000 V. Then wait until Newton’s162

Figure C.1: Anodic bonding setup.

163

Figure C.2: Anodic bonding setup.

164

Figure C.3: Anodic bonding setup.

165
rings disappear.
•Turn oﬀ temperature controller and let cool.

C.3

Etching Channels•Soak pyrex/silicon unit in Piranha etch solution for >30 min at 65◦ C in clean room.
•Rinse pyrex and silicon wafers in ultra pure water.
•Rinse pyrex and silicon wafers in ethanol.
•Dry wafers with nitrogen air stream.
•Follow manufacturer’s instructions for coating with SU-8 2015 (MicroChem). Bake device

at 200◦ C for 2 min to evaporate residual water.
•Center and mount device (silicon side up) on spin coater and dispense a few milliliters of
SU-8 2015 (MicroChem) on top. Follow directions to achieve a ﬁnal thickness ≈38 μm (spin at 500
RPM for 10 s then 1000 RPM for 30 s).
•Continue to follow manufacturer’s instructions. Pre bake on hotplate at 65◦ C for 2 min.
•Soft bake on hotplate at 95◦ C for 2 min.
•Let cool.
•Cover with channel mask and expose with UV for 30 s (follow clean room procedure foraligning wafers and masks).
•Post bake on hotplate at 65◦ C for 1 min.
•Post bake on hotplate at 95◦ C for 3 min.
•Rinse in SU-8 developer until channels are dissolved.
•Etch away channels in REI etcher according to clean room procedure. Use SF6 ﬂow rate of
22 at 450 W. Continue etching until all of the silicon is removed from the channels when examined
under a microscope (≈1.5 hrs).
•Remove remaining SU-8 by soaking device in Remover PG with heat.

166

C.4

Drilling Port Holes•Bond a sacriﬁcial cover glass to a piece of aluminum and then bond the 2 in pyrex wafer

onto that cover glass using crystal bond.
•Create water dam with putty (modeling clay) and ﬁll with rustlick.
•Drill holes using diamond core bit (≈ 1 mm diameter) held in sensitive chuck on mill. Do
not cut into aluminum (it will ruin the bit). Unclog the bit after each hole with wire or sonicator.
Drill with short taps using ﬁngers to lower the sensitive chuck.
•Use hotplate to release crystal bond when ﬁnished and use acetone to clean remaining crystal
bond.

167

Appendix D
Electrical Diagrams

D.1

Custom Operational Ampliﬁer

7

8

6

4

5
4-40
H1

2

3

1

V1

4-40
H2

+V
C3
0.1uF

D

Maximum PMT current (100uADC) gives 1V (DC).

C1
47pF

Install C1 OR C2DC OUT: BW ~350 kHz

R5
51

1
2

C2
5.5-25pF

PMT IN
J1

D

J2

+V

R1
10k
R2
100

1
2

D1
1N4148

2
3

C7
0.1uF

7
+
4

1

C5
1uF

6

U1
5

R3
1k

7

3

LF356N
R4
1M

C6
15nF

+
U2

2

4

C

-V

1

0.1uF
C4

5

6
LF356N

R651

1AC OUT
J3

2

2k
R7

High Pass time constant = (R3+R4)*C5.
Low Pass time constant = (R3//R4)*C6.
Displayed values give 0.16 Hz HP, 10.6 kHz LP.
Design (high pass TC)/(low pass TC) > 10.

R8
220

CW

C

220pF
C9

10k
R9

-V

0.1uF
C8

LF356 gives 500kHz BW at gain 10.
For 3 MHz BW use AD825 with a SOIC8 to DIP8 adapter.
B

B

MOUNTED IN A CU-124 BOX
JILA #: 0211
Power Input Connector:
Lumberg PN: SFV50, JILA #: 2550
+V: PIN 1
RTN: PIN 4-V: PIN 2
SOLDER SIDE VIEW
2
4
5

A

1

J4

J5
HV IN

+V

J6
HV OUT

C12
10nF
+

1

C10
4.7uF

2
3

+

C11
4.7uF

-V

3

el
ec t roni cs

JILA ELECTRONICS LABPMT Transimpedance Amplifier

DESIGN BY: T. Brown

BUILT FOR: Jimemez / Gibson

UPDATE: 12/16/2008 VERSION:
DN: JR014A1

8

7

6

5

4

3

V1 DRAWN BY: T. Brown

DATE: 12/02/2008 SHEET 1

2

OF 1

1

A

169

D.2

Custom I/Q Demodulator

7

8

6

4

5

2

3

1

A2

+5V

J1
1

D

+
10UF C7
18uF

2
3

M1 M2 M3 M44-40 4-40 4-40 4-40

D+
10UF C10
18uF

+5V
-5V

U4
LT1963AET-3.3
4
2
IN
OUT
5
1
SHDN SEN
RTN
3

C8
0.1uF

C3
0.1uF

C
R1

C1 100
0.1uF

C9
0.1uF

VDD
1
ICLK/X1
5
8
CLK
X2

R8
100

6

6
S0
4
S1

U1
PSC-2-1
0.1 - 400MHz

1

OE

R7
49.9

J3

7

GND
3

J2

C

2

ICS501A
U2

5

C6
0.1uF

+5V

B

B

C13
0.1uF
R4
10k

3
C15
0.1uF

7

+
U3
2
-

R3
10k

MAX4251ESA
50
6

L2
3.5uH

L3
3.5uH

C16
10nF

4
10k
R5
10k

R9

R6

C14
0.1uF

J6

A

A

JILA

-5V

PHASE DETECTOR AUX BOARD<TITLE2>

CU-Boulder & NISTELECTRONICSSHEET1DESIGN BY: C. Sauer

BUILT FOR: J. Lubbeck

UPDATE: 5/18/2012 VERSION:
DN:JR021

8

7

6

5

4

3

DATE:

A2

DRAWN BY: CS

5/2012 SHEET 1

2

OF 1

1