Time-Resolved Fluorescence Techniques for the Development andCharacterization of Genetically-Encoded Biosensors
byBrett L. Fiedler
B.S., State University of New York at Geneseo, 2011

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 & Sciences Department of Chemistry & Biochemistry
2017

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iiThis thesis entitled:
Time-Resolved Fluorescence Techniques for the Development and Characterization of
Genetically-Encoded Biosensors.
written by Brett L. Fiedler
has been approved for the College of Arts & Sciences Department of Chemistry andBiochemistry

Ralph Jimenez

Amy E. Palmer

Date

The final copy of this thesis has been examined by the signatories, and wefind that both the content and the form meet acceptable presentation standardsof scholarly work in the above mentioned discipline.

iii

Fiedler, Brett L. (Ph.D., Physical Chemistry)Time-Resolved Fluorescence Techniques for the Development and Characterization of
Genetically-Encoded Biosensors.
Thesis directed by Prof. Ralph Jimenez

Fluorescent biosensors are important measurement tools for in vivo quantification of pH,
concentrations of metal ions and other analytes, and physical parameters such as membranepotential. Both the development of these sensors and their implementation in examining cellular
heterogeneity requires technology for measuring and sorting cells based on the fluorescencelevels before and after chemical or physical perturbations. We developed a droplet microfluidic
platform for the screening and separation of cell populations on the basis of the in vivo responseof expressed fluorescence-based biosensors after addition of an exogenous analyte. Screening
with this instrument reveals increased heterogeneity in an array of targeted Zn2+ biosensors in
HeLa cells that helps shed light on the complexities of these sensors in different chemical
environments. Subsequently, the instrument is used to screen and assess diversity in a number of
HeLa-cell based genetic linker libraries for a family of genetically-encoded Zn2+ sensors.
Progress on sensor characterization is made using time-resolved fluorescence techniques toadvance a deeper molecular understanding of these sensors to guide further development.

iv

Dedication

For Mom, who saw who I could be.
For Dad, who drove who I would be.
For Helen, who believed in who I became.

v

Acknowledgements

I would like to acknowledge and thank those who took part in the journey that broughtme here, whether only for a stroll or for the full trek. I’ll begin with my advisor, Ralph Jimenez,
who has supported me through these six years. At every turn, Ralph would ensure that I had arich graduate career, both scientifically and personally. He always made sure he was available
when I needed help and allowed me the independence to push forward in my own pursuits. It has
been an honor to work with him. I would also like to show my appreciation for Amy Palmer.
Amy treated me as a member of her group and included me in every meeting and celebration.
She was always available to be a second advisor whenever I needed it and for that I thank her.
I would like to thank all those at CU that I have come to know over these years. I thankthe Jimenez group, both past and present for their support and putting up with the amount of
biology I forced on them. In particular, I acknowledge Felix Vietmeyer for his part as friend andmentor even after his time as a labmate. I thank the Palmer group for their conversation, endlesswillingness to help, and putting up with the amount of physics I forced on them. I thank thelasting friends I’ve made at CU: Jasper Cook, Carla and Steele Reynolds. Even after they’vegone off to other ventures, I can count on them every day. I thank the PER department and those
involved with PISEC for enriching the last few years of graduate school and welcoming me in.

viLastly, I would like to thank my family for their unstoppable belief that I could
accomplish any goal I put before myself, even from halfway across the country. My father
reminds me often that I am the first in my family line to pursue a Ph.D and I am proud to takethat mantle. Though she is not here, my mother was always proud of my academic pursuits and I
take solace in knowing she would be proud of me now. I would be nowhere without my wife,
Helen. She experienced the challenges that come with being the spouse of a doctoral student andnever backed down from being the pillar that held me up.

viiContents
Chapter 1 – Introduction
1.1

Fluorescent Proteins ............................................................................................................. 1

1.2

Genetically Encoded Biosensors.......................................................................................... 2

1.3

Quantitative Use of FRET Biosensors ................................................................................. 3

1.4

Genetically Encoded Zn2+ Biosensors ................................................................................. 9

1.5

Strategies for FRET Biosensor Optimization .................................................................... 13

1.6

Conclusions ........................................................................................................................ 16

Chapter 2 - Methods and Instrumentation
2.1

Publication Note................................................................................................................. 17

2.2

Microfluidics ...................................................................................................................... 17

2.2.1

Design Considerations ................................................................................................ 17

2.2.2

Droplet Generation ..................................................................................................... 19

2.2.3

Reagent Mixing .......................................................................................................... 22

2.2.4

Droplet Occupation..................................................................................................... 24

2.2.5

Droplet Sorting ........................................................................................................... 26

viii

2.2.6
2.3

Microfluidic Fabrication ............................................................................................. 29

Experimental Design .......................................................................................................... 30

2.3.1

Optical Configuration ................................................................................................. 30

2.3.2

Pressure Driven Flow Configuration .......................................................................... 33

2.3.3

Electronics .................................................................................................................. 34

2.3.4

Sample Preparation and Instrument Operation ........................................................... 36

2.3.5

Cell viability ............................................................................................................... 42

Chapter 3 - Droplet Microfluidic Flow Cytometer for Sorting on Transient Cellular
Responses of Genetically-Encoded Sensors
3.1

Publication Note................................................................................................................. 44

3.2

Motivation .......................................................................................................................... 45

3.3

Experimental Setup ............................................................................................................ 46

3.3.1

Sensor Constructs ....................................................................................................... 46

3.3.2

Design of ZapCV2 and ZapCV5 Zn2+ sensors ........................................................... 47

3.3.3

In vitro characterization of ZapCV2 and ZapCV5 ..................................................... 47

3.3.4

Cellular imaging for in vivo calibration ..................................................................... 49

3.3.5

Sample Preparation ..................................................................................................... 51

3.3.6

Microfluidic Design and Experiment Parameters ...................................................... 52

3.4

Calibration experiments for FRET sensors ........................................................................ 53

3.5

Tracking cells through pair-matching ................................................................................ 57

3.6

Time-resolved differentiation of ZapCV2/ZapCV5 sensor mixture.................................. 59

3.7

Sorting of a mixed population............................................................................................ 62

3.8

Impact of throughput and operation on instrument applications ....................................... 66

3.9

Conclusions ........................................................................................................................ 73

ix

Chapter 4 - Systematic Evaluation of ER Sensors and Development of a Sensor Linker
Library
4.1

Publication Note and Author Contributions ...................................................................... 74

4.2

Genetically Encoded Zn2+ Sensors Targeted to Subcellular Organelles ........................... 74

4.3

Experimental Setup ............................................................................................................ 76

4.3.1

ER Sensor Cloning and Cell Culture .......................................................................... 76

4.3.2

Library Cloning .......................................................................................................... 77

4.3.3

Mammalian Cell Culture for Libraries ....................................................................... 79

4.3.4

Viral Transduction for Libraries ................................................................................. 79

4.3.5

Genomic DNA Extraction .......................................................................................... 81

4.4

Zn2+ Sensor Response in Subcellular Organelles ............................................................. 82

4.4

Heterogeneity in the Unbound State .................................................................................. 82

4.5

Dynamic Response of ER and Cytosol Targeted Sensors and Sensor Variability ............ 91

4.6

Generation of Linker Randomized Targeted Zn2+ Sensor Libraries .................................. 94

4.6.1

Library Rationale ........................................................................................................ 94

4.6.2

Library Design and Selection Process ........................................................................ 97

4.6.2

Initial Library Screening and Platform Iteration ...................................................... 100

4.7

Microfluidic Screening and Initial Sorting of Sensor Libraries....................................... 104

4.7.1

Pro/Ser Library ......................................................................................................... 104

4.7.2

VVC1-2 Library ....................................................................................................... 104

4.7.3

VVC3 Library ........................................................................................................... 107

4.7.4

VVC1-2 Sort and Troubleshooting........................................................................... 107

4.8

Conclusions and Future Directions .................................................................................. 113

xChapter 5 - Time-Resolved Photophysical Characterization of Zn2+ FRET Sensors
5.1

Time-Resolved Fluorescence of Genetically-Encoded Sensors ...................................... 114

5.1.1

Fluorescence Lifetime Measurements ...................................................................... 114

5.1.2

Fluorescence Anisotropy .......................................................................................... 116

5.1.3

Fluorescent Proteins as Anisotropy Probes .............................................................. 120

5.1.4

Time-resolved FRET ............................................................................................... 120

5.2

Experimental Setup .......................................................................................................... 123

5.2.1

Sample Preparation ................................................................................................... 123

5.2.2

Instrumentation ......................................................................................................... 124

5.3

Fluorescence Anisotropy Calculations ............................................................................ 126

5.4

Time-resolved Fluorescence Results ............................................................................... 131

5.4.1

TRF of a Small Molecule Dye .................................................................................. 131

5.4.2

Effect of His-tag on ECFP Zn2+ Response using TRF ............................................ 133

5.4.3

Time-resolved Fluorescence Measurements on Zinc Sensors .................................. 137

5.4.4

Time-resolved Fluorescence Anisotropy Results for Zinc Sensors.......................... 141

5.5

Conclusions and Future Directions .................................................................................. 151

Bibliography .............................................................................................................................. 152

Appendix

Appendix A - Protocols .............................................................................................................. 166
A.1

PDMS Device Manufacturing Protocol ....................................................................... 166

A.2

Microfluidic Operation Protocol .................................................................................. 171

Appendix B - Code ..................................................................................................................... 175

xi

B.1

Microcontroller Code ................................................................................................... 175

B.2

Mathematica Code for Anisotropy Calculations .......................................................... 191

B.3

Mathematica Code for Microfluidic Device ................................................................ 195

xii

TablesTable
3.1

ZapCV2/ZapCV5 Sorting Results .................................................................................. 64

4.1

FRET Distribution Statistics for ZapCY1, eCALWY-4, and e-ZinCh-2 ....................... 89

4.2

FRET Distribution Statistics for Screened Libraries.................................................... 109

5.1

Cyan and Yellow Fluorescent Protein Absorption Coefficients .................................. 123

5.2

Fluorecein Dianion Anisotropy Fit Results.................................................................. 131

5.3

ECFP Anisotropy Fit Results ....................................................................................... 135

5.4

ZapCY1 TCSPC Fit Results ......................................................................................... 140

5.5

eCALWY-4 TCSPC Fit Results................................................................................... 140

5.6

eZinCh-2 TCSPC Fit Results ....................................................................................... 140

5.7

ZapCY1 FRET Channel Anisotropy Fit Results .......................................................... 146

5.8

ZapCY1 CFP Channel Anisotropy Fit Results ............................................................ 146

5.9

eCALWY-4 and eZinCh-2 FRET Channel Anisotropy Fit Results ............................. 150

5.10 eCALWY-4 and eZinCh-2 CFP Channel Anisotropy Fit Results ............................... 150

xiii

FiguresFigure1.1

Jablonski Diagram for FRET ........................................................................................... 6

1.2

Crystal Structure of ECFP ................................................................................................ 7

1.3

Zap family Zinc Sensor Schematic ................................................................................ 12

2.1

Droplet Failure in Absence of Surfactant ....................................................................... 21

2.2

Y-shaped Mixing Junction ............................................................................................. 23

2.3

Poisson Distrubution and Single Cell Droplet Occupation ............................................ 25

2.4

Droplet Sorting and Droplet Sorting Junction................................................................ 28

2.5

Droplet Merging After Dilelectrophoretic Field Exposure ............................................ 29

2.6

Epifluorescence Microscope and Microfluidic Implentation ......................................... 32

2.7

Raw Signal from HeLa Cells Passing Interrogation Beam ............................................ 33

2.8

Microcontroller Setup and Streaming Data Trace.......................................................... 35

2.9

Sensor Expression in HeLa-S3 and HeLa Cells ............................................................. 37

2.10 Image of Inlet with Cell Aggregation ............................................................................ 39
2.11 OptiPrep Optimization Experiment Images ................................................................... 41

xiv

3.1

ZapCV2/ZapCV5 Schematic and In Vitro Titrations..................................................... 50

3.2

Optical Configuration and Microfluidic Assay Schematic ............................................ 54

3.3

ZapCV2 and ZapCV5 Calibrations ................................................................................ 55

3.4

Schematic Comparing Calibrations to Microfluidic Assay............................................ 56

3.5

Schematic of Sorting Logic and Example Time Delay Histogram ................................ 60

3.6

FRET Histograms for Zinc Sensor Mixtures for Dynamic Range Differentation ......... 61

3.7

Kinetic Resolution of ZapCV2 and ZapCV5 ................................................................. 63

3.8

Pairmatched Scattergram of Mixed ZapCV2/ZapCV5 Sorting Experiment .................. 65

3.9

Fluorescence Images Demonstrating Sorting Enrichment ............................................. 66

3.10 Zinc Concentration Profile in Mixing Junction.............................................................. 70

4.1

FRET Histograms for ZapCY1 in Three Cellular Compartments ................................. 83

4.2

Calibrations for Three Zinc Sensors in the ER and Cytosol .......................................... 84

4.3

ZapCY1 Calibration Illustrating Response Time Window in Microfluidic ................... 86

4.4

FRET Histograms for ZapCY1, eCALWY-4 and eZinCh-2 ......................................... 88

4.5

Fluorescence Images and Western Blot of Three Zinc Sensors ..................................... 90

4.6

Intensity Scattergram and Histograms of Three Zinc Sensors ....................................... 93

4.7

Workflow for Library Generation and Sorting .............................................................. 99

4.8

FRET Histogram for Transiently Expressed ER-ZapCY2........................................... 100

4.9

FRET Histogram for Virally Transduced Expression of ER-ZapCY2 ........................ 101

4.10 Sequence Homology for ECFP and mCitrine .............................................................. 102
4.11 FRET Histogram for ER-OptiZap ................................................................................ 103
4.12 Cytometry Data for Pro/Ser Library............................................................................. 105
4.13 Cytometry Data for VVC1-2 Library ........................................................................... 106
4.14 Cytometry Data for VVC3 Library .............................................................................. 108

xv

4.15 Sorting Data for VVC1-2 Library ................................................................................ 110
4.16 DNA Gels for gDNA PCR Amplification of Zinc Sensor Sequence .......................... 112

5.1

Molecular Definitions for FRET .................................................................................. 119

5.2

Overlaid ECFP and mCitrine Absorption and Emission Spectra ................................. 125

5.3

Time-Resolved Anisotropy Calculation Results .......................................................... 130

5.4

Fluorescein Dianion TCSPC and TRFA Histograms ................................................... 132

5.5

Steady-State Anisotropy of Zinc Sensors..................................................................... 134

5.6

TCSPC Histograms for TEV Treated ECFP ................................................................ 136

5.7

TCSPC Histograms for ZapCY1 .................................................................................. 138

5.8

TCSPC Histograms for eCALWY-4 ............................................................................ 139

5.9

TCSPC Histograms for eZinCh-2 ............................................................................... 139

5.10 Raw FRET TRFA Traces of Zinc Sensors ................................................................... 141
5.11 TRFA Traces for ZapCY1 in the FRET Channel ........................................................ 144
5.12 TRFA Traces for ZapCY1 in the CFP Channel ........................................................... 145
5.13 TRFA Traces for ZapCY1 in the FRET Channel ........................................................ 148
5.14 TRFA Traces for eCALWY-4 and eZinCh-2 in the CFP Channel .............................. 149

1

Chapter 1
Introduction

1.1

Fluorescent Proteins
The discovery of the fluorescent protein shuttled in an unparalleled shift in the way

researchers studied cellular structure and processes in vivo. Beginning with the green fluorescentprotein (GFP) derived from Aequorea victoria, it was possible to avoid the problems associatedwith the loading of cells with exogenous small molecule dyes, as well as difficulties with dye
localization to subcellular organelles. GFP is a highly structured, cylindrical protein characterized
by an 11-stranded beta barrel and a buried chromophore formed by the autocatalytic reaction of
three amino acids (Ser65-Tyr66-Gly67) in aerobic conditions.1 The chromophore is nonfluorescent in the absence of a rigidifying environment (e.g. embedded within an amino acidpocket or frozen in a solvent) and highly sensitive to changes in that environment.2,3 This has
allowed for extensive study and alteration of the chromophore environment to tune its spectral
properties through mutation of surrounding amino acids. GFP is easy to incorporate into the
genome of any organism compatible with genome editing and is amenable to the inclusion oftargeting sequences to localize the protein to subcellular organelles.4

2
The existing palette of fluorescent proteins (FPs) has expanded substantially through thediscovery of similar proteins from other organisms, as well as mutations to the chromophore andits surrounding amino acids to change its electronic structure and consequently the absorption andemission wavelengths. The available range of fluorescent proteins now expands from the near-UV
to the near-infrared with notable visible categories including the blue, cyan, green, yellow, orange
and red fluorescent proteins.5 This has enabled the ability to generate experiments withmultiplexed FPs or FPs and dyes. Additionally, the segregation of FPs into separate but
overlapping optical windows have made them ideal candidates for utilization in Förster Resonance
Energy Transfer (FRET) experiments. Fluorescent proteins are simple to incorporate in fusion
protein constructs, making them invaluable for tagging intracellular proteins or adaptation as
biological analyte sensors (biosensors).6–9

1.2

Genetically Encoded Biosensors
Spatiotemporal investigations of molecular species are among the cornerstones on which

the modern understanding of cellular function is built. Biosensors based on fluorescent proteins
have been constructed to quantify free levels of many important cellular analyte including Ca 2+,
pH, NAD+/NADH, ADP/ATP, cGMP, halides and many metal cations.1,6,10,11 Sensing is achievedby a number of mechanisms including, but not limited to Förster Energy Resonance Transfer(FRET) and emission intensity or wavelength shifts modulated by conformational changes andenvironmental sensitivity.12 Full characterization of a biosensor’s dynamic range, a criticalparameter for determining its usefulness, requires fluorescence measurements prior to and after
cellular perturbation. These fluorescence changes in vivo typically occur on the tens of
milliseconds to several minutes timescale, for example in response to a signal cascade, such as3
Ca2+ and IP3 oscillations after stimulation by glutamate13 or cGMP production in response toNO14,15; transient perturbations in the cellular environment, such as cellular pH changes andreactive oxygen species bursts16,17; or changes in membrane potential.18,19 In addition tobiosensing, cellular control via optogenetic tools intrinsically involves the measurement oftransient responses. For example, when photoexcited at 488 nm, components of the CRY2
optogenetics system cluster on the second timescale and then revert to a diffuse state after exposure
is terminated.20,21 In all these cases, quantifying the heterogeneity of these responses anddetermining whether it is intrinsic to the sensor or to genetic or epigenetic cell variability is adifficult subject of investigation which is impeded by the low-throughput microscopy techniques
traditionally employed in this field. Beyond applications to existing biosensors, development of
next-generation sensors and optogenetics tools would be facilitated by the high throughput
screening and sorting of genetic libraries based on transient cellular response.

1.3

Quantitative Use of FRET Biosensors
This thesis will focus specifically on the use of sensors utilizing FRET as their sensing

mechanism. Measurements of FRET sensors in live cells generally involve using standard
widefield fluorescence microscopy or confocal microscopy, as this is the most available
instrumentation. As such, FRET ratios are generally measured by sensitized emission, rather thanmore complicated methods such as lifetime or anisotropy microscopy. Sensitized emission ismeasured by exciting at the donor wavelength and measuring the emission at the donor detection
window (CFP donor) and the acceptor detection window (FRET channel). FRET ratios, specificto CFP/YFP sensors are defined as:

4
𝑅=

𝐼𝐹𝑅𝐸𝑇 −𝐼𝐹𝑅𝐸𝑇 𝑏𝑘𝑔𝑑
𝐼𝐶𝐹𝑃 −𝐼𝐶𝐹𝑃 𝑏𝑘𝑔𝑑

(1.1)

where I is the intensity in the respective channels and ‘bkgd’ indicates the background signal inthat channel. Determining the dynamic range of a sensor requires the measurement of R during
depletion of the analyte, denoted Rmin or Rapo, and again during saturation of the analyte, denoted
Rmax. The dynamic range can then be defined either as the ratio
𝐷𝑅 = 𝑅𝑚𝑎𝑥 /𝑅𝑚𝑖𝑛 ,(1.2)

𝐷𝑅 = 𝑅𝑚𝑎𝑥 − 𝑅𝑚𝑖𝑛 ,(1.3)

𝐷𝑅 = (𝑅𝑚𝑎𝑥 − 𝑅𝑚𝑖𝑛 )/𝑅𝑚𝑖𝑛 .

(1.4)

the differenceor the normalized ratio

The first formulation is the one most often cited in the literature. Put together, the measurementof a resting R, Rmax and Rmin are the basis of a calibration experiment. The fractional saturation,
Y, can be also be determined at an arbitrary value of R between these bounds in a one site
binding model as
𝑌 = (𝑅 − 𝑅𝑚𝑖𝑛 )/(𝑅𝑚𝑎𝑥 − 𝑅𝑚𝑖𝑛 ).

(1.5)

Combining Y with the measured in vitro KD for the sensor results in a measured concentration of
the ligand, given that the concentration falls within a factor of 10 on either side of the KD. This is
one reason an array of sensors must be produced and no one sensor for a given analyte issufficient or appropriate in all biological contexts.
The quantitative use of intramolecular FRET sensors is complicated by numerous factors
as mentioned in the previous section. First, the dynamic range of the sensors is dependent on theparameters that determine the FRET efficiency between the donor/acceptor pair. FRET is theradiationless transfer of energy from one singlet to a coupled singlet state, where the rate of5transfer is steeply dependent on both the distance and orientation between the transitions dipole
moments (TDMs) of the donor and acceptor chromophore. A simplified Jablonski diagram forthe relevant transitions for the ground and first excited state of the donor and acceptor is shownin Figure 1.1. The formulation of the FRET rate, kT, is9000 ln 10

𝑘𝑇 = (128 𝜋5𝑛4 𝑁 )𝐴𝜅 2 𝑄𝐷 𝐽(𝜆)𝑅 6 𝜏𝐷(1.6)

where n is the refractive index of the surrounding media, NA is Avogadro’s number, κ2 is theorientation factor based on the TDMs of the FRET pair, QD is the fluorescence quantum yield of
the donor, J(λ) is the normalized overlap integral between the emission of the donor andabsorption of the acceptor, R is the distance between the chromophores, and τD is thefluorescence lifetime of the donor. For any given FRET pair, all terms but κ and R are constants.
Thus, for any FRET pair, to optimize the dynamic range of a sensor, both the distance andorientation between the two fluorescent proteins must change substantially. Fluorescent proteinspresent an immediate drawback when used for FRET for the same reason the chromophore
shows remarkable resilience to environmental conditions, namely the protective amino acidbarrel. This barrel makes the protein relatively large compared to the length scales over which
FRET takes place (nm). The width of the barrel is ~2 nm for most FPs, meaning two FPs could
not get closer than 2 nm, restricting the value of R in the high FRET case. See Figure 1.2 for the
top-down view of a fluorescent protein with chromophore and barrel distances visible. The
implications of the distance and orientation factor are discussed in detail in Chapter 5.
Secondly, all three portions (donor, acceptor, binding domain) of the construct are
sensitive to environmental factors including pH, oxidation/reduction, and crowding to name a6Figure 1.1. Jablonski diagram for the relevant transitions for the ground and first excitedstate of the donor and acceptor. Transition are shown for (1) the attosecond absorption of aphoton for the donor, (2) femtosecond to picosecond vibrational relaxation to the firstexcited state for donor, (3a) piscosecond–nanosecond donor fluorescence, (3b) picosecond–nanosecond radiationless energy transfer, and (4) picosecond–nanosecond acceptor
fluorescence.

Figure 1.2. Crystal structure of ECFP (top-down view and side view). The barrel of fluorescentproteins is fairly consistent across variants with a minimum radius of ~1 nm. This results in aminimum separation of ~2 nm between 2 juxtaposed fluorescent proteins. PDB 2WSN.

7

8
few. The most popular FRET pair is the CFP/YFP pair with ECFP remaining the most prevalent
donor despite a number of improved variants now available. Extensive work has been done toshow that ECFP has high sensitivity to a number of biological molecules and ions throughinteraction with outward facing amino acids (His148, Tyr145).22 The YFP family, especiallythose that have been circularly permuted, such as mVenus, show high sensitivity to pH.23 FPs,
originally derived from dimers, have shown tendencies to form weak homodimers and tetramers
even after mutations meant to convert them to monomeric forms.2 The analyte binding domains,
especially those responsible for binding metal ions, are generally pH sensitive and redox
sensitive. Zn2+ specific sensors often contain cysteine residues with their redox capable
sulfihydryl groups and potential for disulfide bond formation. All of these factors must be
accounted for when optimizing FRET sensors for localized in vivo measurements.
The ideal sensor is difficult to achieve given the number of variables needed to control. In
a perfect world, a sensor would show high brightness (above background fluorescence ofendogenous autofluorescence), high photostability (does not bleach over the course of theexperiment) with no photoconversion, fast maturation (sensor is quickly and completely
expressed in as many organisms as possible), monomeric (does not dimerize or aggregate unless
it is a feature of the sensor), environmentally insensitive (only changes due to ligand
concentration contribute to signal change) and, lastly, a large dynamic range (high signal tonoise).

9

1.4

Genetically Encoded Zn2+ Biosensors
The field of Zn2+ homeostasis is of particular interest due to the implication of Zn2+ in all

aspects of cellular function. Zn2+ has long been known to play a critical role in biological
function, ranging from a cofactor in enzymes, a structural component in many proteins, and anecessary component of many transcription factors.24 Studies have shown Zn2+ release inglutamatergic neurons25,26, as well as a discovery of a Zn2+ ‘spark’ accompanying expected Ca2+
dynamics during human egg activation.27,28 These discoveries also implicate Zn2+ as a signalingion, shedding a whole new light on Zn2+ in biology. Fluorescent Zn2+ sensors are invaluable intheir ability to spatially and dynamically resolve Zn2+ homeostasis in vivo. There are a number ofexisting platforms with which to make these measurements, each with its own advantages andpitfalls.
The first molecules made available for in vivo Zn2+ monitoring were small molecule dyes
combined with a specific metal chelator for intensity-based sensing. Such Zn2+-dependent
fluorescent sensors are now available over a range of binding constants, wavelengths, andbrightness (molar extinction coefficient × fluorescence quantum yield).29 These sensors also
boast impressive dynamic ranges from 2-200-fold. Ratiometric sensors also exist, which utilize
emission changes at different wavelengths, albeit with smaller dynamic ranges. Whilecustomization of small molecule sensors is extensive and invaluable for in vitro Zn2+
determination, their use in vivo is hampered by the need for the molecules to bypass the plasma
membrane and localize to the cellular compartment of interest. The uptake and localization ofthese dyes is complex and heterogeneous. There are many strategies for membrane permeation
including the incorporation of a cleavable AM ester to the molecule or microinjection.7 Manydyes show significant mislocalization and aggregation within cells due to a number of10
environmental factors outside of a researcher’s control.30 Several properties influence thelocalization of a sensors including molecular charge, lipophilicity, and solubility in the cellularenvironment.29 In some cases, these properties can be manipulated to influence accumulation in
specific organelles, such as the use of sensors designed from a positively-charged rhodamine
derivative, which is driven across the negative membrane potential of the mitochondria.31
Controlling the amount of dye that is present in the cells presents an additional challenge. With
respect to measuring metal ion homeostasis, which is effectively measuring the unboundconcentration of metal ions in the cell, accumulation of excess sensor can deplete free ion
concentration. Depletion of the free ion pool can sway ion homeostasis and greatly affect cellularfunction.9,32
Genetically encoded Zn2+ sensors generally incorporate a fluorescent protein as afluorescent readout, either intensity-based or wavelength ratiometric, and a Zn2+ binding domain.
FRET-based sensors undergo a conformational change upon Zn2+ binding such that the distanceand orientation between the FPs is altered, changing the measured FRET ratio measured as the
IFRET/. The first fully genetically encoded Zn2+ sensor were based on the FRET based Ca2+
sensors. These incorporated a ECFP/Citrine FRET pair flanking a single zinc finger binding
domain for Zn2+ measurement in mammalian cells.33 Since then, several groups have introduced
a variety of platforms for FP-based FRET-mediated Zn2+ sensing. Two popular sensor platforms,
the eCALWY family and the eZinCh family, were generated by the Merkx lab utilizing different
Zn2+ binding motifs and modifications to the FPs to optimize the FRET properties of thesensors.34,35 These sensors will be discussed in more detail in Chapter 4. Other geneticallyencoded Zn2+ sensor platforms exist, including but not limited to a hybrid platform utilizing a red
FP and a small molecule dye, intensity-based single FP sensors based on Zn2+ binding induced

11chromophore quenching, and a sensor utilizing bioluminescence resonance energy transfer.36–38
These sensors are currently underutilized outside of the labs in which they were generated.
The work presented in this thesis was performed in close collaboration with the Palmer
lab at CU-Boulder, which generates genetically encoded Zn2+ sensors and utilizes them to study
Zn2+ dynamics in an array of cell lines and their subcellular organelles. The FRET sensors
constructed by this group include CFP/YFP sensors, as well as the red-shifted sensors using
GFP/RFP, across a range of affinities and dynamic ranges. The sensors developed to date share azinc finger binding domain consisting of either the mammalian derived zinc finger Zif268 or the
first two fingers of the yeast derived Zap1 zinc finger. A schematic of the Zap1 containing
sensors is found in Figure 1.3. The zinc finger has been shown to be largely unstructured in theabsence of Zn2+, taking on the characteristic zinc finger structure upon binding.39
The panel of genetically encoded Zn2+ sensors is quickly growing, but all current sensors
share a number of problems complicating their use. First and foremost is a lack of dynamic
range. The goal of these sensors is, when combined with the affinity of the sensor, to use the fullrange of the sensor to establish a fractional saturation (% of sensor bound to ligand) and correlatethat with a Zn2+ concentration. When the dynamic range of a sensor is small, it is difficult todistinguish response from the noise. It was shown that a decrease in dynamic range can affect themeasured fractional saturation for sensors with similar affinity.40 Furthermore, measurementswith sensors of small dynamic range, may miss important signaling events or transient fluxesthat could be mistaken for noise. It has been rigorously shown that these sensors responddifferently, often less well, when targeted to organelles other than the cytosol.41,42 This indicatesthe importance of studying these sensors in varying environmental conditions including thevariance of pH, redox, and molecular crowding. These sensors should be optimized for the12cellular environment for which they will be used. This topic will be discussed and analyzed in
more detail with respect to the FRET-based Zn2+ sensors in Chapter 4. A keen molecularunderstanding of these constructs will be necessary for developing more robust and highdynamic range sensors.

Figure 1.3. Sensor schematic for the Zap family of Zn2+ sensors. The sensors incorporate
ECFP as the FRET donor and either mCitrine or mVenus circularly permuted at position
173 as the acceptor. The FPs are connected to the Zap1 Zn2+ binding domain by smallamino acid linkers designated by the nucleic acid restriction sites and small glycine/serine
repeats. Sizes are shown next to each domain in number of amino acids (aa).

13

1.5

Strategies for FRET Biosensor Optimization
FRET biosensor optimization can be performed using a myriad of methods. It is common

for initial construction of a sensor to consist of choosing a FRET pair and sandwiching it arounda domain known to bind the ligand of interest. While this may generate a functional sensor, themutations required to increase dynamic range and sensor kinetics are not well understood forevery sensor platform. It is common to make incremental mutations to the FPs, linker regions, orbinding domains with literature precedent and test each variant generated.32 This method rarely
samples the full space of potential mutations that could lead toward an optimized sensor and isslow due to the laborious manual screening process of expressing and calibrating each variant.
An example of this type of methodology that has resulted in significant improvement is thecircular permutation of the acceptor domain in genetically encoded calcium indicators.43 Anotherincludes the simple lengthening of the linker region in a family of Zn2+ sensors.34
Higher throughput methods for sensor optimization employ large libraries of mutations.
The mutations result in randomization of the amino acids in regions of the sensor guided byrational engineering of a parent sensor and literature precedent. Manually screening large librarysizes (>102) is laborious, requiring expression and in vivo testing of each mutant. The process of
screening and selecting improved variants benefits greatly from automation via high-throughput
methods. This is further complicated by transient responses of many sensors, which required
more vigilant monitoring after addition of ligand. The ideal method would probe the FRET stateof the molecule both before and after exposure to ligand with adequate mixing times (msec-sec)
to assess the full dynamic range of the sensor. Additionally, this method would allow for sensor
screening in mammalian cells which are of great interest to researchers due to their use in14
medicine. An overview of the instrumentation available to screen large libraries of geneticallyencoded fluorescent sensors is detailed below.
Fluorescence activated cell sorters (FACS) and flow cytometers are laser-based
technologies that probe cell suspensions at the single cell level based on their fluorescence or lightscattering properties. This is often used for cell counting or fluorescently-labeled biomarker
detection.44 In the case of FACS, cells with desired optical properties can also be physicallyseparated from the initial suspension. These technologies are highly optimized for speed(thousands of cells analyzed per second), and single-time point, multi-wavelength excitation andemission measurements, but they are not suitable for introducing analytes and measuring transient
biosensor responses on the msec-to-sec timescale. Commercial instruments operate at flow
velocities of m/s, with a maximum delay of hundreds of microseconds between laser excitation
points. Customized flow cytometers adapted for kinetic measurements on the order of ms tominutes have been developed.45–48 In these instruments, the addition of electronic timers, mixing
networks during sample introduction, and nozzle modifications have enabled reagent mixing.
These developments introduced the capability to measure population shifts as a function of delay
time and reagent concentration by in-line dilution, but they do not offer cell-by-cell response
measurements or sorting.
Plate-based colony screening platforms in which each colony represents a single memberof a library offer a robust method for determining and improving sensor dynamic range, but they
are typically limited to screening or selection in bacterial or yeast cell lines. Plate screening
technology measures the fluorescence intensity changes on the colony level through image
analysis after chemical permeabilization and addition of ligands or chelators.49–51 Whileautomation of colony plate screening offers the potential for measurements on large sensor

15
libraries (> 103 clones), current technology employs manual chemical application and image
capture which is inherently labor and time intensive. This technology does not permit
measurements of single cells and averages out cell-to-cell variability, thereby decreasing overallsensitivity. Furthermore, techniques for uniformly and reproducibly applying solutions to thecolonies are still under development. Depending on the application technique, reported coefficientsof variation for the FRET ratio changes range from 67% when a manual sprayer was employed fordispensing ligand to 37% when a spraygun with a lasersight was used.49 Some of the limitations
in plate screening technology could be improved with the use of high throughput-high content
microscopy which could allow for single-cell measurements, though the implementation of cellselection in this technology is not straightforward. Finally, it is known that sensor performancevaries between organisms,52 and even within different cellular compartments of the sameorganism.42 Technology for screening and selection of biosensors based on responses inmammalian cell lines has not yet been reported.
Droplet microfluidics flow cytometry provides potential throughput comparable to FACS
while vastly expanding the types of single-cell measurements possible.53–55 Most efforts involving
time-dependent phenomena capitalize on the stable encapsulation of cells to enable directed
evolution of enzyme function, which requires incubation of single cells for minutes to hours, ordrug toxicity assays also on timescales of hours.56 These assays employ single cell encapsulation,
incubation, and droplet reinjection in separate devices,57,58 so responses are not individually
tracked. Designs incorporating minute to hour-long delay lines for monitoring kinetics have beendeveloped and implemented for enzymatic assays in picoliter reaction volumes, but thus far do notprovide single-cell tracking and sorting.59–61 Prior to this thesis work there has been no report of asingle microfluidic device that incorporates all three aspects required for biosensor

16characterization and development: on-chip mixing for initiation of a cellular response,interrogation before and after the reaction, and sorting based on the response of each cell prior to
the publication of this instrument.

1.6

Conclusions
Genetically encoded FRET sensors have been derived for a wide range of biologicalligands, but the vast majority suffer from a small dynamic range. While there are existing strategies
to improve these sensors, complications arise when the sensor response cannot stably report boththe analyte free and analyte bound form for long times. The work in this thesis presents alternative,
high throughput methodology for the characterization and optimization of the dynamic range ofsuch sensors with a focus in the field of Zn2+ homeostasis. Furthermore, work towards a bettermolecular understanding of the current array of FRET-based Zn2+ sensors is presented.
In chapter 2, an in-depth discussion of the design principles guiding the creation of a timeresolved microfluidic cell sorter is presented. In chapter 3, the microfluidic sorter is applied to aset of previously designed Zn2+ sensors to verify its ability to sort on the basis of a time-resolved
response to Zn2+ perturbation. In chapter 4, the microfluidic system is applied as a screening toolfor heterogeneity and population analysis of the leading Zn2+ sensors in the endoplasmic reticulum(ER). This is followed by the construction and screening of a number of randomized linker libraries
for the optimization of the Zap sensor family in the ER and cytosol. In chapter 5, the many factorsthat influence the behavior of these sensors at the molecular level is discussed. Progress incharacterizing the orientational and time-resolved behavior of several Zn2+ sensors is presented.

17

Chapter 2
Methods and Instrumentation
2.1

Publication Note
Portions of this chapter were adapted from Fiedler, B. L. et al. Droplet Microfluidic Flow

Cytometer for Sorting On Transient Cellular Responses of Genetically-Encoded Sensors. Anal.
Chem. 89, 711–719 (2017) and the corresponding Supplementary Information. Figures present in
the publication are unmodified in this chapter.

2.2

Microfluidics

2.2.1 Design Considerations
The benefits of using microfluidic devices are derived from the micron-scale features thatdefine their name, which include low volume use and a high level of fluid control. While this is
not meant to be an exhaustive review of fluid dynamics at the microscale, the basics as they
apply to the design of the microfluidic devices used during this thesis will be discussed.
Interested readers are referred to thorough introduction to microscale fluid physics for
microfluidics.62 At this scale, the fluid behavior is simplified beyond the usual Navier-Stokes

18
formulation due to the lack of inertial forces present. The Reynold’s number is the ratio ofinertial forces to viscous forces and can be defined as,
𝑅𝑒 =

𝜌𝑢𝑙
𝜂

(2.1)

Where ρ is the fluid’s density, u is the flow speed, l is the length scale, and η is the viscosity. For
the typical buffers used in biological applications and nm - μm length scales used for
microfluidics, even at speeds exceeding meters per second, Re will remain << 1. Fluid dynamics
at this low Reynold’s number are defined by viscous forces and this regime is known as laminar
flow. Laminar flow is defined by its predictable nature, lacking the complicated flow dynamics
and vortices present in the turbulent flow regime (Re > 1000). For microfluidics, this means thatfluid motion in the channels is entirely governed by the applied force (pressure). If the force isreversed, the fluid motion will also be reversed returning it to its original state.63 This allows forhighly controllable and sophisticated microfluidic designs.
Device design for this work was initially based on the earlier work of Weitz et al.57 The
overall scale of the devices was designed to accommodate the size of cultured HeLa cells whichhave a diameter on the order of 15-20 µm when in suspension.64 The total length of the devices
from inlet to outlet is long to accommodate the use of long time delays between measurement
regions and therefore steps were taken to decrease the overall resistance of the device andtherefore the required driving pressure for a given fluid velocity. The resistance of a microfluidic
device is determined by performing an Ohm and Kirchoff’s laws type calculation in which eachchannel segment is assigned a resistance value and added either in parallel or series to determine
total device resistance. The resistance of a rectangular channel can be calculated by65:
𝑅=

12𝜂𝑥
ℎ
(ℎ3 𝑤)(1−0.630 )
𝑤

(2.2)

19
Where η is the viscosity of the medium, x is the length of the channel segment, w is the width ofthe channel segment, and h is the height of the channels. In 2D microfluidics, the height willremain constant across the design. It should be noted in the above equation that the resistance has
a strong dependence on the height of the device. The resistance of the device used in this work
was limited by keeping the designs between 40-50 µm in channel height to accommodate cellsize. For the devices used in this work, the total device resistance was on the order of 1-10 x1014
N s m-5. Increasing the input resistance relative to the output resistance will increase the stabilityof the device as fluids will move naturally from high to low resistance. See Appendix B for
example code for calculating the device resistance.

2.2.2 Droplet Generation
Droplet generation can be achieved by a number of different structures in microfluidics.
Popular designs are a T-junction, flow-focusing junction, or a coaxial junction.66 The flowfocusing junction was chosen for these devices due to the superior control of droplet size fromsheering forces on both sides of the dispersed phase.53,67 Droplets can be generated by a numberof active methods including, but not limited to, the use of surface acoustic waves68, pulsed laser
excitation69, and pneumatic chopping’.70,71 Passive generation of droplets, including pressure
driven, relies on the balance between surface tension and the shear forces introduced by thecontinuous phase (oil) on the dispersed phase (aqueous) and is separated into three regimes:squeezing, dripping, and jetting. The regime is determined by the capillary number (Ca = μU/γ, μviscosity, U characteristic velocity, γ surface tension between water/oil interface), which
increases from squeezing to jetting. Squeezing is characterized by the contact of the dispersed
phase with the channels walls dominated by surface tension. Dripping is increasingly dominated20
by shear forces. Jetting is visualized by a thread of dispersed phase past the droplet junction fromwhich droplets begin generating. Droplet size is most controllable in the squeezing and dripping
regimes. These are the regimes targeted in the devices for this work.
Droplet generation and stability in fluorinated oils (such as Novec 7500 or FC-40), which
are known to be biocompatible with a range of organisms, is hindered without the addition of afluorinated surfactant (fluorosurfactant). These surfactants decrease the surface tension to allow
greater droplet control through external pressure modulation.72 For purposes of droplet
incubation (or where droplet-droplet contact is possible), the surfactants also prevent droplet
merging. Many fluorosurfactants obtainable through commercial means are proprietary and theexact structure is not available, but extensive work has gone into detailing the synthesis ofsuitable and biocompatible fluorosurfactants.73,74 The surfactants are most often characterized by
a fluorophilic head (such as Krytox-FSH) and a hydrophilic tail (such as PEG). Initial studies(Chapter 3) in this thesis made use of a “Droplet Generator Oil” (BioRad) diluted 100-fold in
Novec 7500 to concentrations in which droplet generation was observed. Later studies (Chapter4) switched to a proprietary surfactant made by RAN Biotechnologies used at 0.1% w/v. The
lack of a proper fluorosurfactant can lead to a failure of droplet formation as shown in Figure
2.1.

21

Figure 2.1. A microfluidic device using only Krytox-FSH as a surfactant. Droplet
formation was not observed at all pressures tested.

22
2.2.3 Reagent Mixing
The implications of the laminar flow as it pertains to designing microfluidics extends tothe mixing process. The lack of inertial forces and turbulence, in a straight channel where twosolutions meet, means the mixing is dominated by diffusion. The characteristic mixing time t =l2/D, where l is the distance over which mixing takes place and D is the diffusion coefficient fora particle, can be used as a rough estimate of the mixing time for small molecules in a straightchannel where two solutions meet. The diffusion coefficient for small molecules is on the orderof 10-5 cm2/s, which corresponds to a diffusion time of 100 ms - seconds across 10-100 µm
channels.
Mixing in fluidic channels with a finite fluid velocity becomes more complicated due tothe uneven velocity of particles across the width of the channel. In pressure driven channels in
the laminar regime, particles travel in a parabolic flow profile due to the no-slip boundary
condition at the walls of the microfluidic channel (no fluid flow at the surface of an object).63
This leads to enhanced diffusion across the channel and the calculation of an effective diffusion
coefficient, Deff = l2U2/σD, where σ is a dispersion constant for the cross section of the channel
equal to 0.0084 for approximately square channels and 1/210 for planar channels.75 Thisenhanced diffusion is known as Taylor-Aris diffusion. Application of Taylor-Aris diffusion to
our system using flow velocities on the order of 1-100 mm/s in 50-100 µm channels yields Deffvalues that are 2 orders of magnitude higher than D. Analysis of Deff for our system reveals thatmixing times are in the 100s of ms for our system. All of the work in this thesis employed a two
spot interrogation design to probe the state of cells prior to mixing with analyte and again after
mixing with the analyte. The long mixing time relative to our flow rates and length of the mixing
channel (100 – 300 µm) is a desirable feature to prevent premature sensor response.

23
Approximate mixing time calculations for the most utilized microfluidic design in this work arepresented in Chapter 3.
Characterizing mixing time after droplet formation becomes much more complicated as
droplet convection begins due to contact with the walls of the channel. Extensive numericalcalculations have been done to characterize droplet convection in context of droplet
microfluidics and the resulting mixing effects.76 Mixing within droplets is enhanced further whena moving droplet encounters a turn in the channel, resulting in a ‘folding’ effect. Given thevariability of the cell position (also affected by convection) within the droplet, it is assumed thecell is exposed to saturating analyte-ionophore concentrations soon after droplet encapsulation.
The Y-mixing junction used in these devices and an example of the position of the cells relative
to the reagent stream is shown in Figure 2.2.

Figure 2.2. Y-shaped mixing junction used in the microfluidic devices presented in thisthesis. The presence of OptiPrep in the cell buffer changes the refractive index of the media,
leadingto a clear visual differentiation between the cell buffer and the added ligand buffer.
The cell buffer (bottom channel of the Y) is distinguishable by the presence of cells in thechannel.

24
2.2.4 Droplet Occupation
Single-cell interrogation and sorting hinges on the ability to isolate single-cells. In thecontext of droplet microfluidics, this means the accuracy of any experiment is limited by thecapacity to generate droplets with only one cell. Droplet occupation follows a Poisson
distribution according to P(X) = exp(-λ)(λX/X!) where X is the number of cells per droplet, P(X)
is the probability, and λ is the average number of cells in the droplet volume. As shown in Figure
2.3, the probability of multiple cells in a droplet remains below 2% for values of λ between 0.1 –0.2. This results in most of the droplets generated containing no cell at all, which is a necessary
loss in throughput to keep the chance of multiple occupancy low. Strategies to increase the rateof single cell occupation have been discussed at length elsewhere 66, but add an additional layerof complexity to any device. Droplets with a volume of 150 pL correspond to an input cell (orfluorescent bead) concentration of ~1x106 cells/mL. It should be noted that while theseconcentrations work well for cells that naturally grow in single-cell suspension or with beads,
cultured adherent cells that show a high degree of cell-cell adhesion and surface adhesion willcomplicate the arrival of single cells. Strategies to reduce cell aggregation will be discussed
further in this chapter. An example of single HeLa cell encapsulation in our system is shown in
Figure 2.3 (bottom). A detailed discussion of the interplay between droplet generation, droplet
occupation and screening/sorting throughput is located in Chapter 3.

25

Figure 2.3. (Top) Poisson distribution derived probabilities of finding a cell in a droplet
for values of λ relevant to this work. (Bottom) Example image of single cell
encapsulation in droplets.

26
2.2.5 Droplet Sorting
The benefits of droplets extend beyond the ability to use them as discrete reaction
vessels. A water-in-oil droplet configuration was chosen for the microfluidic assays in this thesis
as it is simple to set up a sorting mechanism by coupling the droplet device to electrodes capableof dielectrophoresis (DEP). There are many methods for sorting cells including optical trapping,valve-based sorting, surface acoustic waves, and electro-osmosis.77 Dielectrophoresis is an easily
implemented sorting strategy based on the generation of an induced dipole in the droplet by an
oscillating electric field gradient. The force applied to move a particle by dielectrophoresis is
𝐹𝐷𝐸𝑃 = 2𝜋𝜀𝑚 𝑅 3 𝐶𝑀(∇𝐸 2 )(2.3)

where εm is the absolute permittivity of the surrounding medium, R is the radius of the particle,
CM is the Clausius-Mossotti factor (the real portion of a complex variable dependent on thepolarizability of the particle at the applied frequency), and ∇E is the gradient of the rms
amplitude of the electric field. The direction of the force is determined by the dielectricdifference between the continuous phase and the dispersed phase. It is immediately apparent why
droplet sorting is advantageous from Equation 2.3 based on the low permittivity of thefluorinated oil phase and the large size of the droplets relative to the cells. This enables lower
voltages to be used in the sorting process to greatly reduce the risk of electric field induced cell
death. The sharp natured shape of the electrodes used in the device is designed to increase theterm ∇E2. Sorting actuation occurs when FDEP exceeds the Stokes’ drag force which is
proportional to the size and velocity of the particle, as well as the viscosity of the surrounding
media. Experimentally, this is determined by modulating the applied voltage until droplets begin
to move toward the sorted channel, typically on the order of 0.2-0.5 kV for these flow conditions.
The frequency of the applied field is based on literature precedent for droplets of a similar size

27
and set to 30 kHz square pulses. An example of a sorting event and the asymmetric, pointed
electrodes is shown in Figure 2.4 (top).
The geometry of the sorting junction and the implementation of cell collection also plays
a factor in the efficiency of sorting. For two-way sorting, the junction breaks off to a ‘waste’channel and a ‘keep’ channel. The resistance of these channels is fixed so that the resistance of
the keep channel is at least 1.5 times higher than the waste channel. Droplets will naturally flow
toward the channel of least resistance, allowing passive disposal of undesirable cells. Problems
occur for this portion of the device if the droplet spacing is small. The divergence of theinterrogation channel into the sorting channels necessitates a region the is wider than theprevious channel and therefore droplets slow down in this area (Figure 2.4, bottom left). Thiswas modified in later iterations to reduce the size of this region (Figure 2.4, bottom right) This
allows for two closely spaced droplets to collide or increase the local resistance of the waste
channel, resulting in the latter droplet to improperly enter the keep channel. The presence of theislets between the keep and waste channels is designed to reduce the effect of one droplet
modifying the resistance of a channel for subsequent droplets by permitting the free movementof the continuous phase.78 The addition of microtubing to the device outlets can have a drastic
effect on the ratio of the output resistances. The tubing rises vertically out of the device before
curving down to the output reservoirs (which are kept at ambient pressure). This introduces asmall gravity head that can be used advantageously by modifying the height of the microtubing if
droplets are moving down the keep channel in the absence of an electric field.
An interesting effect of the electric field is shown in Figure 2.5. Droplets that have been
exposed to the 200V, 30 kHz, 15 ms pulse as they passed the electrodes merge over after making

28

Figure 2.4: (Top) Example image of a sorting event of an encapsulated cell after
determination of a gated FRET response. (Bottom) Evolution of the droplet sorting junction
over the course of this thesis. Channel widths were decreased and rate of growth from delay
channel to sorted channels was decreased to reduce droplet slowdown in the area resulting indroplet collision. Blue arrow indicates direction of flow.

29
contact in the slower output channel. This is due to an electric field mediated rearrangement of
the surfactant molecules on the surface of the droplet with recovery times on the order of
seconds. This effect has been taken advantage of for applications involving droplet merging as ameans to introduce reagents to already formed droplets.55,79 This effect could be useful for thecurrent assay if sorted droplets were able to collect in close proximity. The current
implementation, however, has sorting events that are too spaced and a continuous flow schemethat prevents droplet pooling until the outlet reservoir.

Figure 2.5: Three consecutive frames showing the merging of three droplets after exposure
to the electric field present at the electrodes.

2.2.6 Microfluidic Fabrication
Microfluidic devices are prepared using standard soft lithographic techniques. SU8 3000
photoresist (MicroChem) is spin-coated onto a 3” silicon wafer and exposed to uniform UV
illumination under an acetate mask printed with the inverted image of the device design (CAD/Art
Services). The wafers are developed and hard-baked to complete the master, then exposed to(1H,1H,2H,2H-perfluorooctyl)trichlorosilane (TCS) for several hours under vacuum. Polydimethyl-siloxane (PDMS) pre-polymer and bonding agent (Sylgard 184, Dow Corning) are mixed
in a 10:1 weight ratio and poured onto the master to achieve 4 mm thickness. PDMS slabs andclean 40x22 mm No. 1.5 glass coverslips are O2 plasma treated, then pressed together to achieve

30
an irreversible seal. Lastly the devices are treated with TCS in Novec 7500 (3M) to passivate thesurface to the fluorinated continuous phase (Novec 7500), then blown out with air. The devices
are then placed, one at a time, on a 150 oC hotplate and the electrode channels are filled with a low
melting point In-Sn solder (Custom Thermoelectric). Small lengths of 1000V tolerant wire areinserted in contact with the solder and the assembly is sealed with epoxy to prevent liquids fromentering the electrode inlets during the experiment. Devices are stable for months if stored in an
opaque container at room temperature until use. A detailed protocol for the fabrication of PDMS
microfluidic chips used in this work is provided in Appendix A.

2.3

Experimental Design

2.3.1 Optical Configuration

The optical layout is a standard inverted microscope utilizing epifluorescence capture. See
Figure 2.6A for a picture of the setup. Excitation in two positions is provided by a 445 nm laser
diode beam-shaped with a cylindrical lens to achieve a relatively flat beam profile across the widthof the channel with a 1 µm focus and 1 kW/cm2 irradiance in the interrogation region of the
microfluidic channel. As discussed previously, this beam shape minimizes signal variations due tovariation in cell position across the channel width.80 In the reported system, this led to a 35-fold
reduction in the signal variation when compared to circularly shaped beams.
Detector gains are adjusted to accommodate the diversity of cell brightness observed inthese experiments such that droplet scatter/fluorescence is not observed, however it is evident thatthe background is constant from droplet to droplet by modifying the detector gain. Droplet scattercan be increased by increasing laser power or detector gain. This is useful when characterizing thesystem to identify droplet generation frequency.

31
The optical components are as follows, as they relate to Figure 2.6A: a) 50:50 beamsplitter,b) cylindrical lens, c) 445/20 nm bandpass, d) epifluorescence longpass filter, e) 580 nm camera
longpass filter, f) 510 nm fluorescence dichroic filter, g) 483/32 nm CFP bandpass filter, h) 542/27
nm YFP bandpass filter, and i) 590 nm transmitted light longpass filter. Epifluorescence iscollected with a 20X (0.75 N.A.) objective (Olympus) and passed through a series of dichroics toseparate the emission from light used for excitation and visualization, ending with a dichroic topartition FRET donor (CFP) and acceptor (YFP) emission. The emission is split at the image planeby a pair of 1” broadband dielectric D-shaped mirrors. Emission is further selected by a bandpass
filter on each PMT.
The incorporation of a second detector arm enabled a significant simplification of thealgorithm necessary to do real-time pairmatching of cells between the two interrogation regions.
The algorithm no longer needed to identify which spot the cell event belonged to, which waspreviously identified using the FWHM of the Gaussian-like signal. This split the image into two
regions that then go on to a separate set of PMTs. The second detector also prevented the loss ofcell events due to the simultaneous arrival of two cells at both Spot 1 and Spot 2. Figure 2.7 shows
the overlap in two simultaneous cell events that would not have been resolvable in a 2 PMT setup.
The presence of very bright events (large fluorescence signal), introduces the possibility ofcrosstalk between the spots. Minimization of the crosstalk can be achieved by sufficient separationof the beams (650 µm in the current iteration of the instrument) without approaching the edges ofthe field of view to avoid aberration at the edges of the objective.

Figure 2.6: A.) Image of the epifluorescence microscope setup with labels as detailed in the text. B.) Imageof the pressure controller setup used in this work for pressure driven flow. Pressure is down-regulated frombuilding provided compressed air at 75 PSI. C.) Image of a fully connected microfluidic device as
implemented during an experiment.
32

33

Figure 2.7: Raw data generated from ADC processed data from thephotomultiplier tube. The graph shows two cell events that arrived
simultaneously at interrogation beam 1 (yellow and cyan) and 2 (green
and blue) that would not be distinguishable in a single detector arm
setup.
2.3.2 Pressure Driven Flow Configuration

Three 25 PSI pressure regulators (Figure 2.6B) are connected via microtubing to three offchip reservoirs to maintain buffer/solution isolation prior to mixing on-chip. The three reservoirs
consist of the cell suspension/microfluidic buffer, the metal-ionophore solution, and fluorinatedphase. Microtubing from the reservoirs is inserted into the microfluidic device (Figure 2.6C).
Microtubing is inserted into the outlets of the device after it has been started and an approximate
time delay has been set. Inlets on the microfluidic device are punched smaller than the outer34
diameter of the microtubing. This is sufficient to create an airtight seal at the pressures used, aslong as the thickness of the PDMS slab is 4 mm or greater. The outlet tubing is inserted intocollection vials maintained at ambient pressure.

2.3.3 Electronics

Signals from the two photomultiplier tubes (R9880U-20, Hamamatsu) are sampled at 50
kSamples/sec by an A/D converter on an LPC1769 microcontroller (Figure 2.8) in a home built
circuit. Parameters such as signal threshold, FRET ratio thresholds for the two measurement points
FRET 1 (YFP1/CFP1) and FRET 2 (YFP2/CFP2) and the FRET ratio change, R 2/R1 or FRET
2/FRET 1, threshold, as well as timing parameters for the pair-matching and sorting signal are sent
over a FSUSB connection to the microcontroller. Data are simultaneously collected by a National
Instruments DAQ (NI 6251) board and processed by custom LabView software for calculations
that are too CPU-intensive for the microcontroller. For example, compiling the signal timing
histograms for determining delay times between interrogation regions requires operations on large
arrays. Encapsulated cells with signals satisfying the gate conditions are sorted using
dielectrophoresis. Sorting is triggered by a TTL signal from the microcontroller for the durationof the desired sorting time to a function generator (Keysight Technologies) which sends a squarewave pulse to a high voltage amplifier (TREK) connected to the electrodes (visible in Figure 2.6C)
on the microfluidic device. The code driving the microcontroller, including the pairmatchingalgorithm, is located in Appendix B.

Figure 2.8: Microcontroller setup used in this work. Inset graph is streaming data with colored bars indicating
3 peaks which would be matched in the pairmatching algorithm due to the constant time spacing between them.
Green peaks refer to spot 1 signal, while purple peaks refer to spot 2 signal.

35

36
2.3.4 Sample Preparation and Instrument Operation

The success of any screening or sorting experiment is highly dependent on the
preparation steps leading up to the cytometry portion. Determination of the proper cellconcentration to prepare using the Poisson statistics previously mentioned becomes useless if theexperiment does not begin with a single cell suspension. This section provides some details andreasoning for the preparation of the cells and the buffers used in the microfluidic experiment, as
well as some procedural guidelines, which are pertinent to a successful screen on single cells.
Adherent mammalian cell lines such as HeLa, MIN-6, and HEK-293 grow with high cellto-cell contact on cell culture plates that must be broken up prior to further preparation. In thisthesis, HeLa and HeLa-S3 cell lines are used for all cytometry data presented. HeLa-S3 is a line
derived from HeLa cells that was selected through several rounds of growth and was found togrow successfully in suspension.81 The HeLa-S3 line has proven easier to create cluster-freesingle cell suspension than the parent HeLa line. However, the HeLa-S3 cell line suffers from adecrease in transfection efficiency when treated with the same chemical transfection conditions
as HeLa cells. An example is shown in Figure 2.9, in which HeLa cells and HeLa-S3 cells areboth treated with equal concentrations of Lipofectamine 3000 reagent components and 1 µg of
two different CFP/YFP Zn2+ sensor plasmids (pcDNA3.1+ backbone for each) for transienttransfection. The images are taken 48 hours after transfection with identical exposure settings
using a GFP filter set. The decreased efficiency of gene introduction has also been verified forTransit-LT1 and adenovirus transfection. Stable line creation is possible for HeLa-S3 cells, butmust be FACS enriched for high intensity prior to use. Harvesting either line of these cells byphysical detachment (cell scraper) did not generate proper separation of cell clusters; instead,
harvesting requires trypsinization to fully isolate the cells.

37

Figure 2.9. Microscope images of ER localized Zn2+ sensor expressing cells underidentical transfection conditions. The left column shows HeLa cells, while the rightcolumn contains HeLa-S3 cells. Images are taken under identical illumination andexposure settings.

38
All steps prior to introduction of the cells into the microfluidic device should avoid
exposure to high concentrations of divalent cations (Ca2+, Mg2+). The typical protocol fordeactivating trypsin and suspending mammalian cells includes the addition of serum, however
without treating the serum with chelator, the metals are immediately reintroduced. Cells will
begin to aggregate in these conditions. The current protocol involves adding Ca2+ and Mg2+ free
HEPES buffer Hank’s Balanced Salt solution (HHBSS), pH 7.4, with added 0.1% bovine serum
albumin (BSA) to the trypsinized cells. Otherwise, normal FACS protocol for the preparation ofcells should be followed. This includes any precaution to avoid cell damage during preparationsuch as: avoid bubbles during pipetting, avoid aspirating the entire solution during buffer
exchange, and use the fewest possible centrifugation steps as required by the assay.44 Cells andsolutions should be kept on ice at all times until the experiment. Cell passage number can also
have a significant impact on the ability to isolate single cells. If no work-up of the cells isnecessary after the microfluidic experiment, DNase I can be added to discourage cellaggregation. Lastly, all buffer solutions should be filtered through 0.2 µm filters and cellsolutions through 40 µm filters.
The cell buffer contains two key components to ensure steady arrival of cell events in the
microfluidic. The first is BSA added to 0.1-1% by weight to discourage the re-aggregation of thecells during incubation. Additionally, the BSA coats the PDMS and glass coverslip upon contact
when the cells and reagent are introduced to the microfluidic device. The devices are initiallycoated with a fluorophilic compound to increase wettability of the fluorinated oil phase. The
aqueous channels cannot be selectively treated with a BSA solution prior to device startup. The
presence of BSA prevents cells from adhering to the walls of the device and encourages a steadystream of cell events. Figure 2.10 shows a problematic buildup of poorly separated cells at the39

Figure 2.10: Image of the inlet of a microfluidic device in which cells
were poorly isolated prior to introduction in the device and BSA was not
used in the cell buffer.

40
inlet of the microfluidic device in the absence of BSA which led to very slow arrival of cells to
the interrogation region of the device.
The second critical component in the cell buffer is a density matching reagent. Anotherproblem encountered during long cytometry experiments is the sedimentation of cells due to themismatch in density between cells and the surrounding media. One method for preventing cell
sedimentation is to add a micro stir bar to the cell reservoir (or syringe for many microfluidic
flow systems). However, this method potentially promotes cell death at the speeds required tomaintain a homogeneous distribution of cells over long experiments (> 1 hr).54 Additionally, themicro stir bar does not prevent cell sedimentation in the micro tubing. OptiPrep (AxisShield,
60% iodixanol) is a suitable reagent for this purpose. OptiPrep is added directly to the buffer in apercentage that adjusts the density to the density of the cells and prevents cell sedimentation overtime. A simple experiment was conducted to determine the optimal concentration of OptiPrep forsuspending HeLaS3 cells (it was not assumed that HeLa cells and HeLa-S3 cells have the same
properties). Increasing concentrations of OptiPrep were added to separate vials of cells at aconcentration of 1 million/mL in HHBSS (pH 7.4). Cells were collected from the top 1/3 of thesolution and plated immediately for t = 0 hr. After 4 hours of unperturbed incubation, cells wereagain collected from the top 1/3 of the solution. As shown in Figure 2.11, at concentrations
above 16%, cells were still present in the top portion of the solution, indicating they had notsettled to the bottom due to density differences. The lowest concentration that contained cells,
16% v/v, was chosen as the working concentration for HeLa-S3 cells. The percentage indicatesvolume of OptiPrep, not iodixanol.
With regards to instrument operation, it is important to introduce each fluid into themicrofluidic device in a specific order. The fluorinated oil (continuous phase) should beFigure 2.10: Experimental determination of the optimal concentration of Optiprep to keep cells suspended for several
hours. Time increases down, while OptiPrep concentration increases to the right. Optiprep concentration is in unites of %
v/v.
41

42
introduced first and allowed to fill the delay channel prior to turn on the aqueous buffers(dispersed phase). This prevents the aqueous phase interfering with the fluorophilic coating in
the channels and prevents wetting of the aqueous phase to the coverslip. Next, both aqueous
phases should be introduced simultaneously in order to avoid either channel from backing up
into the other at the Y-mixing junction. If reagent is allowed to back up into the cell channel,
cells may begin an early response to the high levels of Zn2+/pyrithione, altering the FRET 1
value. Additionally, the system should be started at higher speeds (pressures > 7 PSI per channel)
than may be used in the experiment. The pressures can be decreased to operating pressures after
this. Stopped or very slow flow can cause cell buildup in the inlet which will need to be clearedwith high pressure prior to the start of the experiment. See Appendix A for a detailed protocol for
microfluidic operation.

2.3.5 Cell viability
Cell viability in this system has been an ongoing problem that is coupled to themicroscopy experiments discussed in Chapter 3 and 4. The introduction of Zn2+ and pyrithione (amembrane permeable Zn2+ ionophore) results in a complete lack of cell viability for the exposed
cells. Cell viability was explored multiple times across multiple experiments in which cells wererecovered from droplets and plated in DMEM (30% fetal bovine serum) after exposure toZn2+/pyrithione on the microfluidic device, both in the presence and absence of the electricsorting field. The resulting cells remained non-adherent and morphologically necrotic after 24hours in cell culture conditions (37 oC, 5% CO2) with no washing. Additional experiments which
excluded Zn2+/pyrithione resulted in ~90% adherent vs. non-adherent cells after 24 hours (as acrude measure of viability without added stain) in the absence of an electric field and ~73% in43
the presence of a 300V, 30kHz field as they passed the electrodes. The implications of the 100%cell death rate are discussed in Chapter 3 and strategies to work around it in Chapter 4.

44

Chapter 3
Droplet Microfluidic Flow Cytometer for Sorting on Transient
Cellular Responses of Genetically-Encoded Sensors

3.1

Publication Note
This chapter was adapted from Fiedler, B. L. et al. Droplet Microfluidic Flow Cytometer

for Sorting On Transient Cellular Responses of Genetically-Encoded Sensors. Anal. Chem. 89,
711–719 (2017) and the corresponding Supplementary Information. Figures present in the
publication are unmodified in this chapter.

45

3.2

Motivation
Fluorescent biosensors are important measurement tools for in vivo quantification of pH,

concentrations of metal ions and other analytes, and physical parameters such as membranepotential. Both the development of these sensors and their implementation in examining cellular
heterogeneity requires technology for measuring and sorting cells based on the fluorescencelevels before and after chemical or physical perturbations. We developed a droplet microfluidic
platform for the screening and separation of cell populations on the basis of the in vivo responseof expressed fluorescence-based biosensors after addition of an exogenous analyte. We
demonstrate the capability to resolve the responses of two genetically-encoded Zn2+ sensors at a
range of time points spanning several seconds and subsequently sort a mixed-cell population ofvarying ratios with high accuracy.
Specifically, we describe a droplet microfluidic system that introduces each cell
expressing a FRET sensor to the appropriate analyte, probes the baseline FRET level and thesensor response at multi-second time delays set by the geometry of the fluidic network anddriving pressures, and then sorts on the basis of the response with dielectrophoresis (DEP). This
work represents a significant advance over a previous study conducted by the Jimenez group ofcytosolic and extracellular-membrane-expressed D3cpV and D3pdD Ca2+ and ZapCY1 Zn2+
sensor response in HeLa-S3 cells.82 This previous study established that cellular response
occurred on the ms-sec timescale in a two-dimensional hydrodynamic flow-focusing geometry.
The response of cytosolic sensors was ~30 fold slower than the response of sensors expressed on
the cell surface, and the response appeared to be limited by cation transport across the plasma
membrane. Our previous study also revealed heterogeneity in the ZapCY1 Zn2+ sensor response.
The sensor response heterogeneity was attributed to viral recombination during stable

46
transfection of mammalian cells with the sensor resulting in response and unresponsive sensor
populations. A major limitation of the instrument employed in the previous study was that signal
post-processing was required to determine the single-cell response. Here, we demonstrate realtime signal processing and sorting with a microcontroller-based system, and clear measurement
resolution and sorting based on the responses of two closely-related Zn2+ sensors. This new
instrument provides a platform for directed evolution of new cellular sensors. Although we
specifically optimized the system for screening genetically encoded Zn2+ FRET sensors, thetechnology is directly applicable to many other sensors.

3.3

Experimental Setup

3.3.1 Sensor Constructs
Two genetically-encoded Zn2+ sensors, ZapCV2 and ZapCV5, were chosen for study due
to their structural similarity, yet differing dynamic ranges as would be expected for a library ofthese sensors. These sensors are comprised of a fluorescent protein FRET pair including a 11residue truncated enhanced Cyan Fluorescent Protein (CFP) as the donor and circularly permuted
Venus (cp173) as the acceptor. The FRET pair is connected by a Zn2+ binding domain (ZBD)
consisting of the first two Zn-fingers in the Zap1 transcription factor adopted from S. cerevisiae.
These sensors only differ by two mutations. The native Zn-finger contains two Cys and two His
as coordinating ligands. In ZapCV2, one of these Cys residues is mutated to His in both fingers.
In ZapCV5 all four Cys residues are mutated to His. Both constructs contain N-terminal nuclear
exclusion sequences (NES) to achieve cytosolic localization. The FRET response is defined as
the ratio of the acceptor to donor fluorescence intensities upon donor excitation, R = IFRET/ICFP.
Upon Zn2+ binding, the donor-acceptor pair undergoes a conformational change and the value of

47
R increases.40,41,83 The in vitro KD values (Hill coefficient, n) of the sensors are 2.3 nM (n = 0.53)
and 0.3 µM (n = 0.55) in buffer (150 mM HEPES, 100 mM NaCl, 10% glycerol, pH 7.4) and the
dynamic ranges (defined as the ratio Rmax/Rmin) are 2.1 and 1.5 for ZapCV2 and ZapCV5
respectively. A schematic of the Zn2+ sensors with sequence differences highlighted and the data
used to obtain in vitro binding affinities can be found in Figure 3.1.

3.3.2 Design of ZapCV2 and ZapCV5 Zn2+ sensors
ZapCV sensors are modified based on previously characterized ZapCY2 sensors.41 The
FRET acceptor (citrine fluorescent protein, YFP) in ZapCY2 was replaced by circularly
permuted (at amino acid 173 position) Venus fluorescent protein, which generated ZapCV2(YFP changed to Venus). The lower affinity ZapCV5 was created by quick change mutagenesisof the Zn2+ binding domain (C586H and C623H). The cytosolic NES-ZapCV sensors include an
N-terminal nuclear export signal (MLQLPPLERLTL). These sensors were cloned in the
pcDNA3.1 and PiggyBac Transposon vector backbone for mammalian cell expression and in thepBAD vector backbone for bacterial expression.

3.3.3 In vitro characterization of ZapCV2 and ZapCV5
For in vitro studies, the sensors were cloned into pBAD and expressed in Top-10
Escherichia coli with the addition of 0.2% arabinose. Sensor proteins were purified using Ni2+
ion affinity chromatography and the His tag was removed with TEV protease in 20mM Tris, 100mM NaCl, pH8 buffer at room temperature overnight (sensor to TEV protease ratio was 10:1).
The sensor was then concentrated with an Amicon centrifugal filter to 50-100 µM as determinedby the absorbance at 516 nm using  = 77000 M-1 cm-1.

48
Purified sensor was titrated with Zn2+ to determine the disassociation constants of Zn2+
from the sensors. Zn2+ titrations were performed in HEPES buffer (150 mM Hepes, 100 mM
NaCl, and 10% glycerol, pH 7.4) with 1 µM sensor protein, treated for 5 minutes immediately
prior to titration with 0.5 mM TCEP, and different concentration of free Zn2+ as described
previously.41 The in vitro fluorescence measurements used for Zn2+ titrations were made on a
Tecan Safire-II fluorescence plate reader using the following parameters: excitation: 420 nm,
emission: 435-649 nm, and the emission bandwidth was 10 nm. The apparent dissociation
constant was determined using the fitting method described by Pomorski et al.84 The intensitiesof the fluorescence emission at 481 nm were used as 1 and at 529 nm for 2. The ratios of these
intensities were plotted against the zinc concentration and fitted using one of the following
equations depending on the ratio used (1/2 vs. 2/1).

∙𝑍 𝑛 )+(𝐼(𝐼∙𝐾 𝑛 )

𝑅2 = (𝐼2𝑏∙𝑍 𝑛)+(𝐼2𝑢∙𝐾𝑑 𝑛 )
1𝑏

1

1𝑢

∙𝑍 𝑛 )+(𝐼(𝐼𝑑∙𝐾 𝑛 )

𝑅1 = (𝐼1𝑏∙𝑍 𝑛)+(𝐼1𝑢∙𝐾𝑑 𝑛 )
2

2𝑏

2𝑢

𝑑

(3.1)

(3.2)

Where the I2b, I2u, I1b, I1u are fixed using the average intensity at 1 or 2 at the five most or least
concentrated Zn2+ conditions, Z is equal to the –log of the free zinc concentration, Kd is thedisassociation constant, and n is the hill coefficient. The reported Kd’s are the average of 2 ormore titrations of the sensor.

49

3.3.4 Cellular imaging for in vivo calibration
Imaging experiments were performed on a Nikon Ti-E wide-field fluorescencemicroscope equipped with Nikon elements software, Ti-E perfect focus system, an iXon3
EMCCD camera (Andor), mercury arc lamp, and YFP FRET (434/16 excitation, 458 dichroic,535/20 emission), CFP (434/16 excitation, 458 dichroic, 470/24 emission), and YFP (495/10
excitation, 515 dichroic, 535/20 emission) filter sets. External excitation and emission filter
wheels were controlled by a Lambda 10-3 filter changer (Sutter Instruments), while dichroic
mirrors were placed on cubes in the dichroic turret. Images were collected using a 60X oil
objective (NA 1.40), 100 ms exposure time, EM gain 1 MHz at 16-bit readout mode with an EM
gain multiplier of 100, and a neutral density filter with 12.5% light transmission.
For the calibration experiments shown in Figure 3.5, sensor constructs were transfected
into HeLa cells with TransIT-LT1 (Mirus Bio). Cells were imaged 48 hours post-transfection.
Cells were maintained at 37°C and 5% CO2 in a LiveCell™ environment chamber (Pathology
Devices) during the experiment. Cells were imaged in phosphate-free HEPES-buffered HBSS,
pH = 7.4 to prevent zinc precipitation. Resting images were collected for 5 minutes, followed bytreatment with 150 µM TPEN to collect Rmin data until a stable signal was achieved. Cells werethen washed with phosphate, calcium, and magnesium-free HEPES-buffered HBSS, pH = 7.4,
then cells were treated with 1 µM pyrithione and 50 µM ZnCl2 to obtain Rmax data. Resting andRmin images were collected every 1 minute, and Rmax images were collected every 20 seconds.
All data were analyzed in MATLAB (Mathworks). Images were background corrected by

2+

2+

2+

binding domain(green), Venus (yellow). Zn ions are shown as red spheres. Sequences of the Zn binding domains of ZapCV2and ZapCV5 are shown below with the differing residues bolded and highlighted in red. B) In vitro titration
curves showing the binding affinities and Hill coefficients for ZapCV2 and ZapCV5. Experimental details
shown below.

2+

terminus to C-terminus consists of a localization signal sequence, ECFP (blue), Zap1 Zn

Figure 3.1: A) A cartoon of a representative FRET-based Zn sensor used in this work. The structure from N-

50

51
drawing a region of interest (ROI) in a blank area of image and subtracting the average
fluorescence intensity of the background ROI from the average intensity of each cell. FRET
ratios for each cell were calculated by dividing the background-corrected YFP FRET intensity by
the background-corrected CFP intensity ((Icellular FRET – Ibackground FRET)/(Icellular CFP – Ibackground CFP)).
This background subtraction and FRET response measurement is done in image post-processing,though it should, in theory, be possible to speed up the process of dynamic range determination
by incorporating a real-time algorithm in the acquisition software.

3.3.5 Sample Preparation
HeLa-S3 cells stably transfected by the PiggyBAC transposon system (SBI) with thedesired genetically encoded Zn2+ sensor, NES-ZapCV2 co-transfected with mCherry-NLS or
NES-ZapCV5, are grown in DMEM (Gibco) supplemented with 10% fetal bovine serum and 1%
penicillin/streptomycin. They are harvested using 0.05% trypsin-EDTA in DMEM andresuspended in Ca2+, Mg2+, PO42--free HEPES-buffered Hanks Balanced Salt Solution (HHBSS)
prepared with chelex treated water. Cells are pre-treated with 50 µM N,N,N',N'-tetrakis(2pyridylmethyl)ethane-1,2-diamine (Sigma Aldrich), TPEN, for 10 minutes to generate the apoform of the sensor prior to the experiment. Cells are washed twice with HHBSS, before being
resuspended in a microfluidic HHBSS solution with 0.5 µM TPEN and 16% v/v OptiPrep(Sigma Aldrich) to prevent cell sedimentation during the course of the experiment. A solution of
50 µM ZnCl2 and 25 µM pyrithione (2-Mercaptopyridine N-oxide, Sigma Aldrich) in HHBSS
for the reagent reservoir is prepared fresh at the same time. The fluorinated oil phase for droplet
generation is Novec 7500 (3M) mixed with 10% BioRad droplet generator oil (which contains asurfactant to stabilize the droplet generation and prevent droplet merging). After sorting, 100-400

52
𝜇L of phenol-free growth media is added to the droplets, along with a volume of 2,2,3,3,4,4,4Heptafluoro-1-butanol (Sigma Aldrich) equal to 1% of the oil phase. After < 5 minutes of
incubation, the emulsion is centrifuged at 50g for 1 min to break the droplet emulsion and extract
cells into the aqueous phase, which is then collected for analysis.

3.3.6 Microfluidic Design and Experiment Parameters
A PDMS microfluidic device was designed to initiate the cell reaction with exogenous
Zn2+, monitor the time-resolved response, and subsequently sort on the single cell response.
HeLa cells pre-treated with TPEN to chelate free Zn2+ and desaturate the sensors enter the
PDMS chip parallel to the input of a buffer containing a pre-defined concentration of ZnCl2 andpyrithione, a cell membrane permeable Zn2+ ionophore. As detailed in Figure 3.2B&C, the twochannels meet at a Y-junction where mixing begins, followed by a flow focus junction droplet
generator to create water-in-fluorinated-oil droplets stabilized by a fluorinated surfactant.
Encapsulated cells pass an initial laser excitation spot to measure the unbound FRET state, R1
and flow along a delay channel outside the field of view. Cells return to the field of view at alater time defined by the channel geometry and input pressures, pass through a second
interrogation spot to measure a bound FRET state, R2, with the time between the interrogation
spots defining ∆t.
Three 25 PSI pressure regulators are connected via microtubing to three off-chip
reservoirs to maintain buffer/solution isolation prior to mixing on-chip. The three reservoirs
consist of the cell suspension/microfluidic buffer, the metal-ionophore solution, and fluorinatedphase. Microtubing from the reservoirs is inserted into the microfluidic device. The
implementation and pressure control is fully described in Chapter 2 (section 3.2).

53
Channels have a uniform height of 50 µm and widths vary from 50 µm in the input
channels, to 100 µm in the middle section, and 200 µm in the outlets. The input channels use
fluidic resistors to compensate for the output resistance of the chip. Resistance of the “keep” orsorted channel is 1.5 times higher than that of the “waste” channel to bias droplet flow awayfrom the sorted channel. The sorting junction features several small islets to prevent resistancechanges when a droplet occupies an outlet channel.78
The optical scheme (detailed in Figure 3.2A) is described in full in Chapter 2 (section
3.1). Briefly, the optical layout is a standard inverted microscope utilizing epifluorescence
capture. Excitation in two positions is provided by a 445 nm laser diode beam-shaped with acylindrical lens to achieve a relatively flat beam profile across the width of the channel with a 1µm focus and 1 kW/cm2 irradiance in the interrogation region of the microfluidic channel.
Epifluorescence is collected through a train of dichroic and bandpass filters to a set of PMT
detectors. The image is split in two at the focal plane, segregating signals from either
interrogation spot.

3.4

Calibration experiments for FRET sensors
The microfluidic system is designed to measure the sensor parameters which areconventionally obtained from low-throughput fluorescence microscopy. In a typical microscopy
measurement (Figure 3.5) a resting FRET ratio, R, is observed and subsequently a cell permeable
Zn2+ chelator (N,N,N',N'-tetrakis(2-pyridylmethyl)ethane-1,2-diamine: TPEN) is added todesaturate the sensor and obtain an Rmin. The cell is then treated with an agent to permeabilize
the plasma membrane or an ionophore, such as pyrithione, and ZnCl2 to saturate the sensor andestablish an Rmax. After the full saturation of the sensor has been achieved, the Rmax signal inFigure 3.2. A) Epifluorescence microscope optical layout consisting of a) 50:50 beamsplitter, b) cylindrical lens,c) laser clean-up bandpass filter, d) epifluorescence longpass filter, e) camera longpass filter, f) fluorescencedichroic filter, g) CFP bandpass filter, h) YFP bandpass filter, and i) transmitted light longpass filter. B)
Microfluidic device design used in this study. C) Close-up of analysis and sorting region of the microfluidic
device. Optical details are provided in Chapter 2.

54

55
mammalian cells often diminishes by a currently unknown mechanism. The decay of the
response may be due to cell death or homeostatic regulation of the Zn2+ levels. The transient
nature of these signals define the requirements for the time-resolution of the cytometer.
Figure 3.4 provides a schematic that connects the calibration experiments to theschematic of the microfluidic assay as well as some of the data obtainable. Spot 1, the firstinterrogation beam (prior to complete mixing) measure the desaturated FRET signal, Rmin. This
results in the black histogram (left-most) in Fig. 3.4C. At a time after Zn2+/ionophore addition,
defined by the delay (Δt) in the device, the cells are probed again at spot 2 which corresponds toFigure 3.3. Representative calibration plots of ZapCV2 (red, n = 4 cells) and
ZapCV5 (black, n = 9 cells). TPEN (150 µM) and Pyr/Zn (1 µM pyrithione and 50µM ZnCl2) were added at the indicated times. Data are displayed as R – Rmin foreach sensor, where R is the FRET ratio at each time point and Rmin is the minimum
FRET ratio obtained after incubation with TPEN. Error bars are the standard errorof the mean. Regions used to determine Rmin and Rmax are denoted with bars at theappropriate locations.

Figure 3.4. Schematic indicating the connection between the A) traditional microscope calibration experiments, B)
microfluidic assay, and C) data obtained by the cytometer. Spot 1 corresponds to Rmin in this setting, while Spot 22+
corresponds to the sensor FRET value at some time delay after Zn addition.

56

57
a point in the rise of the sensor response shown in Fig. 3.4A and the right-most (red)histogram in Fig. 3.4C. This window can be modulated by adjusting the pressures and delay
channel geometry to encompass early time or saturation for cytosolic sensors.

3.5

Tracking cells through pair-matching
Sorting requires real-time analysis of cellular response based on the fluorescence signals

from spatially and temporally separated points in the flow channel. We previously reported asimple post-processing algorithm with a sliding time window and linear velocity distribution formatching the response of each cell at two time-points (i.e. “pair-matching”) using a single pair ofdonor/acceptor PMTs The successful time-demultiplexing of signals into matched pairs fromindividual cells in a single PMT-pair arrangement is limited by the number of cells, ncrowd, thatoccupy the delay channel. The previous system suffers a loss in pair-matching accuracy for ncrowd> 5.82. Here, we implemented new signal collection and signal processing to enable real-timeflow analysis and sorting. We spatially separated the signals from the two interrogation points,
each of which is then directed into a pair of PMTs. This separation permits the sensor response
from each cell to be tracked by a simple time window based pair-matching algorithm
implemented on a microcontroller. Additionally, spatially segregated signals eliminate theproblem of simultaneous cell arrival events at each interrogation region. This scheme would also
generally apply to measurements with a larger number of interrogation beams. In the newscheme, there is no realistic limitation for the algorithm to the number of cells that can occupy
the delay channel and is instead affected most by the presence of multiple cells per droplet or cellclumping, in which cell events are either potentially mismatched or must be discarded. In aproperly dispersed single-cell emulsion, with the appropriate input cell density and droplet
spacing (which is

58

considered in the discussion), pair-matching efficiencies exceed 90% for cells that pass the signal
threshold at both interrogation regions (likely due to either photobleaching at spot 1 or theincrease in FRET signal due to a FRET change near the signal threshold at spot 2). For thisstudy, ~5% of detected cells were discarded in this manner, with 98% pair-matching efficiency
for the remaining events (% of those detected cells at spot 2 that were matched to a previous cell
event at spot 1).
A flowchart describing the sorting logic is presented in Figure 3.5A. Emission from eachsingle cell passing through an interrogation beam is observed to have a near-Gaussian time
profile. The peak times are compiled into a histogram. Given the discrete and stable dynamics ofdroplet generation, there is a narrow distribution of droplet transit times between the twointerrogation spots. For an elapsed run time the average time delay is determined from thedifference in peak positions of the frequency histogram from spot 1 (from PMT#1) and spot 2(from PMT#2). An example time-delay histogram is given in Figure 3.5B. The largest amplitude
peak in the histogram, or the most frequent time difference between cells detected, indicates thetime delay between interrogation spots. In the event of clusters of cells or frequent multiple cell
droplet occupation due to incorrect calculation of input cell density, the timing histogram loses
the clean, separated nature of peaks and we observe a broadening in the width of the timing
delay, w∆t. This can have a detrimental effect on pair-matching, which stresses the need forsingle-cell encapsulation, as discussed later. To determine the time delay for a sorting run, anumber of cells are screened prior to sorting. Once the time window has been established, every
event detected on PMT#2 is checked against the history of PMT#1 peaks and matched if it falls
within the established window. As shown in Figure 3.5A, if no match is made no action is taken.
With this process the FRET ratio, R1, at spot 1 can be matched to the ratio, R2, at spot 2 to

59
determine the ∆FRET, R2/R1. This parameter provides a very useful sorting gate because it
permits identification and sorting of cells that have a large response, yet do not fall in the lowest
portion of the unsaturated population and the highest portion of the saturated population as may
be found in a heterogeneous sample such as a library.

3.6 Time-resolved differentiation of ZapCV2/ZapCV5 sensor
mixtureDetermination of the instrument’s ability to resolve a mixed-cell population consisting ofdifferent binding domains was initially tested through a ~50:50 mixture (determined by a
Neubaur cytometer separately and mixed) of NES-ZapCV2 and NES-ZapCV5 expressing HeLaS3 cells. Figure 3.6A shows the results of this screening experiment at a time delay of 6 seconds.
The two populations are unresolvable in their FRET 1 distributions, but following Zn2+ exposure,separate into two resolvable peaks by the end of the delay channel. Figure 3.6B is a histogramfor data obtained in a separate experiment conducted much later (Apr 2017, compared to Jan2015 for the previous dataset), but is relevant to the present discussion. A ~50:50 mixture of
NES-ZapCY2 and NES-ZapCV2 expressing HeLa cells was screened after Zn2+-pyrithione
exposure. The purpose of this experiment was to verify the resolvability seen in the two sensors
with different ligand kinetics was also seen in two sensors with the same ligand binding domain,
but different acceptor proteins (ZapCY2, ECFP-Zap2-mCitrine; ZapCV2, ECFP-Zap2cpVenus173).

60

A)

B)

Figure 3.5. a.) Schematic data flow and sorting logic diagram, and b.) a typical histogramof cell event time differences observed between detection spots with annotations
representing the droplet spacing in the time domain, ∆tdroplet, and the width of the delay
time measurement, w∆t, around the largest peak which indicates the highest frequency
time difference (i.e. time delay) used to determine the pair-matching time window gates.
Top graph shows an expanded window of the same data set.

61

Figure 3.6. A) ~50:50 mixture of NES-ZapCV2 and NESZapCV5 after 6 s of Zn2+/pyrithione exposure. An
unresolvable FRET 1 distribution separates into two
distinct FRET 2 peaks. n = 2000. B) ~50:50 mixture of
NES-ZapCY2 and NES-ZapCV2 after 6 s of
Zn2+/pyrithione exposure. Two FRET 1 distributions
respond with different dynamic ranges. Identity of thepeaks was determined by a screening run done immediately
after with isolated ZapCY2. n = 2000.

62
To demonstrate the kinetics of response to Zn2+, HeLa-S3 cells expressing ZapCV2 andZapCV5 in the cytosol were run separately and the FRET changes were measured over a rangeof times achievable in a single device by tuning the input pressure (Figure 3.7). The rise time of
the response does not significantly differ between sensors and is limited by the timescale for theZn-ionophore complex to diffuse across the cellular membrane and equilibrate with the sensor
pool. Sensor expression and hence concentration is similar for both cell lines. At time delays
exceeding 5.5 seconds, the population peak FRET values exceed the width of the FRET
distributions for reliable separability. Coefficients of variation for the measurements of thesesensors are 9% for spot 1 measurements and range from 10-20% for spot 2 measurements.

3.7

Sorting of a mixed population
To verify sorting accuracy, a stable cell line of HeLa-S3 cells was constructed in which

NES-ZapCV2 was co-expressed with mCherry-NLS (nuclear-localization sequence). The
fluorescence of mCherry falls outside the optical window for FRET detection and therefore does
not contribute to the response. The two cell lines were mixed in either an 80:20 or 90:10 ratio(NES-ZapCV5:NES-ZapCV2), where cell numbers were estimated using a Neubaur
hemocytometer. These mixtures were sorted to a target of 3500 cells over the course of 2 hours
to enrich the lower percentage ZapCV2 population. Based on the response kinetics determinedpreviously, the cells were sorted with a time delay of 5.6 seconds to provide a balance ofresolution vs. sorting speed. Figure 3.8 displays a pair-matched scattergram for the sorting
experiment with applied gates shown as colored lines and the sorting region shaded in red. Aftersorting, an aliquot of pre-sorted cells and sorted cells were imaged to quantify the ratio of NLSmCherry tagged NES-ZapCV2 stable cells versus NES-ZapCV5 stable cells. Cell counts to

63

A)

B)

Figure 3.7. A) Histograms of peak FRET ratio (IFRET/ICFP)
2+

distribution for the two Zn sensors at a selection of delay times, ∆t,between interrogation regions (each delay time is represented by adifferent color) and B) ratio of peak FRET ratio as a function of ∆t.
Error bars indicate peak half width. Time points (2000 cells each)were obtained on a single device by varying only the pressure inputs.
∆t = 0 refers to FRET population at FRET spot 1.

64determine enrichment54 were done manually and indiscernible particles/cells were reported as
negatives, therefore reported sorting accuracies are likely artificially low. Example images used
for determining sorting accuracy can be found in Figure 3.9. Sorting parameters used for theexperiments are summarized in the caption of Figure 3.8. Enrichment information is summarizedin Table 3.1. For an estimation of maximum possible enrichment attainable in this experiment,
results shown in Figure 3.8 were fit to 2D Gaussian peaks to estimate population overlap. We
estimate the percent of ZapCV5 cells that match the gates applied to the sorting experiment havean upper bound of 2.5% of the total cells sorted (with 97.5% maximum ZapCV2 sorting potential
or a 130-fold enrichment).

Initial % NES-ZapCV2

Sorted % NES-ZapCV2

Enrichment (η)

23

96

75.1

10

92

96.3

Table 3.1. Cell sorting results demonstrating enrichment of ZapCV2 from an initial mixture of
𝑁 𝑠𝑜𝑟𝑡𝑒𝑑

𝑁 𝑖𝑛𝑖𝑡𝑖𝑎𝑙

𝑁𝑍𝑎𝑝𝐶𝑉5

𝑁𝑍𝑎𝑝𝐶𝑉5

𝑍𝑎𝑝𝐶𝑉2
ZapCV2 and ZapCV5. The enrichment, η, is defined as ( 𝑍𝑎𝑝𝐶𝑉2
𝑠𝑜𝑟𝑡𝑒𝑑 )⁄( 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 ).

65

Figure 3.8. Time resolved pair-matched Ratio Spot 2 vs. Ratio Spot 1 forsensors in HeLaS3 cells (17,000 cells). FRET 1 Gate: 1.21 - 1.60, FRET 2
Gate: 2.50 - 3.00, ∆FRET Gate: 1.75 - 2.50, time delay: 5.6 ± 0.012 seconds
with pair-matching gates applied at 5.6 ± 0.020 sec. The region
corresponding to the cells sorted with the applied gates is shaded in red.

66

Figure 3.9: Representative, pseudo-colored images using CFP (434/16 excitation, 458
dichroic, 470/24 emission) and mCherry channels (560/20 excitation, 585 LP, 610/50
emission) showing pre- and post-sort collections of cells used to collect data in Figure
3.8. Images were collected using a 20X air objective (NA 0.8), 100 ms exposure time
for CFP and 200 ms for mCherry, EM gain 1 MHz at 16-bit readout mode with an EM
gain multiplier of 100, and a neutral density filter with 12.5% light transmission. Cell
expressing ZapCV2 are coexpressed with an NLS-mCherry. Scale bar = 100 uM.

3.8 Impact of throughput and operation on instrument
applications
We now evaluate the scope of potential applications for this instrument in light of its
design and operation characteristics. For quantifying heterogeneity of cellular response and fordevelopment of new sensors, measurement precision and number of cells screened per session
are important performance metrics that are impacted heavily by the inter-dependent factors
influencing droplet generation and loading, the time needed for sensor response (delay time), thetiming parameters needed for successful pair-matching, and run-time. These factors in turn

67
determine the sorting rate and accuracy, which are additional considerations for screening andsorting genetic libraries of sensors.
We begin with a discussion of the operational parameters governing droplet generationand the loading of single cells into the droplets, which linearly affects the screening rate of agiven device. The factors contributing to droplet control have been discussed previously but will
be described here briefly.85 The parameters used in the discussion of device performance are best
illustrated in Figures 2 and 3. Droplet spacing, size, and speed can be adjusted by changing theratio of aqueous inlet pressures to the oil inlet pressure, Pa/Po. Increasing the ratio decreases
droplet speed and spacing while increasing droplet size. This control is critical for optimizing
throughput while preventing droplet collision at the sorting junction, which causes droplets toincorrectly enter the sorted channel. As discussed later, controlling the droplet spacing is also
key to successful sorting. The input cell concentration dramatically impacts both sort throughput
and sort accuracy. Droplet occupancy, the average number of cells per droplet, follows Poisson
statistics.66 For sorting operations, input cell concentration is chosen to ensure most droplets are
empty, which maximizes the probability of single occupation. If the cells are too concentrated(>2x106 cells/mL) such that a large number of droplets are doubly occupied, the sorting
throughput decreases as potentially desirable cells will be discarded when gating or potentially
undesirable cells will be sorted if one is below the set signal threshold which will decreasesorting purity. At the opposite extreme, the throughput will be low if the cell concentration isdecreased (<1x106 cells/mL). Occupancy also depends on the droplet size. Generally, the cellconcentration is tuned such that 90% of droplets are unoccupied and therefore < 1% chancedouble or higher occupancy. For the measurements presented here, with a cell input

68
concentration of ~1-1.5x106 cells/mL, the droplet volume was ~ 200 pL with half of the volume
taken by the cell input fraction.
To continue the discussion of throughput and to justify the use of this technique for arange of sensors, it is necessary to point out the factors in determining and setting the delay time
for a given microfluidic device. The available range of delay times permitting the screening ofalternate sensors that have much faster (> 50 ms) or moderately slower (< 15 sec) kinetics of
response is determined by the input pressures and the device geometry. Longer delays are
preferably achieved by increasing the length of the channel between the two interrogation
regions. The increased output resistance is then compensated with an increase in the fluidic
resistance of the input channels. Increasing the delay by decreasing input pressure, necessitates adecrease in fluid speed, which is undesirable because it decreases cell throughput given thelimitation in cell concentration that can be used to maintain single cell droplet occupancy.
Assuming cell concentration cannot be arbitrarily raised as discussed previously and delay line
crowding is not a factor, by increasing the delay channel length, the throughput and delay time
both increase. We found that delay channel lengths beyond 250 mm (> 15 seconds delay) pose
operational challenges that limit performance. For example, longer channels present more
opportunities for pressure fluctuations and vibrations to affect droplet spacing and thus theconsistent flow of droplets between interrogation points. This becomes limiting at a point as it
requires higher driving pressures to sustain desired time delays and stable flow: irreversible
bonding of PDMS to borosilicate glass is rated for ~30-50 PSI input before
delamination/failure.86 For the experiments presented in this work, a delay channel of 140 mm
was used. Flow speeds in this device are adjustable from 10 mm/s to 250 mm/s with continuous
phase driving pressures of 1 – 20 PSI. It should be noted that, while this delay length was chosen

69
for this experiment, several lengths were tested and utilized, from a direct path between
interrogation beams as might be ideal for rapid extracellular sensor response to the 250 mm
device mentioned above, for sensor screening at a variety of ligand concentrations to observe
heterogeneity and response timing. In adjusting pressures to generate a particular delay time it isimportant that cells do not dwell in the channel prior to the low FRET interrogation region, as
increasing the droplet spacing will slow the aqueous phase flow velocity relative to thecontinuous phase. It important to limit pre-interrogation exposure to ensure proper assessment of
the low FRET state ratio as arbitrarily slowing the flow speed can eventually exceed the time ittakes for Zn-pyrithione to penetrate the cell membrane and begin equilibrating with the sensor,
causing a rise in response at the first interrogation beam. The mixing times for the small ionmetal complex can be estimated for the short, straight channel after mixing but before droplet
generation and we provide simulated results in Figure 3.10. In short, within the operating rangeof flow velocities, mixing is incomplete prior to the cell reaching the droplet generator where cell
position becomes highly variable and more complicated advection takes place within the droplet.
It is reasonable at this point that the cell experiences saturating ligand concentrations due to theuse of ligands concentrations several orders of magnitude above the KD of the sensors and therise of the sensor response is expected to be dominated by membrane permeation. Under theconcentrations and geometry used in this study, no increase in FRET is found for the first spot
until flow speeds in the mixing region decrease below 2.5 mm/s or 120 ms spent in the mixing
region. In practice, it is best to modulate the input pressure ratio and monitor the low FRET
population to verify it does not show an increase at the across the desired delay times.

70

Figure 3.10: A slice of the calculated concentration profile at x = 300 µm (where x isthe distance along the axis of flow) after the mixing junction. The y-axis shows theconcentration C, normalized by the input concentration Co. The x-axis is the position
along the width of the channel, beginning in the middle. Equation used is
1
2

𝑦ξ𝑈

(1 − 𝑒𝑟𝑓 ( 4𝐷
ඥ

𝑒𝑓𝑓 𝑥

𝐶(𝑥,𝑦)
𝐶𝑜=)), where y is the distance perpendicular the axis of flow, U is the2flow velocity, and Deff is the effective diffusion constant (estimated at 1E-9 m /s for thesmall ion/ionophore complex). Plotted at a series of flow velocities relevant to chip
operation.

71
Lastly, the parameters necessary to obtain successful pair-matching and accurate
determination of the timing variables are the most stringent and limiting with regards tothroughput and are inextricably linked to the parameters discussed in previous paragraphs. The
width of the time window for pair-matching and sorting is limited by the closest spacing between
occupied droplets and the timing variation introduced by the cell position in the droplet. The
spacing between droplets can be increased by increasing the pressure of the oil input to provide atime interval during which a cell event is not detected. The key to successful pair-matching is tomaintain a droplet spacer larger than the variation in cell arrival time. The largest contributor tothis variation is typically the variation in cell position in the droplet along the axis of flow. This
spatial variation increases with the size of the droplet. For constant volume, the timing widthscales linearly with the droplet speed. At the sorting speeds employed here, the contribution fromcell position in the timing width is about 3 ms at 25 mm/s and a 5.6 s delay. Droplet spacing in
the time domain is typically 15 ms or greater, owing to the large spacing required to prevent
droplet collision at the sorting junction. This generally limits our potential cell sorting rates to <7 s-1 at our cell concentrations and desired droplet occupation frequency (~9 %). For shorter
delay lengths, faster responding sensors (i.e. extracellular sensors), and less stringent sorting
accuracy the cell throughput can be raised.
A potentially powerful application of this instrument would be screening a library of
sensors in mammalian cells and selecting winning sensors based on direct measurement ofsensor response in the target environment of interest (i.e. mammalian cells). This could beaccomplished by generating a library of sensor plasmids with mutations specifically targeting
linker and ligand binding domains, and incorporating the library into mammalian cells by viraltransduction. A low multiplicity of infection could be used to ensure that each mammalian cell

72
is statistically likely to be infected by a single virion, and hence express only a single library
member.87 Transduced cells could then be enriched, if necessary, by FACS or antibiotic selection
markers. This heterogeneous population of cells, each expressing a single library clone, could
then be screened with our instrument for ligand response. The diversity of FRET responses
within a library is likely to far exceed the difference between the two sensors in the mixture
reported here. Cells that respond above a designated threshold could be collected and analyzed
for high dynamic range clones. Depending on the nature of the reagents, the cells could then be
expanded and subjected to additional rounds of sorting or, if the reagent treatment renders cells
non-viable, DNA could be immediately extracted and analyzed by deep sequencing.88,89 The
sorter is set up to run with minimal interference for several hours with sorting rates typically on
the order of 2-5 s-1, or up to 20,000 hr-1, facilitating the screening of libraries on the order of 104
with several-fold coverage over a run time of a few hours.
While the throughput is inherently limited by the timing parameters required tosuccessfully make a measurement, we remark that it is orders of magnitude faster than theequivalent throughput for screening large numbers sensor variants in mammalian cells. This
contrasts droplet technology for single time point fluorescence interrogation, which approaches
FACS-like speeds on the order of kHz cell throughputs.57 Our instrument is comparable ofsorting speeds on the order of 1-10 s-1 and 50-150 fold enrichment found in technologies
utilizing optical trapping, electro-osmosis, and hydrodynamic mechanisms for cell sorting.90–97
Low coefficients of variation < 20% are achievable, likely due to the consistent and fast exposure
each cell experiences to the saturating ligand and ionophore complex. This offers an
improvement to the plate-based technologies for FRET sensors mentioned in the introduction.

73

3.9

Conclusions
This work presents a cell sorter for the screening and enrichment of biological systems

that exhibit transient intensity signal or ratiometric signal changes on a millisecond to second
timescale. Due to the flexibility of microfluidic technology, the idea can be easily extended toother systems that do not require mixing steps, but rather other methods of signal modulation(i.e. optical photoswitching or clustering events) or require more time points.

74

Chapter 4
Systematic Evaluation of ER Sensors and Development of a Sensor
Linker Library
4.1

Publication Note and Author Contributions
Portions of this chapter were adapted from Carter, K.P; Carpenter, M.C.; Fiedler, B.L. et

al. Critical Comparison of FRET-sensor Functionality in the Cytosol and Endoplasmic
Reticulum and Implications for Quantification of Ions. Anal. Chem. (2017) (in review at time ofthesis submission) and corresponding Supplementary Information. Figures present in the
publication are unmodified in this chapter. Microfluidic work was performed by BLF. In vitro
studies were performed by MCC. Imaging experiments were performed by KPC and MCC.

4.2 Genetically Encoded Zn2+ Sensors Targeted to Subcellular
Organelles
Fluorescent sensors are powerful tools for visualizing and quantifying ions, metabolites,
and other species in cells, offering the potential to define the concentration and spatial
distribution of such species. Realizing this potential requires systematic and careful

75
characterization of sensor platforms in the complex environment of a living cell and in different
subcellular locations. The need for a robust analytical framework for comparing sensor
performance and defining sensor functionality in different cellular locations is illustrated bycomparing the three families of genetically-encoded FRET-based sensors, Zap, eCALWY, andeZinCh, which have been applied to define Zn2+ pools within the cell.
Each family is engineered by fusing fluorescent proteins (FP) to Zn2+ responsive protein
domains. The Zap family links a donor CFP to an acceptor citrine YFP through the zinc-binding
domain of the yeast transcription factor Zap1.41 In the eCALWY family of sensors, cerulean FP
and citrine YFP were mutated to recognize each other in the apo state of the sensor, and themetal-binding domains of Atox1 and WD4 were engineered to coordinate Zn2+.34 In the eZinChfamily a Zn2+ coordination site was directly engineered into an interface between the -barrels of
cerulean and citrine, which were connected by a flexible linker.35 A notable benefit ofgenetically-encoded sensors is that they can be targeted to organelles by fusing small peptide
signaling sequences to the sensors. Members of these families of sensors have been targeted todifferent cellular compartments including: cytoplasm, mitochondria, Golgi apparatus, nucleus,
and endoplasmic reticulum (ER).41,35,98,83,42 Application of multiple platforms for measuring
analytes in cells can increase confidence in the robustness of both the tools and theirmeasurements. These three sensor platforms have been used in the cytosol to estimate labile Zn2+
within an order of magnitude, 82–600 pM.30,99 In contrast, when the same sensors were targetedto the ER of HeLa cells the estimates of labile Zn2+ ranged widely from 0.9–1600 pM.41,35,42
Qualitatively, ZapCY1 is more saturated in the cytosol than in the ER, while eCALWY-4
displays the opposite saturation pattern and eZinCh-2 is about equally saturated in both the ER
and the cytosol.41,35,42

76
It is possible the chemical environment of the ER (pH, crowding, or redox state)differentially affects sensors, impacting sensor functionality and accuracy of quantifications.
Defining whether the labile Zn2+ concentration is higher in the ER than in the cytosol is an
important fundamental question in Zn2+ biology in order to establish whether the ER might serve
as a store of Zn2+ that could be mobilized for cellular signaling. For these reasons, it is becoming
increasingly clear that methodology for the optimization of sensors in the chemical environment
they will ultimately be used is crucial. Additionally, establishment of the parameters that can be
modified to improve these sensors should also be identified.
The overall goal of this work is to establish a framework for critically comparing
ratiometric FRET-based sensors in an organelle of interest by comparing ZapCY1, eCALWY-4,
and eZinCh-2 in the ER and cytosol of HeLa cells. In this thesis, focus will be drawn to theresults generated from the microfluidic work. These findings will be extended to the generationof a linker library for the purpose of generating an improved ER sensor.

4.3

Experimental Setup

4.3.1 ER Sensor Cloning and Cell Culture
The instrument and assay are used as described in Chapters 2 and 3. For the ER sensor
screening experiments, cells used in the microfluidic experiments are grown and transfected
identically to those used in the microscopy experiments done alongside (presented in Figure 4.2).
Plasmids containing eCALWY-4 and eZinCh-2 were obtained from Addgene, catalog numbers
22236 and 70009 respectively. In order to control for possible difference in transfection
efficiency and protein expression due to differences in plasmid backbone or organelle targeting
sequence, all three sensors were cloned into the same pcDNA3.1 backbone. The sensors were77
targeted to the cytosol using the same nuclear exclusion peptide sequence (MLQLPPLERLTL),
and targeted to the ER using the same calreticulin sequence (MLLPVPLLLGLLGA). eCALWY4 and eZinCh-2 were PCR amplified and cloned into pcDNA3.1 between restriction sites BamHI
and EcoRI using InFusion homologous recombination kit (Clontech) according to the
manufacturer’s instructions. HeLa cells (ATCC CCL-2) were cultured in DMEM medium(Invitrogen) supplemented with 10% fetal bovine serum and penicillin/streptomycin at 37°C with
5% CO2. All sensor constructs were transiently transfected with TransIT-LT1 (Mirus Bio)according to the manufacturer’s instructions.
After the 48 hour transfection, cells are harvested using 0.05% trypsin-EDTA. Cells arecentrifuged and resuspended in phosphate, calcium, and magnesium-free HEPES-buffered HBSS
(Rmax buffer) with 150 µM TPEN and incubated at room temperature for 10 minutes. Cells
centrifuged again and resuspended in Rmax buffer to rinse. Cells are subjected to one final spin
down and resuspended in a microfluidic suspension buffer consisting of 0.1% BSA, 16% v/v
Optiprep and 1 µM TPEN in Rmax buffer on ice. The reagent buffer contains 100 µM ZnCl2, 100µM pyrithione, and 0.1% saponin for a final droplet concentration that is half of the statedvalues. Cells are kept on ice until placement in the microfluidic setup and are then kept at room
temperature in a pressurized reservoir for the ~1-2 hour duration of the experiment. Delaychannel length used in this experiment was 140 mm with applied pressures ~7 PSI per fluid
reservoir to achieve the 7.5 second delay.

4.3.2 Library Cloning
For the library screening and sorting experiments, identical conditions were used toharvest and prepare the cells, but HeLa-S3 cells were used in place of HeLa cells.

78
CloneAmp HiFi PCR Premix (Clontech) was used for all PCRs according to the manufacturer’s
instructions. All restriction enzymes were obtained from New England Biolabs (NEB). Allrecombination or ligation reactions were transformed into Stellar competent E. coli cells(Clontech) and plated on LB with 100 µg/mL ampicillin.
Codon-diverged mCitrine, codon-diverged ECFP and codon-optimized Zap2 Zn2+-binding
domain were ordered as gBlocks (IDT) and recombined into a pBAD vector backbone using the
In-Fusion HD Cloning Kit. The backbone was prepared by digesting ZapCY2 in pBAD with
SphI-HF and EcoRI-HF. The sensor fragment (OptiZap) was digested out of the vector using
BamHI and EcoRI-HF, then recombined using the In-Fusion HD Cloning Kit into a pcDNA3.1+
vector backbone (Invitrogen) with a 5’–calreticulin signal sequence(MLLPVPLLLGLLGAAADG) and a 3’–ER retention tag (KDEL). The sensor was thentransferred to pLVX using the In-Fusion HD Cloning Kit.
To construct the libraries, the ZBD was amplified using OptiZap in the pLVX vector as atemplate using degenerate primers and recombined into pLVX using the In-Fusion HD Cloning
Kit. Prior to the recombination reaction, all PCR products were gel purified using the Monarch
DNA Gel Extraction Kit (NEB). The small libraries (< 1,000 variants) were plated on standard
10 cm agar dishes, while the larger libraries were plated on 20 cm dishes.
Once the genomic DNA was purified from cells (vide infra), the ZBD could be amplified by
PCR. Initially, the ZBD was amplified with optizapZBDseqV1fwd (5'–GGCGCATTCAAGATCAGACAC–3') and optizapZBDseqV1rev (5'–GGGCAAAACACATCAGGCCAT–3'). The thermal cycler protocol used was 98°C for 3 minutes,
then 23 cycles of 98°C for 15 seconds, 72°C for 75 seconds, then finally 72°C for 3 minutes,
then the product was gel purified. For verifying cell lines, the purified PCR product was ligated79
into the pJET2.1 vector using the CloneJET PCR Cloning Kit (Thermo Fisher Scientific),transformed into Stellar cells and plated on LB with 100 µg/mL ampicillin. For cloning thesorted cells, the pLVX vector was amplified from OptiZap in pLVX using PLVXsensorhits.rev(5'–GTTGTGTCTGATCTTGAAGTGCGCCTTGATTCCGTTTTTTTGCTTGT CGG–3') and
PLVXsensorhits.fwd (5'–GGCTATGGCCTGATGTGTTTTGCCCGGTACCCAG
ATCATATGAAACAG –3'), gel purified, and recombined with the ZBD PCR product using the
In-Fusion HD Cloning Kit. Alternatively, to minimize parent sensor contamination during thisstep, the ZBD was amplified from gDNA using KeepersPLVXfwd (5'–TTCCGGTGAATTCCTCGAGACTAG TGCGGC–3') and KeepersPLVXrev (5'–CGAGGGTGCGTACGGCCCGG–3') with 0.5 M betaine and 5 % DMSO. The pLVX vector wasdigested with SpeI-HF and XmaI-HF for 16 hours at 37°C. Both products were gel purified andrecombined together using the In-Fusion HD Cloning Kit.

4.3.3 Mammalian Cell Culture for Libraries
HeLa S3 cells (ATCC CL-2.2) and HEK293T cells (ATCC CRL-3216) were cultured in
Dulbecco’s Modified Eagle Medium (DMEM) (Gibco) supplemented with 10 % fetal bovine
serum (FBS) at 37°C with 5% CO2. Cells were split twice per week at a splitting ratio of 1:8(HeLa S3) or 1:10 (HEK). HEK cells were transfected with viral plasmids using TransIT-LT1(Mirus) according to the manufacturer’s instructions.

4.3.4 Viral Transduction for Libraries
HEK293T cells were seeded in 15 cm plates at a density of 4  106 cells per plate. 24
hours later, the cells were transfected with lentiviral packaging plasmids and viral expression

80
plasmid (pLVX) containing a single Zn2+ sensor or a library of linker variants. The packaging
plasmids were a gift from Professor Hubert Yin (CU Boulder). There are three plasmids required
to package lentiviral particles: pRev (which contains the reverse transcriptase), pMDL (contains
the Gag and Pol genes), and pVSV-G (contains the envelope protein). The packaging plasmids
were mixed with equivalent amounts of each other and pLVX, usually 5 µg each plasmid per 15
cm plate, in 1 mL of OptiMEM reduced serum medium (Thermo Fisher). TransIT-LT1 (3 µL per
1 µg of DNA) was then added dropwise to the DNA mixture and the transfection complexes
incubated at room temperature for 20 minutes before adding to cells. After 24 hours, thetransfection complexes were removed from the HEK293T cells and fresh medium was added.
The viral supernatant was harvested between 48-72 hours after transfection. Supernatant could
be harvested multiple times over this period, always replenishing the medium on the HEK293T
cells until the protocol was finished. The supernatant was gently filtered through a 0.45 µM
surfactant-free cellulose acetate filter and either used immediately to transduce HeLa S3 cells orstored at 4°C. Supernatant from multiple harvests of the same virus could be pooled together. If
desired, the viral particles were concentrated using a SW-28 rotor in an L-70 Ultracentrifuge(Beckman) at 25,000 rpm for 2 hours at 20°C. The supernatant was carefully aspirated and theviral pellet was resuspended in 1 mL DMEM.
Viral particles were added to HeLa S3 cells when the cells were 50-70 % confluent in a 6-wellplate, 10 cm dish, or 15 cm dish, depending on the number of cells needed. Growth medium wasremoved from the HeLa S3 cells and replaced with fresh medium. Viral supernatant was thenadded (no more than 1/3 of the total medium on the cells) as well as hexadimethrine bromide(polybrene) at a final concentration of 8 µg/mL). 24 hours after adding viral particles, themedium was replaced on the HeLa S3 cells and they were trypsinized and transferred to a larger

81
dish if needed. Puromycin was added at 2 µg/mL 48-72 hours after adding viral particles toselect cells that were successfully transduced. After 2-3 days of puromycin selection, themedium was replenished and the cells were allowed to grow.

4.3.5 Genomic DNA Extraction
Genomic DNA (gDNA) was purified from cells using phenol/chloroform organic
extraction and ethanol precipitation. After harvesting or sorting, cells were resuspended in 200–
400 µL gDNA extraction buffer (10 mM Tris, 1 mM EDTA, 0.1 M NaCl, 1 % SDS, pH 8.0) with
2 µL Proteinase K (NEB) and incubated for 16 hours at 37°C in a shaker at 200 rpm. The
mixture was then extracted twice with equal volume phenol–chloroform–isoamyl alcohol
mixture 25:24:1 (Sigma Aldrich) then twice with chloroform:isoamyl alcohol 24:1 (Sigma
Aldrich). For extractions, organic solvents were added to the buffer and the mixed thoroughly byvortexing for 30 seconds. The organic and aqueous phases were separated by centrifugation at
13,000  g for 2 minutes. The upper aqueous layer was transferred to a new tube. After the last
phenol:chloroform extraction, DNA was precipitated by adding 20 µg RNA-grade glycogen(Thermo Fisher), 1/10 volume 3 M sodium acetate pH 5.2, and 3 volumes 100 % ethanol, mixing
thoroughly, and incubating at –20°C for 4–6 hours. The gDNA was then pelleted bycentrifugation at 16,000  g for 30 minutes at 4°C. After aspirating the supernatant, the pellet
was washed with 200 µL ice-cold 70 % ethanol and centrifuged at 16,000  g for 5 minutes at
4°C. The resulting pellet was first air dried for 5 minutes, then resuspended in 20 µL TE buffer(10 mM Tris, 1 mM EDTA, pH 8.0) for 16 hours at 37°C in a shaker at 200 rpm.

82

4.4

Zn2+ Sensor Response in Subcellular Organelles
It was previously shown that the microfluidic cytometer introduced in the last chapter

was capable of screening and differentiating sensor response with sensors targeted to the cytosol.
General interest led to the examination of a high dynamic range sensor targeted to several
cellular organelles. ZapCY1 was used in a few preliminary screening experiments in which it
was localized to the cytosol, mitochondria and endoplasmic reticulum. The previously published
dynamic ranges for this sensor is ~4, ~3.2, and 2.16 respectively.41,98 As shown in Figure 4.1, it
is clear that the cytometer is capable of resolving a response in each organelle on a timescale of afew seconds when transiently expressed in HeLa-S3 cells. However, it is also apparent that on
this timescale, in this cell line, that the response does not achieve the same response magnituderelative to their respective dynamic ranges shown through long timescale microscopy
experiments. The responses do appear in the same relative order as their dynamic range, with the
ER sensor responding the least and the cytosolic sensor responding the most. Therefore, thecytometer was deemed capable of analyzing the response of organelle-targeted Zn2+ sensors.

4.4

Heterogeneity in the Unbound State
This cytometer permits measurement of variability in FRET states and FRET responses

in thousands of cells to assess variability at the population-level. Here, the microfluidic
cytometer is used to compare cytosolic and ER-targeted sensors for all three sensor platforms
alongside traditional microscope calibration experiments (Figure 4.2). A distinct advantage ofthis platform is the ability to introduce reagents in a controlled manner that does not vary fromcell to cell or experiment to experiment. However, the time window to resolve a sensor response

83

Figure 4.1. FRET histograms for the high dynamic range sensor, ZapCY1,
localized to the mitochondria (top), cytosol (middle), and ER (bottom).

+ 5 M pyrithione (grey lines), 2 nM buffered Zn + 0.75 M pyrithione (black lines, not done for NES sensors
2+(d-f)), or 2 nM buffered Zn + 0.75 M pyrithione + 0.001% (w/v) saponin (lines in color) to cells treated for 40
minutes with 50 M TPEN. (credit: KPC and MCC)

2+

Figure 4.2. Optimization of zinc conditions for ER (a-c) and NES (d-f) sensors: ZapCY1 (green), eZinCh-2 (red),eCALWY-4 (blue). Arrows indicate washing of cells followed by addition of solutions containing 100 mM ZnCl284

85
is limited by the high resistance device geometry. Therefore, these measurements assess thesensor response within a short time window (7.5 sec), which only correspond to a part of the fullmagnitude of the response. Figure 4.3 shows an estimated window of sensor FRET response on a
microscopy-obtained calibration trace of ER-ZapCY1 based on the conditions used in the
microfluidic screening experiments. This calibration trace was obtained using the same, highZn2+/pyrithione concentrations used on the device, rather than the low concentrations used for acalibration experiment. During the first several seconds there is a significant rise in the response,but milliliter scale volume diffusion Zn2+/pyrithione results in slower mixing times used in the
microscopy calibrations. The full response does not occur for hundreds of seconds. It should be
noted that this figure corresponds to ER-localized sensor only and that response in the cytosolnears saturation as shown in the previous chapter. In measuring the response to Zn2+, it isimportant to note that transport and accumulation of Zn2+ in cells is rate limiting as proven in aprevious publication.82 Additionally, these measurements are made in HeLa cells to remain
consistent with the microscopy experiments done alongside this work. It has not been
demonstrated to achieve the same response under the same Zn2+ and pyrithione conditions andthe same time delays when compared to HeLa-S3 cells for the ER localized sensors. There is anotable size difference observed for non-adherent HeLa cells and non-adherent HeLa-S3 cells.
HeLa cells appear to have sizes ranging from 15-25 μm while HeLa-S3 vary between 10-15 μm.
The smaller sensor response is likely due to Zn2+ diffusion and equilibration with the sensor poolacross a larger volume. It is also possible that the Zn2+ buffering capacity of HeLa cells and
HeLa-S3 cells differ. Therefore, the FRET 2/FRET 1 values do not represent the full dynamic
range of the sensor under saturating conditions, and instead indicate the ratios at 7.5 sec after
Zn2+ treatment. Given these considerations, the platform is used to define the initial FRET ratios

86
and variability of ratios in the apo state, and examine the qualitative responsiveness of sensors
upon addition of Zn2+, as opposed to the magnitude of response (i.e. dynamic range), within thedefined time window.

Figure 4.3. ER-ZapCY1 sensor calibration trace with a red box denoting the general
region of sensor response probed by the microfluidic cytometer using reagent
concentrations used for the microfluidic experiments. It is important to note that thistime window, which is set by the microfluidic geometry, is too short to allow for a fullsensor response in the ER. Cells were pre-treated with 150 µM TPEN for 10 minutes.
Cells are washed and exposed to 5 µM TPEN (start of imaging) for 30 minutes before
exposure to 50 µM pyrithione and 50 µM ZnCl2. n = 7 cells.

Since the measured FRET populations did not exhibit a normal distribution, medianbased statistics were used to assess the center and heterogeneity of the cell populations. The
robust coefficient of variation (RCV) was used to describe the width of the distribution, which
serves as an indicator of the heterogeneity of a sensor in the apo state. RCV is defined as

87
𝑅𝐶𝑉 = 0.75 ×𝑖𝑛𝑡𝑒𝑟𝑞𝑢𝑎𝑟𝑡𝑖𝑙𝑒 𝑟𝑎𝑛𝑔𝑒
𝑚𝑒𝑑𝑖𝑎𝑛,(4.1)

and expressed as a percentage.
Figures 4.4a and 4.4b show histograms of single cell FRET populations at FRET 1 and
FRET 2 for ER- and cytosol-targeted sensors, respectively. The median and RCV values forsensors in each compartment are presented in Table 4.1. Comparison of these values for eachsensor provides evidence to whether there were differences in the FRET state in different
compartments. ZapCY1 showed similar RCV in the two locations (11.1% and 9.3% in ER andcytosol, respectively) suggesting comparable sensor heterogeneity, and a small, but significant
shift in the median FRET 1 ratio when localized to the ER (1.01 versus 0.92 in the ER andcytosol, respectively). The eCALWY-4 sensor showed similarly broad FRET 1 distributions in
the ER and cytosol (22.8% and 26.5%, respectively), indicative of increased sensor heterogeneity
in both locations compared to ZapCY1, and a significant shift in the median FRET ratio from0.97 (ER) to 1.55 (cytosol). The eZinCh-2 sensor yielded the tightest FRET 1 distribution in thecytosol (RCV = 5.1%), indicating minimal heterogeneity, but the broadest distribution in the ER(RCV = 32.4%). For eZinCh-2 the FRET 1 median ratio shifted from 1.42 in the ER to 1.03 inthe cytosol. Indeed, in all sensors there is a shift in FRET 1 in the direction of sensor response(shift high in ZapCY1 and eZinCh-2, shift low in eCALWY-4) when the sensor is moved from

88

Figure 4.4. Microfluidic analysis of FRET sensor response. FRET ratio histogramsfor HeLa cells expressing ER (a) or cytosol (b) targeted sensors indicating FRET 1
and FRET 2 cell populations. Corresponding pairmatched FRET ratio histograms2+
showing FRET response to pyrithione and Zn for ER (c) and cytosol (d) targeted
sensors. Inset scattergrams show FRET response on the single cell level. Time delay
between FRET 1 and FRET 2 for all data shown is 7.5 seconds. Sample size foreach plot is 6500 cells.

89
the cytosol. Collectively, these data reveal that localization to the ER can introduce significant
heterogeneity into the apo state of a sensor, as well as modifying the baseline FRET state of thesensors.
NES-

ZapCY1eCALWY-4
eZinCh-2

ER -Median (xc)

RCVMedian (xc)

RCV

0.92
1.55 (1.21, 1.75)
1

9.3%
26.5%
5.1%

1.01 (0.77, 1.03)
0.97
1.42

11.1%
22.8%
32.4%

Table 4.1. Median and robust coefficient of variation (RCV) statistics calculated from the FRET
1 distributions in Figures 4.4 and 4.5. ER-ZapCY1 and NES-eCALWY-4 were fit with two
Gaussians due to the bimodal distribution observed. The center of each Gaussian was used toestimate the centers (xc) of the apparent multiple peaks. For these sensors the median represents
the weighted average of the centers of the two peaks.
It was demonstrated that sensor performance can be adversely impacted by the oxidizing
environment of the ER which can lead to formation of protein oligomers and reduced dynamic
range. Although all three sensor platforms showed some degree of perturbation in the ER, assuggested by formation of disulfide linked oligomers and reduced dynamic range, the platforms
were differentially affected. See Figure 4.5e for a blot showing extensive oligomerization in theabsence of a reducing agent. eZinCh-2 was the most strongly perturbed (dynamic range reduced
from 2.06 to 1.47, increase in variability in FRET ratio as measured by RCV from 5.1% to32.4%, and perturbation of ER morphology in 25% of cells), perhaps due to surface exposed
cysteine residues. eCALWY-4 was also perturbed, but to a lesser extent (dynamic range reduced
from 1.83 to 1.32, but no change in variability in FRET ratio as measured by RCV: 26.5% vs22.8% in cytosol and ER, respectively). Finally, ZapCY1 was the least perturbed in the ER
environment (dynamic range reduced from 2.62 to 2.55, variability in FRET ratio increased from

90

Figure 4.5. Measurement of localization to and aggregation in the ER. ERZapCY1 (a) and ER-eCALWY-4 (b) localize properly to the ER. Some cells
expressing ER-eZinCH-2 have proper localization (c) but approximately 25% of
cells exhibit bright puncta (d) regardless of signal sequence used (Figure S4).
Western blot (e) of sensors in the ER under non-reducing and reducing conditions(denoted by – or + DTT) reveals that all sensors form DTT-sensitive oligomers.
(credit: KPC)

91
9.3% to 11.1% in the cytosol versus ER, respectively). Despite some degree of perturbation in
the ER environment, the estimated Kd values for each sensor closely matched in vitro
measurements done alongside. Combined, these results suggest that eCALWY-4 and ZapCY1
are more suitable than eZinCh-2 to estimate relative amount of ER Zn2+, and of the two sensor
platforms ZapCY1 has a higher dynamic range (from Figure 4.2) and less evidence of
perturbation.

4.5 Dynamic Response of ER and Cytosol Targeted Sensors andSensor Variability
This platform also enabled measurement of sensor responses to added analyte in a shorttime window and identification of sub-populations of cells with sensors that are non-responsive
sensor or that respond significantly differently than the average. Figure 4.4c and 4.4d show thepair-matched single cell response in the form of a histogram (FRET 2/ FRET 1), and thecorresponding scattergram that illustrates the FRET change in individual cells (inset). All of thesensors gave rise to responsive populations, but the overall shape of the scattergrams varies fromsensor to sensor and is not univariate as the binning of the histograms may suggest. This is mostexaggerated in ER-eCALWY-4 (Figure 4.4c, bottom), in which the histogram appears broad andunimodal, but the scattergram shows additional sub-populations indicating variability in the
kinetics of the response from cell to cell.
Comparison of scattergrams for ER-targeted versus cytosolic sensors revealed a greater
degree of heterogeneity in responsiveness for all ER sensors. While some of this heterogeneity
may derive from increased variability in the apo FRET state, some may also arise from variable
Zn2+ uptake kinetics in different cells. It is clear the cytosolic sensors show a large response
relative to the ER sensors. NES-eZinCh-2 is remarkable in the variability in the response, with a92tight apo population showing considerable kinetic variation across several FRET units in thistime window. In contrast, ER-eZinCh-2 shows considerable FRET 1 variability, most of which
responds in a similar magnitude. This is interesting as ER-eZinCh-2 cells showed a large
increase in signal intensity relative to the ER versions of ZapCY1 and eCALWY-4 when under
the same illumination conditions and transfection conditions. Plotting the signal intensity of eachsensor in the donor and acceptor channel, ZinCh-2 shows a large intensity variation compared tothe other two sensors. Figure 4.6a shows scattergrams of an independent screening of thesesensors. These are plotted as a histogram, as shown in Figure 4.6b, and an arbitrary highthreshold is set in the tail of the distribution at 2V. As shown in the histograms, both ERZapCY1 and ER-eCALWY-4 have ~18% of the cells acquired above this higher intensity gate.
In contrast, ER-eZinCh-2 expands the entire range capable of detection using the current
acquisition settings and 44% of cells acquired are above the high intensity gate. This is consistent
with the higher intensity observed in the microscopy experiments and the bright puncta observedin a large percentage of cells of this sensor (visible in Figure 4.5d). The cytometer is insensitive
to sensor localization and cannot differentiate between sensor that is properly localized in theER, sensor that has not properly localized, and sensor that has aggregated to form bright puncta.

Figure 4.6. a) Intensity scatter plot for each sensor in the ER. b) Intensity histograms for the YFP channel showing thepercentage of cells above 2 V. Data collected on the same day under identical illumination and gain settings. n = 7500 cells.

93

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4.6 Generation of Linker Randomized Targeted Zn2+ Sensor
Libraries
4.6.1 Library Rationale
Several issues were discovered in the sensors explored in the previous sections thatcomplicated their use in the ER for free or labile Zn2+ quantification. First, there is a decrease in
the dynamic range seen with the sensors when localized to the ER. Second, there is an increasein heterogeneity and a shift in the Rmin of each sensor when move from the cytosol to the ER,
potentially correlated to the decrease in dynamic range. Lastly, there is oxidation sensitivity in all
three sensors investigated that decreases their reliability in the oxidative environment of the ER.
It is increasingly clear that the generation of a large dynamic range cytosolic sensor does notguarantee large dynamic range within the ER, as is the case with eZinCh-2. Taking into accountthe need for an optimized ER sensor, methods to optimize these sensors for the complex
environment of the ER will be discussed.
To address the issue of oxidation sensitivity, the sensors would need to be engineered toreduce the use of redox sensitive cysteine residues. Removal of the cysteine residues in each of
the domains of the sensor, including the FPs used for FRET, needs consideration. Work has been
done by the Snapp group to engineer redox insensitive variants of the FPs used frequently in theliterature, in which outward facing cysteine residues in the beta-barrel of the FPs are mutated.100
However, based on results from Western blot experiments done for the three sensors (Figure4.5e), it does appear that the Zap family of sensors utilizing ECFP and mCitrine show
signification aggregation due to barrel-barrel interactions in an oxidative environment. It is also
likely that the decrease in dynamic range is due to the oxidation of the cysteines within thebinding domain, both the metal coordination ligands and non-binding cysteines. Alteration of the

95
non-binding cysteines within the zinc-finger would likely have an effect on the folding of thezinc-finger structure and affect the dynamic range and binding affinity of the sensor. If the
dynamic range is decreased, it would need to be restored by iteratively mutating out the cysteines
for other residues or creating libraries in which those positions are randomized and sensor
response is screened. Alternatively, molecular modeling using a program such as Rosetta could
be done to optimize the folding of the zinc-finger at those positions held by cysteine residues.101
The cysteines within the binding domain have been iteratively changed to histidines in several
variants of the ZapCY sensors.41 Each mutation that was done to change the wild-type Cys2His2
pocket (both fingers) led to a decrease in binding affinity and a decrease in dynamic range.
Cysteine residues help maintain Zn2+ specificity and tight binding constants associated with thezinc finger.102 This is an effective method for creating an array of sensors with varying binding
constants, but makes it difficult to eliminate cysteine residues entirely without further
optimization of the dynamic range.
Improving the dynamic range of the sensors requires an alteration of the Rmax value, theRmin value, or both. The shift in Rmin for the ZapCY1 sensor indicates that modification of the
dynamic range of a sensor in the ER may be achieved by lowering the Rmin. In order to modify
the FRET values of either the bound or unbound state, the parameters that govern the RET
efficiency need to be modified. Recalling Equation 1.1, the two parameters that are variable for agiven FRET pair is the distance (R) and relative orientation (κ2) of the donor and acceptor
chromophores. Libraries in the FRET sensor field have been generated using a number ofapproaches to achieve increased dynamic range. One method for altering the orientation has been
to circularly permute one or both of the FPs in the construct. Circular permutation is thechanging of the positions of the N and C termini within the amino acid sequence of the protein.

96
Circular permutation has been successfully done for fluorescent proteins for the use in sensors
and has generated improved variants due to the altered donor-acceptor orientation in the boundand unbound states.103,5,104,32,10 Circularly permuted versions of the Zap sensors (with cpVenus at
position 173) have been created, but have not yet been successfully expressed when localized to
the ER.* This presents a desirable path for optimization, but will require further work to
implement in all localizations.
Modification of the distance and orientation leading toward increased dynamic range has
also been achieved by modifying the linker and binding domain within FRET sensors.50,105–107
This strategy varies based on the structure of the binding domain and mechanism of sensor
response. In the parallel field of Ca2+ binding biosensors, significant work has been done to yieldhigh dynamic range by linker modification. One example is the optimization of TN-XXL, a Ca2+
FRET sensor developed by the Griesbeck group.49 In this work, short linkers (1-4 residues)flanking both sides of the binding domain were fully randomized to alter the orientation intandem with circular permutation of the acceptor, Citrine. This method is attractive for the Zap
family Zn2+ sensors given the similarity between the constructs (CFP-metal binding domainYFP). It is not apparent if modification of the orientation or the distance is more important toincrease sensor response in the complex, potentially crowded environment of the ER. Therefore,
it will be important to assess libraries that both increase the distance between the donor-acceptor
pair, as well as libraries targeting an orientational change between donor and acceptor.

*

Personal communication with Prof. Amy Palmer.

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4.6.2 Library Design and Selection Process
Three libraries were devised to generate optimized Zn2+ sensors using the Zap platform.
The libraries are designed with consideration of the cell-throughput restrictions imposed by the
microfluidic sorter. Given a sorting rate of the 2-3 cells/s for mammalian cells, it is reasonable tosort on the order of ~10,000 cells per hour. The size of the library is then set by the desired
coverage of the library. In the case that a single library member should be screened multiple
times, then libraries must be kept to a size of several thousand (103-104). This corresponds to arandomization of 2-3 amino acids if all 20 amino acids are included. The libraries generated forthis purpose consist of biased codons that decrease the variability in the amino acid pool. One
codon was chosen to increase the rate of charged and hydrogen-bond forming amino acids inorder to enhance amino acid interaction and increase linker rigidity. This is done byincorporating a VVC codon in the constructed primers which limit the possible nucleic acids ateach position (V = A/G/C). This codon restriction limits the possible amino acids to
Ala/Asp/Gly/His/Asn/Pro/Arg/Ser/Thr. Two libraries were created using this codon as a basis.
The first contains a mixture of lengths in which 1 or 2 residues have been added on either side of
the zinc binding domain for sensor constructs of the form ECFP-Linker-ZBD-Linker-Citrine.
ECFP has been truncated 11 residues at the C-terminus to make the attachment point at a rigid
portion of the FP as indicated by crystallographic structures.108 This results in the possiblecombinations of 1+1, 1+2, 2+1 or 2+2. This library was named VVC1-2 and contains 7371
library members. This library could reach 3-5 fold coverage in a few hours. Another library was
generated in which the VVC repeats were included in a 3+3 combination, resulting in a library(VVC3) with 531,441 members. This library cannot be fully covered by the microfluidic sorter,
but is interesting in whether increasing the linker length has any effect on the sensor. A third98
library was devised that incorporated the YCC codon, which limits the residues to only Pro/Ser.
Proline is an amino acid with a rigid, cyclic side chain the locks the peptide into fixed angles.
Chains of prolines form a very stable polyproline helix, (e.g. the secondary structure ofcollagen). This codon was implemented in a combinatorial fashion up to 4 amino acids,
generating a library (named Pro/Ser) with 276 members.
The library workflow and selection process is shown in Figure 4.7. The steps weredeveloped to accommodate the lack of cell viability upon Zn2+/pyrithione exposure. No cellenrichment through post-sort growth is possible, therefore the workflow relies on genomic DNA(gDNA) extraction, amplification, and identification. The process begins with library design aslaid out in the previous paragraph. Once cloning of the sensor library into a viral vector iscompleted resulting in a pool of library members incorporated in a viral vector, then plasmids arevirally transduced to induce expression of the sensor library in the target cell line (HeLa-S3).
Viral transduction was chosen to control the multiplicity of plasmids introduced to the cells,
which follow Poisson statistics.87 It is undesirable for a single cell to express multiple sensors, so
only one plasmid should be incorporated in each cell. Chemical transfection techniques areinferior to viral techniques for control of plasmid multiplicity. Once an expressive population ofcells has been selected (through antibiotic selection and/or FACS), the cells are simultaneously
screened on the microscope for localization verification and screened on the microfluidic
platform. If interesting variants are observed, the library is re-run on the microfluidic and sorted
for high response. The collected cells are immediately processed to extract their gDNA, which isthen PCR amplified and reintroduced into the viral vector for a second cycle. The process canrepeat until one or more improved sensors are isolated. Sequencing can occur at any of thesesteps to ensure losses do not occur.

Figure 4.7. Workflow for library generation and optimization of a biosensor library based on gDNA amplification
rather than cell enrichment. The process begins with library design, cloning, and verification before expression incells. Each step of this cycle has points for troubleshooting.

99

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4.6.2 Initial Library Screening and Platform Iteration
The parent of each of the three libraries was initially chosen to be ZapCY2, a Zap family
Zn2+ sensor consisting of truncated ECFP and mCitrine with a modified Cys1His3 binding pocketwith Kd = 100 nM. A looser binding sensor was chosen, as a tight binding sensor (ZapCY1, Kd =4 pM) already exists for the ER. A lower affinity sensor with high dynamic range is greatly
needed to help solve the controversy with respect to Zn2+ levels in the ER. Transient transfection(TransIT-LT1) of this sensor in the ER of HeLa-S3 cells was done and the resulting expressive
cell population was screened in the microfluidic to verify contextual response. Figure 4.8 shows
the resulting FRET 1 and 2 histograms after a time delay of 6.2 seconds. The response is small(~1.1), but a FRET population shift is resolved.

Figure 4.8. FRET histogram of ER-ZapCY2 expressing HeLa-S3 cells. Cells weretransiently transfected using TransIT-LT1 in a PiggyBAC vector. Δt = 6.2 s. n =3500 cells.

101
The parent sensor was initially cloned into the retroviral vector pCLNCX (Novus Bio) for
viral transduction of HeLa-S3 cells. After antibiotic selection and recovery, the cells were again
screened on the microfluidic. Figure 4.9 shows the FRET 1 histogram from this construct. It isclear a large amount of heterogeneity was introduced during the viral steps that was not present
in the transient expression. A low FRET population appears that is consistent with the presenceof multiple populations. These cells were run on a FACS to isolate cells with both CFP and YFP,
however, this did not eliminate a majority population that was unresponsive to Zn2+/pyrithione(data not shown). The cause of these extra populations is likely recombination during viral
replication due to the high sequence similarity between ECFP and mCitrine. Figure 4.10 is acomparison of the aligned ECFP and Citrine sequences. Indeed, many FPs derived from GFP
show sequence homology, which makes cloning difficult when multiple FPs are involved in acloning step.

Figure 4.9. FRET Histogram of HeLa-S3 cells expressing ERZapCY2 in pCLNCX. Cells were virally transduced and selected using
puromycin. n = 1050 cells.

Figure 4.10. Nucleic acid sequence of ECFP aligned with Citrine. Red (#) denote disparities in the sequence.
Differences are concentrated in the region around the chromophore. The region leading up to the C-terminus isnearly identical, with only 2 nucleic acids difference, leading to primer mismatch and recombination.

ECFP
Citrine102

103
Reduction of the recombination was achieved by Molly Carpenter by codon diverging thetwo FPs. This resulted in a codon diverged ECFP, a codon diverged Citrine and a ZBD (Zap2)
that was optimized to the most prevalent human codons for each amino acid. The resulting
construct, coined OptiZap, is then ECFPtrunc,diverged-Zap2optimized-Citrinediverged. This construct wascloned into the lentiviral vector pLVX (Clontech). The change in viral vector was done toincrease transduction efficiency. After antibiotic selection and recovery, the cells were screenedon the microfluidic. As shown in the resulting data (Figure 4.11), this cell line shows a unimodal
responsive population. All of the libraries discussed previously were recreated using OptiZap as
the parent construct. Library creation was verified using commercial sequencing on ~10 samples
and verifying the absence or low presence of the parent sequence in the ZBD.

Figure 4.11. FRET histograms for ER-Optizap in pLVX. Cells were virallytransduced and selected with puromycin. (Inset) Pairmatched scattergram. n =13,000 cells. Δt = 7.9 s.

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4.7

Microfluidic Screening and Initial Sorting of Sensor Libraries

4.7.1 Pro/Ser Library
The pLVX|ER-OptiZap derived Pro/Ser library was the first screened for Zn2+ response.
The results of this screen are shown in Figure 4.12. Statistics for this screen are compiled inTable 4.2. There is apparent heterogeneity in the scatterplot (Figure 4.12a), with very low FRET,
unresponsive cells. There is no apparent shift relative to OptiZap in the large population
dominating a ~1.1 fold response with 33,000 cells screen. The library created should be sampledover 10 times per clone, so it is possible the events outside of the main population are instrument
noise or improved sensor variants.

4.7.2 VVC1-2 Library
The VVC1-2 library was screened in conjunction with OptiZap on the same day to makea direct comparison and assess if diversity was introduced in the library. The results of thisscreen are shown in Figure 4.13. Statistics for this screen are compiled in Table 4.2. It is evident
based on the FRET change histogram (Figure 4.13c) that there is not an overall increase in
response due to the linker modifications on the microfluidic platform. One interestingobservation is found in the FRET histograms (Figure 4.13b). The median FRET 1 (Rmin)
distribution for VVC1-2 is decreased relative to OptiZap by 0.05 FRET units. The reduction in
FRET 1 can possibly be explained by the lengthening of the linkers, consistent with an increasein distance, therefore lowering the FRET efficiency of the construct. However, it is not clear
from the data that diversity has been introduced in this library that is resolvable by the
microfluidic platform.

Figure 4.12. HeLa-S3 cells expressing Pro/Ser library. (a) Pairmatched scattergram. (b) FRET histograms
corresponding to Spot 1 and Spot 2. (c) FRET Change histogram. Δt = 8.1 s. n = 33,000 cells.

105

Figure 4.13. HeLa-S3 cells expressing VVC1-2 library. (a) Pairmatched scattergram. (b) FRET histograms corresponding to
Spot 1 and Spot 2. Gaps included to increase visibility of overlap. (c) FRET Change histogram. Δt = 9 s. n = 29,000 cells.

106

107

4.7.3 VVC3 Library
The VVC3 library was also run on the same day as the parent, Optizap, in order to makea direct comparison. This library has a size much larger than the microfluidic platform is capableof covering in a reasonable timespan, but was generated to check for trends in the pool resulting
from 6 additional amino acids in the linker. The results of this screen are shown in Figure 4.14.
Statistics for this screen are compiled in Table 4.2. As seen in the scatter plot (Figure 4.14a),
VVC3 contains extra events at low FRET, suggesting recombination is present in this library. No
significant increase in FRET change is apparent. There is a reduction in the median FRET 1(Rmin) by 0.09. This is larger than the shift seen in VVC1-2, potentially consistent with theaddition of 2 more residues in the linker, though it should be noted that the magnitude of the shiftis small. While there are more spurious cell events in this library, there is also more
recombination evident by the small low FRET population. Again, it is not clear from this datathat resolvable diversity has been introduced, despite a small increase in the RCV of theindividual FRET distributions relative to OptiZap.

4.7.4 VVC1-2 Sort and Troubleshooting
A sort on the VVC1-2 library (named VVC1-2R1) was conducted in order to step
through the entire cycle of library processing shown in Figure 4.9. The sorting experiment scatterdata is shown in Figure 4.15a with gates overlaid. Statistics for this distribution are found in
Table 4.2. The FRET change gate was set to a 1.2 fold change and the individual FRET gateswere opened up to include most cells. The 450 sorted cells were immediately lysed in gDNA
extraction buffer and incubated with Proteinase K overnight. The resulting gDNA was PCR

Figure 4.14. HeLa-S3 cells expressing VVC3 library. (a) Pairmatched scattergram. (b) FRET histograms corresponding to
Spot 1 and Spot 2. Gaps included to increase visibility of overlap. (c) FRET Change histogram. Δt = 10 s. n = 45,000 cells.

108

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Table 4.2. Compilation of population statistics from library screening
experiments. SD = Standard Deviation. CV = Coefficient of Variation(SD/mean). IQR = Interquartile Range. RCV = Robust Coefficient of
Variation (IQR/median).

110
amplified and reintroduced to the viral vector for transduction in a fresh plate of HeLa-S3 cells.
After selection and recovery, the cells were re-screened on the microfluidic platform. A
comparison of this sample and OptiZap are shown in Figure 4.15b. No shift in the FRET
response of the population is evident. The explanations for the lack of shift in the responding

Figure 4.15. (a) Scattergram for VVC1-2R1 sort. Gates: F1 = 0.4, F2 = 0.4, F2/F1 = 1.2. Δt = 7.4 s.
n = 17500 cells. Cells sorted = 450. (b) FRET change histogram on a log scale for the second roundof VVC1-2 after sorting. Δt = 8.2 s. n = 2500 cells.

population include a lack of coverage or diversity in the original library, the inability for the

111
microfluidic to resolve the response of these targeted libraries, and thecontamination/modification of the library during the gDNA workup and amplification.
Possible issues resulting from the amplification steps was explored through a control
experiment on cells harvested directly from a plate. The gDNA extraction protocol was
conducted on the parent (OptiZap), the Pro/Ser library, and the VVC1-2 library with decreasingcell numbers. The protocol was also conducted cell-free as a ‘mock’ experiment. The resulting
products were split and half was subjected to PCR amplification of the ZBD. DNA was run on
agarose gels, displayed in Figure 4.16 at two different contrast levels. Figure 4.16a shows theresulting gDNA bands with no bands visible in the mock. Figure 4.16b show the resulting bands
after PCR amplification at the size expected for the ZBD. The mock experiment shows a band
even in the absence of cells, suggesting possible contamination. Sequencing on this band showed
the presence of the parent across several sequencing attempts. Figure 4.16c shows the ability of
the PCR step to amplify DNA at a variety of cell counts. Bands are visible above 100 cells, but
readily detected above 1000 cells.
Problems occurring due to the lack of library coverage are not readily apparent without
the use of high throughput sequencing, but sequencing of > 10 samples suggests the linker
modifications are present. It is difficult to decouple lack of diversity with the inability of the
microfluidic to resolve the library response on this timescale for the ER sensors. This problem
can be disentangled with the creation of a cytosol localized sensor library using the same linkers.

Figure 4.16. Genomic DNA extraction (a) and ZBD amplification (b-c) from OptiZap and library cell lines. Genomic DNA
was extracted from virally-transduced HeLa S3 cells stably expressing the libraries and the linker-ZBD-linker regions were
amplified by PCR. Genomic DNA from the cell lines (a) is a high-running band with several smaller bands and smears
when incubated with a fluorescent DNA stain. Contrast is adjusted in the right panel to highlight the absence of signal in
the mock (no cells) lane for the gDNA extraction. (b) PCR amplification of the linker-ZBD-linker region from mock,
OptiZap, Pro/Ser, and VVC1-2 gDNA. Contrast is adjusted in the lower panel to highlight the contamination band present
in the mock PCR. (c) PCR amplification of the linker-ZBD-linker region from a dilution of VVC1-2 cells. Bands from PCR
products are readily detectable from reactions using gDNA from > 1,000 cells. Contrast is adjusted in the lower panel tohighlight the bands from reactions using 100 and 500 cells as template, as well as the fainter bands from reactions using
gDNA from 50 or zero cells as template. (credit: KPC)

112

113

4.8

Conclusions and Future Directions
In this chapter, the microfluidic platform was used to assess the population heterogeneityof three unique Zn2+ FRET sensors. It was determined that all three sensors were perturbed whenlocalized in the ER relative to the cytosol. This resulted in a shift and sometimes spread in theminimum FRET state and a lowering of the dynamic range. Additionally, heterogeneity wasobserved in the response of these sensors, whether cytosol or ER targeted. All of theseobservations indicate a need for sensors that are tailored to the environment they are to be used.
The initial steps in creating an optimized sensor were laid out and 3 linker libraries were
presented. Diversity in each of these libraries was not clearly identified using the microfluidic
platform. The most beneficial experiments to be conducted next are those that relocate the libraryto the cytosol. This will bypass any kinetic limitation with respect to the microfluidic platform
and assess if the linker libraries as devised are capable of producing diversity and, ideally,
improved sensors. If diversity is established and the sorter is unable to kinetically resolve theresponse of the ER targeted library, it may be that a different library is required or the
microfluidic platform will need to be modified to accommodate the timescales necessary forsensor response. However, this will require an overhaul of the microfluidic devices required andnecessitate a decrease in throughput to reach these timescales.
Beyond the parameters required to resolve an ER sensor response, the microfluidic
platform is already established to be useful in the measurement of cytosolic sensors. The
instrument is readily adaptable to other optical windows. There is a great need for high dynamic
range Zn2+ sensors that utilize a GFP-RFP FRET pair. Generation of higher dynamic range
sensors in the cytosol could be accomplished with the current library designs if they are provento modify the sensor response in the CFP/YFP sensor platform.

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Chapter 5
Time-Resolved Photophysical Characterization of Zn2+ FRET
Sensors
5.1

Time-Resolved Fluorescence of Genetically-Encoded Sensors

5.1.1 Fluorescence Lifetime Measurements
Time-resolved fluorescence spectroscopy provides a wealth of information unattainablethrough steady-state fluorescence measurements. It gives information on the excited state
dynamics of a fluorophore, specifically the factors affecting the fluorescence lifetimes of theexcited states. Factors range from the solvation dynamics of the excited chromophore to excitedstate reactions to energy transfer processes.
There are two methods for measuring fluorescence lifetimes, frequency-domain and thetime-domain. Frequency-domain measurements are also sometimes referred to as phasemodulation measurements because they are performed by amplitude modulating an excitation
source with an oscillation period on the order of the lifetime to be observed and measuring thephase delay resulting from the finite lag time of fluorescence after excitation. The tangent of thatphase delay is proportional to the single exponential lifetime and modulation frequency of theexcitation.109 While simple in setup, frequency-domain measurements are difficult to analyze

115
when a fluorophore shows a multi-exponential decay: measurements at multiple frequencies are
required to precisely determine the lifetime components.
Time-domain measurements, also referred to as pulse fluorometry, require a pulsed
excitation source with pulse durations on a picosecond timescale. In the most common form of
this method, excitation intensity and emission intensity are attenuated to ensure that less than one
photon is detected per pulse. Detected photons are binned according to their arrival time relative
to the excitation pulse. This method is called time-correlated single-photon counting (TCSPC).
After sufficient photon counts, the resulting histogram of counts is fit to a model, generally
exponential, and the fluorescence lifetime (τi) components are extracted. Measured intensity
traces typically fit the form110:t

I(t) = ∑i αi exp (− τ ) ⊗ 𝐼𝑅𝐹
i

(5.1)

where αi is the ith amplitude of the fitted exponential. The measurement time is generally longer
compared to the frequency-domain method, since measurement time is limited by the low rate ofphoton arrival, but time-domain measurements are more robust in multi-exponential decay
determination without the need for multiple measurements (i.e. multiple frequencies needed for
frequency domain). Given the finite transit time for signals in the TCSPC, which is generally
limited by the detector response, sub-nanosecond lifetime components are difficult to determine
accurately in the absence of a measured instrument response function (IRF). The IRF is used tode-convolute the time response from the measured photon counts.110

116

5.1.2 Fluorescence Anisotropy
Isotropic emission lifetime measurements by TCSPC are generally taken with linearlypolarized excitation and an emission polarizer set to 54.7o (the “magic angle”) relative to theexcitation polarization to avoid effects resulting from molecular rotation after photoselection,
which would give rise to a decay more complex than from the fluorescence lifetimes alone.
However, the polarization of the emission relative to excitation contains significantly more
information when used in conjunction with the lifetime measurements. Fluorescence anisotropy,
defined as:I(t)−GI(𝑡)r(t) = I VV(t)+2GIVH (𝑡)
VV

VH

(5.2)

where IVV is the emission intensity measured with (traditionally) vertically polarized excitation
through an emission polarizer aligned with the excitation. IVH is the intensity of emission
measured with the polarizer set 90o off vertical. G is the G-factor which corrects for polarization
bias in the detector.110 Fluorescence anisotropy provides a concentration-independentmeasurement of any process that causes a rotation of the transition dipole moment vector during
the time between the absorption of a photon and the subsequent emission of a photon. The
probability of photon absorption is proportional to cos2ψ, where ψ is the angle from the axis
defined by the linearly polarized excitation. The result is an angular distribution of excited
fluorophores. This phenomenon is called photoselection and limits the theoretical maximum
anisotropy value for a one-photon excitation to 0.4.
Time-resolved fluorescence anisotropy (TRFA) can be measured using a TCSPC setup
by rotating the excitation and emission polarizers. In TRFA experiments, parallel andperpendicular components of the emission are measured. The intensities of these twocomponents are given by:

117
1

t

IVV (t) = 3 ∑i αi exp(− τ )[1 + 2 ∑𝑗 r(0)𝑗 e−t/φj ]i1t

IVH (t) = 3 ∑i αi exp(− τ )[1 − ∑𝑗 r(0)𝑗 e−t/φj ]
i

(5.3)
(5.4)

where φ is the correlation time corresponding to the jth component of the fluorescence
depolarization. The most common cause of depolarization is molecular motion, i.e. rotations that
occur during the lifetime of a fluorophore. The timescale and amplitude of the anisotropy decay
gives information on the environment of the molecule and the effect of that environment on the
excited state of that molecule.
Another process which can result in fluorescence depolarization is FRET. For a suitable
donor-acceptor pair (discussed in Chapter 1), radiationless energy transfer can occur between the
excited singlet states of two molecules upon excitation of the donor. A Jablonski schematic of
the possible processes following an absorption event is shown in Figure 5.1a. Contributions to
the fluorescence depolarization of a system undergoing FRET include rotation of the donor, therotation of the acceptor, and a change in acceptor emission polarization relative to thepolarization used to excite the donor resulting from energy transfer. Given the initial orientationof the donor, emission from the acceptor will occur at a different angle after energy transfer
occurs unless the donor and acceptor transition dipole moments are perfectly aligned. canprovide information on the relative orientations of the two chromophores.111,112 For a molecular
picture of energy transfer, the relevant vectors and angles between the pair of chromophores areshown in Figure 5.1b. For a given FRET pair, the rate of energy transfer is proportional to thedonor-acceptor distance and the relative orientation between their transition dipole moments. The
orientation factor, κ2, is defined relative to Fig. 5.1b in terms of the angles θT, θD, and θA suchthat
κ2 = (cos θT − 3 cos θD cos θA )2

(5.5)

118
Time-resolved fluorescence anisotropy can be used to determine the average transfer angle, θT,, andsubsequently place limits on the value of κ2, allowing a more precise calculation of R (distance between
chromophores, equation 1.6).113

b

θT
θA

θD
R

D

θA

A

2

ns), kA,F is the rate of acceptor fluorescence (ns). b) Schematic representation of the angles used in the determination of κ .
D and A are the transition dipole moments (TDM) of the donor and acceptor, respectively. R is the distance between the
TDMs. θT, θD, and θA are the angles between A and D, D and R, and A and R, respectively.

Figure 5.1. a) Jablonksi diagram for a donor-acceptor pair undergoing FRET between singlet excited states. The energy
transfer process must be competitive with the fluorescence rate of the donor. kabs is the rate of absorption (1/k ≈ attoseconds),
kIC is the rate of internal conversion (fs-ps), kD,F is the rate of donor fluorescence (ns), kFRET is the rate of energy transfer (ps-

a

119

120

5.1.3 Fluorescent Proteins as Anisotropy Probes
Fluorescent proteins are uniquely suited as probes using anisotropy. The chromophore ina fluorescent protein is rigidly fixed within the barrel of the protein and thus rotates with theprotein as a whole. The ~27 kDa (for GFP) protein rotates much slower than small molecule
fluorophores. The rotational correlation times in low viscosity buffers for FPs are in the range of
15-20 ns, which is much longer than their lifetime (1-4 ns).112,114 For example, mVenus has afluorescence lifetime of ~3 ns, and has a steady-state anisotropy of ~0.33 across its emission
bandwidth (470 nm excitation).115 Fluorescein dianion has a lifetime of ~4 ns and a steady-stateanisotropy of ~0 due to molecular rotations on the order of several picoseconds.116 Given theslow depolarization that occurs due to molecular rotation during the fluorescence lifetime,anisotropy measurements on FPs can illuminate other processes that lead to depolarization suchas energy transfer.

5.1.4

Time-resolved FRET
Measurement of FRET efficiency and molecular separation is typically estimated withsteady-state FP FRET measurements. However, FPs, hence show broad excitation and emission
spectra, can contaminate the FRET emission channel with cross-talk. The FRET efficiency isdefined as the fraction of photons absorbed by the donor transferred to the acceptor. Donor
lifetime measurements are more accurate in the determination of FRET efficiency (E) due tofewer contributions to signal in the donor emission channel. FRET efficiency can be calculated
through the formula110:
τ

E = 1 − τDA
D

(5.6)

121
where τDA is the lifetime of the donor in the presence of the acceptor and τD is the lifetime of thedonor alone. It should be noted that in many systems, the donor lifetime is artificially inflated
due to the presence of free donors that are not interacting with acceptors, which are notaccounted for in this equation. The determination of R using E requires knowledge of κ2, whichin the case of FPs in unlikely to be the dynamically-averaged value of 2/3. This is due to theaforementioned slow rotation during their fluorescence lifetime. An alternative formulation for
determined R from E has been proposed for FRET pairs within the static random isotropic
orientational regime by Vogel et al.117 This regime accounts for the slow rotation of an FP(approximately static over the fluorescence lifetime), but an isotropic distributions of dipole
orientations. Table 1 of reference 7 presents values for the determination of R from E based on
experimentally derived values.
One alternative method for the determination of the FRET efficiency is the measurementof the rise of acceptor fluorescence in the FRET channel.118,119 Since only active FRET donors
will lead to acceptor emission in the absence of significant donor bleedthrough, the rise time is areporter on the rate of transfer between donor and acceptor. However, this method excels only
when there is minimal bleed-through of the donor emission into the FRET channel, because thiscontamination causes an artificial shortening of the rise time.120,121
Utilization of time-resolved anisotropy to measure FRET has been sparse, however a few
examples exist in the literature.121–123 Steady-state imaging anisotropy is more readily used in
microscopy as an alternative to sensitized emission and fluorescence lifetime imaging
microscopy.124–127 One notable example with close parallels to the sensor in use for Zn2+, is a
study of the Ca2+ ECFP-calmodulin-cpVenus FRET sensor, YC3.60 by Borst et al.120 In thisstudy, the researchers utilized, among other techniques, time-resolved fluorescence anisotropy to122determine the energy transfer angle, FRET rate, and flexibility of the construct in the ligand freeand bound states. YC3.60 was suggested to become more flexible in its Ca2+ free state, as
determined by an increase in the rotational correlation time of the acceptor. This study suggested
that the large dynamic range of YC3.60 was due primarily to the distance change, rather than anorientational change through the use of TRFA and fluorescence correlation spectroscopy, whichmeasures diffusion rates of molecules by means of fluorescence emission.
Presently, there is little information on the molecular dynamics of the geneticallyencoded Zn2+ sensors discussed in this thesis. Steps have been taken to understand how they
perform in vivo (Chapter 4). This chapter makes progress on understanding the sensors in more
molecular detail by assessment of their photophysics using time-resolved fluorescence (TRF)
and anisotropy. Calculations are done to assess the effect of crosstalk and active FRET
populations have on the measurements. The sensors used in this study (introduced in Chapter 4)
are: ZapCY1, eCALWY-4, and eZinCh-2. Additionally, two sensors with circular permutation of
the acceptor FP were purified to investigate the how changes in the relative orientation of thedonor and acceptor ion the affect. These sensors, named ZifCV1.173 and ZifCV1.157(ECFP(trunc)-Zif1-cpVenus), use a single zinc-finger ZBD (Zif1) and the last three-digit number
indicates the position in mVenus to which the N-terminus was relocated. cpVenus173 relocatesthe fusion site to a position on the opposite end of the barrel. cpVenus157 retains the N-terminuson the same side of the barrel as the parent, but placed within a turn of the beta-sheet instead of
the original N-terminal helix.40 These sensors were purified and initial measurements made on
their donor and acceptor lifetime and anisotropy in ligand free and saturated conditions.

123

5.2

Experimental Setup

5.2.1 Sample Preparation
All sensors and fluorescent proteins were cloned into pBAD between the BamHI andEcoRI restriction sites and expressed in Top-10 Escherichia coli with the addition of 0.2% m/v
arabinose. Unless otherwise stated, proteins were purified using Ni2+ ion affinity
chromatography and the His tag was removed with TEV protease (Promega) in 1 mM DTT,
20mM Tris, 100 mM NaCl, pH 8 buffer at 30oC for 6 hours and 4oC overnight with a sensor to
TEV protease ratio of 10:1. The sample was again run on a Ni2+ ion affinity column to separate
cleaved protein from uncleaved protein and cleaved His tag. Purified constructs wereconcentrated with an Amicon-Ultra centrifugal filter (EMD Millipore) to 20-80 µM asdetermined by the absorbance at a specific wavelength governed by the isolated FP or acceptor
FP and the corresponding absorption coefficient listed in Table 5.1 obtained from the literature.5

Fluorescent Protein
ECFP
mCeruleanmVenusmCitrine

λ (nm)
433
433
515
516

ε (M-1cm-1)
32500
43000
94500
77000

Table 5.1. Fluorescent protein absorption coefficients at specified wavelengths used todetermine protein concentrations during protein purification.
Purified sensor was subjected two conditions: saturating Zn2+ and Zn2+ chelating
conditions. A saturating Zn2+ condition was achieved by creation of a buffered Zn2+ solution (3:7
Zn/Ca/EGTA:Ca/EGTA), made at pH 7.4 using a pH titration method described previously.128
The concentration of accessible Zn2+ in the buffer was 12 μM. 100 μM EDTA (prepared from 25
mM EDTA, pH 8.0) was used as the Zn2+ chelating condition with no added Zn2+. Experiments

124
were performed in HEPES buffer (150 mM HEPES, 100 mM NaCl, pH 7.4 made with chelexedwater) with 500 nM sensor protein, treated for 5 minutes prior to Zn2+ or EDTA addition with 1
mM TCEP. TCEP is included to reduce cysteine residues that are present in all sensors studied.
All samples were 0.2 μM filtered prior to the experiment.

5.2.2 Instrumentation
Steady state polarization measurements were made on a QM-6 fluorimeter (Photon
Technology International) with a T-configured PMT detector scheme for simultaneous detectionof the parallel and perpendicular fluorescence emission signal. Calibration was checked using
Erythrosin B in water,129 which has an anisotropy of 0.235 measured at 525 nm excitation and
549 nm emission wavelengths. All slit widths (excitation and emission) were set to 6 nm.
Emission was scanned from 460-560 nm with 444 nm excitation.
Time-resolved measurements were made on a FluoTime 100 fluorescence lifetime
spectrometer (PicoQuant) equipped with a TimeHarp 260 PICO TCSPC module andphotomultiplier detector assembly (PMA 182). Plane polarized excitation (further denoted asvertically polarized) is provided by a 440 nm pulsed diode laser at a frequency of 10 MHz andpower adjusted to ~ 1 µW/cm2. The IRF (typically 200 ps FWHM) was measured using a dilute
Ludox suspension. Donor and FRET emission were collected through 483/32 nm and 542/27 nm
bandpass filters (Semrock), respectively. Figure 5.2 shows the detection window overlaid with
ECFP and mCitrine absorption and emission spectra. Vertically polarized emission was collected
at 1.1×105 counts/sec (adjusted by an iris in front of the emission filter) for 600 s. Data werecollected, without touching the iris, in the following order for each sample: FRET channel: VV,
VH, VM; Donor channel: VV, VH, VM. VM is vertically polarized excitation and emission
collected with the polarizer angle set to 54.7o (magic angle). The G-Factor for the instrument,

125
which corrects for polarization bias, was assumed to be 1 for all fits and confirmed by multiple
measurements of HV and HH emission in initial experiments. Iterative reconvolution and global
analysis of the data was performed using the provided FluoFit software (PicoQuant). Error
analysis for individual decay curves was conducted at the 67% confidence level for lifetimes androtational correlation times. Traces are offset from zero by 21500 ps. All results shown in thischapter are single replicate values. Therefore, error analysis across multiple experiments was notconducted.

Figure 5.2. Detection windows overlaid on amplitude normalized ECFP and
mCitrine absorption/emission spectrum. Considerable donor emission can be foundin the acceptor emission window.

126

5.3

Fluorescence Anisotropy Calculations
To estimate the effect of the parameters that convolute the signal in the FRET channel,

calculations were performed to simulate behavior of the FRET anisotropy as a function oftransfer angle (i.e. angle between donor and acceptor transition dipole moments, Fig. 5.1b). It is
assumed here that the rotation of the TDMs themselves relative to the separation vector are fixed.
For the constructs under study, it would be beneficial to understand the effect of non-interacting
fractions of donor emission and donor bleed-through on the FRET anisotropy measurements. For
measurements on FPs, the FRET channel contains contributions from multiple sources: acceptor
lifetime, FRET transfer (acceptor rise), quenched donor emission (bleed-through), FRET inactive
or unquenched donor emission, and direct acceptor excitation. Calculations of the timedependent polarized emission signals were adapted from Masters et al for a one photon process
and can be found in Appendix B.122 Assuming equal ground state populations (Ngs), thepopulations of interacting and non-interacting donors and acceptors (NI,D,NI,A, NNI,D, and NNI,A)
are:
NI,D(t)=TDNgsFIexp(-(kfret+kdi,f)t)
NI,A(t)=TDNgsFIX[exp(-(kfret+kdi,f)t)-exp(-kai,ft)](5.7)

NNI,D(t)=TDNgs(1-FI)exp(-kdni,ft)
NNI,A(t)=TANgsexp(-kani,ft)
where X = kFRET/(kFRET + kdi,f-kai,f). TD and TA are the one-photon excitation probabilities for thedonor and acceptor. FI is the interaction fraction from 0 to 1 (1 indicating that all molecules
undergo FRET). kdi,f, kai,f, kdni,f and kani,f are the inverse of the fluorescence lifetimes forinteracting and non-interacting ECFP and mCitrine. After a short pulsed excitation, the intensityof each of these components is proportional to the fraction of emission in the detection window

127
and the radiative rate of that component. Application of these intensities to Equations 5.3 and 5.4
yields the total intensity in the parallel and perpendicular channels. These components can beapplied to Equation 5.2 and summed over their weighted components to derive the total
anisotropy in the FRET channel. This yields the following equation, dependent on the transfer
angle, θda:
(FI (1-(BX)/2)(3(cos[θda]2)-1) exp[-(kFRET +kaf )t] exp[-krot t]))+(0.5)FI BXexp[-kaf t]((3(cos[θda]2)-1)exp[-krot t])+(1-FI )exp[-kdf t]exp[-krot t]+Bδexp[-krot t]exp[-kaf t]
FI (1-BX)exp[-(kFRET +kaf )t])+FI BXexp[-kaf t]+(1-FI )Exp[-kdf t]+Bδexp[-kaf t]

2

r(t,θda) = (5)
(

(5.8)
)

where B is the ratio of acceptor to donor emission in the detection window and δ is the ratio ofacceptor absorption to donor absorption at the excitation wavelength. For simplicity, theorientational relaxation of the donor and acceptor have assumed to be equivalent to the rotationof the entire FRET sensor (1/krot = 30 ns for these calculations). This assumption has thepotential to introduce inaccuracies as shown in the study of YC3.60, if there is a difference inflexibility for the donor and acceptor domains. The value of kFRET is determined at fixed R = Ro
(Forster distance for ECFP and mCitrine = 4.9 nm). δ is assumed ~ 1%, which is reasonablegiven the low absorption probability of the mVenus and mCitrine at 440 nm. B is calculatedusing commercially available spectra for filters and fluorophores. B was calculated for threepairs of fluorophores in our system using the following equation:
𝐵=

𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑎𝑐𝑐𝑒𝑝𝑡𝑜𝑟∗𝑅𝑎𝑑𝑖𝑎𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 𝑎𝑐𝑐𝑒𝑝𝑡𝑜𝑟
𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑎𝑐𝑐𝑒𝑝𝑡𝑜𝑟
𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑑𝑜𝑛𝑜𝑟∗𝑅𝑎𝑑𝑖𝑎𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 𝑑𝑜𝑛𝑜𝑟
𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒𝑑 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑑𝑜𝑛𝑜𝑟(5.9)

The values for B for ECFP/Citrine, ECFP/Venus, and Cerulean/Citrine are 5.89, 5.41, and 3.54
respectively. The decreasing values of B indicate the higher proportion of donor emission in theacceptor channel. For comparison, in a EGFP/mCherry filter set for the two fluorophores, B =128
19.9.130 The small value of B is a result of the broad emission spectrum of ECFP, which also
makes it a good FRET donor for the YFPs, leading to a large value of the spectral overlap
integral. Cerulean, derived from ECFP, has a higher quantum yield, while retaining the broad
emission leading to higher bleed-through into the FRET channel.
The results of the calculations are shown in Figure 5.3. Figure 5.3a shows the results in
the initial 10 ns window. The B and FI parameters are varied at numerical extremes. A value of B
with little donor emission was chosen (30) and compared to a value corresponding to the
ECFP/mCitrine (5.89) for 100% interacting molecules. Then, the effect of decreasing the fractionof donors interacting with acceptors is shown for 100% vs. 10%. A number of qualitative trends
are observed. The first observation is that an increase in transfer angle, θda, correlates with adecrease in the magnitude of r at all times and that large transfer angles can result in a negative
anisotropy value that begins to rise as molecular rotation, trot, becomes dominant. Decreasing
values of B result in an increase in the measure anisotropy due to the large presence of donoremission. Decrease in the interacting fraction also leads to a more modest increase in themeasured anisotropy at in the first 2 nanoseconds. Figure 5.3b expands the timescale to highlightthe eventual convergence of the anisotropy values to 0 due to the isotropic reorientation of thewhole molecule. However, it is unlikely this will be observed outside of the fluorescence lifetimeof the fluorophore at which time the signal to noise will be limiting.
The measured anisotropy curves will be affected by bleed-through effects in these
CFP/YFP sensors due to a low B value in the range of 3-6. Any measured curve will exhibit
higher anisotropy values than would be calculated in the absence of donor emission in the FRET
channel. However, initial experiments should give a qualitative picture of the magnitude ofreorientation due to the binding of ligand in these sensors. This information may provide insight

129
into what factors drive the FRET changed observed upon ligand binding and whether the FRET
change is dominated by reorientation or distance changes between the two fluorescent proteins.
Future calculations that correspond to the fitted data will need to include parameters obtained
from the measurements including treating the two FPs separately with respect to theirreorientation. Additionally, noise and orientational/distance population distributions (based on
Gaussian distributions)110 could be included to more accurately reflect the measured data andobtain quantitative results.

r

t (s)

θda

r

Figure 5.3. Left: First 10 ns of anisotropy decay (r) following vertically polarized excitation. Increasein transfer angle (θda) results in a decrease in the amplitude of the slow anisotropy component(anisotropy past the first few nanosecond). Right: Time window opened to 60 ns to display theconvergence of the anisotropy to zero after molecules have rotated sufficiently to achieve an isotropic
distribution. Parameters shown above (in order of amplitude in r) with corresponding colors.

t (s)

θda

Gray | FI = 1, B = 5
Green| FI = 0.1, B = 30
Red | FI = 1, B = 30

130

131

5.4

Time-resolved Fluorescence Results

5.4.1 TRF of a Small Molecule Dye
Fluorescein dianion (H2O, pH 11) was measured at 500 nM to validate the ability of the
instrument to accurately reflect the magnitude of fluorescence anisotropy, as well as picosecond
rotational correlation times (φ). The results of this experiment are shown in Figure 5.4 withparameters obtained from exponential reconvolution in Table 5.2. The average lifetime is inagreement with the 4 ns lifetime reported in the literature. The vertical and horizontal
components are displayed in Fig. 5.4a, while the anisotropy trace (convoluted with the IRF) isshown in Fig. 5.4b. The rotational correlation time is on the same order as similarly sized dyes inwater.114 The value of r(0) matches closely with the literature value of 0.38. The estimatedanisotropy at steady-state is approximately 0, as is expected for a small molecule with a long
fluorescence lifetime. The molecule can sufficiently reach an isotropic distribution oforientations on a timescale much faster than the fluorescence lifetime.

2<t> (ns)βϕ (ps)r(0)r(ss), estχ , red

3.9

0.39 (±0.015)

120 (± 4)

0.39

0.01

1.055

Table 5.2. Parameters for exponential reconvolution fits for Fluorescein traces in Figure
5.4.

Figure 5.4. a) Intensity component traces and IRF for fluorescein in H2O (pH 11). B) Raw anisotropy tracethat quickly approaches zero due to the rapid reorientation of the small molecule dye during thecomparatively long fluorescence lifetime.

132

133

5.4.2

Effect of His-tag on ECFP Zn2+ Response using TRF
Steady-state measurements on the Zif sensors and ECFP were conducted to verify

response of the sensors following Zn2+ addition and probe the system for an anisotropy change(Figure 5.5). Upon 100 μM Zn2+ addition, both sensors showed a slight increase in the anisotropy
in the donor region corresponding to the decrease in lifetime of ECFP. ZifCV1.173 showed anincrease in anisotropy in the acceptor region, while ZifCV1.157 showed a decrease. The relative
positioning of the anisotropy curves suggests a greater orientation difference in ZifCV1.173
between donor and acceptor, however time-resolved measurements are required to confirm thisinterpretation. Notably, truncated ECFP also showed a response to added Zn2+, suggesting
modification of the fluorescence lifetime. For these proteins, the 6xHis-tag was not removed
after purification and concentration and the change in lifetime could result from Zn2+ binding tp
the His tag, particularly given the high concentration of Zn2+ included.
To investigate the effects of the His-tag on the ECFP lifetime, time-resolved fluorescence
was measured for truncated ECFP with and without the His-tag in the presence and absence of
Zn2+. Figure 5.6 shows the TCSPC traces and fitted parameters are in Table 5.3. Visualinspection of the traces shows a clear shift in the lifetime for the tagged ECFP in the presence of
Zn2+. The average lifetime (amplitude weighted) decreases by 18% relative to the cleaved protein
in EDTA. Cleaved protein also shows a 6% decrease, either indicative of a sensitivity

134

Figure 5.5. Emission scan steady-state anisotropy for two zinc sensors with ECFP as adonor protein, but different acceptor proteins (cpVenus157 or cpVenus173). Proteins2+
were treated with either 100 μM Zn or 100 μM EDTA. Excitation was set to 444 nm.

135
of ECFP or incomplete cleavage of the tag. The decrease in lifetime comes from the decrease inamplitude of the long lifetime component. As mentioned previously, ECFP has shown sensitivityof the His148 and Tyr145 positions to changes in the environment. Molecules or ions caninteract with these residues and alter the conformation of the chromophore. A TRF study shows
an increase in lifetime of ECFP due to interaction with ATP.22 His-tags have been shown to bind
Zn2+ and are the basis of a reported Zn2+ sensor.131 This result shows the impact modification of
the FP can have and requirement of controls to guarantee tags and other fusions do not modify
response. All subsequent measurements are made after treatment with TEV protease to remove
the His-tag and additional column purification to remove uncleaved protein from the sample.

α1

τ1 (ns)* α2τ2 (ns) α3

τ3 (ns) <t>, ns

ECFP(trunc)

EDTA 0.39

3.64

0.33

1.79

0.28

0.51

2.15

ECFP(trunc)

Zn

0.34

3.64

0.35

1.82

0.31

0.5

2.02

6xHis-ECFP(trunc) EDTA 0.38

3.64

0.34

1.8

0.27

0.54

2.16

6xHis-ECFP(trunc) Zn

3.64

0.50

1.93

0.32

0.56

1.78

0.17

Table 5.3. Parameters for exponential reconvolution fits for ECFP traces in Figure 5.6. *indicates this parameter was globally fit across data sets.

136

Figure 5.6. TCSPC traces for the ECFP treated with and without
TEV protease for removal of an N-terminal His-tag in the presence2+
and absence of 100 μM Zn or 100 μM EDTA.

137

5.4.3 Time-resolved Fluorescence Measurements on Zinc Sensors
Time-resolved measurements of ZapCY1, eCALWY-4 and eZinCh-2 are shown in
Figures 5.7, 5.8 and 5.9, respectively. Parameters for the reconvolution fits are included inTables 5.4, 5.5 and 5.6, respectively. ECFP and Cerulean are included as reference traces forcorresponding sensors. For each sensor, the amplitude-weighted lifetime in the donor channel iscalculated and a corresponding efficiency (EFRET) is calculated (Equation 5.6). As discussed
earlier in this chapter, this lifetime is likely influenced by any inactive FRET donors which
would render the FRET efficiency artificially low. RDA, the distance between donor and acceptor
TDMs, is calculated using the empirically derived separations for static random isotropic
orientational relaxation found in the publication by Vogel et al mentioned earlier.117 The risetime (negative amplitude in the fit) is determined for each sensor in the FRET channel and acorresponding FRET efficiency is calculated using this as the FRET rate (EFRET, rise).
ZapCY1 shows a 13% decrease in donor lifetime upon Zn2+ binding. This change inlifetime suggests a lower FRET efficiency. Acceptor rise times are in the nanosecond range, witha decrease in rise time upon binding, consistent with a higher FRET efficiency. The efficiency
calculated using acceptor rise time is higher than that using the donor lifetimes. This is consistent
with the results observed in YC3.60. eCALWY-4 shows a 30% increase in donor lifetime,
consistent with the mode of FRET sensing for this platform, in which binding of Zn2+ leads to a
separation of the donor and acceptor. Smaller separations are calculated for eCALWY-4 in thehigh FRET state compared to ZapCY1. Rise times for eCALWY-4 likewise show higher FRET
efficiencies. eZinCh-2 shows the largest change in donor lifetime (53%), corresponding to thelargest change in FRET efficiency, and therefore largest change in RDA. However, the acceptor
rise times for eZinch-2 are on the order of the width of the IRF in this system and do not show

138
significant change. Either the transfer occurs on a timescale faster than we can resolve, orconfounding factors are contributing to the fast rise. As shown previously, the overlap factor (B)is higher for the Cerulean variants which greatly affects the measured rise time.132 Additionally,
eZinCh-2 has been shown to readily form aggregates in vivo and in vitro (Chapter 4). Theselarger aggregates could enhance scattered light, artificially increasing amplitudes of shorter
lifetimes. Future replicates of these data should utilize a non-denaturing gel to check for thepresence of aggregates and/or size-exclusion chromatography should be run to isolate monomers.
ZifCV1.173 and ZifCV1.157 were excluded from this measurement due to low dynamic range
and irreproducible lifetime changes.

Figure 5.7. Amplitude normalized magic angle intensity traces for ZapCY1. Sensors
2+
are treated with 100 μM EDTA or 12 μM buffered Zn - Ca2+/EGTA. 1 mM TCEP
is present in all samples.

139

Figure 5.8. Ampliude normalized magic angle intensity traces for eCALWY-4. . Sensors
2+
are treated with 100 μM EDTA or 12 μM buffered Zn - Ca2+/EGTA. 1 mM TCEP ispresent in all samples.

Figure 5.9. Amplitude normalized magic angle intensity traces for eZinCh-2.
2+
Sensors are treated with 100 μM EDTA or 12 μM buffered Zn - Ca2+/EGTA. 1 mM
TCEP is present in all samples.

140

Table 5.4. Parameters for exponential reconvolution fits for ZapCY1 traces in Figure
5.7.

Table 5.5. Parameters for exponential reconvolution fits for eCALWY-4 traces in
Figure 5.8.

Table 5.6. Parameters for exponential reconvolution fits for eZinCh-2 traces in Figure
5.9.

141

5.4.4 Time-resolved Fluorescence Anisotropy Results for Zinc Sensors
A combined plot of FRET anisotropy traces for all sensors is shown in Figure 5.10 for the
purpose of a qualitative comparison of the sensors. Recalling the results of the steady-statemeasurements on ZifCV1.173/157, the same ordering of relative FRET anisotropy is observed.
Compared to all sensors, ZifCV1.173 has the lowest anisotropy value past 4 ns, even extending
into the negatives. This is notable as this sensor is the only one to have the acceptor attached at
the opposite end of the protein. The CV family uses Venus, however, Citrine is a related FP witha difference of only a few residues within the barrel without significant change of the TDM
vector.23,133 A second observation is that there is an increase in anisotropy upon Zn2+ binding for

Figure 5.10. Raw FRET anisotropy traces for all sensors showing a qualitatively
diverse landscape of acceptor orientations. Data are convoluted with the IRF,
making r0 impossible to visualize. ZifCV1.173 (purple) shows a negative
amplitude for the slow anisotropy component. Sensors treated with 100 μM
2+
EDTA or 12 μM buffered Zn - Ca2+/EGTA. 1 mM TCEP is present in all
samples.

142
all sensors, which could correspond to an increased alignment of the donor/acceptor TDMs as amechanism of sensor response.
Analysis of the anisotropy traces for three replications of ZapCY1 show considerable
variability for different protein preparations and over time for the same protein preparation.
Figure 5.11 and 5.12 shows the FRET channel and donor channel data, respectively. Fitparameters are listed in Tables 5.7 and 5.8 for FRET and donor channel, respectively. It isremarkable that across these experiments, the donor anisotropy and lifetime shows consistent
results, however, the acceptor anisotropy change and relative amplitude changes more
drastically. FRET channel lifetimes also remain consistent across the data set. Preliminary datasuggests that all sensors under study show decreased dynamic range in oxidizing conditions.†
The variation in FRET channel anisotropy and comparative consistency of the donor channel
anisotropy may provide structural evidence for the loss of sensor response in which angularchange is restricted for the acceptor. Figure 5.11 also displays the calculated transfer angle foreach data set. This is calculated similar to Borst et al for YC3.60 using the amplitude of therotational correlation time corresponding to the rotation of the molecule as a whole, which has
the lowest error (see β3 in Figure 5.10). θT is calculated using:𝛽 ≅ 𝑟0†3〈𝑐𝑜𝑠2 𝜃𝑇 〉2

Unpublished work from in vitro sensor study by Molly Carpenter in Chapter 4.

(5.10)

143
Since r0 for Citrine is undetermined using this instrument, lacking the necessary excitation
wavelength, it is assumed to be 0.37 in accordance with the limiting anisotropy measured for
ECFP (less than 0.4 due to instrument limitations).112 As time increases past protein purification,
ZapCY1 shows a decrease in the change of transfer angle (41o → 37o vs. 41o → 40o). ZapCY1 isunique in that it required 3 rotational exponentials to fit the FRET data with minimal reduced χ2.
It is also notable that there is a fast depolarization in the first nanosecond in the donor channel
for ZapCY1, despite an

oFigure 5.11. (Top left) Raw FRET anisotropy traces for three data sets of ZapCY1 on different daysor different preps. (Top right) Calculated transfer angles based on the amplitude of the slow
2+
rotational correlation time (β3) Sensors are treated with 100 μM EDTA or 12 μM buffered Zn Ca2+/EGTA. 1 mM TCEP is present in all samples.

θT,1 = 41 → 37ooθT,2 = 41 → 40ooθT,3 = 44 → 43

o

144

145

Figure 5.12. Raw donor anisotropy traces for three data setsof ZapCY1 on different days or different preps. Sensors are2+treated with 100 μM EDTA or 12 μM buffered Zn Ca2+/EGTA. 1 mM TCEP is present in all samples.

146

a. globally fit across data sets., b. negative amplitude corresponding to fluorescence rise.

Table 5.7. Compiled fitted parameters for three data sets of ZapCY1 in the FRET channel withassociated errors. α is the amplitude of the corresponding fluorescence decay (τ). β is theamplitude of the corresponding rotational correlation time (φ). r(0) and r(ss) are calculated by
FluoFit during the fitting process.

a. globally fit across data sets.

Table 5.8. Compiled fitted parameters for three data sets of ZapCY1 in the CFP
channel with associated errors. α is the amplitude of the corresponding fluorescencedecay (τ). β is the amplitude of the corresponding rotational correlation time (φ). r(0)and r(ss) are calculated by FluoFit during the fitting process. ECFP fit: (r0 = 0.377, φ= 15.0 ± 0.7 ns, rss = 0.312)

147
overall increase in steady-state anisotropy upon Zn2+ binding. This fast depolarization is present
in all sensors containing ECFP (data not shown). The cause is presently undetermined, but likely
related to the complex dynamics characteristic of the ECFP, such as a conformational change ofthe chromophore.
FRET anisotropy traces for the Cerulean sensors, eCALWY-4 and eZinCh-2, are found in
Figure 5.13 and fit data in Table 5.9. Donor traces are found in Figure 5.14 and fit data in Table
5.10. There is no evidence of the fast depolarization in the Cerulean sensors that is present in the
ECFP sensors. This may be attributed to the stabilization of the chromophore through mutationof His148.134 eZinCh-2 has the largest change in transfer angle (39o → 25o). As reported in theliterature, knowledge of θT permits limits to be placed on the value of κ2. However, without
knowledge of θD and θA, Equation 5.5 shows κ2 values still span almost the entire range ofpossible values for θT = 41o.120 It is possible that θD and θA may be determined in analog
constructs in which the coinciding FRET partner is dark.111
The rotational correlation times for all sensors are > 20 ns, as measured by donor
anisotropy. These times are associated with large uncertainties due to the magnitude of the decay
relative to the fluorescence lifetime. The long correlation times fit in the FRET anisotropy data
are unreliable without direct measurement of the YFP anisotropy. While correlation times fromthe fits do change between ligand conditions and across data sets, the large errors complicate
drawing precise conclusions about the flexibility of either the CFP or YFP domains. To
corroborate these data, fluorescence correlation spectroscopy (FCS) could be done on the sensors
to determine diffusion constants and rotational times. There needs to be rigorous characterizationof the protein samples that are subject to such measurements.

148

a

o

o

o

o

θT = 39 → 25b

θT = 34 → 31

Figure 5.13. (a) Raw FRET anisotropy traces for ZinCh-2 and calculated transfer angles
based on the amplitude of the slow rotational correlation time (β1). (b) Raw anisotropy traces
for eCALWY-4 and calculated transfer angles based on the amplitude of the slow rotational2+
correlation time (β1). EDTA = 100 μM. Zn = 12 μ M buffered (Ca/EGTA). 1 mM TCEP
present in all samples.

149

Figure 5.14. Raw donor anisotropy traces for eZinCh-2 andeCALWY-4. Sensors are treated with 100 μM EDTA or 12 μM
2+buffered Zn - Ca2+/EGTA. 1 mM TCEP is present in all
samples.

150#*globally fit across data sets., negative amplitude corresponding to fluorescence rise.

Table 5.9. Compiled fitted parameters for eZinCh-2 and eCALWY-4 traces in the FRET
channel with associated errors. α is the amplitude of the corresponding fluorescence decay(τ). β is the amplitude of the corresponding rotational correlation time (φ). r(0) and r(ss) arecalculated by FluoFit during the fitting process.

*globally fit across data sets.

Table 5.10. Compiled fitted parameters for eZinCh-2 and eCALWY-4 traces in the CFP
channel with associated errors. α is the amplitude of the corresponding fluorescence decay(τ). β is the amplitude of the corresponding rotational correlation time (φ). r(0) and r(ss) arecalculated by FluoFit during the fitting process.

151

5.5

Conclusions and Future Directions
This chapter presents initial results on the characterization of a panel of geneticallyencoded Zn2+ sensors using time-resolved fluorescence techniques. The instrument is validated
using a control fluorophore. The effect of His-tag fusion on ECFP during Zn2+ studies isdetermined, indicating that cleavage of the affinity tag is required to more accurately reflect invivo response. There is clear variability in the response of the sensors over time and acrossprotein preparations. This is the primary path for future experiments prior to refinement of thedata analysis. The purification process for oxidation sensitive proteins should be optimized,
either by the addition of reducing agents throughout the process or using anaerobic techniques.
Unpublished work by Molly Carpenter has determined sensor responsiveness varies acrosscolonies from a single transformation experiment, suggesting variation in bacterial translation of
a sensor from the obtained plasmid. As mentioned previously, more rigorous characterization or
isolation of the purified sensor should be done by non-denaturing gels and/or size-exclusion
chromatography. Measurements of a sensor’s response to zinc and confirmation of the binding
constant, KD, should also be performed. There are likely problems associated with the length oftime it takes to cleave the His-tag (several hours to overnight), which increase the duration theprotein is exposed to ambient conditions before measurement. Pulsed laser excitation at ~ 500
nm should be implemented to directly measure acceptor anisotropy. FCS can be done tocorroborate the correlation times measured for the protein rotation. Lastly, time studies should
also be performed for eZinCh-2 and eCALWY-4 to determine if they show similar FRET
anisotropy variability as observed for ZapCY1. When optimized, this technique should prove
crucial in investigating the effects of oxidation, crowding and other environmental effects on thesensors.

152

Bibliography
1.

Tsien, R. Y. The Green Fluorescent Protein. Annu. Rev. Biochem. 67, 509–544 (1998).

2.

Shaner, N. C., Steinbach, P. A. & Tsien, R. Y. A guide to choosing fluorescent proteins.
Nat. Methods 2, 905–9 (2005).

3.

Niwa, H. et al. Chemical nature of the light emitter of the Aequorea green fluorescentprotein (imidazolone͞excited state͞phenolate anion͞low-temperature fluorescence͞photoisomerization). Biochem. Commun. by Martin D. Kamen 93, 13617–
13622 (1996).

4.

VanEngelenburg, S. B. & Palmer, A. E. Fluorescent biosensors of protein function. Curr.
Opin. Chem. Biol. 12, 60–65 (2008).

5.

Day, R. N. & Davidson, M. W. The fluorescent protein palette: tools for cellular imaging.
Chem. Soc. Rev. 38, 2887 (2009).

6.

Newman, R. H., Fosbrink, M. D. & Zhang, J. Genetically Encodable Fluorescent
Biosensors for Tracking Signaling Dynamics in Living Cells. Chem. Rev. 111, 3614–3666(2011).

7.

Carter, K. P., Young, A. M. & Palmer, A. E. Fluorescent Sensors for Measuring Metal

153
Ions in Living Systems. Chem. Rev. 114, 4564–4601 (2014).
8.

Tamura, T. & Hamachi, I. Recent Progress in Design of Protein-Based Fluorescent
Biosensors and Their Cellular Applications. ACS Chem. Biol. (2014).
doi:10.1021/cb500661v

9.

Lindenburg, L. & Merkx, M. Engineering genetically encoded FRET sensors. Sensors(Basel). 14, 11691–713 (2014).

10.

Frommer, W. B., Davidson, M. W. & Campbell, R. E. Genetically encoded biosensors
based on engineered fluorescent proteins. Chem. Soc. Rev. 38, 2833 (2009).

11.

Sample, V., Mehta, S. & Zhang, J. Genetically encoded molecular probes to visualize andperturb signaling dynamics in living biological systems. J. Cell Sci. 127, 1151–1160(2014).

12.

Palmer, A. E., Qin, Y., Park, J. G. & McCombs, J. E. Design and application ofgenetically encoded biosensors. Trends Biotechnol. 29, 144–52 (2011).

13.

Matsu-ura, T. et al. Cytosolic inositol 1,4,5-trisphosphate dynamics during intracellular
calcium oscillations in living cells. J. Cell Biol. 173, 755–765 (2006).

14.

Nausch, L. W. M., Ledoux, J., Bonev, A. D., Nelson, M. T. & Dostmann, W. R.
Differential patterning of cGMP in vascular smooth muscle cells revealed by single GFPlinked biosensors. Proc. Natl. Acad. Sci. U. S. A. 105, 365–370 (2008).

15.

Russwurm, M. et al. Design of fluorescence resonance energy transfer (FRET)-basedcGMP indicators: a systematic approach. Biochem. J. 407, 69–77 (2007).

16.

Cano Abad, M. F. et al. Mitochondrial pH Monitored by a New Engineered Green
Fluorescent Protein Mutant. J. Biol. Chem. 279, 11521–11529 (2004).

17.

Wang, W. et al. Superoxide Flashes in Single Mitochondria. Cell 134, 279–290 (2008).

154
18.

Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity.
Nature 499, 295–300 (2013).

19.

Abdelfattah, A. S. et al. A Bright and Fast Red Fluorescent Protein Voltage Indicator That
Reports Neuronal Activity in Organotypic Brain Slices. J. Neurosci. 36, 2458–2472(2016).

20.

Taslimi, A. et al. An optimized optogenetic clustering tool for probing protein interaction
and function. Nat. Commun. 5, 4925 (2014).

21.

Taslimi, A. et al. Optimized second-generation CRY2–CIB dimerizers andphotoactivatable Cre recombinase. Nat. Chem. Biol. 12, 425–430 (2016).

22.

Borst, J. W. et al. ATP changes the fluorescence lifetime of cyan fluorescent protein via
an interaction with His148. PLoS One 5, 1–7 (2010).

23.

Nagai, T. et al. A variant of yellow fluorescent protein with fast and efficient maturation
for cell-biological applications. Nat. Biotechnol. 20, 87–90 (2002).

24.

Blindauer, C. a. Advances in the molecular understanding of biological zinc transport.
Chem. Commun. (2015). doi:10.1039/C4CC10174J

25.

Grewer, C. & Gameiro, A. How do glutamate transporters function as transporters and ionchannels? Biophys. J. 107, 546–7 (2014).

26.

Frederickson, C. J. et al. Concentrations of extracellular free zinc (pZn)e in the central
nervous system during simple anesthetization, ischemia and reperfusion. Exp. Neurol.
198, 285–93 (2006).

27.

Duncan, F. E. et al. The zinc spark is an inorganic signature of human egg activation. Sci.
Rep. 6, 24737 (2016).

28.

Que, E. L. et al. Quantitative mapping of zinc fluxes in the mammalian egg reveals the155
origin of fertilization-induced zinc sparks. Nat. Chem. (2014). doi:10.1038/nchem.2133
29.

Dean, K. M., Qin, Y. & Palmer, A. E. Visualizing metal ions in cells: an overview ofanalytical techniques, approaches, and probes. Biochim. Biophys. Acta 1823, 1406–15(2012).

30.

Qin, Y. et al. Direct comparison of a genetically encoded sensor and small molecule
indicator: Implications for quantification of cytosolic Zn2+. ACS Chem. Biol. 8, 2366–
2371 (2013).

31.

Sensi, S. L., Ton-That, D., Weiss, J. H., Rothe, A. & Gee, K. R. A new mitochondrial
fluorescent zinc sensor. Cell Calcium 34, 281–284 (2003).

32.

Campbell, R. E. Fluorescent-protein-based biosensors: Modulation of energy transfer as adesign principle. Anal. Chem. 81, 5972–5979 (2009).

33.

Dittmer, P. J., Miranda, J. G., Gorski, J. a & Palmer, A. E. Genetically encoded sensors to
elucidate spatial distribution of cellular zinc. J. Biol. Chem. 284, 16289–97 (2009).

34.

Vinkenborg, J. L. et al. Genetically encoded FRET sensors to monitor intracellular Zn2+
homeostasis. Nat. Methods 6, 737–40 (2009).

35.

Hessels, A. M. et al. eZinCh-2: A Versatile, Genetically Encoded FRET Sensor for
Cytosolic and Intraorganelle Zn 2+ Imaging. ACS Chem. Biol. 10, 2126–2134 (2015).

36.

Wang, D., Hurst, T. K., Thompson, R. B. & Fierke, C. a. Genetically encoded ratiometricbiosensors to measure intracellular exchangeable zinc in Escherichia coli. J. Biomed. Opt.
16, 87011 (2011).

37.

Qin, Y., Sammond, D. W., Braselmann, E., Carpenter, M. C. & Palmer, A. E.
Development of an Optical Zn2+ Probe Based on a Single Fluorescent Protein. ACS
Chem. Biol. 11, 2744–2751 (2016).

156
38.

Aper, S. J. A., Dierickx, P. & Merkx, M. Dual readout BRET/FRET-sensors formeasuring intracellular zinc. ACS Chem. Biol. acschembio.6b00453 (2016).
doi:10.1021/acschembio.6b00453

39.

Wang, Z. et al. Solution structure of a Zap1 zinc-responsive domain provides insights into
metalloregulatory transcriptional repression in Saccharomyces cerevisiae. J. Mol. Biol.
357, 1167–83 (2006).

40.

Park, J. G., Qin, Y., Galati, D. F. & Palmer, A. E. New Sensors for Quantitative
Measurement of Mitochondrial Zn 2+. ACS Chem. Biol. 7, 1636–1640 (2012).

41.

Qin, Y., Dittmer, P. J., Park, J. G., Jansen, K. B. & Palmer, A. E. Measuring steady-stateand dynamic endoplasmic reticulum and Golgi Zn2+ with genetically encoded sensors.
Proc. Natl. Acad. Sci. U. S. A. 108, 7351–6 (2011).

42.

Chabosseau, P. et al. Mitochondrial and ER-Targeted eCALWY Probes Reveal HighLevels of Free Zn(2+). ACS Chem. Biol. 9, 2111–20 (2014).

43.

McCombs, J. E. & Palmer, A. E. Measuring calcium dynamics in living cells withgenetically encodable calcium indicators. Methods 46, 152–9 (2008).

44.

Shapiro, H. M. Practical Flow Cytometry. (John Wiley & Sons, Inc., 2003).
doi:10.1002/0471722731

45.

Beumer, T., Lenssinck, H., Pennings, A. & Haanen, C. An easy-to-build-timer for kinetic
measurements in flow cytometry. Cytometry 5, 648–51 (1984).

46.

Scampavia, L. D., Blankenstein, G., Ruzicka, J. & Christian, G. D. A coaxial jet mixer for
rapid kinetic analysis in flow injection and flow injection cytometry. Anal. Chem. 67,
2743–9 (1995).

47.

Blankenstein, G., Scampavia, L. D., Ruzicka, J. & Christian, G. D. Coaxial flow mixer for

157
real-time monitoring of cellular responses in flow injection cytometry. Cytometry 25,
200–204 (1996).
48.

Nolan, J. P., Posner, R. G., Martin, J. C., Habbersett, R. & Sklar, L. A. A rapid mix flow
cytometer with subsecond kinetic resolution. Cytometry 21, 223–9 (1995).

49.

Litzlbauer, J. et al. Large Scale Bacterial Colony Screening of Diversified FRET
Biosensors. PLoS One 10, e0119860 (2015).

50.

Ibraheem, A., Yap, H., Ding, Y. & Campbell, R. E. A bacteria colony-based screen for
optimal linker combinations in genetically encoded biosensors. BMC Biotechnol. 11, 105(2011).

51.

Belal, A. S. F., Sell, B. R., Hoi, H., Davidson, M. W. & Campbell, R. E. Optimization of agenetically encoded biosensor for cyclin B1-cyclin dependent kinase 1. Mol. Biosyst. 10,
191–5 (2014).

52.

Zhao, Y. Y. et al. Microfluidic cell sorter-aided directed evolution of a protein-based
calcium ion indicator with an inverted fluorescent response. Integr. Biol. 6, 714 (2014).

53.

Teh, S.-Y., Lin, R., Hung, L.-H. & Lee, A. P. Droplet microfluidics. Lab Chip 8, 198(2008).

54.

Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat.
Protoc. 8, 870–891 (2013).

55.

Clausell-Tormos, J. et al. Droplet-based microfluidic platforms for the encapsulation andscreening of Mammalian cells and multicellular organisms. Chem. Biol. 15, 427–37(2008).

56.

Agresti, J. J. et al. Ultrahigh-throughput screening in drop-based microfluidics for directed
evolution. Proc. Natl. Acad. Sci. 107, 4004–4009 (2010).

158
57.

Baret, J.-C. et al. Fluorescence-activated droplet sorting (FADS): efficient microfluidic
cell sorting based on enzymatic activity. Lab Chip 9, 1850–8 (2009).

58.

Clausell-Tormos, J., Griffiths, A. D. & Merten, C. a. An automated two-phasemicrofluidic system for kinetic analyses and the screening of compound libraries. Lab
Chip 10, 1302–1307 (2010).

59.

Frenz, L., Blank, K., Brouzes, E. & Griffiths, A. D. Reliable microfluidic on-chip
incubation of droplets in delay-lines. Lab Chip 9, 1344–1348 (2009).

60.

Huebner, A. et al. Development of quantitative cell-based enzyme assays in
microdroplets. Anal. Chem. 80, 3890–6 (2008).

61.

Huebner, A. et al. Quantitative detection of protein expression in single cells using droplet
microfluidics. Chem. Commun. (Camb). 2, 1218–20 (2007).

62.

Stone, H. A. in CMOS Biotechnology 5–30 (2007). doi:10.1007/978-0-387-68913-5_2

63.

Brody, J. P., Yager, P., Goldstein, R. E. & Austin, R. H. Biotechnology at low Reynolds
numbers. Biophys. J. 71, 3430–41 (1996).

64.

Borle, A. B. Kinetic Analyses of Calcium Movements in HeLa Cell Cultures: I. Calcium
influx. J. Gen. Physiol. 53, 43–56 (1969).

65.

Berthier, J. & Silberzan, P. Microfluidics for Biotechnology. (Artech House, 2010).

66.

Collins, D. J., Neild, A., DeMello, A., Liu, A.-Q. & Ai, Y. The Poisson distribution andbeyond: methods for microfluidic droplet production and single cell encapsulation. Lab
Chip 15, 3439–3459 (2015).

67.

Abate, a. et al. Impact of inlet channel geometry on microfluidic drop formation. Phys.
Rev. E 80, 26310 (2009).

68.

Collins, D. J., Alan, T., Helmerson, K. & Neild, A. Surface acoustic waves for on-demand

159
production of picoliter droplets and particle encapsulation. Lab Chip 13, 3225 (2013).
69.

Wu, T.-H. et al. Pulsed laser triggered high speed microfluidic fluorescence activated cell
sorter. Lab Chip 12, 1378 (2012).

70.

Abate, A. R., Romanowsky, M. B., Agresti, J. J. & Weitz, D. a. Valve-based flow
focusing for drop formation. Appl. Phys. Lett. 94, 23503 (2009).

71.

Lee, C.-H., Hsiung, S.-K. & Lee, G.-B. A tunable microflow focusing device utilizing
controllable moving walls and its applications for formation of micro-droplets in liquids.
J. Micromechanics Microengineering 17, 1121–1129 (2007).

72.

Baret, J.-C. Surfactants in droplet-based microfluidics. Lab Chip 12, 422–33 (2012).

73.

Holtze, C. et al. Biocompatible surfactants for water-in-fluorocarbon emulsions. Lab Chip
8, 1632 (2008).

74.

Holt, D. J., Payne, R. J., Chow, W. Y. & Abell, C. Fluorosurfactants for microdroplets:interfacial tension analysis. J. Colloid Interface Sci. 350, 205–11 (2010).

75.

Datta, S. & Ghosal, S. Characterizing dispersion in microfluidic channels. Lab Chip 9,2537 (2009).

76.

Ma, S., Sherwood, J. M., Huck, W. T. S. & Balabani, S. On the flow topology inside
droplets moving in rectangular microchannels. Lab Chip 14, 3611–20 (2014).

77.

Wyatt Shields IV, C., Reyes, C. D. & López, G. P. Microfluidic cell sorting: a review of
the advances in the separation of cells from debulking to rare cell isolation. Lab Chip 15,
1230–1249 (2015).

78.

Cristobal, G., Benoit, J.-P., Joanicot, M. & Ajdari, A. Microfluidic bypass for efficientpassive regulation of droplet traffic at a junction. Appl. Phys. Lett. 89, 34104 (2006).

79.

Guo, M. T., Rotem, A., Heyman, J. a & Weitz, D. a. Droplet microfluidics for high-160
throughput biological assays. Lab Chip 12, 2146–55 (2012).
80.

Lubbeck, J. L., Dean, K. M., Ma, H., Palmer, A. E. & Jimenez, R. Microfluidic flow
cytometer for quantifying photobleaching of fluorescent proteins in cells. Anal. Chem. 84,
3929–37 (2012).

81.

Puck, T. T., Marcus, P. I. & Cieciura, S. J. Clonal Growth of Mammalian Cells In Vitro. J
Exp Med 103, 273–284 (1956).

82.

Ma, H., Gibson, E. A., Dittmer, P. J., Jimenez, R. & Palmer, A. E. High-throughput
examination of fluorescence resonance energy transfer-detected metal-ion response in
mammalian cells. J. Am. Chem. Soc. 134, 2488–2491 (2012).

83.

Miranda, J. G. et al. New alternately colored FRET sensors for simultaneous monitoringof Zn2+ in multiple cellular locations. PLoS One 7, e49371 (2012).

84.

Pomorski, A., Kochańczyk, T., Miłoch, A., Krężel, A. & Krȩzel, A. Method for accurate
determination of dissociation constants of optical ratiometric systems: Chemical probes,
genetically encoded sensors, and interacting molecules. Anal. Chem. 85, 11479–11486(2013).

85.

Abate, A. R. et al. Correction for Agresti et al., Ultrahigh-throughput screening in dropbased microfluidics for directed evolution. Proc. Natl. Acad. Sci. 107, 6550–6550 (2010).

86.

McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane).
Electrophoresis 21, 27–40 (2000).

87.

Dean, K. M. et al. High-speed multiparameter photophysical analyses of fluorophore
libraries. Anal. Chem. 87, 5026–5030 (2015).

88.

Matochko, W. L. et al. Deep sequencing analysis of phage libraries using Illumina
platform. Methods 58, 47–55 (2012).

161
89.

Glanville, J. et al. Deep sequencing in library selection projects: What insight does itbring? Curr. Opin. Struct. Biol. 33, 146–160 (2015).

90.

Sung Hwan Cho, Chen, C. H., Tsai, F. S. & Yu-Hwa Lo. Micro-fabricated fluorescenceactivated cell sorter. in 2009 Annual International Conference of the IEEE Engineering in
Medicine and Biology Society 1075–1078 (IEEE, 2009).
doi:10.1109/IEMBS.2009.5334976

91.

Davis, L. M., Lubbeck, J. L., Dean, K. M., Palmer, A. E. & Jimenez, R. Microfluidic cell
sorter for use in developing red fluorescent proteins with improved photostability. Lab
Chip 13, 2320–2327 (2013).

92.

Fu, A. Y., Spence, C., Scherer, A., Arnold, F. H. & Quake, S. R. A microfabricatedfluorescence-activated cell sorter. Nat. Biotechnol. 17, 1109–11 (1999).

93.

Wolff, A. et al. Integrating advanced functionality in a microfabricated high-throughput
fluorescent-activated cell sorter. Lab Chip 3, 22–7 (2003).

94.

Wang, M. M. et al. Microfluidic sorting of mammalian cells by optical force switching.
Nat. Biotechnol. 23, 83–87 (2005).

95.

Dittrich, P. S. & Schwille, P. An Integrated Microfluidic System for Reaction, HighSensitivity Detection, and Sorting of Fluorescent Cells and Particles. Anal. Chem. 75,
5767–5774 (2003).

96.

Dolega, M. E. et al. Iterative operations on microdroplets and continuous monitoring ofprocesses within them; determination of solubility diagrams of proteins. Lab Chip 12,4022 (2012).

97.

Fu, A. Y., Chou, H. P., Spence, C., Arnold, F. H. & Quake, S. R. An integrated
microfabricated cell sorter. Anal. Chem. 74, 2451–2457 (2002).

162
98.

Park, J. G., Qin, Y., Galati, D. F. & Palmer, A. E. New Sensors for Quantitative
Measurement of Mitochondrial Zn2+. ACS Chem. Biol. 7, 1636–1640 (2012).

99.

Hessels, A. M. & Merkx, M. Simple Method for Proper Analysis of FRET Sensor
Titration Data and Intracellular Imaging Experiments Based on Isosbestic Points. ACS
Sensors 1, 498–502 (2016).

100. Costantini, L. M. et al. A palette of fluorescent proteins optimized for diverse cellularenvironments. Nat. Commun. 6, 7670 (2015).
101. Bender, B. J. et al. Protocols for Molecular Modeling with Rosetta3 and RosettaScripts.
Biochemistry 55, 4748–4763 (2016).
102. Maret, W. & Li, Y. Coordination dynamics of zinc in proteins. Chem. Rev. 109, 4682–707(2009).
103. Baird, G. S., Zacharias, D. A. & Tsien, R. Y. Circular permutation and receptor insertion
within green fluorescent proteins. Proc. Natl. Acad. Sci. 96, 11241–11246 (1999).
104. Chalfie, M., Tu, Y., Euskirchen, G., Ward, W. W. & Prasher, D. C. Green fluorescentprotein as a marker for gene expression. Science (80-. ). 263, 802–805 (1994).
105. Hires, S. A., Zhu, Y. & Tsien, R. Y. Optical measurement of synaptic glutamate spilloverand reuptake by linker optimized glutamate-sensitive fluorescent reporters. Proc. Natl.
Acad. Sci. 105, 4411–4416 (2008).
106. Komatsu, N. et al. Development of an optimized backbone of FRET biosensors forkinases and GTPases. Mol. Biol. Cell 22, 4647–4656 (2011).
107. Mank, M. et al. A FRET-Based Calcium Biosensor with Fast Signal Kinetics and High
Fluorescence Change. Biophys. J. 90, 1790–1796 (2006).
108. Lelimousin, M. et al. Intrinsic dynamics in ECFP and cerulean control fluorescence163
quantum yield. Biochemistry 48, 10038–10046 (2009).
109. Szmacinski, H. & Lakowicz, J. R. Frequency-domain lifetime measurements and sensing
in highly scattering media. Sensors Actuators B Chem. 30, 207–215 (1996).
110. Lakowicz, J. R. Principles of Fluorescence Spectroscopy. (Springer US, 2006).
doi:10.1007/978-0-387-46312-4
111. Dale, R. E., Eisinger, J. & Blumberg, W. E. The orientational freedom of molecular
probes. The orientation factor in intramolecular energy transfer. Biophys. J. 26, 161–193(1979).
112. Borst, J. W., Hink, M. A., Van Hoek, A. & Visser, A. J. W. G. Effects of refractive index
and viscosity on fluorescence and anisotropy decays of enhanced cyan and yellow
fluorescent proteins. J. Fluoresc. 15, 153–160 (2005).
113. Van Der Meer, B. W. Kappa-squared: From nuisance to new sense. Rev. Mol. Biotechnol.
82, 181–196 (2002).
114. Ameloot, M., VandeVen, M., Acuña, A. & Valeur, B. Fluorescence anisotropy
measurements in solution: Methods and reference materials (IUPAC Technical Report).
Pure Appl. … 85, 589–608 (2013).
115. Sarkar, P., Koushik, S. V., Vogel, S. S., Gryczynski, I. & Gryczynski, Z. Photophysical
Properties of Cerulean and Venus Fluorescent Proteins. J. Biomed. Opt. 14, 34047 (2009).
116. Boens, N. et al. Fluorescence lifetime standards for time and frequency domain
fluorescence spectroscopy. Anal. Chem. 79, 2137–49 (2007).
117. Vogel, S. S., Van der Meer, B. W. & Blank, P. S. Estimating the distance separating
fluorescent protein FRET pairs. Methods 66, 131–138 (2014).
118. Lindhoud, S., Westphal, A. H., Van Mierlo, C. P. M., Visser, A. J. W. G. & Borst, J. W.

164
Rise-time of FRET-acceptor fluorescence tracks protein folding. Int. J. Mol. Sci. 15,
23836–23850 (2014).
119. Laptenok, S. P. et al. Global analysis of Förster resonance energy transfer in live cells
measured by fluorescence lifetime imaging microscopy exploiting the rise time ofacceptor fluorescence. Phys. Chem. Chem. Phys. 12, 7593–602 (2010).
120. Borst, J. W. et al. Structural Changes of Yellow Cameleon Domains Observed by
Quantitative FRET Analysis and Polarized Fluorescence Correlation Spectroscopy.
Biophys. J. 95, 5399–5411 (2008).
121. Blacker, T. S. et al. Investigating State Restriction in Fluorescent Protein FRET Using
Time-Resolved Fluorescence and Anisotropy. J. Phys. Chem. C Submitted,
acs.jpcc.6b11235 (2016).
122. Masters, T. A. et al. Restricted State Selection in Fluorescent Protein Förster Resonance
Energy Transfer. J. Am. Chem. Soc. 135, 7883–7890 (2013).
123. Visser, N. V., Borst, J. W., Hink, M. A., Van Hoek, A. & Visser, A. J. W. G. Direct
observation of resonance tryptophan-to-chromophore energy transfer in visible fluorescentproteins. Biophys. Chem. 116, 207–212 (2005).
124. Ishikawa-Ankerhold, H. C., Ankerhold, R. & Drummen, G. P. C. Advanced fluorescencemicroscopy techniques--FRAP, FLIP, FLAP, FRET and FLIM. Molecules 17, 4047–132(2012).
125. Devauges, V. et al. Steady-state acceptor fluorescence anisotropy imaging underevanescent excitation for visualisation of FRET at the plasma membrane. PLoS One 9,(2014).
126. Gautier, I. et al. Homo-FRET microscopy in living cells to measure monomer-dimer

165
transition of GFP-tagged proteins. Biophys. J. 80, 3000–3008 (2001).
127. Matthews, D. R. et al. Time-lapse FRET microscopy using fluorescence anisotropy. J.
Microsc. 237, 51–62 (2010).
128. Tsien, R. & Pozzan, T. in Biomembranes Part S (ed. Enzymology, B. T.-M. in) Volume
172, 230–262 (Academic Press, 1989).
129. Thompson, R. B., Gryczynski, I. & Malicka, J. Fluorescence polarization standards forhigh-throughput screening and imaging. Biotechniques 32, 34,37-38,40,42 (2002).
130. Blacker, T. S., Duchen, M. R. & Bain, A. J. Heterogeneity and restricted state selection in
FRET with fluorescent proteins. in (eds. Enderlein, J., Gregor, I., Gryczynski, Z. K.,
Erdmann, R. & Koberling, F.) 9714, 971401 (2016).
131. Evers, T. H., Appelhof, M. A. M., Meijer, E. W. & Merkx, M. His-tags as Zn(II) binding
motifs in a protein-based fluorescent sensor. Protein Eng. Des. Sel. 21, 529–536 (2008).
132. Blacker, T. S. et al. Investigating State Restriction in Fluorescent Protein FRET Using
Time-Resolved Fluorescence and Anisotropy - Supporting Information. J. Phys. Chem. C
121, (2017).
133. Ansbacher, T. et al. Calculation of transition dipole moment in fluorescent proteins-towards efficient energy transfer. Phys. Chem. Chem. Phys. 14, 4109–17 (2012).
134. Goedhart, J. et al. Structure-guided evolution of cyan fluorescent proteins towards aquantum yield of 93%. Nat. Commun. 3, 751 (2012).

166

Appendix A - Protocols
A.1

PDMS Device Manufacturing Protocol

Master | CLEAN ROOM

Cleaning Si substrate (typically 3’ wafer):-

Use Piranha (do NOT leave heated Pirahna alone, ~95oC, NO higher, keep all organicsaway) or NanoStrip at elevated temperature to remove old photoresist.
o Rinse with Water -> IPA -> Dry with N2.

-

Ash in Reactive Ion Etcher (RIE) with O2/CF4 (look up required power and time).
o Make sure surface is clean or new photoresist may not adhere properly.

-

Dry on hotplate at 200 oC for ~10 min or until certain all water has evaporated.

Making the master: Negative photoresist

167-

See specific SU-8 3000 instructions for spincoater speeds, exposure times, development
times, and bake times (provided by MicroChem, found on website)-

Notes:o When using acetate masks, make sure you have a square of glass to substitute thenormal glass mask that goes into the Karl Suss MJB3/MJB4 exposure tool.
o Place the acetate mask with the acetate bubbles facing up (away from the wafer).
o Exposure time will usually be around 35 seconds, but may change depending on
how the lamp is performing and SU8 thickness.
****Lamp has been replaced – Results in drastically lower times. Checklamp power and calculate proper exposure time****~18 seconds as of June 2017.

o OPTIONAL: After the master has been made, place in a vacuum chamber with afew drops of (tridecafluoro-1,1,2,2-tetrahydrooctyl)-1-trichlorosilane for 3+
hours. Make sure the master is facing away from the vacuum flow.

Device Fabrication | WET LAB-

Place the master in a petri-dish, tape down both sides.

-

Mix a 10:1 weight ratio of pre-polymer and curing agent (Sylgard 184 – Dow Corning) ina weighing dish according to the dimensions of the petri-dish and desired device height(usually 50-60 mm works well to prevent tubing from slipping out). Use 1 g/cm3 in
calculations.

-

Degas in vacuum chamber until all bubbles have been removed (~30 min)

168-

Pour into petri-dish with master. Degas again until all bubbles have been removed (~2030 min). You can leave the lid to the petri dish on to keep out dust.

-

Cover and place in an oven (75 oC) for at least 1 hr 15 min (can be left overnight @ 65 if
the day is ending).

CLEAN ROOM-

Using an exactoknife, carefully cut around the devices, shallowly the first time, then fully
the second time to avoid cracking the master with too much force.

-Cut and separate each device.

-

Use the 1.2 mm biopsy punch to punch inlets, 1.5 mm biopsy punch for outlets. Do thiswith the channels facing up. Wipe off the punch with a clean room cloth between devices
to clean off PDMS debris.

-

Apply frosted scotch tape to both sides of the device. Remove, carefully, the patternedside to remove excess PDMS pieces and dust.

-

Rinse the devices with IPA and blow dry with nitrogen. Let them sit on a hotplate set to~70 oC for ~10 min.

-At the same time, rinse cover slips for each device in Acetone followed by IPA and letthem dry similarly to the devices.

-

Place devices and cover slips in a Reactive Ion Etcher (RIE) with the side you wish tobond facing up. Run under O2 plasma for 15 seconds at 50 W and 50 sccm O2 pressure..

169-

Press together on a flat surface, using flat ended tweezers to push out any bubbles (using
your fingers will likely cause it to crack, also you want to avoid all contact with your(gloved) hands when possible.

-

Let the bonded devices sit for at least 10 minutes on a 70-75 oC hotplate.

-

Prepare a solution of HFE 7500 with 3-4 drops of trichlorosilane compound per mL.
Flush through the oil channel of each device and blow dry with a syringe through one ofthe outlets (HFE 7500 easily wets out of most plastic and glass dispensers, so be gentle
and slow). IF MAKING DROPLET DEVICES THIS STEP IS MANDATORY.

-OPTIONAL: If placing electrodes within a few hours, this is not necessary. Within 15
minutes of pressing together, flow some 0.1 M 3-mercaptopropyltrimethoxysilane inacetonitrile through the electrode channels and allow it to sit under a hood until it has
evaporated (~1 hr). The compound is very pungent and should not leave a chemical hoodunder any circumstance..

-

One at a time, set the device on a hotplate set between 160-180 oC. The device has atendency to bend with the thinner cover slips; press the middle of the device down the flat
ended tweezers with just enough force to keep it flat and no more.

-

Cut a few small pieces (2-3 mm) of solder wire (Typically used: 52 In 48 Sn), and using
tweezers, press into the electrode inlet. If the channels have been properly coated, itshould shoot through quickly and uniformly. If not, add a little more to the other end of
the channel (Adding the wire often adds enough pressure to fill parts you missed).

170-

Using small, equal sections of 1000 V wire (violet), strip 3-4 mm on each end and place
into the electrode channels, making sure it reaches the bottom and makes proper contact
with the solder. It often helps to twist the ends of the wire to prevent the small pieces
from penetrating the PDMS and becoming stuck.

-

At this point you should check to make sure the connection has been made using a
multimeter.

-

If the connection has been made, mix up a small amount of 2-part epoxy and coat aroundthe wire/PDMS contacts. Make sure to spread some up a small length of the wire toprevent liquid from getting in.

-

If the connection has not been made, add more solder or apply some pressure to the holes
to get it to flow through more uniformly.

-

Once the adhesive has been applied, check the connections. Allow it to fully dry per the
instructions. After it has dried, check the connections again.

Additional notes:
Wear gloves for all parts.
Blow off any visible dust with N2.
Clean anything that comes into contact with the PDMS with IPA.

Training required:UV Exposure tool/Mask Aligner (Karl Suss MJB3,4)
PlasmaStar RIE (O2)Laurel Spin coater

171
Pirahna etching, Nanostrip
Clean room procedures

A.2

Microfluidic Operation Protocol

ALL SOLUTIONS 0.2 UM FILTERED (unless small quantities).
Solutions:
1. Buffer: 0.1-1% BSA, 16-18% Optiprep in HHBSS (no metals, no phenol red).
a. Keep in fridge.
2. Oil: FC40 or HFE7500 with appropriate concentration of fluorosurfactant (RAN
Biotechnologies).

Equipment:
1. Turn on laser: Turn key.
a.

Press ‘Output’ on TEC: Wait until settled to 25 oC.

b.

Press ‘Output’ on LASER. (No higher than 500mA, ~180-240 mA has generally resulted

in the right power depending on beam size adjustments.).
2. Turn on Oscilloscope (hooked up to HV supply)
3. Turn on function generator and recall sort parameters.
4. Turn on PMT power box (gains are pre-adjusted to get equivalent signal from PMT 1 & 3
and 2 & 4, do this with beads or dye in droplets).

Device:172
1. Run IPA/EtOH through oil tubing to waste, DI (or chelex) water, buffer through microtubing to waste, dry 2 minutes high pressure. Use syringe to run IPA/EtOH through waste
tubing and dry.
2. BSA treatment (optional)
3. Blow dry ~10 min (5 PSI+).
4. CAREFULLY - Insert all tubing: Cell inlet > reagent inlet > oil inlet. Do not inset Keepoutlet and waste outlet until timing has been roughly established. Make sure that outlet
tubing is only gently placed inside (Do not jam into the coverslip).
5. Connect power to electrodes (1 = Ground (positive), 2 = Negative). Do not turn on HV
supply until ready to start sorting.
6. Secure device to platform with magnets.
____________________________________________________
Microcontroller/Software:

1. Turn on DAQ32 program (DAQ32_PC_Interface.exe).
2. Turn on Labview Data acquisition program.
3. Turn on camera software.
4. Plug in microcontroller microUSB (power).
5. Click ‘Open port’ on DAQ32 interface for COM that microcontroller is on.
6. Click ‘Show’ next to scope A, B (show relevant channels, only 3 channels per scope).
1. Channel 5 is the TTL signal.
7. Set parameters.
8. Click ‘Write Settings’. This starts the program.

173.

If the program glitches (happens when it gets too high of a signal):
1. Close Port
2. Unplug/Replug USB cable
3. Open port
4. Write Settings_____________________________________________________

Running the Experiment:

1. Align device with knobs until within view of camera (if not done already).
a.

Pan around and check for clogs.
2. Get laser spots into position by moving platform/adjusting device position.
3. Set sort parameters on function generator.

.

TREK HV amp multiplies voltage by ~100.
4. ORDER IS IMPORTANT HERE: Turn on oil pressure (higher than you plan to use it.
Easier to start fast and slow down than to start slow for high resistance devices). Turn on
reagent pressure and cell pressure higher than the oil pressure (wait for it to reach thedevice). Try to do this so they arrive nearly simulataneously. Work on getting even flowfor your reagent and cell channels. Once droplet generation is achieved, adjust oilpressure to desired speed and attempt to correct droplet size/spacing by decreasing Celland Reagent pressures (alternate small turns for both to maintain focus). Check timing
using timing histogram until you get where you want.
5. Insert outlet tubing and check speed.

174
6. Watch for fluctuations in timing and correct.
7. When ready to start sorting, turn on sorting signal and adjust voltage until you see sorting
and use those parameters going forward.

Ending experiment:
1. Allow experiment to run for a few minutes past the end to allow droplets
to reach the reservoirs.
2. Add recovery media to the top of the cells (or gDNA extraction buffer)
3. Add emulsion breaking chemical (SIGMA H1604)
1. Sit for a few minutes
4. Spin @ 50g for 1-2 min to separate phases
5. Carefully pull off supernatant (aqueous).
6. If gDNA extraction, add proteinase K and incubate overnight @ 37oC.
1. gDNA extraction and PCR amplification protocols in Kyle Carter’s
thesis.
7. Discard μfluidic chip in bio-waste (feel free to try to reuse if not twophase)
8. Run EtOH/IPA through tubing first (H2O or bleach may cause cells toburst). Then H2O or Bleach -> H2O and dry.

175

Appendix B - Code
B.1

Microcontroller code#include "mbed.h"#include "adc.h"#include "main.h"#include "filtering.h"#include "USBSerial.h"//settings - set to default values
float FRET_threshold=0;//FRET threshold , a non zero number turns on fret sorting
float FRET_thresholdUpper = 10;
float threshold = 200;//threshold in adc counts - ranges from 0 to 4092
uint32_t sorting_delay_us=40;//sorting delay - time to turn on sorting signal after a peak isdetected
uint32_t sorting_delay_off_us=70; //sorting delay off - time to turn off the sorting signal after apeak is detected
float sorting_delay=0.04;//sorting delay in seconds
float sorting_delay_off=0.07;//sorting delay in seconds must be greater thant sorting_delay
uint32_t sample_delay_us=200;//time between adc samples in us
float sample_delay_seconds=((float)sample_delay_us)/((float)1000000); //time between adc
samples in s. equal to 1/sampling ratebool sortOnChannel3=true;
int pwmPeriod_us = 100;int pwmPulseWidth_us = 50;
bool sendPeakData = false;
int pwmStirrerMotorPulseWidth_us = 50;

176bool useMedianFilter;bool computerConnected;bool computerConnected2;//Timer
Timer t;//PWM - defines the sorting signal
PwmOut myPwmOut(p21);//Stirrer Motor
PwmOut myStirrerMotor(p22);
DigitalOut TTL_sortingSignal(p20);//MODSERIAL pc(USBTX,USBRX);
USBSerial serial;//booleans to control program flowbool data;//used to signal that there is new data from the ADC to process - true when
there is new databool in_peak1=false; //used to signal that we are currently "in a peak" on adc 1 - true when weare in a peakbool in_peak2=false; //used to signal that we are currently "in a peak" on adc 2 - true when weare in a peakbool in_peak3=false; //used to signal that we are currently "in a peak" on adc 3 - true when weare in a peakbool in_peak4=false; //used to signal that we are currently "in a peak" on adc 4 - true when weare in a peakbool peakDetected1 = false;bool peakDetected2 = false;bool peakDetected3 = false;bool peakDetected4 = false;
int peak_index_counter1;
int peak_index_counter2;
int peak_index_counter3;
int peak_index_counter4;

177
int peak_array_index_ch1=0;
int peak_array_index_ch2=0;
int peak_array_index_ch3=0;
int peak_array_index_ch4=0;
Peak peak_array_ch1[20];Peak peak_array_ch2[20];Peak peak_array_ch3[20];Peak peak_array_ch4[20];uint16_t peakDataBuffer1[500];
uint16_t peakDataBuffer2[500];
uint16_t peakDataBuffer3[500];
uint16_t peakDataBuffer4[500];

const int firstSpotFRETbufferSize=100;
float firstSpotFRETbuffer[100];
int firstSpotFRETbufferIndex;
int firstSpotFRETbufferIndexMatching;//peak pair matchinguint16_t second_peak_min_delay_ms=40;
uint16_t second_peak_max_delay_ms=70;
float second_peak_min_delay=0.04;
float second_peak_max_delay=0.07;
Timeout secondSpotMinDelayTime[100];
Timeout secondSpotMaxDelayTime[100];

volatile int searchForSecondSpot=0;
int dataPointsBuffered=0;bool dataBufferLock;volatile ISRdata ISRdata_point[100];
volatile int numBufferedDataPoints=0;//stores the data of the current ADC resultbool isrBufferLock=false;
ISRdata *ISRdata_point_pointer; //used to pass the data of the current ADC results to other dataprocessing functions
uint16_t filter_Buffer_1[3];uint16_t filter_Buffer_2[3];uint16_t filter_Buffer_3[3];uint16_t filter_Buffer_4[3];//used to store the adc data for the median filter for adc 1//used to store the adc data for the median filter for adc 2//used to store the adc data for the median filter for adc 3//used to store the adc data for the median filter for adc 4

178
Ticker adcTicker;//used to trigger ADC read events at a specific time interval
Timeout sortingSignalOn1[10]; //An array of Timeout objects used to start and stop the sorting
signalTimeout sortingSignalOff1[10];Ticker signalGeneratorTest;bool sendReportFilpper;
int peak_packet_data_marker=1;
bool peakDataReadyToSend = false;

Timeout peakPairMatcher;int peakMatchTime = 1500; //time in us allowed for a peak on ch1 and ch2 or on ch3 and ch4 to
be matched

uint16_t sorting_signal_indicator=0; //used to keep track of when the sorting signal is on oroff, when the sorting signal is on =1 and when its off ==0
int lastSortIndex;
int sorting_signal_index1=0;
int f_data_index=0;//an index variable to keep track of where we are in
the filtered data buffer
ISRdata f_data_buffer[1000];// a data buffer to store filtered data and use
within the main loop for data processing (ie peak detection, FRET ratios.....), holds the last 1000
data points
void ADCTimerInterrupt(){
if(computerConnected2) {dataBufferLock=true;
ISRdata_point[dataPointsBuffered].adc_value1=data_of_pin(p16); //read the ADC value
off of pin 16
ISRdata_point[dataPointsBuffered].adc_value2=data_of_pin(p17); //read the ADC value
off of pin 17
ISRdata_point[dataPointsBuffered].adc_value3=data_of_pin(p18); //read the ADC value
off of pin 16
ISRdata_point[dataPointsBuffered].adc_value4=data_of_pin(p19); //read the ADC value
off of pin 17

179//ISRdata_point_pointer=&ISRdata_point;//create a pointer to this data so we canaccess it from other functions
dataPointsBuffered++;//data=true;//turn on the signal to indicate that there is new data to readin the main function loop
dataBufferLock=false;}}

int main(){serial.attach(&packetRecieved);wait(0.5);
searchForSecondSpot=0;//setup PWM
myPwmOut.period_us(100); //set pwm period
myStirrerMotor.period_us(100);//setup the ADC in burst mode and attach a tiker to sample at a predefined rate
init_ADC(180000, 1);//initialize the ADC
setupADC(p16,1);//setup the ADC on pin 16
setupADC(p17,1);//setup the ADC on pin 17
setupADC(p18,1);//setup the ADC on pin 18
setupADC(p19,1);//setup the ADC on pin 19
LPC_ADC->ADCR |= (1 << 16);//enable burst mode, so the ADC on the
microcontroller is constantly generating new results as fast as possible that we can read at any
timewait(0.5);//found we need to add this pause for some reason not sure why?
uint16_t packet_size=64;
packet to be sent to the computer
uint8_t send_report[packet_size];
uint8_t send_report2[packet_size];
uint8_t peak_data_send_report[packet_size];
int testCounter=1;//the number of bytes in the outgoing data180//sorting_delay=((float)sorting_delay_ms)/1000;//in seconds//sorting_delay_off=((float)sorting_delay_off_ms)/1000;//in seconds must be greater thantsorting_delay
int mycount = 0;
numBufferedDataPoints=0;//signalGeneratorTest.attach_us(&sort,300000);//wait(0.5);

adcTicker.attach_us(&ADCTimerInterrupt,sample_delay_us); //attatch a function to becalled to read the ADC at a specified interval - effectively setting the sampling ratewait(0.5);

while(1) {if(computerConnected)
computerConnected2=true;
if(dataPointsBuffered>0) {while(dataBufferLock);//dataBufferLock=true;
dataPointsBuffered--;if(useMedianFilter==true){
getFilteredData();}else{
getRawData();}//dataBufferLock=false;

if((f_data_buffer[f_data_index].adc_value1>threshold)&&(!in_peak1)) {just entering a peak on adc 1//if we arein_peak1=true;

peak_index_counter1=0;
peakDataBuffer1[peak_index_counter1] = f_data_buffer[f_data_index].adc_value1;}

181

if(in_peak1) {//if we are in peak 1
peak_index_counter1++;
peakDataBuffer1[peak_index_counter1] = f_data_buffer[f_data_index].adc_value1;}
if(in_peak1 && (f_data_buffer[f_data_index].adc_value1<threshold)) {//if we areleaving a peak 1
in_peak1=false;
peak_array_ch1[peak_array_index_ch1].width = peak_index_counter1;
peak_array_ch1[peak_array_index_ch1].height =max_array(peakDataBuffer1,peak_index_counter1);
if(peak_array_index_ch1==9||peak_array_index_ch1==19) {peakDataReadyToSend=true;}
if(peak_array_index_ch1==19) {peak_array_index_ch1=0;} else {peak_array_index_ch1++;}
if(!sortOnChannel3) {peakDetected1 = true;
peakPairMatcher.attach_us(&matchFRETpeaksExpiration, peakMatchTime);}}
if((f_data_buffer[f_data_index].adc_value2>threshold)&&(!in_peak2)) {just entering a peak on adc 2
in_peak2=true;//if we are

peak_index_counter2=0;
peakDataBuffer2[peak_index_counter2] = f_data_buffer[f_data_index].adc_value2;}
if(in_peak2) {//if we are in peak 2
peak_index_counter2++;
peakDataBuffer2[peak_index_counter2] = f_data_buffer[f_data_index].adc_value2;}
if(in_peak2 && (f_data_buffer[f_data_index].adc_value2<threshold)) {//if we areleaving a peak on adc 2
in_peak2=false;
peak_array_ch2[peak_array_index_ch2].width = peak_index_counter2;
peak_array_ch2[peak_array_index_ch2].height =max_array(peakDataBuffer2,peak_index_counter2);
if(peak_array_index_ch2==19) {peak_array_index_ch2=0;

182} else {peak_array_index_ch2++;}if(!sortOnChannel3) {peakDetected2 = true;
peakPairMatcher.attach_us(&matchFRETpeaksExpiration, peakMatchTime);}}if((!sortOnChannel3) && peakDetected1 && peakDetected2) {int lastIndex1=peak_array_index_ch1-1;
if(lastIndex1<0)
lastIndex1=19;
int lastIndex2=peak_array_index_ch2-1;
if(lastIndex2<0)
lastIndex2=19;
float FRET_spot1 =((float)peak_array_ch1[lastIndex1].height/(float)peak_array_ch2[lastIndex2].height);
firstSpotFRETbuffer[firstSpotFRETbufferIndex]=FRET_spot1;

secondSpotMinDelayTime[firstSpotFRETbufferIndex].attach(&secondSpotStartSearch,second_peak_min_delay);secondSpotMaxDelayTime[firstSpotFRETbufferIndex].attach(&secondSpotEndSearch,second_p
eak_max_delay);if(firstSpotFRETbufferIndex==99) {firstSpotFRETbufferIndex=0;} else {firstSpotFRETbufferIndex++;}//if(FRET_ratio>FRET_threshold && FRET_ratio<FRET_thresholdUpper)//{// sort();//}peakDetected1 = false;
peakDetected2 = false;
peakPairMatcher.detach();}183

if((searchForSecondSpot > 0) || sortOnChannel3) {if((f_data_buffer[f_data_index].adc_value3>threshold)&&(!in_peak3)) {are just entering a peak on adc 3//if wein_peak3=true;

peak_index_counter3=0;
peakDataBuffer3[peak_index_counter3] = f_data_buffer[f_data_index].adc_value3;}
if(in_peak3) {//if we are in peak 3
peak_index_counter3++;
peakDataBuffer3[peak_index_counter3] = f_data_buffer[f_data_index].adc_value3;}
if(in_peak3 && (f_data_buffer[f_data_index].adc_value3<threshold)) {//if we areleaving a peak 3
in_peak3=false;
peak_array_ch3[peak_array_index_ch3].width = peak_index_counter3;
peak_array_ch3[peak_array_index_ch3].height =max_array(peakDataBuffer3,peak_index_counter3);
if(peak_array_index_ch3==9||peak_array_index_ch3==19) {peakDataReadyToSend=true;}
if(peak_array_index_ch3==19) {peak_array_index_ch3=0;} else {peak_array_index_ch3++;}if(sortOnChannel3) {sort();}
if(!sortOnChannel3) {peakDetected3 = true;
peakPairMatcher.attach_us(&matchFRETpeaksExpiration2, peakMatchTime);}}
if((f_data_buffer[f_data_index].adc_value4>threshold)&&(!in_peak4)) {are just entering a peak on adc 4//if we184
in_peak4=true;
peak_index_counter4=0;
peakDataBuffer4[peak_index_counter4] = f_data_buffer[f_data_index].adc_value4;}
if(in_peak4) {//if we are in peak 4
peak_index_counter4++;
peakDataBuffer4[peak_index_counter4] = f_data_buffer[f_data_index].adc_value4;}
if(in_peak4 && (f_data_buffer[f_data_index].adc_value4<threshold)) {//if we areleaving a peak on adc 4
in_peak4=false;
peak_array_ch4[peak_array_index_ch4].width = peak_index_counter4;
peak_array_ch4[peak_array_index_ch4].height =max_array(peakDataBuffer4,peak_index_counter4);
if(peak_array_index_ch4==19) {peak_array_index_ch4=0;} else {peak_array_index_ch4++;}if(!sortOnChannel3) {peakDetected4 = true;
peakPairMatcher.attach_us(&matchFRETpeaksExpiration2, peakMatchTime);}}if((!sortOnChannel3) && peakDetected3 && peakDetected4) {int lastIndex3=peak_array_index_ch3-1;
if(lastIndex3<0)
lastIndex3=19;
int lastIndex4=peak_array_index_ch4-1;
if(lastIndex4<0)
lastIndex4=19;
float FRET_spot2 =((float)peak_array_ch3[lastIndex3].height/(float)peak_array_ch4[lastIndex4].height);
float FRET_RATIO=FRET_spot2/firstSpotFRETbuffer[firstSpotFRETbufferIndexMatching];
secondSpotMaxDelayTime[firstSpotFRETbufferIndexMatching].detach();secondSpotEndSearch();185

if(FRET_RATIO>FRET_threshold && FRET_RATIO<FRET_thresholdUpper) {sort();}

peakDetected3 = false;
peakDetected4 = false;}}

send_report[0+(8*mycount)]=(uint8_t)(((f_data_buffer[f_data_index].adc_value1>>7))&0x1F)|0x80;
send_report[1+(8*mycount)]=(uint8_t)((f_data_buffer[f_data_index].adc_value1)&0x7F);
send_report[2+(8*mycount)]=(uint8_t)(((f_data_buffer[f_data_index].adc_value2>>7)&0x1F)|(((uint8_t)sorting_signal_indicator)<<5));
send_report[3+(8*mycount)]=(uint8_t)((f_data_buffer[f_data_index].adc_value2)&0x7F);
send_report[4+(8*mycount)]=(uint8_t)(((f_data_buffer[f_data_index].adc_value3>>7))&0x1F);
send_report[5+(8*mycount)]=(uint8_t)((f_data_buffer[f_data_index].adc_value3)&0x7F);
send_report[6+(8*mycount)]=(uint8_t)(((f_data_buffer[f_data_index].adc_value4>>7))&0x1F);
send_report[7+(8*mycount)]=(uint8_t)((f_data_buffer[f_data_index].adc_value4)&0x7F);

mycount++;

if(mycount == 8) {//send_report[0]|= 0x80;

serial.writeBlock(&send_report[0], 64);

186

mycount = 0;//testCounter=1;}
if(((mycount==8) && peakDataReadyToSend) && sendPeakData) {for(int j=0; j<10; j++) {int l;
if(peak_array_index_ch1<9) {l=10;} else {l=0;}
peak_data_send_report[4+(6*j)]= peak_array_ch1[l+j].width;peak_data_send_report[5+(6*j)]= (peak_array_ch1[l+j].height>>8)&0x0F;
peak_data_send_report[6+(6*j)]= (peak_array_ch1[l+j].height)&0xFF;
peak_data_send_report[7+(6*j)]= peak_array_ch2[l+j].width;peak_data_send_report[8+(6*j)]= (peak_array_ch2[l+j].height>>8)&0x0F;
peak_data_send_report[9+(6*j)]= (peak_array_ch2[l+j].height)&0xFF;}
peak_data_send_report[0] = 128;
peak_data_send_report[1] = 128;
peak_data_send_report[2] = 0;
peak_data_send_report[3] = 0;
serial.writeBlock(&peak_data_send_report[0], 64);
peakDataReadyToSend=false;}
endDataTasks();}}}
void getFilteredData(){//f_data_buffer[f_data_index].adc_value1 = medianFilter(filter_Buffer_1,
ISRdata_point_pointer->adc_value1);//f_data_buffer[f_data_index].adc_value2 = medianFilter(filter_Buffer_2,
ISRdata_point_pointer->adc_value2);
f_data_buffer[f_data_index].adc_value1 = medianFilter(filter_Buffer_1,
ISRdata_point[dataPointsBuffered].adc_value1);

187
f_data_buffer[f_data_index].adc_value2 = medianFilter(filter_Buffer_2,
ISRdata_point[dataPointsBuffered].adc_value2);
f_data_buffer[f_data_index].adc_value3 = medianFilter(filter_Buffer_3,
ISRdata_point[dataPointsBuffered].adc_value3);
f_data_buffer[f_data_index].adc_value4 = medianFilter(filter_Buffer_4,
ISRdata_point[dataPointsBuffered].adc_value4);}
void getRawData(){
f_data_buffer[f_data_index].adc_value1 = ISRdata_point[dataPointsBuffered].adc_value1;
f_data_buffer[f_data_index].adc_value2 = ISRdata_point[dataPointsBuffered].adc_value2;
f_data_buffer[f_data_index].adc_value3 = ISRdata_point[dataPointsBuffered].adc_value3;
f_data_buffer[f_data_index].adc_value4 = ISRdata_point[dataPointsBuffered].adc_value4;}
void endDataTasks(){
data=false;
f_data_index++;if(f_data_index>999) {f_data_index=0;}}
void sortingSignalOn(){if(sorting_signal_indicator==1) {sortingSignalOff1[lastSortIndex].detach();}sorting_signal_indicator=1;
myPwmOut.pulsewidth_us(pwmPulseWidth_us); //turn on pwm sorting signal
TTL_sortingSignal=1;//make TTL signal on p20 high}void sortingSignalOff(){sorting_signal_indicator=0;
myPwmOut.pulsewidth_us(0);TTL_sortingSignal=0;}//turn off pwm sorting signal//make TTL signal on p20 low

188void packetRecieved(){
computerConnected2=false;
computerConnected=false;
uint8_t float_bytes[4];float_bytes[0] = serial._getc();
float_bytes[1] = serial._getc();
float_bytes[2] = serial._getc();
float_bytes[3] = serial._getc();
FRET_threshold=*(float *)float_bytes;
float_bytes[0] = serial._getc();
float_bytes[1] = serial._getc();
float_bytes[2] = serial._getc();
float_bytes[3] = serial._getc();
FRET_thresholdUpper=*(float *)float_bytes;
threshold = serial._getc()|(serial._getc()<<8);
sample_delay_us=serial._getc()|(serial._getc()<<8);
sorting_delay_us=serial._getc()|(serial._getc()<<8);
sorting_delay_off_us=serial._getc()|(serial._getc()<<8);second_peak_min_delay_ms=serial._getc()|(serial._getc()<<8);second_peak_max_delay_ms=serial._getc()|(serial._getc()<<8);second_peak_min_delay=((float)second_peak_min_delay_ms)/1000; //in seconds
second_peak_max_delay=((float)second_peak_max_delay_ms)/1000; //in seconds
pwmPulseWidth_us = serial._getc()|(serial._getc()<<8);
pwmPeriod_us = serial._getc()|(serial._getc()<<8);
pwmStirrerMotorPulseWidth_us = serial._getc()|(serial._getc()<<8);if((pwmStirrerMotorPulseWidth_us>100)||(pwmStirrerMotorPulseWidth_us<0)){
pwmStirrerMotorPulseWidth_us=0;}

189
char sortOnFRET = serial._getc();
char sendPData = serial._getc();
char useFilter = serial._getc();
if (sortOnFRET == 100) {sortOnChannel3 = false;} else {sortOnChannel3 = true;}
if (sendPData == 100) {sendPeakData = true;} else {sendPeakData = false;}
if (useFilter == 100) {useMedianFilter = true;} else {useMedianFilter = false;}

myStirrerMotor.write(((float)pwmStirrerMotorPulseWidth_us)/100);
adcTicker.detach();sample_delay_seconds=sample_delay_us/1000000;
adcTicker.attach_us(&ADCTimerInterrupt,sample_delay_us);wait(0.5);//sorting_delay=((float)sorting_delay_us)/1000; //in seconds//sorting_delay_off=((float)sorting_delay_off_us)/1000; //in seconds must be greater thantsorting_delay

myPwmOut.period_us(pwmPeriod_us);//set pwm periodcomputerConnected=true;}
int max_array(uint16_t a[], int num_elements){

190
int i, max=0;
for (i=0; i<=num_elements; i++) {if (a[i]>max) {max=a[i];}}return(max);}
void sort(){
if (sorting_signal_index1 == 9) {sorting_signal_index1 = 0;
lastSortIndex = 9;
} else {lastSortIndex = sorting_signal_index1;
sorting_signal_index1++;}
sortingSignalOn1[sorting_signal_index1].attach_us(sortingSignalOn,sorting_delay_us);
sortingSignalOff1[sorting_signal_index1].attach_us(sortingSignalOff,
sorting_delay_off_us);}
void secondSpotStartSearch(){searchForSecondSpot=searchForSecondSpot+1;}
void secondSpotEndSearch(){searchForSecondSpot=searchForSecondSpot-1;
if(firstSpotFRETbufferIndexMatching==(firstSpotFRETbufferSize-1)) {firstSpotFRETbufferIndexMatching=0;} else {firstSpotFRETbufferIndexMatching++;}}
void matchFRETpeaksExpiration(){

191
if(peakDetected1 && peakDetected2) {peakDetected1 = false;
peakDetected2 = false;}}void matchFRETpeaksExpiration2(){
if(peakDetected3 && peakDetected4) {peakDetected3 = false;
peakDetected4 = false;
}
}

B.2

Mathematica Code for Anisotropy Calculations(*Variables/Constants*)
F1 = 1; (*Interacting Fraction*)
F2 = 0.1;
Ro = 4.8*^-9; (*ECFP/Citrine*)
Ra = 4.8*^-9;
B = 30; (*Acceptor-to-Donor Overlap Factor in Acceptor Channel || low overlap*)
B2 = 5.89; (*Acceptor-to-Donor Overlap Factor in Acceptor Channel || Citrine/ECFP*)
kaf = 1/(3.6*^-9); (*Radiative rate - Acceptor || Citrine*)
kfret = (1/(1/kdf))*(Ro/Ra)^6; (*FRET rate*)
kdf = 1/(3*^-9); (*Radiative rate - Donor || ECFP*)(*θda = 90 Degree; (*Donor-Acceptor TDM angle*)*)δ = 0.01; (*Absorption cross section Acceptor/Donor @ Donor Excitation*)(*ANI = 1;*)(*AI = 1;*)krot=1/(30*^-9);(*X= (kfret/(kfret + kdf - kaf))*)R[t_,θda_]=(2/5)*(((Exp[-(kfret+kaf)*t])*(F1*(1-(B*(kfret/(kfret + kdf - kaf))/2)*(3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t]))+F1*B*(kfret/(kfret + kdf - kaf))*Exp[-kaf*t]*0.5*((3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t])+((1-F1)*Exp[-kdf*t]*Exp[-krot*t])+(B*δ*Exp[-krot*t]*Exp[-

192
kaf*t]))/((F1*(1-B*(kfret/(kfret + kdf - kaf)))*Exp[-(kfret+kaf)*t])+(F1*B*(kfret/(kfret + kdf kaf))*Exp[-kaf*t])+((1-F1)Exp[-kdf*t])+(B*δ*Exp[-kaf*t])));
p1=Plot3D[R[t,θda],{t,0,1*10-8}, {θda,0,90},PlotStyle->RGBColor[0.75,0.61,1.],PlotRange->{0.2,0.4}, AxesLabel->{t,θda,r},LabelStyle->{FontSize->16,FontFamily->"Times",Black,Bold}]

R1[t_,θda_]=(2/5)*(((Exp[-(kfret+kaf)*t])*(F2*(1-(B*(kfret/(kfret + kdf - kaf))/2)*(3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t]))+F2*B*(kfret/(kfret + kdf - kaf))*Exp[-kaf*t]*0.5*((3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t])+((1-F2)*Exp[-kdf*t]*Exp[-krot*t])+(B*δ*Exp[-krot*t]*Exp[kaf*t]))/((F2*(1-B*(kfret/(kfret + kdf - kaf)))*Exp[-(kfret+kaf)*t])+(F2*B*(kfret/(kfret + kdf kaf))*Exp[-kaf*t])+((1-F2)Exp[-kdf*t])+(B*δ*Exp[-kaf*t])));
p2=Plot3D[R1[t,θda],{t,0,1*10-8}, {θda,0,90},PlotStyle->RGBColor[0.5`,0.61`,1.`],PlotRange>{-0.2,0.4}, AxesLabel->{t,θda,r}]R2[t_,θda_]=(2/5)*(((Exp[-(kfret+kaf)*t])*(F1*(1-(B2*(kfret/(kfret + kdf - kaf))/2)*(3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t]))+F1*B2*(kfret/(kfret + kdf - kaf))*Exp[-kaf*t]*0.5*((3*(Cos[θdaDegree]^2)-1)*Exp[-krot*t])+((1-F1)*Exp[-kdf*t]*Exp[-krot*t])+(B2*δ*Exp[-krot*t]*Exp[kaf*t]))/((F1*(1-B2*(kfret/(kfret + kdf - kaf)))*Exp[-(kfret+kaf)*t])+(F1*B2*(kfret/(kfret + kdf- kaf))*Exp[-kaf*t])+((1-F1)Exp[-kdf*t])+(B2*δ*Exp[-kaf*t])));

193
p3=Plot3D[R2[t,θda],{t,0,1*10-8}, {θda,0,90},PlotStyle->RGBColor[0.3`,0.8`,1.`],PlotRange>{-0.2,0.4}, AxesLabel->{t,θda,r}]

pa=Show[p1,p2,p3]

194

195
B.3

Mathematica Code for Microfluidic Device***Adapted from code by Dr. Jennifer Lubbeck***
2 D device (uniform height)
Resistance:
R = Resistance (in units of N s m^-5)\[Eta] = Viscosity (in units of N s (m^-2) )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.
R[x_, w_, h_] := 12 \[Eta] x/((h^3 w) (1 - 0.630 h/w))
Resistance of center input channels :h = 45 10^-6 ;xc = 47.646 10^-3;
xmix = 0 10^-3;
wc = 50 10^-6;\[Eta] = 0.00089;
Rc = N[R[xc, wc, h] + R[xmix, wc, h]]
2.5793*10^14
Resistance of oil input channels :xs = 43.2795272 10^-3;
ws = 50 10^-6;\[Eta] = 0.00124;
Rs = N[R[xs, ws, h]]3.2643*10^14

196
Total input resistance :Rinput = 1/(1/Rc + 2/Rs)
9.9961*10^13
Resistance of delay
xm = 221.744 10^-3;
wm = 100 10^-6;
Rm = N[R[xm, wm, h]]5.0536*10^14
Resistance of outputs :xkj = 0.6697 10^-3;
xk = 8.2904128 10^-3;
xwj = 0.6526 10^-3;xw = 2.926 10^-3;
wo = 200 10^-6;
ww = 140 10^-6;
Ro = N[1/(R[xkj, wm, h] + R[xk, wo, h]) + 1/(R[xwj, ww, h] + R[xw, wo, h])]3.7376*10^-13
Total Output resistance :Routput = 1/Ro
2.67551*10^12
Total Device Resistance :TotalDeviceResistance = N[Rinput + Rm + Routput]
ResistanceRatio = (Routput + Rm)/Rinput
6.07997*10^14

197

5.08234

Necessary Pressure for velocity of (input below) :Vmax = 0.03;
In Helle - Shaw limit : FOR RECTANGULAR CHANNELS THIS IS ONLY APPROXIMATE
Q[h_, w_, v_] := 2 h w v/3
FlowRate = N[Q[h, wm, Vmax]]
9.*10^-11
P = FlowRate*TotalDeviceResistance/6894.7
7.93649