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2021-06-18
A FRET FLOW CYTOMETRY-BASED SCREENING ASSAY FOR MULTIPLEX ANALYSIS OF METABOLITES IN T. BRUCEI
Ronald A. Zegarra BYU
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BYU ScholarsArchive Citation Zegarra, Ronald A., "A FRET FLOW CYTOMETRY-BASED SCREENING ASSAY FOR MULTIPLEX ANALYSIS OF METABOLITES IN T. BRUCEI" (2021). Undergraduate Honors Theses. 196. https://scholarsarchive.byu.edu/studentpub_uht/196
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Honors Thesis
A FRET FLOW CYTOMETRY-BASED SCREENING ASSAY FOR MULTIPLEX ANALYSIS OF METABOLITES IN T. BRUCEI
Ronald Alejandro Zegarra
Submitted to Brigham Young University in partial fulfillment of graduation requirements for University Honors
Department of Chemistry and Biochemistry Brigham Young University June 2021
Advisor: Kenneth A. Christensen Honors Coordinator: Walter Paxton ii
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ABSTRACT
Kinetoplastid parasites are a significant public health issue in some tropical and subtropical regions of the world. Kinetoplastid parasites all require glycolysis for survival, with host glucose key for ATP production. One such parasite, Trypanosoma brucei, exclusively metabolizes glucose in its bloodstream form. Trypanosomal glycolysis is unique because it displays unconventional structural features. Hence, glucose metabolism has been studied extensively in T. brucei and is a therapeutic target in kinetoplastid parasites.
The lack of in vivo analytical techniques for measuring vital glycolytic metabolites in situ has restricted the ability of researchers to test, with high sensitivity and specificity, the essential roles glycolytic metabolites play in both parasite differentiation and regulation of metabolism. Using endogenously expressed Förster resonance energy transfer (FRET) biosensors, we have been able to track in vivo glucose concentrations via flow cytometry in cultured T. brucei parasites, which has enabled unprecedented analysis of the dynamics of intracellular glucose under changing extracellular conditions, including a screening assay for potential glucose uptake inhibitors.
We are currently expanding our FRET biosensor suite to measure glycolytic metabolites and simultaneously screen for specific glycolytic inhibitors throughout the metabolic pathway. We expect to use biosensors for glucose, ATP, pH, and pyruvate.
However, these sensors typically use the same fluorescent proteins, making it extraordinarily difficult to monitor multiple biosensors in the flow cytometer. To overcome this limitation, we present a cellular barcoding method in which multiple cell lines expressing a different biosensor have unique labels to separate individual sensor responses via flow cytometry.
Each cell line is distinguishable from one another using a cell surface staining scheme or vi barcode. Using this approach, we demonstrated the separation of up to four unique populations, thus allowing the analysis of multiple analytes in a single screening assay. As proof of principle, we demonstrated the barcoding of two cell lines expressing cytosolic glucose or ATP sensors and simultaneously measured cytosolic glucose and ATP. We found that cellular barcoding does not interfere with parasite cell viability, and cell populations remained distinguishable over time. We also showed that the individual FRET biosensor responses remain unaffected by barcoding. Finally, we demonstrated that this method could be used in a high-throughput screening assay for T. brucei and other kinetoplast parasites to identify compounds that inhibit glycolysis. vii viii
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ACKNOWLEDGEMENTS
I would like to thank the BYU College of Physical and Mathematical Sciences, the
BYU Department of Chemistry and Biochemistry, the BYU Honors Department, and the
National Health Institute for funding research presented as a part of this thesis. Without their support, progress on this project and my personal development would have been substantially less.
Also, I want to thank my advisor and mentor, Dr. Kenneth A. Christensen, for accepting an inexperienced and ambitious undergraduate and taking the time to help me become a scientist. I want to thank Dr. Christine Ackroyd for being a second mentor in the lab and constantly feeding my curiosity. Thank you to Charles Voyton for initially conceiving the idea of barcoding biosensor-expressing trypanosomes and Jacob Vance for his initial work selecting and testing various dyes as barcodes. Thank you to the Jim Morris
Laboratory at Clemson University for transfecting the cell lines used.
I am incredibly grateful to my constant companion, Isabelle Anne Zegarra. Her support means more than she could ever know. And finally, for my parents, Ronald I.
Zegarra and Violeta Y. Zegarra, for the long hours of work and the immense sacrifice they made to give my siblings and me the opportunity to a better life in the United States. I could not have done this without any of them—esto es para ustedes. xii
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CONTENTS Title i
Abstract v
Acknowledgments xi
Table of Contents xv
Introduction 1
Results & Discussion 6
1.1 Barcode staining scheme could potentially distinguish four 6
different parasitic populations, simultaneously
1.2 Biosensor response is unaffected by barcoding 7
1.3 Biosensor response is statistically significant 9
1.4 Multiplex screening assay 11
1.5 Drug assay supports multiplex biosensor efficiency 13
Conclusions 15
Methods 15
1.1 Cells, Constructs, and Reagents 15
1.2 T. brucei transfection and localization of biosensors 16
1.3 T. brucei culture 16
1.4 Sample preparation and flow cytometry 16
1.5 Selection and optimization of barcoding dyes 17
1.6 Testing parasite viability and population distribution of barcoded cells 18
over time
1.7 Creating a Z-prime experiment and analyzing high and low glucose
Response over time 18 xvi
1.8 Running a barcoded multiplex assay 19
1.9 Testing barcoded biosensor response with drug treatments 19
Further Acknowledgements 20
Abbreviations 21
References: 21 xvii xviii
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INTRODUCTION
According to the World Health Organization, the kinetoplastids Trypanosoma brucei,
Trypanosoma cruzi, and Leishmania spp. parasites are the causative agents of some of the most significant neglected tropical diseases endemic, mainly in tropical and subtropical areas. 1,2
Roughly half a billion people are at risk of contracting these diseases. Current estimates indicate that more than 20 million individuals are infected with some form of a kinetoplastid pathogen, resulting in extensive morbidity and more than 100,000 yearly deaths combined. 3
Unfortunately, many treatments for kinetoplastid parasite infections have serious adverse side effects, including death.4,5,6 Hence, identifying additional drug targets and developing kinetoplastid-specific inhibitors that could become new therapeutics are unmet needs.
Kinetoplastid parasites are motile protozoans and include the human pathogens T. brucei,
T. cruzi, and Leishmania spp. Kinetoplast protozoa share significant similarities in both cellular biology and metabolism7 that are distinct from mammalian cells. For example, kinetoplastids have unique peroxisomes (known as glycosomes) in which the bulk of glycolysis occurs.7,8 In addition, kinetoplastid parasites are vector-borne diseases. As a result, they have complex life cycles in which they survive in different life stages in insects or mammalian hosts.7-9 Although the abrupt changes in environmental conditions between life stages could negatively affect the protozoan parasite, T. brucei, has evolved survival mechanisms to adapt to nutrient availability changes. 10-13 Importantly, the changes in life stages mean that all kinetoplastid protozoan parasites depend on host metabolism for survival.7-9 In its mammalian bloodstream form, the parasites rely heavily on glucose metabolism.
Glucose is essential for kinetoplastid survival in the mammalian host, where it acts as both a signal and a metabolic energy source.4,13-15 For example, when T. brucei moves from the 2 tsetse fly to the mammalian bloodstream, the parasite experiences increased glucose.13,16 High glucose is the signal that induces parasite differentiation to the bloodstream form (BSF), which can survive in the mammalian host.10-13 Importantly, BSF T. brucei relies nearly exclusively on glucose metabolism for survival within the host. Glucose also plays a pivotal role in Leishmania spp. infections16,17 where glucose and its metabolites are essential for the anaplerotic synthesis of glutamate, the primary free amino acid needed to differentiate from the insect form to the intracellular form found in its mammalian host.16,17
Since glucose metabolism plays a critical role in the kinetoplastid life cycle and viability, targeting glucose metabolism is an attractive strategy for generating anti-parasite therapeutics.4
For example, a recent report outlines how a specific inhibitor of trypanosome phosphofructokinase, an essential enzyme in glycolysis, kills BSF T. brucei in a mouse model in which the parasites had migrated to the brain. Importantly, this phosphofructokinase inhibitor had no substantial host toxicity.18 Since this mouse model of T. brucei infection is similar to the late-stage disease in humans,18 this work demonstrates that parasite-specific glycolysis inhibitors are viable therapeutic targets.
Our focus here is to develop a screening approach to identify small molecules which impact specific steps of T. brucei glycolysis. Such inhibitors could be used as either metabolic probes to illuminate individual steps of metabolism or act as trypanocidal compounds that would serve as lead compounds for drug development. Regardless of the final use of identified compounds, the screen itself requires analysis of the concentrations of individual metabolites consumed or created in different stages of glycolysis. Advances made in the measurement of glycolytic metabolites, as carried out here, therefore have significance for the field of multiplex 3 assay development and metabolomics, the study of small-molecule metabolites in cells, tissues, and organisms.19
Obtaining broad coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules in question.19,20 Metabolomic studies currently utilize primarily two techniques; mass spectrometry and nuclear magnetic resonance
(NMR).19,20 While both techniques can measure a high number of metabolites simultaneously, neither is ideally suited for measuring metabolites in intact organisms.19-21 Small-molecule fluorescent probes have a long history quantifying relative and absolute concentrations of intracellular analytes such as pH and Ca2+.22 For example, the Christensen lab has used probes coupled with fluorescein, which can report pH, for pH quantification in T. brucei glycosomes.23,24
Using a signal peptide fluorescein conjugate, such as F-PTS1, pH conditions inside glycosomes were dynamically analyzed under starvation conditions.24
Recently, a wide variety of genetically encoded Förster resonance energy transfer (FRET) sensors have been developed to measure various metabolites, as well as pH, protein activity, and protein-protein interactions.15, 22-27 Such biosensors can be targeted to individual cellular locations, including individual organelles, thus allowing unprecedented analysis of changes in individual metabolite concentration in live cells. Moreover, to increase the efficiency of these non-destructive monitoring techniques, a fluorescent protein glucose biosensor (FLII12Pglu-
700μδ6 ) has been used to measure intracellular glucose with flow cytometry. This combination of techniques has enabled the measurement of observable changes in glucose concentrations within the cytosol and the glycosomes of live T. brucei.25 These intracellularly expressed biosensors offer invaluable tools for monitoring in situ metabolic response(s) to changing environmental conditions. 4
Our goal was to apply biosensors to screen multiple glycolytic metabolites in kinetoplastid parasites.15,22-26 If such biosensors could be analyzed together, a single assay could be used to screen for inhibitors of more than one step of glycolysis. The combination of biosensors would then provide a critical tactical advantage over a standard single metabolite screening assay. In addition, since inhibitors of earlier steps of glycolysis affect glycolytic downstream metabolic steps, the assay itself contains the opportunity for secondary hit validation; molecules identified as hits of early stages of glycolysis could be validated by their impact on downstream metabolites, including the end-product ATP.
FRET sensor utility in studies of multiple metabolites in a single in vivo application is hampered by the significant spectral overlap between different FRET sensors, which has made their combined use impossible. Fluorescence barcoding has previously been used to measure multiple signals of different origins in a single sample simultaneously. 28 This barcoding technique is performed by staining different cells with unique fluorescent dyes, mixing the samples together before flow cytometry analysis, and running them as single samples. Notably, the staining of different cell types allows separate analysis of cells containing different biosensors and thus eliminates the problem of spectral overlap between different biosensors in the same sample. Here we demonstrate the use of fluorescence barcoding to create a proof of principle multiplexed screening assay to identify inhibitors of multiple steps of glycolysis. The assay relies on measurements of glucose (the first metabolite used in glycolysis) and ATP, the final product of glycolysis.
The protein biosensors used in the assay are FLII12Pglu-700μδ6 (FLIP700) and
AT1.03YEMK (YEMK). FLIP700 measures cytosolic glucose levels, while YEMK measures cytosolic adenosine triphosphate (ATP) levels. FLIP700 and YEMK were cloned into pXS6.Q 5
25,29,30 and transfected into BSF T. brucei. The glucose and ATP sensors used here contain no signal (targeting) sequence and are therefore expressed in the cytosol.31,32
We used these two biosensors to design a screening assay that allows analysis of glucose and ATP in the cytosol using a single flow cytometry assay. In short, fluorescent protein biosensors that monitor important metabolites were genetically encoded and expressed in live T. bruci BSF parasites. Biosensor-expressing parasites were labeled with desired concentrations of one or two surface reactive dyes (cellular barcoding), then mixed into a single sample. A similar barcoding approach27 has been used to obtain multiplexed measurements of enzymatic activity with FRET biosensors. Here we report the first use of cellular barcoding to monitor changes in intracellular metabolites and demonstrate its effectiveness when applied into a multiplexed screening assay.
Using a model staining scheme, we show that four distinct populations could be identified within a mixture. The concentrations of dye in this mixture have an insignificant effect on parasite viability and do not diminish in brightness over time. Using these dyes, we demonstrate that readout from two sensors can be monitored in a single assay. Moreover, we show that cellular barcoding does not affect FRET biosensor response. In other words, whether a transgenic parasite expressing a sensor is stained or unstained, the response of the biosensor to changing extracellular conditions remains the same. Finally, we demonstrate that multiplexed biosensors can be effectively used to simultaneously screen for inhibitors of multiple glycolytic steps in a single assay. The resulting assay is proof of principle that a barcoding approach can be used to eliminate problems associated with spectral overlap and, therefore, allow the creation of a singel assay to analyze multiple metabolites.
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RESULTS & DISCUSSION
A barcode staining scheme can distinguish four different parasite populations.
Cellular barcoding combined with the readout of multiple FRET biosensors via flow cytometry should enable the simultaneous detection of multiple unique analytes in a single assay.
However, effective cellular barcoding requires selecting fluorescent barcoding dyes with emission spectra that do not overlap with other barcoding dyes or with biosensor fluorescence.
We tested the feasibility of cellular barcoding using covalent cell-surface labels for different biosensor-expressing cell populations, using multiple N-hydroxysuccinimidyl (NHS) protein reactive dyes. Importantly, these dyes have minimal spectral overlap with the FRET biosensor emission. We first tested whether multiple FRET biosensor-expressing populations could be distinguished using the barcoding NHS-conjugated dyes, Sulfo-Cyanine 3 (Cy3) and Sulfo-
Cyanine 7 (Cy7). BSF wild-type T. brucei were stained with either no dye, Cy3, Cy7, or both barcoding dyes. The dye combination was designed to create four different populations that could be distinguished from each other by flow cytometry. Figure 1A shows each of the four barcoded populations, run as separate samples and then combined into a single plot. These data demonstrate that this staining scheme can achieve sufficient separation of the fluorescence intensities to allow the individual analysis of four distinct populations. Indeed, when the barcoded populations were combined into a single sample and then analyzed in a single flow cytometry experiment, each labeled population was clearly distinguished from the other (Figure
1B). 7
A B
Figure 1. Using Sulfo-Cyanine3 (Cy3) and Sulfo-Cyanine7 (Cy7) dyes, four distinguishable populations can be made. (A) Scatter plots of a four-population staining scheme using BF WT cells with each population run separately and (B) all combined and run simultaneously.
These data, shown in Figure 1, show proof of principle separation of four different barcoded populations in a single flow cytometry experiment, using two different dyes. However, more populations could be created by adjusting the individual dye concentrations. For example, using three intensity levels for each dye would allow nine different stained populations when analyzed using flow cytometry.33,34 Therefore, we conclude that this staining scheme is an efficient model that could be modified to match the desired number of analytes in a myriad of unique experiments.
Biosensor response is unaffected by barcoding
FRET biosensors can be highly sensitive. If the barcoding itself alters either biosensor response or cellular behavior, cellular barcoding could not be used. For example, covalent attachment to surface receptors could impact the delivery of extracellular cargo to the cytosol 8 and alter the analyte concentrations the biosensors measure. In addition, the attachment of fluorophores to the molecule of interest can potentially alter the molecular properties of the cell surface receptors and may affect the relevant conformational states and dynamics of flexible biomolecules like intrinsically disordered proteins (IDP).35 Depending on the size of the molecule being studied, the fluorophores could also compete with extracellular ligands by blocking key signals for nutritional intake.35 To minimize this effect, the concentration of the dyes used for labeling was previously optimized.23-25,27
To ensure that our labeling methods did not alter either the biosensor response or intracellular analyte levels, we compared the biosensor response in cells that were barcoded differently. BSF cells expressing the cytosolic glucose sensor FLII12Pglu-700μδ6 (FLIP700)23-
25,27 cells were divided into four unique populations by cellular barcoding with Cy3 and Cy7 dyes. Specifically, one group was barcoded with Cy3 and Cy7, and the other groups barcoded with either Cy3 or Cy7. The last group was not barcoded as a control. The four barcoded populations were placed in increasing glucose concentrations, and the biosensor response of each population was recorded by flow cytometric analysis. As shown in Figure 2, there was no significant difference between each barcoded population and the unbarcoded control sample.
This result indicates that the glucose biosensor response is not influenced by the use of barcoding dyes over the entire range of glucose concentrations tested, consistent with the conclusion that barcoding dyes allow reliable measurements of the metabolites their barcoded signals represent. 9 Figure 3
Figure 2. Barcoding does not affect sensor response to differing concentrations of glucose. BSF cells expressing FLIP700 were divided into four unique populations by cellular barcoding with Cy3 and Cy7 dyes. The barcoded sample was placed in increasing concentrations of glucose, and the biosensor response of each population is shown overlapped with an unbarcoded sample control for comparison.
Biosensor response is statistically significant
In a screening assay, it is necessary to distinguish between a positive result (inhibition of
the target) and the noise inherent in individual measurements. For example, a lower experimental
uncertainty and a greater separation between a positive and a negative results in a more powerful
screening assay. The Z' factor is the most commonly used statistical parameter to gauge assay
quality and track assay performance during a screening experiment. 36 A higher Z' value indicates
better screening assay performance because it separates positive and negative control
populations, which is necessary to distinguish actual inhibition from experimental noise.
Specifically, Z' values closer to 1 show a complete statistical separation between positive and
negative controls in an assay.36The advantage of a Z' analysis is its simplicity as it reduces the 10 myriad of variables found within the signal assay to a single parameter.37 Because of its suitability to interpret information-rich data sets in multiple parameters (i.e., barcoded biosensors), the value of a Z' analysis in our multiplexed screening assay is pivotal to assessing its feasibility.
Initially, a Z' experiment was performed separately for each biosensor. Using unbarcoded
BSF T. brucei cells expressing FLIP700, one sample was equally divided into two populations and treated with either 0 mM or 7 mM extracellular glucose. The FLIP700 biosensor response under each condition represented the physiological range of extracellular glucose concentrations and were our positive and negative controls. Next, we performed an identical experiment using the YEMK biosensor since the presence or absence of extracellular glucose would result in depleted intracellular ATP. Specifically, unbarcoded BSF T. brucei cells expressing YEMK were separated into two equal populations and treated with either 0 mM or 7 mM extracellular glucose.
Figure 3 shows the results of these experiments. There was a unique and significant difference between high and low glucose responses in each separate cell line. The FLIP700
(Figure 3A) had a Z' value of 0.81, while YEMK (Figure 3B) had a corresponding Z' value of
0.82. These Z’ values reflect a high dynamic range between controls and repeatability within each population.
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Figure 3. Plots representing FRET/CFP of high and low controls in BSF cells expressing the
ATP biosensor in cytosol (1), or glucose biosensor in the cytosol (2). Plots show the ratio of ECFP/EYFP intensity emission (x-axis) and ECFP emission (y-axis) of BSF cells expressing pDR-GW AT1.03YEMK (YEMK) in cytosol (1) or FLII12Pglu-700μδ6 (FLIP700) in cytosol
(2). High controls correspond to cells incubated in 7 mM extracellular glucose (1.C, 2.D), and low controls corresponds to cells incubated in 0 mM extracellular glucose (1.A, 2.B). Z-prime values for cytosol and cytosol assays are calculated as 0.81 and 0.82, respectively.
Multiplexed screening assay
Supported by the previous experiments, it was evident that a viable multiplex assay could be performed using the combination of covalent dyes and biosensor-expressing cell lines described above. BSF cells expressing FLIP700 were barcoded with Cy3.32,33 Similarly, BSF cells expressing YEMK were barcoded with Cy7.32,33 Once barcoded, FLIP700 and YEMK samples were combined and divided equally into two heterogeneous populations and treated with either 0 mM glucose or 7 mM glucose as our high and low controls.
The uniquely treated heterogenous barcoded populations were separately loaded into the same 384 well plate (Corning 384 Well Deep Well Plate). The low (0 mM glucose) control population and the high (0.7 mM glucose) control multiplexed population each had (n=192) samples distributed equally in the plate, adding up to 384 samples total. The plate was run 12 simultaneously, allowing for all barcoded biosensors to run as a single sample. The biosensor response for each population was recorded by the flow cytometer. As depicted in Figure 4, there was a significant difference between each barcoded population with a Z' for YEMK and
FLIP700 of 0.83 and 0.79, respectively. This suggests that a specific biosensor reflects the concentration of its specific metabolite in the cellular location of the biosensor and can therefore be used to measure metabolite concentration.
Figure 4. Plots representing FRET/ECFP emissions of combined (n: 384) C7 barcoded pDR- GW AT1.03YEMK (YEMK) (A, C), and Cy3 barcoded FLII12Pglu-700μδ6 (Flip700) (B, D). Plots show the ratio of ECFP/EYFP intensity emission (x-axis) and ECFP Intensity emission (y- axis) of BSF cells simultaneously expressing pDR-GW AT1.03YEMK (YEMK) in cytosol (A, C) or FLII12Pglu-700μδ6 in cytosol (B, D). High control (n=192) corresponds to cells incubated in 7 mM glucose (C, D), and low controls (n=192) corresponds to cells incubated in 0 mM glucose (A, B). Z-prime values for cytosolic ATP and cytosolic glucose assays are calculated as 0.83 and 0.79, respectively.
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Drug assay supports multiplex biosensor efficiency
To demonstrate that multiplexed biosensors can be used to simultaneously screen for inhibitors of multiple glycolytic steps in a single assay, we screened a small selection of available anti-kinetoplastid drugs as well as reference anti-trypanosomal compounds.
We picked inhibitors that would specifically impact single targets or aspects of glycolysis
(i.e., ATP only or glucose only), except for 2-deoxyglucose (2-DOG), which due to its biochemical composition influences both glucose levels and therefore ATP levels that result from glucose metabolism.38 High concentrations of salicylhydrozamic acid (SHAM) + glycerol are known to only affect ATP and NADH production; therefore, it should only lower ATP signals in the YEMK biosensor while not impacting glucose metabolism or the signal of the
FLIP700 glucose bioscensor.39 6-deoxyglucose (6-DOG) was used to allosterically compete with the regular intake of glucose into the cell. Because glucose transport across the plasma membrane is likely to be the rate-limiting step of glucose utilization within metabolism40, treatment with 6-DOG should lower glucose uptake while not affecting ATP production.
Therefore, treatment with 6-DOG should only lower the FLIP700 signal while not affecting
YEMK signals within the cell samples.
Each compound was tested in duplicate at 50 mM with 7 mM glucose and 0 mM glucose used as controls. As shown in Figure 5, there was a significant difference in biosensor response within the combined barcoded BSF cell populations. Compounds were initially scored as active if they inhibited the FRET response at a minimum of three standard deviations below the average of high (7 mM glucose) controls (Figure 5) in all replicates without altering BSF parasite viability. Figure 5 also showed a significant and simultaneous response difference between the controls and the treated samples. 2-deoxyglucose (2-DOG) reduced both glucose and ATP 14 concentration in the cytosol. Salicylhydrozamic acid (SHAM) + glycerol only reduced ATP concentration in the cytosol. While 6-deoxyglucose (6-DOG) only reduced glucose concentration in the cytosol. Since our biosensors are highly specific and only correspond to their assigned metabolite, the findings described above indicate that our barcoded biosensor cell lines give reliable and robust measurements of their metabolite when used simultaneously.
Figure 5. Bar graph representing FRET emission of combined Cy3 barcoded FLII12Pglu- 700μδ6 (Flip700) in Blue and C7 barcoded pDR-GW AT1.03YEMK (YEMK) in Red. Bars show the ratio of ECFP/EYFP intensity emission (x-axis) and the treatment administered (y- axis). High control corresponds to cells incubated in 7 mM extracellular glucose; low control corresponds to cells incubated in 0 mM extracellular glucose. 50 mM of 2-deoxyglucose (2Dog) was administered to combined BSF C3/C7 barcoded cells in 0.7 mM extracellular glucose (High+2-Dog). 50 mM of combined Salicylhydroxaminc acid (SHAM) and 5 mM glycerol was administered to combined BSF C3/C7 barcoded cells in 0.7 mM extracellular glucose (High+SHAM+glycerol). 50 mM of 6-deoxyglucose (6-Dog) was administered to combined BSF C3/C7 barcoded cells in 0.7 mM extracellular glucose (High+6Dog). 15
CONCLUSIONS
We have shown that multiple populations can be distinguished using two unique barcoding dyes. With the selection and optimization of barcoding dyes, including the use of three dyes rather than two, we hypothesize that the number of populations could be modified to fit a large suite of FRET biosensors. We showed the method's robustness by demonstrating that cellular barcoding has no effect on biosensor response and that the method appropriately models single-cell analysis. We showed that barcoding can be used to simultaneously measure multiple analytes in multiple locations, that each sensor responds sensitively and robustly to its respective analyte, and that the method can highlight relationships between metabolically related analytes
(glucose and ATP). This approach could enable flow cytometric monitoring of multiple metabolites in different cellular locations in a single experiment.
In the future, we propose that this method could be used for a variety of applications, including high-throughput screening in T. brucei and other kinetoplast parasites. Additionally, we anticipate that multiplexed measurements of important metabolic analytes will be crucial for developing future drug therapies for kinetoplastid-caused diseases, such as trypanosomiasis, leishmaniasis, and Chagas disease.
METHODS
Cells, Constructs, and Reagents
All experiments were performed using 427 BSF parasites. BSF cells were continuously cultured in HMI9 media supplemented with 5% fetal bovine serum and 5% Corning Nu-Serum
IV. Cell culture media components, including the selection antibiotics (blasticidin, G418, and 16 hygromycin) were purchased from Sigma (St. Louis.; MO). Cy3 and Cy7 NHS ester dyes were purchased from Lumiprobe (Hunt Valley, Maryland). To control for the potential effects of fluorescent media components, parasites were suspended in phosphate-buffered saline (137 mM
NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4) for all experiments.
T. brucei transfection and localization of biosensors
The plasmids for the FRET biosensors, pRSET-FLII12Pglu-700μδ6 (FLIP700) and pDR-
GW AT1.03YEMK (YEMK), were obtained from Addgene (Addgene plasmids #13568 and
#28004 respectively).22,24,25 Each biosensor was cloned into the pXS6 expression vector using the
Q5 mutagenesis kit, restriction endonucleases, and other cloning reagents from New England
Biolabs (Ipswich, MA). The FLIP700 sensor was localized to the glycosomes by appending a C- terminal peroxisomal targeting sequence using site-directed mutagenesis as described by Voyton et al.22 Parasites were transfected and isolated as described by Wang et al.29
T. brucei culture
BSF cultures were counted via flow cytometry on a Beckman Coulter CytoFLEX flow cytometer and diluted daily to a concentration of 2.0 X 105 cells/mL to maintain cell densities at approximately 1.0 X 106 cells/mL for experimental use the following day. Parasites expressing a
FRET biosensor were grown in media containing blasticidin (5 µg/mL), hygromycin (5 µg/mL), and G418 (1.5 µg/mL) antibiotics. WT cells were grown in media without antibiotics.
Sample preparation and flow cytometry 17
For flow cytometry experiments, cells were centrifuged at 1000 RCF for 10 minutes, washed three times in PBS to remove media (centrifuging at 1000 RCF for 5 minutes in between each wash) and stained at varying concentrations for a period of 30 minutes. After staining, cells were washed two to four times before flow cytometric analysis. Experiments were performed on a Thermo Fisher Scientific Attune NxT flow cytometer with a four-laser (405 nm, 488 nm, 561 nm, 638 nm) excitation configuration. Emissions due to YFP (530/30) and FRET (530/30) were detected by excitation with the 488 nm and 405 nm lasers, respectively. Cy3 (585/42) and Cy7
(780/60) were excited with the 561 nm and 638 nm lasers. Gain settings for each fluorescent channel were adjusted to minimize the robust coefficient of variation (rCV) for the signal in each.
Flow cytometric data were analyzed using FlowJo (FlowJo LLC, Ashland, OR). For all experiments, dead cells and doublets were excluded from analysis using forward scatter (FSC) and side scatter (SSC) parameters (FSC-height vs. SSC-height and FSC-area vs. FSC-height, respectively). For barcoding experiments, cells were then separated into distinct populations according to their Cy3 and Cy7 fluorescence characteristics before analyzing biosensor response in each population. Biosensor response was determined according to the ratio FRET/CFP.
Selection and optimization of barcoding dyes
Cy3 and Cy7 dyes were selected due to their fluorescence spectra that overlap minimally with FRET biosensor spectra. Additionally, the high water-solubility of the sulfonated dyes allows for more efficient washing after staining. Dye concentrations were determined empirically by staining BSF cells with a 24-point 1:2 serial dilution of each dye. By comparing relative fluorescence intensities at each point in the dose-response, concentrations were selected that 18 showed minimal (less than 5%) overlap between adjacent populations. The selected dyes were then tested for effects on viability and sustained fluorescent intensity over a period of 3 hours to determine whether the staining concentration of dye were toxic to parasites. Both Cy3 and Cy7 were shown to have a minimal effect on parasite viability (less than 5%) and negligible loss in fluorescence intensity over the period tested. Graph created through Sigmaplot 11.0 (Systat
Software Inc.; San Jose, CA).
Testing parasite viability and population distinction of barcoded cells over time
Approximately 2-4 X 107 BSF WT cells were washed, stained with Cy3, Cy7, both, or neither, washed, and combined as described above in "sample preparation and flow cytometry".
The barcoded cells were analyzed at 10-15 µL/min for 3 hours. To measure the effect of the dyes on viability, the FSC-height histograms of 15-minute timepoints at the beginning and end of the
3-hour time course assay was compared. FSC was used to estimate live cells. Graph created through Sigmaplot 11.0 (Systat Software Inc.; San Jose, CA).
Creating a Z-prime experiment and analyzing high and low glucose response over time
Approximately 2-4 X 107 BSF pRSET FLII12Pglu-700μδ6 or BSF AT1.03YEMK
(YEMK) cells were washed and prepared as described above in "sample preparation and flow cytometry". Following final wash, equal amount of cells/µL were separately resuspended in either PBS (0 mM glucose) or 7mM glucose and incubated in ambient condition for 35 mins before being analyzed via flow cytometry as described above. FSC was used to estimate live cells. 100 µL of the cell suspension (~20,000 cells per well) was pipetted into the wells; high controls contained 7 mM glucose, and low controls contain no glucose. FSC was used to 19 estimate live cells. Graph created through Sigmaplot 11.0 (Systat Software Inc.; San Jose, CA).
The following equations was utilized to calculate the Z-prime:
3�( ) = �������� ��������� �� 7�� "high" �������
3�( ) = �������� ��������� �� 0 �� "low" samples
3�( ) + 3�( ) � = 1 − |�( ) − �( )|
�( ) = ���� �� 7�� "high" �������
�( ) = ���� �� 0 �� "low" �������
Running a barcoded multiplex screening assay
Approximately 3--6 X 107 BSF pRSET FLII12Pglu-700μδ6 and BSF AT1.03YEMK
(YEMK) cells were washed and separately prepared as described above in "sample preparation and flow cytometry." Following final wash, equal amount of cells/µL were resuspended in PBS and treated. BSF pRSET FLII12Pglu-700μδ6 was barcoded with C3 and BSF AT1.03YEMK was barcoded with C7. Barcoded incubation was done at ambient conditions for 30 minutes.
Following barcoded incubation, each barcoded population was respectively quenched with 1%
BSA in PBS, washed at 1000 RCF for 5 minutes and recombined as one joined sample. 100 µL of the cell suspension (~40,000 cells per well) was pipetted into the wells containing high control
(7mM glucose) or low control (0 mM glucose). After a 35-minute incubation, the barcoded cells were analyzed at 10-15 µL/min for 2 hours. FSC was used to estimate live cells. Graph created through Sigmaplot 11.0 (Systat Software Inc.; San Jose, CA).
Testing barcoded multiplexed screening assay with drug treatments with known impacts 20
Approximately 2-4 X 107 BSF pRSET FLII12Pglu-700μδ6 and BSF AT1.03YEMK cells were washed and prepared as described above in "Running a barcoded multiplex assay." 100 µL of the cell suspension (~40,000 cells per well) was pipetted into the wells containing the test compounds to a final concentration of 50 mM in 0.7 mM glucose. Treatment administered consisted of 3 replicates. 2-Deoxy-D-Glucose "2-Dog" (Cayman Chemical Company Inc.; Ann
Arbor, MI) was administered at 50 mM in mQH2O as solvent. 6-Deoxy-D-Glucose "6-Dog"
(Cayman Chemical Company Inc.; Ann Arbor, MI) was administered at 50 mM in mQH2O as solvent. Salicylhydroxaminc acid "SHAM" (Sigma-Aldrich Inc.; St. Louis, MO) was administered at 50 mM in 1% DMSO as solvent and combined with 5 mM glycerol (Sigma-
Aldrich Inc.; St. Louis, MO). Low control consisted of a starved (0 mM glucose) C3/C7 barcoded cell population; while high control consisted of a C3/C7 barcoded cell population re- suspended in 0.7 mM glucose. FSC was used to estimate live cells. Graph created through
Sigmaplot 11.0 (Systat Software Inc.; San Jose, CA).
FURTHER ACKNOWLEDGEMENT
pRSET FLII12Pglu-700μδ6 was a gift from Wolf Frommer (Addgene plasmid # 13568).
The plasmid was cloned into pXS6.Q by Charles Voyton.18,22-25 Transfection was done by the
Morris Lab (Clemson University.; Clemson, SC).
AT1.03YEMK (Addgene plasmid #28004) was purchased from Addgene. The plasmid was cloned into pXS6.Q by Jacob Vance and Garrett Parker. Transfection was done by the
Morris Lab (Clemson University.; Clemson, SC). 21
Funding was provided by the NIH grant number R21AI105656 and in part by a NIH
Center for Biomedical Excellence (COBRE) grant under award number P20GM109094.
ABBREVIATIONS
T. brucei, Trypanosoma brucei; BSF, bloodstream form; T. brucei, Trypanosoma brucei;
MCF, metacyclic form; NMR, nuclear magnetic resonance; FRET, Förster resonance energy transfer; WT, wild-type; NHS, N-hydroxysuccinimidyl; Cy3, Sulfo-Cyanine3; Cy7, Sulfo-
Cyanine7; FLIP700, FLII12Pglu-700μδ6; FSC, forward scatter; SSC, side scatter.
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