i DEVELOPMENT OF DROPLET MICROFLUDIC TECHNOLOGIES FOR NEGATIVE-STRAND RNA VIRUSES by Mallory Marie Blackwood A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Microbiology and Immunology MONTANA STATE UNIVERSITY Bozeman, Montana May 2025 ©COPYRIGHT by Mallory Marie Blackwood 2025 All Rights Reserved ii DEDICATION This dissertation is dedicated to everyone committed to protecting the integrity of scientific research, evidence-based policy, and critical thinking in our society. Creating a world that is equitable and just, for people and planet, requires empathy and collaboration from us all. iii ACKNOWLEDGEMENTS I would like to acknowledge the support and guidance of my family, friends, and collaborators, without who this work would not be possible. Most importantly I would like to thank my advisor, Dr. Connie Chang, for years of scientific training and personal friendship. Thank you to my loving partner Hannah for your unwavering kindness, patience, and support. I could not do this without you. Thank you to my parents and my brothers for cheering me along the whole way, I hope to make you proud. Thank you especially to my mother Ann, who taught me it is never too late to start something new. Thank you to my grandparents, Elizabeth and Howard Blackwood, who inspired me to be curious about the world and the people in it. I wish you were here to share this day. Thank you to my friends, old and new, who help me remember that there is nothing more important in life than adventure and laughter. Thank you especially to my friend Patrick who I once promised, and perhaps am contractually obligated, to acknowledge in this document. Thanks for being willing to go down the rabbit hole with me. I would like to thank all members of the Chang Lab, especially the co-authors of this work. Thank you to Emma Loveday, whose mentorship contributed significantly to this research. Thank you to the Brooke lab at UIUC, the Ke lab at Los Alamos National Laboratory, the Galanis and Sung labs at Mayo Clinic, and the Fields and Keil labs at Montana State. Thank you to the Mayo Clinic Genomics Core and Bioinformatics Core for your contributions to sequencing data acquisition and analysis. Thank you to the members of my committee for your feedback and direction. And thank you to the Montana State Graduate School and Molecular Biosciences Fellowship Program for funding my PhD program. iv TABLE OF CONTENTS 1. INTRODUCTION .............................................................................................................. 1 Research Motivation ........................................................................................................... 1 Funding and Collaborations ................................................................................................ 1 Dissertation Overview ........................................................................................................ 2 Chapter 2: “Literature Review” .............................................................................. 3 Chapter 3: “Single-cell Infection of Influenza A Virus Using Drop- based Microfluidics” ............................................................................................... 3 Chapter 4: “Influenza A Viral Burst Size from ...................................................... 4 Thousands of Infected Single-cells Using .............................................................. 4 Droplet Quantitative PCR (dqPCR)” ...................................................................... 4 Chapter 5: “Droplet Microfluidic Platform............................................................. 5 for Single-cell Sequencing of Measles ................................................................... 5 Virus During Oncolytic Virotherapy” .................................................................... 5 Chapter 6: “Conclusion” ......................................................................................... 6 2. LITERATURE REVIEW ................................................................................................... 7 Overview ............................................................................................................................. 7 RNA Virus Population Diversity ........................................................................................ 7 Clinically Relevant Negative-Strand RNA Viruses .......................................................... 10 Influenza A Virus (IAV) ....................................................................................... 10 Measles Virus (MV) ............................................................................................. 12 Single-cell Virology .......................................................................................................... 15 Droplet Microfluidics........................................................................................................ 18 Droplet Generation................................................................................................ 19 Virus Culture in Droplets ...................................................................................... 21 Droplet Quantitative PCR ..................................................................................... 22 Single-cell Sequencing.......................................................................................... 23 References Cited ............................................................................................................... 25 3. SINGLE-CELL INFECTION OF INFLUENZA A VIRUS USING DROP- BASED MICROFLUIDICS ............................................................................................. 33 Contribution of Authors and Co-Authors ......................................................................... 33 Manuscript Information .................................................................................................... 34 4. INFLUENZA A VIRAL BURST SIZE FROM THOUSANDS OF INFECTED SINGLE-CELLS USING DROPLET QUANTITATIVE PCR (dqPCR) ............................................................................................................................ 49 Contribution of Authors and Co-Authors ......................................................................... 49 Manuscript Information .................................................................................................... 51 v 5. DROPLET MICROFLUIDIC PLATFORM FOR SINGLE-CELL SEQUENCING OF MEASLES VIRUS DURING ONCOLYTIC VIROTHERAPY .............................................................................................................. 75 Contribution of Authors and Co-Authors ......................................................................... 75 Manuscript Information .................................................................................................... 76 Abstract ............................................................................................................................. 77 Introduction ....................................................................................................................... 78 Materials and Methods ...................................................................................................... 81 Cells and Virus ...................................................................................................... 81 Microfluidic Device Fabrication ........................................................................... 82 Hydrogel Bead (HB) Synthesis. ............................................................................ 83 HB Washing .......................................................................................................... 83 BOP Split-and-Pool Ligation ................................................................................ 84 Quality Control of Barcoding Hydrogel Beads (BHBs) ....................................... 87 Co-Encapsulation of BHBs and MV Infected Cells ............................................. 89 cDNA Synthesis and Barcoding in Drops ............................................................ 90 Sequencing Library Preparation ........................................................................... 93 Quality Control of Sequencing Library Preparation ............................................. 94 Sequence Assembly .............................................................................................. 95 Results ............................................................................................................................... 96 Single-cell mRNA Sequencing of Attenuated Measles Virus (MV) Using Droplet Microfluidics. ................................................................................ 96 Barcoding Hydrogel Bead (BHB) Synthesis ........................................................ 98 Droplet cDNA Synthesis and Barcoding ............................................................ 101 Sequencing Library Preparation ......................................................................... 105 Assembly and Analysis of MV P Gene mRNA scRNA-seq .............................. 105 Discussion ....................................................................................................................... 107 Conclusion ...................................................................................................................... 113 References Cited ............................................................................................................. 115 6. CONCLUSIONS AND OUTLOOK............................................................................... 118 Conclusions ..................................................................................................................... 118 Outlook ........................................................................................................................... 122 References Cited ............................................................................................................. 125 APPENDICES ...................................................................................................................... 127 SUPPLEMENTAL INFORMATION FOR CHAPTER 4 ............................................. 128 SUPPLEMENTAL INFORMATION FOR CHAPTER 5 ............................................. 192 GENOMIC SURVELLIANCE OF SARS-COV-2 VIRUS IN MONTANA 2020-2024 ........................................................................................................... 224 vi Introduction ..................................................................................................................... 225 Materials and Methods .................................................................................................... 227 SARS-CoV-2 Genomic Data from GISAID. ...................................................... 227 Clinical Specimen Collection. ............................................................................ 227 High-throughput Viral RNA Extraction. ............................................................ 227 Illumina Sequencing using the ARTIC Protocol. ............................................... 228 Genome Assembly and Variant Identification. ................................................... 228 Data Sharing........................................................................................................ 229 Population Diversity Analysis. ........................................................................... 229 General Regression Model for Clinical Associations. ........................................ 229 Results and Discussion ................................................................................................... 229 Conclusion ...................................................................................................................... 235 CUMULATIVE REFERENCES CITED ............................................................................. 238 vii LIST OF TABLES Table Page 1. Table 3.1 Percentage of Cells/Drop in Loaded Drops Containing Cells .......................... 42 2. Table 1. Barcoding Oligonucleotide Primer (BOP) Sequences ...................................... 204 3. Table 2. BOP FISH Probe Sequences ............................................................................. 206 4. Table 3. PCR Primers for Detection of MV Barcoded cDNA ........................................ 207 5. Table 4. Sequencing Library Preparation Primers .......................................................... 208 6. Table 5. Sequencing Library Quantification Results by Qubit Assay ............................ 209 7. Table 6. Synthetic RNA Gene Block Sequences ............................................................ 210 8. Table 7. 1M NaCl Solution ............................................................................................. 211 9. Table 8. 10% Triton Solution.......................................................................................... 211 10. Table 9. 1X TBSET Buffer Solution .............................................................................. 211 11. Table 10. 4X AB Crosslinking Agent Solution .............................................................. 212 12. Table 11. 10% APS Solution .......................................................................................... 212 13. Table 12. 20% PFO Solution .......................................................................................... 212 14. Table 13. 1% Span 80 Solution....................................................................................... 212 15. Table 14. 5% (w/w) RAN Solution................................................................................. 213 16. Table 15. 3% RAN Solution ........................................................................................... 213 17. Table 16. HB Synthesis Aqueous Phase Solution .......................................................... 214 18. Table 17. HB Synthesis Oil Phase Solution.................................................................... 214 19. Table 18. Primer 2 Ligation Mixture Solution................................................................ 214 20. Table 19. Primer 3 to 6 Ligation Mixture Solution ........................................................ 214 21. Table 20. Primer 7 Ligation Mixture Solution................................................................ 215 viii LIST OF TABLES CONTINUED Table Page 22. Table 21. 10% Tween 20 Solution .................................................................................. 215 23. Table 22. Hydrogel Bead Wash (HBW) Buffer Solution ............................................... 215 24. Table 23. Denaturation Buffer Solution.......................................................................... 216 25. Table 24. Neutralization Buffer Solution........................................................................ 216 26. Table 25. Hybridization Buffer Solution ........................................................................ 216 27. Table 26. BHB Pre-Encapsulation Wash Solution ......................................................... 217 28. Table 27. BHB Activation Wash Solution ...................................................................... 217 29. Table 28. Enzyme Mix Solution ..................................................................................... 218 30. Table 29. 80% EtOH Solution ........................................................................................ 218 31. Table 30. gBlock Resuspension Solution ....................................................................... 218 32. Table 31. DTT 10-1 Dilution Solution............................................................................. 219 33. Table 32. DTT 10-2 Dilution Solution............................................................................. 219 34. Table 33. DTT 10-3 Dilution Solution............................................................................. 219 35. Table 34. Bulk cDNA Synthesis and Barcoding Assay .................................................. 220 36. Table 35. cDNA Enzymatic Cleanup Assay ................................................................... 220 37. Table 36. Power SYBR qPCR Assay ............................................................................. 221 38. Table 37. Second Strand Synthesis Assay ...................................................................... 221 39. Table 38. In Vitro Transcription – NTP/ Buffer Master Mix ......................................... 221 40. Table 39. In Vitro Transcription Assay .......................................................................... 222 41. Table 40. Secondary Reverse Transcription Assay ........................................................ 222 42. Table 41. PCR for Library Amplification and Adaptor Ligation Assay......................... 222 43. Table 42. Primer/Dye Master Mix for Illumina Library qPCR Assay ........................... 223 ix LIST OF TABLES CONTINUED Table Page 44. Table 43. qPCR Assay for Illumina Library Quantification ........................................... 223 x LIST OF FIGURES Figure Page 1. Figure 1. Comparison between viral mutation and substitution rates. ................................ 8 2. Figure 2. Single-Cell Microfluidic Technologies and Their Typical Applications in Virology................................................................................................... 17 3. Figure 3. Single-cell Analysis Using Droplet Microfluidics. ........................................... 19 4. Figure 4. Microfluidic drop maker geometries and manipulation modules applied in single‐cell workflows ....................................................................................... 20 5. Figure 3.1 General Workflow for Single Cell Infection of IAV ....................................... 37 6. Figure 3.2 IAV Infection in Different Cell Lines ............................................................. 38 7. Figure 3.3 Drop Stability in 100 m Drops ...................................................................... 39 8. Figure 3.4 Cell Viability in 100 m Drops ....................................................................... 40 9. Figure 3.5 Cell Encapsulation in Microfluidic Drops ....................................................... 41 10. Figure 3.6 Comparison of Drop and Bulk IAV Infections in A549 and MDCK cells ...................................................................................................................... 43 11. Figure 4.1 Drop-based Microfluidic Platform for Measuring Viral Burst Size from Single-cell Infection ......................................................................................... 55 12. Figure 4.2 Droplet Quantitative PCR (dqPCR) Model for Converting Drop Fluorescence to Nucleic Acid Concentration ................................................................... 56 13. Figure 4.3 Resolving IAV M Gene RNA Concentration from Single and Mixed Droplet Populations ............................................................................................... 58 14. Figure 4.5 H3N2 and H1N1 Single-cell Infection Burst Size Distributions ..................... 59 15. Figure 5. ScRNA-seq of Attenuated Measles Virus (MV) P gene mRNA. ...................... 97 16. Figure 6. Barcoding Hydrogel Bead (BHB) Synthesis. .................................................. 100 17. Figure 7. Droplet cDNA Synthesis and Barcoding. ........................................................ 102 18. Figure 8. Illumina Sequencing Library Preparation. ...................................................... 104 xi LIST OF FIGURES CONTINUED Figure Page 19. Figure 9. Single-cell mRNA Sequencing of MV P Gene Attenuation Site. ................... 106 20. Figure 10. MV P gene specific barcoding oligonucleotide primer (BOP). .................... 193 21. Figure 11. Control BHBs Synthesized with FITC-dextran and a Single Barcode Sequence. .......................................................................................................... 194 22. Figure 12. Hydrogel Bead (HB) Size. ............................................................................. 195 23. Figure 13. BHB Loading by Flow Rate Fraction. ........................................................... 196 24. Figure 14. Single-cell Loading by Suspension Concentration........................................ 197 25. Figure 15. Barcoded cDNA (BC-cDNA) average nucleotide length. ............................ 198 26. Figure 16. Protocol validation with single-barcode BHBs and synthetic RNA template. ................................................................................................................ 199 27. Figure 17. Protocol validation with single-barcode BHBs and MV infected cells in bulk. .................................................................................................................... 200 28. Figure 18. Protocol validation with single-barcode BHBs and MV infected cells in drops. .................................................................................................................. 201 29. Figure 19. Sanger Sequencing of Library from Single Barcode BHBs + Synthetic RNA Gene Block Template in Bulk. .............................................................. 202 30. Figure 20. Libraries from Single Barcode BHBs Controls. ............................................ 203 31. Figure 21. Genomic Survelliance of SARS-CoV-2 (SCoV2) Virus in Montana from 2020-2025. .............................................................................................. 231 32. Figure 22. SARS-CoV-2 Whole Genome Nucleotide Diversity from 2020- 2025. ............................................................................................................................... 233 33. Figure 23. Clinical Presentation of SARS-CoV-2 Infection from Bozeman, MT in Spring 2021. ........................................................................................................ 235 xii ABSTRACT Viruses infect all organisms and play a key role in host population dynamics and evolution. RNA viruses evolve extremely rapidly, infect a wide range of host cell types, and cause severe diseases. The incredible diversity of RNA virus populations, combined with the heterogeneity of host cells they infect, makes studying RNA virus infection quite challenging. Averaging outcomes across RNA virus infection in bulk often misses minor variants and phenotypes that can drive overall population dynamics. Therefore, it is increasingly important to study RNA virus infections at the single-cell level. Droplet microfluidics is a powerful tool for single-cell analysis. By physically isolating individual cells within microfluidic droplets, and manipulating those droplets to conduct various biological assays, we can study RNA virus infection one cell at a time. In this work I describe the establishment of three new droplet microfluidic methods for studying clinically relevant negative-strand RNA viruses, influenza A virus (IAV) and measles virus (MV), at the single-cell level. The first method involves culturing IAV from single infected cells encapsulated in microfluidic droplets. We show that cell viability, genome replication, and viral infectivity in droplets is comparable to bulk infection conditions. The second method builds on this model system by developing a way to directly quantify the number of viral particles produced by each cell. We find that IAV production varies widely between individual cells and across different IAV strains. Together, these platforms provide a high-throughput solution for studying IAV infection at the single-cell level that can be used to explore the effects of antiviral interventions on IAV replication. The third method focuses on viral genome diversity at the single-cell level. We developed a single-cell sequencing approach to study variation in the MV P gene, which contains attenuation markers important for MV function as a cancer immunotherapy treatment. Preliminary data shows the development of custom single-cell sequencing beads to capture the MV P gene attenuation site and generation of Illumina sequencing libraries. Further optimization is required to improve sequencing yield for diversity analysis. Overall, this work introduces novel methodologies for high-throughput single- cell analysis of negative-strand RNA virus infection dynamics and population diversity. 1 INTRODUCTION Research Motivation Negative-strand RNA viruses can be highly pathogenic and cause severe disease in human populations worldwide. RNA genomes exhibit the highest single-nucleotide mutation rate in nature, enabling the frequent emergence of novel virus strains that can evade host immune responses and antiviral treatments. The genetic diversity within negative-strand RNA virus populations also leads to variable infection outcomes at both the cellular and organismal levels. Understanding how single-cell heterogeneity contributes to differences in infection outcome is crucial for developing effective antiviral treatments and vaccines. Droplet microfluidics is a powerful tool for studying single-cell heterogeneity. However, assays must be customized for each new model system. In this study we describe the development of three droplet microfluidic platforms designed to investigate single-cell heterogeneity during infection of influenza A virus (IAV) and measles virus (MV), two clinically relevant negative-strand RNA viruses. Funding and Collaborations Research funding was supplied by the Defense Advanced Research Projects Agency (DARPA) grant W911NF-17-2-0034, National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under award number 1R56AI156137, National Science Foundation (NSF) CAREER grant 1753352, the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH) under NOSI NOT-GM- 21-031, Montana State IDeA Network of Biomedical Research Excellence (INBRE), and Mayo Clinic internal funding sources. Specific PhD program funding was provided by the Montana 2 State Graduate School and Molecular Biosciences Fellowship Program. The Brooke lab at UIUC and the Ke lab at Los Alamos National Laboratory collaborated significantly on all influenza projects. The Mayo Clinic Genomics Core and Bioinformatics Core provided data acquisition and analysis support. The Galanis lab and Sung lab at Mayo Clinic collaborated on all measles virus work. The Fields lab and Keil lab at Montana State collaborated on all coronavirus projects. We thank all these funding sources and collaborators for their support. Dissertation Overview The remainder of this dissertation is comprised of four chapters and a series of appendices. Chapter 2 reviews current literature on RNA virus diversity and the use of single- cell technologies such as droplet microfluidics to study these diverse populations. We specifically highlight two negative-strand RNA viruses, influenza a virus (IAV) and measles virus (MV), whose relevance in clinical research and biotechnology make them important species to study. Chapter 3 introduces a droplet microfluidic platform for culturing IAV from single-cells. Chapter 4 presents a method for quantifying the number of IAV particles produced from single-cells in drops, expanding the capabilities for IAV single-cell infection study. Chapter 5 outlines a single-cell RNA sequencing platform customized to target a small region of interest on the MV P gene, containing virus attenuation markers, that is relevant to MV function as a cancer immunotherapy. The body of this dissertation is followed by an appendix containing sections A and B. These appendices include (A) supplemental information for the manuscript in chapter 4; (B) supplemental information for the manuscript pre-print in chapter 5. 3 Chapter 2: “Literature Review” Chapter Overview: “This dissertation focuses on the development of novel droplet microfluidics techniques for studying highly heterogeneous negative-strand RNA virus populations at the single-cell level. It addresses the need for customized methods to culture viruses in droplets, quantify virus replication within those droplets, and capture the diversity of gene expression profiles that may explain cell-to-cell variation in observed infection dynamics. Here, I will outline the necessity for these types of analysis techniques and review existing methods for studying influenza A virus and measles virus at the single-cell level.” Chapter 3: “Single-cell Infection of Influenza A Virus Using Drop-based Microfluidics” Chapter Hypothesis: Influenza A virus infection kinetics, such as genome copy number and infectivity titer, remain consistent between single cell infection in microfluidic droplets and bulk infection in traditional tissue culture flasks. Chapter Abstract: “Drop-based microfluidics has revolutionized single-cell studies and can be applied toward analyzing tens of thousands to millions of single-cells and their products contained within picoliter-sized drops. Drop-based microfluidics can shed insight into single-cell virology, enabling higher-resolution analysis of cellular and viral heterogeneity during viral infection. In this work, individual A549, MDCK, and siat7e cells were infected with influenza A virus (IAV) and encapsulated into 100-μm-size drops. Initial studies of uninfected cells encapsulated in drops demonstrated high cell viability and drop stability. Cell viability of uninfected cells in the drops remained above 75%, and the average drop radii changed by less than 3% following cell encapsulation and incubation over 24 h. Infection parameters were analyzed over 24 h from individually infected cells in drops. The number of IAV viral genomes 4 and infectious viruses released from A549 and MDCK cells in drops was not significantly different from bulk infection as measured by reverse transcriptase quantitative PCR (RT-qPCR) and plaque assay. The application of drop-based microfluidics in this work expands the capacity to propagate IAV viruses and perform high-throughput analyses of individually infected cells.” Chapter 4: “Influenza A Viral Burst Size from Thousands of Infected Single-cells Using Droplet Quantitative PCR (dqPCR)” Chapter Hypothesis: Influenza A viral burst size is heterogeneously distributed across individual infected cells within a population, and the population-level average burst size varies between distinct influenza strains. Chapter Abstract: “An important aspect of how viruses spread and infect is the viral burst size, or the number of new viruses produced by each infected cell. Surprisingly, this value remains poorly characterized for influenza A virus (IAV), commonly known as the flu. In this study, we screened tens of thousands of cells using a microfluidic method called droplet quantitative PCR (dqPCR). The high-throughput capability of dqPCR enabled the measurement of a large population of infected cells producing progeny virus. By measuring the fully assembled and successfully released viruses from these infected cells, we discover that the viral burst sizes for both the seasonal H3N2 and the 2009 pandemic H1N1 strains vary significantly, with H3N2 ranging from 101 to 104 viruses per cell, and H1N1 ranging from 101 to 103 viruses per cell. Some infected cells produce average numbers of new viruses, while others generate extensive number of viruses. In fact, we find that only 10% of the single-cell infections are responsible for creating a significant portion of all the viruses. This small fraction produced approximately 60% of new viruses for H3N2 and 40% for H1N1. On average, each infected cell 5 of the H3N2 flu strain produced 709 new viruses, whereas for H1N1, each infected cell produced 358 viruses. This novel method reveals insights into the flu virus and can lead to improved strategies for managing and preventing the spread of viruses.” Chapter 5: “Droplet Microfluidic Platform for Single-cell Sequencing of Measles Virus During Oncolytic Virotherapy” Chapter Hypothesis: The measles virus vaccine strain attenuation site, located on the Phosphoprotein gene, can be selectively sequenced from individual infected cells using a custom single-cell RNA sequencing platform. Chapter Abstract: “As an RNA virus, measles virus (MV) genomes are subject to frequent mutations and selective advantages in diverse host cell environments. During in vitro passaging of the MV vaccine strain, two random mutations in the P gene resulted in a new attenuated MV strain with capabilities for cancer immunotherapy. Attenuated MVs can specifically target cancer cells that overexpress the CD46 membrane receptor. MVs infect and kill these cells, simultaneously recruiting a localized immune response to the tumor site. This represents a promising new form of oncology treatment, currently undergoing Phase I and II clinical trials. However, there is considerable heterogeneity in tumor susceptibility to MV infection during these trials. This variability may be related to further mutation of the P gene attenuation site during therapeutic infection. Single-cell analysis of attenuation site diversity could inform the development of MV strains with more consistent infection outcomes and greater success as a cancer immunotherapy. In this work, we develop a customized single-cell RNA sequencing (scRNA-seq) platform to capture the MV P gene attenuation site. Using droplet microfluidics, we synthesize barcoding hydrogel beads (BHBs) with primer sequences specific to 6 P gene mRNA. These BHBs are then co-encapsulated with single Vero cells infected with attenuated MV. We present preliminary data from two independent replicates that demonstrates the success of our platform in generating Illumina sequencing libraries. This proof-of-concept study will be further developed to optimize protocols for increased sequencing library yield, enabling multiple sequence alignments and diversity analysis of MV P gene expression across thousands of single infected cells.” Chapter 6: “Conclusion” Chapter Overview: “The results presented in this dissertation expand the use of droplet microfluidics to the study of two clinically relevant negative-strand RNA viruses, IAV and MV. Specifically, we focus on the adaptation of molecular biology assays, such as RT-qPCR and next generation sequencing, to a single-cell platform, for nucleic acid quantification and sequencing from IAV and MV RNA genomes and transcripts. Ultimately, the goal of this work is to develop high-throughput methods that can be used to study various features of RNA virus population diversity and evolution at the single-cell level.” 7 CHAPTER TWO LITERATURE REVIEW Overview This dissertation focuses on the development of novel droplet microfluidics techniques for studying highly heterogeneous negative-strand RNA virus populations at the single-cell level. It addresses the need for customized methods to culture viruses in droplets, quantify virus replication within those droplets, and capture the diversity of gene expression profiles that may explain cell-to-cell variation in observed infection dynamics. Here, I will outline the necessity for these types of analysis techniques and review existing methods for studying influenza A virus and measles virus at the single-cell level. RNA Virus Population Diversity Viruses are taxonomically classified by features of their genome structure, including nucleic acid type (RNA or DNA), sense (positive or negative), and strand (single stranded or double stranded) [1]. These genome features determine the molecular mechanisms used by a virus to complete the central dogma of biology, converting genetic information from nucleic acid to messenger RNA to protein [2]. Viruses can be further classified by genome packaging (linear or circular, single or multiple segments), capsid symmetry (icosahedral, helical, or random), envelope (presence / absence), genome and amino acid sequence similarity, order of genes on the genome, mechanism of mRNA synthesis, and enzyme usage [3]. 8 One of the major classification groups are RNA viruses, which are characterized by populations with high genetic diversity and rapid evolution. Genetic diversity in RNA virus populations is mediated by frequent mutation of genomes across large population sizes [4],[5]. RNA genomes have the highest single nucleotide mutation rate in nature (Fig 1) due to the low- fidelity RNA dependent RNA polymerase required for replication [6]-[9]. This is evolutionarily advantageous because it increases the probability of generating a genome variant with enhanced fitness against novel selection pressures [10],[11] in heterogeneous host cell environments [12]. As such, RNA viruses exist as populations of closely related, but genetically distinct, viral genome variants referred to as viral quasispecies [13]. The high genetic diversity of viral quasispecies enables evasion of host immune responses and antiviral treatments, as well as infection across a broad host range [14]-[25]. Figure 1. Comparison between viral mutation and substitution rates. Adapted from [6], with permission from the journal. The ranges of mutation rates, given as mutations per site per round of replication, for viruses with different genomic architectures are summarized in the upper part of the figure. The ranges of average substitution rates, given as substitutions per site per year, are shown in the lower part of the figure. 9 Many RNA viruses have specifically impacted human populations through repeated disease outbreaks. Two such RNA viruses were studied in this work: influenza A virus (IAV) and measles virus (MV). Both viruses have single-stranded negative-sense RNA genomes, a group that consists of other widespread and deadly viruses such as Ebola virus, Rabies virus, and Lassa fever virus. Negative-sense RNA genomes are packaged into a helical nucleocapsid that is immediately transcribed into positive-sense messenger RNA (mRNA), by a RNA dependent RNA polymerase (RdRP) enzyme packaged within the virion, upon infection. After the genome is transcribed into mRNA, viral protein translation and genome replication can begin. This strategy for RNA genome replication introduces single point mutations that contribute to high levels of genetic diversity in the population. While IAV and MV have the same genome type, their infection cycles and disease presentations vary significantly. Both viruses spread from host to host through aerosolization of respiratory droplets. However, the rate of transmission varies dramatically. Viral transmission potential is measured by reproductive number (R0), which quantifies the average number of secondary infections produced by one infected individual in a fully susceptible population. R0 essentially describes how easily a disease could spread through an unprotected population. Low R0 values are associated with low rates of spread, and high R0 values are associated with high rates of spread. Interestingly, IAVs have a lower R0 value (median 1.28-1.80) while MVs have a higher R0 value (median 12-18), that may vary widely by region [26]. There are also key differences in the recommended vaccination regimens to protect against IAV and MV infection. Individuals should be vaccinated for IAV annually to protect against seasonal emergence of new antigenic variants, to which we do not have pre-existing 10 immunity. However, because IAV has a low rate of person-to-person transmission, fewer individuals need to be vaccinated to achieve heard immunity. In contrast, there is only a single antigenic variant of MV in circulation. Meaning that once a person is vaccinated, they will be protected with life-long immunity against MV infection. However, high rates of MV transmission from person-to-person do mean that a large proportion of the population needs to be vaccinated against MV to achieve heard immunity. These key differences between IAV and MV infection dynamics highlights an important nuance in negative-strand RNA virus research – the relationship between genetic diversity and antigenic variation. Specific to this work, both IAV and MV populations contain high genetic diversity due to RdRp induced point mutations that cause genetic drift over time. However, IAV populations typically have high antigenic diversity while MV populations have low antigenic diversity [6]. This is because IAV populations are subject to larger genetic shifts in the population that create antigenic variation. The reasons for this are rooted in the different mechanisms for genetic diversity generation in each virus species, which will be further discussed below. Clinically Relevant Negative-Strand RNA Viruses Influenza A Virus (IAV) IAV is a negative-strand RNA virus belonging to the family Orthomyxoviridae. It has an enveloped virion that ranges from 80-120 nm in size and carries a linear single-stranded negative-sense RNA genome that is 10-15kb in length [3]. IAV genomes exhibit an extremely high level of genetic diversity [27]. The genome is split into 8 segments that each encode one or two viral proteins. Frequent genome point mutation and reassortment between co-infecting 11 viruses results in rapid evolution and continued emergence of novel strains, posing a significant threat to public health through seasonal disease epidemics and global pandemic events [3]. There are four primary subtypes of influenza virus (A, B, C, and D) that circulate within multiple animal species and can move between animal species. IAV is the most common subtype found in human populations [28]. IAV infects epithelial cells in the upper respiratory tract and causes respiratory disease with symptoms including fever, sore throat, cough, headache, and muscle pain. IAVs can be further classified by the amino acid sequence of two surface glycoproteins, Hemagglutinin (HA) and Neuraminidase (NA), which facilitate virion entry into host cells through interactions with sialic acid residues [3]. It is also the unique structure of different the HA and NA proteins that is used to classify IAV into different species subtypes, or strains. The primary IAV strains currently circulating in human populations are H1N1 and H3N2 [29]. The H1N1 strain originated from a spill over event from pigs to humans in 2009, and the H3N2 strain from a similar spillover event from birds to pigs to humans in 1998 [30]. Novel IAV strains such as H1N1 and H3N2 have been the source of multiple global pandemics, leading to 50 million human deaths, over the last century. In addition to historic pandemic events, IAV remains a significant public health threat by causing seasonal epidemics that average 25 million cases, 20,000 deaths, and 10 billion dollars in medical costs each year in the United States [31]. The repeated emergence of novel IAV strains is a result of extreme genetic diversity which allows for rapid adaptation to selective pressures that directly impact transmissibility, pathogenicity, and virulence. Although there are vaccine and antiviral treatments against IAVs, new strains emerge each year that can escape established host immune response. Development of 12 more effective vaccines and antiviral treatments to reduce transmission and pathogenesis of IAVs will require a better understanding of the mechanisms that drive their diversity. IAVs exist as a genetically diverse quasispecies with significant heterogeneity in viral gene expression [32],[33] and host innate immune activation [34] that varies both within and between host cell types and IAV strains [35]. This diversity is attributed to unique features of the IAV genome. Like other RNA viruses, IAV genomes are characterized by high mutational frequency during each round of replication (averaging 10-5 mutations per nucleotide per replication cycle) [6],[9]. In addition to point mutation, IAV genomes also acquire large deletion regions during replication that produce defective viral genomes (DVGs) [36], [37]. DVGs are missing large regions of protein encoding sequence, while maintaining the sequences required for replication and packaging [38]-[40]. The truncated length of DVGs means they are selectively replicated and packaged over full-length WT segments [41],[42] and often outcompete WT virus [43]-[45] to accumulate in the population. The presence of DVGs means that 90-99% of IAV virions will contain incomplete genomes [46] and require co-infection with wild type (WT) particles to replicate [37]. During co-infection, mutated genome segments derived from multiple viruses are frequently packaged into a single progeny virion [47]. This process is called genome reassortment and underlies large genetic shifts in the IAV population that accelerate adaptive evolution [48]. As a result, IAVs respond quickly to changing host environments through repeated zoonotic emergence of novel strains [30],[49], evasion of established host immunity [47], and resistance to antiviral drugs and vaccine treatments [50]. Measles Virus (MV) 13 Measles viruses (MV) is a negative-strand RNA virus belonging to the family Paramyxoviridae. MV has an enveloped virion with a diameter of 150-300 nm that contains a non-segmented genome of linear single-stranded negative-sense RNA measuring 15-18 kb in length [3]. MV RNA genomes have a high single nucleotide mutation rate, averaging 10-5 mutations per nucleotide per replication cycle in vitro [6],[51] and 10-3 mutations per nucleotide per year during genomic surveillance [52]. However, unlike other RNA viruses, MV populations do not undergo significant genetic and antigenic shifts. There is only one wild type MV serotype which circulates across a narrow host range and remains antigenically stable over time [52]. Symptoms of MV infection, which mostly occurs in children, include a characteristic red blotchy rash that is accompanied by fever, red eyes, cough, runny nose, and spots in the mouth. The attachment and entry of MV into host cells is mediated by two envelope glycoproteins, hemagglutinin (H) and fusion (F) proteins. Wild Type MV infects immune cells such as B lymphocytes, T lymphocytes, dendritic cells, and monocytes via the CD150 ‘SLAM’ cell surface protein [3]. Infection of immune cells may contribute to the high pathogenicity and transmission associated with MV infection. The MV vaccine strain, however, has been adapted to bind to many different cell types via the CD46 cell surface protein [3]. Thanks to widespread vaccination, MV has not been a public health concern in the United States for most of the 21st century but has posed a significant threat across Africa and Asia [54]. Development of the Edmonston live-attenuated vaccine in 1961 decreased case numbers from 4 million per year to 500 per year in the United States. Continued genomic surveillance of MV shows limited antigenic variation or emerging strains in the population over time [55]. As a result, serial 14 passaging derivatives of the original 1961 vaccine strain have remained highly effective at conferring lifelong immunity to vaccinated individuals. The MV genome contains 6 genes that are translated to produce 1 to 3 proteins each, for a total of 8 proteins encoded in the genomes [3]. This work focuses on measurement of the MV phosphoprotein (P) gene, whose product is a component of the MV RNA polymerase complex [3]. The P gene also encodes two non-structural C and V proteins. Together, the P, C, and V proteins help regulate the host immune response to infection by blocking interferon signaling [56]-[57]. Specifically, the P protein tyrosine 110 is required for interferon blocking [56]. Mutations within the P gene, acquired by serial passaging of vaccine strain derivatives, reduce MVs ability to block host interferon responses [58]. This type of mutation further attenuates the virus so that it cannot cause disease but can still elicit a strong immune response. This, along with an affinity for the CD46 receptor [59], which is over expressed in cancerous tumor tissue [60]-[62], has made MV useful as an exciting new form of oncolytic virotherapy to treat cancerous tumors [63]-[66]. Immunotherapy is widely used in cancer treatment, where specific antigens are introduced to train a patient’s own immune system to recognize and eliminate cancerous cells and tumors. Many types of immunotherapies exist, including checkpoint inhibitors, adoptive cell therapy, monoclonal antibodies, immune system modulators, cancer vaccines, and oncolytic virotherapies. Oncolytic virotherapy works by direct injection into cancerous tumors, where viruses selectively replicate in and kill cancer cells through syncytia formation and lysis, while also recruiting a localized immune response to the tumor site [66]. MV is highly advantageous as an oncolytic virotherapy because of the existence of a safe and effective vaccine strain, that has a 15 high affinity for CD46 receptors that are overexpressed on cancerous tumor tissue and can be further attenuated by mutations in the P gene to allow for host interferon signaling to proceed during infection and recruit a localized immune response to tumor sites. Single-cell Virology Characterizing the infection dynamics of RNA viruses, like IAV and MV, is challenging due to extreme genomic and phenotypic heterogeneity in their populations. Most research on RNA viruses is conducted using bulk assays, which average outcomes across a population. While informative, bulk studies do not capture important subsets of heterogenous RNA virus populations. This challenge can be overcome by observing viral infection at the single-cell level [67]. Many methods exist for single-cell isolation, the most common being flow cytometry, laser microdissection, manual cell picking, limiting dilution, and microfluidics [68]. Microfluidics is perhaps the most widely used method of single-cell analysis, utilizing microwell, valve, and drop based technologies (Fig 2) [67]. Once single-cells are isolated, they can be analyzed using various imaging [69] and multi-omics [70] techniques. These techniques have been applied to single-cell virology to address questions about progeny production, virus kinetics, defective viral genomes, and viral gene expression [71]-[72]. Investigation of these infection dynamics at the single-cell level has revealed extreme heterogeneity in response to antiviral agents [73], which is critical for the development of highly effective treatments. Previous investigations of single-cell IAV infections have found high variability in viral gene expression [32]-[33] (Heldt 2015), host innate immune activation [34], defective viral 16 genome content [40] and viral burst size [74]. Single-cell IAV infection dynamics have also shown to be MOI dependent [75] and vary between host cell types and IAV strains [35]. Single-cell studies of MV infection are more limited and have primarily focused on the portion of the viral lifecycle where MV infection moved from epithelial cells to immune cells. These studies have helped identify which immune cells are most susceptible to MV infection [76], the cellular functions that enable movement of infection centers from respiratory epithelium to immune cells [77]-[78], and the role of interferon signaling in stopping the dissociation of infection centers [79]. 17 Figure 2. Single-Cell Microfluidic Technologies and Their Typical Applications in Virology. Adapted from [67], with permission from the journal. Microwell-, valve-, and droplet-based strategies are applied in virology studies. Features and typical application opportunities of these methods are presented. Abbreviations: t-SNE 1 and t-SNE 2, dimension 1 and dimension 2 of the data space with t-distributed stochastic neighbor embedding (t-SNE) technique for dimensionality reduction. 18 Droplet Microfluidics One powerful method of single-cell analysis is droplet microfluidics, which physically isolates single-cells into droplets ranging 10-200 m in diameter. Droplets can be generated at a rate of thousands per second and modified downstream to provide miniaturized and high- throughput replacements of many laboratory assays [80] addressing multi omics questions (Fig 3) [81]. Droplet microfluidics has been widely applied to the field of single-cell virology [67], to track infection dynamics, identify subpopulations with important genotype or phenotype, and perform high-throughput screening of both. It is a critical tool for clinical research, serving as infectious disease diagnostics [82] for detection of pathogens like RNA viruses [83] such as IAV and MV [84]. Additionally, droplet microfluidics helps to uncover more basic properties about the diversity of viral replication [85], infectivity [86], genome recombination [87],[88], and evolutionary trajectory [89]-[91]. In this section we will discuss the basics of droplet microfluidics and describe its application to three important laboratory assays (cell culture, RT- qPCR, and next generation sequencing) which are prevalent in the remainder of this work. 19 Figure 3. Single-cell Analysis Using Droplet Microfluidics. Adapted from [81], with permission from the journal. Overview of droplet microfluidics approaches to gain information on all key processes in individual cells. Technologies are categorized in sections as indicated in the figure. Droplet Generation Microfluidic droplets at their most basic form are water-in-oil emulsions. The aqueous phase and the oil phase do not mix within the droplet or between different droplets. Enabling full isolation of encapsulated contents from neighboring droplets. Droplets are formed by flowing small liquid volumes into microfluidic devices at high-speed using either a flow or pressure- 20 based systems. Microfluidic devices are typically made of polydimethylsiloxane (PDMS) polymer chips that contain micron scale channels and junctions molded from SU8 features printed onto silicon wafers [92]. Fabricated PMDS chips are then bound to a glass slide using plasma surface treatment, and the channels are made hydrophobic or hydrophilic, depending on the fluids being used. This is the basic device fabrication protocol for the devices described in this work. However, many methods and materials exist for microfluidic device fabrication. Figure 4. Microfluidic drop maker geometries and manipulation modules applied in single‐cell workflows. Adapted from [81], with permission from the journal. Droplets can be generated using microfluidic devices using three different geometries: T‐junction, flow focusing, and co‐ flow. After droplet generation, microfluidic modules are available for droplet manipulation: merging of droplets with different contents, splitting of bigger droplets into droplets with smaller volume, re‐loading of the emulsion into a new device, static on‐chip incubation of the emulsion at different temperatures, detection of (fluorescence) signal in droplets, and sorting droplets of interest (using e.g., electric field). 21 Custom device design enables complex fluid flow and manipulation, allowing for the miniaturization of almost any bulk assay onto a microfluidic chip. Devices can be designed to include multilayered channels or embedded electric elements [93]. Droplets can be split, merged, reinjected, heated, detected, and sorted (Fig 4) [81]. Droplet microfluidics has revolutionized single-cell biology by providing a platform that decreases the cost and increases the throughput of traditional laboratory assays. Single-cells can be encapsulated into drops at 10 kHz rates, which allows for millions of drops to be produced in a short period of time [94]. This yields a corresponding increase in statistical power of the conclusions made from droplet microfluidic assays. Three droplet microfluidic assays will be discussed in detail below – virus culture, droplet quantitative PCR, and single-cell sequencing – that are critical to the research described in this dissertation. Virus Culture in Droplets The most fundamental method to virology research is virus culture. To culture viruses from single-cells encapsulated into microfluidic droplets we must ensure that cells remain viable and virus replication and infection is comparable to bulk infection conditions [85],[86],[95]. Without these basic quality control measures, single-cell assays cannot be compared to bulk assay counterparts. One important parameter is the biocompatibility of oil used for droplet generation. We use fluorinated oils and surfactants for single-cell encapsulation. These fluorinated oils are nontoxic to cells and allow for gas exchange between the aqueous phase and the atmosphere [96]. Another important factor is media nutrient concentration within the droplets. Small volumes used during single-cell culture in drops may require optimization of media components, compared to media used during bulk cell culture. Additionally, virus culture 22 typically requires that infected cells are incubated at warm temperatures (averaging at 35C). This presents a challenge to droplet stability, where drops may coalesce above room temperature. This condition can be overcome by again optimizing the contents of single-cell infection media and may require the addition of chemicals meant to stabilize the drop interface [97]. However, these chemical additions may also disrupt cell viability and virus infection cycles, therefore testing of concentration gradients is typically required. Droplet Quantitative PCR One of the most widely used methods for quantifying species abundance is measurement of nucleic acid concentration through quantitative polymerase-chain reaction (qPCR) assay. To measure the concentration of RNA viruses in a sample, we can target regions of the RNA genome for detection through qPCR [98],[99]. When utilized in a droplet microfluidic system, qPCR assays increase in speed, sensitivity, and throughput. This technique is commercially available through digital droplet PCR (ddPCR), which splits a bulk sample into drops and counts the number of drops that produce a positive fluorescence read-out through qPCR [100]. The number of positive drops corresponds to target concentration in the starting sample. We can also use qPCR in drops to measure viral RNA concentration from single infected cells [74]. Previous work on RT-qPCR of single infected cells (limiting dilution) shows that IAV progeny virus output and intracellular viral RNA concentration can vary up to 3 orders of magnitude [33],[101]. However, these single-cell methods are limited in through-put. This dissertation describes an improved upon system for single-cell RT-qPCR, which samples viral RNA directly from single-cells in drops. Our method, termed droplet quantitative PCR (dqPCR) [74], splits secreted extracellular virus from the host cell and merges it with qPCR mix. Drops 23 are thermocycled off -chip and the fluorescence intensity of individual drops are read in series using high-speed flow-based detection on a microfluidic chip [93]. The fluorescence intensity of each drop is converted to a pre-split RNA copy number using a sigmoidal curve fitting model. The model relies on generation of an RNA standard droplet amplification curve library that is specific to a target sequence of interest, to relate droplet fluorescence at a given cycle number back to a starting RNA concentration [97]. The dqPCR method enables quantification of viral RNA from tens of thousands of single-cell infections. The goal of dqPCR is to quantify heterogeneity in viral RNA replication across single-cell infections and compare results to existing bulk infection data. Single-cell Sequencing Much of the work in the field of single-cell virology has focused on single-cell RNA sequencing (scRNA-seq) technologies [102]. scRNA-seq is typically used to quantify diversity in gene expression at a single-cell level and identifies important subpopulations that are often masked by bulk RNA sequencing methods. Understanding the true heterogeneity of RNA expression during viral infection may inform fundamental concepts of viral spread and persistence, activation of immune responses, and the design of antiviral therapeutics. Sc-RNAseq relies on microfluidic technologies [103]. Most scRNA-seq platforms are designed for transcriptomic sequencing of cellular messenger RNA (mRNA). The technology was originally developed by the inDrop [104] and Dropseq [105] protocols, which were later commercialized by 10x Genomics. These platforms utilize microfluidic devices to construct barcoded hydrogel beads (BHBs) carrying DNA primers with sequences for mRNA capture and barcoding, then to co-encapsulate BHBs with single-cells in droplets. Once isolated in 24 microfluidic drops, reverse transcription of cellular mRNA yields cDNA barcoded by drop of origin. This barcoded cDNA (BC-cDNA) is processed for Illumina sequencing, producing tens of thousands of scRNA-seq libraries derived from individual cells. Since the development of Dropseq and InDrop, and subsequent commercialization by 10x Genomics, many other scRNA-seq methods have been commercialized. These new technologies allow for scRNA-seq of genomes, epigenomes, and proteomes, in addition to traditional transcriptomic sequencing. NanoString has expanded the field of single-cell transcriptomics to spatial monitoring of gene expression in tissue layers. Mission Bio specializes in single-cell genomics and multiomics, using bead-based microfluidic technologies to simultaneously capture DNA and proteins. Scale Biosciences and Parse Biosciences are microfluidic-free scRNA-seq technologies that build sequencing barcodes directly onto cells themselves, reducing the technical skill, equipment need, and cost of the assay. Even the industry leader in next generation sequencing, Illumina, has recently released their own protocol for multiomics scRNA-seq. 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Loveday Contributions: Designed studies, Performed Experiments, Analyzed data, Wrote Manuscript. Co-Author: Humberto S. Sanchez Contributions: Performed Experiments, Analyzed data, Wrote Manuscript. Co-Author: Mallory M. Thomas Contributions: Performed Experiments, Analyzed data, Wrote Manuscript. Co-Author: Connie B. Chang Contributions: PI on the Study, Acquired funding, Designed studies, Wrote Manuscript. 34 Manuscript Information Emma K. Loveday, Humberto S. Sanchez, Mallory M. Thomas, Connie B. Chang Microbiology Spectrum Status of Manuscript: ☐ Prepared for submission to a peer-reviewed journal ☐ Officially submitted to a peer-reviewed journal ☐ Accepted by a peer-reviewed journal Published in a peer-reviewed journal American Society for Microbiology Volume 10, Number 5, October 26th, 2022 DOI 10.1128/spectrum.00993-22 Single-Cell Infection of Influenza A Virus Using Drop-Based Microfluidics Emma Kate Loveday,a,b Humberto S. Sanchez,a,b Mallory M. Thomas,a,c Connie B. Changa,b,d aCenter for Biofilm Engineering, Montana State University, Bozeman, Montana, USA bDepartment of Chemical and Biological Engineering, Montana State University, Bozeman, Montana, USA cMicrobiology and Cell Biology, Montana State University, Bozeman, Montana, USA dDepartment of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA ABSTRACT Drop-based microfluidics has revolutionized single-cell studies and can be applied toward analyzing tens of thousands to millions of single cells and their products contained within picoliter-sized drops. Drop-based microfluidics can shed insight into single-cell virology, enabling higher-resolution analysis of cellular and viral heterogeneity during viral infection. In this work, individual A549, MDCK, and siat7e cells were infected with influenza A virus (IAV) and encapsulated into 100-mm-size drops. Initial studies of uninfected cells encapsulated in drops demonstrated high cell viability and drop stability. Cell viability of uninfected cells in the drops remained above 75%, and the average drop radii changed by less than 3% following cell encapsulation and incubation over 24 h. Infection parameters were analyzed over 24 h from individually infected cells in drops. The number of IAV viral genomes and infectious viruses released from A549 and MDCK cells in drops was not significantly different from bulk infection as measured by reverse transcriptase quantitative PCR (RT-qPCR) and plaque assay. The application of drop-based microfluidics in this work expands the capacity to propagate IAV viruses and perform high- throughput analyses of individually infected cells. IMPORTANCE Drop-based microfluidics is a cutting-edge tool in single-cell research. Here, we used drop-based microfluidics to encapsulate thousands of individual cells infected with influenza A virus within picoliter-sized drops. Drop stability, cell loading, and cell viability were quantified from three different cell lines that support influenza A virus propagation. Similar levels of viral progeny as determined by RT-qPCR and pla- que assay were observed from encapsulated cells in drops compared to bulk culture. This approach enables the ability to propagate influenza A virus from encapsulated cells, allowing for future high-throughput analysis of single host cell interactions in iso- lated microenvironments over the course of the viral life cycle. KEYWORDS microfluidics, influenza, single cell, drops Single-cell studies of viral infection enable high-resolution examination of heterogeneous virus populations. Differential selective pressures in both the host cell and virus popula- tion lead to variability in virus replication and production that enables antiviral escape, zoo- notic spillover events, and changes in virulence or pathogenesis (1). Influenza A virus (IAV) is a negative-strand RNA virus with populations containing high genetic diversity due to its segmented genome, rapid replication rate, and low-fidelity RNA-dependent RNA polymer- ase (RdRp) (1). As such, IAV infection results in a diverse swarm of unique variants exhibiting heterogeneous genotypes and phenotypes (2, 3). Cutting-edge technologies such as drop-based microfluidics (4–8) and single-cell sequenc- ing (9–14) are enabling higher-resolution analysis of the effects of cellular heterogeneity on vi- ral infection dynamics. Single-cell virology studies have revealed a more detailed snapshot of the heterogeneous kinetics of virus production in relation to innate immune activation and Editor Abimbola O. Kolawole, Wright State University Copyright © 2022 Loveday et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Address correspondence to Connie B. Chang, connie.chang@montana.edu. The authors declare no conflict of interest. Received 13 April 2022 Accepted 22 August 2022 Month YYYY Volume XX Issue XX 10.1128/spectrum.00993-22 1 RESEARCH ARTICLE D ow nl oa de d fr om h ttp s: //j ou rn al s. as m .o rg /jo ur na l/s pe ct ru m o n 23 S ep te m be r 20 22 b y 15 3. 90 .1 18 .1 46 . 35 https://orcid.org/0000-0002-1154-7728 https://orcid.org/0000-0002-1154-7728 https://orcid.org/0000-0002-1154-7728 https://orcid.org/0000-0002-1154-7728 https://orcid.org/0000-0001-7218-2868 https://orcid.org/0000-0001-7218-2868 https://orcid.org/0000-0001-7218-2868 https://orcid.org/0000-0001-7218-2868 https://orcid.org/0000-0001-9555-8223 https://orcid.org/0000-0001-9555-8223 https://orcid.org/0000-0001-9555-8223 https://orcid.org/0000-0001-9555-8223 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-00-00 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-00-00 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-00-00 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-00-00 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-9-20 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-9-20 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-9-20 https://crossmark.crossref.org/dialog/?doi=10.1128/spectrum.00993-22&domain=pdf&date_stamp=2022-9-20 responses (15–18) and the variability of viral gene expression between individually infected cells (9–12). Heterogeneous IAV populations can be examined using methods that capture diversity at the single-cell level. Such methods include microfluidic techniques to perform single-cell transcriptomics (9–11) or isolation of cells using limiting dilutions in well plates to assess single infection events (12, 13, 16, 19) or by using two-dimensional (2D) microfluidic chambers (15). While these studies have identified large variations in total viral mRNA expressed, infectious virus produced, and host transcriptional response to infection, the low-throughput methods were limited to analyzing hundreds to a few thousand individual cells (12, 13, 15, 20). To process tens of thousands or up to millions of single cells, a high- throughput continuous flow method such as fluorescence-activated cell sorting (FACS) can be utilized to study virus infection (9–11, 14). Yet FACS sorting is mostly performed at early time points postinfection to ensure that virus spread is reduced and that the timing of infec- tion, viral gene expression, and cellular response to infection are comparable from cell to cell (9–11, 14, 17). In contrast, analysis of infected cells from bulk cultures at later time points postinfection can be difficult due to virus spread. A method to study single cells at high throughput is drop-based microfluidics, which offers the ability to compartmentalize and rapidly assay individual cells (6, 21–25). A microfluidic de- vice is used to create microscale aqueous drops that are surrounded by an immiscible oil and stabilized with a biocompatible surfactant (26). Compared to larger-scale in vitro culturing methods where cells are grown in flasks or well plates, drop-based microfluidics creates mil- lions of discrete bioreactors that house single cells in which viruses can replicate (4, 7), ena- bling viral replication events to be analyzed independently. Furthermore, these compartmen- talized cells in drops allow for single-cell analysis at any time point postinfection since virus spread cannot occur between neighboring drops. Thus, drops provide a way to analyze virus replication, production, and cellular responses of single cells at multiple time points. To date, drop-based viral infections have only been performed with murine norovirus (MNV-1), a positive-sense RNA virus in a small icosahedral capsid that lacks an outer envelope and is extremely stable at a range of environmental conditions, using cell lines adapted to spinner cultures or already grown in suspension (4, 7, 8, 27). Drop-based microfluidics was used to study infectivity (4, 7) and recombination (8) of MNV-1, with data demonstrating the ability to successfully encapsulate and infect mammalian cells within drops (4, 6–8). Expanding the applicability of drop-based microfluidics to other viruses, specifically for enveloped viruses such as IAV, and the ability to encapsulate a broad range of host cells would greatly increase capacity for single-cell virology studies. In this work, drop-based microfluidics was applied toward culturing and studying IAV infections at the single-cell level. Three different cell lines, alveolar basal epithelial (A549) cells, Madin-Darby Canine Kidney (MDCK) cells, and MDCK plus human siat7e gene (siat7e) cells (28, 29), were tested for their viability and ability to support IAV infection in drops com- pared to bulk tissue culture. Drop stability, cell loading, and viability were tested within 100- mm-diameter drops and quantified after 24 h of incubation, sufficient time for one productive round of IAV infection. Additionally, this study demonstrates the use of drop-based microflui- dics to culture and propagate enveloped viruses, which generally have reduced environmental stability. The cells were infected with A/California/07/2009 (H1N1) at a multiplicity of infection (MOI) of 0.1 to compare virus production over multiple time points between infected cells on standard tissue culture plates and encapsulated in drops. Our findings demonstrate that standard adherent cell lines can be used in drops to propagate IAV, thereby expanding single- cell capabilities for studying viral infections, which, until recently, has only been demonstrated for nonadherent, spinner-adapted host cells (4, 7, 8). The drop-based microfluidic methods developed in this work will enable future studies of single-cell IAV infections using multiple cell lines. The methods expand the application of drop-based microfluidics from the nonenveloped positive-sense MNV-1 (4, 7, 8) to include the enveloped and negative-sense IAV, thus broadening the scope of single-cell virology assays. Additionally, this work can serve as a blueprint for testing, comparing, and implementing drop-based microfluidics methods in future single-cell studies. With the recent interest and development of single-cell omic technologies for Single-Cell Infection of Influenza A Virus in Drops Microbiology Spectrum Month YYYY Volume XX Issue XX 10.1128/spectrum.00993-22 2 D ow nl oa de d fr om h ttp s: //j ou rn al s. as m .o rg /jo ur na l/s pe ct ru m o n 23 S ep te m be r 20 22 b y 15 3. 90 .1 18 .1 46 . 36 https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 studying viral infections (13–15, 20, 30–36), we expect this work to further expand capabilities and increase applications of single-cell technologies in virology. RESULTS Single-cell virus infection using drop-based microfluidics. A schematic of the virus infection workflow comparing drops to bulk is outlined in Fig. 1. Naive cells were infected with IAV at an MOI of 0.1 prior to encapsulation such that most cells were infected with a single infectious virus particle (Fig. 1A). Following inoculation, cells were dissociated from the tissue culture plates and processed for encapsulation into 100-mm drops (Fig. 1B), while bulk samples were replated (Fig. 1D). Both encapsulated and bulk cells were similarly proc- essed to ensure that trypsinization from the tissue culture plates and subsequent centrifuga- tion and washing did not interfere with infection kinetics. A 0 h bulk and drop sample was obtained immediately following replating or encapsulation. The remaining bulk and drop samples were incubated for 24 h at 37°C to allow for a minimum of one round of virus repli- cation (Fig. 1C and D). At 24 hours post infection (hpi), viral supernatant was collected from both the bulk and drop samples for analysis of viral genome copies and infectious progeny via reverse transcriptase quantitative PCR (RT-qPCR) and plaque assay. To sample viral super- natant from encapsulated infections, the drops were placed in the freezer and, after thaw- ing, treated with 20% 1H,1H,2H,2H-perfluoro-1-octanol (PFO) in HFE7500 to break the emul- sions and release the cells and viral supernatant for collection (Fig. 1E). To assess cell viability, drops were not frozen and were broken with 20% PFO in HFE7500 only. We then compared cell viability and number of viral genomes and infectious progeny from different cell lines maintained in bulk culture and encapsulated in drops (Fig. 1F). Validating IAV infection in different cell lines. Before performing IAV infection in drops, IAV infection was studied in bulk using three different cell lines, A549, MDCK, and siat7e cells. Both the A549 and MDCK cells are anchorage dependent and are commonly used to propagate IAV and investigate viral infection phenotypes. The siat7e cells are a modified MDCK cell line that expresses the siat7e gene (ST6GalNac V), which is important for cellular adhesion, allowing the cells to grow in a suspension culture, and they are distinc- tive from the standard MDCK-Siat1 cells used commonly for IAV production (28, 29). They were developed to simplify cell culture-based IAV vaccine production (28, 29). The siat7e cells are anchorage independent and were tested for their potentially more favorable com- patibility to drop encapsulation (4, 7). IAV production was compared between the different cell lines to determine the kinetics of viral infection in a bulk infection. The cells were infected at an MOI of 0.1, and viral RNA was measured from supernatant at 0, 6, 12, 24, 30, 36, and 48 hpi using RT-qPCR, in which the 0 h measurement represents the inoculating dose (Fig. 2A to C). A549 cells demonstrated the most robust viral RNA production during FIG 1 Graphical workflow. (A) Cells were infected with IAV. (B) A suspension of infected cells was encapsulated into drops using a fluorinated oil continuous phase. (C) Drops were incubated for 24 h at 37°C and 5% CO2. (D) A portion of infected cells was replated onto standard tissue culture dishes to recapitulate a bulk infection and incubated for 24 h at 37°C and 5% CO2. (E) To analyze virus production, the drops were broken and pooled, and cells and/or viral supernatant were recovered. (F) Cells and virus from drop and bulk infections were analyzed using LIVE/DEAD staining to determine cell viability, RT-qPCR to determine viral genome copy number, and plaque assays to determine viral infectivity. Single-Cell Infection of Influenza A Virus in Drops Microbiology Spectrum Month YYYY Volume XX Issue XX 10.1128/spectrum.00993-22 3 D ow nl oa de d fr om h ttp s: //j ou rn al s. as m .o rg /jo ur na l/s pe ct ru m o n 23 S ep te m be r 20 22 b y 15 3. 90 .1 18 .1 46 . 37 https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://journals.asm.org/journal/spectrum https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 https://doi.org/10.1128/spectrum.00993-22 infection, with 9.5 � 104 genome copies/mL at 0 hpi, which increased to 3.9 � 108 genome copies/mL at 24 hpi, a 1,000-fold increase. Between 24 and 48 hpi, the amount of RNA detected fluctuated slightly but remained between 1.6 and 6.1 � 108 genome copies/mL. MDCK cells demonstrated a slower increase in viral production, with 1.1� 105 genome copies/mL at 0 hpi, increasing to 3.5 � 107 genome copies/mL at 24 hpi, a 100-fold increase, before reaching 1.9� 108 genome copies/mL at 48 hpi. IAV production in siat7e cells was limited with no expo- nential increase over the course of 48 hpi, with 6.0 � 105 genome copies/mL at 0 hpi and 5.4� 106 genome copies/mL at 48 hpi. For A/California/07/2009, peak hemagglutinin (HA) titers in siat7e cells were previously observed at 24 hpi, although 50% tissue culture infective dose (TCID50) measurements showed similar kinetics to our observed viral RNA measurements, with limited increases in titers from 0 to 120 hpi, suggesting that replication and production of infec- tious virus in the siat7e cells is limited (29). In addition, viral growth, as measured by TCID50, of A/Brisbane/59/2007 IVR-148 (H1N1) and A/Uruguay/716/2007 X-175C (H3N2) in the siat7e cells was also not observed until 96 and 84 h, respectively, suggesting that there is variability in viral replication and kinetics in the siat7e cells (11). Overall, the