The authors would like to thank the NIAID VRC Flow Cytometry Core and the MHRP Flow Cytometry Core facilities for maintenance and operation of FACS instruments and sorting equipment; Maria Montero, Vishakha Sharma, Kaimei Song for expert technical assistance; Michael Piatak, Jr. (deceased) for assistance with SIV qPCR assay design; and Brandon Keele and Matthew Scarlotta for SIV isolate sequences. The views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army or the Department of Defense. Research was conducted under an approved animal use protocol in an AAALAC accredited facility in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals, NRC Publication, 2011 edition.

NOTE: A schematic of the protocol workflow is shown in Figure 1. It consists of three principal steps: FACS, RT and cDNA pre-amplification, and qPCR for up to 96 genes simultaneously. Two versions of the protocol, sorting cells in limiting dilutions and sorting single cells, are described in greater detail in step 5 and step 6, respectively. These strategies address different research questions but follow similar procedures.
1. Prerequisite or Prior Analyses
<ol...

<ol>
	<li>Macaulay, I. C., Ponting, C. P., Voet, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-Cell+Multiomics:+Multiple+Measurements+from+Single+Cells.">Single-Cell Multiomics: Multiple Measurements from Single Cells.</a> Trends Genet. 33 (2), 155-168 (2017).</li><li>Pasternak, A. O., Lukashov, V. 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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intrinsic+host+restrictions+to+HIV-1+and+mechanisms+of+viral+escape.">Intrinsic host restrictions to HIV-1 and mechanisms of viral escape.</a> Nat Immunol. 16 (6), 546-553 (2015).</li><li>Kirchhoff, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immune+evasion+and+counteraction+of+restriction+factors+by+HIV-1+and+other+primate+lentiviruses.">Immune evasion and counteraction of restriction factors by HIV-1 and other primate lentiviruses.</a> Cell Host Microbe. 8 (1), 55-67 (2010).</li><li>Wigzell, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunopathogenesis+of+HIV+infection.">Immunopathogenesis of HIV infection.</a> J Acquir Immune Defic Syndr. 1 (6), 559-565 (1988).</li><li>Reynolds, M. 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E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-Cell+Characterization+of+Viral+Translation-Competent+Reservoirs+in+HIV-Infected+Individuals.">Single-Cell Characterization of Viral Translation-Competent Reservoirs in HIV-Infected Individuals.</a> Cell Host Microbe. 20 (3), 368-380 (2016).</li><li>Grau-Exposito, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Novel+Single-Cell+FISH-Flow+Assay+Identifies+Effector+Memory+CD4++T+cells+as+a+Major+Niche+for+HIV-1+Transcription+in+HIV-Infected+Patients.">A Novel Single-Cell FISH-Flow Assay Identifies Effector Memory CD4+ T cells as a Major Niche for HIV-1 Transcription in HIV-Infected Patients.</a> MBio. 8 (4), (2017).</li><li>Eriksson, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+analysis+of+measures+of+viral+reservoirs+in+HIV-1+eradication+studies.">Comparative analysis of measures of viral reservoirs in HIV-1 eradication studies.</a> PLoS Pathog. 9 (2), e1003174 (2013).</li><li>Finzi, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+a+reservoir+for+HIV-1+in+patients+on+highly+active+antiretroviral+therapy.">Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy.</a> Science. 278 (5341), 1295-1300 (1997).</li><li>Chomont, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HIV+reservoir+size+and+persistence+are+driven+by+T+cell+survival+and+homeostatic+proliferation.">HIV reservoir size and persistence are driven by T cell survival and homeostatic proliferation.</a> Nat Med. 15 (8), 893-900 (2009).</li><li>DeMaster, L. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Subset+of+CD4/CD8+Double-Negative+T+Cells+Expresses+HIV+Proteins+in+Patients+on+Antiretroviral+Therapy.">A Subset of CD4/CD8 Double-Negative T Cells Expresses HIV Proteins in Patients on Antiretroviral Therapy.</a> J Virol. 90 (5), 2165-2179 (2015).</li><li>Bolton, D. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combined+single-cell+quantitation+of+host+and+SIV+genes+and+proteins+ex+vivo+reveals+host-pathogen+interactions+in+individual+cells.">Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells.</a> PLoS Pathog. 13 (6), e1006445 (2017).</li><li>Quintans, J., Lefkovits, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precursor+cells+specific+to+sheep+red+cells+in+nude+mice.+Estimation+of+frequency+in+the+microculture+system.">Precursor cells specific to sheep red cells in nude mice. Estimation of frequency in the microculture system.</a> Eur J Immunol. 3 (7), 392-397 (1973).</li><li>Finak, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAST:+a+flexible+statistical+framework+for+assessing+transcriptional+changes+and+characterizing+heterogeneity+in+single-cell+RNA+sequencing+data.">MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.</a> Genome Biol. 16, 278 (2015).</li><li>McDavid, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+bi-modality+improves+characterization+of+cell+cycle+on+gene+expression+in+single+cells.">Modeling bi-modality improves characterization of cell cycle on gene expression in single cells.</a> PLoS Comput Biol. 10 (7), e1003696 (2014).</li><li>McDavid, A., Finak, G., Gottardo, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contribution+of+cell+cycle+to+heterogeneity+in+single-cell+RNA-seq+data.">The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.</a> Nat Biotechnol. 34 (6), 591-593 (2016).</li><li>Kok, Y. L., Ciuffi, A., Metzner, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unravelling+HIV-1+Latency,+One+Cell+at+a+Time.">Unravelling HIV-1 Latency, One Cell at a Time.</a> Trends Microbiol. , (2017).</li><li>Rato, S., Golumbeanu, M., Telenti, A., Ciuffi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+viral+infection+using+single-cell+sequencing.">Exploring viral infection using single-cell sequencing.</a> Virus Res. 239, 55-68 (2017).</li><li>Wagner, A., Regev, A., Yosef, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revealing+the+vectors+of+cellular+identity+with+single-cell+genomics.">Revealing the vectors of cellular identity with single-cell genomics.</a> Nat Biotechnol. 34 (11), 1145-1160 (2016).</li><li>Nestorowa, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+single-cell+resolution+map+of+mouse+hematopoietic+stem+and+progenitor+cell+differentiation.">A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.</a> Blood. 128 (8), e20-e31 (2016).</li><li>Soh, K. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous,+Single-Cell+Measurement+of+Messenger+RNA,+Cell+Surface+Proteins,+and+Intracellular+Proteins.">Simultaneous, Single-Cell Measurement of Messenger RNA, Cell Surface Proteins, and Intracellular Proteins.</a> Curr Protoc Cytom. 75, 41-47 (2016).</li><li>Kochan, J., Wawro, M., Kasza, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+detection+of+mRNA+and+protein+in+single+cells+using+immunofluorescence-combined+single-molecule+RNA+FISH.">Simultaneous detection of mRNA and protein in single cells using immunofluorescence-combined single-molecule RNA FISH.</a> Biotechniques. 59 (4), 209-212 (2015).</li><li>Stahlberg, A., Thomsen, C., Ruff, D., Aman, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+PCR+analysis+of+DNA,+RNAs,+and+proteins+in+the+same+single+cell.">Quantitative PCR analysis of DNA, RNAs, and proteins in the same single cell.</a> Clin Chem. 58 (12), 1682-1691 (2012).</li><li>Assarsson, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Homogenous+96-plex+PEA+immunoassay+exhibiting+high+sensitivity,+specificity,+and+excellent+scalability.">Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability.</a> PLoS One. 9 (4), e95192 (2014).</li><li>Fredriksson, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+detection+using+proximity-dependent+DNA+ligation+assays.">Protein detection using proximity-dependent DNA ligation assays.</a> Nat Biotechnol. 20 (5), 473-477 (2002).</li><li>Genshaft, A. 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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spliced+human+immunodeficiency+virus+type+1+RNA+is+reverse+transcribed+into+cDNA+within+infected+cells.">Spliced human immunodeficiency virus type 1 RNA is reverse transcribed into cDNA within infected cells.</a> AIDS Res Hum Retroviruses. 20 (2), 203-211 (2004).</li><li>Stegle, O., Teichmann, S. A., Marioni, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+and+analytical+challenges+in+single-cell+transcriptomics.">Computational and analytical challenges in single-cell transcriptomics.</a> Nat Rev Genet. 16 (3), 133-145 (2015).</li><li>Wu, M., Singh, A. 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The protocol described here, termed tSCEPTRE, integrates single-cell surface protein quantitation by multiparameter flow cytometry with quantitative single-cell mRNA expression by highly multiplexed RT-qPCR. The union of these two technologies enables high-content snapshots of the combined transcriptional and protein profile of single cells in a high-throughput format. We use the method to identify heretofore elusive cells infected with SIV in vivo, and describe differentially expressed host genes and proteins. ...

Many intracellular pathogens rely on host cell machinery to replicate, often altering host cell biology or targeting very specific subpopulations of host cells to maximize their chances of propagation. As a result, cell biological processes are commonly disrupted, with deleterious consequences for the overall health of the host. Understanding the interactions between viruses and the host cells in which they replicate will elucidate disease mechanisms that may aid in the development of improved therapies and strategies to prevent infection. Direct analytic tools that enable the study of host-pathogen interactions are essential toward this end. Single-cell analysis provides the only means to unambiguously attribute a cellular phenotype to a particular genotype, or infection status1. For example, pathogenic infections frequently induce both direct and indirect changes in host cells. Therefore, distinguishing infected cells from their uninfected counterparts is necessary to attribute host cell changes to either direct infection or secondary effects, such as generalized inflammation. Moreover, for many pathogens, like SIV and human immunodeficiency viruses (HIV), host cell infection proceeds through multiple stages, such as early, late, or latent, each of which may be characterized by distinct gene and protein expression profiles2,3,4,5. Bulk analyses of cell mixtures will fail to capture this heterogeneity6. By contrast, highly multiplexed single-cell analyses able to quantify the expression of both viral and host genes offer a means to resolve infection-specific cellular perturbations, including variations across infection stages. Further, analyzing host-pathogen interactions in physiologically relevant settings is critical for the identification of events that occur in infected organisms. Thus, methods that can be applied directly ex vivo are likely to best capture in vivo processes.
SIV and HIV target CD4+ T cells, in which they counteract host antiviral &#34;restriction&#34; factors and downregulate antigen presenting molecules to establish productive infection and avoid immune surveillance7,8,9,10,11. Without treatment, the infection results in massive loss of CD4+ T cells, ultimately culminating in acquired immunodeficiency syndrome (AIDS)12. In the setting of antiretroviral therapy, latently infected cell reservoirs persist for decades, posing a formidable barrier to curative strategies. Understanding the properties of in vivo HIV/SIV-infected cells has the potential to reveal host cell features instrumental in pathogenesis and persistence. However, this has been highly challenging, primarily due to the low frequency of infected cells and lack of reagents able to readily identify them. Cells that transcribe viral RNA, are estimated to be present at 0.01&#8211;1% of CD4+ T cells in blood and lymphoid tissue13,14,15. Under suppressive therapy, latently infected cells are even less frequent at 10-3&#8211;10-7 16,17,18. Viral protein staining assays that work well for studying in vitro infections, such as for intracellular Gag, are suboptimal due to background staining of 0.01&#8211;0.1%, similar to or greater than the frequency of infected cells13,14. Surface staining for Env protein using well-characterized SIV/HIV Env-specific monoclonal antibodies has also been proven to be difficult, likely for similar reasons. Recently, novel tools aim to improve the detection of cells expressing Gag by either incorporating assays specific for gag RNA or by using alternative imaging technologies14,15,19. However, such approaches remain limited in the number of quantitative measurements performed on each cell.
Here, we describe methodology that (1) identifies single virus-infected cells directly ex vivo by sensitive and specific viral gene quantitative qPCR and (2) quantifies the expression of up to 18 surface proteins and 96 genes for each infected (and uninfected) cell. This methodology combines single-cell surface protein measurement by FACS followed by immediate cell lysis and gene expression analysis using multiplexed targeted qPCR on the Biomark system. The integrated fluidic circuit (IFC) technology allows multiplexed quantitation of 96 genes from 96 samples simultaneously, accomplished by a matrix of 9,216 chambers in which the individual qPCR reactions are performed. The live cell FACS sorting records high-content protein abundance measurements while preserving the entire transcriptome for analysis performed immediately downstream. To identify virus-infected cells, assays specific for alternatively spliced and unspliced viral RNAs (vRNA) are included in the qPCR analysis, along with a panel of user-defined assays totaling up to 96 genes, the maximum number of assays currently accommodated in the IFC. The gene expression and protein information collected for each cell are linked by well position. We previously reported results from this analysis elsewhere20. Here, we provide more detailed methodological guidelines as well as further descriptive phenotyping of SIV-infected CD4+ T cells.
This approach, which we term tSCEPTRE, can be applied to the suspensions of any viable cell population reactive to fluorescently labeled antibodies and expressing a transcriptome compatible with available qPCR assays. For example, it can be used for characterizing differential gene and protein expression in rare cells or cells not readily distinguished by surface protein markers. The sample preparation relies on a standard staining protocol using commercially available antibodies. Cytometers with single-cell sorting capability are also commercially available, but additional biosafety precautions are required for processing infectious live cells. Recording the single-cell protein expression profile for each cell by well position, referred to herein as indexed sorting, is a common feature of commercially available FACS sorting software. Computational analysis of differentially expressed host genes among cell populations of interest is not described here, but references are provided to previously published methods.

<table>
	<tbody>
		<tr>
			<td>
			RNA extraction and PCR reagents and consumables
			</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			Genemate 96-Well Semi-Skirted PCR Plate
			</td>
			<td>
			BioExpress/VWR
			</td>
			<td>
			T-3060-1
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Adhesive PCR Plate Seals
			</td>
			<td>
			ThermoFisher
			</td>
			<td>
			AB0558
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Armadillo 384-well PCR Plate
			</td>
			<td>
			ThermoFisher
			</td>
			<td>
			AB2384
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			MicroAmp Optical Adhesive Film
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			4311971
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			DEPC Water
			</td>
			<td>
			Quality Biological
			</td>
			<td>
			351-068-101
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Glass Distilled Water
			</td>
			<td>
			Teknova
			</td>
			<td>
			W3345
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Superscript III Platinum One-Step qRT-PCR Kit
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			11732088
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			SUPERase-In Rnase Inhibitor
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			AM2696
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Platinum Taq
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			10966034
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			dNTP Mix
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			18427088
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			ROX Reference Dye (if separate from kit)
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			12223012
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			DNA Suspension Buffer
			</td>
			<td>
			Teknova
			</td>
			<td>
			T0223
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			RNAqueous kit
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			AM1931
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			TaqMan gene expression assays not listed in Table 2
			</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			CD6
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			Hs00198752_m1
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			TLR3
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			Hs1551078_m1
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Biomark reagents
			</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			Control Line Fluid Kit
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			89000021
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			TaqMan Universal PCR Mix
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			4304437
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Assay Loading Reagent
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			85000736
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Sample Loading Reagent
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			85000735
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Dynamic Array 96.96 (chip)
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			BMK-M-96.96
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			FACS reagents
			</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			SPHERO COMPtrol Goat anti-mouse (lambda)
			</td>
			<td>
			Spherotech Inc.
			</td>
			<td>
			CMIgP-30-5H
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			CompBeads Anti-Mouse Ig,k
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			51-90-9001229
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			5 ml Polystyrene tube with strainer cap
			</td>
			<td>
			FALCON
			</td>
			<td>
			352235
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Aqua Live/Dead stain
			</td>
			<td>
			Invitrogen/ThermoFisher
			</td>
			<td>
			L34976
			</td>
			<td>
			dilute 1:800
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD3 BV650 clone SP34-2
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			563916
			</td>
			<td>
			dilute 1:40
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD4 BV786 clone L200
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			563914
			</td>
			<td>
			dilute 1:20
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD8 BUV496 clone RPA-T8
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			564804
			</td>
			<td>
			dilute 1:10
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD28 BV711 clone CD28.2
			</td>
			<td>
			Biolegend
			</td>
			<td>
			302948
			</td>
			<td>
			dilute 1:20
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD95 BUV737 clone DX2
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			564710
			</td>
			<td>
			dilute 1:10
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD14 BV510 clone M5E2
			</td>
			<td>
			Biolegend
			</td>
			<td>
			301842
			</td>
			<td>
			dilute 1:83
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD16 BV510 clone 3G8
			</td>
			<td>
			Biolegend
			</td>
			<td>
			302048
			</td>
			<td>
			dilute 1:167
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD20 BV510 clone 2H7
			</td>
			<td>
			Biolegend
			</td>
			<td>
			302340
			</td>
			<td>
			dilute 1:37
			</td>
		</tr>
		<tr>
			<td>
			Anti-CD38-R PE clone OKT10
			</td>
			<td>
			NHP reagent recource
			</td>
			<td>
			N/A
			</td>
			<td>
			dilute 1:100
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human CD69 BUV395 clone FN50
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			564364
			</td>
			<td>
			dilute 1:10
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human HLA-DR APC-H7 clone G46-6
			</td>
			<td>
			BD Biosciences
			</td>
			<td>
			561358
			</td>
			<td>
			dilute 1:20
			</td>
		</tr>
		<tr>
			<td>
			Mouse Anti-Human ICOS Alexa Fluor 700 clone C398.4A
			</td>
			<td>
			Biolegend
			</td>
			<td>
			313528
			</td>
			<td>
			dilute 1:80
			</td>
		</tr>
		<tr>
			<td>
			Instruments
			</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			BioPrptect Containment Enclosure
			</td>
			<td>
			Baker
			</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			BD FACS Aria
			</td>
			<td>
			BD Biosciences
			</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>
			ProtoFlex Dual 96-well PCR system
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			4484076
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Quant Studio 6 qPCR instrument
			</td>
			<td>
			Applied Biosystems/ThermoFisher
			</td>
			<td>
			4485694
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			IFC controller HX
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			IFC-HX
			</td>
			<td></td>
		</tr>
		<tr>
			<td>
			Biomark HD
			</td>
			<td>
			Fluidigm
			</td>
			<td>
			BMKHD-BMKHD
			</td>
			<td></td>
		</tr>
	</tbody>
</table>

single-cell quantitation of mrna and surface protein expression in simian immunodeficiency virus-infected cd4+ t cells isolated from rhesus macaques

Single-cell analysis is an important tool for dissecting heterogeneous populations of cells. The identification and isolation of rare cells can be difficult. To overcome this challenge, a methodology combining indexed flow cytometry and high-throughput multiplexed quantitative polymerase chain reaction (qPCR) was developed. The objective was to identify and characterize simian immunodeficiency virus (SIV)-infected cells present within rhesus macaques. Through quantitation of surface protein by fluorescence-activated cell sorting (FACS) and mRNA by qPCR, virus-infected cells are identified by viral gene expression, which is combined with host gene and protein measurements to create a multidimensional profile. We term the approach, targeted Single-Cell Proteo-transcriptional Evaluation, or tSCEPTRE. To perform the method, viable cells are stained with fluorescent antibodies specific for surface markers used for FACS isolation of a cell subset and/or downstream phenotypic analysis. Single cells are sorted followed by immediate lysis, multiplex reverse transcription (RT), PCR pre-amplification, and high throughput qPCR of up to 96 transcripts. FACS measurements are recorded at the time of sorting and subsequently linked to the gene expression data by well position to create a combined protein and transcriptional profile. To study SIV-infected cells directly ex vivo, cells were identified by qPCR detection of multiple viral RNA species. The combination of viral transcripts and the quantity of each provide a framework for classifying cells into distinct stages of the viral life cycle (e.g., productive versus non-productive). Moreover, tSCEPTRE of SIV+ cells were compared to uninfected cells isolated from the same specimen to assess differentially expressed host genes and proteins. The analysis revealed previously unappreciated viral RNA expression heterogeneity among infected cells as well as in vivo SIV-mediated post-transcriptional gene regulation with single-cell resolution. The tSCEPTRE method is relevant for the analysis of any cell population amenable to identification by expression of surface protein marker(s), host or pathogen gene(s), or combinations thereof.

The workflow for the entire protocol is depicted in Figure 1. It consists of two variations defined by the number of cells sorted: either limiting dilution or as single cells, as described in the text. Examples of primer-probe qualification analyses on 2-fold serial RNA dilutions are shown in Figure 2. The gating strategy to identify potential SIV+ cells is shown in Figure 3. A successful,...

Watch this Scientific Journal Video about Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques at JoVE.c

Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques

Described is a methodology to quantitate the expression of 96 genes and 18 surface proteins by single cells ex vivo, allowing for the identification of differentially expressed genes and proteins in virus-infected cells relative to uninfected cells. We apply the approach to study SIV-infected CD4+ T cells isolated from rhesus macaques.

Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques

Single-cell analysis is an important tool for dissecting heterogeneous populations of cells. The identification and isolation of rare cells can be ...

This method can help answer key questions in the field of host-pathogen interactions such as what are the unique characteristics of the productively infected cells, what host cofactors allow viruses to establish productive infection, and how to target these cells in therapeutic interventions. The main advantage of this technique is that it provides quantitative measures for both protein and mRNA within single cells, and most reagents are commercially available. Demonstrating the cell-sorting procedure will be Matthew Creegan, a senior technician from the flow cytometry core in Doctor Michael Eller's laboratory.To begin, in a designated pre-PCR molecular biology workstation, prepare the assay mix by combining 96 gene expression assays into an RNase, DNase free tube making sure to add each assay to a final concentration of 180 nanomolar of forward and reverse primers. Add DNA suspension buffer to achieve the appropriate dilution of the assay mix. For each anticipated 96 by 96 chip array, pipette six microliters of each assay into a designated well of a 96 well PCR plate, then seal the plate with an adhesive.To begin surface staining viable cells, in a tissue culture biosafety cabinet, first prepare the compensation samples and a master mix of fluorescent antibody cocktail as outlined in the text protocol. Thaw the cryopreserved cells in a 37 degree Celsius water bath for two minutes. After this, add between 5 and two milliliters of the cell suspension to a 15 milliliter tube containing 12 milliliters of PBS.Centrifuge at 500 G for three minutes at 25 degrees Celsius, then aspirate the PBS and resuspend the pellet in three milliliters of fresh PBS. Transfer this mixture to a five milliliter polystyrene tube. Centrifuge once again at 500 G for three minutes at 25 degrees Celsius.Aspirate the supernatant, leaving approximately 10 microliters residual PBS. Next, resuspend up to 20 million washed cells in 80 microliters of antibody cocktail. Incubate for 20 minutes at 25 degrees Celsius while protected from light.After this, add three milliliters of PBS. Centrifuge at 500 G for three minutes and aspirate the supernatant. Thoroughly resuspend the cells in 300 to 500 microliters of PBS.Filter this solution by pipetting it through a 35 micrometer nylon cell-strainer cap. then keep the cells on ice and protected from light until the sort. Back in the pre-PCR workstation, first prepare the RT preAMP reaction mix in a single RNase, DNase free sterile tube as outlined in the text protocol.Using a multichannel pipette, dispense 10 microliters of this reaction mix into the desired number of 96 well PCR sort collection plates. Seal the plate with adhesive film and then place the plate on a pre-chilled 96 well aluminum block. After this, establish the cell-sorting gating scheme and prepare the flow cytometric cell sorter as outlined in the text protocol.Enter the appropriate instrument settings to specify the number and subset of cells to be sorted into each well. Remove the adhesive seal, then FACS sort the cells into the prepared 96 well PCR collection plates. Reseal the plate with fresh adhesive film.Immediately vortex the resealed plate and then centrifuge at 2000 G for one minute at four degrees Celsius. Thermocycle the plate in a PCR machine with a preheated lid at 50 degrees Celsius for 15 minutes followed by 95 degrees Celsius for two minutes. Finally, thermocycle for 18 cycles of 95 degrees Celsius for 15 seconds and 60 degrees Celsius for four minutes, then in a post-PCR workstation, transfer five microliters of cDNA into 20 microliters of DNA suspension buffer in a new 96 well PCR plate.To begin, prepare the qPCR assay plate by pipetting four microliters of assay-loading reagent in each well. Next, transfer four microliters of each assay from the 2X assay plate to the qPCR plate. Maintain the qPCR assay plate at four degrees Celsius.Dispense the control line fluids from the priming syringes into the two intake valves of the chip. Remove the protective plastic from beneath the plate. Place the chip on an IFC controller with the notched side at the A1 position, then select and run the prime script.For each microfluidic chip, mix 50 microliters of the sample loading reagent with 500 microliters of PCR master mix to prepare the real-time reaction mix. Pipette 4.4 microliters of this reaction mix into each well of a new 96 well PCR plate now designated as the sample plate. Next, pipette 3.6 microliters of the previously diluted cDNA into every well of the sample plate.After this, transfer five microliters from the assay plate to the corresponding well on the notched side of the chip. Transfer five microliters from the sample plate into the corresponding well on the other side of the chip. Insert the loaded chip into the IFC controller and run the load mix script.Next, transfer the chip to the BioMark platform and set up the instrument as outlined in the text protocol, then save the chip run file in a designated folder. When the chip run is complete, open the real-time PCR analysis software and then select file, open, to open the chiprun. bml file.In the upper left corner of the software window, locate chip explorer and chip run summary. Under chip run summary, click on detector setup. Under task, click new and select the container type SBS plate and container format SBS 96.Click on the button with the ellipsis next to mapping, then select M96-Assay-SBS96.dsp. Click on analysis views. In the qPCR tab under task, select baseline correction for linear derivative and CT threshold method for user detectors.In the CT thresholds tab, check the initialize with auto box and then click the analyze button. In the upper right quadrant of analysis views, click on the second tab, results table. From the dropdown menu, select heat map view and the heat map with the data will appear.Under the heat map, click threshold and log graph. Adjust the CT thresholds manually for each detector by clicking on assays, represented by columns on the heat map, and dragging the threshold as necessary to intersect the amplification curves in the exponential phase. When done, click analyze.Next, export the qPCR data as a csv file. Transfer qPCR data from the BioMark computer to your desktop. Import the data into a spreadsheet or statistical analysis software and map the results by sample and assay positions on the chip.Create a new column and use a conditional formula to organize the cells into groups based on the expression of viral genes. Under analyze, select fit Y by X and plot gene expression versus group. After this, use FlowJo version nine to open FCS files from FACS sort corresponding to a 96 well plate.With the file name highlighted, select platform, event number gate, and create indexed sort gates. Individual cells will appear displayed by row. Highlight all 96 cells and then select workspace, export, select all compensated floors.Under data, select FCS file, click export, and select a designated folder. Drag the new FCS files for individual cells into a new FlowJo workspace. Highlight all of the cells and click add statistics, represented by the sigma button, mean, and all fluorescence parameters.Open the table editor, then highlight all the floors of the first cell and drag them into the table editor window. In this window, click create and view table. After this, copy and output into a statistical software either by copy-pasting or by clicking the save and launch application button.Merge the single cell FACS data with the qPCR data in JMP by the plate number and well position. Finally, perform graphical and statistical analyses on the combined data. In this study, single cell surface protein quantitation by multi-parameter flow cytometry is integrated with quantitative single cell mRNA expression by highly multiplexed RTqPCR.Shown here is single cell quantitative viral gene expression from FACS sorted Rhesus macaque CD4 positive T cells. Tat/rev positive N of negative cells express fewer copies of tat/rev RNA than the N of positive cells in dark green which is consistent with an early stage of infection prior to stabilization and a nuclear export of partially spliced viral RNA. A high abundance of unspliced Gag-RNA in tat/rev positive N of positive cells is consistent with late stage productive infection during which abundant genomic RNA is expressed and packaged into budding virions.This trivariate plot displaying the single cell viral gene, host gene, and host surface protein expression shows that surface CD4 and CD3 protein expression is decreased in tat/rev positive cells shown in green even though CD4 and CD3 transcripts are sustained. Once mastered, this technique can be done in two to three days. After watching this video, you should have a good understanding of how to perform targeted single cell prototranscriptional evaluation to address questions pertaining to differentially expressed genes and proteins in virally infected cells.

single-cell-quantitation-mrna-surface-protein-expression-simian

Research

JoVE Journal

Immunology and Infection

10.5K vistas.  Walter Reed Army Institute of Research. Este método puede ayudar a responder preguntas clave en el campo de las interacciones huésped-patógeno, como cuáles son las características únicas de las células infectadas productivamente, qué cofactores del huésped permiten a los virus establecer la infección productiva y cómo atacar estas células en intervenciones terapéuticas. La principal ventaja de esta técnica es que proporciona medidas cuantitativas para proteínas y ARNM dentro de células individuales, y la mayoría de...

Video: Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques