Name: single-cell quantitation of mrna and surface protein expression in simian immunodeficiency virus-infected cd4+ t cells isolated from rhesus macaques
Uploaded: 2018-09-25T13:00:00
Description: 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

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. V., Berkhout, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-associated+HIV+RNA:+a+dynamic+biomarker+of+viral+persistence.">Cell-associated HIV RNA: a dynamic biomarker of viral persistence.</a> Retrovirology. 10, 41 (2013).</li><li>Mailler, 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=The+Life-Cycle+of+the+HIV-1+Gag-RNA+Complex.">The Life-Cycle of the HIV-1 Gag-RNA Complex.</a> Viruses. 8 (9), (2016).</li><li>Varmus, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retroviruses.">Retroviruses.</a> Science. 240 (4858), 1427-1435 (1988).</li><li>Frankel, A. D., Young, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HIV-1:+fifteen+proteins+and+an+RNA.">HIV-1: fifteen proteins and an RNA.</a> Annu Rev Biochem. 67, 1-25 (1998).</li><li>Dominguez, M. H., 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=Highly+multiplexed+quantitation+of+gene+expression+on+single+cells.">Highly multiplexed quantitation of gene expression on single cells.</a> J Immunol Methods. 391 (1-2), 133-145 (2013).</li><li>Tokarev, A., Guatelli, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Misdirection+of+membrane+trafficking+by+HIV-1+Vpu+and+Nef:+Keys+to+viral+virulence+and+persistence.">Misdirection of membrane trafficking by HIV-1 Vpu and Nef: Keys to viral virulence and persistence.</a> Cell Logist. 1 (3), 90-102 (2011).</li><li>Malim, M. H., Bieniasz, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HIV+Restriction+Factors+and+Mechanisms+of+Evasion.">HIV Restriction Factors and Mechanisms of Evasion.</a> Cold Spring Harb Perspect Med. 2 (5), a006940 (2012).</li><li>Sugden, S. M., Bego, M. G., Pham, T. N., Cohen, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Remodeling+of+the+Host+Cell+Plasma+Membrane+by+HIV-1+Nef+and+Vpu:+A+Strategy+to+Ensure+Viral+Fitness+and+Persistence.">Remodeling of the Host Cell Plasma Membrane by HIV-1 Nef and Vpu: A Strategy to Ensure Viral Fitness and Persistence.</a> Viruses. 8 (3), 67 (2016).</li><li>Simon, V., Bloch, N., Landau, N. 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. R., 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=Ex+vivo+analysis+of+SIV-infected+cells+by+flow+cytometry.">Ex vivo analysis of SIV-infected cells by flow cytometry.</a> Cytometry A. 77 (11), 1059-1066 (2010).</li><li>Baxter, A. 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. 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=targeted+profiling+of+single-cell+proteomes+and+transcriptomes+in+a+single+reaction.">targeted profiling of single-cell proteomes and transcriptomes in a single reaction.</a> Genome Biol. 17 (1), 188 (2016).</li><li>Mahnke, Y. D., Roederer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimizing+a+multicolor+immunophenotyping+assay.">Optimizing a multicolor immunophenotyping assay.</a> Clin Lab Med. 27 (3), 469-485 (2007).</li><li>Liang, C., Hu, J., Russell, R. S., Kameoka, M., Wainberg, M. 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. K. <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+protein+analysis.">Single-cell protein analysis.</a> Curr Opin Biotechnol. 23 (1), 83-88 (2012).</li><li>Haque, A., Engel, J., Teichmann, S. A., Lonnberg, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+practical+guide+to+single-cell+RNA-sequencing+for+biomedical+research+and+clinical+applications.">A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.</a> Genome Med. 9 (1), 75 (2017).</li></ol>

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 ...

Research

JoVE Journal

Immunology and Infection

Multicolor Flow Cytometry Analyses of Cellular Immune Response in Rhesus Macaques

The rhesus macaque model is currently the best available model for HIV-AIDS with respect to understanding the pathogenesis as well as for the development ...

Retroviral Transduction of T-cell Receptors in Mouse T-cells

T-cell receptors (TCRs) play a central role in the immune system. TCRs on T-cell surfaces can specifically recognize peptide antigens presented by antigen ...

Quantitative High-throughput Single-cell Cytotoxicity Assay For T Cells

Cancer immunotherapy can harness the specificity of  immune response to target and eliminate tumors. Adoptive cell therapy (ACT)  based on the adoptive ...

Accelerated Type 1 Diabetes Induction in Mice by Adoptive Transfer of Diabetogenic CD4+ T Cells

The nonobese diabetic (NOD) mouse spontaneously develops autoimmune diabetes after 12 weeks of age and is the most extensively studied animal model of ...

Adenoviral Transduction of Naive CD4 T Cells to Study Treg Differentiation

Regulatory T cells (Tregs) are essential to provide immune tolerance to self as well as to certain foreign antigens. Tregs can be generated from naive CD4 ...

Isolation and Th17 Differentiation of Na&iuml;ve CD4 T Lymphocytes

Isolation and Th17 Differentiation of NaÃƒÆ’Ã‚Â¯ve CD4 T Lymphocytes

Th17 cells are a distinct subset of T cells that have been found to produce interleukin 17 (IL-17), and differ in function from the other T cell subsets ...

Induction of Murine Intestinal Inflammation by Adoptive Transfer of Effector CD4+CD45RBhigh T Cells into Immunodeficient Mice

Induction of Murine Intestinal Inflammation by Adoptive Transfer of Effector CD4+CD45RBhigh T Cells into Immunodeficient Mice

There are many different animal models available for studying the pathogenesis of human inflammatory bowel diseases (IBD), each with its own advantages ...

Mouse Na&#239;ve CD4+ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Mouse NaÃƒÆ’Ã‚Â¯ve CD4+ T Cell Isolation and In vitro Differentiation into T Cell Subsets

Antigen inexperienced (na&#239;ve) CD4+ T cells undergo expansion and differentiation to effector subsets at the time of T cell receptor (TCR) recognition ...

Imaging CD4 T Cell Interstitial Migration in the Inflamed Dermis

The ability of CD4 T cells to carry out effector functions is dependent upon the rapid and efficient migration of these cells in inflamed peripheral ...

Phenotypic and Functional Analysis of Activated Regulatory T Cells Isolated from Chronic Lymphocytic Choriomeningitis Virus-infected Mice

Regulatory T (Treg) cells, which express Foxp3 as a transcription factor, are subsets of CD4+ T cells. Treg cells play crucial roles in immune tolerance ...