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.

<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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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. 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This work was supported by a cooperative agreement (W81XWH-07-2-0067) between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense (DOD). 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 AAALACi 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.

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 ...

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Immunology and Infection

10.5K Views.  Walter Reed Army Institute of Research. 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.

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

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 of vaccines and therapeutics1,2,3. Here, we describe a method for the detailed phenotypic and functional analyses of cellular immune responses, specifically intracellular cytokine production by CD4+ and CD8+ T cells as well as the individual memory subsets. We obtained precise quantitative and qualitative measures for the production of interferon gamma (INF-) and interleukin (IL) -2 in both CD4+ and CD8+ T cells from the rhesus macaque PBMC stimulated with PMA plus ionomycin (PMA+I). The cytokine profiles were different in the different subsets of memory cells. Furthermore, this protocol provided us the sensitivity to demonstrate even minor fractions of antigen specific CD4+ and CD8+ T cell subsets within the PBMC samples from rhesus macaques immunized with an HIV envelope peptide cocktail vaccine developed in our laboratory. The multicolor flow cytometry technique is a powerful tool to precisely identify different populations of T cells 4,5 with cytokine-producing capability6 following non-specific or antigen-specific stimulation 5,7.

We demonstrate the utility of multicolor flow cytometry for detailed phenotypic and functional characterization of total as well as memory subsets of CD4+ and CD8+ T cells in rhesus macaques, the ideal model for HIV/AIDS vaccine studies.

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 presenting cells (APCs)1. This recognition leads to the activation of T-cells and a series of functional outcomes (e.g. cytokine production, killing of the target cells). Understanding the functional role of TCRs is critical to harness the power of the immune system to treat a variety of immunology related diseases (e.g. cancer or autoimmunity).
It is convenient to study TCRs in mouse models, which can be accomplished in several ways. Making TCR transgenic mouse models is costly and time-consuming and currently there are only a limited number of them available2-4. Alternatively, mice with antigen-specific T-cells can be generated by bone marrow chimera. This method also takes several weeks and requires expertise5. Retroviral transduction of TCRs into in vitro activated mouse T-cells is a quick and relatively easy method to obtain T-cells of desired peptide-MHC specificity. Antigen-specific T-cells can be generated in one week and used in any downstream applications. Studying transduced T-cells also has direct application to human immunotherapy, as adoptive transfer of human T-cells transduced with antigen-specific TCRs is an emerging strategy for cancer treatment6.
Here we present a protocol to retrovirally transduce TCRs into in vitro activated mouse T-cells. Both human and mouse TCR genes can be used. Retroviruses carrying specific TCR genes are generated and used to infect mouse T-cells activated with anti-CD3 and anti-CD28 antibodies. After in vitro expansion, transduced T-cells are analyzed by flow cytometry.

We present a protocol to produce antigen-specific mouse T-cells using retroviral transduction

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 transfer of T cells genetically modified to express a chimeric antigen receptor (CAR) has shown considerable promise in clinical trials1-4. There are several advantages to using CAR+ T cells for the treatment of cancers including the ability to target non-MHC restricted antigens and to functionalize the T cells for optimal survival, homing and persistence within the host; and finally to induce apoptosis of CAR+ T cells in the event of host toxicity5.
Delineating the optimal functions of CAR+ T cells associated with clinical benefit is essential for designing the next generation of clinical trials. Recent advances in live animal imaging like multiphoton microscopy have revolutionized the study of immune cell function in vivo6,7. While these studies have advanced our understanding of T-cell functions in vivo, T-cell based ACT in clinical trials requires the need to link molecular and functional features of T-cell preparations pre-infusion with clinical efficacy post-infusion, by utilizing in vitro assays monitoring T-cell functions like, cytotoxicity and cytokine secretion. Standard flow-cytometry based assays have been developed that determine the overall functioning of populations of T cells at the single-cell level but these are not suitable for monitoring conjugate formation and lifetimes or the ability of the same cell to kill multiple targets8.
Microfabricated arrays designed in biocompatible polymers like polydimethylsiloxane (PDMS) are a particularly attractive method to spatially confine effectors and targets in small volumes9. In combination with automated time-lapse fluorescence microscopy, thousands of effector-target interactions can be monitored simultaneously by imaging individual wells of a nanowell array. We present here a high-throughput methodology for monitoring T-cell mediated cytotoxicity at the single-cell level that can be broadly applied to studying the cytolytic functionality of T cells.

We describe a single-cell high-throughput assay to measure cytotoxicity of T cells when incubated with tumor target cells. This method employs a dense, elastomeric array of sub-nanoliter wells (~100,000 wells/array) to spatially confine the T cells and target cells at defined ratios and is coupled to fluorescence microscopy to monitor effector-target conjugation and subsequent apoptosis.

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 human Type 1 diabetes (T1D). Cell transfer studies in irradiated recipient mice have established that T cells are pivotal in T1D pathogenesis in this model. We describe herein a simple method to rapidly induce T1D by adoptive transfer of purified, primary CD4+ T cells from pre-diabetic NOD mice transgenic for the islet-specific T cell receptor (TCR) BDC2.5 into NOD.SCID recipient mice. The major advantages of this technique are that isolation and adoptive transfer of diabetogenic T cells can be completed within the same day, irradiation of the recipients is not required, and a high incidence of T1D is elicited within 2 weeks after T cell transfer. Thus, studies of pathogenesis and therapeutic interventions in T1D can proceed at a faster rate than with methods that rely on heterogenous T cell populations or clones derived from diabetic NOD mice.

We provide a reproducible method to induce type 1 diabetes (T1D) in mice within two weeks by the adoptive transfer of islet antigen-specific, primary 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 T cells in vitro with TCR- and co-stimulation in the presence of TGF&beta; and IL-2. This bears enormous potential for future therapies, however, the molecules and signaling pathways that control differentiation are largely unknown.
Primary T cells can be manipulated through ectopic gene expression, but common methods fail to target the most important naive state of the T cell prior to primary antigen recognition. Here, we provide a protocol to express ectopic genes in naive CD4 T cells in vitro before inducing Treg differentiation. It applies transduction with the replication-deficient adenovirus and explains its generation and production. The adenovirus can take up large inserts (up to 7 kb) and can be equipped with promoters to achieve high and transient overexpression in T cells. It effectively transduces naive mouse T cells if they express a transgenic Coxsackie adenovirus receptor (CAR). Importantly, after infection the T cells remain naive (CD44low, CD62Lhigh) and resting (CD25-, CD69-) and can be activated and differentiated into Tregs similar to non-infected cells. Thus, this method enables manipulation of CD4 T cell differentiation from its very beginning. It ensures that ectopic gene expression is already in place when early signaling events of the initial TCR stimulation induces cellular changes that eventually lead into Treg differentiation.

Adenoviral gene transfer into naive CD4 T cells with transgenic expression of the Coxsackie adenovirus receptor enables the molecular analysis of regulatory T cell differentiation in vitro.

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

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 including Th1, Th2, and regulatory T cells. Th17 cells have emerged as a central culprit in overzealous inflammatory immune responses associated with many autoimmune disorders. In this method we purify T lymphocytes from the spleen and lymph nodes of C57BL/6 mice, and stimulate purified CD4+ T cells under control and Th17-inducing environments. The Th17-inducing environment includes stimulation in the presence of anti-CD3 and anti-CD28 antibodies, IL-6, and TGF-&#946;. After incubation for at least 72 hours and for up to five days at 37 &#176;C, cells are subsequently analyzed for the capability to produce IL-17 through flow cytometry, qPCR, and ELISAs. Th17 differentiated CD4+CD25- T cells can be utilized to further elucidate the role that Th17 cells play in the onset and progression of autoimmunity and host defense. Moreover, Th17 differentiation of CD4+CD25- lymphocytes from distinct murine knockout/disease models can contribute to our understanding of cell fate plasticity.

Isolation and Th17 Differentiation of Na&#239;ve CD4 T Lymphocytes

With effector functions distinct from other T cell subsets, Th17 cells have been centrally implicated in inflammatory autoimmunity. This in vitro Th17 differentiation protocol provides a means to determine whether na&#239;ve CD4+ T lymphocytes can differentiate into Th17 cells, and to further examine their role in autoimmunity and host response.

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

There are many different animal models available for studying the pathogenesis of human inflammatory bowel diseases (IBD), each with its own advantages and disadvantages. We describe here an experimental colitis model that is initiated by adoptive transfer of syngeneic splenic CD4+CD45RBhigh T cells into T and B cell deficient recipient mice. The CD4+CD45RBhigh T cell population that largely consists of na&#239;ve effector cells is capable of inducing chronic intestinal inflammation, closely resembling key aspects of human IBD. This method can be manipulated to study aspects of disease onset and progression. Additionally it can be used to study the function of innate, adaptive, and regulatory immune cell populations, and the role of environmental exposures, i.e., the microbiota, in intestinal inflammation. In this article we illustrate the methodology for inducing colitis with a step-by-step protocol. This includes a video demonstration of key technical aspects required to successfully develop this murine model of experimental colitis for research purposes.

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

Here, we present a protocol to induce colonic inflammation in mice by adoptive transfer of syngeneic CD4+CD45RBhigh T cells into T and B cell deficient recipients. Clinical and histopathological features mimic human inflammatory bowel diseases. This method allows the study of the initiation of colonic inflammation and progression of disease.

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

Antigen inexperienced (na&#239;ve) CD4+ T cells undergo expansion and differentiation to effector subsets at the time of T cell receptor (TCR) recognition of cognate antigen presented on MHC class II. The cytokine signals present in the environment at the time of TCR activation are a major factor in determining the effector fate of a na&#239;ve CD4+ T cell. Although the cytokine environment during na&#239;ve T cell activation may be complex and involve both redundant and opposing signals in vivo, the addition of various cytokine combinations during naive CD4+ T cell activation in vitro can readily promote the establishment of effector T helper lineages with hallmark cytokine and transcription factor expression. Such differentiation experiments are commonly used as a first step for the evaluation of targets believed to promote or inhibit the development of certain CD4+ T helper subsets. The addition of mediators, such as signaling agonists, antagonists, or other cytokines, during the differentiation process can also be used to study the influence of a particular target on T cell differentiation. Here, we describe a basic protocol for the isolation of na&#239;ve T cells from mouse and the subsequent steps necessary for polarizing na&#239;ve cells to various T helper effector lineages in vitro.

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

Na&#239;ve CD4+ T cells polarize to various subsets depending on the environment at the time of activation. The differentiation of na&#239;ve CD4+ T cells to various effector subsets can be achieved in vitro through the addition of T cell receptor stimuli and specific cytokine signals.

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

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 and homeostasis maintenance by regulating the immune response. The primary role of Treg cells is to suppress the proliferation of effector T (Teff) cells and the production of cytokines such as IFN-&#947;, TNF-&#945;, and IL-2. It has been demonstrated that Treg cells&#39; ability to inhibit the function of Teff cells is enhanced during persistent pathogen infection and cancer development. To clarify the function of Treg cells under resting or inflamed conditions, a variety of in vitro suppression assays using mouse or human Treg cells have been devised. The main aim of this study is to develop a method to compare the differences in phenotype and suppressive function between resting and activated Treg cells. To isolate activated Treg cells, mice were infected with lymphocytic choriomeningitis virus (LCMV) clone 13 (CL13), a chronic strain of LCMV. Treg cells isolated from the spleen of LCMV CL13-infected mice exhibited both the activated phenotype and enhanced suppressive activity compared with resting Treg cells isolated from na&#239;ve mice. Here, we describe the basic protocol for ex vivo phenotype analysis to distinguish activated Treg cells from resting Treg cells. Furthermore, we describe a protocol for the measurement of the suppressive activity of fully activated Treg cells.

Here, we describe a protocol to analyze the phenotype of regulatory T (Treg) cells isolated from na&#239;ve and chronic lymphocytic choriomeningitis virus-infected mice. In addition, we provide a process to evaluate the suppressive activity of the Treg cells.

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 ...

Determination of Biofilm Initiation on Virus-infected Cells by Bacteria and Fungi

The study of polymicrobial interactions across the taxonomic kingdoms that include fungi, bacteria and virus have not been previously examined with respect to how viral members of the microbiome affect subsequent microbe interactions with these virus-infected host cells. The co-habitation of virus with bacteria and fungi is principally present on the mucosal surfaces of the oral cavity and genital tract. Mucosal cells, particularly those with persistent chronic or persistent latent viral infections, could have a significant impact on members of the microbiome through virus alteration in number and type of receptors expressed. Modification in host cell membrane architecture would result in altered ability of subsequent members of the normal flora and opportunistic pathogens to initiate the first step in biofilm formation, i.e., adherence. This study describes a method for quantitation and visual examination of HSV&#39;s effect on the initiation of biofilm formation (adherence) of S. aureus and C. albicans.

A method is described herein for the determination of inter-Kingdom association and competition (bacterial and fungal) for adherence to virus-infected HeLa cell monolayers. This protocol can be extended to multiple combinations of prokaryotes, eukaryotes, and viruses.

The study of polymicrobial interactions across the taxonomic kingdoms that include fungi, bacteria and virus have not been previously examined with ...

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

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