Name: a combinatorial single-cell approach to characterize the molecular and immunophenotypic heterogeneity of human stem and progenitor populations
Uploaded: 2018-10-25T12:30:00
Description: Watch this Scientific Journal Video about A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations at JoVE.com

This work is supported by grants from the Swedish Cancer Society, The Swedish Research Council, The Swedish Society for Medical Research, The Swedish Childhood Cancer Foundation, The Ragnar S&#246;derberg Foundation, and The Knut and Alice Wallenberg Foundation

1. Preparation of Lysis Plates
<ol>
	<li>Using a RNA/DNA free bench, prepare enough lysis buffer for 96 wells, with 10% extra, by mixing 390 &#181;L nuclease free water, 17 &#181;L of 10% NP-40, 2.8 &#181;L 10 mM dNTP, 10 &#181;L 0.1 M DTT and 5.3 &#181;L RNAse inhibitor (see Table of Materials). Vortex and spin down.</li>
	<li>Distribute 4 &#181;L of lysis buffer to each well of a 96 well PCR plate and seal the plates with adhesive film. Spin down tubes to collect liquid at the bottom of the plates. K...

<ol>
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Y., 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=Batch+effects+and+the+effective+design+of+single-cell+gene+expression+studies.">Batch effects and the effective design of single-cell gene expression studies.</a> Scientific Reports. 7, 39921 (2017).</li><li>Teles, J., Enver, T., Pina, C. <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+PCR+profiling+of+gene+expression+in+hematopoiesis.">Single-cell PCR profiling of gene expression in hematopoiesis.</a> Methods in Molecular Biology. , 21-42 (2014).</li><li>Hayashi, 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=Single-cell+gene+profiling+of+planarian+stem+cells+using+fluorescent+activated+cell+sorting+and+its+&amp;#34;index+sorting&amp;#34;+function+for+stem+cell+research.">Single-cell gene profiling of planarian stem cells using fluorescent activated cell sorting and its &amp;#34;index sorting&amp;#34; function for stem cell research.</a> Development, Growth, &amp; Differentiation. 52 (1), 131-144 (2010).</li><li>Lang, 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=SCExV:+A+webtool+for+the+analysis+and+visualisation+of+single+cell+qRT-PCR+data.">SCExV: A webtool for the analysis and visualisation of single cell qRT-PCR data.</a> BMC Bioinformatics. 16 (1), 320 (2015).</li><li>de Klein, 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=A+cellular+oncogene+is+translocated+to+the+Philadelphia+chromosome+in+chronic+myelocytic+leukaemia.">A cellular oncogene is translocated to the Philadelphia chromosome in chronic myelocytic leukaemia.</a> Nature. 300 (5894), 765-767 (1982).</li><li>. indexed-sorting Available from: <a target="_blank" href="https://github.com/FlowJo-LLC/indexed-sorting">https://github.com/FlowJo-LLC/indexed-sorting</a> (2016)</li><li>Warfvinge, 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=Single-cell+molecular+analysis+defines+therapy+response+and+immunophenotype+of+stem+cell+subpopulations+in+CML.">Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML.</a> Blood. 129 (17), 2384-2394 (2017).</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>Breton, 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=Human+dendritic+cells+(DCs)+are+derived+from+distinct+circulating+precursors+that+are+precommitted+to+become+CD1c++or+CD141++DCs.">Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs.</a> The Journal of Experimental Medicine. 213 (13), 2861-2870 (2016).</li><li>Alberti-Servera, 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=Single-cell+RNA+sequencing+reveals+developmental+heterogeneity+among+early+lymphoid+progenitors.">Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors.</a> The EMBO Journal. 36 (24), 3619-3633 (2017).</li><li>Giustacchini, 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=Single-cell+transcriptomics+uncovers+distinct+molecular+signatures+of+stem+cells+in+chronic+myeloid+leukemia.">Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia.</a> Nature Medicine. 23, 692 (2017).</li><li>Hansmann, L., Han, A., Penter, L., Liedtke, M., Davis, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+expansion+and+interrelatedness+of+distinct+B-lineage+compartments+in+multiple+myeloma+bone+marrow.">Clonal expansion and interrelatedness of distinct B-lineage compartments in multiple myeloma bone marrow.</a> Cancer Immunology Research. 5 (9), 744-754 (2017).</li><li>Psaila, B., 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+profiling+of+human+megakaryocyte-erythroid+progenitors+identifies+distinct+megakaryocyte+and+erythroid+differentiation+pathways.">Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways.</a> Genome Biology. 17 (1), 83 (2016).</li><li>Schulte, 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=Index+sorting+resolves+heterogeneous+murine+hematopoietic+stem+cell+populations.">Index sorting resolves heterogeneous murine hematopoietic stem cell populations.</a> Experimental Hematology. 43 (9), 803-811 (2015).</li></ol>

In recent years, single-cell gene expression analysis has become a valuable addition to define the heterogeneity of various cell populations23. The advent of RNA sequencing technologies theoretically provides a possibility to measure the entire transcriptome of a cell, however these methods are complicated by variations in cell-to-cell sequencing depths and drop-outs. Single-cell qPCR offers a sensitive and robust analysis of the expression of hundreds of critical genes where all cells are treated...

Each individual blood cell is believed to reside in a cellular hierarchy, where stem cells form the apex on top of a series of increasingly committed intermediate progenitors that eventually terminally differentiate into the final effector cells carrying specific biological functions1. Much of the knowledge about how stem cell systems are organized has been generated in the hematopoietic system, largely because of the ability to prospectively isolate distinct hematopoietic populations highly enriched for stem cells or various progenitors2 by FACS sorting. This has allowed for many of these populations to be analyzed functionally or molecularly, predominantly through gene expression profiling3,4. However when analyzing gene expression of bulk populations individual differences between cells are averaged out and lost5. Thus, incapacity to detect cell-to-cell variations within heterogeneous cell fractions may confound our understanding of critical biological processes if small subsets of cells account for the inferred biological function of that population6,7. Conversely, investigation of gene expression signatures at single-cell resolution offer a possibility to delineate heterogeneity and circumvent overshadowing influences from overrepresented subsets of cells8.
To date many protocols for single-cell gene expression analysis have been developed; with each approach having its own caveats. The earliest method was RNA Fluorescent in situ hybridization (RNA-FISH), which measures a limited number of transcripts at a time but is unique in that it allows for investigation of RNA localization9,11. Early methods using PCR and qPCR to detect a single or very few transcripts were also developed12. However, these have lately been replaced by microfluidics-based methods which can simultaneously analyze the expression of hundreds of transcripts per cell in hundreds of cells through qPCR and thus allow for high-dimensional heterogeneity analysis using pre-determined gene panels10,13. Recently RNA sequencing-based technologies have become widely used for single cell analysis, as these theoretically can measure the entire transcriptome of a cell and thereby add an exploratory dimension to heterogeneity analysis10,14. Multiplexed qPCR analysis and single-cell RNA sequencing have different features, thus the rationale for using either of the methods depends on the question asked as well as the number of cells in the target population. The high-throughput and low cost per cell together with unbiased, exploratory characteristics of single-cell RNA sequencing are desirable when unknown cell or large populations are investigated. However, single-cell RNA sequencing is also biased towards sequencing high abundant transcripts more frequently while transcripts with low abundance are prone to dropouts. This can lead to considerably complex data that puts high-demands on bioinformatic analysis to reveal important molecular signals that are often subtle or hidden in technical noise15. Thus, for well-characterized tissues, single-cell qPCR analysis using pre-determined primer panels selected for functionally important genes or molecular markers can serve as a sensitive, straightforward approach to determine the heterogeneity of a population. However, it should be noted that compared to single-cell RNA-seq, the cost per cell is generally higher for single-cell qPCR methods. Here, we describe an approach that combines single-cell RT-qPCR (modified from Teles J. et al.16), to FACS index sorting17 and bioinformatics analysis18 in order to simultaneously characterize the molecular and immunophenotypic heterogeneity within populations.
In this approach, the cell population of interest is stained, and single-cells are sorted by FACS directly into lysis buffer in 96-well PCR plates. Simultaneously, the expression levels of an additional set of cell-surface markers are recorded for each single cell during FACS-sorting, a method that is referred to as index-sorting. The lysed cell material is subsequently amplified and the gene expression of a selected set of genes analyzed with RT-qPCR, using a microfluidic platform. This strategy enables molecular analysis of the sorted single-cell as well as simultaneous characterization of each individual cell&#39;s cell-surface marker expression. By directly mapping molecularly distinct subsets of cells to the expression of the indexed sorted markers, the subpopulations can be linked to a specific immunophenotype that can be used for their prospective isolation. The method is outlined step by step in Figure 1. A pre-determined gene panel further contributes to a higher resolution of the targeted gene expression, since it circumvents measurement of irrelevant abundant genes that can otherwise occlude subtle gene expression signals. Moreover, the specific target amplification, one step reverse transcription, and amplification allows for robust measurement of low expressed transcripts, like transcription factors or non-poly-adenylated RNAs. Importantly, qPCR methods allow for measurement of mRNA from fusion proteins, which are important when investigating certain malignant diseases19. Finally, the focused number of genes investigated, low drop-out rates, and limited technical differences between cells make this method easily analyzed compared to higher dimensional methods, such as single-cell RNA-seq. By following the protocol, an entire experiment can be performed, from sorting cells to analyzed results, within three days, making this an uncomplicated and quick method for sensitive, high-throughput single-cell gene expression analysis.

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			<td>BioRad</td>
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			<td>Thermo scientific&#160;</td>
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			<td>Invitrogen</td>
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			<td>Invitrogen</td>
			<td>11753-100</td>
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			<td>Neuclease free water</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
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			<td>2X Reaction Mix</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
			<td>from CellsDirect kit</td>
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			<td>SuperScript III RT/Platinum Taq Mix</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
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			<td>Platinum&#160;Taq&#160;DNA Polymerase</td>
			<td>Invitrogen</td>
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			<td>Xeno RNA Control</td>
			<td>Invitrogen</td>
			<td>4386995</td>
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			<td>20X Xeno RNA Control Taqman Gene Expression Assay</td>
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			<td>4386995</td>
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			<td>Fluidigm</td>
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			<td>From 96.96 Sample/Loading Kit</td>
		</tr>
		<tr>
			<td>20X GE Sample Loading Reagent</td>
			<td>Fluidigm</td>
			<td></td>
			<td>From 96.96 Sample/Loading Kit</td>
		</tr>
		<tr>
			<td>Control line fluid&#160;</td>
			<td>Fluidigm</td>
			<td></td>
			<td>From 96.96 Sample/Loading Kit</td>
		</tr>
		<tr>
			<td>TaqMan Gene Expression Master Mix</td>
			<td>Applied Biosystems</td>
			<td>4369016</td>
			<td></td>
		</tr>
		<tr>
			<td>BioMark HD</td>
			<td>Fluidigm</td>
			<td>BMKHD-BMKHD</td>
			<td></td>
		</tr>
		<tr>
			<td>96.96 Dynamic Array IFC</td>
			<td>Fluidigm</td>
			<td>BMK-M10-96.96GT</td>
			<td></td>
		</tr>
		<tr>
			<td>Excel</td>
			<td>Microsoft</td>
			<td>Microsoft</td>
			<td></td>
		</tr>
		<tr>
			<td>FlowJo V10</td>
			<td>TreeStar</td>
			<td>TreeStar</td>
			<td></td>
		</tr>
		<tr>
			<td>Fluidigm real time PCR analysis</td>
			<td>Fluidigm</td>
			<td>Fluidigm</td>
			<td></td>
		</tr>
		<tr>
			<td>CD179a.VPREB1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00356766_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>ACE</td>
			<td>Thermofisher scientific</td>
			<td>Hs00174179_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>AHR</td>
			<td>Thermofisher scientific</td>
			<td>Hs00169233_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>BCR_ABL.52</td>
			<td>Thermofisher scientific</td>
			<td>Hs03043652_ft</td>
			<td></td>
		</tr>
		<tr>
			<td>BCR_ABL41</td>
			<td>Thermofisher scientific</td>
			<td>Hs03024541_ft</td>
			<td></td>
		</tr>
		<tr>
			<td>BMI1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00995536_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNA2</td>
			<td>Thermofisher scientific</td>
			<td>Hs00996788_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNB1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01030099_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNB2</td>
			<td>Thermofisher scientific</td>
			<td>Hs01084593_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNC</td>
			<td>Thermofisher scientific</td>
			<td>Hs01029304_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNE1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01026535_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCNF</td>
			<td>Thermofisher scientific</td>
			<td>Hs00171049_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CCR9</td>
			<td>Thermofisher scientific</td>
			<td>Hs01890924_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD10.MME</td>
			<td>Thermofisher scientific</td>
			<td>Hs00153510_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD11a</td>
			<td>Thermofisher scientific</td>
			<td>Hs00158218_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD11c.ITAX</td>
			<td>Thermofisher scientific</td>
			<td>Hs00174217_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD123.IL3RA</td>
			<td>Thermofisher scientific</td>
			<td>Hs00608141_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD133.PROM1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01009250_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD151</td>
			<td>Thermofisher scientific</td>
			<td>Hs00911635_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD220.INSR</td>
			<td>Thermofisher scientific</td>
			<td>Hs00961554_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD24.HSA</td>
			<td>Thermofisher scientific</td>
			<td>Hs03044178_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>NCOR1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01094540_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD26.DPP4</td>
			<td>Thermofisher scientific</td>
			<td>Hs00175210_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD274</td>
			<td>Thermofisher scientific</td>
			<td>Hs01125301_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD276</td>
			<td>Thermofisher scientific</td>
			<td>Hs00987207_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD32.FCGR2B</td>
			<td>Thermofisher scientific</td>
			<td>Hs01634996_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD33</td>
			<td>Thermofisher scientific</td>
			<td>Hs01076281_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD34</td>
			<td>Thermofisher scientific</td>
			<td>Hs00990732_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD344.FZD4</td>
			<td>Thermofisher scientific</td>
			<td>Hs00201853_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD352.SLAMF6</td>
			<td>Thermofisher scientific</td>
			<td>Hs01559920_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD38</td>
			<td>Thermofisher scientific</td>
			<td>Hs01120071_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD4</td>
			<td>Thermofisher scientific</td>
			<td>Hs01058407_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD41.ITGA2B</td>
			<td>Thermofisher scientific</td>
			<td>Hs01116228_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD49f.ITGA6</td>
			<td>Thermofisher scientific</td>
			<td>Hs01041011_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD56.NCAM1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00941830_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD9</td>
			<td>Thermofisher scientific</td>
			<td>Hs00233521_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD97</td>
			<td>Thermofisher scientific</td>
			<td>Hs00173542_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CD99</td>
			<td>Thermofisher scientific</td>
			<td>Hs00908458_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CDK6</td>
			<td>Thermofisher scientific</td>
			<td>Hs01026371_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CDKN1A</td>
			<td>Thermofisher scientific</td>
			<td>Hs00355782_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CDKN1B</td>
			<td>Thermofisher scientific</td>
			<td>Hs01597588_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CDKN1C</td>
			<td>Thermofisher scientific</td>
			<td>Hs00175938_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CEBPa</td>
			<td>Thermofisher scientific</td>
			<td>Hs00269972_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>CSF1r</td>
			<td>Thermofisher scientific</td>
			<td>Hs00911250_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>CSF2RA</td>
			<td>Thermofisher scientific</td>
			<td>Hs00531296_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>CSF3RA</td>
			<td>Thermofisher scientific</td>
			<td>Hs01114427_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>E2A.TCF3</td>
			<td>Thermofisher scientific</td>
			<td>Hs00413032_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>EBF1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01092694_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>ENG</td>
			<td>Thermofisher scientific</td>
			<td>Hs00923996_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>EPOR</td>
			<td>Thermofisher scientific</td>
			<td>Hs00959427_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>ERG</td>
			<td>Thermofisher scientific</td>
			<td>Hs01554629_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>FLI1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00956711_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>FLT3</td>
			<td>Thermofisher scientific</td>
			<td>Hs00174690_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>FOXO1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01054576_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>GAPDH</td>
			<td>Thermofisher scientific</td>
			<td>Hs02758991_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>GATA1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00231112_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>GATA2</td>
			<td>Thermofisher scientific</td>
			<td>Hs00231119_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>GATA3</td>
			<td>Thermofisher scientific</td>
			<td>Hs00231122_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>GFI1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00382207_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>HES1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01118947_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>HLF</td>
			<td>Thermofisher scientific</td>
			<td>Hs00171406_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>HMGA2</td>
			<td>Thermofisher scientific</td>
			<td>Hs00171569_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>HOXA5</td>
			<td>Thermofisher scientific</td>
			<td>Hs00430330_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>HOXB4</td>
			<td>Thermofisher scientific</td>
			<td>Hs00256884_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>ID2</td>
			<td>Thermofisher scientific</td>
			<td>Hs04187239_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IGF2BP1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00198023_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IGF2BP2</td>
			<td>Thermofisher scientific</td>
			<td>Hs01118009_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IKZF1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00172991_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IL1RAP</td>
			<td>Thermofisher scientific</td>
			<td>Hs00895050_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IL2RG</td>
			<td>Thermofisher scientific</td>
			<td>Hs00953624_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>IRF8</td>
			<td>Thermofisher scientific</td>
			<td>Hs00175238_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>ITGB7</td>
			<td>Thermofisher scientific</td>
			<td>Hs01565750_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>KIT</td>
			<td>Thermofisher scientific</td>
			<td>Hs00174029_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Lin28B</td>
			<td>Thermofisher scientific</td>
			<td>Hs01013729_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>LMO2</td>
			<td>Thermofisher scientific</td>
			<td>Hs00153473_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>LYL1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01089802_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>Meis1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01017441_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>mKi67</td>
			<td>Thermofisher scientific</td>
			<td>Hs01032443_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>MPL</td>
			<td>Thermofisher scientific</td>
			<td>Hs00180489_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>MPO</td>
			<td>Thermofisher scientific</td>
			<td>Hs00924296_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>NFIB</td>
			<td>Thermofisher scientific</td>
			<td>Hs01029175_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Notch1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01062011_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Pten</td>
			<td>Thermofisher scientific</td>
			<td>Hs02621230_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>RAG2</td>
			<td>Thermofisher scientific</td>
			<td>Hs01851142_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>RPS18</td>
			<td>Thermofisher scientific</td>
			<td>Hs01375212_g1</td>
			<td></td>
		</tr>
		<tr>
			<td>RUNX1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00231079_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Shisa2</td>
			<td>Thermofisher scientific</td>
			<td>Hs01590823_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Spi1</td>
			<td>Thermofisher scientific</td>
			<td>Hs02786711_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile.IgH</td>
			<td>Thermofisher scientific</td>
			<td>Hs00378435_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>TAL1</td>
			<td>Thermofisher scientific</td>
			<td>Hs01097987_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>THY1</td>
			<td>Thermofisher scientific</td>
			<td>Hs00264235_s1</td>
			<td></td>
		</tr>
		<tr>
			<td>Tim.3.HAVCR2</td>
			<td>Thermofisher scientific</td>
			<td>Hs00958618_m1</td>
			<td></td>
		</tr>
		<tr>
			<td>VWF</td>
			<td>Thermofisher scientific</td>
			<td>Hs00169795_m1</td>
			<td></td>
		</tr>
	</tbody>
</table>

a combinatorial single-cell approach to characterize the molecular and immunophenotypic heterogeneity of human stem and progenitor populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

The protocol described is quick, easily performed and highly reliable. An overview of the experimental set-up is presented in Figure 1. The entire protocol, from sorting of single-cells, to specific target amplification, gene expression measurements and preliminary analysis can be performed in three days. An example of analyzed results in the form of a heat map that represents preliminary analyzed data from single-cell gene expression analysis using 96 primer...

Watch this Scientific Journal Video about A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations at JoVE.com

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Research

JoVE Journal

Genetics

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ...

Highly Efficient Gene Disruption of Murine and Human Hematopoietic Progenitor Cells by CRISPR/Cas9

Advances in the hematopoietic stem cell (HSCs) field have been aided by methods to genetically engineer primary progenitor cells as well as animal models. ...

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Preparation and Gene Modification of Nonhuman Primate Hematopoietic Stem and Progenitor Cells

Hematopoietic stem and progenitor cell (HSPC) transplantation has been a cornerstone therapy for leukemia and other cancers for nearly half a century, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Generation of Defined Genomic Modifications Using CRISPR-CAS9 in Human Pluripotent Stem Cells

Human pluripotent stem cells offer a powerful system to study gene function and model specific mutations relevant to disease. The generation of precise ...