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>
	<li>Seita, J., Weissman, I. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hematopoietic+stem+cell:+Self-renewal+versus+differentiation.">Hematopoietic stem cell: Self-renewal versus differentiation.</a> Wiley Interdiscip Rev Syst Biol Med. 2 (6), 640-653 (2010).</li><li>Orkin, S. H., Zon, L. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hematopoiesis:+An+evolving+paradigm+for+stem+cell+biology.">Hematopoiesis: An evolving paradigm for stem cell biology.</a> Cell. 132 (4), 631-644 (2008).</li><li>Ye, F., Huang, W., Guo, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Studying+hematopoiesis+using+single-cell+technologies.">Studying hematopoiesis using single-cell technologies.</a> Journal of Hematology &amp; Oncology. 10, 27 (2017).</li><li>Hoppe, P. S., Coutu, D. L., Schroeder, 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+technologies+sharpen+up+mammalian+stem+cell+research.">Single-cell technologies sharpen up mammalian stem cell research.</a> Nature Cell Biology. 16 (10), 919-927 (2014).</li><li>Wills, Q. F., 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+expression+analysis+reveals+genetic+associations+masked+in+whole-tissue+experiments.">Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments.</a> Nature Biotechnol. 31 (8), 748-752 (2013).</li><li>Velten, 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=Human+haematopoietic+stem+cell+lineage+commitment+is+a+continuous+process.">Human haematopoietic stem cell lineage commitment is a continuous process.</a> Nat Cell Biol. 19 (4), 271-281 (2017).</li><li>Wilson, N. K., Nicola, 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=Combined+single-cell+functional+and+gene+expression+analysis+resolves+heterogeneity+within+stem+cell+populations.">Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations.</a> Cell Stem Cell. 16 (6), 712-724 (2015).</li><li>Saliba, A. E., Westermann, A. J., Gorski, S. A., Vogel, J. <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-seq:+Advances+and+future+challenges.">Single-cell RNA-seq: Advances and future challenges.</a> Nucleic Acids Research. 42 (14), 8845-8860 (2014).</li><li>Femino, A. M., Fay, F. S., Fogarty, K., Singer, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualization+of+Single+RNA+Transcripts+in+Situ.">Visualization of Single RNA Transcripts in Situ.</a> Science. 280 (5363), 585-590 (1998).</li><li>Kalisky, 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=A+brief+review+of+single-cell+transcriptomic+technologies.">A brief review of single-cell transcriptomic technologies.</a> Briefings in Functional Genomics. , elx019 (2017).</li><li>Crosetto, N., Bienko, M., van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatially+resolved+transcriptomics+and+beyond.">Spatially resolved transcriptomics and beyond.</a> Nature Reviews Genetics. 16, 57 (2014).</li><li>Bengtsson, M., St&#229;hlberg, A., Rorsman, P., Kubista, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+profiling+in+single+cells+from+the+pancreatic+islets+of+Langerhans+reveals+lognormal+distribution+of+mRNA+levels.">Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels.</a> Genome Research. 15 (10), 1388-1392 (2005).</li><li>Bengtsson, M., Hemberg, M., Rorsman, P., St&#229;hlberg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+mRNA+in+single+cells+and+modelling+of+RT-qPCR+induced+noise.">Quantification of mRNA in single cells and modelling of RT-qPCR induced noise.</a> BMC Molecular Biology. 9 (1), 63 (2008).</li><li>Picelli, 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=Smart-seq2+for+sensitive+full-length+transcriptome+profiling+in+single+cells.">Smart-seq2 for sensitive full-length transcriptome profiling in single cells.</a> Nature Methods. 10, 1096 (2013).</li><li>Tung, P. -. 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>

The authors have nothing to disclose.

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.

<table>
	<tbody>
		<tr>
			<td>CD14 PECY5</td>
			<td>eBioscience</td>
			<td>15-0149-42</td>
			<td>Clone: 61D3</td>
		</tr>
		<tr>
			<td>CD16 PECY5</td>
			<td>Biolegend</td>
			<td>302010</td>
			<td>Clone: 3G8</td>
		</tr>
		<tr>
			<td>CD56 PECY5</td>
			<td>Biolegend</td>
			<td>304608</td>
			<td>Clone: MEM-188</td>
		</tr>
		<tr>
			<td>CD19 PECY5</td>
			<td>Biolegend</td>
			<td>302210</td>
			<td>Clone: HIB19</td>
		</tr>
		<tr>
			<td>CD2 PECY5</td>
			<td>Biolegend</td>
			<td>300210</td>
			<td>Clone: RPA-2.10</td>
		</tr>
		<tr>
			<td>CD3 PECY5</td>
			<td>Biolegend</td>
			<td>300310</td>
			<td>Clone: HIT3a</td>
		</tr>
		<tr>
			<td>CD123 PECY5</td>
			<td>Biolegend</td>
			<td>306008</td>
			<td>Clone: 6H6</td>
		</tr>
		<tr>
			<td>CD235A PECY5</td>
			<td>BD Pharma</td>
			<td>559944</td>
			<td>Clone GAR2</td>
		</tr>
		<tr>
			<td>CD34 FITC</td>
			<td>Biolegend</td>
			<td>343604</td>
			<td>Clone: 561</td>
		</tr>
		<tr>
			<td>CD38 APC</td>
			<td>Biolegend</td>
			<td>303510</td>
			<td>Clone: Hit2</td>
		</tr>
		<tr>
			<td>CD90 PE</td>
			<td>Biolegend</td>
			<td>328110</td>
			<td>Clone: 5E10</td>
		</tr>
		<tr>
			<td>CD45RA BV421</td>
			<td>BD bioscience</td>
			<td>560362</td>
			<td>Clone: HI100</td>
		</tr>
		<tr>
			<td>CD49f Pecy7</td>
			<td>eBioscience</td>
			<td>25-0495-82</td>
			<td>Clone: eBioGOH3</td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>HyClone</td>
			<td>SV30160.3</td>
			<td></td>
		</tr>
		<tr>
			<td>PBS</td>
			<td>HyClone</td>
			<td>SH30028.02</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well u-bottom Plate</td>
			<td>VWR</td>
			<td>10861-564</td>
			<td></td>
		</tr>
		<tr>
			<td>SFEM</td>
			<td>Stem cell technologies</td>
			<td>9650</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin streptomycin</td>
			<td>HyClone</td>
			<td>SV30010</td>
			<td></td>
		</tr>
		<tr>
			<td>TPO</td>
			<td>Peprotech</td>
			<td>300-18</td>
			<td></td>
		</tr>
		<tr>
			<td>SCF</td>
			<td>Peprotech</td>
			<td>300-07</td>
			<td></td>
		</tr>
		<tr>
			<td>FLT3L</td>
			<td>Peprotech</td>
			<td>300-19</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon Tube 15 mL</td>
			<td>Sarstedt</td>
			<td>62.554.502</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorph tube</td>
			<td>Sarstedt</td>
			<td>72.690.001</td>
			<td></td>
		</tr>
		<tr>
			<td>CST beads</td>
			<td>BD</td>
			<td>642412</td>
			<td></td>
		</tr>
		<tr>
			<td>Accudrop Beads</td>
			<td>BD</td>
			<td>345249</td>
			<td>6-&#181;m particles&#160;</td>
		</tr>
		<tr>
			<td>Adhesive film Clear</td>
			<td>Thermo scientific&#160;</td>
			<td>AB-1170</td>
			<td></td>
		</tr>
		<tr>
			<td>Adhesive film Foil</td>
			<td>Thermo scientific&#160;</td>
			<td>AB-0626</td>
			<td></td>
		</tr>
		<tr>
			<td>96 well PCR plate</td>
			<td>Axygen</td>
			<td>PCR-96M2-HS-C</td>
			<td></td>
		</tr>
		<tr>
			<td>PCR 1.5 mL tube</td>
			<td>Axygen</td>
			<td>MCT-150-L-C</td>
			<td></td>
		</tr>
		<tr>
			<td>T100 PCR cycler</td>
			<td>BioRad</td>
			<td>186-1096</td>
			<td></td>
		</tr>
		<tr>
			<td>10% NP40</td>
			<td>Thermo scientific&#160;</td>
			<td>85124</td>
			<td></td>
		</tr>
		<tr>
			<td>10mM dNTP</td>
			<td>Takara</td>
			<td>4030</td>
			<td></td>
		</tr>
		<tr>
			<td>0.1M DTT</td>
			<td>Invitrogen</td>
			<td>P2325</td>
			<td></td>
		</tr>
		<tr>
			<td>RNAsout</td>
			<td>Invitrogen</td>
			<td>10777-019</td>
			<td>RNAse inhibitor</td>
		</tr>
		<tr>
			<td>CellsDirect One-Step qRT-PCR Kit</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
			<td></td>
		</tr>
		<tr>
			<td>Neuclease free water</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
			<td>from CellsDirect kit</td>
		</tr>
		<tr>
			<td>2X Reaction Mix</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
			<td>from CellsDirect kit</td>
		</tr>
		<tr>
			<td>SuperScript III RT/Platinum Taq Mix</td>
			<td>Invitrogen</td>
			<td>11753-100</td>
			<td>from CellsDirect kit</td>
		</tr>
		<tr>
			<td>Platinum&#160;Taq&#160;DNA Polymerase</td>
			<td>Invitrogen</td>
			<td>10966026</td>
			<td></td>
		</tr>
		<tr>
			<td>TaqMan Cells-to-CT Control Kit</td>
			<td>Invitrogen</td>
			<td>4386995</td>
			<td></td>
		</tr>
		<tr>
			<td>Xeno RNA Control</td>
			<td>Invitrogen</td>
			<td>4386995</td>
			<td>From TaqMan Cells-to-CT Control Kit</td>
		</tr>
		<tr>
			<td>20X Xeno RNA Control Taqman Gene Expression Assay</td>
			<td>Invitrogen</td>
			<td>4386995</td>
			<td>From TaqMan Cells-to-CT Control Kit</td>
		</tr>
		<tr>
			<td>96.96 Sample/Loading Kit&#8212;10 IFCs</td>
			<td>Fluidigm</td>
			<td>BMK-M10-96.96</td>
			<td></td>
		</tr>
		<tr>
			<td>2X Assay Loading Reagent</td>
			<td>Fluidigm</td>
			<td></td>
			<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 ...

This method can answer key questions in the stem cell and cancer fields such as what stem cell heterogeneity looks like and how therapy in sensitive cancer cells can be isolated. The main advantage of this study is that it combines technically robust single-cell gene expression analysis together with vaccinosis for further downstream characterization of target subpopulations. Though this method can be used to investigate the cancer and the stem cell heterogeneity, it can also give insights into gene priming and lineage potential.To begin the protocol, on an RNA/DNA-free bench, prepare enough lysis buffer for 96 wells with 10%extra by mixing 390 microliters nuclease free water, 17 microliters of 10%NP-40, 2.8 microliters 10 millimolar dNTP, 10 microliters 0.1 molar DTT, and 5.3 microliters RNAse inhibitor. Vortex and spin down the tube. Next, distribute four microliters of lysis buffer to each well of a 96-well PCR plate and seal the plates with adhesive film.Spin down the plates to collect the liquid at the bottom. Keep the plates on ice until cell sorting for a maximum of 24 hours. Once the FACS machine has been properly setup, perform reanalysis of the target population by sorting at least 100 target cells into a new microcentrifuge tube with 100 microliters of stain buffer.FACS analyze the sorted cells by recording the sorted sample and make sure that they end up in the sort gate. Then set up single-cell plate sorting by centering the drop in well A1 in the 96-well plate. When it is centered, sort 50 to 106 micrometer particles in all the wells around the edge of an empty 96-well plate to ensure that all wells will get a cell in the center of each well.Remove the adhesive film from the plates. Sort a single cell of Lin-CD34 plus CD38 minus cells into 92 out of the 96 wells. Activate index sorting in the FACS sorting software to save the immunophenotypic profile for CD45RA, CD49F and CD90 for each single cell.Sort two wells with 10 and 20 cells respectively for linearity controls in the PCR amplification. Wells H1 and H2 are usually used. Keep two wells without any cells as no template controls, usually wells H3 and H4.Seal the plates with clear adhesive film and spin the plates at 300 times gravity for one minute.Snap freeze the plates on dry ice. Then store them at minus 80 degrees Celsius. Make reverse transcription and specific target amplification mix by adding 632.5 microliters of two times reaction mix, 101.2 microliters Taq/SuperscriptIII, 151.8 microliters primer mix, and 0.7 microliters spiked in control RNA.Vortex the solution and spin it down to collect the liquid at the bottom of the tube. Keep the mix on ice until it is ready to be added to the sample. Next, thaw the lysate plates on ice.Add 8.75 microliters of the previously prepared reverse transcription and specific target amplification mix to 92 wells, including the linearity and no template controls. Add 8.75 microliters of no reverse transcription control mix to the four remaining wells. Then seal the plates with clear adhesive film and spin the plates to collect liquid at the bottom.Perform reverse transcription and specific target amplification by running the plate in a PCR machine according to the preamp program. Prepare the assay loading plate by pipetting three microliters of assay loading reagent to each well of a 96-well plate. Add three microliters of each primer to individual wells in the assay loading plate.Seal the plate with adhesive film and spin it down. After spinning the plate, prepare the dilution plate by pipetting eight microliters of nuclease free water into all the wells of a 96-well plate. Add two microliters of amplified sample to the dilution plate.Seal the plate with adhesive film. Mix by vortexing the plate for 10 seconds. Then spin down the plate.Next, prepare the sample loading mix by carefully mixing 352 microliters of master mix with 35.2 microliters of sample loading reagent. Prepare the sample loading plate by aliquoting 3.3 microliters of loading mix to each well of a 96-well plate. Add 2.7 microliters from the diluted sample into each well of the sample loading plate.Seal the plate with adhesive film and spin it down. Take out a new 96 by 96 microfluidic chip. Prepare inlets by poking them with a syringe with a cap on to make sure that they can be moved.Add the full volume of the syringes to each valve while tilting the chip to 45 degrees and pressing down the valve. Prime the chip with the IFC controller. Load each assay inlet with 4.25 microliters from each of the wells in the assay loading plate and avoid bubbles.If bubbles appear in the well, remove them with a pipette tip. Continue loading each sample inlet with 4.25 microliters from each of the wells in the sample loading plate, avoiding bubbles, and if bubbles appear removing them with a pipette tip. Load the chip with the Integrated Fluidics Circuit controller or IFC controller.Check that the chip looks even and that all chambers have been loaded. Remove dust from the chip's surface by touching it with tape. Finally, run the chip in the multiplex microfluidic gene expression platform.A heat map of a successful run displays the evenly spread expression across samples with a strong signal of around seven to 25 CTs. This amplification curve of spiked in control RNA verifies that all wells have clear expression of around 10 CT with little to no variation, validating that the pre-amplification and qPCR reactions had been successful for all cells. Principle Component Analysis or PCA which visualizes the similarities among cell groups using dimension reduction can be useful to distinguish clusters of molecularly distinct cells from each other here identifying four subgroups.To identify the immunophenotype of cells from different molecular clusters, load the index FCS file into FlowJo. Open the script editor and paste the index script, press Run. Each cell will now be in a separate gate.Add each cell into the layout editor and color them according to the molecularly-defined grouping from SCXV available in the file sample_complete_data.xls. FACS plots show the cell surface marker expression for the hematopoietic stem cell markers CD90 and CD45RA which can be used to discriminate between the clusters and isolate them for functional analysis. Following this procedure, other technique like RNA sequencing of newly discovered subpopulations or in vitro and in vivo experiments can be performed to answer additional question like what molecular mechanism causes this heterogeneity and how does the subpopulation differ functionally.This strategy paved the way for researchers to not only investigate heterogeneity in normal and malignant hematopoiesis, but can also be used to prospectively isolate the discovered subpopulations to further study them.

a-combinatorial-single-cell-approach-to-characterize-molecular

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JoVE Journal

Genetics

6.5K 조회수.  Lund University. 이 방법은 줄기 세포 이질성이 어떻게 보이는지 및 민감한 암세포에 있는 치료가 어떻게 고립될 수 있는지와 같은 줄기 세포 및 암 필드에 있는 중요한 질문에 대답할 수 있습니다. 이 연구의 주요 장점은 표적 하위 인구의 추가 하류 특성화를 위해 암시노시스와 함께 기술적으로 견고한 단세포 유전자 발현 분석을 결합한다는 것입니다. 이 방법은 암과 줄기 세포 이질성을 ?...