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. Keep plates on ice until cell sorting (maximum 24 h).</li>
</ol>
2. Preparation of Cells for Cell Sorting
<ol>
	<li>Thaw appropriate number of cells (here, CD34 enriched hematopoietic stem and progenitor cells) for the experiment. 1 x 106 cells are appropriate for sorting approximately three 96-well plates of single-cells with controls.</li>
	<li>Transfer thawed cells to a 15 mL conical tube and add 1 mL FBS every 30 s until a total volume of 8 mL is reached. Spin cells in a centrifuge at 350 x g for 10 min at 4 &#176;C and remove supernatant.</li>
	<li>Resuspend cells in 8 mL staining buffer (PBS with 2% FBS) and centrifuge at 350 x g for 10 min at 4 &#176;C and remove supernatant.</li>
	<li>Resuspend cells in 200 &#181;L staining buffer and remove cells for control stains.</li>
	<li>Make Fluorescence minus one controls (FMOs) for each fluorophore, by staining a fraction of cells in 50 &#181;L staining buffer. In this example, 6 microcentrifuge tubes with 20,000 cells are used as FMOSs. Note that the number of cells should be adjusted depending on the population investigated. Add all antibodies at the same concentration as in the sample stain except for one to each tube.</li>
	<li>Make single stains for each fluorophore by staining a fraction of cells in 50 &#181;L buffer for each fluorophore used. In this example 6 microcentrifuge tubes with 20,000 cells are used. Note that the target for each antibody needs to be expressed by the cells used for controls. Add each antibody at the same concentration as in the sample stain in individual tubes. Additionally keep 20,000 unstained cells in 50 &#181;L as an unstained control.</li>
	<li>To the cell sample, add antibodies at their appropriate concentration. Used here are CD34-FITC at a 1/100 concentration, CD38-APC 1/50, CD90-PE 1/10, CD45RA-bv421 1/50, CD49F-PECy7 1/50 and Lineage Mix: CD3-PECy5 1/50, CD2-PECy5 1/50, CD19-PECy5 1/50, CD56-PECy5 1/50, CD123-PECy5 1/50, CD14-PECy5 1/50, CD16-PECy5 1/50, and CD235a-PECy5 1/1000.</li>
	<li>Incubate cells with antibodies for 30 min on ice in the dark.</li>
	<li>Wash cells with 3 mL staining buffer. Centrifuge cells at 350 x g for 10 min at 4 &#176;C and remove supernatant.</li>
	<li>Resuspend cells and repeat step 2.9.</li>
	<li>Resuspend sample in 500 &#181;L and FMOs in 100 &#181;L staining buffer with 1/100 7AAD and filter cells through a 50 &#181;m filter to get a single-cell suspension.</li>
</ol>
3. Cell Sorting
<ol>
	<li>Make sure that the FACS machine is set up correctly with drop delay and cytometer setup and tracking (CST) that have recently been performed according to manufacturer&#39;s instructions, to ensure that the appropriate cells are sorted. For hematopoietic cells, the use of the 85 micron nozzle and maximum speed of 4 is recommended, while the optimal event rate is between 800 and 2000 events/s.</li>
	<li>Correct for spectral overlap by performing fluorescence compensation and set gates according to FMO controls or internal negative controls.</li>
	<li>Perform reanalysis of the target population by sorting at least 100 target cells into a new microcentrifuge tube with 100 &#181;L of stain buffer. FACS analyze the sorted cells by recording the sorted sample and make sure that they end up in the sort gate.</li>
	<li>Set-up single cell plate sorting by centering the drop in well A1 in a 96 well plate. When it is centered, sort 50&#8211;100 6 &#181;m particles into all 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.</li>
	<li>If possible, an additional control to ensure that viable cells are sorted can be added by sorting single-cells for in vitro growth. Haematopoietic cells can be grown in U-bottom 96 well plates in 100 &#181;l SFEM with 1% penicillin streptomycin, 100 ng/mL FLT3L, TPO and SCF. Analyze each well after 3 days in culture for cell colonies using a microscope.</li>
	<li>Remove adhesive film from plates. Sort a single cell of interest (here Lin-CD34+CD38- cells) into 92 out of the 96 wells, activate INDEX-sorting in the FACS sorting software to save the immunophenotypic profile for other markers of interest (here CD45RA, CD49f, and CD90) for each single cell.</li>
	<li>Sort two wells with 10 and 20 cells respectively for linearity controls in the PCR amplification. Wells H1 and H2 are usually used.</li>
	<li>Keep two wells without any cells as no-template controls, usually wells H3 and H4.</li>
	<li>Seal the plates with clear adhesive film and spin the plates at 300 x g for 1 min before snap freezing on dry ice.</li>
	<li>Store frozen plates at -80 &#176;C. 
	NOTE: Safe stopping point. Sorted and lysed cells can be kept at -80 &#176;C for long-term storage.</li>
</ol>
4. Reverse Transcription and Specific Target Amplification
<ol>
	<li>Prepare the primer mix for all 96 gene targets by adding 2 &#181;L of each primer pair, including housekeeping gene primers and primers for spiked-in control RNA, in a 1.5 mL RNAse free tube on a RNA/DNA free bench. If using less than 96 primers, add an equivalent volume of nuclease free water for the missing primers. Primers are ordered separately to match the desired gene panel.</li>
	<li>Make reverse transcription and specific target amplification mix by adding 632.5 &#181;L 2x reaction mix, 101.2 &#181;L Taq/SuperscriptIII, 151.8 &#181;L Primer mix, and 0.7 &#181;L spiked in control RNA. Preform this step on a DNA free bench. Mix by vortexing and spin down to collect the liquid in the bottom of the tube. Keep on ice until addition to the sample.</li>
	<li>Make no-reverse transcription control mix for four wells by mixing 27.5 &#181;L of 2x reaction mix, 1.76 &#181;L of Taq enzyme, 6.6 &#181;L of primer mix and 2.64 &#181;L of nuclease free water. Spike in control RNA. Perform this step on a DNA free bench. Vortex and spin down to collect the liquid in the bottom of the tube and keep on ice until addition to sample.</li>
	<li>Thaw lysate plates on ice. Add 8.75 &#181;L of the previously prepared reverse transcription and specific target amplification mix to 92 wells, including the linearity and no-template controls. Add 8.75 &#181;L of no-reverse transcription control mix to the four remaining wells. Seal plates with clear adhesive film and spin down to collect liquid at the bottom of the plates.</li>
	<li>Preform reverse transcription and specific target amplification by running the plate in a PCR machine according to the preamp program; step 1: 50 &#176;C for 60 min, step 2: 95 &#176;C for 2 min, step 3: 95 &#176;C for 15 s, step 4: 60 &#176;C for 4 min, repeat steps 3&#8211;4 24 times and finally step 5: 8 &#176;C forever.</li>
	<li>After PCR is complete, keep the plate at 8 &#176;C for short term storage and -20 &#176;C for long term storage. 
	NOTE: Safe stopping point. Amplified material can be kept at 8 &#176;C for short-term storage and at -20 &#176;C for long-term storage.</li>
</ol>
5. Preparation of Sample and Assay Plates for Multiplex Microfluidic Gene Expression Analysis
<ol>
	<li>Prepare assay loading plate by pipetting 3 &#181;L Assay loading reagent to each well of a 96 well plate. Add 3 &#181;L of each primer to individual wells in the assay loading plate.</li>
	<li>Seal plate with adhesive film and spin down to collect liquid at the bottom of the plates.</li>
	<li>Prepare dilution plate by pipetting 8 &#181;L of nuclease free water into all wells of a 96 well plate. Add 2 &#181;L of amplified sample to the dilution plate, making a final dilution of 1:5.</li>
	<li>Seal plate with adhesive film, mix by vortexing plate for 10 s, and finally spin down to collect liquid at the bottom of the plates.</li>
	<li>Prepare sample loading mix by carefully mixing 352 &#181;L of master mix with 35.2 &#181;L of sample loading reagent. Prepare sample loading plate by aliquoting 3.3 &#181;L of loading mix to each well of a 96 well plate.</li>
	<li>Add 2.7 &#181;L from the diluted sample into each well of the sample loading plate.</li>
	<li>Seal plate with adhesive film and spin down to collect liquid at the bottom of the plates.</li>
</ol>
6. Loading of Microfluidic Chip
<ol>
	<li>Take out a new 96 x 96 microfluidic chip. Prepare inlets by poking them with a syringe with cap on to make sure that they can be moved.</li>
	<li>Remove bubbles from syringes. Add full volume of syringes to each valve while tilting the chip 45 degrees and pressing down the valve. Prime chip with the IFC controller.</li>
	<li>Load each assay inlet with 4.25 &#181;L from each of the wells in the assay loading plate. Avoid bubbles. If bubbles appear in the well, remove them with a pipette tip.</li>
	<li>Continue loading each sample inlet with 4.25 &#181;L from each of the wells in the sample loading plate, avoid bubbles, and if bubbles appear, remove them with pipette tip.</li>
	<li>Load chip with the IFC controller.</li>
	<li>Check that the chip looks even and that all chambers have been loaded. Remove dust from the chip surface by touching it with tape. Run the chip in the multiplex microfluidic gene expression platform.</li>
</ol>
7. Running Chip on Multiplex Microfluidic Gene Expression Platform
<ol>
	<li>After loading chip into the multiplex microfluidic gene expression platform, name the sample.</li>
	<li>Set ROX as passive dye. Set single probe and FAM-MGB as fluorescence. Use 96 x 96 standard v2 as protocol. Start run.</li>
	<li>Remove chip when run is complete.</li>
</ol>
8. Preliminary Analysis of Chip Run
<ol>
	<li>Load data into Real-Time PCR analysis software.</li>
	<li>Load gene names and cell names by pasting cell and gene layouts from a tab delimited file.</li>
	<li>Open image view and select ROX as dye. Check if all wells have ROX passive dye.</li>
	<li>Investigate if all amplification plots look ok, with a smooth amplification curve with no spikes (similar to Figure 3E).</li>
	<li>Ensure that all single-cells have expression of spiked-in control RNA to make sure that all have been loaded properly.</li>
	<li>Ensure that all cells have housekeeping gene expression and thus have been sorted properly.</li>
	<li>Ensure that 10 and 20 cell linearity controls have approximately 1 CT difference to validate linear amplification.</li>
	<li>Check if there is expression in the noRT control samples. If expression is detected in noRT consider changing probes to probes which do not detect genomic DNA for subsequent runs.</li>
	<li>Export data in csv files for further analysis.</li>
</ol>
9. Single-cell Analysis Using SCExV
NOTE: An introductory film is present20 to introduce the tool. Here, a short recommendation of how to do analysis using the controls introduced in the protocol is presented.
<ol>
	<li>Connect to the SCexV website20.</li>
	<li>Upload exported CSV files.</li>
	<li>Chose the spiked-in control RNA as positive control. Remove any cell which has a control RNA CT above 25. Normalize the data to median expression of control RNA.</li>
	<li>Click &#34;Done here -&#62; Analyze&#34;. Remove noRT, notemplate, 10 and 20 cell controls in the exclude cell option.</li>
	<li>Cluster cells with the clustering approach of choice with the expected number of clusters. Export analyzed values.</li>
</ol>
10. Index-sorting Analysis
<ol>
	<li>Open FACS analysis software and load the indexed samples.</li>
	<li>Open the script editor and run the script available from Quinn J. et al.21</li>
	<li>Now that the FACS analysis software should have made a gate for each of the single cells, open layout editor and color the cells according to the grouping from SCexV (available in the file named &#34;Sample_complete_Data.xls&#34;).</li>
</ol>

<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 ...

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Genetics

6.6K Views.  Lund University. 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.

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

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 activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

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 microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

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

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. Complete gene ablation in HSCs required the generation of knockout mice from which HSCs could be isolated, and gene ablation in primary human HSCs was not possible. Viral transduction could be used for knock-down approaches, but these suffered from variable efficacy. In general, genetic manipulation of human and mouse hematopoietic cells was hampered by low efficiencies and extensive time and cost commitments. Recently, CRISPR/Cas9 has dramatically expanded the ability to engineer the DNA of mammalian cells. However, the application of CRISPR/Cas9 to hematopoietic cells has been challenging, mainly due to their low transfection efficiencies, the toxicity of plasmid-based approaches and the slow turnaround time of virus-based protocols.
A rapid method to perform CRISPR/Cas9-mediated gene editing in murine and human hematopoietic stem and progenitor cells with knockout efficiencies of up to 90% is provided in this article. This approach utilizes a ribonucleoprotein (RNP) delivery strategy with a streamlined three-day workflow. The use of Cas9-sgRNA RNP allows for a hit-and-run approach, introducing no exogenous DNA sequences in the genome of edited cells and reducing off-target effects. The RNP-based method is fast and straightforward: it does not require cloning of sgRNAs, virus preparation or specific sgRNA chemical modification. With this protocol, scientists should be able to successfully generate knockouts of a gene of interest in primary hematopoietic cells within a week, including downtimes for oligonucleotide synthesis. This approach will allow a much broader group of users to adapt this protocol for their needs.

A protocol for fast CRISPR/Cas9-mediated gene disruption in mouse and human primary hematopoietic cells is described in this article. Cas9-sgRNA ribonucleoproteins are introduced via electroporation with sgRNAs generated through in vitro transcription and commercial Cas9. High editing efficiencies are achieved with limited time and financial cost.

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 is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

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

Here we describe the Replica Set method, an approach to quantitatively measure C. elegans lifespan/survival and healthspan in a high-throughput and robust manner, thus allowing screening of many conditions without sacrificing data quality. This protocol details the strategy and provides a software tool for analysis of Replica Set data.

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 as initial massive cell death and low therapeutic effects, impaired their broad clinical translation. Genetic engineering of stem cells prior to transplantation emerged as a promising method to optimize therapeutic stem cell effects. However, safe and efficient gene delivery systems are still lacking. Therefore, the development of suitable methods may provide an approach to resolve current challenges in stem cell-based therapies.
The present protocol describes the extraction and characterization of human dental follicle stem cells (hDFSCs) as well as their non-viral genetic modification. The postnatal dental follicle unveiled as a promising and easily accessible source for harvesting adult multipotent stem cells possessing high proliferation potential. The described isolation procedure presents a simple and reliable method to harvest hDFSCs from impacted wisdom teeth. Also this protocol comprises methods to define stem cell characteristics of isolated cells. For genetic engineering of hDFSCs, an optimized cationic lipid-based transfection strategy is presented enabling highly efficient microRNA introduction without causing cytotoxic effects. MicroRNAs are suitable candidates for transient cell manipulation, as these small translational regulators control the fate and behavior of stem cells without the hazard of stable genome integration. Thus, this protocol represents a safe and efficient procedure for engineering of hDFSCs that may become important for optimizing their therapeutic efficacy.

This protocol describes the transient genetic engineering of dental stem cells extracted from the human dental follicle. The applied non-viral modification strategy may become a basis for the improvement of therapeutic stem cell products.

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, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

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, underlies the only known cure of human immunodeficiency virus (HIV-1) infection, and shows immense promise in the treatment of genetic diseases such as beta thalassemia. Our group has developed a protocol to model HSPC gene therapy in nonhuman primates (NHPs), allowing scientists to optimize many of the same reagents and techniques that are applied in the clinic. Here, we describe methods for purifying CD34+ HSPCs and long-term persisting hematopoietic stem cell (HSC) subsets from primed bone marrow (BM). Identical techniques can be employed for the purification of other HSPC sources (e.g., mobilized peripheral blood stem cells [PBSCs]). Outlined is a 2 day protocol in which cells are purified, cultured, modified with lentivirus (LV), and prepared for infusion back into the autologous host. Key readouts of success include the purity of the CD34+ HSPC population, the ability of purified HSPCs to form morphologically distinct colonies in semisolid media, and, most importantly, gene modification efficiency. The key advantage to HSPC gene therapy is the ability to provide a source of long-lived cells that give rise to all hematopoietic cell types. As such, these methods have been used to model therapies for cancer, genetic diseases, and infectious diseases. In each case, therapeutic efficacy is established by enhancing the function of distinct HSPC progeny, including red blood cells, T cells, B cells, and/or myeloid subsets. The methods to isolate, modify, and prepare HSPC products are directly applicable and translatable to multiple diseases in human patients.

The goal of this protocol is to isolate nonhuman primate CD34+ cells from primed bone marrow, to gene-modify these cells with lentiviral vectors, and to prepare a product for infusion into the autologous host. The total protocol length is approximately 48 h.

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 normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

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 heterozygous genetic modifications is challenging due to CRISPR-CAS9 mediated indel formation in the second allele. Here, we demonstrate a protocol to help overcome this difficulty by using two repair templates in which only one expresses the desired sequence change, while both templates contain silent mutations to prevent re-cutting and indel formation. This methodology is most advantageous for gene editing coding regions of DNA to generate isogenic control and mutant human stem cell lines for studying human disease and biology. In addition, optimization of transfection and screening methodologies have been performed to reduce labor and cost of a gene editing experiment. Overall, this protocol is widely applicable to many genome editing projects utilizing the human pluripotent stem cell model.

This protocol provides a method to facilitate the generation of defined heterozygous or homozygous nucleotide changes 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 ...

Isolation of Adipose Tissue Nuclei for Single-Cell Genomic Applications

Brown and beige fat are specialized adipose tissues that dissipate energy for thermogenesis by UCP1 (Uncoupling Protein-1)-dependent and independent pathways. Until recently, thermogenic adipocytes were considered a homogeneous population. However, recent studies have indicated that there are multiple subtypes or subpopulations that are distinct in developmental origin, substrate use, and transcriptome. Despite advances in single-cell genomics, unbiased decomposition of adipose tissues into cellular subtypes has been challenging because of the fragile nature of lipid-filled adipocytes. The protocol presented was developed to circumvent these obstacles by effective isolation of single nuclei from adipose tissue for downstream applications, including RNA sequencing. Cellular heterogeneity can then be analyzed by RNA sequencing and bioinformatic analyses.

This publication describes a protocol for the isolation of nuclei from mature adipocytes, purification by fluorescence-activated sorting, and single-cell level transcriptomics.

Brown and beige fat are specialized adipose tissues that dissipate energy for thermogenesis by UCP1 (Uncoupling Protein-1)-dependent and independent ...