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

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

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

<ol>
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Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Batch+effects+and+the+effective+design+of+single-cell+gene+expression+studies.">Batch effects and the effective design of single-cell gene expression studies.</a> Scientific Reports. 7, 39921 (2017).</li><li>Teles, J., Enver, T., Pina, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+PCR+profiling+of+gene+expression+in+hematopoiesis.">Single-cell PCR profiling of gene expression in hematopoiesis.</a> Methods in Molecular Biology. , 21-42 (2014).</li><li>Hayashi, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+gene+profiling+of+planarian+stem+cells+using+fluorescent+activated+cell+sorting+and+its+&amp;#34;index+sorting&amp;#34;+function+for+stem+cell+research.">Single-cell gene profiling of planarian stem cells using fluorescent activated cell sorting and its &amp;#34;index sorting&amp;#34; function for stem cell research.</a> Development, Growth, &amp; Differentiation. 52 (1), 131-144 (2010).</li><li>Lang, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCExV:+A+webtool+for+the+analysis+and+visualisation+of+single+cell+qRT-PCR+data.">SCExV: A webtool for the analysis and visualisation of single cell qRT-PCR data.</a> BMC Bioinformatics. 16 (1), 320 (2015).</li><li>de Klein, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cellular+oncogene+is+translocated+to+the+Philadelphia+chromosome+in+chronic+myelocytic+leukaemia.">A cellular oncogene is translocated to the Philadelphia chromosome in chronic myelocytic leukaemia.</a> Nature. 300 (5894), 765-767 (1982).</li><li>. indexed-sorting Available from: <a target="_blank" href="https://github.com/FlowJo-LLC/indexed-sorting">https://github.com/FlowJo-LLC/indexed-sorting</a> (2016)</li><li>Warfvinge, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+molecular+analysis+defines+therapy+response+and+immunophenotype+of+stem+cell+subpopulations+in+CML.">Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML.</a> Blood. 129 (17), 2384-2394 (2017).</li><li>Nestorowa, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+single-cell+resolution+map+of+mouse+hematopoietic+stem+and+progenitor+cell+differentiation.">A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.</a> Blood. 128 (8), e20-e31 (2016).</li><li>Breton, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+dendritic+cells+(DCs)+are+derived+from+distinct+circulating+precursors+that+are+precommitted+to+become+CD1c++or+CD141++DCs.">Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs.</a> The Journal of Experimental Medicine. 213 (13), 2861-2870 (2016).</li><li>Alberti-Servera, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+reveals+developmental+heterogeneity+among+early+lymphoid+progenitors.">Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors.</a> The EMBO Journal. 36 (24), 3619-3633 (2017).</li><li>Giustacchini, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+transcriptomics+uncovers+distinct+molecular+signatures+of+stem+cells+in+chronic+myeloid+leukemia.">Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia.</a> Nature Medicine. 23, 692 (2017).</li><li>Hansmann, L., Han, A., Penter, L., Liedtke, M., Davis, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+expansion+and+interrelatedness+of+distinct+B-lineage+compartments+in+multiple+myeloma+bone+marrow.">Clonal expansion and interrelatedness of distinct B-lineage compartments in multiple myeloma bone marrow.</a> Cancer Immunology Research. 5 (9), 744-754 (2017).</li><li>Psaila, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+profiling+of+human+megakaryocyte-erythroid+progenitors+identifies+distinct+megakaryocyte+and+erythroid+differentiation+pathways.">Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways.</a> Genome Biology. 17 (1), 83 (2016).</li><li>Schulte, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Index+sorting+resolves+heterogeneous+murine+hematopoietic+stem+cell+populations.">Index sorting resolves heterogeneous murine hematopoietic stem cell populations.</a> Experimental Hematology. 43 (9), 803-811 (2015).</li></ol>

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

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

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

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

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

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

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

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

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

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

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

Research

JoVE Journal

Genetics

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

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

Canalostomy As a Surgical Approach to Local Drug Delivery into the Inner Ears of Adult and Neonatal Mice

Local delivery of therapeutic drugs into the inner ear is a promising therapy for inner ear diseases. Injection through semicircular canals (canalostomy) has been shown to be a useful approach to local drug delivery into the inner ear. The goal of this article is to describe, in detail, the surgical techniques involved in canalostomy in both adult and neonatal mice. As indicated by fast-green dye and adeno-associated virus serotype 8 with the green fluorescent protein gene, the canalostomy facilitated broad distribution of injected reagents in the cochlea and vestibular end-organs with minimal damage to hearing and vestibular function. The surgery was successfully implemented in both adult and neonatal mice; indeed, multiple surgeries could be performed if required. In conclusion, canalostomy is an effective and safe approach to drug delivery into the inner ears of adult and neonatal mice and may be used to treat human inner ear diseases in the future.

Here we describe canalostomy procedure which allows local drug delivery into the inner ears of adult and neonatal mice through the semicircular canal with minimum damage to hearing and vestibular function. This method can be used to inoculate viral vectors, pharmaceuticals, and small molecules into the mouse inner ear.

Local delivery of therapeutic drugs into the inner ear is a promising therapy for inner ear diseases. Injection through semicircular canals (canalostomy) ...

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 ...