Name: single-cell gene expression profiling using facs and qpcr with internal standards
Uploaded: 2017-02-25T14:00:00.000+00:00
Duration: 10 min 50 s
Description: Watch this Scientific Journal Video about Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards at JoVE.com

Wir möchten, dass bei der Durchführung der Zellsortierung bei der Entwicklung dieses Protokolls V. Kapoor in der CCR ETIB Flow Cytometry Kern für ihre Hilfe zu danken. Wir danken auch M. Raffeld und die CCR LP Molecular Diagnostics Unit und J. Zhu und die NHLBI DNA-Sequenzierung und Genomics Core-für ihre Hilfe in der qPCR bei der Entwicklung dieses Protokolls durchgeführt wird. Diese Forschung wurde von der Intramural Programm des NIH unterstützt.

<ol>
	<li>Feinerman, O., Veiga, J., Dorfman, J. R., Germain, R. N., Altan-Bonnet, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variability+and+Robustness+in+T+Cell+Activation+from+Regulated+Heterogeneity+in+Protein+Levels.">Variability and Robustness in T Cell Activation from Regulated Heterogeneity in Protein Levels.</a> Science. 321 (5892), 1081-1084 (2008).</li><li>Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M., Sorger, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-genetic+origins+of+cell-to-cell+variability+in+TRAIL-induced+apoptosis.">Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis.</a> Nature. 459 (7245), 428-432 (2009).</li><li>Geva-Zatorsky, N., Rosenfeld, N., 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=Oscillations+and+variability+in+the+p53+system.">Oscillations and variability in the p53 system.</a> Mol. Syst. Biol. 2 (1), (2006).</li><li>Colman-Lerner, A., Gordon, 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=Regulated+cell-to-cell+variation+in+a+cell-fate+decision+system.">Regulated cell-to-cell variation in a cell-fate decision system.</a> Nature. 437 (7059), 699-706 (2005).</li><li>Chong, S., Chen, C., Ge, H., Xie, X. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanism+of+Transcriptional+Bursting+in+Bacteria.">Mechanism of Transcriptional Bursting in Bacteria.</a> Cell. 158 (2), 314-326 (2014).</li><li>Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., Tyagi, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stochastic+mRNA+Synthesis+in+Mammalian+Cells.">Stochastic mRNA Synthesis in Mammalian Cells.</a> PLoS Biol. 4 (10), e309+ (2006).</li><li>Senecal, A., Munsky, 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=Transcription+Factors+Modulate+c-Fos+Transcriptional+Bursts.">Transcription Factors Modulate c-Fos Transcriptional Bursts.</a> Cell Rep. 8 (1), 75-83 (2014).</li><li>Dey, S. S., Foley, J. E., Limsirichai, P., Schaffer, D. V., Arkin, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orthogonal+control+of+expression+mean+and+variance+by+epigenetic+features+at+different+genomic+loci.">Orthogonal control of expression mean and variance by epigenetic features at different genomic loci.</a> Mol. Syst. Biol. 11 (5), 806+ (2015).</li><li>Dar, R. D., Razooky, B. 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=Transcriptional+burst+frequency+and+burst+size+are+equally+modulated+across+the+human+genome.">Transcriptional burst frequency and burst size are equally modulated across the human genome.</a> Proc. Natl. Acad. Sci. U.S.A. 109 (43), 17454-17459 (2012).</li><li>Thattai, 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=Stochastic+gene+expression+in+fluctuating+environments.">Stochastic gene expression in fluctuating environments.</a> Genetics. 167 (1), 523-530 (2004).</li><li>Porter, J. R., Fisher, B. E., Batchelor, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=p53+Pulses+Diversify+Target+Gene+Expression+Dynamics+in+an+mRNA+Half-Life-Dependent+Manner+and+Delineate+Co-regulated+Target+Gene+Subnetworks.">p53 Pulses Diversify Target Gene Expression Dynamics in an mRNA Half-Life-Dependent Manner and Delineate Co-regulated Target Gene Subnetworks.</a> Cell Syst. 2 (4), 272-282 (2016).</li><li>Lahav, G., Rosenfeld, N., 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=Dynamics+of+the+p53-Mdm2+feedback+loop+in+individual+cells.">Dynamics of the p53-Mdm2 feedback loop in individual cells.</a> Nat. Genet. 36 (2), 147-150 (2004).</li><li>Levine, A. J., Oren, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+first+30+years+of+p53:+growing+ever+more+complex.">The first 30 years of p53: growing ever more complex.</a> Nat. Rev. Cancer. 9 (10), 749-758 (2009).</li><li>Riley, T., Sontag, E., Chen, P., Levine, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+control+of+human+p53-regulated+genes.">Transcriptional control of human p53-regulated genes.</a> Nat. Rev. Mol. Cell Biol. 9 (5), 402-412 (2008).</li><li>Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S., Madden, T. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Primer-BLAST:+A+tool+to+design+target-specific+primers+for+polymerase+chain+reaction.">Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction.</a> BMC Bioinf. 13 (1), 134 (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PCR Technologies: A Technical Guide. , (2014).</li><li>Sambrook, J., Russell, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Molecular Cloning: A Laboratory Manual. , (2001).</li><li>Batchelor, E., Mock, C. S., Bhan, I., Loewer, A., Lahav, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recurrent+initiation:+a+mechanism+for+triggering+p53+pulses+in+response+to+DNA+damage.">Recurrent initiation: a mechanism for triggering p53 pulses in response to DNA damage.</a> Mol. Cell. 30 (3), 277-289 (2008).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Flow Cytometry: Principles and Applications. , (2007).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Real-time PCR. , (2006).</li><li>Song, L., Langfelder, P., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+co-expression+measures:+mutual+information,+correlation,+and+model+based+indices.">Comparison of co-expression measures: mutual information, correlation, and model based indices.</a> BMC Bioinf. 13 (1), 328 (2012).</li><li>Margolin, A. A., Nemenman, I., 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=ARACNE:+An+Algorithm+for+the+Reconstruction+of+Gene+Regulatory+Networks+in+a+Mammalian+Cellular+Context.">ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context.</a> BMC Bioinf. 7 (Suppl 1), (2006).</li><li>Haff, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+quantitative+PCR+using+nested+primers.">Improved quantitative PCR using nested primers.</a> PCR Methods Appl. 3 (6), 332-337 (1994).</li><li>Hashimshony, T., Senderovich, N., 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=CEL-Seq2:+sensitive+highly-multiplexed+single-cell+RNA-Seq.">CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.</a> Genome Biol. 17, 77 (2016).</li><li>Ronander, E., Bengtsson, D. C., Joergensen, L., Jensen, A. T. R., Arnot, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Single-cell+Gene+Transcription+by+RNA+Fluorescent++In+Situ+Hybridization+(FISH).">Analysis of Single-cell Gene Transcription by RNA Fluorescent In Situ Hybridization (FISH).</a> J. Vis. Exp. (68), e4073 (2012).</li><li>Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., Tyagi, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+individual+mRNA+molecules+using+multiple+singly+labeled+probes.">Imaging individual mRNA molecules using multiple singly labeled probes.</a> Nat. Methods. 5 (10), 877-879 (2008).</li><li>Lubeck, E., Cai, L. <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+systems+biology+by+super-resolution+imaging+and+combinatorial+labeling.">Single-cell systems biology by super-resolution imaging and combinatorial labeling.</a> Nat. Methods. 9 (7), 743-748 (2012).</li><li>Battich, N., Stoeger, T., Pelkmans, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image-based+transcriptomics+in+thousands+of+single+human+cells+at+single-molecule+resolution.">Image-based transcriptomics in thousands of single human cells at single-molecule resolution.</a> Nat. Methods. 10 (11), 1127-1133 (2013).</li></ol>

The authors have nothing to disclose.

Wir haben zur Isolierung von einzelnen Säugerzellen aus einer Population von anhaftenden Zellen in der Kultur und zur Untersuchung der Expression von etwa 96 Gene in jeder Zelle gezüchtet ein Verfahren dargestellt. Gute rechtzeitige Vorbereitung ist entscheidend für diese Methode gut zu funktionieren. Insbesondere Entwerfen und Testen Primerpaare spezifisch für die Transkripte von Interesse (Schritte 1,2-1,3) sind zeitaufwendig, aber wichtige Schritte, wie die Primer, die Qualität der Einzelzellmessungen bestimmen....

Einzelne Zellen in einer Population kann zu einem einheitlichen physiologischen Reiz 1, 2, 3, 4 sehr unterschiedliche Reaktionen zeigen. Die genetische Veränderung der Zellen in einer Population eines Mechanismus für diese Vielzahl von Antworten, aber es gibt auch einige nicht-genetischen Faktoren, die die Variabilität von Antworten erhöhen können, selbst in einer klonalen Population von Zellen. Zum Beispiel können die Ebenen der einzelnen Proteine ​​und andere wichtige Signalmoleküle variieren auf einer Zelle-für-Zelle-Basis, was zu geben Variation in nachgeschalteten Genexpressionsprofilen. Zusätzlich können kurzzeitige Bursts von Transkripten 5 treten Genaktivierung in 6, der 7 auf eine relativ kleine Anzahl von Transkripten pro Burst begrenzt sein kann, 8, 9. Eine solchestochasticity in Genaktivierung kann stark von Schwankungen der biologischen Reaktionen beitragen und einen selektiven Vorteil in Mikroorganismen 10 und in Säugetierzellen 1, 2 reagiert auf einen physiologischen Reiz bieten kann. Aufgrund beiden genetischen und nicht-genetischen Variationsquellen kann das Genexpressionsprofil eines beliebigen gegebenen Zelle in Reaktion auf einen Reiz deutlich vom Durchschnitt Genexpressionsprofil unterscheiden sich von der Messung der Massenantwort erhalten. Das Ausmaß, in dem einzelne Zellen Variabilität in Reaktion auf einen Reiz zeigen, erfordert Techniken zur Isolierung von einzelnen Zellen, die Messung der Expressionsspiegel für Transkripte von Interesse, und die Computeranalyse der resultierenden Expressionsdaten. Es gibt mehrere Ansätze zur Untersuchung der Genexpression in einzelnen Zellen, eine breite Palette von Kosten abdeckt, die Anzahl der Transkripte sondiert, undGenauigkeit der Quantifizierung. So bietet zum Beispiel Single-Cell-RNA-Seq eine große Tiefe transcript Abdeckung und der Möglichkeit, Tausende von verschiedenen Transkripte für den am höchsten exprimierten Gene in einzelnen Zellen zu quantifizieren; jedoch verbunden sind die Kosten, die mit solchen Sequenzierungstiefe kann unerschwinglich sein, auch wenn die Kosten weiter sinken. Im Gegensatz dazu bietet Einzelmolekül RNA Fluoreszenz in situ Hybridisierung (FISH smRNA) genaue Quantifizierung von Transkripten für sogar Low-Expression von Genen zu vernünftigen Kosten pro Gen von Interesse; jedoch nur eine geringe Anzahl von Zielgenen können durch diese Vorgehensweise in einer gegebenen Zelle untersucht werden. Quantitative PCR-basierten Assays, in diesem Protokoll beschrieben, einen Mittelweg zwischen diesen Techniken bereitzustellen. Diese Assays verwenden eine mikrofluidische Echtzeit-PCR-Maschine zu einem Zeitpunkt, zu 96 Transkripte von Interesse zu quantifizieren bis in bis zu 96 Zellen. Während jedes der vorgenannten Verfahren erforderliche Hardwarekosten hat, sind die Kosten eines einzelnen qPCR Assay relativniedrig. Dieses Protokoll wird von einem durch den Hersteller eines mikrofluidischen Echtzeit-PCR-Maschine (Protokoll ADP 41, Fluidigm) vorgeschlagen angepasst. Um die Abschätzung der absoluten Nummer jedes Transkript in einem PCR-basierten Ansatz zu ermöglichen, haben wir das Protokoll erweitert, um die Verwendung von internen Kontrollen vorbereiteter Zielgen Amplikons machen, die über mehrere Experimente verwendet werden kann. Als ein Beispiel für diese Technik ist 11 beschrieben die Quantifizierung der Expression von Genen durch den Tumorsuppressor p53 in menschlichen MCF-7 - Brustkarzinomzellen reguliert. Die Zellen werden mit einem chemischen Mittel in Frage gestellt, die DNA-Doppelstrangbrüche induziert. Frühere Studien haben gezeigt , dass die p53 - Antwort auf DNA - Doppelstrangbrüchen eine große Heterogenität in einzelnen Zellen aufweist, sowohl in Bezug auf p53 Ebenen 12 und bei der Aktivierung verschiedener Zielgene 11. Darüber hinaus reguliert p53 die Expression von mehr als 100gut charakterisierten Zielgene in zahlreichen Downstream Wegen beteiligt, einschließlich Zellzyklusarrest, Apoptose und Seneszenz 13, 14. Da die p53-vermittelte Reaktion in jeder Zelle sowohl komplex und variabel ist, kann die Analyse des Systems profitiert von einem Ansatz, bei dem nahezu 100 Zielgene gleichzeitig in einzelnen Zellen werden untersucht, wie das unten beschrieben wird. Mit leichten Modifikationen (wie alternative Verfahren zur Einzelzellisolierung und Lyse), kann das Protokoll leicht ein breites Spektrum von Säugerzelltypen, Protokolle und zelluläre Antworten zu untersuchen angepasst werden. Mit der richtigen rechtzeitige Vorbereitung kann eine Runde der Zellsortierung und Genexpressionsmessung durchgeführt werden, gemäß diesem Protokoll über einen Zeitraum von drei Tagen. Das folgende Zeit wird vorgeschlagen: im Voraus, die Transkripte von Interesse auswählen, zu identifizieren und die Primerpaare zu validieren, die die cDNA von jenen transcr verstärkenIPTS und bereite die Standards und Zündmittelmischungen diese Primer verwendet. Am Tag 1 nach Zellbehandlung, Ernte und sortieren Sie die Zellen, führen die reverse Transkription und spezifische Ziel-Amplifikation und Behandlung der Proben mit einer Exonuklease unincorporated Primer zu entfernen. Am Tag 2, führen an der sortierten Zellen Qualitätskontrolle mit qPCR. Schließlich, am Tag 3, messen die Genexpression in den sortierten Zellen mikrofluidischen qPCR verwendet wird. In Abbildung 1 sind die Schritte beteiligt.

<table><tbody><tr><td>RNeasy Plus Mini Kit</td><td>Qiagen</td><td>74134</td><td></td></tr><tr><td>High Capacity cDNA Reverse Transcription Kit with RNase Inhibitor</td><td>ThermoFisher</td><td>4374966</td><td></td></tr><tr><td>Phusion High-Fidelity DNA Polymerase</td><td>New England BioLabs</td><td>M0530S</td><td></td></tr><tr><td>QIAquick Gel Extraction Kit</td><td>Qiagen</td><td>28704</td><td></td></tr><tr><td>Quant-iT High-Sensitivity dsDNA Assay Kit</td><td>ThermoFisher</td><td>Q33120&amp;nbsp;</td><td></td></tr><tr><td>2.0 ml low adhesion microcentrifuge tubes</td><td>USA Scientific</td><td>1420-2600</td><td></td></tr><tr><td>DNA Suspension Buffer</td><td>Teknova</td><td>T0221</td><td></td></tr><tr><td>Axygen 0.2 ml Maxymum Recovery Thin Wall PCR Tubes</td><td>Corning</td><td>PCR-02-L-C</td><td></td></tr><tr><td>GE 96.96 Dynamic Array DNA Binding Dye Sample &amp;amp; Assay Loading Reagent Kit</td><td>Fluidigm</td><td>100-3415</td><td></td></tr><tr><td>HyClone RPMI 1640 media</td><td>GE Healthcare Life Sciences</td><td>SH30027.01</td><td></td></tr><tr><td>Fetal Bovine Serum, Certified (US)</td><td>ThermoFisher</td><td>16000-044</td><td></td></tr><tr><td>Antibiotic-Antimycotic Solution</td><td>Corning</td><td>30-004-CI</td><td></td></tr><tr><td>Neocarzinostatin</td><td>Sigma</td><td>N9162</td><td></td></tr><tr><td>ELIMINase</td><td>Decon Labs</td><td>1101</td><td></td></tr><tr><td>SUPERase-In</td><td>ThermoFisher</td><td>AM2696</td><td></td></tr><tr><td>CellsDirect One-Step qRT-PCR Kit</td><td>ThermoFisher</td><td>11753500</td><td></td></tr><tr><td>&lt;em&gt;E. coli&lt;/em&gt; DNA</td><td>Affymetrix</td><td>14380 10 MG</td><td></td></tr><tr><td>ThermalSeal Sealing Film, Sterile</td><td>Excel Scientific</td><td>STR-THER-PLT</td><td></td></tr><tr><td>BD FACSAria IIu</td><td>BD Biosciences</td><td></td><td></td></tr><tr><td>HyClone Trypsin 0.05%</td><td>GE Healthcare Life Sciences</td><td>SH30236.01</td><td></td></tr><tr><td>PBS, 1x</td><td>Corning</td><td>21-040-CV</td><td></td></tr><tr><td>Falcon 40 &amp;micro;m Cell Strainer</td><td>Corning</td><td>352340</td><td></td></tr><tr><td>Exonuclease I</td><td>New England BioLabs</td><td>M0293S</td><td></td></tr><tr><td>SsoFast EvaGreen Supermix with Low ROX</td><td>Bio-Rad</td><td>172-5210</td><td></td></tr><tr><td>96.96 Dynamic Array IFC for Gene Expression (microfluidic qPCR chip)</td><td>Fluidigm</td><td>BMK-M-96.96</td><td></td></tr><tr><td>IFC Controller HX (loading machine)</td><td>Fluidigm</td><td></td><td></td></tr><tr><td>BioMark or BioMark HD (microfluidic qPCR machine)</td><td>Fluidigm</td><td></td><td></td></tr><tr><td>Real-Time PCR Analysis software&amp;nbsp;</td><td>Fluidigm</td><td></td><td></td></tr><tr><td>MATLAB software</td><td>MathWorks</td><td></td><td></td></tr></tbody></table>

single-cell gene expression profiling using facs and qpcr with internal standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

Eine allgemeine Übersicht über das Protokoll ist in Abbildung 1, mit Schritten zur Zellbehandlung, die Isolierung von einzelnen Zellen durch FACS, die Erzeugung gezeigt und Vorverstärkung von cDNA - Bibliotheken aus single-Zelllysaten, die Bestätigung der Einzelzell - cDNA - Bibliotheken in sortierter Wells, und die Messung der Genexpression durch qPCR. In Vorbereitung auf die Analyse Einzelzellisolierung ...

Watch this Scientific Journal Video about Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards at JoVE.com

Einzelzell-Genexpressionsprofilierung mithilfe von FACS und qPCR mit internen Standards | Genetik | Video

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Einzellige Genexpressionsprofile mittels FACS und qPCR mit internen Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

single-cell-gene-expression-profiling-using-facs-qpcr-with-internal

Research

Education

JoVE Journal

JoVE Core

Biology

Genetics

Gene Expression

SCIENTISTS IN ACTION

16.4K Aufrufe.  National Cancer Institute. We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Serie: Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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.

Einzellige Gene Expression Mit Multiplex-RT-qPCR zum Charakterisieren Heterogene Rare Lymphatische Populations

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

Forschung

Genetik

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Gleichzeitige DNA-RNA-Extraktion aus Küstensedimenten und Quantifizierung von 16S rRNA-Gene und Transkripte von Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

Umwelt

Isolation and Characterization of Single Cells from Zebrafish Embryos

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been described at the morphological level, little is known about the molecular basis of cellular changes that occur as the organism develops. With recent advancements in microfluidics and multiplexing technologies, it is now possible to characterize gene expression in single cells. This allows for investigation of heterogeneity between individual cells of specific cell populations to identify and classify cell subtypes, characterize intermediate states that occur during cell differentiation, and explore differential cellular responses to stimuli. This study describes a protocol to isolate viable, single cells from zebrafish embryos for high throughput multiplexing assays. This method may be rapidly applied to any zebrafish embryonic cell type with fluorescent markers. An extension of this method may also be used in combination with high throughput sequencing technologies to fully characterize the transcriptome of single cells. As proof of principle, the relative abundance of cardiac differentiation markers was assessed in isolated, single cells derived from nkx2.5 positive cardiac progenitors. By evaluation of gene expression at the single cell level and at a single time point, the data support a model in which cardiac progenitors coexist with differentiating progeny. The method and work flow described here is broadly applicable to the zebrafish research community, requiring only a labeled transgenic fish line and access to microfluidics technologies.

This protocol describes a method for isolating single cells from zebrafish embryos, enriching for cells of interest, capturing zebrafish cells in microfluidic based single cell multiplex systems, and assessing gene expression from single cells.

Isolierung und Charakterisierung von Einzelzellen aus Zebrafischembryonen

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been ...

Entwicklungsbiologie

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.

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.

Eine kombinatorische einzellige Ansatz zur molekularen Charakterisierung und Immunophenotypic Heterogenität der menschlichen Stammzellen und Vorläuferzellen Populationen

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

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Optimierung zur Sequenzierung und Analyse degradierter FFPE-RNA-Proben

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding ...

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk population methods (Northern blot, PCR, and RNAseq) that measure mRNA levels lack the ability to provide information on cell-to-cell variation in responses. Precise single cell and allelic visualization and quantification is possible via single molecule RNA fluorescence in situ hybridization (smFISH). RNA-FISH is performed by hybridizing target RNAs with labeled oligonucleotide probes. These can be imaged in medium/high throughput modalities, and, through image analysis pipelines, provide quantitative data on both mature and nascent RNAs, all at the single cell level. The fixation, permeabilization, hybridization and imaging steps have been optimized in the lab over many years using the model system described herein, which results in successful and robust single cell analysis of smFISH labeling. The main goal with sample preparation and processing is to produce high quality images characterized by a high signal-to-noise ratio to reduce false positives and provide data that are more accurate. Here, we present a protocol describing the pipeline from sample preparation to data analysis in conjunction with suggestions and optimization steps to tailor to specific samples.&#160;

Single molecule RNA fluorescence in situ hybridization (smFISH) is a method to accurately quantify levels and localization of specific RNAs at the single cell level. Here, we report our validated lab protocols for wet-bench processing, imaging and image analysis for single cell quantification of specific RNAs.

Einzelzellanalyse transkriptionell aktiver Allele mittels Einzelmolekül FISH

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk ...

Biochemie

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their translational repression. Changes in miRNA expression have been shown to be involved in the development of all major complex diseases. Furthermore, recent findings showed that miRNAs can be secreted to the extracellular environment and enter the bloodstream and other body fluids where they can circulate with high stability. The function of such circulating miRNAs remains largely elusive, but systematic high throughput approaches, such as miRNA profiling arrays, have lead to the identification of miRNA signatures in several pathological conditions, including neurodegenerative disorders and several types of cancers. In this context, the identification of miRNA expression profile in the cerebrospinal fluid, as reported in our recent study, makes miRNAs attractive candidates for biomarker analysis.
There are several tools available for profiling microRNAs, such as microarrays, quantitative real-time PCR (qPCR), and deep sequencing. Here, we describe a sensitive method to profile microRNAs in cerebrospinal fluids by quantitative real-time PCR. We used the Exiqon microRNA ready-to-use PCR human panels I and II V2.R, which allows detection of 742 unique human microRNAs. We performed the arrays in triplicate runs and we processed and analyzed data using the GenEx Professional 5 software.
Using this protocol, we have successfully profiled microRNAs in various types of cell lines and primary cells, CSF, plasma, and formalin-fixed paraffin-embedded tissues.

We describe a protocol of real time PCR to profile microRNAs in the cerebrospinal fluid (CSF). With the exception of RNA extraction protocols, the procedure can be extended to RNA extracted from other body fluids, cultured cells, or tissue specimens.

Liquor microRNA Profiling mittels quantitativer Real-Time PCR

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their ...

Medizin

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene transcripts from infected tissues. Pathogen RNA constitutes a tiny portion of the total RNA isolated from infected tissue samples. Both microarray and RNAseq technologies have difficulties in generating reliable reads for weakly expressed pathogen genes. Mutant pathogen strains with reduced in vivo proliferation pose an even bigger challenge. Here we describe an in vivo gene expression profiling protocol that is very fast, extremely sensitive and highly reproducible. We developed this protocol during our investigation of the fungal pathogen Candida albicans in a murine model of hematogenously disseminated candidiasis. Using this protocol, we have documented time courses of dynamically regulated C. albicans gene expression during kidney infection, and discovered unexpected features of gene expression responses to antifungal drug treatment in vivo.

We developed a protocol that is fast, sensitive and reproducible for pathogen gene expression profiling during an infection.

Gene Expression Profiling von Infecting Mikroben Mit einem Digital-Strichcode-Plattform

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene ...

Immunologie und Infektion

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

Single Cell Transcriptional Profiling der erwachsenen Maus Kardiomyozyten

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

Biologie

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different lineages. A single cell transcriptome assay can be a new approach for dissecting individual variation. We have developed the single cell qRT-PCR method, and confirmed that this method works well in several gene expression profiles. In single cell level, each human embryonic stem cell, sorted by OCT4::EGFP positive cells, has high expression in OCT4, but a different level of NANOG expression. Our single cell gene expression assay should be useful to interrogate population heterogeneities.

Single cell gene expression assay is needed for understanding stem cell heterogeneities.

Profiling Individuelle humanen embryonalen Stammzellen durch quantitative RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different ...