私たちは、このプロトコルの開発中に細胞選別を行うの彼女の援助のためにCCR ETIBフローサイトメトリーコアでV.カプールに感謝したいと思います。また、このプロトコルの開発中に定量PCRを行う際の援助のためにM. RaffeldとCCR LP分子診断ユニットとJ.朱とNHLBI DNA配列決定およびゲノミクスコアに感謝します。この研究は、NIHの学内プログラムによってサポートされていました。

<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.

我々は、培養で増殖させた接着性細胞の集団から個々の哺乳動物細胞を単離するため、各セル内の約96の遺伝子の発現をアッセイするための方法を提示しています。この方法がうまく機能するために良好な事前準備が重要です。プライマーは、単セル測定の品質を決定するように、特に興味のある転写物（1.2〜1.3ステップ）に特異的な設計およびテストプライマーペアは、時間がかかるが、?...

集団内の個々の細胞は均一な生理的刺激1、2、3、4に大幅に異なる応答を示すことができます。集団中の細胞の遺伝的変異は、応答のこの品種のための1つのメカニズムであるが、それでも細胞のクローン集団で、応答の変動性を向上させることができ、いくつかの非遺伝的要因もあります。例えば、個々のタンパク質および他の重要なシグナル伝達分子のレベルは、下流の遺伝子発現プロファイルの変化を生じる、セル単位で変化させることができます。さらに、遺伝子活性化は、バースト7,8,9あたりの転写物の比較的少数に限定することができる転写5,6の短時間のバーストで発生することができます。そのような遺伝子活性化における偶然性が大幅に生物学的応答の変動に寄与することができ、生理的刺激に応答して、微生物10におよび哺乳動物細胞1に2を選択的利点を提供することができます。変化の遺伝的および非遺伝的供給源の両方に、刺激に応答する任意の細胞の遺伝子発現プロファイルは、バルク応答の測定から得られた平均遺伝子発現プロファイルとは大きく異なっていてもよいです。個々の細胞は、刺激に応答して可変性を示す程度を決定することは、個々の細胞の単離のための技術は、目的の転写物の発現レベルを測定し、得られた発現データのコンピュータ分析を必要とします。 コストの広い範囲をカバーし、単一細胞における遺伝子発現をアッセイするためのいくつかのアプローチがあり、転写産物の数はプローブ、及び定量化の精度。例えば、単一細胞RNA-のSeqは、転写物のカバレッジと、個々の細胞の中で最も高度に発現される遺伝子のための別個の転写産物の数千を定量する能力の広い深さを提供しています。コストが減少し続けるが、しかし、このようなシーケンシングの深さに関連するコストは、法外することができます。逆に、in situハイブリダイゼーション（FISH smRNA） における単一分子RNAの蛍光は、目的の遺伝子につき合理的なコストであっても低発現遺伝子のための転写物の正確な定量を提供しています。しかしながら、標的遺伝子の少数は、このアプローチによって、所定の細胞においてアッセイすることができます。このプロトコールに記載定量的PCRベースのアッセイは、これらの技術の間の妥協点を提供します。これらのアッセイは、最大96個の細胞で一度に関心の96転写物まで定量化するために、マイクロ流体リアルタイムPCR装置を採用しています。前述の方法のそれぞれは、必要なハードウェアのコストを有しているが、任意の個々のqPCRアッセイのコストは比較的です低いです。このプロトコルは、（プロトコルADP 41、フリュー）マイクロ流体リアルタイムPCR装置の製造元が推奨する1から適応されます。 PCRベースのアプローチで各転写産物の絶対数の推定を可能にするために、我々は、複数の実験で使用できる準備標的遺伝子アンプリコンの内部統制を利用するプロトコルを拡大してきました。 この技術の一例として、MCF-7ヒト乳癌細胞において腫瘍抑制因子p53によって調節される遺伝子の発現の定量化は、11に記載されています。細胞は、DNA二本鎖切断を誘発する化学物質でチャレンジされています。以前の研究は、DNA二重鎖切断に対するp53応答は、p53レベル12の観点と異なる標的遺伝子11の活性化の両方で、個々の細胞における異質性を大いに発揮することが示されています。さらに、p53は100以上の発現を調節します細胞周期停止、アポトーシス及び老化13、14を含む多くの下流経路に関与する標的遺伝子を十分に特徴付け。各細胞におけるp53媒介性応答は、複雑かつ可変でもあるので、アプローチからシステムの利点の分析とは、約100の標的遺伝子は、以下に記載されるような個々の細胞、同時にプローブすることができます。 （例えば、単一細胞の単離および溶解のための代替方法として）わずかな変更を加えて、プロトコルは、容易に、哺乳動物の細胞型、転写、および細胞応答の広い範囲を研究するために適合させることができます。 適切な事前準備で、細胞選別および遺伝子発現測定のラウンドは、3日間にわたって、このプロトコルに従って行うことができます。 、事前に関心の転写産物を選択し、それらのtranscrからcDNAを増幅するプライマー対を識別し、検証：以下のタイミングが示唆されていますIPTSは、および標準とそれらのプライマーを用いてプライマーミックスを準備します。細胞治療の後、1日目に、収穫と逆転写および特定の標的増幅を行い、細胞を選別し、取り込まれなかったプライマーを除去するためにエキソヌクレアーゼでサンプルを扱います。 2日目、定量PCRを使用して選別した細胞上の品質管理を行います。最後に、3日目に、マイクロ流体定量PCRを使用して選別した細胞における遺伝子発現を測定します。 図1は、必要な手順をまとめたものです。

<table border="0" cellpadding="0" cellspacing="0" width="988">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:488px;">RNeasy Plus Mini Kit</td>
			<td style="width:187px;">Qiagen</td>
			<td style="width:147px;">74134</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">High Capacity cDNA Reverse Transcription Kit with RNase Inhibitor</td>
			<td>ThermoFisher</td>
			<td>4374966</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phusion High-Fidelity DNA Polymerase</td>
			<td>New England BioLabs</td>
			<td>M0530S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">QIAquick Gel Extraction Kit</td>
			<td>Qiagen</td>
			<td>28704</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Quant-iT High-Sensitivity dsDNA Assay Kit</td>
			<td>ThermoFisher</td>
			<td>Q33120&#160;</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2.0-mL low adhesion microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1420-2600</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">DNA Suspension Buffer</td>
			<td>Teknova</td>
			<td>T0221</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Axygen 0.2-mL Maxymum Recovery Thin Wall PCR Tubes</td>
			<td>Corning</td>
			<td>PCR-02-L-C</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">GE 96.96 Dynamic Array DNA Binding Dye Sample &#38; Assay Loading Reagent Kit</td>
			<td>Fluidigm</td>
			<td>100-3415</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone RPMI 1640 media</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30027.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fetal Bovine Serum, Certified (US)</td>
			<td>ThermoFisher</td>
			<td>16000-044</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Antibiotic-Antimycotic Solution</td>
			<td>Corning</td>
			<td>30-004-CI</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Neocarzinostatin</td>
			<td>Sigma</td>
			<td>N9162</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ELIMINase</td>
			<td>Decon Labs</td>
			<td>1101</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SUPERase-In</td>
			<td>ThermoFisher</td>
			<td>AM2696</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">CellsDirect One-Step qRT-PCR Kit</td>
			<td>ThermoFisher</td>
			<td>11753500</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">E. coli DNA</td>
			<td>Affymetrix</td>
			<td>14380 10 MG</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ThermalSeal Sealing Film, Sterile</td>
			<td>Excel Scientific</td>
			<td>STR-THER-PLT</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BD FACSAria IIu</td>
			<td>BD Biosciences</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone Trypsin 0.05%</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30236.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">PBS, 1x</td>
			<td>Corning</td>
			<td>21-040-CV</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Falcon 40&#181;m Cell Strainer</td>
			<td>Corning</td>
			<td>352340</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Exonuclease I</td>
			<td>New England BioLabs</td>
			<td>M0293S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SsoFast EvaGreen Supermix with Low ROX</td>
			<td>Bio-Rad</td>
			<td>172-5210</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">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 height="20">
			<td height="20" style="height:20px;">IFC Controller HX (loading machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BioMark or BioMark HD (microfluidic qPCR machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Real-Time PCR Analysis software&#160;</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">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.

プロトコルの一般的な概要は、細胞治療、FACSによる単一細胞の単離、単一細胞溶解物からのcDNAライブラリーの生成および前増幅、単一細胞cDNAライブラリーを確認するステップを含む、 図1に示されていますソートされたウェル、および定量PCRにより遺伝子発現を測定します。 単一細胞の単離および遺伝子発?...

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

内部標準を使用した FACS および qPCR を使用した単一細胞遺伝子発現プロファイリング |遺伝学 |ビデオ

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.

内部標準でFACSおよび定量PCRを用いた単一細胞遺伝子発現プロファイリング

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

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16.3K ビュー.  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.

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

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental changes. Considerable progress in our understanding of the tight connection between the circadian clock and most aspects of physiology has been made in the field over the last decade. However, unraveling the molecular basis that underlies the function of the circadian oscillator in humans stays of highest technical challenge. Here, we provide a detailed description of an experimental approach for long-term (2-5 days) bioluminescence recording and outflow medium collection in cultured human primary cells. For this purpose, we have transduced primary cells with a lentiviral luciferase reporter that is under control of a core clock gene promoter, which allows for the parallel assessment of hormone secretion and circadian bioluminescence. Furthermore, we describe the conditions for disrupting the circadian clock in primary human cells by transfecting siRNA targeting CLOCK. Our results on the circadian regulation of insulin secretion by human pancreatic islets, and myokine secretion by human skeletal muscle cells, are presented here to illustrate the application of this methodology. These settings can be used to study the molecular makeup of human peripheral clocks and to analyze their functional impact on primary cells under physiological or pathophysiological conditions.

Here, we describe settings to monitor in parallel circadian bioluminescence and the secretory activity of human islet cells and primary myotubes. For this, we employed lentiviral gene delivery of a luciferase core clock reporter, followed by in vitro synchronization and collection of outflow medium by continuous cell perifusion.

ヒト初代細胞培養物における概日時計遺伝子発現やホルモン分泌のパラレル測定

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

遺伝学

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

過敏性腸症候群におけるmiRNA循環の摂動は多重化ハイスループット遺伝子発現プラットフォームを使用して検出します

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

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.

レアリンパ集団の不均一性を特徴づけるためにマルチプレックスRT-qPCRのを使用した単一細胞の遺伝子発現

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.

単一細胞のシーケンシングを使用したコピー数変化の検出

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

Detection of microRNA Expression in Peritoneal Membrane of Rats Using Quantitative Real-time PCR

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been investigated in various organs and tissues in rat. However, standard methods for the purification of miRNAs and detection of their expression in rat peritoneal membrane have not been well established. We have developed an effective and reliable method to purify and quantify miRNAs using quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) in rat peritoneal membrane. This protocol consists of four steps: 1) purification of peritoneal membrane sample; 2) purification of total RNA including miRNA from peritoneal membrane sample; 3) reverse transcription of miRNA to produce cDNA; and 4) qRT-PCR to detect miRNA expression. Using this protocol, we successfully determined that the expression of six miRNAs (miRNA-142-3p, miRNA-21-5p, miRNA-221-3p, miRNA-223-3p, miRNA-327, and miRNA-34a-5p) increased significantly in the peritoneal membrane of a rat peritoneal fibrosis model compared with those in control groups. This protocol can be used to study the profile of miRNA expression in the peritoneal membrane of rats in many pathological conditions.

Here we present a protocol for the detection of microRNA expression in rat peritoneal membrane using quantitative real-time reverse-transcription polymerase chain reaction. This method is suitable for studying the microRNA expression profile in rat peritoneal membrane in several pathological conditions.

定量的リアルタイムPCRを用いたラットの腹膜におけるマイクロRNA発現の検出

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been ...

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such as cancer. Cell cycle-regulated gene expression analysis depends on cell synchronization into specific phases. Here we describe a method utilizing two complementary synchronization protocols that is commonly used for studying periodic variation of gene expression during the cell cycle. Both procedures are based on transiently blocking the cell cycle in one defined point. The synchronization protocol by hydroxyurea (HU) treatment leads to cellular arrest in late G1/early S phase, and release from HU-mediated arrest provides a cellular population uniformly progressing through S and G2/M. The synchronization protocol by thymidine and nocodazole (Thy-Noc) treatment blocks cells in early mitosis, and release from Thy-Noc mediated arrest provides a synchronized cellular population suitable for G1 phase and S phase-entry studies. Application of both procedures requires monitoring of the cell cycle distribution profiles, which is typically performed after propidium iodide (PI) staining of the cells and flow cytometry-mediated analysis of DNA content. We show that the combined use of two synchronization protocols is a robust approach to clearly determine the transcriptional profiles of genes that are differentially regulated in the cell cycle (i.e. E2F1 and E2F7), and consequently to have a better understanding of their role in cell cycle processes. Furthermore, we show that this approach is useful for the study of mechanisms underlying drug-based therapies (i.e. mitomycin C, an anticancer agent), because it allows to discriminate genes that are responsive to the genotoxic agent from those solely affected by cell cycle perturbations imposed by the agent.

We report two cell synchronization protocols that provide a context for studying events related to specific phases of the cell cycle. We show that this approach is useful for analyzing the regulation of specific genes in an unperturbed cell cycle or upon exposure to agents affecting the cell cycle.

2つの相補的細胞同期プロトコルによる細胞周期制御遺伝子発現の研究

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

サンプル準備およびゼブラフィッシュから RNASeq による遺伝子発現データの解析

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in which the gene of interest has been disrupted. A gene-disruption DNA construct containing a suitable antibiotic resistance marker gene is useful for the generation of gene-disrupted strains in bacteria. However, conventional construction methods, which require gene cloning steps, involve complex and time-consuming protocols. Here, a relatively facile, rapid, and cost-effective method for targeted gene disruption in Streptococcus mutans is described. The method utilizes a 2-step fusion polymerase chain reaction (PCR) to generate the disruption construct and electroporation for genetic transformation. This method does not require an enzymatic reaction, other than PCR, and additionally offers greater flexibility in terms of the design of the disruption construct. Employment of electroporation facilitates the preparation of competent cells and improves the transformation efficiency. The present method may be adapted for the generation of gene-disrupted strains of various species.

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

We describe a facile, rapid, and relatively inexpensive method for the generation of gene-disrupted Streptococcus mutans strains; this technique may be adapted for the generation of gene-disrupted strains of various species.

遺伝子破壊ミュータンス連鎖球菌緊張せず遺伝子クローニングの生成

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in ...

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

スループットを向上させると衝撃されたオオムギ糊粉層における遺伝子発現を測定

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc2155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

We describe an original transfer RNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). SPOt simultaneously measures cellular levels of all tRNAs in biological samples, in only three steps, and in less than 24 hours.

代謝ラベリングと Macroarrays を使用して転移 Rna のプロファイリング

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for ...