Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

被引:4867
作者
Macosko, Evan Z. [1 ,2 ,3 ]
Basu, Anindita [4 ,5 ]
Satija, Rahul [4 ,6 ,7 ]
Nemesh, James [1 ,2 ,3 ]
Shekhar, Karthik [4 ]
Goldman, Melissa [1 ,2 ]
Tirosh, Itay [4 ]
Bialas, Allison R. [8 ]
Kamitaki, Nolan [1 ,2 ,3 ]
Martersteck, Emily M. [9 ,10 ]
Trombetta, John J. [4 ]
Weitz, David A. [5 ,11 ]
Sanes, Joshua R. [9 ,10 ]
Shalek, Alex K. [4 ,12 ,13 ,14 ]
Regev, Aviv [4 ,15 ,16 ]
McCarroll, Steven A. [1 ,2 ,3 ]
机构
[1] Harvard Univ, Sch Med, Dept Genet, Boston, MA 02115 USA
[2] Broad Inst Harvard & MIT, Stanley Ctr Psychiat Res, Cambridge, MA 02142 USA
[3] Broad Inst Harvard & MIT, Program Med & Populat Genet, Cambridge, MA 02142 USA
[4] Broad Inst Harvard & MIT, Klarman Cell Observ, Cambridge, MA 02142 USA
[5] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[6] New York Genome Ctr, New York, NY 10013 USA
[7] NYU, Dept Biol, New York, NY 10003 USA
[8] Childrens Hosp, Program Cellular & Mol Med, Boston, MA 02115 USA
[9] Harvard Univ, Dept Mol & Cellular Biol, Cambridge, MA 02138 USA
[10] Harvard Univ, Ctr Brain Sci, Cambridge, MA 02138 USA
[11] Harvard Univ, Dept Phys, Cambridge, MA 02138 USA
[12] Ragon Inst MGH MIT & Harvard, Cambridge, MA 02139 USA
[13] MIT, Inst Med Engn & Sci, Cambridge, MA 02139 USA
[14] MIT, Dept Chem, Cambridge, MA 02139 USA
[15] MIT, Dept Biol, Cambridge, MA 02139 USA
[16] Howard Hughes Med Inst, Chevy Chase, MD 20815 USA
基金
美国国家科学基金会;
关键词
RNA-SEQ; AMACRINE CELLS; GENE; RETINA; LENS;
D O I
10.1016/j.cell.2015.05.002
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution.
引用
收藏
页码:1202 / 1214
页数:13
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