| Literature DB >> 26000488 |
Evan Z Macosko1, Anindita Basu2, Rahul Satija3, James Nemesh4, Karthik Shekhar5, Melissa Goldman6, Itay Tirosh5, Allison R Bialas7, Nolan Kamitaki4, Emily M Martersteck8, John J Trombetta5, David A Weitz9, Joshua R Sanes8, Alex K Shalek10, Aviv Regev11, Steven A McCarroll12.
Abstract
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. VIDEO ABSTRACT.Entities:
Mesh：
Year: 2015 PMID： 26000488 PMCID： PMC4481139 DOI： 10.1016/j.cell.2015.05.002
Source DB: PubMed Journal: Cell ISSN： 0092-8674 Impact factor: 41.582