Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
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Shalek_Highly parallel.pdf
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2.82 MB
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Author(s)
Macosko, Evan Z.
Satija, Rahul
Nemesh, James
Shekhar, Karthik
Goldman, Melissa
Tirosh, Itay
Bialas, Allison R.
Kamitaki, Nolan
Martersteck, Emily M.
Trombetta, John J.
Date Issued
May 2015
Journal
Cell
Publisher
Elsevier
Citation
Macosko, Evan Z.; Basu, Anindita; Satija, Rahul; Nemesh, James; Shekhar, Karthik; Goldman, Melissa; Tirosh, Itay et al. “Highly Parallel Genome-Wide Expression Profiling of Individual Cells Using Nanoliter Droplets.” Cell 161, no. 5 (May 2015): 1202–1214 © 2015 Elsevier Inc
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.cell.2015.05.002
