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Cell. 2015 May 21;161(5):1187-1201. doi: 10.1016/j.cell.2015.04.044.

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Author information

1
Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
2
School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA; Vilnius University Institute of Biotechnology, Vilnius LT-02241, Lithuania.
3
School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA.
4
Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA.
5
School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA 02138, USA. Electronic address: weitz@seas.harvard.edu.
6
Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: marc@hms.harvard.edu.

Abstract

It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.

PMID:
26000487
PMCID:
PMC4441768
DOI:
10.1016/j.cell.2015.04.044
[Indexed for MEDLINE]
Free PMC Article
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