Journal
CELL
Volume 161, Issue 5, Pages 1187-1201Publisher
CELL PRESS
DOI: 10.1016/j.cell.2015.04.044
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Funding
- NIH SCAP [R21DK098818]
- Burroughs-Wellcome Fund
- Marie Curie International Outgoing Fellowship [300121]
- HSCI Medical Scientist Training Fellowship
- Harvard Presidential Scholars Fund
- NIH [5R01HD073104-03, R01 GM026875, R01 GM103785, R01 HD073104, P01HL120839]
- NSF [DMR-1310266]
- Harvard Materials Research Science and Engineering Center [DMR-1420570]
- DARPA [HR0011-11-C-0093]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1310266] Funding Source: National Science Foundation
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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.
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