期刊
NATURE BIOTECHNOLOGY
卷 36, 期 1, 页码 89-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/nbt.4042
关键词
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资金
- NIH [R01AR071522, R21AI133337, F30DK115167, K25HL121295, R03DE025665]
- Department of Defense [W81WH-16-2-0018]
- [U01HL137182]
- NATIONAL CENTER FOR CHRONIC DISEASE PREV AND HEALTH PROMO [U01DP005120] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [K25HL121295, U01HL137182] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [R21AI133337, U19AI090023] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [R01AR071522, P30AR070155] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DENTAL & CRANIOFACIAL RESEARCH [R03DE025665] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [F30DK115167] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM067547, T32GM007618] Funding Source: NIH RePORTER
Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-beta and perform eQTL analysis on 23 pooled samples.
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