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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.

Abstract：

: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)-β and perform eQTL analysis on 23 pooled samples.

journal_name

Nat Biotechnol

journal_title

Nature biotechnology

authors

Kang HM,Subramaniam M,Targ S,Nguyen M,Maliskova L,McCarthy E,Wan E,Wong S,Byrnes L,Lanata CM,Gate RE,Mostafavi S,Marson A,Zaitlen N,Criswell LA,Ye CJ

doi

10.1038/nbt.4042

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

89-94

issue

1

eissn

1087-0156

issn

1546-1696

pii

nbt.4042

journal_volume

36

pub_type

杂志文章

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