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Nat Biotechnol. 2018 Jan;36(1):89-94. doi: 10.1038/nbt.4042. Epub 2017 Dec 11.

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

Kang HM1, Subramaniam M2,3,4,5,6, Targ S2,3,4,5,6,7, Nguyen M8,9,10, Maliskova L3,11, McCarthy E7, Wan E3, Wong S3, Byrnes L12, Lanata CM13,14, Gate RE2,3,4,5,6, Mostafavi S15, Marson A8,9,10,13,16,17, Zaitlen N3,13,18, Criswell LA3,13,14,19, Ye CJ3,4,5,6.

Author information

1
Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
2
Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, California, USA.
3
Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, California, USA.
4
Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, California, USA.
5
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
6
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.
7
Medical Scientist Training Program (MSTP), University of California, San Francisco, San Francisco, California, USA.
8
Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, California, USA.
9
Diabetes Center, University of California, San Francisco, San Francisco, California, USA.
10
Innovative Genomics Institute, University of California, Berkeley, Berkeley, California, USA.
11
Department of Neurology, University of California, San Francisco, San Francisco, California, USA.
12
Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, California, USA.
13
Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
14
Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, University of California, San Francisco, San Francisco, California, USA.
15
Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
16
UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.
17
Chan Zuckerberg Biohub, San Francisco, California, USA.
18
Lung Biology Center, University of California, San Francisco, San Francisco, California, USA.
19
Department of Orofacial Sciences, University of California, San Francisco, San Francisco, USA.

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.

PMID:
29227470
PMCID:
PMC5784859
DOI:
10.1038/nbt.4042
[Indexed for MEDLINE]
Free PMC Article

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