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

doi:10.1038/nbt.4042

. 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

Hyun Min Kang¹, Meena Subramaniam^{2

3

4

5

6}, Sasha Targ^{2

3

4

5

6

7}, Michelle Nguyen^{8

9

10}, Lenka Maliskova^{3

11}, Elizabeth McCarthy⁷, Eunice Wan³, Simon Wong³, Lauren Byrnes¹², Cristina M Lanata^{13

14}, Rachel E Gate^{2

3

4

5

6}, Sara Mostafavi¹⁵, Alexander Marson^{8

9

10

13

16

17}, Noah Zaitlen^{3

13

18}, Lindsey A Criswell^{3

13

14

19}, Chun Jimmie Ye^{3

4

5

6}

Affiliations

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.

PMID: 29227470
PMCID: PMC5784859
DOI: 10.1038/nbt.4042

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

Hyun Min Kang et al. Nat Biotechnol. 2018 Jan.

. 2018 Jan;36(1):89-94.

doi: 10.1038/nbt.4042. Epub 2017 Dec 11.

Authors

Affiliations

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.

PMID: 29227470
PMCID: PMC5784859
DOI: 10.1038/nbt.4042

Erratum in

Author Correction: Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.
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. Kang HM, et al. Nat Biotechnol. 2020 Nov;38(11):1356. doi: 10.1038/s41587-020-0715-9. Nat Biotechnol. 2020. PMID: 33057163

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.

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Figures

**Figure 1. Demuxlet: demultiplexing and doublet identification from single cell data**
a) Pipeline for experimental multiplexing of unrelated individuals, loading onto droplet-based single-cell RNA-sequencing instrument, and computational demultiplexing (demux) and doublet removal using demuxlet. Assuming equal mixing of 8 individuals, b) 4 genetic variants can recover the sample identity of a cell, and c) 87.5% of doublets will contain cells from two different samples.

**Figure 2. Performance of demuxlet**
a) Experimental design for equimolar pooling of cells from 8 unrelated samples (S1-S8) into three wells (W1-W3). W1 and W2 contain cells from two disjoint sets of 4 individuals. W3 contains cells from all 8 individuals. b) Demultiplexing single cells in each well recovers the expected individuals. c) Estimates of doublet rates versus previous estimates from mixed species experiments. d) Cell type identity determined by prediction to previously annotated PBMC data. e) t-SNE plot of two individuals (S1 and S5) from different wells are qualitatively concordant.

**Figure 3. Inter-individual variability in IFN-β response**
a) t-SNE plot of unstimulated (blue) and IFN-β-stimulated (red) PBMCs and the estimated cell types. b) Cell type-specific expression in stimulated (left) and unstimulated (right) cells. Differentially expressed genes shown (FDR < 0.05, |log(FC)| > 1). Each column represents cell type-specific expression for each individual from demuxlet. c) Observed variance (y-axis) in mean expression over all PBMCs from each of the 8 individuals versus expected variance (x-axis) over synthetic replicates sampled across all cells (light blue, pink) or replicates matched for cell type proportion (blue, red). d) Cell type proportions for each individual in unstimulated and stimulated cells. e) Correlation between sample replicates in control and stimulated cells. f) Number of significantly variable genes in each cell type and condition.

**Figure 4. Genetic control over cell type proportion and gene expression (N=23)**
a) Observed variance (y-axis) in mean expression over all PBMCs from each individual versus expected variance (x-axis) over synthetic replicates sampled across batch 1 (left, N=8) and batch 3 (right, N=15). b) Association of chr10:3791224 with NK cell type proportions. c) Genome-wide and chromosome 6 Manhattan plots across all major cell types. Horizontal lines correspond to FDR < 0.1 (blue) and FDR < 0.05 (red). d) Q-Q plots across all genes and subsets of previously published eQTLs in relevant cell types are shown for B, cM, and Th populations. e) Notable cis-eQTLs across all major immune cell types are marked with *(FDR < 0.25), ** (FDR < 0.1), and *** (FDR < 0.05). Lack of association is marked with NS (not significant).

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[1] Macosko EZ, et al. Cell. 2015;161:1202–1214. - PMC - PubMed

[2] Macosko EZ, et al. Cell. 2015;161:1202–1214. - PMC - PubMed

[3] Pollen AA, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotech. 2014;32:1053–1058. - PMC - PubMed

[4] Pollen AA, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotech. 2014;32:1053–1058. - PMC - PubMed

[5] Buenrostro JD, et al. Nature. Vol. 523. Nature Research; 2015. pp. 486–490. - PMC - PubMed

[6] Buenrostro JD, et al. Nature. Vol. 523. Nature Research; 2015. pp. 486–490. - PMC - PubMed

[7] Nagano T, et al. Nature. 2013;502:59–64. - PMC - PubMed

[8] Nagano T, et al. Nature. 2013;502:59–64. - PMC - PubMed

[9] Patel AP, et al. Science. Vol. 344. American Association for the Advancement of Science; 2014. pp. 1396–1401. - PubMed

[10] Patel AP, et al. Science. Vol. 344. American Association for the Advancement of Science; 2014. pp. 1396–1401. - PubMed

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

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

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