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


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Demuxlet: demultiplexing and doublet identification from single-cell data.
Figure 2: Performance of demuxlet.
Figure 3: Inter-individual variability in IFN-β response.
Figure 4: Genetic control over cell type proportion and gene expression (N = 23).

Accession codes

Primary accessions

Gene Expression Omnibus


  1. 1

    Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  2. 2

    Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  Article  Google Scholar 

  3. 3

    Stegle, O., Teichmann, S.A. & Marioni, J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    CAS  Article  Google Scholar 

  4. 4

    Gawad, C., Koh, W. & Quake, S.R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    CAS  Article  Google Scholar 

  5. 5

    Streets, A.M. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048–7053 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44–73 (2017).

    CAS  Article  Google Scholar 

  7. 7

    Zheng, G.X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    CAS  Article  Google Scholar 

  8. 8

    Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012).

    CAS  Article  Google Scholar 

  9. 9

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  Article  Google Scholar 

  10. 10

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  11. 11

    Auton, A. et al. The Genomes Project. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  12. 12

    Aguirre-Gamboa, R. et al. Differential effects of environmental and genetic factors on T and B cell immune traits. Cell Rep. 17, 2474–2487 (2016).

    CAS  Article  Google Scholar 

  13. 13

    Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110.e14 (2016).

    CAS  Article  Google Scholar 

  14. 14

    Mostafavi, S. et al. Parsing the interferon transcriptional network and its disease associations. Cell 164, 564–578 (2016).

    CAS  Article  Google Scholar 

  15. 15

    Stark, G.R., Kerr, I.M., Williams, B.R.G., Silverman, R.H. & Schreiber, R.D. How cells respond to interferons. Annu. Rev. Biochem. 67, 227–264 (1998).

    CAS  Article  Google Scholar 

  16. 16

    Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    Article  Google Scholar 

  17. 17

    Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).

    Article  Google Scholar 

  18. 18

    Andrés, A.M. et al. Balancing selection maintains a form of ERAP2 that undergoes nonsense-mediated decay and affects antigen presentation. PLoS Genet. 6, e1001157 (2010).

    Article  Google Scholar 

  19. 19

    Palmer, C., Diehn, M., Alizadeh, A.A. & Brown, P.O. Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 7, 115 (2006).

    Article  Google Scholar 

  20. 20

    Saveanu, L. et al. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 6, 689–697 (2005).

    CAS  Article  Google Scholar 

  21. 21

    Franco, L.M. et al. Integrative genomic analysis of the human immune response to influenza vaccination. eLife 2, e00299 (2013).

    Article  Google Scholar 

  22. 22

    Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Preprint at bioRxiv (2017).

  23. 23

    Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

    CAS  Article  Google Scholar 

  24. 24

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

    CAS  Article  Google Scholar 

  25. 25

    Jaitin, D.A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167, 1883–1896.e15 (2016).

    CAS  Article  Google Scholar 

  26. 26

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS  Article  Google Scholar 

  27. 27

    Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  Article  Google Scholar 

  28. 28

    Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

    CAS  Article  Google Scholar 

  29. 29

    Tung, P.-Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).

    CAS  Article  Google Scholar 

  30. 30

    Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    CAS  Article  Google Scholar 

  31. 31

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  32. 32

    Wang, X., Spandidos, A., Wang, H. & Seed, B. PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update. Nucleic Acids Res. 40, D1144–D1149 (2012).

    CAS  Article  Google Scholar 

  33. 33

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  Google Scholar 

  34. 34

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  Article  Google Scholar 

  35. 35

    Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  Article  Google Scholar 

  36. 36

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  Article  Google Scholar 

  37. 37

    Dabney, A., Storey, J.D. & Warnes, G.R. qvalue: Q-value estimation for false discovery rate control. R package version 1 (2010).

  38. 38

    Falconer, D.S. & Mackay, T.F. Introduction to Quantitative Genetics, 4th edn. (Pearson, 1996).

  39. 39

    Loh, P.R., Palamara, P.F. & Price, A.L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  Article  Google Scholar 

  40. 40

    Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  Article  Google Scholar 

Download references


M.S. and C.J.Y. are supported by NIH R01AR071522 and R21AI133337. S.T. is supported by NIH F30DK115167. H.M.K. is supported by U01HL137182. N.Z. is supported by NIH K25HL121295, R03DE025665, and Department of Defense W81WH-16-2-0018.

Author information




H.M.K. and C.J.Y. conceived the project. M.S., S.T., L.M., R.G., L.B., E.W., S.W., and M.N. performed all experiments. H.M.K., M.S., S.T., E.M., S.M., and C.J.Y. analyzed the data. C.L. and L.A.C. provided the patient samples. N.Z. and A.M. provided helpful comments and discussion. H.M.K., M.S., S.T., and C.J.Y. wrote the manuscript.

Corresponding authors

Correspondence to Hyun Min Kang or Chun Jimmie Ye.

Ethics declarations

Competing interests

A.M. is a founder of Spotlight Therapeutics and serves as an advisor to Juno Therapeutics and PACT Pharma; the Marson laboratory has received sponsored research support from Juno Therapeutics and Epinomics.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–21 (PDF 3273 kb)

Life Sciences Reporting Summary (PDF 130 kb)

Supplementary Table 1

Cell type specific differentially expressed genes (XLSX 680 kb)

Supplementary Table 2

Pathway enrichment for differentially expressed genes (XLSX 97 kb)

Supplementary Table 3

Cell type specific eQTLs (PDF 369 kb)

Supplementary Table 4

Base Call Probabilities (XLSX 2726 kb)

Supplementary Code

Implementation of demuxlet (ZIP 4903 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kang, H., Subramaniam, M., Targ, S. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol 36, 89–94 (2018).

Download citation

Further reading