VOOZH about

URL: https://www.nature.com/articles/nbt.4042?error=cookies_not_supported&code=5f0e9c5d-ffcb-4500-b867-1f4ce93501d2

⇱ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation | Nature Biotechnology


Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

An Author Correction to this article was published on 14 October 2020

This article has been updated

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)-Ξ² and perform eQTL analysis on 23 pooled samples.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

$32.99 / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

$259.00 per year

only $21.58 per issue

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Demuxlet: demultiplexing and doublet identification from single-cell data.
The alternative text for this image may have been generated using AI.
Figure 2: Performance of demuxlet.
The alternative text for this image may have been generated using AI.
Figure 3: Inter-individual variability in IFN-Ξ² response.
The alternative text for this image may have been generated using AI.
Figure 4: Genetic control over cell type proportion and gene expression (N = 23).
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Change history

References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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. Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Preprint at bioRxiv https://doi.org/10.1101/104844 (2017).

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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

Author notes
  1. Hyun Min Kang, Meena Subramaniam and Sasha Targ: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA

    Hyun Min Kang

  2. Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, California, USA

    Meena Subramaniam, Sasha Targ & Rachel E Gate

  3. Institute for Human Genetics (IHG), University of California,, San Francisco, San Francisco, California, USA

    Meena Subramaniam, Sasha Targ, Lenka Maliskova, Eunice Wan, Simon Wong, Rachel E Gate, Noah Zaitlen, Lindsey A Criswell & Chun Jimmie Ye

  4. Institute for Computational Health Sciences, University of California,, San Francisco, San Francisco, California, USA

    Meena Subramaniam, Sasha Targ, Rachel E Gate & Chun Jimmie Ye

  5. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA

    Meena Subramaniam, Sasha Targ, Rachel E Gate & Chun Jimmie Ye

  6. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA

    Meena Subramaniam, Sasha Targ, Rachel E Gate & Chun Jimmie Ye

  7. Medical Scientist Training Program (MSTP), University of California, San Francisco, San Francisco, California, USA

    Sasha Targ & Elizabeth McCarthy

  8. Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, California, USA

    Michelle Nguyen & Alexander Marson

  9. Diabetes Center, University of California, San Francisco, San Francisco, California, USA

    Michelle Nguyen & Alexander Marson

  10. Innovative Genomics Institute, University of California, Berkeley, Berkeley, California, USA

    Michelle Nguyen & Alexander Marson

  11. Department of Neurology, University of California, San Francisco, San Francisco, California, USA

    Lenka Maliskova

  12. Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, California, USA

    Lauren Byrnes

  13. Department of Medicine, University of California, San Francisco, San Francisco, California, USA

    Cristina M Lanata, Alexander Marson, Noah Zaitlen & Lindsey A Criswell

  14. Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, University of California, San Francisco, San Francisco, California, USA

    Cristina M Lanata & Lindsey A Criswell

  15. Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada

    Sara Mostafavi

  16. UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA

    Alexander Marson

  17. Chan Zuckerberg Biohub, San Francisco, California, USA

    Alexander Marson

  18. Lung Biology Center, University of California, San Francisco, San Francisco, California, USA

    Noah Zaitlen

  19. Department of Orofacial Sciences, University of California, San Francisco, San Francisco, USA

    Lindsey A Criswell

Authors
  1. Hyun Min Kang
  2. Meena Subramaniam
  3. Sasha Targ
  4. Michelle Nguyen
  5. Lenka Maliskova
  6. Elizabeth McCarthy
  7. Eunice Wan
  8. Simon Wong
  9. Lauren Byrnes
  10. Cristina M Lanata
  11. Rachel E Gate
  12. Sara Mostafavi
  13. Alexander Marson
  14. Noah Zaitlen
  15. Lindsey A Criswell
  16. Chun Jimmie Ye

Contributions

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 (download PDF )

Supplementary Figures 1–21 (PDF 3273 kb)

Supplementary Table 1 (download XLSX )

Cell type specific differentially expressed genes (XLSX 680 kb)

Supplementary Table 2 (download XLSX )

Pathway enrichment for differentially expressed genes (XLSX 97 kb)

Supplementary Table 3 (download PDF )

Cell type specific eQTLs (PDF 369 kb)

Supplementary Table 4 (download XLSX )

Base Call Probabilities (XLSX 2726 kb)

Supplementary Code (download ZIP )

Implementation of demuxlet (ZIP 4903 kb)

About this article

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). https://doi.org/10.1038/nbt.4042

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/nbt.4042

Search

Advanced search

Quick links

πŸ‘ Nature Briefing

Sign up for the Nature Briefing newsletter β€” what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing