Subjects
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
Similar content being viewed by others
Long-read whole-genome analysis of human single cells
Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references
Accession codes
Change history
References
Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202β1214 (2015).
Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187β1201 (2015).
Stegle, O., Teichmann, S.A. & Marioni, J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133β145 (2015).
Gawad, C., Koh, W. & Quake, S.R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175β188 (2016).
Streets, A.M. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048β7053 (2014).
Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44β73 (2017).
Zheng, G.X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839β848 (2012).
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156β2158 (2011).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078β2079 (2009).
Auton, A. et al. The Genomes Project. A global reference for human genetic variation. Nature 526, 68β74 (2015).
Aguirre-Gamboa, R. et al. Differential effects of environmental and genetic factors on T and B cell immune traits. Cell Rep. 17, 2474β2487 (2016).
Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099β1110.e14 (2016).
Mostafavi, S. et al. Parsing the interferon transcriptional network and its disease associations. Cell 164, 564β578 (2016).
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, 227β264 (1998).
Lee, M.N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).
Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).
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).
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).
Saveanu, L. et al. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 6, 689β697 (2005).
Franco, L.M. et al. Integrative genomic analysis of the human immune response to influenza vaccination. eLife 2, e00299 (2013).
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).
Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853β1866.e17 (2016).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867β1882.e21 (2016).
Jaitin, D.A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167, 1883β1896.e15 (2016).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297β301 (2017).
Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337β343 (2015).
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, 155β160 (2015).
Tung, P.-Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331β338 (2017).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15β21 (2013).
Wang, X., Spandidos, A., Wang, H. & Seed, B. PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update. Nucleic Acids Res. 40, D1144βD1149 (2012).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096β1098 (2013).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171β181 (2014).
Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495β502 (2015).
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
Dabney, A., Storey, J.D. & Warnes, G.R. qvalue: Q-value estimation for false discovery rate control. R package version 1 (2010).
Falconer, D.S. & Mackay, T.F. Introduction to Quantitative Genetics, 4th edn. (Pearson, 1996).
Loh, P.R., Palamara, P.F. & Price, A.L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811β816 (2016).
Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353β1358 (2012).
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.
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 1β21 (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)
Rights and permissions
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, 89β94 (2018). https://doi.org/10.1038/nbt.4042
Received:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/nbt.4042
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
This article is cited by
-
A sequence knowledge-guided deep learning method for single-cell multi-omics translation
Genome Biology (2026)
-
Benchmarking algorithms for generalizable single-cell perturbation response prediction
Nature Methods (2026)
-
FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models
Nature Communications (2026)
-
Characterizing gene perturbations in single cells via network divergence analysis
Nature Communications (2026)
-
The GENEVA platform models tumor mosaicism to reveal variations of responses to KRAS inhibitors and identify improved drug combinations
Nature Cancer (2026)
