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

Kang, Hyun Min; Subramaniam, Meena; Targ, Sasha; Nguyen, Michelle; Maliskova, Lenka; McCarthy, Elizabeth; Wan, Eunice; Wong, Simon; Byrnes, Lauren; Lanata, Cristina M; Gate, Rachel E; Mostafavi, Sara; Marson, Alexander; Zaitlen, Noah; Criswell, Lindsey A; Ye, Chun Jimmie

doi:10.1038/nbt.4042

Cited by 1,339Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesInnovative Microfluidic and Catalytic Techniques Innovation

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

Open access

Kang, Hyun Min, Subramaniam, Meena, Targ, Sasha, Nguyen, Michelle, Maliskova, Lenka, McCarthy, Elizabeth, Wan, Eunice, Wong, Simon +8 more·2018·Nature Biotechnology

DOI 10.1038/nbt.4042PMC PMC5784859

Published version of a preprint

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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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Cites

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Claims the paper makes

22 of 30 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured

Findingcitation
IFN-β stimulation induces widespread transcriptomic changes observed as a shift in t-SNE projections of singlets.
“IFN-β stimulation induces widespread transcriptomic changes observed as a shift in the t-SNE projections of singlets24”
Quote in the paperNot a substantial claim1 of 1 sources checked
- cites [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). source
  The Results section on UPR branch delineation describes t-SNE clustering of cells by UPR perturbation, not a shift following IFN-β stimulation. Searches for IFN-β, interferon beta, and singlets found no passage in our copy addressing the claimed response; page numbers were unavailable. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
Premisecitation
A 6-hour activation time point maximizes expression of interferon-sensitive genes in dendritic cells and T cells.
“a time point we previously found to maximize the expression of interferon-sensitive genes (ISGs) in dendritic cells (DCs) and T cells26, 27.”
Quote in the paperSubstantial claim2 of 2 sources checked
- cites [26] Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017). source
  The Methods section on anti-CD3/CD28 stimulation (p. 11) describes stimulating Jurkat T cells for 4 hours, not a comparison of activation time points. Searches for six-hour activation and interferon-sensitive genes found no passage establishing when their expression peaks in dendritic cells or T cells. (LLM, conf=0.75)
  Experimental — accuracy not yet measured
- cites [27] Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015). source
  The Introduction’s discussion of immune-cell enhancers reports gene induction after T-cell stimulation, but not interferon-sensitive gene expression or its peak time. The Supplementary Methods describe an eight-hour T-cell stimulation; searches for a six-hour ISG maximum and for dendritic-cell results found no passage addressing the claim. (LLM, conf=0.74)
  Experimental — accuracy not yet measured
Interpretationown data
Multiplexing reduces technical effects due to separate sample processing.
“suggesting multiplexing reduces technical effects due to separate sample processing22, 23.”
Quote in the paperSubstantial claim2 of 2 sources checked
- cites [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). source
- cites [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). source
  The Introduction distinguishes pooled screens from separately assessed, arrayed screens, and the Results report that Perturb-seq steps were performed in a single pool. Our copy does not state or test whether pooling reduces technical effects caused by separate sample processing, so that attributed benefit remains unsupported. (LLM, conf=0.78)
  Experimental — accuracy not yet measured
Premisecitation
Simultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.
“While strategies to demultiplex cells from different species1, 10, 17 or host and graft samples17 have been reported, simultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.”
Quote in the paperSubstantial claim3 of 3 sources checked
- cites [1] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source
- cites [10] Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). source
  The Introduction and Methods sections describe the SAM/BAM alignment format and SAMtools, not cell demultiplexing. Searches for simultaneous demultiplexing, doublet detection, and more than two individuals found no passage addressing the claimed limitation. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
- cites [17] Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014). source
  The section ‘An experimental and analytic strategy to dissect inter-individual variation’ describes measuring T-cell transcripts from multiple donors, but does not address demultiplexing cells or detecting doublets. Searches for those methods and for the claimed limit of two individuals found no relevant passage in our copy. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
Interpretationcitation
Analysis of multiplexed dscRNA-seq data recovers cell type-specific gene regulatory programs affected by interferon stimulation consistent with published IFN-β signatures in mouse and humans.
“The analysis of multiplexed dscRNA-seq data recovers cell type-specific gene regulatory programs affected by interferon stimulation consistent with published IFN-β signatures in mouse and humans29.”
Quote in the paperSubstantial claim1 of 1 sources checked
- cites [29] Tung, P.-Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017). source
  The Abstract describes a study of batch effects in single-cell RNA-seq data from human iPSC lines, and the Results section on regulatory noise compares genes across individuals. Searches for interferon stimulation, IFN-β signatures, and multiplexed dscRNA-seq found no passage in our copy addressing the claimed finding. Page numbers were not provided. (LLM, conf=0.75)
  Experimental — accuracy not yet measured

Findingown data
For each cell type, variance across individuals is higher than variance across synthetic replicates (Lin's concordance = 0.007-0.20).
“for each cell type, the variance across individuals22, 30 is also higher than the variance across synthetic replicates (Lin’s concordance = 0.007–0.20)”
Quote in the paperSubstantial claim2 of 2 sources checked
- cites [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). source
  The ‘Identifying cell types’ section discusses variation between two C. elegans experiments and suggests developmental timing or preparation, rather than technical variation, as a cause. The ‘Robustness of sci-RNA-seq’ section reports correlations between experiments, but neither section compares variance across individuals with synthetic replicates or reports Lin’s concordance of 0.007–0.20 (searched those terms and the variance comparison). (LLM, conf=0.75)
  Experimental — accuracy not yet measured
- cites [30] Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017). source
  The section “The mechanism axis: modeling gene regulation” discusses variation between cells, not variance across individuals within each cell type. Searches for synthetic replicates and Lin’s concordance found no comparison or reported range of 0.007–0.20 in our copy. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
Findingown data
t-SNE projections of the pool and each individual are not confounded by well-to-well effects.
“t-SNE projections of the pool and each individual are not confounded by well-to-well effects (Supplementary Fig. 11a).”
Quote in the paperSubstantial claim
Findingown data
In simulation of 6,145 cells from 8 related individuals from the 1000 Genomes Project, 50 SNPs per cell would allow demuxlet to correctly assign over 98% of cells.
“we simulated 6,145 cells from a set of 8 related individuals from 1000 Genomes21. In this simulation, 50 SNPs per cell would allow demuxlet to correctly assign over 98% of cells”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites 1000 Genomes21
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Findingown data
Detected doublets form distinct clusters near the periphery of other clusters defined by cell type.
“Detected doublets form distinct clusters near the periphery of other clusters defined by cell type (Supplementary Fig. 13).”
Quote in the paperNot a substantial claim
Findingown data
For a pool of 8 individuals and uncorrelated SNPs with 50% minor allele frequency, 4 reads overlapping SNPs are sufficient to uniquely assign a cell to its donor.
“For a pool of 8 individuals and a set of uncorrelated SNPs each with 50% minor allele frequency (MAF), 4 reads overlapping SNPs are sufficient to uniquely assign a cell to the donor of origin (Fig. 1b)”
Quote in the paperSubstantial claim
Findingown data
50 SNPs can be obtained with as few as 1,000 unique molecular identifiers per cell.
“we determined that 50 SNPs can be obtained with as few as 1,000 unique molecular identifiers (UMIs) per cell”
Quote in the paperSubstantial claim
Findingreasoning
A small number of reads overlapping common SNPs is sufficient to accurately identify each cell.
“A small number of reads overlapping common SNPs is sufficient to accurately identify each cell.”
Quote in the paperSubstantial claim
Findingown data
For a pool of 8 individuals and uncorrelated SNPs with 50% minor allele frequency, 20 reads overlapping SNPs can distinguish every sample with >98% probability in simulation.
“20 reads overlapping SNPs can distinguish every sample with >98% probability in simulation (Supplementary Fig. 1).”
Quote in the paperSubstantial claim
Findingown data
From wells W1 and W2, each containing cells from two disjoint sets of 4 individuals, the demultiplexing error rate was less than 1% of singlets (W1: 2/3332, W2: 0/3864).
“we estimated a demultiplexing error rate (number of cells assigned to individuals not in the pool) of less than 1% of singlets (W1: 2/3332, W2: 0/3864)”
Quote in the paperSubstantial claim
Findingown data
Associating >150,000 genetic variants (MAF > 20%) with proportion of 8 major immune cell populations identified a SNP (chr10:3791224) significantly associated (P = 1.03×10−5, FDR < 0.05) with NK cell proportion.
“Associating >150,000 genetic variants (MAF > 20%) with the proportion of 8 major immune cell populations, we identified a SNP (chr10:3791224) significantly associated (P = 1.03×10−5, FDR < 0.05) with the proportion of NK cells”
Quote in the paperSubstantial claim
Findingown data
Previously reported local eQTLs from bulk CD14+ monocytes, CD4+ T cells, and lymphoblastoid cell lines are more significantly associated with gene expression in the most similar cell types (cM, Th, and B cells, respectively) than other cell types.
“Previously reported local eQTLs from bulk CD14+ monocytes, CD4+ T cells and lymphoblastoid cell lines are more significantly associated with gene expression in the most similar cell types (cM, Th and B cells, respectively) than other cell types”
Quote in the paperSubstantial claim
Findingown data
Five clusters of downregulated genes are enriched for antibacterial response and natural killer cell mediated toxicity.
“The five clusters of downregulated genes are enriched for antibacterial response (KEGG Legionellosis: Cluster II monocyte down P < 5.5×10−3) and natural killer cell mediated toxicity (Cluster IV NK/Th cell down: P < 3.6×10−2).”
Quote in the paperNot a substantial claim
Findingown data
Estimated doublet rates of 18%, 18%, and 25% are consistent with increased concentrations of loaded cells.
“The estimated doublet rates of 18%, 18% and 25% are consistent with the increased concentrations of loaded cells (Fig. 2C).”
Quote in the paperNot a substantial claim
Findingown data
Over all PBMCs, variance of mean expression across individuals is higher than variance across synthetic replicates whose cells were randomly sampled (Lin's concordance = 0.022, Pearson correlation = 0.69).
“Over all PBMCs, the variance of mean expression across individuals is higher than the variance across synthetic replicates whose cells were randomly sampled (Lin’s concordance = 0.022, Pearson correlation= 0.69, Fig. 3C).”
Quote in the paperSubstantial claim
Findingown data
Inverse variance weighted meta-analysis identified genes with pan-cell type eQTLs, including MHC class I antigen presentation pathway genes ERAP2 and HLA-C.
“We used an inverse variance weighted meta-analysis to identify genes with pan-cell type eQTLs, including those in the major histocompatibility complex (MHC) class I antigen presentation pathway including ERAP2 (P < 3.57×10−32, meta-analysis), encoding an aminopeptidase known to cleave viral peptides34, and HLA-C (P < 1.74×10−29, meta-analysis), which encodes the MHC class I heavy chain”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [34] Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014). source
Findingown data
Estimates of abundances for ~2000 homologous genes in each cell type and condition correlated with similar data from mice.
“Estimates of abundances for ~2000 homologous genes in each cell type and condition correlated with similar data from mice (Supplementary Fig. 16).”
Quote in the paperNot a substantial claim
Findingown data
Differentially expressed genes cluster into modules of cell type-specific responses enriched for distinct gene regulatory programs.
“Differentially expressed genes cluster into modules of cell type-specific responses enriched for distinct gene regulatory programs (Fig. 3B, Supplementary Table 2).”
Quote in the paperSubstantial claim
Findingown data
3,055 differentially expressed genes (logFC > 2, FDR < 0.05) were identified in at least one cell type.
“We identified 3,055 differentially expressed genes (logFC > 2, FDR < 0.05) in at least one cell type (Supplementary Table 1).”
Quote in the paperNot a substantial claim
Findingown data
Comparing to 407 genes previously profiled in bulk monocyte-derived dendritic cells, proportion of variance explained by inter-individual variability is more correlated in myeloid cells after stimulation (R = 0.26-0.3) than before (R = 0.05-0.19).
“Comparing to 407 genes previously profiled in bulk monocyte-derived dendritic cells, the proportion of variance explained by inter-individual variability is more correlated in myeloid cells after stimulation (R = 0.26 – 0.3) than before (R = 0.05 – 0.19).”
Quote in the paperSubstantial claim
Interpretationown data
Profiling 22,000 cells multiplexed from 26 individuals generates 23-fold more singlets at the same effective doublet rate.
“profiling 22,000 cells multiplexed from 26 individuals generates 23-fold more singlets at the same effective doublet rate (Supplementary Fig. 3).”
Quote in the paperSubstantial claim
Interpretationown data
By multiplexing even a small number of individuals, the probability that a doublet contains cells from different individuals is very high, equal to 1 - 1/N, for example 87.5% for N=8.
“We note that by multiplexing even a small number of individuals, the probability that a doublet contains cells from different individuals is very high (1 – 1/N, e.g., 87.5% for N=8 samples) (Fig. 1C).”
Quote in the paperSubstantial claim
Interpretationreasoning
Recommended sequencing depths of standard dscRNA-seq workflows would capture hundreds of SNPs.
“recommended sequencing depths of standard dscRNA-seq workflows would capture hundreds of SNPs.”
Quote in the paperSubstantial claim
Interpretationown data
Cell type composition contributes to expression variability.
“suggesting a contribution of cell type composition on expression variability.”
Quote in the paperSubstantial claim
Interpretationown data
Inter-individual variation exists beyond sampling.
“further indicating inter-individual variation beyond sampling (Fig. 3E).”
Quote in the paperSubstantial claim
Interpretationcitation
Compared to sorting known cell types followed by bulk RNA-seq, multiplexed dscRNA-seq is a more efficient and unbiased method for obtaining cell type-specific immune traits.
“Compared to sorting known cell types followed by bulk RNA-seq, multiplexed dscRNA-seq is a more efficient and unbiased method for obtaining cell type-specific immune traits41.”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [41]
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Interpretationown data
There is additional inter-individual variability not due to cell type composition.
“suggesting additional inter-individual variability not due to cell type composition (Supplementary Fig. 19).”
Quote in the paperSubstantial claim
Interpretationown data
The optimal number of samples to multiplex is approximately 20, based on sample processing time and empirical doublet rates of current microfluidic devices.
“We find the optimal number of samples to multiplex is approximately 20, based on sample processing time and empirical doublet rates of current microfluidic devices”
Quote in the paperSubstantial claim

Classified as probably not a substantial claim. 11
May combine two claims. 28

Classified as probably not a substantial claim.
Premisecitation
Droplet single-cell RNA sequencing has made it possible to profile transcriptomes rapidly and at large scale.
“Droplet single cell RNA-sequencing (dscRNA-seq) has increased substantially the throughput of single cell capture and library preparation1, 10, enabling the simultaneous profiling of thousands of cells.”
Quote in the paperSubstantial claim2 of 2 sources checked
- cites [1] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source
- cites [10] Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). source
  The Introduction and Methods passages (pages not supplied) describe the SAM alignment format and SAMtools, not droplet single-cell RNA sequencing. Searches for droplet sequencing, cell-capture throughput, and profiling thousands of cells found no passage addressing the claim. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
Premisecitation
IFN-β induces genome-scale changes in the transcriptional profiles of immune cells.
“IFN-β, a potent cytokine that induces genome-scale changes in the transcriptional profiles of immune cells24, 25.”
Quote in the paperSubstantial claim1 of 2 sources checked
- cites [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). source
  The Results section on UPR transcriptional programs describes responses to thapsigargin and tunicamycin, not IFN-β in immune cells. The Discussion mentions dendritic-cell activation only as a parallel study. Searches for IFN-β and interferon-related transcriptional responses found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.75)
  Experimental — accuracy not yet measured
- cites [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). source
May combine two claims.
Interpretationcitation
Demuxlet is fast, accurate, scalable, and compatible with standard input formats.
“Inspired by models and algorithms developed for detecting contamination in DNA sequencing18, demuxlet is fast, accurate, scalable, and compatible with standard input formats17, 19, 20.”
Quote in the paperNot a substantial claim2 of 3 sources checked
- cites [17] Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014). source
  The section “An experimental and analytic strategy to dissect inter-individual variation” describes Nanostring measurements and eQTL analysis, not demuxlet. Searches for demuxlet’s speed, accuracy, scalability, input-format compatibility, and single-cell demultiplexing found no passage addressing those properties in our copy. (LLM, conf=0.77)
  Experimental — accuracy not yet measured
- cites [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). source
  The Methods sections describe leukocyte gene-expression microarrays and data filtering, not demuxlet. Searches for demuxlet, demultiplexing, scalability, and standard input formats found no passage in our copy addressing the claimed properties. No page numbers were available for these sections. (LLM, conf=0.78)
  Experimental — accuracy not yet measured
- cites [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). source
May combine two claims.
Interpretationcitation
The capability to demultiplex and identify doublets using natural genetic variation reduces per-sample and per-cell library preparation cost, does not require synthetic barcodes or split-pool strategies, and captures biological variability among individual samples while limiting unwanted technical variability.
“The capability to demultiplex and identify doublets using natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies36–40, and captures biological variability among individual samples while limiting unwanted technical variability.”
Quote in the paperSubstantial claim3 of 5 sources checked
- cites [36] Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010). source
  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
  Experimental — accuracy not yet measured
- cites [37] Dabney, A., Storey, J.D. & Warnes, G.R. qvalue: Q-value estimation for false discovery rate control. R package version 1 (2010).
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [38] Falconer, D.S. & Mackay, T.F. Introduction to Quantitative Genetics, 4th edn. (Pearson, 1996).
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [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). source
  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
  Experimental — accuracy not yet measured
- cites [40] Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012). source
  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
  Experimental — accuracy not yet measured
May combine two claims.
Findingcitation
Among other cell type-specific local eQTLs are CD52 only in monocyte populations and DIP2A only in NK cells associated with immune response to vaccination.
“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood35.”
Quote in the paperNot a substantial claim1 of 1 sources checked
- cites [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). source
  The Results section on dispersed cell populations discusses gene expression in zebrafish embryos, not cell type-specific eQTLs in human immune cells. Searches for CD52, DIP2A, monocytes, NK cells, and vaccination found no passage in our copy addressing the claim. (LLM, conf=0.78)
  Experimental — accuracy not yet measured
May combine two claims.
Findingown data
For 709 genes, estimates of fold change in response to IFN-β in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells and CD4+ T cells, and correlated with qPCR results of sorted CD4+ T cells.
“For 709 genes, estimates of fold change in response to IFN-β stimulation in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells28 and CD4+ T cells27, respectively (Supplementary Fig. 17) and correlated with qPCR results of sorted CD4+ T cells (Supplementary Fig. 18).”
Quote in the paperSubstantial claim2 of 2 sources checked
- cites [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). source
  The source’s “Cell cycle variation affects global gene expression” section describes CD4+ T cells stimulated with IL-4 to study TH2 differentiation, not IFN-β responses. Searches for 709 genes, IFN-β fold changes, dendritic-cell comparisons, and sorted-cell qPCR found no passage addressing the claimed agreement or correlation. (LLM, conf=0.77)
  Experimental — accuracy not yet measured
- cites [27] Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015). source
  The Supplementary Methods (Cell Isolation and Culture; Analysis of ex vivo Stimulation-dependent Enhancers, pages not provided) describe CD4+ T-cell sorting and stimulation with PMA/ionomycin or anti-CD3/CD28, not IFN-β. Searches for “709 genes,” IFN-β fold changes, and the stated comparisons and qPCR correlation found no passage addressing the claim. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
May combine two claims.
Findingown data
In simulation of 6,145 cells from 2-64 individuals from the 1000 Genomes Project, 50 SNPs per cell allows demultiplexing of 97% of singlets and identification of 92% of doublets in pools of up to 64 individuals.
“We show that 50 SNPs per cell allows demultiplexing of 97% of singlets and identification of 92% of doublets in pools of up to 64 individuals”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
Six genes were differentially expressed between wells W1 and W2, while only two genes were differentially expressed between W1 and W2 individuals in well W3.
“While 6 genes were differentially expressed between wells W1 and W2 (DESeq2 on pseudobulk counts, FDR < 0.05), only 2 genes were differentially expressed between W1 and W2 individuals in well W3 (FDR < 0.05)”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
In wells W1, W2, and W3, demuxlet identified 91%, 91%, and 86% of droplets as singlets, with 25%, 25%, and 12.5% mapping to each donor, consistent with equal mixing.
“In wells W1, W2 and W3, demuxlet identified 91% (3332/3645), 91% (3864/4254), and 86% (5348/6205) of droplets as singlets (likelihood ratio test, L(singlet)/L(doublet) > 2), of which 25% (+/− 2.6%), 25% (+/− 4.6%) and 12.5% (+/− 1.4%) mapped to each donor, consistent with equal mixing of individuals in each well.”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
The estimated doublet rate was 10.9% in each condition, consistent with predicted rates, and observed and expected doublet frequencies for each pair of individuals were highly correlated (R=0.98).
“The estimated doublet rate of 10.9% in each condition is consistent with predicted rates (Fig. 2C) and the observed and expected frequencies of doublets for each pair of individuals are highly correlated (R=0.98)”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
Applied to real data from W1, W2, and W3, demuxlet identified doublet rates of 5.0%, 5.2%, and 7.1%, consistent with expected doublet rates from mixed species experiments.
“demuxlet identified 138/3645, 165/4254, and 384/6205 doublets corresponding to doublet rates of 5.0%, 5.2% and 7.1%, consistent with the expected doublet rates estimated from mixed species experiments”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
Applied to simulated synthetic doublets, demuxlet identified 91% (426/466) as doublets or ambiguous and correctly recovered the sample identity of both cells in 95% (403/426) of doublets.
“demuxlet identified 91% (426/466) of synthetic doublets as doublets or ambiguous, correctly recovering the sample identity of both cells in 403/426 (95%) doublets”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
In the IFN-β experiment, demuxlet identified 83% (12,138) of control and 84% (12,167) of stimulated droplets as singlets.
“We obtained 14,619 (control) and 14,446 (stimulated) cell-containing droplets, of which demuxlet identified 83% (12,138) and 84% (12,167) as singlets.”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
Singlets identified by demuxlet in all three wells cluster into known immune cell types and correlate with bulk RNA-sequencing of sorted cell populations (R=0.76-0.92).
“Singlets identified by demuxlet in all three wells cluster into known immune cell types (Fig. 2d) and are correlated with bulk RNA-sequencing of sorted cell populations (R=0.76–0.92)”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
For the same individuals from different wells, t-SNE of dscRNA-seq data are qualitatively consistent, and estimates of cell type proportions are highly correlated (R=0.99).
“For the same individuals from different wells, t-distributed stochastic neighbor embedding (t-SNE) of dscRNA-seq data are qualitatively consistent, and estimates of cell type proportions are highly correlated (R = 0.99)”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
In CD14+CD16− monocytes, correlation of mean expression between pairs of synthetic replicates from the same individual is >99%, greater than ~97% from different individuals.
“In CD14+CD16− monocytes, the correlation of mean expression between pairs of synthetic replicates from the same individual (>99%) is greater than from different individuals (~97%)”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
Between 15 and 827 genes had statistically significant inter-individual variability in control cells and 7 to 613 in stimulated cells, with most found in classical monocytes and CD4+ helper T cells.
“We found between 15 to 827 genes with statistically significant inter-individual variability in control cells and 7 to 613 in stimulated cells (Pearson correlation, FDR < 0.05), with most found in classical monocytes (cM) and CD4+ helper T (Th) cells.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown data
In each sequencing experiment, more than 90% of captured transcripts were recovered at the sequencing depths used.
“At these depths, we recovered >90% of captured transcripts in each sequencing experiment.”
Quote in the paperNot a substantial claim
May combine two claims.
Findingown data
Across 23 donors, eQTL analysis found 32 local eQTLs (+/− 100kb, FDR < 0.1), 22 of which were detected in only one cell type.
“We found a total of 32 local eQTLs (+/− 100kb, FDR < 0.1), 22 of which were detected in only one cell type”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
IFN-β did not affect cell type proportions between control and stimulated cells, and these proportions were consistent with flow cytometry measurements (R=0.88).
“IFN-β did not affect cell type proportions between control and stimulated cells (Supplementary Fig. 14), and these were consistent with flow cytometry measurements (R=0.88)”
Quote in the paperNot a substantial claim
May combine two claims.
Findingown data
In additional pools, demuxlet identified 71%, 73%, and 60% of droplets as singlets, correctly assigning 99% of singlets from the first two pools W1 and W2.
“Demuxlet identified 71% (10,766/15,250), 73% (16,618/22,619) and 60% (15,596/25,918) of droplets as singlets, correctly assigning 99% of singlets from the first two pools, W1 and W2 (10,740/10,766 and 16,616/16,618).”
Quote in the paperSubstantial claim
May combine two claims.
Findingown data
HLA-DQA1 has local eQTLs only in some cell types while HLA-DQA2 has local eQTLs in all antigen presentation cells.
“HLA-DQA1 has local eQTLs only in some cell types (P <2.11×10−15, Cochran’s Q) while HLA-DQA2 has local eQTLs in all antigen presentation cells (P < 1.02e10−43, Cochran’s Q).”
Quote in the paperNot a substantial claim
May combine two claims.
Findingown data
Expression variability was determined by variability in cell type proportion and reproducible between batches.
“we found that expression variability was determined by variability in cell type proportion (Fig. 4A) and reproducible between batches (Supplementary Fig. 20).”
Quote in the paperSubstantial claim
May combine two claims.
Interpretationreasoning
If a 1,000-cell run without multiplexing yields 990 singlets with a 1% undetected doublet rate, multiplexing 1,570 cells each from 63 samples can theoretically achieve the same undetected doublet rate, producing up to 37-fold more singlets (36,600) if sample identity of every droplet can be perfectly demultiplexed.
“For example, if a 1,000-cell run without multiplexing results in 990 singlets with a 1% undetected doublet rate, multiplexing 1,570 cells each from 63 samples can theoretically achieve the same rate of undetected doublets, producing up to a 37-fold more singlets (36,600) if the sample identity of every droplet can be perfectly demultiplexed”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationreasoning
If droplets containing multiple cells from different individuals could be detected, pooled cells could be loaded at higher concentrations, enabling additional reduction in per-cell library preparation cost.
“Furthermore, if droplets containing multiple cells from different individuals could be detected, pooled cells could be loaded at higher concentrations, enabling additional reduction in per-cell library preparation cost.”
Quote in the paperNot a substantial claim
- Rests on an assumption: In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
Classified as probably not a substantial claim. May combine two claims.
Interpretationown data
Optimal multiplexed designs pool cells from tens of unrelated individuals, load at concentrations 2-10x higher than standard workflows, and sequence to at least 1,000 UMIs per cell.
“These results suggest optimal multiplexed designs where cells from tens of unrelated individuals should be pooled, loaded at concentrations 2–10x higher than standard workflows, and sequenced to at least 1,000 UMIs per cell.”
Quote in the paperSubstantial claim
May combine two claims.
Interpretationown data
The ability to demultiplex cells depends on the number of individuals multiplexed, sequencing depth or number of read-overlapping SNPs, and relatedness of multiplexed individuals.
“The ability to demultiplex cells is a function of the number of individuals multiplexed, the depth of sequencing or number of read-overlapping SNPs, and relatedness of multiplexed individuals.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning
If the genetic identity of each cell could be determined, pooling cells from different individuals in one microfluidic run would lower per-sample library preparation cost and eliminate confounding effects.
“If the genetic identity of each cell could be determined, pooling cells from different individuals in one microfluidic run would result in lower per-sample library preparation cost and eliminate confounding effects.”
Quote in the paperNot a substantial claim
- Rests on an assumption: In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
Classified as probably not a substantial claim.
Study design and conductown data
Batch information and the first three principal components of the expression matrix were included as covariates in the eQTL mapping.
“Batch information for each sample as well as the first 3 principal components of the expression matrix were used as covariates.”
Quote in the paperSubstantial claim
Classified as probably not a substantial claim.
Study design and conductreasoning
Every doublet and ambiguous droplet was discarded so that singlets would be estimated conservatively.
“we remove all doublets and ambiguous droplets to conservatively estimate singlets.”
Quote in the paperSubstantial claim
May combine two claims.
Interpretationown data
Demuxlet enables reliable estimation of cell type proportion, recovers cell type-specific transcriptional response to stimulation, and could facilitate further genetic and longitudinal analyses.
“Demuxlet enables reliable estimation of cell type proportion, recovers cell type-specific transcriptional response to stimulation, and could facilitate further genetic and longitudinal analyses”
Quote in the paperSubstantial claim
Classified as probably not a substantial claim.
Study design and conductnone given
Demultiplexed individuals were used as replicates in the differential expression analysis.
“Demultiplexed individuals were used as replicates for differential expression analysis.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitation
Differential expression analysis controlled the false discovery rate at 0.05 using the qvalue package.
“the qvalue package was used to calculate FDR < 0.05 55.”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [55]
  Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown data
These results demonstrate the ability of multiplexed dscRNA-seq to characterize inter-individual variation in immune response and, when integrated with genetic data, reveal cell type-specific genetic control of gene expression undetectable in bulk tissues.
“These results demonstrate the ability of multiplexed dscRNA-seq to characterize inter-individual variation in immune response and when integrated with genetic data, reveal cell type-specific genetic control of gene expression, which would be undetectable when bulk tissues are analyzed.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone given
While demuxlet could in principle be applied to sequencing solid tissue, standardizing sample processing and preservation remain major challenges.
“While demuxlet could in principle be applied to sequencing solid tissue, standardizing sample processing and preservation remain major challenges.”
Quote in the paperNot a substantial claim

35 of 35

The paper relies on this without support.
In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
“However, in standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.”
The authors state this as an assumption.
When a base call is incorrect, the probability of the observed base call given the genotype and error follows the distribution in Supplementary Table 4.
“When ecvi = 1, we assume that Pr(bcvi|g, ecvi) follows Supplementary Table 4.”
The authors state this as an assumption.
The latent variable indicating whether a base call is correct follows a Bernoulli distribution with parameter 10−qcvi10.
“ecvi is assumed to follow Bernoulli (10−qcvi10) where qcvi is a phred-scale quality score of the observed base call.”
The paper relies on this without support.
The prior probability of genotype g follows a Binomial(2, pv) distribution, where pv is the population allele frequency of the alternate allele.
“Pr(g) ~ Binomial(2, pv) and pv is the population allele frequency of the alternate allele.”
The authors state this as an assumption.
The expected rate of multiplets for a given number of loaded cells follows an exponential model: d(x)=1−(1−d0)^(x/x0).
“Then the expected multiplet rates when x cells are loaded can be modeled exponentially as d(x)=1−(1−d0)xx0.”

5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Demultiplexing into anonymous contributors dissociates other sample information from sequencing data, reducing the power of conclusions.
“However, since the identities of the samples cannot be determined, this approach dissociates other sample information from the sequencing data, thereby reducing the power of the conclusions [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
The Methods section, “Identifying the sample identity of each single cell” (page unavailable), describes matching each cell to a sample using its genotype. The paper also reports recovering the sample identity of more than 99% of singlets in a pool of eight samples. This contradicts the claim that sample identities cannot be determined and therefore must be dissociated from the sequencing data. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
The integration of scRNA-seq with eQTL was first reported in 2018.
“The integration of scRNA-seq with eQTL was first reported in 2018 [94].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [94]
The Abstract reports using droplet single-cell RNA-seq to perform eQTL analysis on 23 pooled samples, supporting the integration described. Neither the Abstract nor the Methods passage on quantitative trait mapping establishes that this was the first such report; the paper’s 2018 publication year alone does not establish priority. (LLM, conf=0.86)
Experimental — accuracy not yet measured
The maturation of single-cell technologies enables large-scale comparisons of cell states within complex tissues, providing the appropriate resolution to dissect cell-type-specific responses to perturbation.
“The maturation of single-cell technologies now enables large-scale comparisons of cell states within complex tissues, thus providing the appropriate resolution to dissect cell-type-specific responses to perturbation5,6.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
The paper reports profiling thousands of cells and identifying cell-type-specific responses to IFN-β stimulation in peripheral blood immune cells (Abstract; page unavailable). It does not demonstrate large-scale comparisons within complex tissues: the same section says applying the method to solid tissue remains challenging. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Failing to account for the intrinsic variability of biological replicates produced hundreds of DE genes between randomly assigned replicates in control human PBMC samples not exposed to interferon.
“Failing to account for the intrinsic variability of biological replicates produced hundreds of DE genes between randomly assigned replicates (Fig. 4d and Supplementary Fig. 6a, b).”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
The paper reports substantial inter-individual expression variability in control PBMCs, including 15–827 genes with statistically significant variability (results passage, labeled Abstract in our copy; page unavailable). But those are genes variable across individuals, not hundreds of differentially expressed genes found between randomly assigned control replicates. The Methods describe using individuals as replicates for differential-expression analysis, but do not report the claimed random-assignment comparison. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Demuxlet showed that as few as 50 genetic variants were required to separate up to 64 samples.
“Demuxlet, one of the first bioinformatic tools developed, showed that as few as 50 genetic variants were determined to be required to separate up to 64 samples [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
The Abstract reports that, in simulations, 50 SNPs per cell were sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. It does not establish that 50 variants were required, or that every cell was successfully separated. (LLM, conf=0.85)
Experimental — accuracy not yet measured

Kang et al. used multiplexed droplet scRNA-seq to profile eight immune cell populations from 23 donors and identified 32 cis-eQTLs, 22 of which were cell-specific.
“Kang et al. utilized multiplexed droplet scRNA-seq to profile eight immune cell populations from 23 donors and subsequently conducted eQTL analysis, identifying 32 cis-eQTLs, 22 of which were cell-specific.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [94]
Within the compendium, fourteen studies had control groups that included six or more samples.
“Within our compendium, we identified a total of fourteen studies with control groups that included six or more samples5,6,15,49–59.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
SNP-based approaches have been benchmarked and shown to be highly effective at separating human samples.
“SNP-based approaches have been benchmarked and shown to be highly effective at separating human samples (Kang et al, 2018; Huang et al, 2019; Xu et al, 2019; Heaton et al, 2020; Weber et al, 2021).”
From Evaluation of genetic demultiplexing of single-cell sequencing data from model species (2023) · cites this paper as Kang et al, 2018
The number of SNPs from RNA-Seq was not as high as from the array but exceeded the minimum number reported necessary for demultiplexing.
“However, while the number of SNPs from RNA-Seq was not as high as from the array, they exceeded the minimum number reported necessary for demultiplexing [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
Increasing biological replicates through sample pooling prior to library preparation followed by demultiplexing is an effective strategy to overcome issues of high cost and reduce batch effects.
“Increasing biological replicates through sample pooling prior to library preparation followed by demultiplexing (i.e., identifying and isolating the sequencing data related to each sample within the pool) to the original biological sample is an effective strategy to overcome these issues as well as reduce batch effects [5–9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
SingleR differentiated human peripheral blood mononuclear cell subtypes such as memory and naive B and T cells using Blueprint and Encode references.
“SingleR differentiated cellular subtypes such as memory and naïve B and T cells”
From Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage (2019) · cites this paper as [22]
Between IFN-β and control conditions in all cells, the top 25 differentially active TFs, including the top-ranked SREBP, are consistent with previously reported interferon induction of lipogenesis.
“Between IFN-β and control conditions in all cells, the top 25 differentially active TFs, including the top-ranked SREBP (Supplementary Fig. 16d), are consistent with previously reported interferon induction of lipogenesis57, which has not been described in the previous scRNA-seq analysis56.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [56]
In a published PBMC dataset from eight genotyped human donors, ground-truth multiplets were barcodes with reads containing polymorphisms from more than one individual.
“The authors identified ground truth multiplets as cell barcodes associated with reads containing polymorphisms from more than one individual (Figure 4A).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Kang et al., 2018
In our own data set and published studies, doublet rates are slightly higher than shown in reference 77.
“However, we observe in our own data set as well as in published studies47,78 slightly higher doublet rates than shown in77.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
Doublet detection methods such as Demuxlet and Scrublet enable faithful detection of doublets to exclude them from downstream analysis.
“Doublet detection methods such as Demuxlet47 and Scrublet76 enable faithful detection of those to exclude the doublets from the downstream analysis.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
The default doublet rate is based on 10X Genomics reference values and is a lower bound compared to doublet rates estimated for our own data and reported by other studies.
“The default for the doublet rate is based on reference values from 10X Genomics and is a lower bound compared to the doublet rates we estimate for our own data and to rates reported by other studies47,78.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
Using strategies such as cell tagging or via genetic variation, it is possible to demultiplex cells that were pooled in the experiment.
“Using strategies such as cell tagging (preprint: Gehring et al, 2018), or via genetic variation (Kang et al, 2018), it is possible to demultiplex cells that were pooled in the experiment.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kang et al, 2018
This set-up tells us how individual cell identities react transcriptionally under particular experimental conditions.
“This set‐up tells us how individual cell identities react transcriptionally under particular experimental conditions (Kang et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kang et al, 2018

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

W1 and W2 are assigned to different pools
Minor
The initial W1 and W2 wells contain four donors each, but the later passage calls the first two additional pools W1 and W2 even though they contain seven and eight donors. The text does not distinguish these as reused labels.

1 of 1

The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · cited 1× · see citing works
“Inspired by models and algorithms developed for detecting contamination in DNA sequencing 18 , demuxlet is fast, accurate, scalable, and compatible with standard input formats 17, 19, 20 .”
Method
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citation
The variant call format and VCFtools
Danecek, P., Auton, A., Abecasis, G. R., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G. A., Marth, G., Sherry, S. T., et al. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr330 · Cited by 16,008 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · cited 1× · see citing works
“Cellular suspensions were loaded onto the 10x Chromium instrument (10x Genomics) and sequenced as described in Zheng et al 17 .”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 2× · see citing works
“Let ecvi ∈ {0, 1} be a latent variable indicating whether the base call is correct (0) or not (1), then given ecvi = 0, bcvi ∈ {R = 0, A = 1} and ∼ Binomial 2 2 , g    when g ∈ {0, 1, 2} is the true genotype of the sample corresponding to c-th droplet at v-th variant.”
Background
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1× · see citing works
“To identify known immune cell populations in PBMCs, we used the Seurat package to perform unbiased clustering on the 2.7k PBMCs from Zheng et al., following the publicly available Guided Clustering Tutorial 17, 53 .”
Method
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063 · see citing works
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · cited 1× · see citing works
“RNA was extracted using the RNeasy Mini kit (#74104), and reverse transcription and tagmentation were conducted according to Picelli et al. using the SmartSeq2 protocol 51, 52 .”
Method
HOW CELLS RESPOND TO INTERFERONS
Stark, G. R., Kerr, I. M., Williams, B. R. G., Silverman, R. H., & Schreiber, R. D. (1998) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.67.1.227 · Cited by 3,932 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · cited 1× · see citing works
“Droplet single cell RNA-sequencing (dscRNA-seq) has increased substantially the throughput of single cell capture and library preparation 1, 10 , enabling the simultaneous profiling of thousands of cells.”
Background
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · cited 1× · see citing works
“RNA was extracted using the RNeasy Mini kit (#74104), and reverse transcription and tagmentation were conducted according to Picelli et al. using the SmartSeq2 protocol 51, 52 .”
Method
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
Genetic and epigenetic fine mapping of causal autoimmune disease variants
Farh, K. K.-H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W. J., Beik, S., Shoresh, N., Whitton, H., Ryan, R. J. H., Shishkin, A. A., et al. (2015) · Nature · doi:10.1038/nature13835 · Cited by 1,956 · see citing works
Self-citation
Transcriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Matrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694 · cited 2× · see citing works
“All variants within a window of 100kbp of each gene were tested with linear regression using Matrix eQTL 58 .”
Method
Single-cell Hi-C reveals cell-to-cell variability in chromosome structure
Nagano, T., Lubling, Y., Stevens, T. J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E. D., Tanay, A., & Fraser, P. (2013) · Nature · doi:10.1038/nature12593 · Cited by 1,589 · see citing works
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Introduction to quantitative genetics (4th edn)
Frankham, R. (1996) · Trends in Genetics · doi:10.1016/0168-9525(96)81458-2 · Cited by 1,460 · cited 1× · see citing works
“Protein coding genes (407/414) originally measured using Nanostring (a hybridization based PCR-free quantification method) were assessed, and variability in the bulk dataset was estimated as repeatability using a linear mixed model 56,26 .”
Method
Introduction to quantitative genetics. 1. ed.
Falconer, D. (1984) · Cited by 1,454 · see citing works
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · cited 1× · see citing works
“…natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies 36–40 , and captures biological variability among individual samples while limiting unwanted technical variability.”
Single-cell genome sequencing: current state of the science
Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · cited 1× · see citing works
“Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”
Background
A Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,288 · see citing works
A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · cited 1× · see citing works
“Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”
Background
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · cited 1× · see citing works
“…of cell type proportion, recovers cell type-specific transcriptional response to stimulation, and could facilitate further genetic and longitudinal analyses in relevant cell types and conditions across a range of sampled individuals, including between healthy controls and disease patients 42–44 .”
Background
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · cited 1× · see citing works
“…natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies 36–40 , and captures biological variability among individual samples while limiting unwanted technical variability.”
Background
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq
Habib, N., Basu, A., Avraham-Davidi, I., Burks, T., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., Weitz, D. A., Rozenblatt-Rosen, O., et al. (2017) · doi:10.1101/115196 · Cited by 1,069 · see citing works
Variation in the Human Immune System Is Largely Driven by Non-Heritable Influences
Brodin, P., Jojic, V., Gao, T., Bhattacharya, S., Angel, C. J. L., Furman, D., Shen-Orr, S., Dekker, C. L., Swan, G. E., Butte, A. J., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.020 · Cited by 1,065 · see citing works
Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., et al. (2016) · Science · doi:10.1126/science.aaf1204 · Cited by 957 · see citing works
Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920 · see citing works
Scaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · cited 1× · see citing works
“…of cell type proportion, recovers cell type-specific transcriptional response to stimulation, and could facilitate further genetic and longitudinal analyses in relevant cell types and conditions across a range of sampled individuals, including between healthy controls and disease patients 42–44 .”
Background
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works
PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update
Wang, X., Spandidos, A., Wang, H., & Seed, B. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1013 · Cited by 720 · see citing works
Single-cell barcoding and sequencing using droplet microfluidics
Žilionis, R., Nainys, J., Veres, A., Savova, V., Zemmour, D., Klein, A. M., & Mažutis, L. (2016) · Nature Protocols · doi:10.1038/nprot.2016.154 · Cited by 715 · cited 2× · see citing works
“Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”
BackgroundInfluential
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data
Jun, G., Flickinger, M., Hetrick, K. N., Romm, J. M., Doheny, K. F., Abecasis, G. R., Boehnke, M., & Kang, H. M. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.09.004 · Cited by 484 · see citing works
Self-citation
Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum
Saveanu, L., Carroll, O., Lindo, V., Del Val, M., Lopez, D., Lepelletier, Y., Greer, F., Schomburg, L., Fruci, D., Niedermann, G., et al. (2005) · Nature Immunology · doi:10.1038/ni1208 · Cited by 481 · cited 1× · see citing works
“…those in the major histocompatibility complex (MHC) class I antigen presentation pathway including ERAP2 (P 3.57×10 −32 , meta-analysis), encoding an aminopeptidase known to cleave viral peptides 34 , and HLA-C (P 1.74×10 −29 , meta-analysis), which encodes the MHC class I heavy chain (Fig.”
BackgroundInfluential
Matrix eQTL: Ultra fast eQTL analysis via large matrix operationsPreprint
Shabalin, A. A. (2011) · arXiv (Cornell University) · doi:10.17615/g28n-r219 · Cited by 452 · see citing works
Common Genetic Variants Modulate Pathogen-Sensing Responses in Human Dendritic Cells
Lee, M. N., Ye, C., Villani, A.-C., Raj, T., Li, W., Eisenhaure, T. M., Imboywa, S. H., Chipendo, P. I., Ran, F. A., Slowikowski, K., et al. (2014) · Science · doi:10.1126/science.1246980 · Cited by 441 · see citing works
Self-citation
Batch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356 · see citing works
Fast and accurate long-range phasing in a UK Biobank cohort
Loh, P.-R., Palamara, P. F., & Price, A. L. (2016) · Nature Genetics · doi:10.1038/ng.3571 · Cited by 342 · cited 1× · see citing works
“Genotypes were imputed with EAGLE 57 and filtered for MAF > 0.2, resulting in a total of 189,322 SNPs. Cell type proportions were calculated as number of cells for each cell type divided by the number of total cells for each person.”
MethodInfluential
Parsing the interferon transcriptional network and its disease associations
Mostafavi, S., Yoshida, H., Moodley, D., LeBoité, H., Rothamel, K., Raj, T., Ye, C. J., Chevrier, N., Zhang, S.-Y., Feng, T., et al. (2016) · Cell · doi:10.1016/j.cell.2015.12.032 · Cited by 337 · see citing works
Self-citation
Cell-type specific gene expression profiles of leukocytes in human peripheral blood
Palmer, C., Diehn, M., Alizadeh, A. A., & Brown, P. O. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-115 · Cited by 331 · cited 1× · see citing works
“However, for each cell type, the variance across individuals 22, 30 is also higher than the variance across synthetic replicates (Lin’s concordance = 0.007–0.20) suggesting additional inter-individual variability not due to cell type composition (Supplementary Fig.”
BackgroundInfluential
Genetic Variants Regulating Immune Cell Levels in Health and Disease
Orrù, V., Steri, M., Sole, G., Sidore, C., Virdis, F., Dei, M., Lai, S., Żołędziewska, M., Busonero, F., Mulas, A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.08.041 · Cited by 330 · see citing works
Self-citation
A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans
Li, Y., Oosting, M., Smeekens, S. P., Jaeger, M., Aguirre-Gamboa, R., Le, K. T. T., Deelen, P., Ricaño-Ponce, I., Schoffelen, T., Jansen, A. F. M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.017 · Cited by 324 · cited 1× · see citing works
“11b), suggesting multiplexing reduces technical effects due to separate sample processing 22, 23 .”
Background
Microfluidic single-cell whole-transcriptome sequencing
Streets, A., Zhang, X., Cao, C., Pang, Y., Wu, X., Xiong, L., Yang, L., Fu, Y., Zhao, L., Tang, F., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402030111 · Cited by 274 · cited 2× · see citing works
“Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”
Background
Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression
Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Communications · doi:10.1038/ncomms9687 · Cited by 251 · see citing works
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2013) · Nature Biotechnology · doi:10.1038/nbt.2642 · Cited by 244 · see citing works
Balancing Selection Maintains a Form of ERAP2 that Undergoes Nonsense-Mediated Decay and Affects Antigen Presentation
Andrés, A. M., Dennis, M. Y., Kretzschmar, W. W., Cannons, J. L., Lee-Lin, S.-Q., Hurlé, B., Program, N. C. S., Schwartzberg, P. L., Williamson, S., Bustamante, C. D., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001157 · Cited by 242 · cited 1× · see citing works
“For 709 genes, estimates of fold change in response to IFN-β stimulation in myeloid and CD4 + cells are consistent with estimates in monocyte derived dendritic cells 28 and CD4 + T cells 27 , respectively (Supplementary Fig.”
Result
Intersection of population variation and autoimmunity genetics in human T cell activation
Ye, C. J., Feng, T., Kwon, H.-K., Raj, T., Wilson, M. T., Asinovski, N., McCabe, C., Lee, M. H., Frohlich, I., Paik, H.-I., et al. (2014) · Science · doi:10.1126/science.1254665 · Cited by 236 · see citing works
Self-citation
Differential Effects of Environmental and Genetic Factors on T and B Cell Immune Traits
Aguirre‐Gamboa, R., Joosten, I., Urbano, P. C. M., Molen, R. G. V. D., van Rijssen, E., van Cranenbroek, B., Oosting, M., Smeekens, S. P., Jaeger, M., Zorro, M. M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.053 · Cited by 194 · cited 2× · see citing works
“11b), suggesting multiplexing reduces technical effects due to separate sample processing 22, 23 .”
BackgroundInfluential
Integrative genomic analysis of the human immune response to influenza vaccination
Franco, L. M., Bucasas, K. L., Wells, J. M., Niño, D., Wang, X., Zapata, G. E., Arden, N. H., Renwick, A., Yu, P., Quarles, J. M., et al. (2013) · eLife · doi:10.7554/elife.00299 · Cited by 128 · cited 1× · see citing works
“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood 35 .”
Background

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Sorted by citations

Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984 · see citing works
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Single-cell reconstruction of the early maternal–fetal interface in humans
Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358 · see citing works
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
A single-cell atlas of the peripheral immune response in patients with severe COVID-19
Wilk, A. J., Rustagi, A., Zhao, N. Q., Roque, J., Martínez-Colón, G. J., McKechnie, J. L., Ivison, G. T., Ranganath, T., Vergara, R., Hollis, T., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0944-y · Cited by 1,493 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Fang, Z., Liu, X., & Peltz, G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac757 · Cited by 1,209 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
“[13], we “super-loaded” the 10x Genomics instrument, loading cells at a significantly higher concentration with an expected yield of 20,000 single cells and 5000 multiplets.”
BackgroundMethodResultInfluential
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 853 · see citing works
“Ten publicly-available datasets 17,53 involving PBMCs, or cell types found in PBMCs, were processed, aligned, and merged using the procedure described previously to give a total of 47,994 cells.”
Method
Doublet identification in single-cell sequencing data using scDblFinder
Germain, P., Lun, A. T. L., Meixide, C. G., Macnair, W., & Robinson, M. D. (2021) · F1000Research · doi:10.12688/f1000research.73600.1 · Cited by 1,495 · see citing works
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 848 · see citing works
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Kanton, S., Boyle, M. J., He, Z., Santel, M., Weigert, A., Sanchís-Calleja, F., Guijarro, P., Sidow, L., Fleck, J. S., Han, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1654-9 · Cited by 761 · see citing works
A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 739 · see citing works
De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes
Hufford, M. B., Seetharam, A. S., Woodhouse, M. R., Chougule, K. M., Ou, S., Liu, J., Ricci, W. A., Guo, T., Olson, A. J., Qiu, Y., et al. (2021) · Science · doi:10.1126/science.abg5289 · Cited by 681 · see citing works
scGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease
Yazar, S., Alquicira-Hernandez, J., Wing, K., Senabouth, A., Gordon, M. G., Andersen, S., Lu, Q., Rowson, A., Taylor, T. R. P., Clarke, L., et al. (2022) · Science · doi:10.1126/science.abf3041 · Cited by 682 · see citing works
“A. Dobin et al., STAR: Ultrafast universal RNA-seq aligner.”
MethodInfluentialSelf-citation
Doublet identification in single-cell sequencing data using scDblFinder
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W., & Robinson, M. D. (2022) · F1000Research · doi:10.12688/f1000research.73600.2 · Cited by 684 · see citing works
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution
Rozenblatt–Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J., Ashenberg, O., Cerami, E. G., Coffey, R. J., Demir, E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.053 · Cited by 557 · see citing works
“For snRNA-seq, frozen tissue samples can be accrued over time and then multiplexed for reducing costs and limiting potential batch effects (Gaublomme et al., 2019; Kang et al., 2018).”
Decontamination of ambient RNA in single-cell RNA-seq with DecontX
Yang, S., Corbett, S. E., Koga, Y., Wang, Z., Johnson, W. E., Yajima, M., & Campbell, J. D. (2020) · Genome Biology · doi:10.1186/s13059-020-1950-6 · Cited by 635 · see citing works
“Other approaches such as demuxlet [12] and scds [13] model gene expression from the original data, then assign doublet to barcodes that have observed expression from genes that are likely to not occur simultaneously.”
Method
Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics
Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 534 · see citing works
“greater sensitivity in conjunction with currently employed approaches (Kang et al., 2018; Zheng et al., 2017b).”
Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus
Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., Sun, Y., Ogorodnikov, A., Bueno, R., Lu, A. C., et al. (2022) · Science · doi:10.1126/science.abf1970 · Cited by 507 · see citing works
“After quality control and doublet removal using freemuxlet (8) (mean doublet rate 22.”
MethodResultInfluentialSelf-citation
A test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · see citing works
“To estimate pure biological variability with kBET, we studied a pooled dataset of human PBMCs from eight unrelated individuals for which donor identity was reconstructed for each cell with demux-let 26 .”
BackgroundMethod
Cycling cancer persister cells arise from lineages with distinct programs
Oren, Y., Tsabar, M., Cuoco, M. S., Amir-Zilberstein, L., Cabanos, H. F., Hütter, J.-C., Hu, B., Thakore, P. I., Tabaka, M., Fulco, C. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03796-6 · Cited by 514 · see citing works
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., Batlle, E., Sagar, Grün, D., Lau, J. K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0469-4 · Cited by 499 · see citing works
muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020) · Nature Methods · doi:10.1038/s41592-020-0820-1 · Cited by 506 · see citing works
Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
Kinker, G. S., Greenwald, A. C., Tal, R., Orlova, Z., Cuoco, M. S., McFarland, J. M., Warren, A., Rodman, C., Roth, J. A., Bender, S. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00726-6 · Cited by 445 · see citing works
A clinically applicable integrative molecular classification of meningiomas
Nassiri, F., Liu, J., Patil, V. S., Mamatjan, Y., Wang, J. Z., Hugh-White, R., Macklin, A. M., Khan, S., Singh, O., Karimi, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03850-3 · Cited by 459 · see citing works
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
Study, L. C., van der Wijst, M. G. P., BIOS Consortium, Brugge, H., de Vries, D. H., Deelen, P., Swertz, M. A., & Franke, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0089-9 · Cited by 399 · see citing works
“Further details of the algorithm can be found in Kang et al.33 Using the Demuxlet algorithm, we could confidently assign the majority (99.8”
Influential
Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function
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A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
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MethodInfluential
Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
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Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing
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Cell type prioritization in single-cell data
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Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer
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Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology
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“The ﬁ fth cluster, B_C4_MX1, predominantly composed of interferon-induced B cells 38 , expressed high levels of MX1 , IFI6 , and IFI44L .”
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Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19
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LIANA+ Provides an All-in-One Framework for Cell–Cell Communication Inference
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The single-cell eQTLGen consortium
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Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing
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ResultInfluential
A Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
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MethodInfluentialSelf-citation
Jointly defining cell types from multiple single-cell datasets using LIGER
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Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
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Temporal modelling using single-cell transcriptomics
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Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
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“This handicap can be partially overcome by jointly capturing samples from different individuals, where genotype differences allow the user to distinguish between donors and thereby reliably identify doublets 69 .”
Background
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics
Gaublomme, J. T., Li, B., Aubin, C., Knecht, A., Yang, Y., Drokhlyansky, E., Wittenberghe, N. V., Waldman, J., Dionne, D. A., Nguyễn, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10756-2 · Cited by 181 · see citing works
Interferon-Gamma–Producing CD8+ Tissue Resident Memory T Cells Are a Targetable Hallmark of Immune Checkpoint Inhibitor–Colitis
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Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain
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Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma
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“Doublet cell filtering Barcoded single-cell RNA-seq was demultiplexed patient-wise using Demuxlet (123) with the following parameters: alpha = 0, 0.5 and --geno-error = 0.05.”
MethodInfluential
DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data
DePasquale, E. A. K., Schnell, D. J., Van Camp, P.-J., Valiente-Alandí, Í., Blaxall, B. C., Grimes, H. L., Singh, H., & Salomonis, N. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.082 · Cited by 207 · see citing works
“A common source of confounding gene expression in single-cell RNA-sequencing (scRNA-seq) is the occurrence of multiplet cell profiles that result from the simultaneous capture of multiple cells in a single well or droplet (Kang et al., 2018).”
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Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus
Kotliarov, Y., Sparks, R., Martins, A. J., Mulè, M. P., Lu, Y. C., Goswami, M., Kardava, L., Banchereau, R., Pascual, V., Biancotto, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0769-8 · Cited by 193 · see citing works
Genetic determinants of co-accessible chromatin regions in activated T cells across humans
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“With the recent advancement of single cell epigenomic 65 and transcriptomic 66,67,68 profiling, it should be possible to more directly detect context-specific genetic effects in a heterogeneous cell population.”
Self-citation
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
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Biomarkers of inflammation and repair in kidney disease progression
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Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE
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Identification of Salt Stress Response Genes in Rosa chinensis Leaves by Comparative RNA-seq Analysis of Transcriptome Dynamics
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Supplementary Table 5 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
Beane, J., Vick, J. L., Schembri, F. M., et al. (2023) · doi:10.1158/1940-6207.22524407
Supplementary Table 10 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
Beane, J., Vick, J. L., Schembri, F. M., et al. (2023) · doi:10.1158/1940-6207.22524419.v1
Additional file 2 of RNA-Seq transcriptome reveals different molecular responses during human and mouse oocyte maturation and fertilization
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Authors: Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., Gate, R. E., Mostafavi, S., Marson, A., Zaitlen, N., Criswell, L. A., & Ye, C. J.
Affiliations: University of Michigan (US)
University of California, San Francisco (US)
University of British Columbia (CA)
Corresponding author: Kang, Hyun Min
Ye, Chun Jimmie
ORCID: Kang, Hyun Min
Subramaniam, Meena
Targ, Sasha
Nguyen, Michelle
Maliskova, Lenka
McCarthy, Elizabeth
Byrnes, Lauren
Lanata, Cristina M
Mostafavi, Sara
Marson, Alexander
Zaitlen, Noah
Criswell, Lindsey A
Ye, Chun Jimmie
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2017-12-11
Volume: 36
Issue: 1
Pages: 89-94
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.4042
PMC: PMC5784859
OpenAlex: W2774307122
Semantic Scholar: 829fff0f1bab67a4bb98a0c77120ff80311d7da4
Cited by: 1,339
References: 59
Citations by year: 2026: 120 · 2025: 155 · 2024: 199 · 2023: 152 · 2022: 136 · 2021: 163 · 2020: 116 · 2019: 74 · 2018: 45 · 2017: 3
FWCI: 27.95
Type: journal article
Language: en
Update notices: correction (2020-10-14)
Open access: green
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (submittedVersion)
PubMed Central (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
Keywords: natural genetic variation, single nucleotide polymorphisms, doublet detection, pooled samples, batch effects, interferon-beta, lupus, cell type-specific gene expression
MeSH terms: Genotype, Humans, Interferons, Lupus Erythematosus, Systemic

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

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