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

By Hyun Min Kang, Meena Subramaniam, Sasha Targ, M. Nguyen, L. Maliskova, Elizabeth McCarthy, Eunice Wan, Simon Wong, Lauren E. Byrnes, C. Lanata and 6 more. Nature Biotechnology, 2017

978 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology, Rheumatology

View Paper: https://www.semanticscholar.org/paper/829fff0f1bab67a4bb98a0c77120ff80311d7da4

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Citations in the corpus

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “A recent study compared multiple patients across stimulated and unstimulated conditions by first computationally separating immune cell types [185] ; to do this, cells from a given cell type were aggregated into a “pseudobulk” RNAseq dataset and DE was performed using standard tools.”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, neutral): “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).”
A test metric for assessing single-cell RNA-seq batch correction (2018, methodology): “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 .”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, methodology): “[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.”