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

Details

• Alternative Title:
  Nat Biotechnol
• Personal Author:
  Kang, Hyun Min ; Subramaniam, Meena ; Targ, Sasha ; Nguyen, Michelle ; Maliskova, Lenka ; McCarthy, Elizabeth ; Wan, Eunice ; Wong, Simon ; Byrnes, Lauren ; Lanata, Cristina ; Gate, Rachel ; Mostafavi, Sara ; Marson, Alexander ; Zaitlen, Noah ; Criswell, Lindsey A ; Ye, Chun Jimmie
• Description:
  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.
• Subjects:
  Genotype High-Throughput Nucleotide Sequencing Humans Interferons Lupus Erythematosus, Systemic Polymorphism, Single Nucleotide Quantitative Trait Loci Single-Cell Analysis Transcriptome
• Source:
  Nat Biotechnol. 36(1):89-94
• Pubmed ID:
  29227470
• Pubmed Central ID:
  PMC5784859
• Document Type:
  Journal Article
• Funding:
  R38 AI140299/AI/NIAID NIH HHSUnited States/ ; U19 AI090023/AI/NIAID NIH HHSUnited States/ ; T32 GM067547/GM/NIGMS NIH HHSUnited States/ ; T32 GM007618/GM/NIGMS NIH HHSUnited States/ ; R03 DE025665/DE/NIDCR NIH HHSUnited States/ ; R21 AI133337/AI/NIAID NIH HHSUnited States/ ; P30 AR070155/AR/NIAMS NIH HHSUnited States/ ; R01 AR071522/AR/NIAMS NIH HHSUnited States/ ; U01 HL137182/HL/NHLBI NIH HHSUnited States/ ; U01 DP005120/DP/NCCDPHP CDC HHSUnited States/ ; K25 HL121295/HL/NHLBI NIH HHSUnited States/ ; U01 DP006486/DP/NCCDPHP CDC HHSUnited States/ ; F30 DK115167/DK/NIDDK NIH HHSUnited States/
• Volume:
  36
• Issue:
  1
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:69618805b1d8713a33aa310a087dea3b053dbe791c6050fd29725460050da2aa
• Download URL:
  https://stacks.cdc.gov/view/cdc/79371/cdc_79371_DS1.pdf
• File Type:
  [PDF - 2.20 MB ]