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Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.
Nature Biotechnology ( IF 46.9 ) Pub Date : 2018-01-01 , DOI: 10.1038/nbt.4042
Hyun Min Kang 1 , Meena Subramaniam 2, 3, 4, 5, 6 , Sasha Targ 2, 3, 4, 5, 6, 7 , Michelle Nguyen 8, 9, 10 , Lenka Maliskova 3, 11 , Elizabeth McCarthy 7 , Eunice Wan 3 , Simon Wong 3 , Lauren Byrnes 12 , Cristina M Lanata 13, 14 , Rachel E Gate 2, 3, 4, 5, 6 , Sara Mostafavi 15 , Alexander Marson 8, 9, 10, 13, 16, 17 , Noah Zaitlen 3, 13, 18 , Lindsey A Criswell 3, 13, 14, 19 , Chun Jimmie Ye 3, 4, 5, 6
Affiliation  

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.

中文翻译:

使用自然遗传变异的多重液滴单细胞 RNA 测序。

液滴单细胞 RNA 测序 (dscRNA-seq) 实现了转录组的快速、大规模并行分析。然而,低效的样本处理和技术批次效应阻碍了评估多个个体之间的差异表达。在这里,我们描述了一种计算工具 demuxlet,它利用自然遗传变异来确定包含单个细胞(单峰)的每个液滴的样本身份,并检测包含两个细胞(双峰)的液滴。这些功能支持多重 dscRNA-seq 实验,其中来自不相关个体的细胞被汇集并以比标准工作流程更高的通量捕获。使用模拟数据,我们表明每个细胞 50 个单核苷酸多态性 (SNP) 足以在多达 64 个个体的池中分配 97% 的单峰并识别 92% 的双峰。给定八个合并样本中每一个的基因分型数据,demuxlet 正确地恢复了 > 99% 的单峰的样本身份,并以与之前估计一致的速率识别双峰。我们应用 demuxlet 来评估接受干扰素 (IFN)-β 治疗的 8 个合并狼疮患者样本中基因表达的细胞类型特异性变化,并对 23 个合并样本进行 eQTL 分析。
更新日期:2017-12-11
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