Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing

被引:134
作者
Datlinger, Paul [1 ]
Rendeiro, Andre F. [1 ]
Boenke, Thorina [1 ]
Senekowitsch, Martin [1 ]
Krausgruber, Thomas [1 ]
Barreca, Daniele [1 ]
Bock, Christoph [1 ,2 ]
机构
[1] Austrian Acad Sci, CeMM Res Ctr Mol Med, Vienna, Austria
[2] Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Inst Artificial Intelligence, Vienna, Austria
基金
奥地利科学基金会; 欧盟地平线“2020”;
关键词
TRANSCRIPTOMICS; CHROMATIN; CIRCUITS; ATLAS;
D O I
10.1038/s41592-021-01153-z
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed 'single-cell combinatorial fluidic indexing' (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation. Combining whole-transcriptome preindexing with standard droplet microfluidics, scifi-RNA-seq enables single-cell RNA-seq with massive throughput and built-in sample multiplexing.
引用
收藏
页码:635 / +
页数:26
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