Microbial single-cell RNA sequencing by split-pool barcoding

被引:144
|
作者
Kuchina, Anna [1 ]
Brettner, Leandra M. [2 ,3 ]
Paleologu, Luana [4 ,5 ]
Roco, Charles M. [2 ]
Rosenberg, Alexander B. [1 ]
Carignano, Alberto [1 ]
Kibler, Ryan [6 ]
Hirano, Matthew [1 ]
DePaolo, R. William [3 ,7 ]
Seelig, Georg [1 ,8 ,9 ]
机构
[1] Univ Washington, Dept Elect & Comp Engn, Seattle, WA 98195 USA
[2] Univ Washington, Dept Bioengn, Seattle, WA 98195 USA
[3] Univ Washington, Sch Med, Ctr Microbiome Sci & Therapeut, Seattle, WA USA
[4] Univ Washington, Dept Microbiol, Seattle, WA 98195 USA
[5] Univ Washington, Dept Biol, Seattle, WA 98195 USA
[6] Univ Washington, Biol Phys Struct & Design, Seattle, WA 98195 USA
[7] Univ Washington, Sch Med, Dept Med, Div Gastroenterol, Seattle, WA 98195 USA
[8] Univ Washington, Mol Engn & Sci Inst, Seattle, WA 98195 USA
[9] Univ Washington, Paul G Allen Sch Comp Sci & Engn, Seattle, WA 98195 USA
关键词
STOCHASTIC GENE-EXPRESSION; BACILLUS-SUBTILIS; BINDING-SITES; DEFECTIVE PROPHAGE; FUNCTIONAL ROLES; GENOME ANALYSIS; MESSENGER-RNA; SIGMA-FACTORS; TRANSCRIPTION; PROTEINS;
D O I
10.1126/science.aba5257
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to highthroughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.
引用
收藏
页码:798 / +
页数:58
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