生物
大规模并行测序
计算生物学
巨量平行
RNA序列
单细胞分析
单细胞测序
细菌
DNA测序
遗传学
细胞
基因
转录组
表型
基因表达
计算机科学
并行计算
外显子组测序
作者
Bruce Wang,Aaron E. Lin,Jiayi Yuan,Kimberly Novak,Matthias D. Koch,Ned S. Wingreen,Britt Adamson,Zemer Gitai
出处
期刊:Nature microbiology
日期:2023-08-31
卷期号:8 (10): 1846-1862
被引量:22
标识
DOI:10.1038/s41564-023-01462-3
摘要
Abstract Bacterial populations are highly adaptive. They can respond to stress and survive in shifting environments. How the behaviours of individual bacteria vary during stress, however, is poorly understood. To identify and characterize rare bacterial subpopulations, technologies for single-cell transcriptional profiling have been developed. Existing approaches show some degree of limitation, for example, in terms of number of cells or transcripts that can be profiled. Due in part to these limitations, few conditions have been studied with these tools. Here we develop massively-parallel, multiplexed, microbial sequencing (M3-seq)—a single-cell RNA-sequencing platform for bacteria that pairs combinatorial cell indexing with post hoc rRNA depletion. We show that M3-seq can profile bacterial cells from different species under a range of conditions in single experiments. We then apply M3-seq to hundreds of thousands of cells, revealing rare populations and insights into bet-hedging associated with stress responses and characterizing phage infection.
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