Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection

Bruce Wang, Aaron E. Lin, Jiayi Yuan, Katherine E. Novak, Matthias D. Koch, Ned S. Wingreen, Britt Adamson, Zemer Gitai

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1846-1862
Number of pages17
JournalNature Microbiology
Volume8
Issue number10
DOIs
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Applied Microbiology and Biotechnology
  • Microbiology (medical)
  • Genetics
  • Cell Biology
  • Microbiology
  • Immunology

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