Toggle Main Menu Toggle Search

Open Access padlockePrints

Single-cell genomics reveals population structures from in vitro evolutionary studies of Salmonella

Lookup NU author(s): Dr Matt BawnORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2022 The Authors. Single-cell DNA sequencing has the potential to reveal detailed hierarchical structures in evolving populations of cells. Single cell approaches are increasingly used to study clonal evolution in human ageing and cancer but have not yet been deployed to study evolving clonal microbial populations. Here, we present an approach for single bacterial genomic analysis for in vitro evolution experiments using FACS isolation of individual bacteria followed by whole-genome amplification and sequencing. We apply this to the experimental evolution of a hypermutator strain of Salmonella in response to antibiotic stress (ciprofloxacin). By analysing sequence polymorphisms in individual cells from populations we identified the presence and prevalence of sub-populations which have acquired polymorphisms in genes previously demonstrated to be associated with ciprofloxacin susceptibility. We were also able to identify that the population exposed to antibiotic stress was able to develop resistance whilst maintaining diversity. This population structure could not be resolved from bulk sequence data, and our results show how high-throughput single-cell sequencing can enhance experimental studies of bacterial evolution.


Publication metadata

Author(s): Bawn M, Hernandez J, Trampari E, Thilliez G, Quince C, Webber MA, Kingsley RA, Hall N, Macaulay IC

Publication type: Article

Publication status: Published

Journal: Microbial Genomics

Year: 2022

Volume: 8

Issue: 9

Online publication date: 20/09/2022

Acceptance date: 28/06/2022

Date deposited: 11/02/2025

ISSN (electronic): 2057-5858

Publisher: Microbiology Society

URL: https://doi.org/10.1099/mgen.0.000871

DOI: 10.1099/mgen.0.000871

PubMed id: 36125951


Altmetrics

Altmetrics provided by Altmetric


Share