Literature DB >> 30718845

Single-cell imaging and RNA sequencing reveal patterns of gene expression heterogeneity during fission yeast growth and adaptation.

Malika Saint1,2, François Bertaux1,2,3,4, Wenhao Tang3, Xi-Ming Sun1,2, Laurence Game1,2, Anna Köferle5,6, Jürg Bähler5, Vahid Shahrezaei7, Samuel Marguerat8,9.   

Abstract

Phenotypic cell-to-cell variability is a fundamental determinant of microbial fitness that contributes to stress adaptation and drug resistance. Gene expression heterogeneity underpins this variability but is challenging to study genome-wide. Here we examine the transcriptomes of >2,000 single fission yeast cells exposed to various environmental conditions by combining imaging, single-cell RNA sequencing and Bayesian true count recovery. We identify sets of highly variable genes during rapid proliferation in constant culture conditions. By integrating single-cell RNA sequencing and cell-size data, we provide insights into genes that are regulated during cell growth and division, including genes whose expression does not scale with cell size. We further analyse the heterogeneity of gene expression during adaptive and acute responses to changing environments. Entry into the stationary phase is preceded by a gradual, synchronized adaptation in gene regulation that is followed by highly variable gene expression when growth decreases. Conversely, sudden and acute heat shock leads to a stronger, coordinated response and adaptation across cells. This analysis reveals that the magnitude of global gene expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote.

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Mesh:

Year:  2019        PMID: 30718845     DOI: 10.1038/s41564-018-0330-4

Source DB:  PubMed          Journal:  Nat Microbiol        ISSN: 2058-5276            Impact factor:   17.745


  14 in total

1.  Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments.

Authors:  Christopher A Jackson; Dayanne M Castro; Richard Bonneau; David Gresham; Giuseppe-Antonio Saldi
Journal:  Elife       Date:  2020-01-27       Impact factor: 8.140

2.  μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells.

Authors:  Tyler N Chen; Anushka Gupta; Mansi D Zalavadia; Aaron Streets
Journal:  Lab Chip       Date:  2020-09-15       Impact factor: 6.799

3.  Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes.

Authors:  Chuan Ku; Arnau Sebé-Pedrós
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-10-07       Impact factor: 6.237

4.  Single-Cell RNA Sequencing in Yeast Using the 10× Genomics Chromium Device.

Authors:  Lieselotte Vermeersch; Abbas Jariani; Jana Helsen; Benjamin M Heineike; Kevin J Verstrepen
Journal:  Methods Mol Biol       Date:  2022

5.  Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.

Authors:  Ayoub Lasri; Vahid Shahrezaei; Marc Sturrock
Journal:  BMC Bioinformatics       Date:  2022-06-17       Impact factor: 3.307

6.  Yeast Single-cell RNA-seq, Cell by Cell and Step by Step.

Authors:  Mariona Nadal-Ribelles; Saiful Islam; Wu Wei; Pablo Latorre; Michelle Nguyen; Eulàlia de Nadal; Francesc Posas; Lars M Steinmetz
Journal:  Bio Protoc       Date:  2019-09-05

7.  A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast.

Authors:  Abbas Jariani; Lieselotte Vermeersch; Bram Cerulus; Gemma Perez-Samper; Karin Voordeckers; Thomas Van Brussel; Bernard Thienpont; Diether Lambrechts; Kevin J Verstrepen
Journal:  Elife       Date:  2020-05-18       Impact factor: 8.140

8.  Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing.

Authors:  Sydney B Blattman; Wenyan Jiang; Panos Oikonomou; Saeed Tavazoie
Journal:  Nat Microbiol       Date:  2020-05-25       Impact factor: 17.745

9.  Method for RNA extraction and transcriptomic analysis of single fungal spores.

Authors:  Ivey A Geoghegan; Richard D Emes; David B Archer; Simon V Avery
Journal:  MethodsX       Date:  2019-12-04

10.  bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.

Authors:  Wenhao Tang; François Bertaux; Philipp Thomas; Claire Stefanelli; Malika Saint; Samuel Marguerat; Vahid Shahrezaei
Journal:  Bioinformatics       Date:  2020-02-15       Impact factor: 6.937

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