Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality

Mengyu Wang, Jing Zhang, Heng Xu, Ido Golding

Research output: Contribution to journalLetter

Abstract

Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression1–3, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically4,5. Here, we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allow us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.

Original languageEnglish (US)
Pages (from-to)2118-2127
Number of pages10
JournalNature Microbiology
Volume4
Issue number12
DOIs
StatePublished - Dec 1 2019

Fingerprint

Individuality
Genes
Messenger RNA
cdc Genes
RNA Stability
Population Characteristics
Cell Cycle
Theoretical Models
Cell Count
Escherichia coli
Bacteria

ASJC Scopus subject areas

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

Cite this

Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality. / Wang, Mengyu; Zhang, Jing; Xu, Heng; Golding, Ido.

In: Nature Microbiology, Vol. 4, No. 12, 01.12.2019, p. 2118-2127.

Research output: Contribution to journalLetter

Wang, Mengyu ; Zhang, Jing ; Xu, Heng ; Golding, Ido. / Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality. In: Nature Microbiology. 2019 ; Vol. 4, No. 12. pp. 2118-2127.
@article{de04a1decbd34537996aeda988bfb95c,
title = "Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality",
abstract = "Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression1–3, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically4,5. Here, we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allow us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.",
author = "Mengyu Wang and Jing Zhang and Heng Xu and Ido Golding",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s41564-019-0553-z",
language = "English (US)",
volume = "4",
pages = "2118--2127",
journal = "Nature Microbiology",
issn = "2058-5276",
publisher = "Nature Publishing Group",
number = "12",

}

TY - JOUR

T1 - Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality

AU - Wang, Mengyu

AU - Zhang, Jing

AU - Xu, Heng

AU - Golding, Ido

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression1–3, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically4,5. Here, we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allow us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.

AB - Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression1–3, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically4,5. Here, we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allow us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.

UR - http://www.scopus.com/inward/record.url?scp=85073834746&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073834746&partnerID=8YFLogxK

U2 - 10.1038/s41564-019-0553-z

DO - 10.1038/s41564-019-0553-z

M3 - Letter

C2 - 31527794

AN - SCOPUS:85073834746

VL - 4

SP - 2118

EP - 2127

JO - Nature Microbiology

JF - Nature Microbiology

SN - 2058-5276

IS - 12

ER -