Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells

By T. Petersen, Elizabeth A. Calle, Liping Zhao, Eun Jung Lee, Liqiong Gui, MichaSam B. Raredon, Kseniya Gavrilov, Tai Yi, Zhuangcheng Zhen, Christopher K. Breuer and 2 more. Science, 2010

2,032 citations recorded by the source corpus.

Devil in the Detail Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533; see the Perspective by Tyagi) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated. Measurement of protein and messenger RNA copy numbers in single Escherichia coli cells gives a system-wide view of stochastic gene expression. Protein and messenger RNA (mRNA) copy numbers vary from cell to cell in isogenic bacterial populations. However, these molecules often exist in low copy numbers and are difficult to detect in single cells. We carried out quantitative system-wide analyses of protein and mRNA expression in individual cells with single-molecule sensitivity using a newly constructed yellow fluorescent protein fusion library for Escherichia coli. We found that almost all protein number distributions can be described by the gamma distribution with two fitting parameters which, at low expression levels, have clear physical interpretations as the transcription rate and protein burst size. At high expression levels, the distributions are dominated by extrinsic noise. We found that a single cell’s protein and mRNA copy numbers for any given gene are uncorrelated.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/23bd7e3f1e495bd57b98ab60e6bb431cb5c54acf

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Citations in the corpus

Revealing the vectors of cellular identity with single-cell genomics (2016, background): “Third, the number of mRNA molecules may sometimes not correlate well with the numbers of their protein products at the same time point 187–191 , and consequently regulatory networks ought to be considered at both the transcript and R E V I E W © 201 6 N a t u r e A m e r i ca , I n c ., p a r t o f…”
Single-cell RNA-seq: advances and future challenges (2014, positive): “As for bacteria, the average copy number of an mRNA in Escherichia coli is 0.4 per cell (40).”
Single-Cell and Single-Molecule Analysis of Gene Expression Regulation (2016, background): “It is now well established that transcription predominately occurs in bursts in eukaryotic cells (22, 33, 120, 126, 159), viruses (130), and bacteria (22, 140).”
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses (2012, positive): “Such strains provide a direct readout of protein abundance and have led to large-scale surveys of protein expression within single cell”
Single cell RNA Seq reveals dynamic paracrine control of cellular variation (2014, methodology): “Using the variance calculated over all cells as a metric of heterogeneity 6,16 conflates these two types of variation.”
Spatially resolved, highly multiplexed RNA profiling in single cells (2015, positive): “Recent advances in imaging and analysismethods have allowed hundreds of smFISH measurements to be performed in an automatedmanner, substantially expanding our knowledge of the RNA expression profile and spatial organization in different organisms (14, 15).”
Hfq and its constellation of RNA (2011, background): “However, most bacterial mRNAs are present in low number”
The quantitative and condition-dependent Escherichia coli proteome (2015, background): “(B), Taniguchi et al.(10) (C), Lu et al.”
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells.”