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

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TLDR
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 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.
Abstract
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.

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疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

TL;DR: Current understanding of the major factors regulating protein expression is summarized to demonstrate a substantial role for regulatory processes occurring after mRNA is made in controlling steady-state protein abundances.
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On the Dependency of Cellular Protein Levels on mRNA Abundance.

TL;DR: It is concluded that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.
References
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Journal ArticleDOI

Probing Gene Expression in Live Cells, One Protein Molecule at a Time

TL;DR: It is found that the protein molecules are produced in bursts, with each burst originating from a stochastically transcribed single messenger RNA molecule, and that protein copy numbers in the bursts follow a geometric distribution.
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Noise Propagation in Gene Networks

TL;DR: A model was developed that explains the complex behavior exhibited by the correlations and reveals the dominant noise sources and successfully predicts the correlations as the network is systematically perturbed.
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Noise in protein expression scales with natural protein abundance.

TL;DR: Deviation of coexpressed genes from this general trend, including high noise in stress-related genes and low noise in proteasomal genes, may indicate fluctuations in pathway-specific regulators or a differential activation pattern of the underlying gene promoters.
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Analytical distributions for stochastic gene expression

TL;DR: An approximation that allows the calculation of not only the mean and variance, but also the distribution of protein numbers is presented, which implies that protein synthesis occurs in geometrically distributed bursts and allows mRNA to be eliminated from a master equation description.
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