Stochastic mRNA Synthesis in Mammalian Cells

By Arjun Raj, Charles S. Peskin, Daniel Tranchina, D. Vargas, S. Tyagi. PLoS Biology, 2006

654 citations recorded by the source corpus.

Individual cells in genetically homogeneous populations have been found to express different numbers of molecules of specific proteins. We investigated the origins of these variations in mammalian cells by counting individual molecules of mRNA produced from a reporter gene that was stably integrated into the cell's genome. We found that there are massive variations in the number of mRNA molecules present in each cell. These variations occur because mRNAs are synthesized in short but intense bursts of transcription beginning when the gene transitions from an inactive to an active state and ending when they transition back to the inactive state. We show that these transitions are intrinsically random and not due to global, extrinsic factors such as the levels of transcriptional activators. Moreover, the gene activation causes burst-like expression of all genes within a wider genomic locus. We further found that bursts are also exhibited in the synthesis of natural genes. The bursts of mRNA expression can be buffered at the protein level by slow protein degradation rates. A stochastic model of gene activation and inactivation was developed to explain the statistical properties of the bursts. The model showed that increasing the level of transcription factors increases the average size of the bursts rather than their frequency. These results demonstrate that gene expression in mammalian cells is subject to large, intrinsically random fluctuations and raise questions about how cells are able to function in the face of such noise.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a3a7308f0b0d22599685c23994452f7e9bfe8826

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Citations in the corpus

Stochastic gene expression and its consequences (2008, methodology): “As mentioned earlier, one possibility often posited is that stochastic events of chromatin remodeling could underlie the bursts by causing the gene to switch between transcriptionally active and inactive states (Becskei et al., 2005; Raj et al., 2006; Raser and O'Shea, 2004; Warren et al., 2006).”
Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells (2009, background): “Transcription of most 'active' genes is not continuous but occurs in pulses of activit”
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses (2012, neutral): “A stochastic switch between 'on' and 'off' states has been suggested for transcription, resulting in bursts of gene expression occurring from bacteria to human”
Single-RNA counting reveals alternative modes of gene expression in yeast (2008, background): “Fluorescence in situ hybridization (FISH) suggested that genes in mammalian cells are expressed as 'bursts' of transcription: infrequent periods of transcriptional activity that produce many transcripts within a short tim”
On Transcriptome Sequencing (2009, methodology): “Genome-wide analysis of gene expression in single cells has great potential for advancing the investigation of gene-expression heterogeneity (Raj et al. 2006). Tang et al. (2009) recently published their work on mRNA-seq of single cells using a modified version of a previously published protocol (Ku”
RNA regulons: coordination of post-transcriptional events (2007, background): “Indeed, it is difficult to understand how transcription can be the sole coordinating process of gene expression, given its spontaneous and stochastic natur”
MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. (2007, background): “The eukaryotic cell is a noisy environment in which transcription often occurs in a bursting manner (Blake et al., 2006; Golding et al., 2005; Raj et al., 2006), causing the number of mRNAs per cell— which can go as low as fractions of a copy when averaged over a population—to fluctuate significantl”
Cellular Decision-Making and Biological Noise: From Microbes to Mammals (2011, background): “Gene expression bursts (Raj et al., 2006) may offer a solution, as highly expressed proteins can be noisy provided that they are expressed in bursts (Newman et al.”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “Previous experimental approaches, such as time-lapse microscopy studies 24,82 , have labelled individual genes and then tracked their expression over time, which has allowed estimation of the rate per unit time at which a gene transitions from the off state to the on state and vice versa, along…”
Comparison and evaluation of statistical error models for scRNA-seq (2021, background): “…is well suited to capture variation driven by stochastic technical loss and sampling noise, it cannot capture other sources of biological heterogeneity between cells that are not driven by changes in cell state, for example, intrinsic variation caused by stochastic transcriptional bursts (26–28).”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “The second is allele-intrinsic variation, namely stochastic factors intrinsic to the molecular mechanisms that control gene expression 40–42 .”
Analytical distributions for stochastic gene expression (2008, background): “Such bursts of gene expression have been measured in bacteria and eukaryotes [12, 13, 14, 28, 29].”
Roles for microRNAs in conferring robustness to biological processes. (2012, neutral): “Transcription occurs in stochastic bursts (Blake et al., 2006; Raj et al., 2006), and higher transcription rates correlate with lower noise (Paulsson, 2004).”
In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus (2016, positive): “Building upon single molecule fluorescence in situ hybridization (smFISH) (Femino et al., 1998; Raj et al., 2006;)”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, background): “Current models of transcriptional dynamics describe promoter bursting (Figure 4A), where the promoter of a gene switches between an active and an inactive state and, once activated, initiates transcript production at a constant rate (Raj et al., 2006; Raser and O’Shea, 2004).”