Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback

AbstractBackgroundTo understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation.ResultWe identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, α-methylene-γ-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment.ConclusionUsing single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

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Selection at the pathway level drives the evolution of gene-specific transcriptional noise

10.1101/104216 ◽

2017 ◽

Author(s):

Gustavo Valadares Barroso ◽

Natasa Puzovic ◽

Julien Y Dutheil

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Networks ◽

Phenotypic Variability ◽

Published Data ◽

Evolutionary Constraint ◽

Stochastic Gene Expression ◽

Transcriptional Noise ◽

Evolutionary Forces ◽

A Genome

ABSTRACTBiochemical reactions within individual cells result from the interactions of molecules, typically in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generate noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, in particular owing to the advent of single cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. We take advantage of two recently published data sets of the single-cell transcriptome of the domestic mouse Mus musculus in order to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We show that noise levels in the mRNA distributions (a.k.a. transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraint on the encoded protein and gene age. The position of the encoded protein in biological pathways, however, is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with mean expression level in functional and evolutionary studies of gene expression.

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Stochastic Gene Expression in a Single Cell

Science ◽

10.1126/science.1070919 ◽

2002 ◽

Vol 297 (5584) ◽

pp. 1183-1186 ◽

Cited By ~ 3422

Author(s):

M. B. Elowitz

Keyword(s):

Gene Expression ◽

Single Cell ◽

Stochastic Gene Expression

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Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation

Genome Biology ◽

10.1186/s13059-021-02414-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Melania Barile ◽

Ivan Imaz-Rosshandler ◽

Isabella Inzani ◽

Shila Ghazanfar ◽

Jennifer Nichols ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Current Velocity ◽

Fetal Liver ◽

Step Change ◽

Chimeric Mouse ◽

Starting Point ◽

Erythroid Maturation ◽

Mutant Cells ◽

Expression Kinetics

Abstract Background Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Results Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. Conclusions By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

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Urn models for stochastic gene expression yield intuitive insights into the probability distributions of single-cell mRNA and protein counts

Physical Biology ◽

10.1088/1478-3975/aba50f ◽

2020 ◽

Vol 17 (6) ◽

pp. 066001

Author(s):

Krishna Choudhary ◽

Atul Narang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Probability Distributions ◽

Stochastic Gene Expression ◽

Urn Models

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Author response: A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

10.7554/elife.55320.sa2 ◽

2020 ◽

Author(s):

Abbas Jariani ◽

Lieselotte Vermeersch ◽

Bram Cerulus ◽

Gemma Perez-Samper ◽

Karin Voordeckers ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Budding Yeast ◽

Author Response ◽

Lag Phase ◽

Stochastic Gene Expression ◽

Rna Seq

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Coordinated Changes in Gene Expression Kinetics Underlie both Mouse and Human Erythroid Maturation

10.1101/2020.12.21.423773 ◽

2020 ◽

Author(s):

Melania Barile ◽

Ivan Imaz-Rosshandler ◽

Isabella Inzani ◽

Shila Ghazanfar ◽

Jennifer Nichols ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Current Velocity ◽

Fetal Liver ◽

Step Change ◽

Chimeric Mouse ◽

Starting Point ◽

Erythroid Maturation ◽

Mutant Cells ◽

Expression Kinetics

AbstractSingle cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single cell RNA-Seq permits prediction of future expression states. Here we applied this ‘RNA velocity concept’ to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Intriguingly, RNA velocity correctly identified epiblast cells as the starting point, but several trajectory predictions at later stages were inconsistent with both real time ordering and existing knowledge. The most striking discrepancy concerned red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes revealed a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be up-regulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1- chimera dataset revealed induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

Download Full-text