Generation of Single-Cell Transcript Variability by Repression

Antolović, Vlatka; Miermont, Agnès; Corrigan, Adam; Chubb, Jonathan R.

doi:10.1016/j.cub.2017.05.028

Cited by 50 publications

(52 citation statements)

References 30 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the correlation analysis between the cell transcriptional uncertainty and biologically meaningful rates of the stochastic gene transcription model showed strong positive correlations with transcriptional burst size and frequency. In agreement with the result of the correlation analysis, several studies have reported an increase in gene transcriptional bursts during transition states in cell differentiation and other recent studies have suggested that both burst frequency and burst size regulate gene expression levels 33,51,52 . Importantly, our comparison of the single-cell transcriptional uncertainty and the single-cell RNA velocity revealed that an increase (decrease) in RNA velocity predicts an increase (decrease) in transcriptional uncertainty after a short delay, and that a peak of RNA velocity preceeds that of the transcriptional uncertainty.…”

Section: Discussionsupporting

confidence: 89%

“…One possible explanation for such change in model parameters is a higher chromatin accessibility during the transition period of cell differentiation. This finding is consistent with the view that stem cells increase its gene expression uncertainty or stochasticity by adopting a more open chromatin state to enable the exploration of the gene expression space 33,[50][51][52] .…”

Section: As Depicted Insupporting

confidence: 89%

See 1 more Smart Citation

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Gao

Gandrillon

Páldi

et al. 2020

Preprint

View full text Add to dashboard Cite

We employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington's epigenetic landscape, that is generalizable to every cell differentiation system. MAINCell differentiation is the process through which unspecialized stem cells become more specialized. Because of its importance in development, cellular repair, and organismal homeostasis, the molecular mechanisms of cell differentiation has been the subject of intense scrutiny. Since roughly 50 years ago -along with the promulgation of the central dogma of molecular biology by Francis Crick and the characterization of the lactose operon by François Jacob and Jacques Monod -the existence of a genetic program has become a prevailing explanation for the cell differentiation process. Although the details were originally not defined, at least not formally, such a genetic program purports a constellation of master genes (i.e., transcription factors) that orchestrate the transcription of downstream target genes in a precise spatiotemporal fashion, resulting in long-lasting alterations in the gene expression patterns 1-3 . A notable experimental evidence substantiating this view is the overexpression of myoD inducing a myogenic phenotype in seemingly naive cells 4 . Over the past few decades, the repertoire of such master genes across numerous stem cell systems, such as Nanog, Oct4, Sox2, BATF and MyoD, begin to coalesce 5-7 .Recent advances in single-cell technologies has revealed new aspects of the cell differentiation that are incompatible with the idea of ordered and programmed (i.e., deterministic) gene expression. More specifically, single-cell data paint a stochastic differentiation process that increases cell-to-cell variability of gene expression.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: As Depicted Insupporting

confidence: 89%

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Gao

Gandrillon

Páldi

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Indeed, in mouse ES The underlying transcriptional variability that accompanies capture of the mid-Nanog transition state is reminiscent of other transition events associated with enhanced heterogeneity [2,3,5,7]. Recently, it was suggested that heterogeneity of expression levels occurs primarily in genes which decrease their expression at fate decisions [34], but that this heterogeneity resolves on commitment to a stable, differentiated state, requiring a dynamic means of transcriptional regulation. In line with this view, Ahrends et al [1] have modelled the contribution of transcriptional noise during commitment and progression and adipocyte differentiation and found that while low levels of noise ensure lineage commitment, noise must be limited for differentiation to progress.…”

Section: Discussionmentioning

confidence: 99%

Histone Acetyltransferase KAT2A Stabilises Pluripotency with Control of Transcriptional Heterogeneity

Moris

Edri

Seyres

et al. 2018

Preprint

View full text Add to dashboard Cite

Naomi Moris: (1) Conception and design, (5) collection and assembly of data, (6) data analysis and interpretation, (7) manuscript writing, (8) final approval of manuscript. Shlomit Edri: (5) Collection and assembly of data, (6) data analysis and interpretation, (8) final approval of manuscript. Denis Seyres: (6) Data analysis and interpretation, (8) final approval of manuscript. Rashmi Kulkarni: (6) Data analysis and interpretation, (8) final approval of manuscript. Ana Filipa Domingues: (5) Collection and assembly of data, (8) final approval of manuscript. Tina Balayo: (5) Collection and assembly of data, (8) final approval of manuscript. Mattia Frontini: (6) Supported data analysis and interpretation, (8) final approval of manuscript. Cristina Pina: (1) Conception and design, (6) data analysis and interpretation, (2) financial support, (7) manuscript writing, (8) final approval of manuscript. ABSTRACTCell fate transitions in mammalian stem cell systems have often been associated with transcriptional heterogeneity, however existing data have failed to establish a functional or mechanistic link between the two phenomena. Experiments in unicellular organisms support the notion that transcriptional heterogeneity can be used to facilitate adaptability to environmental changes and have identified conserved chromatin-associated factors that modulate levels of transcriptional noise. Herein, we show destabilisation of pluripotency-associated gene regulatory networks through increased transcriptional heterogeneity of mouse embryonic stem cells in which paradigmatic histone acetyl-transferase, and candidate noise modulator, Kat2a(yeast orthologue Gcn5) has been inhibited. Functionally, network destabilisation associates with reduced pluripotency and accelerated mesendodermal differentiation, with increased probability of transitions into lineage commitment. Thus, we functionally link transcriptional heterogeneity to cell fate transitions through manipulation of the histone acetylation landscape of mouse embryonic stem cells and establish a general paradigm that could be exploited in other normal and malignant stem cell fate transitions.

show abstract

“…Thus, a larger fraction of cells express very high levels of multiple marker genes compared to the bulk population than one would expect for a normal distribution ( Figure S1A ). Importantly, this type of single cell variability, which is characterized by rare and coordinated large deviations in the expression of multiple genes, is conceptually distinct from the classical "noise" models of non-genetic single cell variability using probabilistic models of gene regulation (Antolović et al, 2017;Chen and Larson, 2016;Corrigan et al, 2016;Golding et al, 2005;Raj and van Oudenaarden, 2008;So et al, 2011;Symmons and Raj, 2016;Thattai and van Oudenaarden, 2001) . Specifically, the classical models have largely described the variability that results in relatively normally distributed counts of mRNAs of a given gene per cell.…”

Section: Introductionmentioning

confidence: 99%

Gene networks with transcriptional bursting recapitulate rare transient coordinated expression states in cancer

Schuh

Saint-Antoine

Sanford

et al. 2019

Preprint

View full text Add to dashboard Cite

Non-genetic transcriptional variability at the single-cell level is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of melanoma cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes. Importantly, these rare cells are able to survive drug treatment and develop resistance. How might these extremely rare states arise and disappear within the population? It is unclear whether the canonical stochastic models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified molecular mechanisms. Here we use mathematical modeling to show that a minimal network comprising of transcriptional bursting and interactions between genes can give rise to rare coordinated high states. We next show that although these states occur across networks of different sizes, they depend strongly on three (out of seven) model parameters and require network connectivity to be ≤ 6. Interestingly, we find that while entry into the rare coordinated high state is initiated by a long transcriptional burst that also triggers entry of other genes, the exit from it occurs through the independent inactivation of individual genes. Finally, our model predicts that increased network connectivity can lead to transcriptionally stable states, which we verify using network inference analysis of experimental data. In sum, we demonstrate that established principles of gene regulation are sufficient to describe this new class of rare cell variability and argue for its general existence in other biological contexts.

show abstract

Generation of Single-Cell Transcript Variability by Repression

Cited by 50 publications

References 30 publications

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Histone Acetyltransferase KAT2A Stabilises Pluripotency with Control of Transcriptional Heterogeneity

Gene networks with transcriptional bursting recapitulate rare transient coordinated expression states in cancer

Contact Info

Product

Resources

About