Dataset Information

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming.

ABSTRACT: Induction and reversal of chromatin silencing is critical for successful development, tissue homeostasis, and the derivation of induced pluripotent stem cells (iPSCs). X-Chromosome inactivation (XCI) and reactivation (XCR) in female cells represent chromosome-wide transitions between active and inactive chromatin states. Although XCI has long been studied, providing important insights into gene regulation, the dynamics and mechanisms underlying the reversal of stable chromatin silencing of X-linked genes are much less understood. Here, we use allele-specific transcriptomics to study XCR during mouse iPSC reprogramming in order to elucidate the timing and mechanisms of chromosome-wide reversal of gene silencing. We show that XCR is hierarchical, with subsets of genes reactivating early, late, and very late during reprogramming. Early genes are activated before the onset of late pluripotency genes activation. Early genes are located genomically closer to genes that escape XCI, unlike genes reactivating late. Early genes also show increased pluripotency transcription factor (TF) binding. We also reveal that histone deacetylases (HDACs) restrict XCR in reprogramming intermediates and that the severe hypoacetylation state of the inactive X Chromosome (Xi) persists until late reprogramming stages. Altogether, these results reveal the timing of transcriptional activation of monoallelically repressed genes during iPSC reprogramming, and suggest that allelic activation involves the combined action of chromatin topology, pluripotency TFs, and chromatin regulators. These findings are important for our understanding of gene silencing, maintenance of cell identity, reprogramming, and disease.

SUBMITTER: Janiszewski A

PROVIDER: S-EPMC6771397 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming.

Janiszewski Adrian A Talon Irene I Chappell Joel J Collombet Samuel S Song Juan J De Geest Natalie N To San Kit SK Bervoets Greet G Marin-Bejar Oskar O Provenzano Caterina C Vanheer Lotte L Marine Jean-Christophe JC Rambow Florian F Pasque Vincent V

Genome research 20190912 10

Induction and reversal of chromatin silencing is critical for successful development, tissue homeostasis, and the derivation of induced pluripotent stem cells (iPSCs). X-Chromosome inactivation (XCI) and reactivation (XCR) in female cells represent chromosome-wide transitions between active and inactive chromatin states. Although XCI has long been studied, providing important insights into gene regulation, the dynamics and mechanisms underlying the reversal of stable chromatin silencing of X-lin ...[more]

PMID: 31515287

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Project description:Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and require analysis approaches that temporally map between cell state distributions across time points. Current approaches to approximate the single-cell time series as a dynamical system suffer from too restrictive assumptions about the type of kinetics, or link together pairs of sequential measurements in a discontinuous fashion. We propose Dynamic Distribution Decomposition (DDD), an operator approximation approach to infer a continuous distribution map between time points. On the basis of single-cell snapshot time series data, DDD approximates the continuous time Perron-Frobenius operator by means of a finite set of basis functions. This procedure can be interpreted as a continuous time Markov chain over a continuum of states. By only assuming a memoryless Markov (autonomous) process, the types of dynamics represented are more general than those represented by other common models, e.g., chemical reaction networks, stochastic differential equations. Furthermore, we can a posteriori check whether the autonomy assumptions are valid by calculation of prediction error-which we show gives a measure of autonomy within the studied system. The continuity and autonomy assumptions ensure that the same dynamical system maps between all time points, not arbitrarily changing at each time point. We demonstrate the ability of DDD to reconstruct dynamically important cell states and their transitions both on synthetic data, as well as on mass cytometry time series of iPSC reprogramming of a fibroblast system. We use DDD to find previously identified subpopulations of cells and to visualise differentiation trajectories. Dynamic Distribution Decomposition allows interpretation of high-dimensional snapshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynamics analysis for a variety of biological processes by means of identifying their dynamically important cell states.

Dataset Information

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming.

Publications

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming.

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