Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Schiebinger, Geoffrey; Shu, Jian; Tabaka, Marcin; Cleary, Brian; Subramanian, Vidya; Solomon, Aryeh; Gould, Joshua; Liu, Siyan; Lin, Stacie; Berube, Peter; Lee, Lia; Chen, Jenny; Brumbaugh, Justin; Rigollet, Philippe; Hochedlinger, Konrad; Jaenisch, Rudolf; Regev, Aviv; Lander, Eric S.

doi:10.1016/j.cell.2019.01.006

Cited by 592 publications

(691 citation statements)

References 72 publications

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“…S2A). This contrasted with previous studies that reported reactivation of X-linked genes only much later in reprogramming, after activation of late pluripotency markers and complete Xist silencing (Pasque et al 2014;Schiebinger et al 2019). Other genes were delayed in transcriptional reactivation and could be segregated into different groups: with reactivation kinetics between day 8 and day 10 ("intermediate"), between day 10 and day 13 ("late"), and after day 13 ("very late") ( Fig.…”

Section: Transcriptional Reactivation Of X-linked Genes During Inductcontrasting

confidence: 98%

“…1G). An analysis of single-cell RNA-seq iPSC reprogramming data in a different reprogramming system and genetic background (Schiebinger et al 2019) confirmed the finding that different groups of X-linked genes are activated at different times during pluripotency induction ( Fig. 2C,D; Supplemental Fig.…”

Section: Transcriptional Reactivation Of X-linked Genes During Inductsupporting

confidence: 67%

“…Reprogramming female somatic cells to iPSCs induces chromosome-wide reversal of gene silencing after rXCI. Previous studies on reprogramming to iPSCs have shown that XCR occurs late (Maherali et al 2007), after the silencing of Xist and the activation of pluripotency markers such as NANOG and DPPA4 (Payer et al 2013;Pasque et al 2014;Schiebinger et al 2019). Xist deletion did not affect these kinetics; however, its ectopic expression caused a delay in XCR, suggesting that silencing of Xist is required for XCR (Pasque et al 2014).…”

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confidence: 84%

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Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming

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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.

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Section: Transcriptional Reactivation Of X-linked Genes During Inductcontrasting

confidence: 98%

Section: Transcriptional Reactivation Of X-linked Genes During Inductsupporting

confidence: 67%

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confidence: 84%

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Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming

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“…S17B). Optimal transport is a technique for measuring distances between equally sized samples that has been applied to a wide variety of data analysis tasks, including scRNA-seq (Schiebinger et al 2019).…”

Section: Endothelial Cells and Lymphocytes Show Distinct Aging Trajecmentioning

confidence: 99%

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

et al. 2019

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Aging is a pleiotropic process affecting many aspects of mammalian physiology. Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear. Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types. We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals. 2

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“…These methods may be used to map and interpret novel cell states 463 predicted by mathematical models or similarly as a method to interpret model-predicted 464 changes in cell heterogeneity following a perturbation. 465 Schiebinger et al compute and predict differentiation trajectories in cell development 466 using optimal transport (OT) [35]. This approach considers the optimal transport of 467 cells as a mass flowing along differentiation trajectories, and is conceptually the most 468 similar to our approach.…”

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confidence: 99%

Mathematical modeling with single-cell sequencing data

Cho¹,

Rockne²

2019

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Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two approaches for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; by solving partial differential equations on a graph representing discrete cell state relationships, and by solving the equations on a continuous cell state-space. We demonstrate how to calibrate model parameters from single or multiple time-point single-cell sequencing data, and examine the effects of data processing algorithms on the model calibration and predictions. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell types during the pathogenesis of acute myeloid leukemia. The mathematical modeling framework we present is general and can be applied to study cell state-transitions in any single-cell genome sequencing dataset. Author summaryHere we compare and contrast graph-and continuum-based approaches for constructing mathematical models of cell state-transitions using single-cell RNA-sequencing data. Using two publicly available datasets, we demonstrate how to calibrate mathematical models of hematopoeisis and how to use the models to predict dynamics of acute myeloid leukemia pathogenesis by mathematically perturbing the process of cellular proliferation and differentiation. We apply these modeling approaches to study the effects of perturbing individual or sets of genes in subsets of cells, or by modeling the dynamics of cell state-transitions directly in a reduced dimensional space. We examine the effects of different graph abstraction and trajectory inference algorithms on calibrating the models and the subsequent model predictions. We conclude that both the graph-and continuum-based modeling approaches can be equally well calibrated to data and discuss situations in which one method may be preferable over the other. This work presents a general mathematical modeling framework, applicable to any single-cell sequencing dataset where cell state-transitions are of interest.July 21, 2019 1/32 47 impact of various graph construction and trajectory inference methods on the geometry 48 of the cell state space, and solve the model on these geometries. We then use the model 49 the study the effects of perturbing 1) the graph structure 2) expression of select subsets 50 of genes 3) and cell state transition dynamics by perturbing flow on the graph or by 51 modifying the dynamics in the continuous space. We predict novel dynamics of leukemia 52July 21, 2019 2/32 65 embedding. While different techniques provide different shapes and differentiation 66 sp...

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Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Cited by 592 publications

References 72 publications

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming

Dynamic reversal of random X-Chromosome inactivation during iPSC reprogramming

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

Mathematical modeling with single-cell sequencing data

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