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Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on...

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Autores principales: Zhou, Peijie, Wang, Shuxiong, Li, Tiejun, Nie, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460805/
https://www.ncbi.nlm.nih.gov/pubmed/34556644
http://dx.doi.org/10.1038/s41467-021-25548-w
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author Zhou, Peijie
Wang, Shuxiong
Li, Tiejun
Nie, Qing
author_facet Zhou, Peijie
Wang, Shuxiong
Li, Tiejun
Nie, Qing
author_sort Zhou, Peijie
collection PubMed
description Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.
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spelling pubmed-84608052021-10-22 Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics Zhou, Peijie Wang, Shuxiong Li, Tiejun Nie, Qing Nat Commun Article Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution. Nature Publishing Group UK 2021-09-23 /pmc/articles/PMC8460805/ /pubmed/34556644 http://dx.doi.org/10.1038/s41467-021-25548-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhou, Peijie
Wang, Shuxiong
Li, Tiejun
Nie, Qing
Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title_full Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title_fullStr Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title_full_unstemmed Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title_short Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
title_sort dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460805/
https://www.ncbi.nlm.nih.gov/pubmed/34556644
http://dx.doi.org/10.1038/s41467-021-25548-w
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