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  Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis

Sagar, S., & Grün, D. (2020). Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis. Annual Review of Biomedical Data Science, 3, 1-22. doi:doi.org/10.1146/annurev-biodatasci-111419-091750.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-3AD5-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-3AD6-8
Genre: Journal Article

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Sagar et al..pdf (Preprint), 956KB
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 Creators:
Sagar , Sagar1, Author
Grün, Dominic1, Author              
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1Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society, ou_2243642              

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Free keywords: cell fate, lineage specification, lineage tree, single-cell RNA-seq,differentiation trajectory, dimensionality reduction, lineage tracing
 Abstract: Cellular differentiation is a common underlying feature of all multicellularorganisms through which naïve cells progressively become fate restrictedand develop into mature cells with specialized functions. A comprehensiveunderstanding of the regulatory mechanisms of cell fate choices during de-velopment, regeneration, homeostasis, and disease is a central goal of mod-ern biology. Ongoing rapid advances in single-cell biology are enabling theexploration of cell fate specification at unprecedented resolution. Here, wereview single-cell RNA sequencing and sequencing of other modalities asmethods to elucidate the molecular underpinnings of lineage specification.We specifically discuss how the computational tools available to reconstructlineage trajectories, quantify cell fate bias, and perform dimensionality re-duction for data visualization are providing new mechanistic insights into theprocess of cell fate decision. Studying cellular differentiation using single-cell genomic tools is paving the way for a detailed understanding of cellularbehavior in health and disease.

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Language(s): eng - English
 Dates: 2020-03-02
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: Peer
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Title: Annual Review of Biomedical Data Science
Source Genre: Journal
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Pages: - Volume / Issue: 3 Sequence Number: - Start / End Page: 1 - 22 Identifier: -