通过单细胞测序综合分析解密细胞命运决策

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sagar, Dominic Grün
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引用次数: 0

摘要

细胞分化是所有多细胞生物体的共同基本特征,通过这种分化,幼稚细胞逐渐成为命运受限细胞,并发育成具有特化功能的成熟细胞。全面了解发育、再生、稳态和疾病过程中细胞命运选择的调控机制是现代生物学的核心目标。单细胞生物学的飞速发展使人们能够以前所未有的分辨率探索细胞命运的规范。在这里,我们回顾了单细胞 RNA 测序和其他方式的测序,它们是阐明细胞系规范的分子基础的方法。我们特别讨论了可用于重建细胞系轨迹、量化细胞命运偏倚以及为数据可视化进行维度重构的计算工具如何为细胞命运决定过程提供新的机理见解。利用单细胞基因组工具研究细胞分化,为详细了解细胞在健康和疾病中的行为铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis.

Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding 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 the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality re- duction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single- cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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