在单细胞水平上解码衰老特征。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Shuai Ma, Xu Chi, Yusheng Cai, Zhejun Ji, Si Wang, Jie Ren, Guang-Hui Liu
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引用次数: 8

摘要

机体衰老在组织、器官和系统的不同细胞类型中表现出广泛的特征。单细胞技术的进步和丰富数据集的产生为科学界提供了以前所未有的范围和分辨率解码这些衰老特征的机会。在这篇综述中,我们描述了技术进步和生物信息学方法,使数据解释在细胞水平。然后,我们概述了这些技术在解码衰老标志和潜在干预目标方面的应用,并总结了全身代表性器官系统的共同主题和特定环境的分子特征。最后,我们简要总结了与老龄化研究相关的现有数据库,并对这一新兴领域的机会进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoding Aging Hallmarks at the Single-Cell Level.

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

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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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