通过单细胞细胞计量技术解密衰老过程。

IF 2.5 4区生物学 Q3 BIOCHEMICAL RESEARCH METHODS

Cytometry Part A Pub Date : 2024-06-07 DOI:10.1002/cyto.a.24852

Lok Ming Tam, Timothy Bushnell

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引用次数: 0

摘要

单细胞细胞计量技术的出现以及单细胞生物学的进步，极大地推动了老年科学领域的发展，增强了我们对老年相关疾病机理的理解。鉴于衰老是多种慢性疾病的主要风险因素，研究单细胞粒度生理结构的动态变化对于阐明生物衰老的分子基础至关重要。我们以衰老的标志为概念框架，回顾了当前的文献，描述了单细胞细胞计量技术的发展及其在探索与衰老相关的分子变化中的关键应用。接下来，我们将从仪器、软件和试剂的发展角度讨论单细胞细胞测量技术的最新进展，强调其在推动未来老龄化研究的突破性发现方面大有可为的关键作用。

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Deciphering the aging process through single-cell cytometric technologies

The advent of single-cell cytometric technologies, in conjunction with advances in single-cell biology, has significantly propelled forward the field of geroscience, enhancing our comprehension of the mechanisms underlying age-related diseases. Given that aging is a primary risk factor for numerous chronic health conditions, investigating the dynamic changes within the physiological landscape at the granularity of single cells is crucial for elucidating the molecular foundations of biological aging. Utilizing hallmarks of aging as a conceptual framework, we review current literature to delineate the progression of single-cell cytometric techniques and their pivotal applications in the exploration of molecular alterations associated with aging. We next discuss recent advancements in single-cell cytometry in terms of the development in instrument, software, and reagents, highlighting its promising and critical role in driving future breakthrough discoveries in aging research.

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来源期刊

Cytometry Part A 生物-生化研究方法

CiteScore

8.10

自引率

13.50%

发文量

183

审稿时长

4-8 weeks

期刊介绍： Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques. The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome: Biomedical Instrumentation Engineering Biophotonics Bioinformatics Cell Biology Computational Biology Data Science Immunology Parasitology Microbiology Neuroscience Cancer Stem Cells Tissue Regeneration.