代谢组学
计算生物学
代谢物
质量细胞仪
代谢组
生化工程
化学
计算机科学
生物
生物信息学
表型
生物化学
工程类
基因
作者
P. Minakshi,Mayukh Ghosh,Rajesh Kumar,Harshad Sudhir Patki,Hari Mohan Saini,Koushlesh Ranjan,Basanti Brar,Gaya Prasad
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2019-01-01
卷期号:: 319-353
被引量:10
标识
DOI:10.1016/b978-0-12-814919-5.00015-4
摘要
Abstract Metabolites are arguably the end product of any biological process performed in the “cell” that yields most of the immediate information about the cellular phenotype. Single-cell metabolomics (SCM) provides much penetration to elucidate cellular heterogeneity of metabolite generation under particular conditions at particular time intervals, and SCM has better potential to provide holistic and comprehensive insights for understanding organisms’ responses under different pathophysiological conditions. However, enormous structural diversity, chemical instability due to rapid turnover, and lack of an amplification option render analysis of single-cell metabolites extremely difficult. The major challenge in SCM is to have sensitive instruments and assays that can identify major metabolites in nano-to-attomole range. Several modern methods—such as improved ionization methods, high-resolution mass spectrometry imaging (MSI), mass cytometry, and other sensitive techniques—are leading the way for SCM. This chapter examines basic sampling methods, SCM platforms, and data analysis programs, along with potential applications and the future of SCM technology.
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