期刊
ANALYTICA CHIMICA ACTA
卷 1143, 期 -, 页码 124-134出版社
ELSEVIER
DOI: 10.1016/j.aca.2020.11.020
关键词
Single cell mass spectrometry; Vacuum-based and ambient mass spectrometry; Biological variance vs technical variance; Single cell metabolomics; Univariate and multivariate analysis; Machine learning
资金
- National Institutes of Health [R01GM116116, R21CA204706]
- National Science Foundation [OCE-1634630]
Single cell metabolomics using mass spectrometry techniques explores cellular metabolism, focusing on cell differences at single-cell level, and is expected to have more potential applications in translational and clinical fields with the implementation of advanced data analysis methods.
Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cellto-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future. (C) 2020 Elsevier B.V. All rights reserved.
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