Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
University of Chinese Academy of Sciences, Beijing, China.
Mass Spectrom Rev. 2023 Jan;42(1):67-94. doi: 10.1002/mas.21704. Epub 2021 May 24.
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
单细胞分析由于能够探究细胞异质性,允许更精细的组织分类并促进新的生物标志物发现,因此引起了研究界越来越多的关注。随着相关仪器和技术的进步,现在可以在单细胞水平上进行多种组学研究,包括基因组学、转录组学、代谢组学甚至蛋白质组学。与其他组学研究相比,单细胞代谢组学(SCM)是一个重大挑战,因为它涉及到许多种类的动态变化的化合物,浓度范围很广。此外,代谢物不能被扩增。尽管困难,但在过去十年中,基于质谱(MS)的 SCM 在处理技术和生化应用方面取得了相当大的进展。在这篇综述中,我们将总结在有前途的 MS 平台、样品制备方法以及各种细胞类型(包括植物细胞、癌细胞、神经元、胚胎细胞和酵母细胞)的 SCM 分析方面的最新进展。还将讨论 SCM 领域的当前限制和未来研究方向。