Mao Xiying, Xia Dandan, Xu Miao, Gao Yan, Tong Le, Lu Chen, Li Weiqi, Xie Runmin, Liu Qinghuai, Jiang Dechen, Yuan Songtao
Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, P. R. China.
The State Key Lab of Analytical Chemistry for Life Science, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210093, P. R. China.
Adv Sci (Weinh). 2025 Jan;12(4):e2411276. doi: 10.1002/advs.202411276. Epub 2024 Dec 4.
Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, the single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed at one single cell, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design not only provides sufficient cytoplasm for SCM but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Integrative analysis of scMeT-seq reveals both dynamical and cell state-specific associations between metabolome and transcriptome in the macrophages with defined metabolic perturbations. Moreover, metabolite signatures are mapped to the single-cell trajectory and gene correlation network of macrophage transition, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from a metabolite snapshot to a functional approach.
单细胞水平的代谢研究能够直接定义最接近生理状态或疾病状态的细胞表型。然而，目前利用质谱技术进行的单细胞代谢组（SCM）研究难以全面呈现细胞内的代谢活性，而且代谢与表型关系的预测受到转录组水平和代谢水平潜在不一致性的限制。在此，基于从细胞中进行亚皮升采样以进行初始代谢组分析，随后进行单细胞转录组测序，在单个细胞上开发了单细胞代谢组和转录组同步分析方法（scMeT-seq）。这种设计不仅为SCM提供了充足的细胞质，还很好地保持了细胞活力，以便在同一细胞中进行准确的转录组分析。对scMeT-seq的综合分析揭示了在具有特定代谢扰动的巨噬细胞中，代谢组和转录组之间的动态关联以及细胞状态特异性关联。此外，代谢物特征被映射到巨噬细胞转变的单细胞轨迹和基因相关网络，这使得对代谢组进行无监督的功能解读成为可能。因此，所建立的scMeT-seq应该会通过将代谢组学从代谢物快照转变为功能方法，为代谢研究带来新的视角。
