Metabolomics usually using tissues, body fluids and cells as samples has been used in various fields of life science including disease research and drug R & D. Traditional cell metabolomics needs millions of cells. However, in some cases, such as for stem cells, circulating tumor cells, primary cells and so on, the number of cells is very rare, and the conventional metabolomics analysis methods exist challenging. Another disadvantage for the population cell analyses is that the individual difference of cells is concealed. Therefore, it is necessary to develop novel analytical techniques for a small number of cells and a single cell [1,2]. Moreover, to achieve representative results, it is important to analyze a large number of single cells under physiological conditions, leading to the requirement for high-throughput single-cell analysis methods [2]. On the other hand, biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue, it is very important to characterize metabolic profile at a single cells level in a spatially resolved mode.
Over the past years, we have concentrated on the development of mass spectrometry (MS)-based single cells analysis techniques including capillary microsampling combined with high-resolution spectral stitching nanoelectrospray ionization direct-infusion MS, laser capture microdissection-sample micromanipulation-MS, inertial microfluidics and pulsed electric field-induced ESI-MS, combined ESI-APCI-MS and related data analysis. In this lecture our recent advancements on the single cells-related metabolomics analysis techniques and their applications in cell heterogeneity studies will be reported [1-3].
Literature:
[1] T. R. Xu, H. Li, D. S. Feng, P. Dou, X. Z. Shi, C. X. Hu, G. W. Xu, Anal. Chem. 2021, 93, 10031−10038. [2] D. S. Feng, H. Li, T. R. Xu, F. J. Zheng, C. X. Hu, X. Z. Shi, G. W. Xu, Anal. Chim. Acta. 2022, 1221, 340116. [3] T. R. Xu, H. Li, P. Dou, Y. Y. Luo, S. M. Pu, H. Mu, Z. H. Zhang, D. S. Feng, X. S. Hu, T. Wang, G. Tan, C. Chen, H. Y. Li, X. Z. Shi, C. X. Hu, G. W. Xu, Adv. Sci. 2024, revised.
