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Updated: Mar 19, 2026

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
Published on: September 16, 2025
Comparison of Liquid Chromatography- and Nano-Electrospray Ionization-Mass Spectrometry Approaches for Single-Cell
Abigail Cook1,2, Claire Davison2, Jordan Pascoe3
1Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, U.K.
Abstract:
Live single-cell metabolomics is a rapidly growing area of research, which offers the potential to provide unique insights into cellular function and heterogeneity. Single-cell isolation approaches based on capillary sampling are in principle compatible with either nano-electrospray ionization-mass spectrometry (nano-ESI-MS), where the cell is lysed and sprayed directly into a mass spectrometer, or liquid chromatography-mass spectrometry (LC-MS) for metabolomics analysis. However, there are no data indicating which approach can provide the best performance (metabolite coverage, reproducibility and sensitivity) for single-cell metabolomics. In this work, we have developed and then compared two semitargeted metabolomics methods (direct nano-ESI-MS and LC-MS) for detecting amino acids and other hydrophilic metabolites in single macrophages. Interestingly, our results show that, even when using analytical-flow LC-MS, the coverage of metabolites is superior to the nano-ESI-MS method. We applied both methodologies to single THP-1 macrophages infected with fluorescent Mycobacterium bovis bacillus Calmette-Guérin (BCG), the vaccine strain of Mycobacterium tuberculosis. Infected cells were identified under a microscope and sampled into glass capillaries. Our results show that the LC-MS approach provides a much clearer distinction between infected and control cells than using nano-ESI-MS. LC-MS detected enrichment of several compounds in infected cells, including methionine, cysteine and taurine, highlighting reprogramming of sulfur metabolism during mycobacterial infection. These findings establish a robust analytical framework for spatially resolved single-cell metabolomics and underscore its potential for uncovering infection-driven metabolic heterogeneity, with broad applications in infectious disease research, drug discovery, and clinical diagnostics.
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