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◆ Analytical Chemistry2026-03-18· Metabolomics

Comparison of Liquid Chromatography- and Nano-Electrospray Ionization-Mass Spectrometry Approaches for Single-Cell Metabolomics

Abigail Cook, Claire Davison, Jordan Pascoe, Harpreet Atwal, George Mayson, Ahmed Ali, Dany JV Beste, Melanie J. Bailey

原始摘要(英文原文)· Original abstract
High Resolution Image Download MS PowerPoint Slide 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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