Advances in Single-Cell Metabolomics and Their Applications in Disease Biology

Single-cell metabolomics (SCM) provides a functional view of cellular heterogeneity by measuring metabolic states at cellular or subcellular resolution. Unlike bulk metabolomics, SCM can reveal rare metabolic cell states, spatially restricted metabolic niches, dynamic pathway activity, and treatment-associated metabolic adaptations. This review summarizes major SCM strategies, including spatial mass spectrometry imaging (MSI), isolated single-cell mass spectrometry (MS), isotope-assisted approaches, fluorogenic probes, and vibrational spectroscopy-based methods. Rather than treating these platforms as interchangeable technologies, we organize the review around a question-driven framework linking biological questions to platform selection, data structures, computational workflows, and interpretation boundaries. We also discuss major analytical challenges, including limited sample amount, missing values, ion suppression, batch effects, metabolite annotation uncertainty, and incomplete standardization. Disease-related studies suggest that SCM can identify recurrent metabolic programs, candidate biomarkers, and intervention-relevant metabolic nodes, but most applications remain at the discovery or early translational stage. Future progress will require standardized workflows, improved quantitative confidence, multimodal integration, and validation in clinically relevant models and cohorts.