Advanced Technologies of Single-Cell Metabolomics Unveiling Cellular Metabolic Heterogeneity for Biological and Biomedical Research

Shang-Che Kuo, Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan.
Ting-Wei Tsao, Graduate Program in Translational Agricultural Sciences, National Cheng Kung University and Academia Sinica, Tainan, Taiwan.
I-Fan Wang, Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan
Ying-Lan Chen, Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan.Follow

Keywords

Single-cell metabolomics, mass spectrometry, MALDI, DESI, LC-MS/MS

Abstract

Metabolomics provides direct insights into cellular physiology, yet it faces greater analytical challenges compared to genomics and transcriptomics due to the chemical diversity, instability, and environmental sensitivity of metabolites. Conventional bulk metabolomics averages signals across cell populations, thereby masking cellular heterogeneity critical for understanding disease mechanisms. Recent breakthroughs in single-cell metabolomics (SCM), driven by advances in mass spectrometry, microfluidics, isotope tracing, and spatial omics, have enabled the detection of metabolic diversity at unprecedented resolution. SCM has uncovered cell-type-specific biomarkers, revealed metabolic reprogramming in cancer and immunity and revealed disease progression. These studies highlight SCM’s transformative potential in biomarker discovery, clinical diagnostics, and precision medicine. Despite rapid progress, SCM remains limited by low metabolite abundance, instability during cell handling, lack of standardized quantification methods, and challenges in integrative multi-omics analysis. Future developments will require innovations to improve sensitivity and spatial resolution, establish cross-laboratory quality control frameworks, and apply artificial intelligence[KS1] for data interpretation. With continued technological convergence, SCM is poised to evolve from a niche research tool into a cornerstone platform for biological and biomedical research[KS2] .

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Recommended Citation

Kuo, Shang-Che; Tsao, Ting-Wei; Wang, I-Fan; and Chen, Ying-Lan (2026) "Advanced Technologies of Single-Cell Metabolomics Unveiling Cellular Metabolic Heterogeneity for Biological and Biomedical Research," Journal of Food and Drug Analysis: Vol. 34 : Iss. 1 , Article 5.
Available at: https://doi.org/10.38212/2224-6614.3590