Comparer des méthodes

	Analyse métabolomique en séries temporelles	Analyse du métabolisme à l'échelle de la cellule unique
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s	2013–2021 (emerging field; major methods established ~2019–2021)
Auteur d'origine	Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues	Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups
Type	Quantitative longitudinal omics pipeline	Analytical pipeline
Source fondatrice	Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736.	Rappez, L., Stadler, M., Triana, S., Gathungu, R. M., Ovchinnikova, K., Phapale, P., Heikenwalder, M., & Alexandrov, T. (2021). SpaceM reveals metabolic states of single cells. Nature Methods, 18(7), 799–805.
Alias	longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics	scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics
Apparentées	6	4
Résumé	Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.	Single-cell metabolomics analysis measures the small-molecule metabolite content of individual cells, revealing cell-to-cell metabolic heterogeneity that bulk methods obscure by averaging. Rooted in mass spectrometry and microfluidics advances, it enables researchers to map metabolic states across cell populations, identify rare subpopulations, and link metabolic phenotypes to cellular function — providing a functional complement to transcriptomics and proteomics at single-cell resolution.