Порівняння методів

	Аналіз метаболоміки часових рядів	Аналіз метаболоміки окремих клітин
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2000s–2010s	2013–2021 (emerging field; major methods established ~2019–2021)
Автор методу	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
Тип	Quantitative longitudinal omics pipeline	Analytical pipeline
Основоположне джерело	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.
Інші назви	longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics	scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics
Пов'язані	6	4
Підсумок	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.