Linganisha mbinu

	Uchambuzi wa Metabolomiki ya Seli Moja	Uchanganuzi wa Metabolomics wa Multi-Omics
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2013–2021 (emerging field; major methods established ~2019–2021)	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)
Mwanzilishi	Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups	Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)
Aina	Analytical pipeline	Integrative computational pipeline
Chanzo asilia	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.	Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.
Majina mbadala	scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics
Zinazohusiana	4	5
Muhtasari	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.	Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone.