Bandingkan metode

	Analisis Metabolomik Sel Tunggal	Analisis RNA-seq Sel Tunggal
Bidang	Bioinformatika	Bioinformatika
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2013–2021 (emerging field; major methods established ~2019–2021)	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Pencetus	Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Tipe	Analytical pipeline	High-throughput single-cell transcriptomic profiling pipeline
Sumber perintis	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.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
Alias	scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Terkait	4	5
Ringkasan	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.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.