পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সময়-সিরিজ মেটাবোলোমিক্স বিশ্লেষণ× | একক-কোষ মেটাবোলোমিক্স বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র | জৈব তথ্যবিজ্ঞান | জৈব তথ্যবিজ্ঞান |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 2000s–2010s | 2013–2021 (emerging field; major methods established ~2019–2021) |
| প্রবর্তক≠ | 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 |
| ধরন≠ | Quantitative longitudinal omics pipeline | Analytical pipeline |
| মৌলিক উৎস≠ | 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. link ↗ | 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. link ↗ |
| অপর নাম | longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics | scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics |
| সম্পর্কিত≠ | 6 | 4 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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