ArticleJournal of the American Society for Mass Spectrometry2024
Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web.
Article in Journal of the American Society for Mass Spectrometry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Applications and technical challenges of MALDI mass spectrometry imaging in microbial ecosystems and metabolic network research.Archives of microbiology · 2026Review
- Metabolomics Data Processing Using the Asari Toolkit.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molecular mechanisms and clinical significance of the glioma metabolic-immune axis: a comprehensive review.Frontiers in immunology · 2026Review
- Triple labeling of metabolites for metabolome analysis (TLEMMA): a stable isotope labeling approach for metabolite identification and network reconstruction.The Plant journal : for cell and molecular biology · 2025Article
- Constructing a consensus serum metabolome.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Stable isotope tracing is a crucial technique for understanding the metabolic wiring of biological systems, determining metabolic flux through pathways of interest, and detecting novel metabolites and pathways. Despite the potential insights provided by this technique, its application remains limited to a small number of targeted molecules and pathways. Because previous software tools usually require chemical formulas to find relevant features, and the data are highly complex, especially in untargeted metabolomics and when the downstream reactions and metabolites are poorly characterized. We report here Khipu version 2 and its new user-friendly web application. New functions are added to enhance analyzing stable isotope tracing data including metrics that evaluate peak enrichment in labeled samples, scoring methods to facilitate robust detection of intensity patterns and integrated natural abundance correction. We demonstrate that this approach can be applied to untargeted metabolomics to systematically extract isotope-labeled compounds and annotate the unidentified metabolites.
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