| 论文作者 |
Cao, YH; Li, SX; Liu, ZY; Jia, ZX; Zhuang, WD; Pan, X; Zhou, JY; Yang, LF; Wang, L |
| 摘要 |
Spatially resolved metabolomics plays a critical role in unraveling tissue-specific metabolic complexities. Despite its significance, this profound technology generates thousands of features, yet accurate annotation significantly lags behind that of LC-MS-based approaches. To bridge this gap, we introduce SMART, an open-source platform designed for precise formula assignment in mass spectrometry imaging. SMART constructs a KnownSet database containing 2.8 million formulas linked by ChemEdges derived from repositories such as HMDB, ChEMBL, PubChem, and BioEdges from KEGG biological reactant pairs. Using a multiple linear regression model, SMART extracts formula networks associated with the m/z of interest and scores potential candidates based on several criteria, including linked formulas, database existence score, ChemEdges/BioEdges, and ppm values. Benchmarking against reference data sets demonstrates that SMART achieves prediction accuracy rates of up to 95.8%. Applied to mass spectrometry imaging, SMART successfully annotated 986 formulas in developing mouse embryos. This robust platform enables systematic formula annotation within tissues, enhancing our understanding of metabolic heterogeneity. |
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