SMART: An Approach for Accurate Formula Assignment in Spatially Resolved Metabolomics

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Abstract
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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Funding Information
  • National Key Research and Development Program of China (2022YFA1106300)
  • Chinese Academy of Medical Sciences (2021-RC350-008)
  • Natural Science Foundation of Beijing Municipality (Z240014)
  • Fundamental Research Funds for the Central Universities (3332023039)
  • National Natural Science Foundation of China (32271354, 92353302)
  • Chinese Academy of Sciences (CASSHB-QNPD-2023-011)
  • State Key Laboratory Special Fund (2060204)

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