AI-assisted mass spectrometry imaging with image segmentation for subcellular metabolomics analysis
Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an art...
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| Published in | Chemical science (Cambridge) Vol. 15; no. 12; pp. 4547 - 4555 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Published |
20.03.2024
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| Online Access | Get full text |
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| Summary: | Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with
in situ
image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4′. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.
A unique artificial intelligence-assisted subcellular mass spectrometry imaging strategy to decrypt the distinct action mechanism of isomeric drugs. |
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| Bibliography: | https://doi.org/10.1039/d4sc00839a signals and tentative assignments corresponding to the peaks in Fig. 2. See DOI z m Electronic supplementary information (ESI) available: Experimental sections, characterization of instrument performance, typical mass spectrum of DOX, metabolites with significant differences in the cytoplasmic region between DOX-cultured and EPI-cultured cells, mass spectra of several typical metabolites, the relational network of key metabolic pathways, and / |
| ISSN: | 2041-6520 2041-6539 |
| DOI: | 10.1039/d4sc00839a |