Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-03-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/96892 |
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| author | Huiyong Cheng Dawson Miller Nneka Southwell Paola Porcari Joshua L Fischer Isobel Taylor J Michael Salbaum Claudia Kappen Fenghua Hu Cha Yang Kayvan R Keshari Steven S Gross Marilena D'Aurelio Qiuying Chen |
| author_facet | Huiyong Cheng Dawson Miller Nneka Southwell Paola Porcari Joshua L Fischer Isobel Taylor J Michael Salbaum Claudia Kappen Fenghua Hu Cha Yang Kayvan R Keshari Steven S Gross Marilena D'Aurelio Qiuying Chen |
| author_sort | Huiyong Cheng |
| collection | DOAJ |
| description | Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies. |
| format | Article |
| id | doaj-art-cf9c6749dd1b455391cf948fb5bb82cd |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-cf9c6749dd1b455391cf948fb5bb82cd2025-08-20T03:01:50ZengeLife Sciences Publications LtdeLife2050-084X2025-03-011310.7554/eLife.96892Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imagingHuiyong Cheng0Dawson Miller1Nneka Southwell2Paola Porcari3Joshua L Fischer4Isobel Taylor5J Michael Salbaum6Claudia Kappen7Fenghua Hu8https://orcid.org/0000-0002-6447-9992Cha Yang9Kayvan R Keshari10Steven S Gross11Marilena D'Aurelio12Qiuying Chen13https://orcid.org/0000-0001-5909-3959Department of Pharmacology, Weill Cornell Medicine, New York, United StatesDepartment of Pharmacology, Weill Cornell Medicine, New York, United StatesBrain and Mind Research Institute, Weill Cornell Medicine, New York City, United StatesMemorial Sloan Kettering Cancer Center, New York, United StatesBruker Daltonics, Billerica, United StatesDepartment of Pharmacology, Weill Cornell Medicine, New York, United StatesPennington Biomedical Research Center, Louisiana State University, Baton Rouge, United StatesPennington Biomedical Research Center, Louisiana State University, Baton Rouge, United StatesCornell University, Department of Molecular Biology & Genetics, Ithaca, United StatesCornell University, Department of Molecular Biology & Genetics, Ithaca, United StatesMemorial Sloan Kettering Cancer Center, New York, United StatesDepartment of Pharmacology, Weill Cornell Medicine, New York, United StatesBrain and Mind Research Institute, Weill Cornell Medicine, New York City, United StatesDepartment of Pharmacology, Weill Cornell Medicine, New York, United StatesMass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.https://elifesciences.org/articles/96892brainembryoadipose |
| spellingShingle | Huiyong Cheng Dawson Miller Nneka Southwell Paola Porcari Joshua L Fischer Isobel Taylor J Michael Salbaum Claudia Kappen Fenghua Hu Cha Yang Kayvan R Keshari Steven S Gross Marilena D'Aurelio Qiuying Chen Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging eLife brain embryo adipose |
| title | Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| title_full | Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| title_fullStr | Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| title_full_unstemmed | Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| title_short | Untargeted pixel-by-pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| title_sort | untargeted pixel by pixel metabolite ratio imaging as a novel tool for biomedical discovery in mass spectrometry imaging |
| topic | brain embryo adipose |
| url | https://elifesciences.org/articles/96892 |
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