Guided Super Resolution Outputs
<p dir="ltr">Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to obtain sub-cellular-level information....
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2024
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| _version_ | 1852024847315501056 |
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| author | Efe Ozturk (20348733) |
| author_facet | Efe Ozturk (20348733) |
| author_role | author |
| dc.creator.none.fl_str_mv | Efe Ozturk (20348733) |
| dc.date.none.fl_str_mv | 2024-11-28T19:25:24Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.27926907.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Guided_Super_Resolution_Outputs/27926907 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medical biochemistry - amino acids and metabolites Spatial metabolomics Guided Super-Resolution Generative artifical intelligence Deep Learning Mass Spectrometry Imaging |
| dc.title.none.fl_str_mv | Guided Super Resolution Outputs |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p dir="ltr">Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to obtain sub-cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages Imaging Mass Cytometry (IMC) images to enhance the spatial granularity of MSI data. By using the detailed IMC images as guides, we achieve a five-fold increase in resolution, creating super-resolved 112 distinct metabolite maps of 85,762 single cells and 24 cell phenotypes across various colorectal cancer tumor samples. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into tissue heterogeneity and cellular interactions within the tumor microenvironment through high-definition spatial metabolomics in individual cells.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7f3da2190cff5779f5a2e2d71bdfa025 |
| identifier_str_mv | 10.6084/m9.figshare.27926907.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27926907 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Guided Super Resolution OutputsEfe Ozturk (20348733)Medical biochemistry - amino acids and metabolitesSpatial metabolomicsGuided Super-ResolutionGenerative artifical intelligenceDeep LearningMass Spectrometry Imaging<p dir="ltr">Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to obtain sub-cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages Imaging Mass Cytometry (IMC) images to enhance the spatial granularity of MSI data. By using the detailed IMC images as guides, we achieve a five-fold increase in resolution, creating super-resolved 112 distinct metabolite maps of 85,762 single cells and 24 cell phenotypes across various colorectal cancer tumor samples. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into tissue heterogeneity and cellular interactions within the tumor microenvironment through high-definition spatial metabolomics in individual cells.</p>2024-11-28T19:25:24ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.6084/m9.figshare.27926907.v1https://figshare.com/articles/figure/Guided_Super_Resolution_Outputs/27926907CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279269072024-11-28T19:25:24Z |
| spellingShingle | Guided Super Resolution Outputs Efe Ozturk (20348733) Medical biochemistry - amino acids and metabolites Spatial metabolomics Guided Super-Resolution Generative artifical intelligence Deep Learning Mass Spectrometry Imaging |
| status_str | publishedVersion |
| title | Guided Super Resolution Outputs |
| title_full | Guided Super Resolution Outputs |
| title_fullStr | Guided Super Resolution Outputs |
| title_full_unstemmed | Guided Super Resolution Outputs |
| title_short | Guided Super Resolution Outputs |
| title_sort | Guided Super Resolution Outputs |
| topic | Medical biochemistry - amino acids and metabolites Spatial metabolomics Guided Super-Resolution Generative artifical intelligence Deep Learning Mass Spectrometry Imaging |