rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios

Sementé, Lluc; Baquer, Gerard; García‐Altares, María; Correig, X.; Ràfols, Pere

doi:10.1016/j.aca.2021.338669

Cited by 15 publications

(20 citation statements)

References 31 publications

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“…To date, the available metabolite annotation tools for MSI are limited. A few tools are specialized for annotation, including METASPACE, , MassPix, rMSIannotation, rMSIcleanup, and others. , Some comprehensive tools nearly cover the entire workflow for MSI data analysis and include annotation pipeline, such as LipostarMSI and MSiReader . For these tools, the principle of annotation generally uses isotopic patterns of metabolite ions in MS spectra, including isotopic ion intensity ratios and/or spatial localization of isotopic ion images.…”

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confidence: 99%

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An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

et al. 2022

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Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC−MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC−MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

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confidence: 99%

“…To date, the available metabolite annotation tools for MSI are limited. A few tools are specialized for annotation, including METASPACE, 20,21 MassPix, 22 rMSIannotation, 23 rMSIcleanup, 24 and others. 25,26 Some comprehensive tools nearly cover the entire workflow for MSI data analysis and include annotation pipeline, such as LipostarMSI 27 and MSiReader.…”

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confidence: 99%

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

et al. 2022

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“…In untargeted chemical analyses, many inter-chemical correlations are often observed due to non-biological causes. They are useful in annotating peaks in the untargeted dataset with isotope information ( Semente et al, 2021 ), chemical fragments ( J Guo et al, 2021 ), and errors during data processing, such as duplicate peaks. These annotations can be transferred to other untargeted studies with many unidentified peaks, with the logic that pairs of the same compound will show similar inter-chemical correlation irrespective of analysis platform.…”

Section: Discussionmentioning

confidence: 99%

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Barupal

Mahajan

Baygi

et al. 2022

Environment International

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“…In untargeted chemical analyses, many inter-chemical correlations are often observed due to non-biological causes. They are useful in annotating peaks in the untargeted dataset with isotope information 85 , chemical fragments 86 , and errors during data processing, such as duplicate peaks. These annotations can be transferred to other untargeted studies with many unidentified peaks, with the logic that pairs of the same compound will show similar inter-chemical correlation irrespective of analysis platform.…”

Section: Discussionmentioning

confidence: 99%

IDSL.CCDB: a database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Barupal

Mahajan

Baygi

et al. 2022

Preprint

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Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among reported chemicals measured in human specimens. With an increase in the size of these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. While co-regulatory genes databases have been developed, a similar database for metabolites and chemicals have not been developed yet. We have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics - Chemical Correlation Database (IDSL.CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views that are clear, readable and meaningful. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 36 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. IDSL.CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The IDSL.CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

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rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios

Cited by 15 publications

References 31 publications

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

IDSL.CCDB: a database for exploring inter-chemical correlations in metabolomics and exposomics datasets

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