METASPACE: A community-populated knowledge base of spatial metabolomes in health and disease

Autor: Mark T. Bokhart, Marta Sans, Guanshi Zhang, Konstantin O. Nagornov, Katja Ovchinnikova, Vetter M, Bernhard Spengler, Carsten Hopf, Weaver E, Michael W. Linscheid, Balluff B, Zoltan Takats, Quiason C, Régis Lavigne, Mario Kompauer, James S. McKenzie, Janfelt C, Violante Gd, Shahidi-Latham S, Livia S. Eberlin, Joensen Am, Sarah Aboulmagd, David C. Muddiman, Lachlan Stuart, Dominik Fay, Luca Rappez, Charles Pineau, Prasad M, Benedikt Geier, Shane R. Ellis, Dinaiz Thinagaran, Lopez Cg, Manuel Liebeke, Erin Gemperline, Christopher R. Anderton, Kovalev, Michael Becker, Berin A. Boughton, Theodore Alexandrov, Nigmetzianov R, Dimitri Heintz, Denis A. Sammour, Sergio Triana, Dušan Veličković, Nicole Strittmatter, Swales J, Lingjun Li, Artem Tarasov, Bagger C, Ruhland E, Rigopoulos A, Kumar Sharma, Pánczél J, Richard J. A. Goodwin, Mathieu Gaudin, Andrew Palmer
Rok vydání: 2019
Předmět:
DOI: 10.1101/539478
Popis: Metabolites, lipids, and other small molecules are key constituents of tissues supporting cellular programs in health and disease. Here, we present METASPACE, a community-populated knowledge base of spatial metabolomes from imaging mass spectrometry data. METASPACE is enabled by a high-performance engine for metabolite annotation in a confidence-controlled way that makes results comparable between experiments and laboratories. By sharing their results publicly, engine users continuously populate a knowledge base of annotated spatial metabolomes in tissues currently including over 3000 datasets from human cancer cohorts, whole-body sections of animal models, and various organs. The spatial metabolomes can be visualized, explored and shared using a web app as well as accessed programmatically for large-scale analysis. By using novel computational methods inspired by natural language processing, we illustrate that METASPACE provides molecular coverage beyond the capacity of any individual laboratory and opens avenues towards comprehensive metabolite atlases on the levels of tissues and organs.
Databáze: OpenAIRE