Periodic Reporting for period 2 - METASPACE (Bioinformatics for spatial metabolomics)
Période du rapport: 2017-01-01 au 2018-06-30
The overarching goal of the METASPACE project is to enable untargeted spatial metabolomics for translational research and clinical applications by providing novel bioinformatics tools, and to demonstrate their potential by using several case studies related to personalized health, precision medicine and quality of life in chronic afflictions.
The work in the project is organised by the following objectives: 1) develop novel bioinformatics for spatial metabolomics, 2) develop novel bioinformatics for knowledge-based downstream interpretation, 3) integrate state-of-the-art methods of LC-MS/MS validation into our approach, 4) create an open, accessible, user-friendly online engine for spatial metabolomics, 5) evaluate and demonstrate the online engine, raise awareness and build trust among potential users.
The consortium unites eight partners from five countries: European Molecular Biology Laboratory (international organization) participating as the headquarters EMBL Heidelberg (DE) and European Bioinformatics Institute (UK), Flanders Institute for Biotechnology (BE), Imperial College London (UK), University of California San Diego (USA), University of Rennes I (FR), SCiLS GmbH (DE), and European Research Services GmbH (DE). The partners combine expertise in metabolomics, imaging mass spectrometry, statistics, bioinformatics, and software development.
Complementing our major efforts on metabolite annotation, we have developed a variety of bioinformatics algorithms and tools for imaging MS particularly machine learning methods for FDR-controlled annotation adapted from proteomics and for in silico fragmentation for LC-MS/MS data as well as signal processing and visualization tools for 3D molecular cartography.
We have published 20 publications including publications in high-level journals as Nature Methods, Nature Protocols, PNAS, and field-relevant journals such as Metabolomics and Analytical Chemistry. We have contributed opinions and reviews in Current Opinion in Chemical Biology and Metabolomics. We have published results of an esophageal cancer study used in our test case in one of the leading journal in cancer, Cancer Research.
The project twitter account (http://twitter.com/metaspace2020) is actively used to disseminate news and engage community and has over 390 followers. The project GitHub software repository (https://github.com/metaspace2020) hosts open-source implementations of key algorithms and software with 15 sub-repositories and over 2300 commits from ten contributors.
We have set up and keep increasing the Advisory Board which includes 25 members and serves as a key channel for dissemination of project results to academia, vendors, pharma, and journals. We organized a special session at the conferences OurCon’15 and ASMS’18 and public trainings at OurCon’16, EMSC’18 and Workshop on Imaging Mass Spectrometry’18.
The key achievements: Data for algorithm development was acquired; bioinformatics for metabolite annotation of HR imaging MS data was developed including a novel score for measuring likelihood of metabolites from a database as well as False Discovery Rate estimation approach for estimating the quality of produced annotations and selecting parameters. The scoring algorithm was improved and mapping onto KEGG metabolic pathways and genome-scale reconstructed metabolic networks were developed. The cloud software engine for metabolite annotation was developed (http://metaspace2020.eu) along with other software tools (`ili, BASIS, ChemDistiller). Proof-of-concept studies were performed by analysis of samples from cystic fibrosis, esophageal, and other cancer samples. In analysis of cancer cohorts, METASPACE has facilitated the interpretation of results not only within sample cohorts but also between cohorts. Analysing cystic fibrosis data, METASPACE helped to reveal a patient-specific metabolism of prescribed medications, differential drug penetration and microbial compartmentalisation resulting in metabolic divergence governed by local microbial interactions. These proof-of-principle studies not only helped evaluate the algorithms and software developed in the project but, importantly, successfully demonstrated how algorithms and tools developed in METASPACE can enhance data interpretation in large-scale spatial metabolomics studies.
The METASPACE engine and the knowledge base are already becoming an indispensable tool in academic labs using imaging MS to address the key questions of metabolism, health and disease. We expect integration of the METASPACE engine into drug discovery and testing workflows of top pharmaceutical companies where imaging MS is rapidly gaining adoption. Moreover, we expect closer integration of the METASPACE engine into open-source software as well as imaging MS software of vendors. This will have enable answering the key questions spatial metabolomics and will have profound impact onto our understanding of metabolism in various problems of biology and medicine.