The project has developed bioinformatics for metabolite annotation of imaging MS data, published in a leading methods journal Nature Methods (Palmer et al., 2016). Using modern software development technologies and practices, we have implemented it as a high-performance cloud computing engine, deployed it onto AWS Amazon Cloud, and provided with a web app (
http://metaspace2020.eu(odnośnik otworzy się w nowym oknie)). Through providing the unique capacity in metabolite annotation supported by various dissemination efforts and trainings, we were able to engage the imaging MS community and by July 2018 have received more than 3000 submissions from over 50 labs from across the world accounting to over 100 TB of raw data. The vast majority of the annotation results are publicly shared that represents the so far largest data sharing effort in imaging MS and is actively used with over 15.000 pageviews every month.
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(odnośnik otworzy się w nowym oknie)) is actively used to disseminate news and engage community and has over 390 followers. The project GitHub software repository (
https://github.com/metaspace2020(odnośnik otworzy się w nowym oknie)) 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(odnośnik otworzy się w nowym oknie)) 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.
