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Bioinformatics for spatial metabolomics

Periodic Reporting for period 1 - METASPACE (Bioinformatics for spatial metabolomics)

Reporting period: 2015-07-01 to 2016-12-31

Metabolomics is recognized as a crucial scientific domain, promising to advance our understanding of cell biology, physiology, and medicine. Metabolomics complements genomics, transcriptomics, and proteomics by analyzing the final read-out of biochemical processes and by revealing the contributions of non-genetic factors, such as the environment, diet, or microbiome. Spatial metabolomics is a next frontier, where the challenge is to localize hundreds of metabolites directly from biological tissue sections with cellular and sub-cellular spatial resolution. Our project is motivated by recent technological advances in high mass resolution imaging mass spectrometry (HR imaging MS) for imaging metabolites in biological tissue sections. Imaging MS is bringing a great promise to multiple biological and medical applications. However, a major bottleneck is still the lack of bioinformatics methods for high-throughput molecular interpretation of information-rich imaging MS data.
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 organized to reach the overarching goal by completing 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.
In the first 18 months of the project, the project has achieved all planned objectives and beyond, with the key achievements summarized below.

We developed bioinformatics for metabolite annotation of imaging MS data, published in Nature Methods (Palmer et al., 2016). We implemented it as a software engine, deployed it onto Amazon Cloud, and developed a web graphical user interface ( We have engaged the imaging MS community and gathered more than 600 datasets from more than 20 labs that arguably represents the largest data sharing effort in the field of imaging MS. Using cloud technologies, we were able to process and reprocess this massive amount of data from more than 50 TB of raw data.

Already in the first 18 months of the project, we have published 10 publications including publications in high-level journals as Nature Methods, PNAS, and Current Opinion in Chemical Biology and more technical journals such as Metabolomics and Analytical Chemistry. We published in Cancer Research results of an esophageal cancer study used in our test case.

We put efforts into engaging the scientific community and establishing open interfaces for communication.

The project website ( serves as the main frontend of the project containing brief information on the project as well as updated list of events, publications, and news with more than 50 unique visitors every month. The project twitter account ( is actively used to disseminate news and engage community and has more than 150 followers. The project GitHub software repository ( hosts open-source implementations of key algorithms and software with 14 sub-repositories and more than 500 commits from seven contributors in the most active repository sm-engine.

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 imaging MS conference OurCon’15 and a public training at OurCon’16.

Relating these key achievements to the work program, the following tasks were completed in the corresponding work packages. Open data management plan and specifications were formulated. 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. Software for performing MS/MS based annotation and annotation verification was developed. The cloud software engine for metabolite annotation was developed ( The case studies are being pursued with the aim to evaluate the algorithms and software developed in the project. One work package includes the dissemination and community engagement activities, and another includes the management activities.
The novel bioinformatics we developed in the project as well as the capacity for processing hundreds of datasets using the online engine go beyond the state of the art. The increasing need for metabolite annotation in the imaging MS field reinforces a strong potential of the project to create a wide impact of the project onto the field of imaging MS and further in biology and medicine.

In the second half of the project, we will provide necessary yet missing methods and software tools, integrated them with the METASPACE engine, and evaluate them in the test-case studies with the aim to solidify the growing recognition of METASPACE and prepare for a wide acceptance and impact.

We expect that the METASPACE engine will become 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.
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The project idea