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Dagstuhl Seminar 17491

Computational Metabolomics: Identification, Interpretation, Imaging

( Dec 03 – Dec 08, 2017 )


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Please use the following short url to reference this page: https://www.dagstuhl.de/17491

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Impacts

Motivation

Metabolomics is the study of metabolites (the small molecules involved in metabolism) in living cells, cell populations, organisms or communities. Metabolites are key players in almost all biological processes, play various functional roles providing energy, building blocks, signaling, communication, and defense and serve as clinical biomarkers for detecting medical conditions such as cancer. Small molecule drugs (many of which are derived from metabolites) account for 90 % of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space.

Mass spectrometry is the predominant analytical technique for detecting and identifying metabolites and other small molecules in high–throughput experiments. Huge technological advances in mass spectrometry and experimental workflows during the last decade enabled novel investigations of biological systems on the metabolite level. Research into computational workflows, the simulation of tandem mass spectra, compound identification and molecular networking have helped disentangle the vast amount of information that mass spectrometry provides. Spatial metabolomics on different spatial scales from single cells to organs and organisms has posed data analysis challenges, in particular due to an unprecedented data volume generated that grows quadratically with the increase of spatial resolution.

Other omics fields have benefited greatly from close cooperations between experimental and computational scientists, which is still in its infancy for metabolomics. Many of the methods established in other omics fields, especially proteomics, are not directly transferable to metabolomics due to the striking chemical variety of metabolites. Here, lessons learned in other fields such as pharmaceutical and environmental sciences, forensics, and toxicology are invaluable to extending the window of metabolomics beyond its current state. This seminar aims to foster communication between the computational and experimental scientists to bring metabolomics in line with the other omics fields.

Continued improvements to instruments, resolution, ionization and acquisition techniques mean that metabolomics mass spectrometry experiments can generate massive amounts of data, and the field is evolving into a “big data” science. This is particularly the case for imaging mass spectrometry, where a single dataset can easily be many gigabytes or even terabytes in size. Despite this dramatic increase in data, much of the data analysis in metabolomics is still performed manually and requires expert knowledge as well as the collation of data from a plethora of sources. Novel computational methods are required to exploit spectral and, in the case of imaging, also spatial information from the data, while remaining efficient enough to process tens to hundreds of gigabytes of data.

This Dagstuhl Seminar will build upon the success of the first Computational Metabolomics Dagstuhl Seminar (15492). It will address the core themes and challenges applicable to computational metabolomics, while adding a parallel focus on spatial aspects and imaging mass spectrometry. The key goals are (i) to foster the exchange of ideas between the experimental and computational communities, (ii) to expose the novel computational developments and challenges, and (iii) establish collaborations to address grand and priority challenges by bridging the best available data with the best methods.

The seminar will start with leaders in the field presenting current opportunities and computational challenges in classical metabolomics, imaging and environmental sciences. Main topics will include MS1 and MS/MS analysis, feature building, annotation and un-annotated “dark matter”, structural identification, substructure recognition, computational workflows and services, as well as network-based or integrative analysis. Several breakout sessions throughout the seminar will facilitate discussion of multiple smaller topics and cater to all participants. Potential topic leaders and speakers will be contacted in advance. We look forward to lively discussions and contributions from all participants.

Copyright Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, Emma Schymanski, and Nicola Zamboni

Summary

Metabolomics is the study of metabolites (the small molecules involved in metabolism) in living cells, cell populations, organisms or communities. Metabolites are key players in almost all biological processes, play various functional roles providing energy, building blocks, signaling, communication, and defense and serve as clinical biomarkers for detecting medical conditions such as cancer. Small molecule drugs (many of which are derived from metabolites) account for 90% of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space.

Mass spectrometry is the predominant analytical technique for detecting and identifying metabolites and other small molecules in high--throughput experiments. Huge technological advances in mass spectrometry and experimental workflows during the last decade enabled novel investigations of biological systems on the metabolite level. Research into computational workflows, the simulation of tandem mass spectra, compound identification and molecular networking have helped disentangle the vast amount of information that mass spectrometry provides. Spatial metabolomics on different spatial scales from single cells to organs and organisms has posed data analysis challenges, in particular due to an unprecedented data volume generated that grows quadratically with the increase of spatial resolution.

Continued improvements to instruments, resolution, ionization and acquisition techniques mean that metabolomics mass spectrometry experiments can generate massive amounts of data, and the field is evolving into a "big data" science. This is particularly the case for imaging mass spectrometry, where a single dataset can easily be many gigabytes or even terabytes in size. Despite this dramatic increase in data, much of the data analysis in metabolomics is still performed manually and requires expert knowledge as well as the collation of data from a plethora of sources. Novel computational methods are required to exploit spectral and, in the case of imaging, also spatial information from the data, while remaining efficient enough to process tens to hundreds of gigabytes of data.

Dagstuhl Seminar 17491 on Computational Metabolomics: Identification, Interpretation, Imaging built on the success of the first Computational Metabolomics Dagstuhl Seminar (15492) in 2015. A number of topics overlapped with the 2015 seminar, while the focus on imaging introduced new perspectives, participants and topics. In contrast to the first seminar, 17491 was a large seminar, with 45 very active participants and a large portion of young scientists. From the first hours of the seminar, effort was made to integrate these young scientists in the discussions and presentations and this paid off leading to lively discussions involving all participants. Many participants were new to Dagstuhl and the concept of Dagstuhl seminars, which led to a seminar that was a combination of being semi-structured and spontaneous. Very positive feedback was received from all during a comprehensive feedback session before lunch on Friday, including constructive ideas for a new focus for a possible new seminar in 2019.

On the scientific side, the seminar covered numerous topics which were found to be most relevant for the computational analysis of mass spectrometry data, and ranged from the "dark matter in metabolomics" to "integrating spatial and conventional metabolomics"; see the full report for a comprehensive description.

The seminar has fully achieved its key goals: to foster the exchange of ideas between the experimental and computational communities; to expose the novel computational developments and challenges; and, to establish collaborations to address grand and priority challenges by bridging the best available data with the best methods.

Copyright Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski

Participants
  • Hayley Abbiss (Murdoch University, AU)
  • Theodore Alexandrov (EMBL Heidelberg, DE) [dblp]
  • Manor Askenazi (Biomedical Hosting - Arlington, US) [dblp]
  • Eric Bach (Aalto University, FI) [dblp]
  • Ruth Birner-Grünberger (Universität Graz, AT)
  • Sebastian Böcker (Universität Jena, DE) [dblp]
  • Corey Broeckling (Colorado State University - Fort Collins, US)
  • Christoph Büschl (Universität für Bodenkultur - Wien, AT) [dblp]
  • Ricardo Da Silva (UC - San Diego, US)
  • Pieter Dorrestein (UC - San Diego, US) [dblp]
  • Edward M. Driggers (General Metabolics - Wilchester, US) [dblp]
  • Kai Dührkop (Universität Jena, DE) [dblp]
  • Madeleine Ernst (UC - San Diego, US)
  • P. Lee Ferguson (Duke University - Durham, US)
  • Nils Hoffmann (ISAS - Dortmund, DE) [dblp]
  • Julijana Ivanisevic (University of Lausanne, CH) [dblp]
  • Fabien Jourdan (INRA-ENVT - Toulouse, FR) [dblp]
  • Alexander Kerner (Lablicate GmbH - Hamburg, DE)
  • Oliver Kohlbacher (Universität Tübingen, DE) [dblp]
  • Martin Krauss (UFZ - Leipzig, DE) [dblp]
  • Martin Loos (looscomputing - Dübendorf, CH) [dblp]
  • Marcus Ludwig (Universität Jena, DE) [dblp]
  • Marnix Medema (Wageningen University, NL) [dblp]
  • Kris Morreel (Ghent University, BE)
  • Rolf Müller (Helmholtz-Institut - Saarbrücken, DE) [dblp]
  • Steffen Neumann (IPB - Halle, DE) [dblp]
  • Louis-Felix Nothias (UC - San Diego, US)
  • Andrew Palmer (EMBL - Heidelberg, DE) [dblp]
  • Alan Race (Universität Bayreuth, DE)
  • Stacey N. Reinke (Murdoch University, AU)
  • Simon Rogers (University of Glasgow, GB) [dblp]
  • Juho Rousu (Aalto University, FI) [dblp]
  • Sarah Scharfenberg (IPB - Halle, DE)
  • Jennifer Schollee (Eawag - Dübendorf, CH)
  • Emma Schymanski (University of Luxembourg, LU) [dblp]
  • Jan Stanstrup (University of Copenhagen, DK)
  • Michael Andrej Stravs (Eawag - Dübendorf, CH)
  • Raf Van de Plas (TU Delft, NL) [dblp]
  • Justin van der Hooft (Wageningen University, NL) [dblp]
  • Kirill Veselkov (Imperial College London, GB)
  • Ron Wehrens (Wageningen University, NL) [dblp]
  • David Wishart (University of Alberta - Edmonton, CA) [dblp]
  • Michael Anton Witting (Helmholtz Zentrum - München, DE)
  • Nicola Zamboni (ETH Zürich, CH) [dblp]

Related Seminars
  • Dagstuhl Seminar 15492: Computational Metabolomics (2015-11-29 - 2015-12-04) (Details)
  • Dagstuhl Seminar 20051: Computational Metabolomics: From Cheminformatics to Machine Learning (2020-01-26 - 2020-01-31) (Details)
  • Dagstuhl Seminar 22181: Computational Metabolomics: From Spectra to Knowledge (2022-05-01 - 2022-05-06) (Details)

Classification
  • bioinformatics

Keywords
  • computational metabolomics
  • computational mass spectrometry
  • imaging mass spectrometry
  • bioinformatics
  • chemoinformatics