9th Annual International Conference of the Metabolomics Society

Last week we attended the 9th Annual International Conference of the Metabolomics Society at the SECC in Glasgow, Scotland. It was our first time at this meeting so it was a great opportunity to find out first-hand what challenges researchers are facing within metabolomics and preview the latest developments in Progenesis CoMet.

The Clyde Auditorium of the Scottish Exhibition and Conference Centre, with the Cylde Arc in the backgroundSenior Software Developer, Ian Reah, and European Sales Support Specialist, Juliet Evans, on the booth at Metabolomics 2013

Oral presentations

With over 700 delegates, a record for this conference series, the talks were often standing room only. In the case of Parallel Session 1B: Fluxomics people were turned away it was so full, showing what a hot topic this is! And as you would expect from an opening plenary lecture by Professor Douglas Kell, co-author of a paper that used ‘metabolome’ in the literature for the first time, it was a busy start. His opening lecture focussed on the role that metabolomics plays in understanding drug effectiveness and improving drug delivery. This set the scene for a scientific program  that showed the diversity of applications within which metabolomics is making an impact.

The approaches presented during the week also demonstrated how metabolomics is generating data from large-population studies to explain how environmental factors affect an organism phenotype. The same techniques applied to large-scale discovery experiments can also be translated to give a personalised view of your own biochemistry.  This shows the potential for diagnosis, prognosis and effective disease prevention or cure based on measuring your individual metabolism.

One talk I attended showed that metabolomics is starting to address the challenges that other ‘omics disciplines have struggled with. Jean-Charles Martin from INRA (Marseille, France) presented data from a large-scale, multi-site study to measure how reproducible results are for untargeted metabolomics on the same sample using different instruments within different labs. His results suggest that it was possible to find a significant level of overlap between results from different labs using different analytical approaches.


The workshops before the main program gave you a good background, the current state of this field and potential for the future.

Workshop 1B, the COSMOS/MetaboLights/NIH common funds session, was invitation only but reports suggested it is building a solid foundation for metabolomics based on data sharing, common standards and collaborative effort. I saw these principles in other sessions and from talking with people like Dr Laura Reed from the University of Alabama. Dr Reed is setting up a Drosophila Metabolomics Consortium, with an inaugural meeting 12-13th October 2013, to establish a common set of protocols and ensure comparability of results between different research groups who use this model organism for metabolomics research.

The Data Processing and Experimental Design workshop (2A), chaired by Dr Gavin Blackburn from the University of Strathclyde and Dr Karl Burgess from the University of Glasgow, provided a presentation on the main “do’s and don’ts” when setting up a metabolomics experiment. These apply to either LC-MS or GC-MS approaches and include:

  1. Always include an internal standard not normally seen in your experiment, so you can measure extraction efficiency.
  2. Include a QC sample, usually a pool of your samples, to show you how well your system can discriminate groups and technical variation within your system.
  3. Include solvent blanks to measure the background signal that comes from your sample extraction protocol.
  4. Measure samples before and after you affect your system, as well as any spent media for extracted metabolites to build up a picture of how the metabolome looks at each stage in the experiment.
  5. Run the right number of replicates to measure the fold change you want to see within the biological variation of your samples, for example at least three for two-fold changes and 30% CV.
  6. Always do a time course so you can look at a continuum rather than state changes.
  7. Put as much care into shipping metabolomics samples as you would in handling them during the experiment.

There were demonstrations of several tools during the workshop, including MZMatch.R, IDEOM, IMZmine2 and MZMatch-ISO. They provide a very flexible way to process raw files and generate quantified and identified metabolomics results. The open source nature, use of R-scripts and additional features mean they are highly customisable and address a key issue presented, namely having to rely on black box software from the MS instrument vendors. Since I was there to help preview the next release of Progenesis CoMet it was encouraging to see that, compared to these solutions, our software gives you the best of both worlds with simple data processing as well as visualising the data at each step. But don’t take my word for it, I would encourage you to download Progenesis CoMet and see for yourself.


Over 250 posters covered the same diversity of applications, advances in technology and use of model organisms as the oral presentations. There were three posters on display from customers of Progenesis software including:

  • P5-22 Method Development for Peptidome Characterisation. Estelle Pujos-Guillot. The purpose of this study was to develop the analytical tools required for bioactive peptide isolation, characterization and identification by mass spectrometry, in plasma and serum samples. This specific application used Progenesis LC-MS.
  • P7-12 Surviving Starvation: Lipidomic Analysis of Algae Under Nutrient Deprivation. Phillip Whitfield. In this study lipidomic strategies, including multivariate data analysis with Progenesis CoMet, were used to define the lipid response of O. tauri to nutrient deprivation, an important adaptive process a model phytoplankton.
  • P9-38 Novel Software Solutions for Liquid Chromatography-High Resolution MS (LC-HRMS) Metabolite Profiling of Legumes Under Drought and Fungal Infection Conditions. Michael Dickinson. This poster describes work to develop methods that accurately deliver metabolite profiles of biotic and abiotic stressed vs. non stressed legume plants. This included demonstrating the effectiveness of Progenesis CoMet in data processing and interpretation. You can read experimental details on our website within a similar poster shown at ASMS.

If you were unable to meet us at the conference you can download Progenesis LC-MS and download Progenesis CoMet to see how they would support proteomics and metabolomics research.

Previewing Progenesis CoMet v3.0

Having had its first preview at ASMS in June this was our first chance to share it with an exclusive audience of metabolomics researchers. Initial response to the main product features, including MetaScope our search tool, has been encouraging. MetaScope is integrated into the software so you can search your own data and return compound identifications, including chemical structures from SDF databases. The preview we had at the conference demonstrated how MetaScope will allow you to compare the MS/MS information associated with compound ions to a theoretical fragmentation of their putative identifications.

Showing a preview of the ms/ms-aided metabolite identification in Progenesis CoMet v3.0

The next release of Progenesis CoMet will include automation of key analysis steps to increase objectivity and free-up your research time, as well as other features added in response to customer feedback. Contact us to get a preview of the next software release.

A high-level look at some of the screens in Progenesis CoMet v3.0's automated analysis process

A preview of the workflow in Progenesis CoMet v3.0 that, once released, will enable you to automate key analysis steps to increase objectivity and free-up your research time.