Issue 25 - September 2013


Online version of this newsletter:

Welcome to the twenty-fifth issue of MetaboNews, a monthly newsletter for the worldwide metabolomics community. In this issue,
we feature a Software Spotlight article on MUSCLE, a multi-platform, user-friendly software tool for the robust, objective, and automated optimisation of targeted LC-MS/MS analyses. In May 2012, we introduced a new section called MetaboInterviews that features interviews with metabolomics experts from around the world. This issue includes an interview with Tim Ebbels of Imperial College London (UK). This newsletter is produced by The Metabolomics Innovation Centre (TMIC,, and is intended to keep metabolomics researchers and other professionals informed about new technologies, software, databases, events, job postings, conferences, training opportunities, interviews, publications, awards, and other newsworthy items concerning metabolomics. We hope to provide enough useful content to keep you interested and informed and appreciate your feedback on how we can make this newsletter better (

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Current and back issues of this newsletter can be viewed from the newsletter archive (


1) Software Spotlight


MUSCLE—Multi-Platform Unbiased Optimisation of Spectrometry via Closed Loop Experimentation

Feature article contributed by Mark Viant1, Shan He1, Warwick Dunn1, Gregory Genta-Jouve1, James Bradbury1, Joshua Knowles2, Roy Goodacre2

1. University of Birmingham, Birmingham, UK and 2. University of Manchester, Manchester, UK

Liquid chromatography mass spectrometry (LC-MS) is an extremely widely used tool in analytical laboratories for selectively measuring a broad range of chemicals. It is also the primary technology in metabolomics. However the development of new LC-MS methods, or even the transfer of an existing method between instruments and laboratories, is both time consuming and challenging and places a considerable burden on the analyst. This bottleneck significantly impedes our ability to establish new bioanalytical methods, e.g., for the targeted analysis of a particular metabolite class or metabolic pathway. The primary reason why optimising an LC-MS method is so challenging stems from the large number of instrument control settings. Varying all of the LC and MS parameters systematically to optimise an analysis is simply impossible because of the astronomical number of combinations that are possible.

Previously we developed a closed-loop strategy for the automated optimisation of metabolite analyses on three platforms: LECO GC-ToF-MS and GCxGC-ToF-MS, and Waters LC-ToF-MS [1-3]. Not only was this procedure fully automated, but it greatly improved the analytical method by detecting three times as many metabolites, and it only took a few days of automated optimisation to achieve this result. However, since it was programmed to control only three specific mass spectrometers, scientists have not been able to deploy this automated optimisation software in their own research laboratories. Also, because of how we wrote our original software, reprogramming it for each additional mass spectrometer would be challenging and time consuming.

With funding from the UK Biotechnology and Biological Sciences Research Council, we have developed and validated a multi-platform, user-friendly software tool, MUSCLE, for the robust, objective and automated optimisation of targeted LC-MS/MS analyses (Figure 1) [4].

Click on the thumbnail below to view a larger version of the image

MUSCLE screenshots

Figure 1. MUSCLE screenshots: Top Left: Experiment setup, Bottom Left: Application Control Script (ACS) setup, Top Right: Output of an optimisation, Bottom Right: Chromatogram with peak detection, Middle: Homepage.

MUSCLE can be used to optimise methods on a wide array of LC-MS/MS platforms in a fully automated manner. Our approach is based on utilising the power of genetic algorithms and closed-loop optimisation to discover optimal solutions of LC and MS parameters, readily enabling analysts with no programming skills to optimise their targeted LC-MS/MS analyses. With user-defined LC and MS parameter selections, as well as user-defined criteria for spectral quality, MUSCLE can increase the speed and quality of one's method development regardless of the instrument manufacturer. Application of this software on a Thermo Scientific TSQ Vantage and Waters Xevo™ TQ, for the analysis of a steroid mixture, decreased the run time and increased the sensitivity of analysis as indicated below (Figure 2).

Optimisation results

Figure 2. Optimisation results: Following an optimisation study that comprised of 170 LC-MS/MS runs, MUSCLE had reduced the run time of the LC method by 20%, while simultaneously increasing the total peak area by 15%. This was all achieved in 28 hours of fully automated optimisation. We estimate that to achieve comparable method optimisation manually would take an analyst 5 full days of manual work.

Could this be of benefit to you?

There are direct and obvious benefits of MUSCLE to scientists who employ LC-MS/MS. Novel analytical method development, the re-implementation of published methods to a new laboratory, and the transfer of methods across instruments within the same laboratory will become easier, more rapid, and less expensive. In turn this could improve the quality of analysis, and will facilitate the analyst to attempt method development and optimisation that they could not previously consider.

Where can I get MUSCLE?

The beta release of MUSCLE will be available in September 2013 for download at, where you can also find training documentation and videos. We are now seeking testers across academia, industry, and government laboratories.

If you are interested in beta testing or have any inquiries, please contact To be kept up to date with all the latest MUSCLE news, please register your interest directly at

The developers

MUSCLE was developed by a team of computer scientists, analytical chemists, and biochemists from the Universities of Birmingham and Manchester in the UK, including Professor Mark Viant and Drs. Shan He, Warwick Dunn, Gregory Genta-Jouve and Mr. James Bradbury from Birmingham, and Dr. Joshua Knowles and Professor Roy Goodacre from Manchester.

[1] S O'Hagan, WB Dunn, M Brown, JD Knowles, DB Kell, Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem. 77: 290-303 (2005). [PMID: 15623308]
[2] S O'Hagan, WB Dunn, JD Knowles, D Broadhurst, R Williams, JJ Ashworth, M Cameron, DB Kell, Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics. Anal. Chem. 79: 464-476 (2007). [PMID: 17222009]
[3] E Zelena, WB Dunn, D Broadhurst, S Francis-McIntyre, KM Carroll, P Begley, S O'Hagan, JD Knowles, A Halsall, ID Wilson, DB Kell, Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal. Chem. 81: 1357-1364 (2009). [PMID: 19170513]
[4] J Bradbury, G Genta-Jouve, WB Dunn, S O’Hagan, R Goodacre, JD Knowles, MR Viant, MUSCLE: A novel multi-platform, user-friendly software tool for the robust, objective and automated optimisation of targeted LC-MS analyses. 9th Annual International Conference of the Metabolomics Society, 1-4 July 2013, Glasgow, UK.

Please note: If you know of any metabolomics research programs, software, databases, statistical methods, meetings, workshops, or training sessions that we should feature in future issues of this newsletter, please email Ian Forsythe at


2) MetaboInterviews

MetaboInterviews features interviews with prominent researchers in the field of metabolomics. The aim of these interviews is to shed light on metabolomics researchers around the world and give them an opportunity to share their metabolomics story. In this issue, we feature an interview with Tim Ebbels.

Tim Ebbels

Reader in Computational Bioinformatics, Imperial College London, London, UK, and Metabolomics Society Board Member

 Tim Ebbels


Tim Ebbels obtained his PhD in astrophysics from the University of Cambridge and in 1998 moved into bioinformatics via postdoctoral work at Imperial College in the metabolic profiling group of Prof Jeremy Nicholson. He was a key post-doctoral member of the Consortium for Metabonomic Toxicology (COMET), a large academic-industry collaboration which developed expert systems for predicting adverse effects in pre-clinical toxicity studies via metabolic profiling. In 2003 he joined Prof David Jones’ group at University College London to work on modelling and visualisation of transcriptomic data. In 2005 he returned to a faculty position at Imperial, within one of the world’s largest metabolic spectroscopy departments. His group focuses on the application of bioinformatic, machine learning and chemometric techniques to post-genomic data, with a particular emphasis on metabolic profiles. He is involved in projects ranging from environmental monitoring, through molecular epidemiology, to in vitro toxicogenomics. Much work focuses on detailed modelling of the analytical technologies used to obtain metabolic profiles, but his group is also addressing problems of data integration, visualisation and time series analysis.

Metabolomics Interview (MN, MetaboNews; TE, Tim Ebbels)

MN: How did you get involved in metabolomics?

TE: At the end of my PhD in astrophysics I decided to get out into the ‘real world’ and took a job with a technical consultancy company. After a few months I realized it wasn’t for me and looked for a way back into academia. I spotted an advert for a post-doc applying Bayesian statistical methods to the analysis of metabolic NMR spectra. Since I had been working with statistical methods to analyze spectra during my PhD I thought this sounded interesting and applied. The post-doc was with Jeremy Nicholson and John Lindon at Imperial College London and I found that my astrophysics training was surprisingly transferable to the area of complex mixture spectroscopy. (In fact optical spectra of remote galaxies are not so different from NMR spectra of urine!) Gradually I realized I had found a niche, applying statistical and machine learning approaches to spectroscopic data in a burgeoning new field. I stayed with the group to work on the Consortium for Metabonomic Toxicology (COMET), a partnership between academia and pharmaceuticals industry set up to apply metabolic profiling on a large scale to preclinical toxicology. We built an ‘expert system’ for predicting the toxicity profile of new drugs based on comparison to a database of ~30,000 NMR urine spectra. This showed that metabolic profiling was possible on a large scale and that chemometric and multivariate statistical methods were key to achieving results.

COMET expert system

Figure 1. COMET expert system.

MN: What are some of the most exciting aspects of your work in metabolomics?

TE: One of the best things about my position is the diversity of people, disciplines and projects I can get involved with. For example, I’m currently involved in one project looking at earthworms as sensors of environmental pollution, another developing in vitro models for toxicity prediction and a further one looking for biomarkers of early stage atherosclerosis. From the statistical point of view, these pose a wide variety of different challenges, from modeling raw analytical data to high level systems biological modeling. I feel very privileged to be embedded within a large group actively involved in metabolomic research in a wide range of areas, and generating vast amounts of data every day. For a bioinformatician, that is an ideal position!

MN: What key metabolomics initiatives are you pursuing at your research centre or institute?

TE: Extracting individual metabolite measurements from complex NMR and mass spectra is one of the key challenges in metabolomics. We have recently developed the BATMAN package, a free and open source R-based tool which applies Bayesian modeling to assign, deconvolve and quantitate metabolites within 1-dimensional NMR spectra. We’ve recently added new features such as an improved ability to model shifting peaks and are keen for new users to give it a try.


Figure 2. BATMAN logo.

Another idea I’m excited about is the concept of differential metabolic correlation networks. In collaboration with Maria De Iorio at University College London, we have developed methods to model how correlations between metabolite levels change in relation to other variables, such as disease status or genotype. We think such changes offer a new level of description of the metabolic phenotype which hasn’t yet been fully exploited.

Valcarcel, B., et al., A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes. PLoS One, 2011. 6(9): p. e24702

        correlation networks

Figure 3. Differential correlation networks

In the context of the wider group at Imperial there are large changes afoot. Many people will have heard of the new MRC-NIHR Phenome Centre, launched on 5th June 2013 as a London Olympics legacy. The centre will deliver a high throughput metabolic phenotyping service, primarily for the UK translational medicine community and expects to profile up to 100,000 samples per year. In addition to this there are also major initiatives such as the Imperial Clinical Phenotyping centre which brings advanced NMR and mass spectrometry instruments directly into a large London teaching hospital (St. Mary’s), enabling clinical decisions to be informed by real-time metabolic profile information, available in minutes or even seconds. This has the potential to transform areas such as cancer treatment and surgery with projects like the intelligent knife, in which smoke from tissue vaporized by surgical instruments is sent to a mass spectrometer giving a readout which can tell the surgeon whether they are cutting normal or diseased tissue.

MN: What is happening in your country in terms of metabolomics?

TE: Metabolomics is becoming more ubiquitous. Many UK universities now have one or more groups using metabolic profiling technologies in a wide variety of areas. However, it is not always easy to find out who is doing what, and the number of groups actively developing new metabolomic methods and technologies is still relatively small. In my area—data analysis for metabolomics—the number of researchers is even smaller. This is where national and international meetings—such as those sponsored by the Metabolomics Society—can really help and it is likely that there will be more of these conferences in the future.

MN: How do you see your work in metabolomics being applied today or in the future?

TE: In my group, we are primarily concerned with developing enabling technologies—statistical, chemometric and machine learning approaches which can help the whole metabolomics community get more information from their data. We are developing tools at many levels, from processing of raw data to modeling the biological systems being studied. As well as the BATMAN tool for NMR mentioned above, we have developed a new statistical framework for modeling LCMS data (Ipsen, A. and T.M. Ebbels, Prospects for a statistical theory of LC/TOFMS data. Journal of the American Society for Mass Spectrometry, 2012. 23(5): p. 779-91) which takes account of the physical design of the instrument.

Detecting coeluting parent
            fragment pairs in LCMS using a Poisson model

Figure 4. Detecting coeluting parent fragment pairs in LCMS using a Poisson model.

We have also looked at how statistical combination of data from multiple analytical methods can relieve the problems of metabolite identification. We’re interested in modeling the processed data, for example we have developed new classification approaches and methods for short highly multivariate time series such as are widely found in metabolomics studies (Berk, M., T. Ebbels, and G. Montana, A statistical framework for biomarker discovery in metabolomic time course data. Bioinformatics, 2011. 27(14): p. 1979-1985). Finally, we are also working on tools which aid biological interpretation of metabolomics experiments, for example, by using pathways or integrating metabolomics and other omics data to discover new connections.

Time series analysis using a
            smoothing splines mixed effects (SME) model
Figure 5. Time series analysis using a smoothing splines mixed effects (SME) model.

MN: As you see it, what are metabolomics' greatest strengths?

TE: Metabolomics has the unique power to monitor both the internal state of an organism and also its response to a vast array of internal and external influences, such as the influence of genotype or diet. The untargeted philosophy—where there is no preselection of the metabolites which might be important—invariably results in the discovery of effects which could not have been predicted beforehand. Another strength is the interdisciplinary nature of the field. Many advances in science have been brought about by people from different fields talking to each other, and the necessity of biologists, data analysts and chemists working together in metabolomics, makes cross fertilization of ideas such as this highly likely.

MN: What do you see as the greatest barriers for metabolomics?

TE: The great strengths mentioned above also pose great challenges. The sensitivity of metabolic profiles to a wide variety of effects means that experiments need to be controlled very carefully. The problem becomes ever more important as sample sizes grow and the power to detect confounding effects increases. This issue is particularly relevant to large molecular epidemiology studies which are becoming very popular. Along with good study design and sensitive, specific analytical instruments, we need innovative data analytical methods to help filter out or compensate for these extraneous signals.

The other big challenge is metabolite identification. There are two aspects: assignment of known signals and de novo identification. A large proportion of the signals recorded in any metabolomics experiment are unassigned and this is still the case even with many targeted approaches. However many of these signals may have been seen before and assigned—they are knowns. The problem is how to match signals and assign them in an automated way across thousands of peaks from hundreds of samples. It’s a very big data analysis challenge and of course depends crucially on the analytical technology used. De novo identification—assignment of something which has never been reported before—is much harder. Statistical correlation approaches such as STOCSY and CAMERA can definitely help, but the full gamut of analytical chemistry tools will usually be necessary to fully assign the unknown.

Example of STOCSY from 1H NMR
                signals of hippuric acid
Figure 6. Example of STOCSY from 1H NMR signals of hippuric acid.

MN: What improvements, technological or otherwise, need to take place for metabolomics to really take off?

TE: As a data analyst, I think the most needed development is the widespread public release of metabolomics data. This is very common in other areas of omics science (e.g., gene expression) and makes the whole field accessible to those who are not linked directly to a data generating group. Of course there are many issues to be addressed before this can become widespread, such as the existence of community accepted standards—both for analytical and meta data—and international data repositories. These are gradually becoming a reality with projects like MetaboLights and COSMOS, both of which I’m very happy to be involved with, and there are new initiatives elsewhere in the world which will help.

MN: How does the future look in terms of funding for metabolomics?

TE: I’m quite optimistic about funding in this area. Europe has traditionally led the way in metabolomics and there is still a good level of funding, particularly from the EU, for projects involving metabolomics. The UK research councils have all funded metabolomics facilities which is encouraging the use of the technology in new disciplines. However, we clearly see a new impetus in the US with the NIH Common Fund initiative, and also an upsurge in other communities such as Korea and Japan. It’s hard to predict what will happen in the next few years, but my hope is that, as the world economy gradually pulls away from the global recession, we will see further improvement in the funding climate.

MN: What role can metabolomics standards play?

TE: They are crucial. It’s a fairly dry topic for many people (including myself) but as mentioned above, the impact and reach of the field could be so much broader if others could easily download, analyze and interpret our data. The important thing is to get agreement within the community, especially from journals, that standardized, public release of data is the default. Of course, nobody is suggesting that one gives away hard won data as soon as it is acquired. There is room for embargoes and delayed release, but the principal of open science and public release remains. The other important thing is to have tools and repositories that actually implement these standards so that they can be easily adopted by those generating and using the data. We are going in the right direction, but there is some way to go yet.

MN: Do you have any other comments that you wish to share about metabolomics?

TE: My final comment is advice to new researchers coming in to the field, especially on the data analysis side. I would say the most important skill is to be able to work with people with different backgrounds and training. You must learn to speak their scientific language and see the world from their scientific viewpoint. This means, for example, statisticians understanding some chemistry and biologists being able to get their message across to computer scientists. Only by getting on the same wavelength can we move forward in such an interdisciplinary science as metabolomics.

Biomarker Beacon

3) Biomarker Beacon

Feature article contributed by Ian Forsythe, Editor, MetaboNews, Department of Computing Science, University of Alberta, Edmonton, Canada

Metabolomics is an emerging field that is complementary to other omics sciences and that is gaining increasing interest across all disciplines. Because of metabolomics' unique advantages, it is now being applied in functional genomics, integrative and systems biology, pharmacogenomics, and biomarker discovery for drug development and therapy monitoring. A substantial number of biomarkers are small molecules or metabolites (MW <1500 Da), which can be used for disease testing, drug testing, toxic exposure testing, and food consumption tracking. While standard clinical assays are limited in the number and type of compounds that can be detected, metabolomics measures many more compounds. Since a single compound is not always the best biomarker (diagnostic, prognostic, or predictive), healthcare practitioners can use metabolomic information about multiple compounds to make better medical decisions. Global metabolic profiling is now being used to determine clinical biomarkers in assessing the pathophysiological health status of patients.

In the following two recent studies, metabolomic approaches were used to develop tools for the identification of biomarkers associated with Parkinson's disease and chronic heart failure, respectively.
  1. Lewitt PA, Li J, Lu M, Beach TG, Adler CH, Guo L; the Arizona Parkinson's Disease Consortium. 3-hydroxykynurenine and other Parkinson's disease biomarkers discovered by metabolomic analysis. Mov Disord. 2013 Jul 19. doi: 10.1002/mds.25555. [Epub ahead of print] [PMID: 23873789]

    Reliable Parkinson's disease (PD) biomarkers are needed to improve the diagnosis and treatment of PD. In this paper, the researchers employed a metabolomics-based approach to identify PD-specific biomarkers. Using ultra-high-performance liquid and gas chromatography linked to mass spectrometry, the investigators quantitatively profiled cerebrospinal fluid (CSF) metabolites from 48 PD subjects (<4 hours post-mortem) and 57 similarly-aged control subjects. They discovered 19 biochemicals, four of which were N-acetylated amino acids, that allowed them to differentiate between PD and control subjects. In the PD samples, the researchers found that the concentration of oxidized glutathione was reduced on average by 40% and 3-hydroxykynurenine was increased by one-third. This study presents a promising approach for distinguishing between PD and healthy patients.

  2. Wang J, Li Z, Chen J, Zhao H, Luo L, Chen C, Xu X, Zhang W, Gao K, Li B, Zhang J, Wang W. Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure. Mol Biosyst. 2013 Aug 20. [Epub ahead of print] [PMID: 23959290]

    For patients that suffer from chronic heart failure (CHF), the heart fails to pump enough blood to meet the metabolic needs of the rest of the body. Many researchers are interested in identifying biomarkers that can be used to diagnose CHF. In this study, the research team sought to utilize a metabolomics-based approach to differentiate between CHF patients and healthy patients. They analyzed plasma metabolites from 39 CHF patients and 15 control subjects using proton NMR spectroscopy and orthogonal partial least square discriminant analysis (OPLS-DA). In CHF patients, the investigators discovered several biochemical perturbations including hyperlipidemia, altered energy metabolism, and changes to other potential CHF-related biological mechanisms. This study demonstrates the effective use of
    a metabolomics-based, NMR approach to the identification of diagnostic plasma markers.
Metabolomics Current

4) Metabolomics Current Contents

Recently published papers in metabolomics:

5) MetaboNews

5 Sep 2013

What scientists can see in your pee

Researchers at the University of Alberta announced today that they have determined the chemical composition of human urine. The study, which took more than seven years and involved a team of nearly 20 researchers, has revealed that more than 3,000 chemicals or "metabolites" can be detected in urine. The results are expected to have significant implications for medical, nutritional, drug and environmental testing.

"Urine is an incredibly complex biofluid. We had no idea there could be so many different compounds going into our toilets," noted David Wishart, the senior scientist on the project.

Wishart's research team used state-of-the-art analytical chemistry techniques including nuclear magnetic resonance spectroscopy, gas chromatography, mass spectrometry and liquid chromatography to systematically identify and quantify hundreds of compounds from a wide range of human urine samples.

To help supplement their experimental results, they also used computer-based data mining techniques to scour more than 100 years of published scientific literature about human urine. This chemical inventory—which includes chemical names, synonyms, descriptions, structures, concentrations and disease associations for thousands of urinary metabolites—is housed in a freely available database called the Urine Metabolome Database, or UMDB. The UMDB is a worldwide reference resource to facilitate clinical, drug and environmental urinalysis. The UMDB is maintained by The Metabolomics Innovation Centre, Canada's national metabolomics core facility.

The Human Urine Metabolome Publication: PLoS One

5 Sep 2013

RTI Forming Metabolomics Center

RTI International has received a contract from the National Institutes of Health Common Fund to create a metabolomics center where metabolites will be synthesized for use by researchers, the nonprofit research institute said today.

The five-year contract for up to $4.1 million will go toward setting up the Metabolite Standards Synthesis Center where RTI scientists will chemically synthesize metabolites that will be made available to the scientific community as a standard of comparison to help identify and detect diseases.

The center, which will be led by the National Heart, Lung, and Blood Institute, "is intended to increase the national capacity for metabolomics services to basic, translational, and clinical investigators," RTI said.

Metabolites that have been nominated by researchers and approved by a selection committee will be synthesized by RTI scientists. Spectral and chromatographic methods will be used to characterize candidate metabolites, and RTI will provide data on physical properties, stability, and analytical methods for use by researchers.

The compounds, RTI said, will be provided so that researchers can compare them with tissue samples to identify and detect diseases.

"With this project, we want to enable research that can contribute to earlier and reliable diagnosis and facilitate a better understanding of diseases," Herbert Seltzman, a senior research scientist at RTI and the project’s director, said in a statement. "Providing scientists with known, postulated, or isotopically labeled compounds that are otherwise unavailable to them could vastly improve the process of therapeutic intervention and drug development."

RTI was recently awarded $5.3 million by NHGRI to expand its web-based tool for the use of phenotypic data in research. 

Source: Genomeweb
12 Aug 2013

University of Alberta metabolomics research team collaborates with prestigious BGI Shenzhen on new cancer screening technology

A new diagnostic test for pre-cancerous polyps, PolypDx™, from Metabolomic Technologies Inc. (MTI), is moving towards commercialization thanks to collaboration between the Alberta government, the University of Alberta (U of A), the renowned BGI Shenzhen (formerly Beijing Genomic Institute) and U of A spin-off company, MTI.

The University of Alberta research team, led by Dr. Richard Fedorak and Dr. Haili Wang, have developed a non-invasive early diagnostic test "PolypDx™" to detect evidence of colonic polyps, the precursor to colorectal cancers. They have formed MTI with the assistance of business incubator TEC Edmonton, to further develop, commercialize, validate and market this innovative diagnostic in Alberta and global markets.

The novelty of MTI's PolypDx™ resides with its significantly higher detection accuracy than current fecal based tests, easier administration, faster results and lower cost. The superior accuracy of MTI's diagnostic test will be a game-changer in the early detection and prevention of colorectal cancers.

With over a million dollars in total project support, PolypDx™ is undergoing validation and clinical trials in Alberta and in China using the large-scale population screening resources of the BGI.

"As one of the leading research universities in the country, the University of Alberta is proud to deliver the kind of cutting-edge research and innovation that leads to great discoveries like those of MTI's," states Dr. Lorne Babiuk, Vice-President of Research, University of Alberta. "We are thrilled to see the provincial government recognize the value of this research on cancer detection at a global scale, which is also a testament to the strength of innovation and technology found right here in Alberta."

"This is an Alberta-developed technology that can save lives and it's the result of the leading talent we have in our Campus Alberta and Alberta Innovates systems," said the Honourable Thomas Lukaszuk, Deputy Premier and Minister of Alberta Enterprise and Advanced Education.

"And, through our collaborative approach, we are bringing together the right partners to move this knowledge from basic research into a commercial application that will make a significant impact in health outcomes."

The U of A's capabilities in metabolomics and clinical biomarkers were a natural fit with BGI's strength in detection capabilities to co-develop diagnostic tests for the global market.

"China is focusing on preventative medicine to avoid the burden of managing long term chronic treatment costs, and MTI's pre-colon cancer test aligns perfectly with our healthcare goals," says Yong Zhang, Head of Proteomic Division, BGI. "BGI is the best positioned to co-develop MTI's diagnostic tests for the Chinese market, assist with the regulatory process and market the technology. We're excited to be collaborating with the University of Alberta and MTI on technology that will save thousands of lives."

Source: CNW

Please note:
If you know of any metabolomics news that we should feature in future issues of this newsletter, please email Ian Forsythe (

Metabolomics Events

6) Metabolomics Events

1-3 Oct 2013

The 10th International Symposium on Milk Genomics and Human Health
Venue: Davis, California, USA

Join us October 1-3, 2013 in Davis, California to celebrate the 10th Anniversary of the International Symposium on Milk Genomics and Human Health. This year's theme is Milk Leading Life Sciences Research in the 21st Century.

The venue for this year's event is the U.C. Davis Conference Center located on the University of California, Davis campus in the United States.

The three day event will bring together international experts in nutrition, genomics, bioinformatics and milk research to discuss and share the latest breakthroughs and their implications.
The Annual Symposium is our flagship event that features scientific research related to milk and human health done throughout the world. The  symposium draws from the diversity of its memberships to cover the breath of genomics themes that reflect the interest of the Consortium. The goal of the Consortium is to bring together the research and dairy communities to share, translate, and interpret data that are happening within the fields of the "-omics" science.  
For more information, visit

7-11 Oct 2013

Metabolomics course: SLC-Tjärnö marinebiological laboratory
Venue: Center for Marine Chemical Ecology at SLC Tjärnö on the Swedish West coast

Do you work, or want to work with metabolomics? This intensive course in mass spectrometry based metabolomics targets the complete procedure, from experimental design and data acquisition to post processing and statistical analysis. A mixture of lectures and hands-on experience guided by international experts will help you develop your metabolomics skills. The course is intended for PhD students and Post Docs. Priority will be given to students in chemical ecology, but we also welcome applications from other disciplines. The course will be held at the Center for Marine Chemical Ecology at SLC Tjärnö on the Swedish West coast. Food and lodging is covered by a generous grant from the Swedish Royal Academy of Science. Students will need to cover travel costs form other sources. The course corresponds to 2.5 HP (ECT).

Application should include a short motivation (<1 page) and a brief CV. Submit by E-mail to

Application deadline 15th of August 2013

Prof. Georg Pohnert, Biorganic Analytics, Friedrich Schiller University, Jena
Prof. Johan Trygg, Department of Chemistry, Umeå University
Dr. Ulf Sommer, NERC Metabolomics Facility, University of Birmingham

Contact and inquiries:
Erik Selander
Göran Nylund
Course Flyer:

7-11 Oct 2013

Hands-on LC-MS for Metabolic Profiling Course
Venue: Imperial International Phenome Training Centre, Imperial College, South Kensington Campus, Exhibition Road, London, UK

This week long course aims to cover how to perform a metabolic profiling experiment, from start to finish. It will cover study design, sample preparation, the use of mass spectrometry for global profiling and targeted methodologies and data analysis.

Day 1
Introductory lectures in mass spectrometry and chromatography, study design and sample preparation.

Days 2 & 3
Analysis of biofluids through global profiling and targeted analyses; one day spent on each of the newest QToF instrumentation and the newest TQ instrumentation. Instrument set up, method development and acquisition will be covered. As we have set a maximum of 4 attendees per instrument this allows for hands-on participation by all.

Day 4
Lectures in data analysis, followed by workshops where attendees will process the data acquired from the previous day, allowing for development of interpretation skills.

Day 5
Application lectures, tips, tricks and troubleshooting.

Download the full programme here: LCMS_Metabolic_Profiling

For more information, visit

17-21 Mar 2014

EMBO Practical Course on Metabolomics Bioinformatics for Life Scientists
Venue: The European Bioinformatics Institute, Hinxton, UK (See map: Google Maps)

Date: Monday, March 17, 2014 - Friday, March 21, 2014


Reza Salek, EMBL-EBI & Cambridge University, UK
Laura Emery, EMBL-EBI, UK

Registration Opens:
Thursday, August 1, 2013
Registration Deadline:
Friday, January 17, 2014 (12:00 midday GMT)
Acceptance Notification Date: Friday, January 31, 2014
Participation: Open application with selection

This course will provide an overview of key issues that affect metabolomics studies, bioinformatics tools, and procedures for the analysis of metabolomics data. It will be delivered using a mixture of lectures, computer-based practical sessions and interactive discussions. The course will provide a platform for discussion of the key questions and challenges in the field of metabolomics.

This course is aimed at PhD students and researchers with a minimum of one year’ s experience in the field of metabolomics who are seeking to improve their skills in metabolomics data analysis. Participants must have experience using R (including a basic understanding of the syntax and ability to manipulate objects) and the UNIX/LINUX operating system.

For more information, visit

24-26 Mar 2014

3rd International Conference and Exhibition on Metabolomics & Systems Biology
Venue: Hilton San Antonio Airport, USA

Theme: Multi-Omic Approaches to Envision the Role of Metabolites in Biological Systems

The annual Metabolomics conference mainly aims in bringing Metabolomics and Systems Biology researchers from around the world under a single roof, where they discuss the research, achievements and advancements in the field.

After the success of Metabolomics-2012 & Metabolomics-2013, OMICS Group is proud to announce the 3rd International Conference and Exhibition on Metabolomics & Systems Biology which is going to be held during March 24-26, 2014 at Hilton San Antonio Airport, USA.

Metabolomics-2014 meeting promises a program full of practical workshops and parallel sessions covering the broad range of biological and technological metabolomics topics, providing rich opportunities for networking and approach towards biomedical and biological scientific research.

Join us at Metabolomics-2014 as we gather together to share ideas, insights and advances  in the field of Metabolomics and Systems Biology.

Conference Highlights
  • Novel Approaches to Cancer Therapeutics
  • Analytical and Bio-Analytical Techniques in Metabolomics
  • Transcriptomics
  • Toxicology and Drug Metabolism
  • Current Trends and Innovations in Metabolomics
  • Computational Biology, Synthetic Biology and Systems Biology
  • Computational Genomics
  • Metabolomics Syndrome
  • Recent Approaches in Proteomics and Genomics
  • Glycomics and Lipidomics

To share your views and research, please click here to register for the Conference.

For more information, visit

23-26 Jun 2014

Metabolomics 2014: 10th Annual International Conference of the Metabolomics Society
The Official Joint Conference of the Metabolomics Society and Plant Metabolomics Platform
The Official Annual Meeting of the Metabolomics Society
Venue: Tsuruoka, Japan

Health, medical, pharmaceutical, nutritional, agricultural, microbial, bioenergy, environmental and plant sciences meet biochemical, analytical and computational technologies.

Early registration and abstract submission due March 31, 2014.

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Please come back later for detailed information about Metabolomics 2014 by visiting

Please note: If you know of any metabolomics lectures, meetings, workshops, or training sessions that we should feature in future issues of this newsletter, please email Ian Forsythe (

Metabolomics Jobs

7) Metabolomics Jobs

This is a resource for advertising positions in metabolomics. If you have a job you would like posted in this newsletter, please email Ian Forsythe ( Job postings will be carried for a maximum of 4 issues (8 weeks) unless the position is filled prior to that date.

Jobs Offered

Job Title Employer Location Posted Closes Source
An engineer-technician in metabolomics CRP - Gabriel Lippmann Belvaux, Luxembourg 23-Jul-2013 31-Dec-2013
Metabolomics Society
Canada Research Chair (Tier II) in Marine Microbial Proteomics and Metabolomics Dalhousie University Halifax, Canada
20-Aug-2013 15-Oct-2013
AIHS Translational Health Chair - Metabolomics, Department of Biological Sciences, Faculty of Science University of Calgary Calgary, Canada 11-Aug-2013 8-Oct-2013
Research Officer (Analytical)  University of Melbourne Melbourne, Australia 9-Sep-2013 29-Sep-2013
University of Melbourne
Biostatistician/Data Analyst in the field of Metabolomics Leiden University Leiden, Netherlands 11-Sep-2013 16-Sep-2013
Mass Spectrometry software developer Genedata Munich, Germany
5-Sep-2013 Not specified

Jobs Wanted

This section is intended for very highly qualified individuals (e.g., lab managers, professors, directors, executives with extensive experience) who are seeking employment in metabolomics. We encourage these individuals to submit their position requests to Ian Forsythe ( Upon review, a limited number of job submissions will be selected for publication in the Jobs Wanted section.
  • Research or Lab Manager Position Sought (Candidate has extensive NMR metabolomics experience and knowledge including NMR instrumentation maintenance): [Candidate's CV]

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