Michael Witting

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Michael Witting

Short Biography


Dr. Michael Witting studied Applied Chemistry with a functional direction into biochemistry at the Georg-Simon-Ohm University of Applied Science, Nuremberg, Germany and obtained his PhD in 2013 from the Technical University of Munich. He is a current member of the Metabolomics Society Board of Directors and since 1st of January he is heading the metabolomics section of the Metabolomics and Proteomics Core at the Helmholtz Zentrum München. His main research interests are LC-MS based metabolomics method development and application, as well as metabolite identification improvement by retention time prediction.

Expert Opinion

Question 1

1. When and why did you start using metabolomics in your investigations?

I have started with metabolomics in 2009 when starting my PhD in the group of Philippe Schmitt-Kopplin at the Helmholtz Zentrum München. My first project funded by the ERA-Net project pathomics was to study host-pathogen interactions of the opportunistic human pathogen Pseudomonas aeruginosa. I applied direct infusion FT-ICR-MS as well as UPLC-UHR-ToF-MS based metabolomics and lipidomics to study the metabolic response of HeLa cells or Caenorhabditis elegans [1]. This was also the time, when my interest in this model organism started, realizing that C. elegans and metabolomics/lipidomics is a fruitful combination.

Question 2

2. What have you been working on recently?

I have currently two main projects running. The first one is a French-German collaboration with the groups of Fabien Jourdan and Reza Salek in France as well as the group of Steffen Neumann in Germany. We are aiming to integrate different networks, such as mass-difference, correlation, spectral similarity networks with genome-scale metabolic reconstructions and other types of networks in a multilayer network to derive a better understanding of the data as well as helping to identify new metabolites and improve genome-scale metabolic models. Within this fantastic group my PhD student Liesa Salzer and I are working on C. elegans metabolomics and lipidomics data and the annotation of metabolites present in the nematode [2]. We are using different spectral libraries as well as in-silico tools to annotate the data as good as possible and feed it into our larger framework of the project. This is very interesting since it will enable us on the one side to define in more detail the metabolome and lipidome of C. elegans and on the other side requires to develop new scripts and pipelines for data handling. Furthermore, we hope to be able to identify several new metabolites and close some gaps on metabolic pathways. We are really looking forward to presenting our first results in the coming months. The second project is a collaborative project with the group of Sebastian Böcker aiming to develop reproducible and transferable retention time prediction. Chromatographic separation is important in metabolite identification since it allows the separation of isomers and represents an orthogonal information to MS and MS/MS giving hints about the polarity of a metabolite. However, it is often only used at a late stage of metabolite identification, typically when comparing against a reference standard. Retention time prediction enables to utilize this information in an early stage, potentially reducing the number of false positive annotations [3]. However, sharing of RTs is not as widespread as sharing of MS2 spectra. So, we first needed to identify good training data for our project. My PhD student Eva-Maria Harrieder did a great job identifying data sets and standardized them as well as curating chromatographic metadata. Throughout the project we realized that especially this metadata is crucial. In contrast to previous retention time prediction approaches, which predict RTs for single chromatographic systems, we aim to develop a transferable system embracing the power of machine and deep learning. The first results are looking very promising. Beside these two, many other projects are currently on my table. Since 1st of January 2021 I’m heading the metabolomics part of the newly fused Metabolomics and Proteomics Core facility. After this fusion we are working on new targeted assays and a major project is the measurement of a large epidemiological study with >4000 samples. Such large-scale experiments are really the future of metabolomics (not only in clinical or epidemiological settings, but also basic science) and further work towards standardization, QC, reporting and data integration are required, but the community is on a good way here.

Question 3

3. How do you think the understanding of C. elegans and their metabolic interactions with beneficial microbes can be applied into health and disease??

C. elegans offers several advantages, not only to study the beneficial effects of microbes. The short generation cycle as well as the genetic tractability makes it possible to grow sufficient amounts of isogenic animals in a short time. Since the worm feeds on live bacteria, virtually all bacteria that can be grown under similar conditions can be used as food for C. elegans. However, in recent years there was shift from seeing bacteria only as food towards a real microbiome and host-microbe interactions. Escherichia coli is normally used as a food source for the nematode, since it is easy to cultivate and was readily available at the time of introduction of C. elegans as model organism by Sidney Brenner. However, this is not the natural food of C. elegans nor its natural microbiome. A group around Hinrich Schulenburg and Buck Samuel recently published the CeMBio resource, a collection of culturable microbes from the natural C. elegans microbiome [4]. With this we have now a reproducible system to study host-microbe interactions, also on a metabolic level. The group of Christoph Kaleta for example added genome-scale metabolic models for the bacteria to the toolset. Based on the available system we can now start to study interaction in more detail, also following genetic components on both sides as well as metabolic interactions etc. This system is still not comparable to e.g. the interaction of humans with their microbiome, but we can derive some basic, conserved principles. The fast growth also helps to perform several experiments in short time, potentially in future also in high-throughput screens (genetic or drug screens), which might include metabolomics as well. Results can be then transferred to e.g. mouse models as next step.

Question 4

4. What are the main challenges when developing methods for metabolomic analysis from limited sample amount?

The challenges are twofold. First, highly sensitive analytical approaches are required to enable the analysis of low amounts of material. In contrast to genomics and transcriptomics we are not able to amplify our molecules of interest. I think the MS field is rapidly developing in this direction and recent instrument releases or publications such as the SpaceM approach from Theodore Alexandrov’s group are great examples [5]. However, depending on the amount of material at your hands you might have to make sacrifices. While normally, one would combine e.g. RP and HILIC based methods to increase the coverage of metabolites, you might be restricted to a single method or even injection. The goal should be to maximize the information you can obtain, e.g. using for example data-independent acquisition to cover “all” detected metabolites with MS2 information. Likewise, optimizing methods further towards better metabolome coverage. We have developed a Tandem-LC method covering HILIC and RP from a single injection. The goal is to further miniaturize this to be compatible with a limited sample amount. Second, with a decreasing amount of sample, even going down to single cells, the demand on sample preparation increases. One might face very low volumes for pipetting, extraction and injection. I believe that novel automated liquid handling solutions can help a lot here. But also, with decreasing amount of sample might be also related to an increasing number of samples. C. elegans is a good example here. Typically, we process 500-5000 worms (each with ~1000 cells) for one biological replicate. The metabolic individuality of the worms is averaged out by extracting them all together. If you lower the amount of worms per sample the more the individuality comes into effect, theoretically going down to single worms. While it is interesting to study this level of detail in metabolism, one has to keep in mind that you would need a larger number of replicates to compensate for this to obtain meaningful data (a problem, which is well-known from studies with “free-living-humans”, e.g. epidemiological scale studies). One need to counter-balance these effects and perform vigorous study design and keep in mind that we are often still measuring in a sequential manner in contrast to genomics and transcriptomics, where a lot of measurements can be parallelized. In the end we are probably not talking about 5 cells of type A vs 5 cells of type B, but 5000 vs 5000 or even worse. That means a lot of measurement time and I even didn’t start on the data analysis and interpretation…

Question 5

5. You are the deputy head of metabolomics and proteomics core of the Helmholtz Zentrum München. What do you think contributed significantly to your career path in becoming a deputy leader?

system is full of rejections and setbacks, but the key is to never give up and believe in the work you are doing. However, there are a few things that can turn the odds in your favor. I was lucky to participate in the Postdoctoral Fellowship Program of the HMGU, which gave me the chance to participate in many different courses on leadership, management, etc. as well as having a professional coaching. Especially the last one helped me a lot. During the process of coaching, it became clear to me that science has a lot of parallels to sales. Especially in the age of social media it makes a difference if you correctly “advertise” your work or not. The other important aspect is communication. Improving on my communication skills helped me a lot to better lead teams of scientist and to focus on common goals. I always try to remember myself that “the biggest misunderstanding in communication is that it happened”. Have an open ear, listen to others, ask questions and engage with people. Also think outside of the box, my personal coach never heard about metabolomics and that was great, because we never talked about science, but everything around it. From the scientific point of view, I was always happy and grateful that I had enough freedom to follow my own ideas. Even if you fail, you learn. And sometimes you learn more from your failures than your successes. And one last thing: All great minds, irrespective of the field, started as a beginner. Don’t be afraid to ask them, engage with them. You can learn a lot…


1. Witting, M., et al., DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model. Analytical and Bioanalytical Chemistry, 2015. 407(4): p. 1059-1073. 2. Salzer, L. and M. Witting, Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode. Metabolites, 2021. 11(5): p. 284. 3. Witting, M. and S. Böcker, Current status of retention time prediction in metabolite identification. Journal of Separation Science, 2020. 43(9-10): p. 1746-1754. 4. Dirksen, P., et al., CeMbio - The Caenorhabditis elegans Microbiome Resource. G3: Genes|Genomes|Genetics, 2020. 10(9): p. 3025-3039. 5. Rappez, L., et al., SpaceM reveals metabolic states of single cells. Nature Methods, 2021. 18(7): p. 799-805.

See also