Metabolism is the driver of all life; it provides the energy and building blocks for any biological process, whether it is cell growth, brain activity or muscle contraction. Metabolic activity is the basis for biotechnological applications and drug biotransformations, and almost any disease or toxic response can be linked to altered metabolic performance, either causal or consequential.
The mission of the O|2 Metabolism Core Facility (O2-MCF) of the Amsterdam Institute of Molecules Medicines and Systems is to provide expertise, technologies and services with respect to all key aspects of metabolism research:
1. Cell physiology: Cell cultivation and sample preparation
2. Measuring metabolites by targeted and untargeted metabolomics and biosensors
3. Systems biology: Data analysis, interpretation and modeling
Head of the facility: prof. dr. Bas Teusink
Managing director: dr. Jurgen Haanstra
Data stewardship, standards and software: dr. Brett Olivier
Program leader: prof. dr. Paul Jennings
Associated experts: dr. Herwig Bachmann, dr. Jan Commandeur, prof.dr. Marjorie van Duursen, dr. Timo Hamer
Program leader: prof. dr. Pim Leonards
Associated experts: prof. dr. Govert Somsen, dr. Jeroen Kool, dr. Marja Lamoree, dr. Johan van Heerden, dr. Isabel Kohler
Program leader: dr. Douwe Molenaar
Associated experts: prof. dr. Hans Westerhoff, prof. dr. Frank Bruggeman
Intracellular metabolism is very sensitive to the physiological state of the cells and for sample handling, and well-defined culture conditions are essential. We are experts in population level cultivation as well as single-cell cultivation methods. For mammalian cells, we have expertise in 2D and 3D cell culturing, organoids and IPS cells. For microorganisms, we have fermentors for continuous culturing and controlled batch. Online gas analysis and Seahorse facilities are available. We also have a high content live cell confocal imager. For single-cell cultivations we developed emulsion-based cultivations (one cell per droplet) and time-lapsed microscopy; we recently have set up microfluidics, which is currently operative for yeast.
We have a large number of platforms for metabolomics, largely mass spectrometry based. We also have (U)HPLC (organic acids, amino acids) and enzymatic methods. MS based methods include CE-MS, LC-MS and GC-MS based (incl. QTOF, triple Quad, Orbitrap, ion-mobility), and LCxLC, GCxGC separation methods. We also have metabolite isolation techniques (LC and GC fractionation) which can be used in combination with NMR and numerous bioactivity assays. We can measure metabolites of central carbon metabolism (glycolysis, TCA cycle, PPP), amino acid metabolism and >500 lipids, but also neurotransmitters, fatty acids, acylcarnitines, oxylipins, (d)NTPs, and toxins. We develop new analytical methods and ionisation methods, e.g. for resolving chiral metabolites. We further have expertise in MS-imaging of snap-freezed tissue samples, providing spatial information on metabolites in tissues, and we developed the iKnife, a surgical knife that uses real-time MS-based detection for distinguishing healthy and cancerous tissue. Finally, we are developing and applying FRET-based biosensors of targeted metabolites that allow us to measure a specific metabolite in real time at single cell resolution.
Generating high-quality data by itself is not very helpful. At the O|2 Metabolic Core Facility we realize that optimal generation of knowledge results from a close interaction between experimenters and data analysts from planning to interpreting experiments. We use multivariable statistical and bioinformatics approaches to find biomarkers or structure in data. We regard it as a key challenge to discover the mechanisms underlying these associations, and to subsequently identify targets for drugs or metabolic engineering. In the O|2 Metabolic Core Facility we have a deep understanding of metabolism, and develop new theory and modeling approaches of metabolic regulation. We develop standards and software and we offer expertise in the handling and interpretation of metabolomics data. This includes machine learning and multivariate analyses, genome-scale metabolic modeling, kinetic modeling, visualization and Metabolic Control Analysis.