Metabolomics is the simultaneous measurement of hundreds to thousands of small molecule metabolites in a cell, tissue, or bio-fluid. For general context, “small” means molecules in the range of 100 ~1000 Daltons, in contrast to “large” molecules like proteins or nucleotides that are often tens-to-hundreds of thousands of Daltons in molecular weight. These small molecules participate directly in most of the energetics (e.g. ATP) and biosynthetic processes in the body – they are the molecules used to build proteins, DNA, and RNA, and to transport nutrients and waste products. Thus, metabolomics provides a direct measurement of real-time cellular physiology and biochemistry of cells and tissues at the most detailed level possible. By analyzing variation in the abundance of large numbers of metabolites simultaneously in a biological sample, which may result from experimental studies or natural variation related to genetics or disease states, researchers have been able to elucidate new mechanistic insight into biological processes, including the discovery of disease markers, treatment responses, and new drug targets.
The analytical tools most commonly used for metabolomics are based on either mass spectrometry or nuclear magnetic resonance spectroscopy (“NMR”). GMet’s platform is based on a suite of rigorous, well validated and highly published mass spectrometry tools.
While a number of academic and industrial researchers and clinicians have demonstrated the potential of metabolomics in recent years, significant time and cost constraints have limited the broader application of this technology. The existing tools available to support metabolomics data acquisition and downstream data handling for QC and interpretation, coupled with the highly specialized knowledge-base needed for metabolism research (which is very different from the knowledge-base for genes, transcripts, and other omics) have created a barrier to entry for the research community to get the most from incorporating metabolomics into their research programs.
For the first time, General Metabolics brings scale to metabolomics to allow these powerful studies to become a regular part of the R&D workflow, enabling researchers to identify markers of disease, to assess target engagement, and to mechanistically link targets to broader cellular physiology or to evaluate patient response to treatment.
Interested to read more about how GMet technology is being used to advance and scale academic metabolism research and bring new insights to industry?
Following are selected peer-reviewed publications with GMet founders and advisors.
Chen, L., et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome.
Nature Medicine 2022, 28: 2333–2343.
Chen L, Wang D, Garmaeva S, et al. The long-term genetic stability and individual specificity of the human gut microbiome.
Temba, G.S., et al. Urban living in healthy Tanzanians is associated with an inflammatory status driven by dietary and metabolic changes.
Nature Immunology 2021, 22:287–300.
Geiger, R. et al. L-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity.
Cell 2016, 167: 829–842.
Xao, T., et al. Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism.
Nature 2017, 548(7666):228-233.
Cherkaoui S, Durot S, Bradley J, et al. A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs.
Mol Syst Biol. 2022;18(11):e11033.
Kazak, L., et al. Genetic Depletion of Adipocyte Creatine Metabolism Inhibits Diet-Induced Thermogenesis and Drives Obesity.
Cell Metabolism 2017, 26(4):660-671.
Lingzi, L., et al. Metabolomics Identifies a Biomarker Revealing in Vivo Loss of Functional ß-Cell Mass Before Diabetes Onset.
Diabetes 2019, 68(12):2272-2286. doi: 10.2337/db19-0131.
Mitroulis, I. et al. Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity.
Cell 2018, 172(1-2):147-161.
Presented Abstract, Rheos Medicines: Camacho D., et al. Transcriptional Subsetting of SLE Patient Cohorts Based on Metabolic Pathway Activity [abstract].
Arthritis Rheumatol. 2021, 73 (suppl 9).
Fuhrer, T., et al. High-Throughput, Accurate Mass Metabolome Profiling of Cellular Extracts by Flow Injection time-of-Flight Mass Spectrometry.
Analytical Chemistry 2011, 83(18):7074-80.
Williams EG, Pfister N, Roy S, Statzer C, Haverty J, Ingels J, Bohl C, Hasan M, Čuklina J, Bühlmann P, Zamboni N, Lu L, Ewald CY, Williams RW, Aebersold R. Multiomic profiling of the liver across diets and age in a diverse mouse population.
Cell Syst. 2022 Jan 19;13(1):43-57.e6.
Holbrook-Smith, D. et al. High-throughput metabolomics predicts drug–target relationships for eukaryotic proteins.
Mol Systems Biology 2022, 18(2): e10767.
Jha, A., et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization.
Immunity 2015, 42(3): 419–430. doi: 10.1016/j.immuni.2015.02.005.
Sergushichev, A., et al. GAM: a web-service for integrated transcriptional and metabolic network analysis.
Nucleic Acids Research 2016, 44(W1): W194-200.
Blasche S, Kim Y, Mars RAT, Machado D, Maansson M, Kafkia E, Milanese A, Zeller G, Teusink B, Nielsen J, Benes V, Neves R, Sauer U, Patil KR. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community.
Nat Microbiol. 2021 Feb;6(2):196-208. doi: 10.1038/s41564-020-00816-5. Epub 2021 Jan 4. PMID: 33398099; PMCID: PMC7610452.
Zampieri, M., Hörl, M., Hotz, F. et al. Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli.
Nat Commun 10, 3354 (2019).
Diether M, Nikolaev Y, Allain FH, Sauer U. Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli.
Mol Syst Biol. 2019 Aug;15(8):e9008. doi: 10.15252/msb.20199008. PMID: 31464375; PMCID: PMC6706640.