Our Technology

General Metabolics’ proprietary technology platform for metabolomics is uniquely suited for delivering deep biological insight at an unprecedented speed and scale.

high-throughput

Ultra-High Throughput

GMet’s technology utilizes direct injection of samples on high resolution mass spectrometers to maximize analytical throughput, allowing metabolite discrimination in highly complex mixtures. By avoiding traditional column chromatography, we’re able to exponentially increase sample throughput relative to LCMS approaches, and run extremely large experiments from 1,000 to +40,000 samples with rapid turnaround.

broad-coverage

Broad Unbiased Coverage

GMet’s non-targeted methods screen for known and unexpected metabolites. Unlike targeted approaches, our non-targeted methods can reveal novel metabolic phenotypes and biomarkers that would otherwise be missed. These tools have helped our clients accelerate therapeutics development, pathway engineering, biomarker discovery and mechanistic cellular research.

data-generation

Automated Data Generation

comparative-analysis

Comparative Analysis

We utilize proprietary software tools to enable fully automated data processing and delivery. This includes metabolite abundance and annotation, differential analysis, cohort statistics, and pathway enrichment analysis. 

multi-omics3

Integrated Multi-Omics

GMet’s platform enables seamless integration of genomics and metabolomics data sets. Our network-based integration methods find relationships between genes and metabolites to uncover novel biological mechanisms.

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MarkerLab®

Collaborative research and reporting software that simplifies metabolomics data management, organization and visualization.

MarkerLab’s web-based tools organize your metabolomics and other data into informative and engaging interactive reports. 

Working in MarkerLab, General Metabolics customers can access or create an extensive collection of data organizing tools including ontologies, pathway maps, annotations, and algorithms. These resources are quickly and easily edited, layered, and combined with unique user insights to create a dynamic knowledge base for individuals and institutions.

Interested to read more about how GMet technology is being used to advance and scale metabolomics research and bring new insights to industry?

Following are selected peer-reviewed publications with General Metabolics founders and advisors.

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in E. coli.

Nature Communications, 2019

Metabolomics Identifies a Biomarker Revealing in Vivo Loss of Functional ß-Cell Mass Before Diabetes Onset.

Diabetes, 2019

High-throughput dynamic metabolomics identifies mode of action for uncharacterized antimicrobial compounds.

Science Translational Medicine, 2018

Antibodies set boundaries limiting microbial metabolite penetration and the resultant mammalian host response.

Immunity, 2018

Synthesis and degradation of FtsZ quantitatively predicts the first cell division in starved bacteria.

Molecular Systems Biology, 2018

Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity.

Cell, 2018

Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli.

Nature Methods, 2017

Genomewide landscape of gene–metabolome associations in Escherichia coli.

Molecular Systems Biology, 2017

Genetic Depletion of Adipocyte Creatine Metabolism Inhibits Diet-Induced Thermogenesis and Drives Obesity.

Cell Metabolism, 2017

Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism.

Nature, 2017

GMet technology has been used in over 50 peer-reviewed publications.