Metabolite Identification Services
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Download NowThe goal of nontargeted metabolomic and lipidomic studies is to discover the most biologically important signals in a patient population, whether they are known molecules or yet to be chemically identified. Accurate metabolite identification analysis is needed to maximize the impact of discovery metabolomics and lipidomics, to effectively map the role of key biomarkers identified in a study and support translation to clinical use.
To that end, Sapient has focused extensively on building metabolite identification analysis capabilities to enhance the speed, quality, and extent of metabolite identification. Our in-house standards library now encompasses >13,000 chemical reference standards, and is enriched for key human and preclinical metabolic pathways, biological processes and disease drivers. The library has strong representation across endogenous human metabolites and lipids as well as from FDA approved drugs.
Why is metabolite identification critical in discovery metabolomics?
Next-generation technologies have expanded the number of metabolites and lipids that can be measured in discovery metabolomics studies. Sapient’s rLC-MS systems, for example, capture more than 15,000 of these molecular features in a single biosample. This has enabled the metabolomics field to move beyond targeted studies of established, well-known small molecules to find novel, biologically important biomarkers in the broader metabolome, lipidome, and exposome.
The challenge is that many of these compounds have yet to be identified, meaning their chemical structures are still unknown. Accurate metabolite identification is needed to chemically fingerprint the molecule and draw meaningful mechanistic biochemical insight into its role in biological and/or disease processes – and to maximize translatability.
How does Sapient perform metabolite identification analysis?
By referencing our comprehensive standards library – all of which have been extensively analyzed using Sapient’s rapid liquid chromatography-mass spectrometry (rLC-MS) systems – we can provide definitive identifications of up to 1,400 metabolites in large-scale studies. These identifications use retention time (RT), MS1 mass-to-charge ratio (m/z), and MS2 data to match the molecule in line with MSI Tier 1 identification standards.
For metabolites and lipids that are still unknown, we leverage our of database of >6 million MS2 spectra representing more than 850,000 compounds to assign putative structural identifications, maximizing discovery potential of novel markers.
We can also perform custom studies to identify biologically significant but unannotated compounds observed in discovery studies and not yet captured in our reference databases.
What makes your standards library unique?
We have focused acquiring standards for metabolites and lipids that are routinely found in human biosamples, spanning diverse endogenous human metabolites, plant-derived small molecules, and molecules originating from food and food additives, FDA approved drugs, environmental toxicants, and pollutants. This means we can maximize identifications for pharma and biotech client studies of compounds that are most relevant to human biology and most likely to translate clinically.
If a molecule of interest is not an exact match in our database, still use the reference standards to identify structural similarities to known compounds and confidently annotate yet-identified metabolites and lipids. This significantly increases both discovery potential and translatability.
Download the fact sheet to learn how Sapient’s metabolite identification services enhance and accelerate small molecule biomarker discovery with:
- An in-house library of >13,000 chemical reference standards: with strong coverage across endogenous human metabolites, lipids, and FDA approved drugs.
- Identification of up to 1,400 metabolites in large studies: using MSI Tier 1 identification standards of matching retention time (RT), MS1 mass-to-charge ratio (m/z), and MS2 chemical fingerprint.
- Putative identifications using our database of >6 million MS2 spectra: to elucidate unannotated molecules representing significant biomarkers in your study.