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Abstract Introduction: Type 2 Diabetes (T2D), the most prevalent form of diabetes, is a metabolic disorder characterized by decreased insulin sensitivity and abnormal hepatic glucose production. Monitoring metabolic alterations during T2D progression may provide better understanding of its pathogenesis and identify potential biomarkers for early diagnosis. Several metabolomics approaches have been applied in diabetic research for identification of metabolites associated with the risk of T2D and related pathways. Here, a semi-targeted workflow was designed to confidently measure known metabolic differentiators, such as branched-chain amino acids, while allowing for the discovery of previously unidentified metabolites that are altered during T2D progression. This approach combines high resolution accurate mass Orbitrap™ technology for maximum detection of known and unknown metabolites in serum samples, with intelligence-driven fragmentation for the identification of knowns and structural elucidation of unknown biomarkers.
Methods: Serum samples were obtained from 3 healthy donors and 3 T2D donors. A pooled sample was created from all samples and was used for quality control and identification of unknowns. Metabolites were extracted with an excess of cold methanol (3x) containing internal standards. Samples were analyzed with a Thermo Scientific™ Vanquish™ UHPLC system and a Thermo Scientific™ Orbitrap ID-X™ Tribrid™ mass spectrometer. A custom library containing fragmentation spectra and retention times for 300 authentic standards was created in-house. Data were processed using Thermo Scientific™ Compound Discoverer™ software for unknown identification, differential analysis and pathway mapping.
Results: A semi-targeted workflow was developed for the robust quantitation of known markers, such as branched chain amino acids, while at the same time, enabling comprehensive metabolic phenotyping of serum samples. Over 3,000 metabolites were detected, 200 of which could be confidently identified (MSI Level 1) against an in-house spectral library. The Orbitrap ID-X Tribrid MS with AcquireX intelligent acquisition software maximized the number of metabolites interrogated by MS/MS, by annotating non-biological and redundant features on-the-fly, resulting in confident metabolite annotations. Putative annotations (MSI Level 2 and 3) were obtained for more than 90% of the metabolites detected through searches against the mzCloud™ library and ChemSpider database. Differential analysis detected metabolite perturbations in amino acids and carnitines in serum from T2D donors, in agreement with previous studies.
Conclusion: The semi-targeted strategy described here presents a promising and facile workflow for the monitoring of known biomarkers, while enabling the discovery of novel disease biomarkers that could lead to further biochemical insights in disease progression and treatment outcome.
For Research Use Only. Not for use in diagnostic procedures.
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= Discovery stage.
= Translation stage.
= Clinically available.
