High Resolution GC-MS for Metabolomics and Biomarker Discovery

Metabolomics demands analytical techniques capable of delivering high confidence, reproducible identification across highly complex biological matrices. In this on‑demand webinar, Dr. Vladimir Tolstikov of Eli Lilly and Company explores how high‑resolution GC‑MS with time‑of‑flight detection strengthens metabolomics workflows—from discovery through pathway analysis and biomarker development.

The presentation begins with a practical overview of the end‑to‑end metabolomics workflow, emphasizing the importance of standardized sample handling, quality control, metadata management, and robust data processing. Dr. Tolstikov highlights how high‑resolution GC‑MS complements multi‑platform metabolomics strategies by providing highly reproducible measurements of derivatized small molecules that are difficult to analyze by other techniques.

A key focus of the webinar is the analytical advantage of routine high mass accuracy and high resolving power in GC‑MS for metabolomics. Using real examples, the session demonstrates how accurate‑mass deconvolution enables “yes/no” metabolite identification by combining spectral similarity with mass accuracy thresholds—significantly improving confidence over probability‑based library matching alone. The discussion also addresses current challenges in spectral libraries, derivative annotation, and the need for high‑quality reference databases tailored to high‑resolution workflows.

The webinar concludes with a detailed case study on pancreatic cancer, showing how GC‑MS‑derived metabolomics data contributes to multivariate statistical modeling, pathway analysis, and biological interpretation when combined with transcriptomic information. The results illustrate how panels of metabolites—rather than single biomarkers—can support disease discrimination and hypothesis generation for further validation.

This webinar is ideal for researchers working in metabolomics, systems biology, biomarker discovery, and translational research who want to understand how high‑resolution GC‑MS improves metabolite identification, data confidence, and biological insight.

Originally presented 21 February 2013.