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Promising new technologies for researching inherited metabolic diseases

4 December 2025
News

Researchers at HFML-FELIX and Radboudumc’s Translational Metabolic Laboratory have published an insightful new review paper in the Journal of Inherited Metabolic Disease. In this paper, they give an overview and outlook on emerging techniques that are important for characterizing inherited metabolic diseases (IMDs) – a large group of disorders typically caused by mutations in genes encoding enzymes. They demonstrate how state-of-the-art mass spectrometry-based metabolomics techniques are used in clinical applications and how techniques pioneered at HFML-FELIX using infrared ion spectroscopy can be applied to address persistent challenges in metabolite identification research.

 

Untargeted metabolomics

The researchers highlight untargeted metabolomics as a promising tool for IMDs, offering simultaneous detection and relative quantification of potentially thousands of metabolites in one go. While traditional targeted assays currently remain important for quantifying known biomarkers for screening and diagnostics, untargeted metabolomics provides a unique and unbiased assessment of much wider portions of the metabolome. This enables the discovery of novel biomarkers and deepens our understanding of disease mechanisms.

Future advances in untargeted metabolomics are expected to address existing challenges. In addition to using infrared ion spectroscopy for metabolite identification, other innovations in computational biology, bioinformatics, and the continued integration of multi-omics datasets using machine learning techniques will surely play pivotal roles in overcoming these challenges, the group of researchers conclude. Their joint paper showcases the value of translating fundamental science to patient care and provides an excellent insight into the primary subject of their collaboration.

 

Collaborators:

Dr. Udo Engelke
Prof. Ron Wevers
Prof. Dirk Lefeber
Dr. Purva Kulkarni

Link to paper:

Untargeted Metabolomics for Diagnosis, Monitoring, and Understanding the Pathophysiology of Inherited Metabolic Disorders

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