MSACL 2025 Abstract

Untargeted Metabolomic Method Development With Clinical Application to Newborn Screening

Étienne Ljóni Poisson (1,3), Freyr Jóhansson (2), Margrét Þorsteinsdóttir (4,5), Jón Jóhannes Jónsson (2), Finnur Freyr Eiríksson (4,5), Óttar Rolfsson (1,3)
(1) Center for Systems Biology, University of Iceland, Reykjavik, Iceland, (2) Department of Genetics and Molecular Biology, Icelandic National Hospital, (3) Faculty of Medicine, University of Iceland, Reykjavik, Iceland, (4) Faculty of Pharmaceutical Sciences, University of Iceland, (5) ArcticMass, Reykjavík, Iceland

Etienne Ljoni Poisson, MsSci (Presenter) University of Iceland

Relevant Financial Disclosures (within past 24 months, reported on Apr 09, 2026) No relevant financial relationship(s) to disclose.

INTRODUCTION:
The clinical application of untargeted metabolomics to newborn screening of inborn errors of metabolism has greatly enhanced our ability to diagnose these rare diseases early. Here we present our progress in developing a library of metabolic features using semi-untargeted liquid chromatography-mass spectrometry with applications in biomarker discovery and improving current newborn screening. Using a catalogue of 387 metabolite standards relevant to inborn errors of metabolism, we have constructed an extensive library containing tens of thousands of spectral features for dried blood spot screening in several different LC-MS methods.

METHODS:
A metabolite catalogue of 387 metabolite standards was split into 20 distinct solutions with a maximized range of LogP, the octanol-water partition coefficient, and monoisotopic masses. We ran this catalogue in several different LC-MS methods; HILIC acidic (A: ACN + 0.1% formic acid, B: H2O + 0.1% formic acid) and HILIC basic (A: 95:5 ACN:5 mM ammonium acetate and 5 mM ammonium hydroxide) and reverse phase chromatography, using three different columns: BEH Amide, HSS T3 and reverse phase C18 column. We also plan on assessing the microfluidic IonKey/MS System. All samples were run on a Waters Xevo QTOF LC-MS in HDMSe mode, acquiring MS/MS data from 50 to 1000 m/z. We are currently optimizing our extraction methods and comparing them to the method used by the Icelandic National Hospital, using various extraction buffers (4:1 and 1:1 MeOH:H2O, 1:1 MeOH:ACN and 100% MeOH) using reconstitution in 1:1 ACN:H2O or supernatant.

OBJECTIVES:
The purpose of our LC-MS method and library development is two-fold: first, use our methods to establish a metabolic baseline of Icelandic newborns by screening a thousand healthy newborns. Once we have established this baseline, we intend on testing our library against samples with known conditions that we expect our method will be able to discriminate, focusing on inborn errors of metabolism such as PKU, IgA Nephropathy, Fabry disease and Charcot-Marie-Tooth disease. Following this, our hope is to investigate possible novel biomarkers for these diseases using an untargeted approach and our library of features. Ultimately, we hope to implement our findings in the regular newborn screening methods of the National Hospital.

RESULTS:
Our current feature library holds over 23,400 individual features, covering 91% of our metabolite catalogue, covering a wide range of molecules from organic acids to fatty acids and nucleosides. We have gathered data on all our detected metabolites, including CCS values, retention time, mass, associated fragments and adducts and relative intensity. Preliminary results of our extraction method optimization shows a clear difference between tested extraction methods, suggesting an improvement in both output and coverage to the current methods used by the Icelandic National Hospital.

DISCUSSION:
Our project is still in development, but we have gathered a significant amount of new spectral data on many metabolites. Following the acceptance of our application to gain access to dried blood spots from the National Hospital, we will begin our goal of screening 1,000 Icelandic newborns and analysing their metabolome using our methods. With our library, we hope to capture valuable information about the normal metabolome of Icelandic newborn babies, which will yield a comprehensive standard against which to compare newborn screening. This metabolic baseline will be a powerful tool in biomarker discovery for inborn errors of metabolism.