Torbjørn Norberg Myhre

Background: Metabolite identification and annotation remain major bottlenecks in untargeted metabolomics because mass spectral features often lack sufficient specificity. High-confidence annotation requires experimental validation using authentic standards analyzed under matched chromatographic and ionization conditions, providing greater reliability than in silico predictions or database matching alone. This study aimed to develop a practical and scalable workflow for constructing a high-quality mass spectral library using a commercially available analytical standards kit. Methods: A total of 603 metabolites from the MSMLS kit were organized into 42 mixtures, each containing approximately 15 compounds. Mixture design was based on molecular mass and distribution coefficient values, specifically logD at pH 3.1, with a minimum logD spacing of 0.15 to improve chromatographic separation and reduce co-elution. This strategy was used to minimize the total number of injections while maintaining spectral quality. The resulting spectra were evaluated against online spectral resources and in silico fragmentation predictions. A preliminary proof-of-concept analysis was also performed using human serum samples. Results: Using this workflow, 471 metabolites, corresponding to approximately 78% of the standards, were successfully detected and incorporated into the spectral library. Comparison with online resources and in silico fragmentation predictions demonstrated improved spectral quality and reliability. The proof-of-concept serum analysis enabled identification of endogenous metabolites using the constructed library. In addition, the robustness and applicability of the workflow were further supported by a method validation study using metabolites derived from this library. Conclusions: This workflow provides a scalable strategy for constructing mass spectral libraries that balances spectral quality with analytical throughput. By using rational mixture design and authentic standards analyzed under matched experimental conditions, the approach enables substantial metabolite coverage while maintaining data reliability and minimizing experimental effort.

Untargeted metabolomics by LC-HRMS is a powerful tool when searching for new biomarkers to enhance our knowledge of pathophysiological processes. When used for large-scale clinical studies, it is crucial to have a comprehensive and robust setup and that the laboratory’s ability to collect reliable data is documented. This study aimed to establish a robust analytical platform for a long-term clinical metabolomics study, assessing the method's fitness-for-purpose. Samples were prepared with an automated liquid handler and four analytical methods were developed and evaluated. The method validation study spanned three batches with twelve runs using individual serum samples and quality control samples. Data was acquired with untargeted acquisition and only metabolites identified with a local mass spectral library including retention time (level 1 identification) were evaluated. Validation parameters included reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Of the four methods tested, two were selected for validation. A total of 47 metabolites in RPLC-ESI+-and 55 metabolites in HILIC-ESI--HRMS met specified validation criteria, and full validation reports are presented. Quality assurance involved system suitability testing, sample release, run release, and batch release. Concentrated and diluted pooled quality control samples were introduced to correct for variance in response linearity across batches. The acquired data files can further be revisited for ad hoc validation of future metabolome and exposome features detected with the same setup. The validated methods proved fit-for-purpose. The laboratory demonstrated its capability to produce reliable results for large-scale, clinical, untargeted metabolomics. This study underscores the value of validation of an assays performance before commencing larger studies to enhance the reliability and impact of the generated hypotheses.

The diatom lipidome actively regulates photosynthesis and displays a high degree of plasticity in response to a light environment, either directly as structural modifications of thylakoid membranes and protein–pigment complexes, or indirectly via photoprotection mechanisms that dissipate excess light energy. This acclimation is crucial to maintaining primary production in marine systems, particularly in polar environments, due to the large temporal variations in both the intensity and wavelength distributions of downwelling solar irradiance. This study investigated the hypothesis that Arctic marine diatoms uniquely modify their lipidome, including their concentration and type of pigments, in response to wavelength-specific light quality in their environment. We postulate that Arctic-adapted diatoms can adapt to regulate their lipidome to maintain growth in response to the extreme variability in photosynthetically active radiation. This was tested by comparing the untargeted lipidomic profiles, pigmentation, specific growth rates and carbon assimilation of the Arctic diatom Porosira glacialis vs. the temperate species Coscinodiscus radiatus during exponential growth under red, blue and white light. Here, we found that the chromatic wavelength influenced lipidome remodeling and growth in each strain, with P. glacialis showing effective utilization of red light coupled with increased inclusion of primary light-harvesting pigments and polar lipid classes. These results indicate a unique photoadaptation strategy that enables Arctic diatoms like P. glacialis to capitalize on a wide chromatic growth range and demonstrates the importance of active lipid regulation in the Arctic light environment.