ArticleScientific reports2023
Optimising a urinary extraction method for non-targeted GC-MS metabolomics.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Urinary metabolomic profiling to identify the impact of the increased intake of whole grains, vegetables, and fruits in healthy adolescents.European journal of clinical nutrition · 2026Article
- Effect biomonitoring in a controlled firefighting setting: an untargeted metabolomic pilot study.Archives of toxicology · 2026Article
- Characterising the urinary metabolome in HIV/TB coinfection and the impact of antiretroviral treatment.BMC infectious diseases · 2026Article
- Comparative metabolomic profiling of bovine urine using different sample processing methods by non-targeted GC-MS analysis.Veterinary research communications · 2026Article
- Urine Metabolomics of Gout Reveals the Dynamic Reprogramming and Non-Invasive Biomarkers of Disease Progression.Metabolites · 2025Article
- Urine organic acid metabolomic profiling by gas chromatography mass spectrometry: Assessment of solvent extract evaporation parameters on the recovery of key diagnostic metabolites.Clinica chimica acta; international journal of clinical chemistry · 2025Article
- The effect of treatment with a non-ionic surfactant vesicular formulation of sodium stibogluconate on host immune responses and serum metabolites in a murine model ofFrontiers in immunology · 2025Article
- Article
- Metabolic insights into HIV/TB co-infection: an untargeted urinary metabolomics approach.Metabolomics : Official journal of the Metabolomic Society · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.