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<p>Sample-processing strategies markedly influence metabolite recovery in non-targeted GC–MS-based urinary metabolomics. This study aimed to evaluate and compare tangential flow filtration (TFF) and urease treatment, applied individually and in combination, to optimise bovine urine sample preparation for non-targeted GC–MS analysis. Urine samples collected from Karan Fries cows (<i>n</i> = 8) were processed using three distinct workflows: (1) TFF-filtered urine, (2) TFF-filtered urine followed by urease treatment, and (3) raw urine treated with urease. All processed aliquots underwent identical extraction procedures and two-step derivatisation prior to GC–MS analysis. Comparative evaluation revealed substantial differences in metabolite yield, diversity and reproducibility among the processing methods. TFF-filtered urine achieved the highest metabolite coverage (282 total metabolites, including 123 unique compounds), followed by the combined TFF and urease treatment (241 total, 74 unique), whereas urease-treated raw urine exhibited markedly reduced coverage (159 total, 37 unique), indicating that TFF filtration enhanced reproducibility and improved matrix removal. Analysis of chemical class distribution demonstrated that TFF-filtered samples contained higher proportions of carbohydrates, esters, hydrocarbons, amino acids and polyols, while urease-treated raw urine was comparatively enriched in hydrocarbons and aromatic compounds. Notably, several compound classes, including dihydrofuranones, pyran derivatives and quinols, were exclusively detected in TFF-processed samples. Pathway enrichment analysis further indicated that TFF-filtered urine provided the most comprehensive metabolic representation, with significant enrichment of pathways associated with carbohydrate metabolism, amino acid biosynthesis, ascorbate and aldarate metabolism, and fatty acid metabolism. Overall, TFF filtration emerged as the most effective sample-processing strategy for maximising metabolite diversity and pathway coverage in bovine urinary metabolomics.</p>

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Comparative metabolomic profiling of bovine urine using different sample processing methods by non-targeted GC–MS analysis

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Swati Ruhil,
  • Rubina Kumari Baithalu,
  • Mohd Akram,
  • Abhijeet Fernandes,
  • Richa Singh,
  • A. Kumaresan,
  • Vishvas Gohil,
  • Sanjay Kumar Singh,
  • Tushar Kumar Mohanty

摘要

Sample-processing strategies markedly influence metabolite recovery in non-targeted GC–MS-based urinary metabolomics. This study aimed to evaluate and compare tangential flow filtration (TFF) and urease treatment, applied individually and in combination, to optimise bovine urine sample preparation for non-targeted GC–MS analysis. Urine samples collected from Karan Fries cows (n = 8) were processed using three distinct workflows: (1) TFF-filtered urine, (2) TFF-filtered urine followed by urease treatment, and (3) raw urine treated with urease. All processed aliquots underwent identical extraction procedures and two-step derivatisation prior to GC–MS analysis. Comparative evaluation revealed substantial differences in metabolite yield, diversity and reproducibility among the processing methods. TFF-filtered urine achieved the highest metabolite coverage (282 total metabolites, including 123 unique compounds), followed by the combined TFF and urease treatment (241 total, 74 unique), whereas urease-treated raw urine exhibited markedly reduced coverage (159 total, 37 unique), indicating that TFF filtration enhanced reproducibility and improved matrix removal. Analysis of chemical class distribution demonstrated that TFF-filtered samples contained higher proportions of carbohydrates, esters, hydrocarbons, amino acids and polyols, while urease-treated raw urine was comparatively enriched in hydrocarbons and aromatic compounds. Notably, several compound classes, including dihydrofuranones, pyran derivatives and quinols, were exclusively detected in TFF-processed samples. Pathway enrichment analysis further indicated that TFF-filtered urine provided the most comprehensive metabolic representation, with significant enrichment of pathways associated with carbohydrate metabolism, amino acid biosynthesis, ascorbate and aldarate metabolism, and fatty acid metabolism. Overall, TFF filtration emerged as the most effective sample-processing strategy for maximising metabolite diversity and pathway coverage in bovine urinary metabolomics.