Untargeted metabolomics of murine wound samples

Importance of the topic

Disturbances of the skin microbiome (dysbiosis) are increasingly recognized as influential factors in wound healing. Untargeted metabolomics of wound tissue can reveal small-molecule mediators that reflect host–microbiome interactions and biochemical states relevant to inflammation, tissue regeneration and angiogenesis. Identifying wound-associated metabolites supports mechanistic understanding, biomarker discovery and the development of targeted interventions to improve healing outcomes.

Study objectives and overview

The study aimed to determine whether untargeted metabolomic profiles of murine wound tissue differ between healthy and diet-induced obese animals and whether any differences are linked to the presence of a microbiome. The work used orthogonal ionization in a single GC-MS run to increase annotation confidence and sought to generate putative identifications of metabolites that may be associated with wound healing processes.

Methodology

Sample collection and design:

• 22 murine wound tissue samples total: healthy controls (n = 14) and high-fat diet induced obese mice (n = 8). The cohort included germ-free mice and short-term colonized animals (one generation with microbiota).

Extraction and sample preparation:

• 10 mg wound tissue homogenized with stainless steel beads.
• Metabolites extracted using precooled acetonitrile/isopropanol/water (3:3:2, v/v/v).
• After centrifugation and vacuum drying, a two-step derivatization was performed: methoximation in pyridine (MeOx) followed by silylation using a FAMEs/MSTFA mixture.

Analytical platform and data processing:

• GC-MS analysis used the ecTOF platform (Bruker Daltonics) combining electron ionization (EI, 70 eV) and chemical ionization (CI, ammonia reagent gas) within a single 25 min chromatographic method.
• CI data were processed in MetaboScape (Bruker Daltonics) and statistically evaluated in MetaboAnalyst.
• EI data were processed with MS-DIAL and also evaluated using MetaboAnalyst.
• Statistical comparisons used volcano plot-based criteria (log2 fold change > 1, raw p < 0.05) to highlight differentially abundant features between healthy and obese wound samples.

Instrumentation used

• ecTOF GC/MS platform (Bruker Daltonics) enabling simultaneous acquisition of EI and CI spectra.
• GC with a 25 min separation method (specific column not reported in the summary).
• Standard laboratory equipment: bead mill homogenizer, centrifuge, vacuum concentrator; reagents for MeOx and MSTFA derivatization.

Main results and discussion

Key findings:

• CI-based analysis (MetaboScape) comparing colonized healthy and obese mice identified five significantly altered features (four elevated in healthy controls, one elevated in obese wounds).
• EI-based analysis (MS-DIAL) also revealed five significant features, all more abundant in healthy wound samples after normalization and scaling.

Putative metabolite annotations of significant features included:

• Lactic acid — known regenerative and anti-inflammatory roles relevant to wound repair.
• 13-docosenamide (Z)- — flagged as a metabolite of potential interest because it was significant in both CI and EI workflows and has prior associations with angiogenesis.
• Glycerol.
• 9-hexadecenoic acid (Z)- (palmitoleic-like fatty acid).
• Ethyl oleate.

Microbiome association and caveats:

• Differential features observed between healthy and obese mice with a microbiome were not present in germ-free animals, suggesting a microbiome-associated effect for at least some metabolites.
• 13-docosenamide (Z)- is of particular interest due to its repeated detection across orthogonal workflows and literature linking it to angiogenesis. However, it is also a known polymer additive and has been reported as a leachable from some plastic pipette tips, raising the possibility of exogenous contamination.
• The feature attributed to 13-docosenamide was not detected in method and derivatization blanks, reducing the likelihood of a simple lab-contamination explanation but not eliminating it.
• All reported identifications remain putative (database matches) and require confirmation with authentic analytical standards and further biological validation.
• Limited sample size (n = 22 total; healthy n = 14, obese n = 8) constrains statistical power and necessitates cautious interpretation of biological significance.

Benefits and practical applications of the method

• Combining EI and CI spectra in a single GC-MS run (ecTOF) improves annotation confidence by providing complementary fragmentation and molecular ion information.
• Standardized extraction and dual-step derivatization enable broad coverage of polar and semi-polar wound metabolites relevant to host and microbial metabolism.
• The approach can be applied to exploratory biomarker discovery in wound research, comparative studies of host–microbiome interactions, and preclinical models assessing the metabolic impact of obesity on healing.
• Potential for downstream targeted assays once putative markers are validated, enabling quantitative monitoring in larger cohorts or intervention studies.

Future trends and potential uses

• Validation studies using authentic standards, isotope-labeled internal standards and orthogonal platforms (e.g., LC-MS/MS) to confirm identities and quantify candidate metabolites.
• Larger, statistically powered cohorts including longitudinal sampling to track temporal dynamics of wound metabolomes during healing phases.
• Integration with microbiome sequencing and host transcriptomics/proteomics to link specific microbes or pathways to metabolite signatures and mechanistic processes such as angiogenesis and inflammation resolution.
• Spatially resolved metabolomics or imaging mass spectrometry to map metabolite distributions within wound tissue and identify local microenvironmental niches.
• Improved contamination control and workflow blanks, plus routine monitoring for common leachables (e.g., polymer additives), to discriminate endogenous signals from exogenous artifacts.
• Standardization of sample preparation and data-processing pipelines to enhance reproducibility across labs and platforms.

Conclusion

The study demonstrates the feasibility of untargeted GC-MS metabolomics on small murine wound tissue samples using an ecTOF platform that acquires EI and CI spectra simultaneously. Several putative metabolites differed between healthy and obese wound samples in colonized animals, with lactic acid and 13-docosenamide among the notable candidates potentially related to wound healing biology. However, limited sample size, reliance on database matches, and possible exogenous sources for some analytes require cautious interpretation. Confirmatory work with analytical standards, expanded cohorts and integrated multi-omic approaches is needed to validate biomarkers and clarify microbiome-associated metabolic effects on wound healing.

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