Untargeted LC-MS metabolomics

Stages

1. 1.
   Rabbit model and tissue collection(library_build)

   This stage generates the plaque and control tissue inputs required for comparative multi-omics profiling.

   Selection: Experimental rabbits were divided into model and sham groups, and abdominal aortas were isolated and collected for downstream analysis.

2. 2.
   Quantitative proteomics profiling(functional_characterization)

   This stage identifies proteins altered between injured and uninjured aortas.

   Selection: TMT-labeled quantitative proteomics was performed on abdominal aorta samples to evaluate protein fingerprints in arterial plaques.

3. 3.
   Untargeted metabolomics profiling(functional_characterization)

   This stage identifies metabolites altered in plaque tissue, including lipid components and pathway-linked metabolites.

   Selection: Untargeted LC-MS metabolomics was performed on abdominal aorta samples to evaluate metabolite fingerprints in arterial plaques.

4. 4.
   Integrated statistical and pathway analysis(secondary_characterization)

   This stage integrates protein and metabolite changes to infer correlated molecular signatures and implicated pathways.

   Selection: Acquired data were analyzed using uni- and multivariate statistics, Pearson correlation, and KEGG enrichment analysis.

Steps

1. 1.
   Assign rabbits to model and sham groups

   Create plaque and control cohorts for comparative analysis.

   Group assignment is required before tissue collection and downstream omics measurements.

2. 2.
   Isolate and collect abdominal aortas

   Obtain tissue samples for proteomic and metabolomic profiling.

   Tissue collection must precede sample preparation and omics analysis.

3. 3.
   Treat collected aortas with proteinase K

   Prepare collected aortic tissue for downstream analysis.

   The abstract places proteinase K treatment after tissue collection and before omics assays.

4. 4.
   Perform TMT-labeled quantitative proteomics analysisassay method

   Measure protein fingerprints in arterial plaques.

   Proteomic measurement follows tissue collection and preparation to generate one molecular layer for integrated analysis.

5. 5.
   Perform untargeted LC-MS metabolomics analysisassay method

   Measure metabolite fingerprints in arterial plaques.

   Metabolomic measurement provides the second molecular layer needed for integrated analysis after tissue collection and preparation.

6. 6.
   Analyze acquired data using uni- and multivariate statistics

   Identify differential molecular features from the acquired omics data.

   Statistical analysis follows data acquisition to determine which proteins and metabolites differ between groups.

7. 7.
   Compute Pearson correlations between differentially abundant proteins and metabolites

   Link altered proteins and metabolites across omics layers.

   Correlation analysis requires differential protein and metabolite features identified from prior assays and statistics.

8. 8.
   Predict involved functional pathways using KEGG enrichment analysis

   Interpret altered molecular features in terms of biological pathways.

   Pathway prediction is performed after differential features and correlations are available for biological interpretation.