Source 1primary paper2025MED
Toolkit/TMT-labeled quantitative proteomics
TMT-labeled quantitative proteomics
Also known as: tandem mass tag (TMT)-labeled quantitative proteomics analysis
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
a tandem mass tag (TMT)-labeled quantitative proteomics analysis
Usefulness & Problems
Why this is useful
This method quantifies protein abundance differences in abdominal aorta samples from rabbit plaque and sham conditions. In this study it was used to identify differentially abundant proteins in injured versus uninjured aortas.; quantifying differentially abundant proteins in rabbit aortic plaque tissue
Source:
This method quantifies protein abundance differences in abdominal aorta samples from rabbit plaque and sham conditions. In this study it was used to identify differentially abundant proteins in injured versus uninjured aortas.
Source:
quantifying differentially abundant proteins in rabbit aortic plaque tissue
Problem solved
It helps reveal protein fingerprints associated with rabbit atherosclerotic plaques.; profiles protein abundance changes associated with atherosclerotic plaques
Source:
It helps reveal protein fingerprints associated with rabbit atherosclerotic plaques.
Source:
profiles protein abundance changes associated with atherosclerotic plaques
Problem links
profiles protein abundance changes associated with atherosclerotic plaques
LiteratureIt helps reveal protein fingerprints associated with rabbit atherosclerotic plaques.
Source:
It helps reveal protein fingerprints associated with rabbit atherosclerotic plaques.
Published Workflows
Objective: Explore potential mechanisms and molecular signatures associated with rabbit atherosclerotic plaques by integrating proteomics and untargeted metabolomics analyses of abdominal aortas.
Why it works: The workflow combines protein and metabolite profiling from plaque tissue, then links these layers using statistical correlation and pathway enrichment to reveal coordinated molecular signatures.
molecular changes associated with plaque formationmetabolic pathway involvement in plaquesTMT-labeled quantitative proteomicsuntargeted LC-MS metabolomicsunivariate and multivariate statisticsPearson correlation analysisKEGG enrichment analysis
Stages
- 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.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.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.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.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.Isolate and collect abdominal aortas
Obtain tissue samples for proteomic and metabolomic profiling.
Tissue collection must precede sample preparation and omics analysis.
- 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.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.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.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.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.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.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Mechanisms
isobaric chemical labeling for relative quantificationmass spectrometry-based peptide/protein abundance measurementTechniques
Functional AssayTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
The abstract indicates that abdominal aortas were isolated, collected, treated with proteinase K, and then analyzed by a TMT-labeled quantitative proteomics workflow.; requires abdominal aorta tissue collection and proteomics workflow execution
The abstract does not show that this method alone establishes causality or validates individual proteins as clinical biomarkers.; the abstract does not specify protein identities, coverage limits, or validation beyond discovery profiling
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Injured rabbit aortas with plaques show 207 significantly altered proteins compared with uninjured aortas, including 133 upregulated and 74 downregulated proteins.
A total of 207 proteins are significantly altered in injured aortas compared to uninjured ones, with 133 upregulated and 74 downregulated proteins (fold changes > 1.2, P < 0.05).
downregulated proteins 74fold change threshold 1.2P value threshold 0.05significantly altered proteins 207upregulated proteins 133
Rabbit plaques show 234 significantly changed metabolites in positive ion mode and 187 in negative ion mode.
In plaques, 234 metabolites are significantly changed under the positive ion mode, and 187 under the negative ion mode.
significantly changed metabolites negative ion mode 187significantly changed metabolites positive ion mode 234
Phosphatidylcholines and lysophosphatidylcholines, including PC 9:0 and LPC 20:2, are increased in rabbit plaques.
Notably, increases are observed in phosphatidylcholines (PCs) [PC 9:0] and lysophosphatidylcholines (LPCs) [LPC 20:2], two key lipid components.
Altered plaque metabolites are involved in purine metabolism and vascular smooth muscle contraction pathways.
These metabolites are involved in some key metabolic pathways, including purine metabolism and vascular smooth muscle contraction.
Approval Evidence
1 source1 linked approval claimfirst-pass slug tmt-labeled-quantitative-proteomics
a tandem mass tag (TMT)-labeled quantitative proteomics analysis
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differential abundancesupports
Injured rabbit aortas with plaques show 207 significantly altered proteins compared with uninjured aortas, including 133 upregulated and 74 downregulated proteins.
A total of 207 proteins are significantly altered in injured aortas compared to uninjured ones, with 133 upregulated and 74 downregulated proteins (fold changes > 1.2, P < 0.05).
Source:
Comparisons
Source-stated alternatives
The same study pairs proteomics with untargeted LC-MS metabolomics, indicating metabolite profiling as a complementary approach rather than a substitute for protein-level measurement.
Source:
The same study pairs proteomics with untargeted LC-MS metabolomics, indicating metabolite profiling as a complementary approach rather than a substitute for protein-level measurement.
Source-backed strengths
supports quantitative proteomic comparison between injured and uninjured aortas
Source:
supports quantitative proteomic comparison between injured and uninjured aortas
Compared with Untargeted LC-MS metabolomics
The same study pairs proteomics with untargeted LC-MS metabolomics, indicating metabolite profiling as a complementary approach rather than a substitute for protein-level measurement.
Shared frame: source-stated alternative in extracted literature
Strengths here: supports quantitative proteomic comparison between injured and uninjured aortas.
Relative tradeoffs: the abstract does not specify protein identities, coverage limits, or validation beyond discovery profiling.
Source:
The same study pairs proteomics with untargeted LC-MS metabolomics, indicating metabolite profiling as a complementary approach rather than a substitute for protein-level measurement.
Ranked Citations
- 1.