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Isobaric tags labeling technique was firstly applied for the simultaneous identification and relative quantification of peptide pairs by Thompson et al. in 2003. Tandem Mass Tag (TMT) system was designed by Thermo Fisher Scientific for the identification and quantification of proteins in different kinds of samples. Each TMT tagging reagent is composed of an amine-reactive NHS-ester group, a spacer arm and an MS/MS reporter. And the isobaric tagging reagent within a set has the same nominal parent (precursor) mass. These reporter ions are in the low mass region of the MS/MS spectrum and are used to report expression changes in cell-based or tissue samples that may not be amenable to metabolic isotopic labeling strategies such as SILAC. This technique enables simultaneous qualitative and quantitative analysis of proteins in different samples (currently, it can label up to 16 samples simultaneously).
What Is The Principle Of TMT Labeling
The isobaric labels TMT (tandem mass tagging) are available in up to 16 tags that can be used for labeling practically any peptide or protein sample. TMT makes it possible to multiplex the analysis, to efficiently use the instrument time and exert further controls for technical variation. Due to its ability to multiplex up to 11 samples, TMT are widely used for quantitative protein biomarker discovery. TMTzero, TMTduplex, TMTsixplex, TMT10plex, TMT11plex and TMT16plex Reagents share an identical structure, allowing efficient transition from methods development to multiplex quantitation.
The TMT reagent consists of three chemical moieties: the reporter group, the balance group, and the reactive group. The reactive group specifically reacts with the amino group of the peptide, covalently attaching the TMT reagent to the peptide. In the MS1 spectrum, peptides originating from the same protein but from different samples appear as a single peak due to the attachment of identical TMT reagents with the same total mass. However, in the collision cell, the balance group undergoes neutral loss, while the reporter group generates corresponding reporter ions (e.g., for a six-plex, the masses are 126, 127, 128, 129, 130, and 131 Da; for a ten-plex, the masses are 126, 127N, 127C, 128N, 128C, 129N, 129C, 130N, 130C, and 131 Da; for a sixteen-plex, the masses are 126, 127N, 127C, 128N, 128C, 129N, 129C, 130N, 130C, 131N, 131C, 132N, 132C, 133N, 133C, and 134N). The intensities of these reporter ions represent the abundance of the corresponding protein/peptide in the respective sample, enabling simultaneous quantification of six/ten/sixteen different samples.
TMT quantification is performed by measuring the intensities of fragment reporter ions released from the labels in the tandem MS mode (MS2) during peptide fragmentation. Precursor ions are selected in the full scan mode (MS1) to be fragmented. Since ion selection step reduces the noise levels, it is advantageous. Ideally, only one selected precursor ion is fragmented during the precursor ion fragmentation. However, it is common that other precursor ions are caught within the specified m/z window and are fragmented together with the selected precursor in practice. This is precursor co-isolation or mixing. Isobaric Labelling works best with mass-spectrometers which allow MS3-level quantitation such as Thermo's Fusion Orbitraps. The additional filtering step allows almost complete correction of co-isolation caused by contamination of MS1 precursors.
Our TMT Labeling Quantitative Proteomic Analysis Service
Instrumentation
Thermo Scientific™ Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer, which offers the most accurate quantitation and the highest number of protein identifications compared to other instrumentation.
Sample Prep
Sample preparation is easy. Just send frozen cell or tissue samples and our team will do the rest. About 100 μg total protein per sample will be enough.
Technical Advantages
Wide range of application: Proteins of most species can be isolated and identified. No species specificity restrictions. Wide range of protein types, membrane proteins, nuclear proteins, secreted proteins, etc. can be detected.
High efficiency: Liquid chromatography coupled with tandem mass spectrometry for fast analysis and good separation.
High throughput: It can simultaneously analyze 6, 10, or 16 samples in a single run.
Good reproducibility: The separation and identification conditions are consistent for all samples.
Rich data: It provides qualitative and quantitative information for all proteins.
High level of automation: Based on high-resolution liquid chromatography-mass spectrometry, the workflow is automated, allowing for fast analysis.
Accurate quantification: It reduces experimental errors caused by sample handling and different runs, resulting in more accurate quantification.
High sensitivity: It can detect proteins with expression abundance at the picogram (pg) level. It can detect low abundance proteins in samples such as blood, cerebrospinal fluid, muscle tissue, and bone tissue.
TMT Labeling Quantitative Proteomic Analysis
| Statistical analysis of protein identification | |
|---|---|
| Functional annotation | Total protein GO analysis |
| Pathway analysis | |
| Differential protein analysis | GO enrichment analysis of differential proteins |
| Pathway pathway enrichment analysis of differential proteins | |
Sample Requirement
| Sample Type | Requirement |
|---|---|
| Protein Extracts | Concentration > 1 μg/μL |
| Total protein amount > 300 μg | |
| Cell Samples | Cell count > 10^7 cells |
| Tissue Samples | Animal tissue > 100 mg |
| Plant material > 200 mg | |
| Liquid Samples | Blood volume > 100 μL |
| Urine > 10 mL | |
| Saliva > 1 mL |
Extended multiplexing of TMT labeling reveals age and high fat diets pecific proteome changes in mouse epididymal adipose tissue
Materials: Epididymal Tissue
Impact Factor: 6.5399
Journal: Molecular & Cellular Proteomics
Technique: Tandem Mass Tag (TMT) Proteomics
Background
Obesity is currently a global epidemic. Due to the lack of effective high-throughput techniques in the past, the study of protein composition in epididymal tissue has been limited, resulting in a very limited understanding of the metabolic and protein network changes induced by aging and a high-fat diet. Building upon previous work, this study improved experimental design and data analysis by employing the Tandem Mass Tag (TMT) proteomics technique with a total of 20 biological replicates. We compared the protein profiles between long-term high-fat diet groups, short-term high-fat diet groups, and age-matched high-fat diet groups with low-fat diet groups. The research revealed alterations in various proteins involved in pathways such as lipid metabolism, amino acid metabolism, immune response, apoptosis, and others. This article provides a simple and reproducible analytical method for proteomic studies of epididymal tissue.
Research Approach
Results
1. Basal Metabolic Assessments
This section includes assessments related to body weight, tissue weight, and serum markers, such as glucose, insulin, cholesterol, triglycerides, and glucose and insulin tolerance tests. These evaluations provide insights into the baseline metabolic status of the subjects and are crucial for the overall understanding of the study's findings.
3. Venn Diagram
Figure | Depicting Intersection of Differential Proteins across Groups Subtitle
4: Differential Protein Expression Fold Changes Caption
Volcano and Bar Charts Highlighting Noticeable Protein Variations in the High Fat Group.
5. Changes in Protein Expression Levels
6. Gene Ontology (GO) Analysis
Revealing Changes in Pathways Related to Lipid Metabolism, Amino Acid Metabolism, Immune Response, and Apoptosis in Light of Differential Protein Expression Fold Changes
7. Protein-Protein Interaction (PPI) Analysis
Identification of Key Node Proteins FGG, Dpt, Mug2, and Subsequent Validation of the Aforementioned Conclusions
Recommendation
In the context of TMT proteomics analysis of research tissue samples, this study stands out as an exemplary case in terms of research approach, technical methodology, and data analysis. It conducted comprehensive metabolic assessments, including insulin tolerance and lactose tolerance, for the selection of model metabolic indicators. Additionally, the experimental design for the proteomic study demonstrated meticulous sample grouping and replication. A total of three sets of ten-plex TMT labeling was employed, resulting in a rich dataset that will facilitate subsequent data analysis.
Creative Proteomics has been dedicated to the field of life sciences and life technology, pioneering multi-omics integration experiments and analysis based on proteomics and metabolomics in the early stages in China. After years of development and accumulation, the company has established proteinomics technology platforms, including iTRAQ/TMT, DIA, PRM, modified proteomics, as well as comprehensive metabolomics technology platforms, including full-spectrum metabolomics, targeted metabolomics, and lipidomics. Corresponding data integration and analysis platforms have also been established, along with a scientifically sound service workflow and precise operating standards.
Reference
- Plubell D. et al., Extended multiplexing of TMT labeling reveals age and high-fat diet-specific proteome changes in mouse epididymal adipose tissue. 2017, Mol. Cell. Proteomics.
Q&A of TMT
Q1: What are the features of labeled quantitative proteomic technologies such as iTRAQ and TMT?
A: iTRAQ and TMT and other labeled quantitative proteomic technologies are high resolution and can identify up to more than 6000 proteins in cell samples, and most proteins have quantitative and qualitative information; secondly, they are high throughput and can complete experiments with up to 8 (iTRAQ) or 10 (TMT) samples at a time, which is especially suitable for simultaneous comparison between multiple groups of samples and dynamic detection of biological processes.
Q2: How should I identify proteins for species without whole genome data?
A: For species with published whole genome sequences, we can build a library locally and conduct local searches after getting the whole sequences; for species without whole genome sequencing, we can use databases of closely related species or EST data and transcriptome data for searches.
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