TMT-Based Quantitative Proteomics
TMT-based quantitative proteomics is a technique that utilizes isotopic labeling for multiplex protein quantification. Its primary advantage lies in high throughput, enabling the simultaneous analysis of multiple samples, thus improving experimental efficiency and minimizing batch effects. Compared to LFQ methods, TMT labeling at the peptide level reduces technical biases, enhancing quantification stability and reproducibility. However, TMT also has limitations, such as co-isolation interference due to sample mixing before mass spectrometry (MS) detection, which affects quantification accuracy. To address this, MS3-based TMT quantification strategies (e.g., TMT-MS3) have been developed, offering more precise fragment selection for improved accuracy. Additionally, TMT experiments require high sample input, making them unsuitable for low-input studies, and the cost of TMT reagents can increase research budgets. Despite these challenges, TMT-based quantitative proteomics is widely applied in cancer, neurodegenerative diseases, and immune disorders to study protein expression changes, identify potential biomarkers, and uncover regulatory mechanisms in complex biological processes.
The experimental workflow of TMT-based quantitative proteomics involves several key steps: sample preparation, TMT labeling, liquid chromatography separation, mass spectrometry analysis, and data processing. First, proteins from tissues, cells, or biofluids undergo extraction, denaturation, reduction, and alkylation, followed by enzymatic digestion using trypsin or other proteases. The resulting peptides react with distinct TMT labels, assigning unique isotopic tags to each sample for differentiation in subsequent analysis. After labeling, all samples are pooled and fractionated using high-performance liquid chromatography (HPLC) to reduce complexity and enhance MS detection sensitivity. The mixed peptides are then analyzed by high-resolution MS (e.g., Orbitrap, Q-TOF), where TMT tags release reporter ions at MS2 or MS3 stages. The relative intensity of these ions determines protein abundance across samples. Finally, the mass spectrometry data is processed using specialized software like Proteome Discoverer or MaxQuant for protein identification, quantification, and functional analysis.
The large-scale data generated from TMT-based quantitative proteomics requires advanced bioinformatics tools for analysis, including protein identification, quantification, differential expression analysis, and functional enrichment. Database search algorithms (e.g., Sequest, Mascot) are used to match mass spectrometry data and determine protein sequences and relative abundances. Statistical analysis then identifies significantly differentially expressed proteins between experimental groups, revealing potential biomarkers or disease-associated molecules. Functional enrichment and pathway analysis using databases such as KEGG, GO, and Reactome help interpret the biological roles of these proteins. Recent advancements in artificial intelligence and machine learning have further enhanced protein function prediction, biomarker discovery, and personalized medicine strategies in proteomics research.
With years of experience in proteomics research, MtoZ Biolabs provides high-quality quantitative proteomics services, including TMT sample labeling, mass spectrometry analysis, data processing, and bioinformatics interpretation.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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