Proteomics analysis involves a comprehensive study of all proteins in an organism, tissue, or cell, focusing on their structure, function, expression levels, and interactions, to reveal their dynamic changes and molecular mechanisms in biological processes. Proteomics analysis encompasses protein identification, quantitative analysis, detection of post-translational modifications (PTMs), protein interaction network analysis, and functional pathway enrichment analysis, aiming to provide a thorough understanding of protein roles under various physiological and pathological conditions.
Proteomics analysis can uncover the connection between genes and phenotypes, elucidate the key molecular mechanisms underlying disease occurrence, progression, and treatment, and identify potential biomarkers and therapeutic targets. Through high-resolution mass spectrometry technologies and advanced bioinformatics tools, proteomics analysis addresses scientific challenges such as disease molecular mechanism analysis, drug target identification, and biomarker discovery, offering robust support for precision medicine, drug development, and fundamental life sciences research.
1. Protein Quantitation
Quantitative proteomics is divided into label-based methods and label-free methods. Labeled quantitative proteomics involves introducing light and heavy isotopes or chemical tags into samples to compare protein abundance under different conditions. Metabolic labeling (e.g., SILAC) uses labeled amino acids introduced during cell culture to metabolically label proteins, while chemical labeling (e.g., ICAT) modifies specific residues with specialized chemical tags. Enzymatic labeling incorporates 18O isotopes during the digestion process using heavy water. After labeling, samples are mixed in fixed ratios and analyzed by LC-MS/MS to quantify relative protein abundance based on differences in signal intensities of mass peaks. Label-based methods offer high precision and reproducibility, making them ideal for quantitative studies in complex samples. Label-free quantitative proteomics does not require additional labeling of samples and directly quantifies relative protein abundance by analyzing differences in peptide signal intensities or spectral counts using mass spectrometry. After separate digestion and mass spectrometry analysis of each sample, the relative abundance of proteins is calculated by comparing the signal intensities of the same peptides across different conditions. The label-free method has a simpler workflow and is suitable for large-scale samples or experiments where labeling is impractical, but it relies heavily on instrument stability and advanced data processing capabilities.
2. Protein Identification
Protein identification holds a central position in proteomics analysis, serving as the foundation for understanding the composition, structure, and function of proteins within biological systems. The protein identification workflow consists of several key steps. First, proteins are extracted from biological samples such as animals, tissues, biofluids, or cells. Next, protein separation is performed to ensure effective differentiation of various proteins. Following this, proteolytic digestion using enzymes like trypsin breaks down proteins into peptides. Subsequently, chemical labeling (an optional step) can be applied to enhance the accuracy of quantitative analysis. The resulting peptides are then analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), generating mass spectra. These spectra are used for peptide identification and quantification, which is further mapped to protein identification and quantification. Leveraging high-precision mass spectrometry technologies and advanced bioinformatics tools, protein identification reveals changes in protein expression, post-translational modifications, and protein-protein interactions under specific conditions, providing critical data for disease mechanism studies, biomarker discovery, and drug target screening.
3. Protein Post-translational Modification Analysis
Based on this diagram, the post-translational modification (PTM) analysis workflow primarily follows three strategies: Bottom-up proteomics, Middle-down proteomics, and Top-down proteomics. In Bottom-up proteomics, protein samples undergo high-grade digestion to generate small peptides (8–30 amino acids). These peptides are separated and selected using LC-MS, followed by MS2 analysis to identify PTM sites. In Middle-down proteomics, proteins are subjected to medium-grade digestion to produce larger peptides (>30 amino acids). These peptides are separated via LC-MS, selected, and analyzed using MS2, preserving more sequence and modification information. In Top-down proteomics, proteins remain undigested and are analyzed as intact entities. They undergo protein separation, proteoform selection, and subsequent analysis via MS1 and MS2, allowing the identification of different proteoforms and PTM patterns. During PTM identification, the measured peptide mass is compared with the theoretically calculated sequence mass. Through the mass shift (Δm), specific modifications (e.g., acetylation, Δm = 42.01 Da) are identified and validated using PTM databases. This comprehensive workflow accurately identifies PTM sites, types, and their biological roles, revealing regulatory mechanisms in cellular processes.
4. Protein-Proten Interaction Analysis
Protein interaction analysis is a crucial approach for uncovering protein functions and regulatory networks within cells. The following are three main strategies:
(a) Affinity Purification: Using antibodies or tags (e.g., Strep-tag, TAP tags), target proteins (Bait) are enriched from cell lysates along with their direct or indirect interacting partners. These complexes are subsequently identified through mass spectrometry. This method is particularly suitable for analyzing stable protein complexes.
(b) Proximity Labeling: Techniques such as BioID or APEX are employed to covalently label neighboring proteins within a defined radius around the target protein using biotin. These biotin-labeled proteins are then purified via streptavidin and identified using mass spectrometry. This approach is ideal for capturing weak or transient protein interactions.
(c) Global Interactome Analysis: Proteins are analyzed globally through fractionation techniques (e.g., SEC, IEX) or thermal proximity coaggregation analysis. These methods enable the identification and quantification of protein complexes on a large scale. Finally, mass spectrometry and bioinformatics are used to integrate the data and construct a comprehensive protein interaction network (Interactome), revealing dynamic protein interactions in complex biological processes.
5. Sample Proteomics Analysis
Sample proteomics analysis involves a comprehensive investigation of the composition, structure, and function of proteins within a sample. First, protein extraction is performed to isolate total proteins from biological samples, such as cells, tissues, or biofluids, ensuring sample integrity and high quality. Next, protein digestion is carried out, typically using trypsin to break proteins into specific peptides, facilitating subsequent analysis. This is followed by MS/MS analysis, where liquid chromatography-tandem mass spectrometry (LC-MS/MS) is used to separate, identify, and quantify the peptides. Subsequently, data analysis is conducted using specialized bioinformatics tools to interpret the mass spectrometry data, identifying and quantifying proteins. Finally, a report generation step compiles the experimental results into a visualized report, revealing patterns of protein expression, interactions, and functional changes in biological processes. Sample proteomics analysis provides robust technical support for disease mechanism research, biomarker discovery, and drug target identification.
