Biomarker Discovery Proteomics
Biomarker discovery proteomics is a scientific discipline that employs proteomic techniques to screen, identify, and validate biomarkers, aiming to uncover key protein molecules involved in disease states, drug responses, or physiological processes. Biomarkers refer to biological molecules that can be objectively measured to assess normal biological processes, pathological states, or responses to therapeutic interventions. Proteins, being the primary functional molecules in living organisms, provide insights into physiological or pathological conditions through changes in their expression levels, post-translational modifications, and interaction networks. Consequently, biomarker discovery proteomics offers robust technical support for early disease diagnosis, personalized medicine, and the development of novel therapeutic targets. In cancer, for instance, the onset and progression of malignant tumors often involve abnormal protein expression or modification patterns. Prostate-specific antigen (PSA) is extensively used for prostate cancer screening, while the overexpression of HER2 is a foundation for breast cancer classification and targeted therapy. By utilizing high-resolution mass spectrometry and bioinformatics, biomarker discovery proteomics systematically identifies potential disease-related proteins, leading to more accurate diagnostic methods. Moreover, this technology has shown clinical value in autoimmune diseases, neurodegenerative diseases, and infectious diseases. For example, the abnormal phosphorylation of Tau protein in Alzheimer's disease patients' cerebrospinal fluid is a recognized marker of disease progression, while inflammation-related proteins serve as indicators of disease severity in infectious diseases like sepsis.
The research process of biomarker discovery proteomics typically involves sample collection, protein separation and identification, quantitative analysis, and data mining. Initially, potential biomarker sources are obtained from blood, urine, cerebrospinal fluid, or tissue samples. Protein identification is then conducted using techniques like liquid chromatography-mass spectrometry (LC-MS/MS) or antibody arrays, with protein expression differences analyzed through techniques like TMT, iTRAQ for label-based quantification, or DIA, SWATH for label-free quantification. During data analysis, bioinformatics tools are employed to screen and statistically validate data, identifying specific and stable biomarkers. Subsequent functional validation experiments, such as Western blot, ELISA, or immunohistochemistry, further ensure the clinical applicability of biomarkers.
In recent years, advancements in single-cell proteomics, multi-omics integration, and artificial intelligence have enhanced the research scope and depth of biomarker discovery proteomics. Single-cell proteomics enables the analysis of protein expression at the individual cell level, revealing tissue heterogeneity and microenvironmental regulatory mechanisms. Multi-omics integration, combining genomics, transcriptomics, and metabolomics, allows for a comprehensive analysis of biomarker dynamics, improving specificity and reliability. Furthermore, machine learning and deep learning algorithms have significantly enhanced the pattern recognition and predictive capabilities of extensive proteomics data, expediting the discovery and translation of valuable biomarkers.
MtoZ Biolabs is dedicated to providing high-quality proteomics analysis services to research institutions and biopharmaceutical companies. With extensive experience in proteomics research, we offer customized experimental strategies, integrating advanced mass spectrometry technology and bioinformatics analysis to ensure precise and reliable biomarker discovery.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
