Serum Proteomics Research

Serum proteomics research primarily focuses on the composition, structure, and function of proteins in serum, as well as their changes under various physiological and pathological conditions. Serum, a blood component without fibrinogen, is an important biological sample for studying human health status, disease diagnosis, treatment monitoring, and new drug discovery. Due to the complexity of the serum proteome and the wide dynamic range of protein expression levels, serum proteomics faces many challenges but also provides rich information and opportunities. Below are the key aspects and technologies in serum proteomics research:

 

1. Sample Preparation

Removal of High-Abundance Proteins: Use specific affinity columns or other techniques to remove high-abundance proteins in serum (such as albumin and immunoglobulins) to better detect and analyze low-abundance proteins.

 

2. Protein Separation and Identification

Two-Dimensional Gel Electrophoresis (2D-PAGE): First separate proteins based on their isoelectric points, followed by a second-dimensional separation according to molecular weight.

Liquid Chromatography-Mass Spectrometry (LC-MS/MS): Used for high-throughput identification and quantitative analysis of serum proteins, especially with high sensitivity for low-abundance proteins.

 

3. Quantitative Analysis

Labeled Quantitative Methods: Such as isotope labeling and Tandem Mass Tag (TMT) labeling, allowing simultaneous analysis of protein expression differences in multiple samples.

Label-Free Quantification: Such as Label-Free Quantification (LFQ) and Data Independent Acquisition (DIA), suitable for large-scale proteomics analysis.

 

4. Data Analysis and Bioinformatics

Use advanced algorithms and bioinformatics tools to process large datasets, identifying protein identification, quantitative differences, and biomarkers.

Protein function annotation, pathway analysis, and network analysis help reveal biological functions and disease associations of proteins.

 

5. Applications

Disease Diagnosis and Biomarker Discovery: Identify disease-related biomarkers by comparing serum proteome differences between health and disease states.

Therapeutic Effect Monitoring: Analyze changes in the serum proteome before and after treatment to evaluate therapeutic effects.

Drug Target Identification: Identify potential drug targets by studying protein functions and interaction networks.