Workflow of Serum Proteomics for Disease Biomarker Discovery

Serum proteomics is the study of the composition and changes of proteins in serum, which has been widely applied in the discovery of disease biomarkers in recent years. Biomarkers are essential tools for disease diagnosis, prognosis evaluation, and treatment monitoring. Serum proteomics, through high-throughput technologies, can provide a large amount of quantitative and qualitative data about proteins.

 

Sample Collection and Processing

In serum proteomics research, sample collection is a critical step. First, it is necessary to select an appropriate study population to ensure the representativeness and diversity of samples. Typically, studies are divided into case groups and control groups, where the case group consists of individuals with specific diseases, and the control group consists of healthy individuals. During sample collection, blood samples should be collected using anticoagulant tubes, and serum should be obtained after centrifugation. To ensure the stability of samples, processing should occur as soon as possible after collection, with samples stored under low-temperature conditions.

 

Protein Extraction and Quantification

After sample processing, the next step is protein extraction. Commonly, buffer solutions and chemical reagents are used to dissolve and separate proteins in serum. The concentration of extracted proteins needs to be quantified through colorimetric methods or BCA assays to ensure accuracy in subsequent analyses.

 

Protein Separation and Identification

Protein separation is typically performed using two common techniques: one-dimensional gel electrophoresis (SDS-PAGE) and two-dimensional gel electrophoresis (2D-GE). These methods allow for the separation of proteins based on their molecular weight and isoelectric point, providing a basis for subsequent mass spectrometric analysis.

 

After separation, proteins need to be identified, commonly using mass spectrometry, particularly LC-MS/MS (liquid chromatography-tandem mass spectrometry). Through mass spectrometry, researchers can identify and quantify proteins and further obtain structural information.

 

Data Analysis

Data analysis is one of the most challenging aspects of proteomics research. The acquired mass spectrometric data needs to be processed through specialized software to identify and quantify proteins. These software programs can analyze complex mass spectrometric data, identify characteristic peptides, and compare them with known databases.

 

Data analysis also requires bioinformatics analysis, where researchers can utilize bioinformatics tools for functional enrichment analysis and pathway analysis to help understand the role of proteins in diseases.

 

Biomarker Validation

After initially screening potential biomarkers, further validation is necessary. This usually involves analyzing a larger scale of samples to confirm their expression changes across different individuals and pathological states. The validation process may include quantitative methods such as enzyme-linked immunosorbent assays (ELISA) to quantify the expression levels of these proteins.

 

Assessment of Clinical Relevance

To ensure that the discovered biomarkers have clinical applicability, researchers must evaluate their relevance in disease diagnosis, prognosis, and treatment monitoring. This may involve comparative analysis with clinical data, such as patient survival rates and disease progression. Through statistical analysis, researchers confirm whether these biomarkers can effectively distinguish between case and control groups.

 

Serum proteomics plays a vital role in the discovery of disease biomarkers, with its workflow encompassing sample collection, data analysis, biomarker validation, and assessment of clinical relevance.