Detailed Workflow of Label-Free Proteomics Analysis

Unlabeled proteomics analysis is a crucial technology used to investigate the composition and expression of proteins. Compared to labeled technology, unlabeled proteomics analysis has advantages such as simplicity, high throughput, and high sensitivity. This article will provide a detailed introduction to the process of unlabeled proteomics analysis, from sample preparation to data interpretation, emphasizing the significance of each step and method selection, and its application value in biomedical research.

 

Sample Preparation

Sample preparation is the first step of unlabeled proteomics analysis. This includes the collection of samples, cell lysis, and protein extraction. For complex samples, such as tissues or serum, layered centrifugation, ultrasonic crushing, or acidic precipitation methods can be used for sample pretreatment. The determination of protein content in the sample is also necessary for the control of sample normalization and loading amount.

 

Protein Separation

Protein separation is a key step in unlabeled proteomics analysis. Common protein separation methods include gel electrophoresis and liquid chromatography. Gel electrophoresis can be used to separate the molecular weight and charge characteristics of proteins, with common methods being SDS-PAGE and 2D electrophoresis. Liquid chromatography can separate the hydrophilicity, hydrophobicity, and affinity characteristics of proteins, with reverse phase chromatography and ion exchange chromatography being common methods.

 

Mass Spectrometry Analysis

Mass spectrometry analysis is the core step of unlabeled proteomics analysis. By ionizing and analyzing protein samples with a mass spectrometer, the mass and sequence information of proteins can be obtained. Common mass spectrometry analysis methods include Time-of-Flight mass spectrometry (TOF-MS), tandem mass spectrometry (MS/MS), and quantitative mass spectrometry. The choice of mass spectrometry analysis is based on factors such as the complexity of the sample, the required resolution, and sensitivity.

 

Data Analysis

Data analysis is the final step in unlabeled proteomics analysis. It involves the preprocessing of mass spectrometry data, peak identification, quantification, and labeling. Preprocessing of mass spectrometry data includes denoising, mass correction, and feature extraction. Peak identification is the process of associating mass peaks with protein labels. Quantification and labeling are used to determine the relative abundance of proteins in different samples and their identification.

 

Unlabeled proteomics analysis is an essential technical means for studying protein composition and expression. Through the steps of sample preparation, protein separation, mass spectrometry analysis, and data analysis, detailed information about the proteome can be obtained and biological processes and disease mechanisms revealed. Reasonable selection and optimization of each step in the analysis process can improve the reliability of the experiment and the accuracy of the data. Unlabeled proteomics analysis has significant application value in biomedical research and drug development.