Quantitative Shotgun Proteomics

Shotgun Quantitative Proteomics is an advanced proteomics technology that allows scientists to simultaneously identify and quantify thousands of proteins in complex biological samples. Shotgun Quantitative Proteomics relies on the combination of High Performance Liquid Chromatography (HPLC) and Tandem Mass Spectrometry (MS/MS). Proteins are enzymatically digested into peptides, separated by liquid chromatography, and finally quantified by mass spectrometry. This technique can perform relative or absolute quantitative analysis. Not only does this method improve the accuracy of protein identification, it also plays a crucial role in biomedical research and disease diagnosis.

 

Advantages of the Technology

1. High-Throughput

It can identify and quantify thousands of proteins at the same time.

 

2. High Sensitivity

It can detect low-abundance proteins.

 

3. Wide Application

It's suitable for different types of samples, such as body fluids, tissues, and cells.

 

Application

1. Discovery of Disease Biomarkers

In fields such as cancer and cardiovascular diseases, Shotgun Quantitative Proteomics helps to discover new biomarkers.

 

2. Study of Drug Mechanism of Action

By analyzing the difference in protein expression before and after drug treatment, the molecular mechanism of the drug can be revealed.

 

3. Systems Biology Research

It provides tools for understanding complex protein networks within cells.

 

Technical Challenges

1. Complexity of Sample Preparation

Sample processing steps need to be precisely controlled to avoid sample loss and contamination.

 

2. Difficulty of Data Processing and Analysis

Handling massive amounts of data requires complex algorithms and powerful computing resources.

 

3. Dynamic Range and Sensitivity Limitations

Detecting low-abundance proteins is still a challenge.

 

With the advancement of mass spectrometry technology and the enhancement of data processing capabilities, Shotgun Quantitative Proteomics will become more precise and efficient. Furthermore, the integrated analysis of other omics data (such as transcriptomics and metabolomics) will provide a more comprehensive perspective for life science research.