Principle of DIA-PRM Proteomics

DIA (Data-Independent Acquisition) and PRM (Parallel Reaction Monitoring) are two critical mass spectrometry techniques in the field of proteomics. As scientific research progresses, proteomics methods continue to evolve, making protein quantification and identification more accurate and efficient.

 

Basic Principles of DIA

DIA is a mass spectrometry technique that collects data independently of pre-selected precursor ions, unlike DDA (Data-Dependent Acquisition). The primary advantage of DIA is its ability to simultaneously capture protein information from all samples, preventing data loss from low-abundance proteins. The working principle of DIA involves segmenting all peptide ions in the sample according to their mass-to-charge ratio (m/z), analyzing and fragmenting them segment by segment, and ultimately interpreting the data and quantifying proteins using sophisticated data analysis software.

 

Basic Principles of PRM

PRM is a mass spectrometry-based quantification method that, compared to traditional SRM (Selected Reaction Monitoring), utilizes high-resolution and high-accuracy mass spectrometers to achieve greater specificity and sensitivity. The PRM workflow includes selecting precursor ions of target peptides via mass spectrometry, fragmenting these precursor ions, and monitoring all resulting fragment ions. Unlike DIA, PRM focuses solely on preselected target proteins or peptides, making PRM particularly suitable for validation experiments and precise quantification of specific proteins in quantitative analysis.

 

Combined Application of DIA-PRM

The combined application of DIA and PRM provides a more flexible and powerful tool for proteomics. DIA can conduct large-scale proteomic screening, offering extensive protein quantification information; PRM can precisely validate the proteins of interest identified by DIA. Specifically, DIA can be used for preliminary screening and discovering potential biomarkers, while PRM can further verify the presence of these biomarkers and their differential expression in various samples. This combined application significantly enhances the efficiency and accuracy of proteomics research.