How to Calculate Protein Expression Levels in Proteomics
In proteomics research, common methods to measure protein expression levels are based on relative or absolute quantification using mass spectrometry data. Common quantification methods include labeling and label-free approaches:
1. Labeling Quantification
Proteins or peptides are labeled using isotope tagging techniques, such as SILAC, ICAT, TMT, and iTRAQ. The relative change in protein expression is calculated by comparing the signal intensity of isotope labels in different samples. Log2-fold change (log2FC) is similar to logCPM in RNA-seq data. Positive and negative log2FC values indicate upregulation or downregulation in the experimental group compared to the control.
2. Label-Free Quantification
Proteins or peptides are not labeled and instead, signal intensity from mass spectrometry is directly compared. Common label-free methods include spectrum counting (SC) and peak intensity-based quantification (e.g., LFQ intensity in MaxQuant). The relative abundance of proteins is estimated by calculating the ratio of peptide spectrum counts or peak intensities under different conditions. Like labeling methods, log2FC can be used to represent changes in protein expression levels.
3. Absolute Quantification
The signal intensity of the target protein is compared to an internal standard protein with a known concentration, providing absolute protein expression levels. Common methods for absolute quantification include MRM (MultiReaction Monitoring) and PRM (Parallel Reaction Monitoring). These methods offer more accurate protein quantification results but are limited in scope, often used for studying specific proteins.
Log ratios (like log2FC) in proteomics can be used to measure protein expression changes and rank expression levels. When ranking protein expression, it is important to select the appropriate metric based on experimental design and mass spectrometry data processing methods, while considering statistical and biological significance to ensure that changes in protein expression are meaningful and biologically relevant.
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