Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN)

Area of Science:

• Proteomics
• Analytical Chemistry
• Biochemistry

Background:

• Label-free MS1 intensity-based quantification in HPLC-ESI-MS/MS workflows aims to identify biological variation at the molecular level.
• These workflows often suffer from poor repeatability and reproducibility due to systematic bias and complex variability.
• Current global normalization strategies are insufficient for addressing complex variability from transient stochastic events.

Purpose of the Study:

• To develop and evaluate a novel local normalization method, proximity-based intensity normalization (PIN), to improve the reliability of HPLC-ESI-MS/MS data.
• To address the limitations of existing normalization strategies in handling complex variability and systematic bias.
• To enhance the discovery of statistically significant biological variation in proteomic studies.

Main Methods:

• Development of proximity-based intensity normalization (PIN), a novel local normalization method.
• Utilizing compositional data analysis as the foundation for the PIN method.
• Comparative evaluation of PIN against common global normalization strategies.

Main Results:

• PIN significantly reduces variance in HPLC-ESI-MS/MS data compared to common normalization strategies.
• PIN identified 20% more proteins with statistically significant abundance differences.
• The method effectively mitigates systematic bias and complex variability, improving data reliability.

Conclusions:

• Proximity-based intensity normalization (PIN) is a superior method for normalizing label-free quantitative proteomic data from HPLC-ESI-MS/MS workflows.
• PIN enables the reliable discovery of statistically significant biological variation that may be missed or falsely reported by other methods.
• This advancement enhances the accuracy and scope of molecular-level biological variation discovery.