Integrating MS1 and MS2 Signals via Linear Mixed-Effects Models for Robust DIA Proteomics

Thomas M. Menino Convention & Exhibition Center 

Data-Independent Acquisition (DIA) proteomics generates precursor (MS1) and fragment (MS2) ion data. Despite their value, different signal interferences cause inconsistencies when analyzed separately. Hypothesizing that integrating MS1 and MS2 signals improves differential protein abundance analysis accuracy, we developed a linear mixed-effects model (LMM) that jointly analyzes MS1 and MS2 intensities-normalized to exosome markers-as technical replicates. The model accounts for within-group variability to compare protein abundance. Simulations show LMM outperforms MS1- or MS2-only analyses, achieving higher significant ratios (SR) for true positives and lower SRs for false positives across various sample sizes. We validated this using urine-derived extracellular vesicles from pancreatic cancer patients and healthy controls (Orbitrap Eclipse/Spectronaut 19.1). The LMM identified more differentially abundant proteins (DAPs) than MS1-only but fewer than MS2, providing a balanced result that avoids t-test overestimation. This integration also improved pathway enrichment, offering a robust, interpretable framework for DIA-based proteomics.

Data-Independent Acquisition (DIA)

Linear Mixed-Effects Model

mass spectrometry MS1/MS2

proteomics

Pancreatic cancer

Urinary extracellular vesicles (EVs) 

Section on Statistics in Genomics and Genetics