Detecting Differential Alternative Splicing in Mass Spectrometry-based Proteomics Data

Abstract

Alternative splicing can substantially diversify biological cell states and influence cellular function. The functional impact of splicing has to be estimated at protein level, typically by mass spectrometry (MS) -based proteomics. Although this technology measures increasingly large peptides sets, distinguishing isoform-specific peptides are rare, limiting detection and quantification of splicing. We introduce MS-EmpiReS, a quantification-based computational approach for differential alternative splicing detection in proteomics data. Its core principle is to differentially quantify peptides mapping to different regions of genes. This approach increased the number of testable peptides hundred-fold in a clinical cancer cohort, resulting in a large number of cancer-relevant splicing candidates. Splicing events detected by both MS-EmpiReS and deep RNA sequencing correlated well but also provided complementary information. The proteomics data allowed us to define a per-sample splicing score to separate cancer conditions. Finally, deep brain proteomes from different mice separated strongly by the lower abundance protein splicing isoform.