Scalable proteomics with multiDIA resolves proteoforms in neurodegenerative disease

Abstract

Deep proteome characterization has traditionally required multidimensional chromatographic fractionation, long acquisition times and large sample amounts, thus limiting scalability. Here, we introduce multiDIA, which combines independent digestions with multiple proteases and data-independent acquisition (DIA) to increase sequence coverage without extensive chromatographic fractionation. By generating complementary and overlapping peptide representations of proteins, multiDIA identifies about 270,000 distinct peptides per sample, including 3,649 phosphosites. This is more than twice the coverage of unfractionated workflows and exceeds the coverage of multidimensional fractionation workflows that use 10-fold more material and time. We demonstrate the approach in Parkinson's disease (PD) brain samples, showing that multiDIA substantially expands sequence coverage while retaining the sensitivity, reproducibility, and scalability of DIA. This enabled the identification of disease-associated proteoform changes undetectable by single-protease DIA and the quantification of many disease-relevant post-translational modifications. MultiDIA thus enables deep, high-confidence proteome characterization from limited sample quantities without the throughput and sensitivity constraints of multidimensional fractionation.