Computation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart

Alternative splicing is prevalent in the heart and implicated in many cardiovascular diseases, but not every alternative transcript is translated and detecting non-canonical isoforms at the protein level remains challenging. Here we show the use of a computation-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart. We build on a recent strategy to integrate deep RNA-seq and large-scale mass spectrometry data to identify candidate translated isoform peptides. A machine learning approach is then applied to predict their fragmentation patterns and design protein isoform-specific parallel reaction monitoring detection (PRM) assays. As proof-of-principle, we built PRM assays for 29 non-canonical isoform peptides and detected 22 peptides in a human heart lysate. The predictions-aided PRM assays closely mirrored synthetic peptide standards for non-canonical sequences. This approach may be useful for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.

Automated summary

Alternative splicing is common in the heart and plays a role in cardiovascular diseases, but detecting non-canonical isoforms at the protein level is challenging. This study used a computational-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart, combining deep RNA-seq and mass spectrometry data to identify candidate translated isoform peptides. By applying machine learning and designing specific PRM assays, they successfully detected non-canonical isoform peptides in a human heart lysate, showing promise for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.