Workflow enhancement of TurboID-mediated proximity labeling for SPY signaling network mapping

TurboID-based proximity labeling coupled to mass spectrometry (PL-MS) has emerged as a powerful tool for mapping protein-protein interactions in both plant and animal systems. Despite advances in sensitivity, PL-MS studies can still suffer from false negatives, especially when dealing with low abundance bait proteins and their transient interactors. Protein-level enrichment for biotinylated proteins is well developed and popular, but direct detection of biotinylated proteins by peptide-level enrichment and the difference in results between direct and indirect detection remain underexplored. To address this gap, we compared and improved enrichment and data analysis methods using TurboID fused to SPY, a low-abundance O-fucose transferase, using an AAL-enriched SPY target library for cross-referencing. Our results showed that MyOne and M280 streptavidin beads significantly outperformed antibody beads for peptide-level enrichment, with M280 performing best. In addition, while a biotin concentration ≤ 50 μM is recommended for protein-level enrichment in plants, higher biotin concentrations can be used for peptide-level enrichment, allowing us to improve detection and data quality. FragPipe's MSFragger protein identification and quantification software outperformed Maxquant and Protein Prospector for SPY interactome enrichment due to its superior detection of biotinylated peptides. Our improved washing protocols for protein-level enrichment mitigated bead collapse issues, improving data quality, and reducing experimental time. We found that the two enrichment methods provided complementary results and identified a total of 160 SPY-TurboID-enriched interactors, including 60 previously identified in the AAL-enriched SPY target list and 100 additional novel interactors. SILIA quantitative proteomics comparing WT and spy-4 mutants showed that SPY affects the protein levels of some of the identified interactors, such as nucleoporin proteins. We expect that our improvement will extend beyond TurboID to benefit other PL systems and hold promise for broader applications in biological research.