A Straightforward Interpretation of Proximity Labeling through Direct Biotinylation Analysis

Proximity labeling (PL) is a revolutionary tool in proteomics, enabling the precise identification of protein interactions in live cells. However, conventional statistical approaches for analyzing biotinylation data often lead to false positives, hindering the accuracy of the PL studies. In this study, we propose a direct biotinylation analysis approach that focuses on identifying only biotinylated peptides rather than relying solely on statistical comparisons. Using LC-MS data from a prior TurboID-based study, we reanalyzed secretome data sets and demonstrated significant improvements in identifying true biotinylated proteins with fewer false positives. By applying this approach to tissue-specific secretome data, we identified fibronectin (FN1) as a pericyte-specific marker. Our findings highlight that the limitations of traditional methods are insufficiently robust, and we advocate for the adoption of direct biotinylation analysis to enhance data reliability in PL-based proteomics. This methodology sets a new standard for studying protein interactions and secretomes, offering deeper insights into cellular- and tissue-specific molecular networks.