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.

邻近标记（Proximity labeling）是蛋白质组学领域的革命性工具，可实现活细胞内蛋白质相互作用的精准鉴定。然而，传统的生物素化数据分析统计方法往往会产生假阳性结果，制约了邻近标记研究的准确性。本研究提出了一种直接生物素化分析方法，仅聚焦于鉴定生物素化肽段，而非单纯依赖统计比对。本研究利用此前基于TurboID的研究中的液相色谱-质谱（Liquid Chromatography-Mass Spectrometry, LC-MS）数据，对分泌组数据集进行重新分析，结果证实，在鉴定真实生物素化蛋白质方面，识别效果得到显著提升，且假阳性率更低。通过将该方法应用于组织特异性分泌组数据，我们鉴定出纤连蛋白（FN1）作为周细胞特异性标志物。本研究结果表明，传统方法的稳健性存在不足，我们倡导采用直接生物素化分析方法，以提升基于邻近标记的蛋白质组学研究的数据可靠性。该方法为蛋白质相互作用及分泌组研究树立了新的标准，可为细胞及组织特异性分子网络研究提供更深入的见解。