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Proximity labeling combined with mass spectrometry (MS)-based proteomics has become an essential tool in interactomics. Proximity-dependent biotin identification (BioID) is a versatile method for identifying interacting and neighboring proteins within their native cellular environments. In BioID, the target (bait) protein is fused to a mutated BirA tag that biotinylates vicinal proteins (preys), which are subsequently purified and analyzed using LC-MS/MS. While data-dependent acquisition (DDA) has been the standard for MS-based proteomics, it suffers from bias toward abundant peptides, leading to missing data. In contrast, data-independent acquisition (DIA) improves the identification of low-abundant peptides, providing a more comprehensive proteomic analysis. This chapter outlines a data analysis workflow for BioID experiments in both DDA and DIA modes, using data from a study on the glucocorticoid receptor (GR) as an example. Data analysis was performed using MaxQuant, FragPipe, and DIA-NN, with downstream processing and statistical analysis conducted in R, incorporating SAINTq to enhance the reliability of bait–prey interaction identification.

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A Ready-to-Use Data Analysis Pipeline for BioID Experiments Using Data-Dependent or Data-Independent Acquisition Mass Spectrometry

  • 实验方案

  • 发表日期：

    2025年

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  • Sara Dufour,
  • Teresa Mendes Maia,
  • Laura Van Moortel,
  • Louis Delhaye,
  • Sven Eyckerman,
  • Simon Devos

摘要

Proximity labeling combined with mass spectrometry (MS)-based proteomics has become an essential tool in interactomics. Proximity-dependent biotin identification (BioID) is a versatile method for identifying interacting and neighboring proteins within their native cellular environments. In BioID, the target (bait) protein is fused to a mutated BirA tag that biotinylates vicinal proteins (preys), which are subsequently purified and analyzed using LC-MS/MS. While data-dependent acquisition (DDA) has been the standard for MS-based proteomics, it suffers from bias toward abundant peptides, leading to missing data. In contrast, data-independent acquisition (DIA) improves the identification of low-abundant peptides, providing a more comprehensive proteomic analysis. This chapter outlines a data analysis workflow for BioID experiments in both DDA and DIA modes, using data from a study on the glucocorticoid receptor (GR) as an example. Data analysis was performed using MaxQuant, FragPipe, and DIA-NN, with downstream processing and statistical analysis conducted in R, incorporating SAINTq to enhance the reliability of bait–prey interaction identification.