PDF(Pubmed)
Abstract：
Proximity labeling (PL) via biotinylation coupled with mass spectrometry (MS) captures spatial proteomes in cells. Large-scale processing requires a workflow minimizing hands-on time and enhancing quantitative reproducibility. We introduced a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. Combining this with optimized quantitative MS based on data-independent acquisition (DIA), we increased sample throughput and improved protein identification and quantification reproducibility. We applied this pipeline to delineate subcellular proteomes across various compartments. Using the 5HT2A serotonin receptor as a model, we studied temporal changes of proximal interaction networks induced by receptor activation. In addition, we modified the pipeline for reduced sample input to accommodate CRISPR-based gene knockout, assessing dynamics of the 5HT2A network in response to perturbation of selected interactors. This PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, enhancing throughput and reproducibility of standard protocols.
摘要：
通过生物素化结合质谱(MS)的邻近标记(PL)捕获细胞中的空间蛋白质组。大规模处理需要最小化操作时间和增强定量再现性的工作流程。我们引入了可扩展的PL管道，该管道以96孔板形式整合了生物素化蛋白质的自动富集。将其与基于数据独立采集（DIA）的优化定量MS相结合，我们提高了样品通量,改善了蛋白质鉴定和定量重现性.我们应用这个管道来描绘不同区室的亚细胞蛋白质组。使用5HT2A血清素受体作为模型，我们研究了受体激活引起的近端相互作用网络的时间变化。此外,我们修改了减少样本输入的管道，以适应基于CRISPR的基因敲除，评估5HT2A网络的动力学，以响应选定交互体的扰动。这种PL方法普遍适用于使用基于生物素化的PL酶的PL蛋白质组学，提高标准方案的吞吐量和可重复性。
