A proximity proteomics pipeline with improved reproducibility and throughput

Proximity labeling coupled with mass spectrometry (proximity proteomics) have emerged as a powerful technique to map proximal protein interactions in living cells. However, the large-scale and unbiased sample analysis in proximity proteomics necessitates a universal and high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a systematic and automated approach, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, optimization of quantitative mass spectrometry acquisition methods, and advanced computational framework to deconvolute spatial and temporal protein interaction networks. The quantitative reproducibility and accuracy of this streamlined workflow clearly outperforms the conventional manual enrichment. We first applied this approach to investigate subcellular proteomics, including cytosol, endosome, Golgi, lysosome, and plasma membrane. Moreover, using serotonin receptor (5HT2A) as a model, we mapped dynamic protein-protein interactions of GPCR induced by agonist. Collectively, this systematic approach could be widely used in proximity proteomics with high throughput, high reproducibility, and minimal manual intervention.