Optimized TMT-based quantitative cross-linking mass spectrometry strategy for large-scale interactomic studies

Cross-linking mass spectrometry is a powerful method for the investigation of protein-protein interactions from highly complex samples. XL-MS combined with tandem mass tag labeling holds the promise of large-scale PPI quantification. However, a robust and efficient TMT-based XL-MS quantification method has not yet been established due to the lack of a benchmarking dataset and thorough evaluation of various MS parameters. To tackle these limitations, we generate a two-interactome dataset by spiking-in TMT-labeled cross-linked E. coli lysate into TMT-labeled cross-linked HEK293T lysate using a defined mixing scheme. Using this benchmarking dataset, we assess the efficacy of cross-link identification and accuracy of cross-link quantification using different MS acquisition strategies. For identification, we compare various MS2- and MS3-based XL-MS methods, and optimize stepped HCD energies for TMT-labeled cross-links. We observed a need for notably higher fragmentation energies compared to unlabeled cross-links. For quantification, we assess the quantification accuracy and dispersion of MS2-, MS3- and synchronous precursor selection-MS3-based methods. We show that a stepped HCD-MS2 method with stepped collision energies 36-42-48 provides a vast number of quantifiable cross-links with high quantification accuracy. This widely applicable method paves the way for multiplexed quantitative PPI characterization from complex biological systems.