The performance of ultrasensitive LC-MS/MS methods, such as Single-Cell Proteomics by Mass Spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint bottlenecks in the LC-MS/MS methods and approaches for resolving them. For example, low signal at MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such bottlenecks by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many search engines, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed bottlenecks but also enabled us to rationally optimize them. For example, we used DO-MS to diagnose poor sampling of the elution peak apex and to optimize it, which increased the efficiency of delivering ions for MS2 analysis by 370 %. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS is available for download from GitHub: https://github.com/SlavovLab/DO-MS
slavovLabUsing affinity purification mass spec, @SBLabUCSF, @KroganLab and colleagues defined an interaction network for all 90 human tyrosine kinases. t.co/17TrDsQlsJ
They found 1,463 mostly novel interactions between these diverse molecular complexes.
Our lab twitter account begins with a link to group trips t.co/StLfHWJFAH, such a group lunch at the restaurant with the best panoramic views of Greater Boston. (It closed after 55 years of dining.)