The performance of ultrasensitive liquid chromatography and tandem mass spectrometry (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 the sources of problems in the LC-MS/MS methods and approaches for resolving them. For example, a low signal at the MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such problems by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many software packages, 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 LC-MS/MS problems but also enabled us to rationally optimize them. For example, by using DO-MS to optimize the sampling of the elution peak apexes, we increased ion accumulation times and apex sampling, which resulted in a 370% more efficient delivery of ions for MS2 analysis. 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 v1.0.8 is available for download from GitHub: https://github.com/SlavovLab/DO-MS. Additional documentation is available at https://do-ms.slavovlab.net.
slavovLab@JimJohnsonSci Press release is sometimes very problematic even with peer reviewed articles. That is why we need strong science journalism. Some journalists are consistently writing solid, well substantiated highlights.
slavovLabMy final lecture for Methods of Bioengineering is on one of the most frequent statistical mistakes. Many people are not even aware of it ... and yes, I have published papers with this mistake (e.g., figure 2a t.co/mLKhOUcFwz) before I knew better t.co/7ZZl1GBNX2
slavovLab@jbloom22 The relationship between truth and measurement is not not a monotonic function. Rather, many RNAs have large sequence specific bias, which is not well understood. It's not simple GC bias.
Relative measurements largely control for these biases. Thus spike-in standards can help.
slavovLabReproducibility ≠ Accuracy
Sequencing can provide very reproducible digital counts of RNAs. Yet, standards are consistently reported to be ten times below or above their known concentrations and estimates from different platforms are highly divergent t.co/fYrOMw60c8t.co/UBaocsxvjt