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J Proteome Res. 2019 Jun 7;18(6):2493-2500. doi: 10.1021/acs.jproteome.9b00039. Epub 2019 May 28.

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods.

Author information

1
Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.
2
Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States.
3
Department of Biology , Northeastern University , Boston , Massachusetts 02115 , United States.

Abstract

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 .

KEYWORDS:

MaxQuant; R; Shiny; method development; optimizing mass spectrometry; quality control; single-cell analysis; single-cell proteomics by mass spectrometry; ultrasensitive proteomics; visualization

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