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A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis.

Jennifer Guergues1, Jessica Wohlfahrt1, John M Koomen2

  • 1Department of Molecular Biosciences, University of South Florida, Tampa, Florida, USA.

Proteomics
|September 5, 2024
PubMed
Summary

A new Python script streamlines parallel reaction monitoring (PRM) method development for targeted proteomics using data independent acquisition (DIA) data. This tool enhances protein quantitation precision and reduces missing data in complex proteomic analyses.

Keywords:
PRM‐PASEFapoptosisdiscovery validationmethod development automationopen scriptparallel accumulation serial fragmentation (PASEF)parallel reaction monitoring (PRM)pathway‐driven analysis

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Targeted proteomics, including parallel reaction monitoring (PRM), offers precise protein detection and quantitation.
  • Data independent acquisition (DIA) provides deep proteome coverage but can have missing data.
  • Developing PRM methods from DIA data is complex and time-consuming.

Purpose of the Study:

  • To develop a Python script for rapid PRM method generation from DIA data.
  • To simplify and accelerate targeted proteomics workflow on the TIMS-TOF platform.
  • To improve quantitation precision and reduce missing data in proteomics.

Main Methods:

  • A Python script was developed to generate PRM methods using DIA output and a target list.
  • The script was evaluated using DIA data from HeLa cell lysate.
  • Pathway information from Ingenuity Pathway Analysis guided PRM method generation for the 'regulation of apoptosis' pathway.

Main Results:

  • The script successfully generated a pathway-driven PRM method.
  • Subsequent PRM analysis showed improved chromatographic data and enhanced quantitation precision.
  • Achieved 100% of peptides below 10% coefficient of variation (CV) with a median CV of 2.9%.

Conclusions:

  • The Python script significantly simplifies and accelerates PRM method development from DIA data.
  • This approach enhances measurement precision and data completeness in targeted proteomics.
  • The script is adaptable for various data outputs and instrument types, offering a versatile framework.