A semi-automated workflow for DIA-based global discovery to
pathway-driven PRM analysis
Abstract
Targeted proteomics, which includes parallel reaction monitoring (PRM),
is typically utilized for more precise detection and quantitation of key
proteins and/or pathways derived from complex discovery proteomics
datasets. Initial discovery-based analysis using data independent
acquisition (DIA) can obtain deep proteome coverage with low data
missingness while targeted PRM assays can provide additional benefits in
further eliminating missing data and optimizing measurement precision.
However, PRM method development from bioinformatic predictions can be
tedious and time-consuming because of the DIA output complexity. We
address this limitation with a Python script that rapidly generates a
PRM method for the TIMS-TOF platform using DIA data and a user-defined
target list. To evaluate the script, DIA data generated from HeLa cell
lysate (200 ng, 45-minute gradient method) as well as canonical pathway
information from Ingenuity Pathway Analysis was utilized to generated a
pathway-driven PRM method. Subsequent PRM analysis of targets within the
example pathway, regulation of apoptosis, resulted in improved
chromatographic data and enhanced quantitation precision (100% peptides
below 10% CV with a median CV of 2.9%, n=3 technical replicates). The
script is freely available at
https://github.com/StevensOmicsLab/PRM-script and provides a framework
that can be adapted to multiple DDA/DIA data outputs and
instrument-specific PRM method types.