Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling

In the context of
bacterial infections, it is imperative that physiological
responses can be studied in an integrated manner, meaning a simultaneous
analysis of both the host and the pathogen responses. To improve the
sensitivity of detection, data-independent acquisition (DIA)-based
proteomics was found to outperform data-dependent acquisition (DDA)
workflows in identifying and quantifying low-abundant proteins. Here,
by making use of representative bacterial pathogen/host proteome samples,
we report an optimized hybrid library generation workflow for DIA
mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to
searching DDA experiment-specific libraries only, the use of hybrid
libraries significantly improved peptide detection to an extent suggesting
that infection-relevant host-pathogen conditions could be profiled
in sufficient depth without the need of a priori bacterial pathogen
enrichment when studying the bacterial proteome. Proteomics data have
been deposited to the ProteomeXchange Consortium via the PRIDE partner
repository with the dataset identifiers PXD017904 and PXD017945.