AI Article Synopsis
- * Data-independent acquisition (DIA) based proteomics was found to be more effective than data-dependent acquisition (DDA) for detecting low-abundance proteins, leading to improved detection sensitivity.
- * An optimized hybrid library generation workflow for DIA mass spectrometry was developed, significantly enhancing peptide detection in various conditions without needing prior bacterial enrichment.
Article Abstract
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 -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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871992 | PMC |
| http://dx.doi.org/10.1021/acs.jproteome.0c00350 | DOI Listing |
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