title: High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation.
abstract: Targeted analysis of data-independent acquisition (DIA) data is a powerful mass spectrometric approach for comprehensive, reproducible and precise proteome quantitation. It requires a spectral library, which contains for all considered peptide precursor ions empirically determined fragment ion intensities and their predicted retention time (RT). RTs, however, are not comparable on an absolute scale, especially if heterogeneous measurements are combined. Here, we present a method for high-precision prediction of RT, which significantly improves the quality of targeted DIA analysis compared to in silico RT prediction and the state of the art indexed retention time (iRT) normalization approach. We describe a high-precision normalized RT algorithm, which is implemented in the Spectronaut software. We, furthermore, investigate the influence of nine different experimental factors, such as chromatographic mobile and stationary phase, on iRT precision. In summary, we show that using targeted analysis of DIA data with high-precision iRT significantly increases sensitivity and data quality. The iRT values are generally transferable across a wide range of experimental conditions. Best results, however, are achieved if library generation and analytical measurements are performed on the same system.
authors.affiliation: Biognosys, Wagistrasse 25, CH-8952 Schlieren, Switzerland.
authors.forename: Roland
authors.initials: R
authors.lastname: Bruderer
authors.affiliation: Biognosys, Wagistrasse 25, CH-8952 Schlieren, Switzerland.
authors.forename: Oliver M
authors.initials: OM
authors.lastname: Bernhardt
authors.affiliation: Biognosys, Wagistrasse 25, CH-8952 Schlieren, Switzerland.
authors.forename: Tejas
authors.initials: T
authors.lastname: Gandhi
authors.affiliation: Biognosys, Wagistrasse 25, CH-8952 Schlieren, Switzerland.
authors.forename: Lukas
authors.initials: L
authors.lastname: Reiter
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D013058:Mass Spectrometry / Q000379:methods*; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*
chemical_list: D020543:Proteome
keywords: Bioinformatics; Chromatography; Data-independent acquisition; Retention time alignment; Retention time normalization; Retention time prediction; iRT