Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

• Published in: Journal of proteome research 2016-10, Vol.15 (10), p.3563-3573
• Main Authors: Kreimer, Simion, Belov, Mikhail E, Danielson, William F, Levitsky, Lev I, Gorshkov, Mikhail V, Karger, Barry L, Ivanov, Alexander R
• Format: Article
• Language: English
• Subjects: Algorithms; Chromatography, Liquid - methods; HeLa Cells; Humans; Peptides - analysis; Proteome - analysis; Proteomics - methods; Proteomics - standards; Tandem Mass Spectrometry - methods
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.6b00312

Conventional TopN data-dependent acquisition (DDA) LC–MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC–MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.