Differences in Uniquely Identified Peptides Between ddaPASEF and diaPASEF

Recent advancements in mass spectrometry-based proteomics have made it possible to conduct comprehensive protein analysis. In particular, the emergence of the data-independent acquisition (DIA) method powered by machine learning has significantly improved protein identification efficiency. However, compared with the conventional data-dependent acquisition (DDA) method, the degree to which peptides are uniquely identified by DIA and DDA has not been thoroughly examined. In this study, we identified over 10,000 proteins using the DDA and DIA methods and analyzed the characteristics of unique peptides identified by each method. Results showed that the number of peptides uniquely identified by DDA and DIA using the same column type was 19% and 32%, respectively, with shorter peptides preferentially detected by the DIA method. In addition, more DIA-specific peptides were identified, especially during the first 10% of elution time, and the overall 1/<i>K</i><sub>0</sub> and <i>m</i>/<i>z</i> shifted toward smaller values than in the DDA method. Furthermore, comparing the phosphorylation and ubiquitination proteome profiles with those of whole-cell lysates by DDA showed that the enrichment of post-translationally modified peptides resulted in wider <i>m</i>/<i>z</i> and 1/<i>K</i><sub>0</sub> ranges. Notably, the ubiquitin peptide-enriched samples displayed lower <i>m</i>/<i>z</i> values than the phospho-proteome. These findings suggest a bias in the types of peptides identified by the acquisition method and the importance of setting appropriate ranges for DIA based on the post-translational modification of peptide characteristics.

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Publication Details

Journal
Cells
Published
2024-11-07
DOI
https://doi.org/10.3390/cells13221848
Citations
6
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.44
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article

Differences in Uniquely Identified Peptides Between ddaPASEF and diaPASEF

Megumi Narita, Yousuke Miyamoto, Tsuyoshi Tabata, Mio Iwasaki et al.
6 citations
Cells
Advanced Proteomics Techniques and Applications
1.44
article

Differences in Uniquely Identified Peptides Between ddaPASEF and diaPASEF

Megumi Narita, Yousuke Miyamoto, Tsuyoshi Tabata, Mio Iwasaki, Tatsuya Yamakawa, Rika Nishimura
article en
6 citations

Abstract

Recent advancements in mass spectrometry-based proteomics have made it possible to conduct comprehensive protein analysis. In particular, the emergence of the data-independent acquisition (DIA) method powered by machine learning has significantly improved protein identification efficiency. However, compared with the conventional data-dependent acquisition (DDA) method, the degree to which peptides are uniquely identified by DIA and DDA has not been thoroughly examined. In this study, we identified over 10,000 proteins using the DDA and DIA methods and analyzed the characteristics of unique peptides identified by each method. Results showed that the number of peptides uniquely identified by DDA and DIA using the same column type was 19% and 32%, respectively, with shorter peptides preferentially detected by the DIA method. In addition, more DIA-specific peptides were identified, especially during the first 10% of elution time, and the overall 1/<i>K</i><sub>0</sub> and <i>m</i>/<i>z</i> shifted toward smaller values than in the DDA method. Furthermore, comparing the phosphorylation and ubiquitination proteome profiles with those of whole-cell lysates by DDA showed that the enrichment of post-translationally modified peptides resulted in wider <i>m</i>/<i>z</i> and 1/<i>K</i><sub>0</sub> ranges. Notably, the ubiquitin peptide-enriched samples displayed lower <i>m</i>/<i>z</i> values than the phospho-proteome. These findings suggest a bias in the types of peptides identified by the acquisition method and the importance of setting appropriate ranges for DIA based on the post-translational modification of peptide characteristics.

CellsVol. 13(22)
Kyoto University (JP)
Openalex Percentile: Top 19%
Advanced Proteomics Techniques and Applications
1.44
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