Poster Presentations
Day 2, June 11（Thu.） Room P (5F 501+502)
- 2P-49
Segmented DIA-Based Proteomics Enhances Protein Identification and Proteome Coverage
(DGIST)
oJunhee Kim, Min-Sik Kim
Data-independent acquisition (DIA) mass spectrometry has emerged as a powerful and highly reproducible approach for large-scale proteome profiling. However, its analytical depth is often limited by the complexity of MS/MS spectra generated from wide precursor isolation windows, which can hinder peptide identification and reduce overall proteome coverage. Segmented DIA-based proteomics, implemented through gas-phase fractionation (GPF), has been proposed as an effective strategy to overcome this limitation by dividing the m/z range into multiple injections, thereby reducing spectral complexity and improving precursor selectivity.
In this study, HeLa cell-derived tryptic peptides were used to systematically evaluate the performance of segmented DIA compared to conventional single-run DIA. Multiple segmented DIA strategies with varying numbers of injections were applied using identical sample inputs. These approaches improved precursor separation and minimized spectral interference, leading to increased peptide detection and higher protein identifications. As a result, segmented DIA workflows consistently achieved deeper proteome coverage than single-run DIA.
These findings demonstrate that segmented DIA-based proteomics significantly enhances both the sensitivity and depth of protein identification while maintaining the inherent reproducibility of DIA. Overall, this approach provides a robust and flexible strategy for comprehensive and high-confidence proteome profiling.