A Comparative Qualitative and Quantitative Assessment of SONAR for High-Throughput Proteomic Applications
Applications | 2018 | WatersInstrumentation
Proteomic analysis increasingly demands high throughput workflows without sacrificing specificity or sensitivity. SONAR, a scanning quadrupole data independent acquisition (DIA) method, addresses these requirements by enabling rapid, robust protein identification and quantitation under fast chromatographic conditions.
This comparative study evaluates SONAR versus SWATH acquisition for analyzing tryptic digests of the K562 cell line using capillary-scale LC at 30 min and 60 min gradients. The goal is to assess qualitative protein identification rates and quantitative precision under matched chromatographic parameters.
Sample Preparation:
Spectral Selectivity:
SONAR enables:
Combining SONAR with advanced data analysis and machine learning could deepen proteome coverage. Expansion to multi-omic workflows and further optimization of collision energy ramps promise even greater fragmentation efficiency under rapid separations.
Scanning quadrupole DIA (SONAR) significantly outperforms SWATH in both protein identification and quantitative precision under high throughput conditions, enabling reliable analyses with condensed LC gradients.
LC/TOF, LC/HRMS, LC/MS, LC/MS/MS
IndustriesProteomics
ManufacturerWaters
Summary
Importance of the Topic
Proteomic analysis increasingly demands high throughput workflows without sacrificing specificity or sensitivity. SONAR, a scanning quadrupole data independent acquisition (DIA) method, addresses these requirements by enabling rapid, robust protein identification and quantitation under fast chromatographic conditions.
Study Objectives and Overview
This comparative study evaluates SONAR versus SWATH acquisition for analyzing tryptic digests of the K562 cell line using capillary-scale LC at 30 min and 60 min gradients. The goal is to assess qualitative protein identification rates and quantitative precision under matched chromatographic parameters.
Methodology and Instrumentation
Sample Preparation:
- Tryptic digest of K562 cell line (Sigma-Aldrich).
- Waters ACQUITY UPLC M-Class system with NanoEase analytical column (300 µm × 100 mm, 1.8 µm HSS T3 C18).
- Flow rate: 7 µL/min at 35 °C.
- Gradient: 3–40% acetonitrile (0.1% formic acid) over 30 min or 60 min.
- SWATH on Sciex TripleTOF 5600: 50–2000 m/z, 60 variable windows with 1 Da overlap.
- SONAR on Waters Xevo G2-XS QTof: scanning quadrupole from 400–900 m/z with 24 Da isolation window, 0.5 s cycle for low and high energy functions.
- Mascot Distiller and Spectronaut Pulsar against a library generated by gas-phase fractionated DDA experiments.
Key Results and Discussion
Spectral Selectivity:
- SONAR’s continuous quadrupole scan reduces co-eluting interferences compared to SWATH’s stepped acquisition, yielding cleaner fragment ion chromatograms.
- 30 min gradient: SWATH ~1748 vs. SONAR ~2325 (≈33% increase).
- 60 min gradient: SWATH ~2221 vs. SONAR ~2560 (≈15% increase).
- Fast SONAR cycles (0.5 s) provide 6–8 data points across 3 s peaks, maintaining signal-to-noise and quantitative accuracy even with shortened gradients.
Benefits and Practical Applications
SONAR enables:
- Enhanced protein coverage in high throughput proteomic workflows.
- Improved selectivity with reduced spectral interference.
- Robust performance on capillary LC systems for routine analysis.
Future Trends and Opportunities
Combining SONAR with advanced data analysis and machine learning could deepen proteome coverage. Expansion to multi-omic workflows and further optimization of collision energy ramps promise even greater fragmentation efficiency under rapid separations.
Conclusion
Scanning quadrupole DIA (SONAR) significantly outperforms SWATH in both protein identification and quantitative precision under high throughput conditions, enabling reliable analyses with condensed LC gradients.
Reference
- Gethings LA et al. Rapid Commun Mass Spectrom. 2017;31(19):1599–1606.
- Moseley AM et al. J Proteome Res. 2017;doi:10.1021/acs.jproteome.7b00464.
- Bruderer R et al. Proteomics. 2016;16(15-16):2246–56.
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