Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition (DIA) methods (Scanning SWATH, or more generally scanning DIA), but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a ZenoTOF 7600+ instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30%-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows.

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Publication Details

Journal
PROTEOMICS
Published
2025-12-26
DOI
https://doi.org/10.1002/pmic.70093
Citations
3
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.27

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article

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

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PROTEOMICS
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article

Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput

Z. Wang, Ludwig Sinn, Vadim Petrovich Demichev, Anjali Chelur, Stephen Tate, Ihor Batruch, Claudia Alvarez, Markus Ralser, Patrick Pribil, Christoph B. Messner, José Castro‐Perez, Daniela Ludwig
article en
3 citations

Abstract

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition (DIA) methods (Scanning SWATH, or more generally scanning DIA), but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a ZenoTOF 7600+ instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30%-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows.

PROTEOMICSVol. 26(1)
Centre for Human Genetics (GB), Nuffield Orthopaedic Centre (GB), University of Oxford (GB), Swiss Institute for Art Research (CH), Concord University (US), Charité - Universitätsmedizin Berlin (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 11%
Advanced Proteomics Techniques and Applications
2.27
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