Optimizing Selectivity and Ion Utilization for Trapped Ion Mobility Spectrometry for Enhanced DIA Performance
Posters | 2025 | Bruker | ASMSInstrumentation
Data independent acquisition (DIA) workflows combined with ion mobility separations have become central to proteomic analyses due to their high sensitivity, reproducibility and completeness of data. Trapped ion mobility spectrometry (TIMS) enhances selectivity and ion utilization by separating ions based on mobility before quadrupole isolation, enabling more efficient sampling and improved detection of peptide precursors.
This work aims to benchmark various diagonal-PASEF acquisition schemes against a py_diAID optimized dia-PASEF method. By assessing fixed and variable slice configurations in m/z and mobility dimensions, the study evaluates their impact on proteome coverage and identification performance across different sample types and input amounts.
Proteomic digests from human (HeLa), yeast and E. coli cells were analyzed using a nanoHPLC system (300 nl/min) equipped with IonOpticks columns (8 cm or 25 cm, 0.075 mm ID) and gradients of 10 or 22 minutes. Acquisitions were performed on a Bruker timsTOF HT instrument. Diagonal-PASEF methods employed fixed or variable window schemes (e.g., 12×20 Da, 12×30 Da) tracing the peptide precursor distribution, while a py_diAID optimized dia-PASEF served as reference, all processed via Spectronaut v19.8 with the directDIA pipeline and Legacy preprocessing mode, applying IM sampling reduction.
Static diagonal-PASEF windows (12×20 Da) consistently achieved the highest number of protein identifications for high-load (400 ng) samples and short gradients, outperforming variable slice configurations that, although reducing cycle time to 0.8 s, did not enhance proteome coverage. The optimized dia-PASEF method matched or exceeded identification metrics in some cases but did not surpass the static diagonal scheme under the tested conditions. These findings highlight the importance of window design and method refinement to fully leverage TIMS-based separations.
Static diagonal-PASEF acquisition provides a balanced approach to maximize ion sampling efficiency and selectivity without compromising throughput, making it suitable for high-throughput proteomic studies where rapid and reproducible results are essential. This method can be readily implemented in routine analysis pipelines for biological research, clinical proteomics and industrial quality control.
Further developments in adaptive window placement, guided by real-time data analytics or machine learning, may enhance dynamic coverage of peptide distributions. Expansion of diagonal-PASEF strategies to a wider range of sample matrices and integration with emerging ion mobility techniques will broaden its applicability. Optimizing software workflows and hardware capabilities will continue to drive improvements in speed and depth of proteomic analyses.
Optimized static diagonal-PASEF acquisition offers a robust and efficient platform for high-load, short-gradient proteomics, outperforming variable slice approaches under the investigated conditions. Ongoing refinements in window configuration and data processing will further elevate its performance and utility in diverse analytical settings.
LC/MS, LC/MS/MS, LC/TOF, LC/HRMS, Ion Mobility
IndustriesProteomics
ManufacturerBruker
Summary
Significance of the Topic
Data independent acquisition (DIA) workflows combined with ion mobility separations have become central to proteomic analyses due to their high sensitivity, reproducibility and completeness of data. Trapped ion mobility spectrometry (TIMS) enhances selectivity and ion utilization by separating ions based on mobility before quadrupole isolation, enabling more efficient sampling and improved detection of peptide precursors.
Objectives and Overview of the Study
This work aims to benchmark various diagonal-PASEF acquisition schemes against a py_diAID optimized dia-PASEF method. By assessing fixed and variable slice configurations in m/z and mobility dimensions, the study evaluates their impact on proteome coverage and identification performance across different sample types and input amounts.
Methodology and Instrumentation Used
Proteomic digests from human (HeLa), yeast and E. coli cells were analyzed using a nanoHPLC system (300 nl/min) equipped with IonOpticks columns (8 cm or 25 cm, 0.075 mm ID) and gradients of 10 or 22 minutes. Acquisitions were performed on a Bruker timsTOF HT instrument. Diagonal-PASEF methods employed fixed or variable window schemes (e.g., 12×20 Da, 12×30 Da) tracing the peptide precursor distribution, while a py_diAID optimized dia-PASEF served as reference, all processed via Spectronaut v19.8 with the directDIA pipeline and Legacy preprocessing mode, applying IM sampling reduction.
Main Results and Discussion
Static diagonal-PASEF windows (12×20 Da) consistently achieved the highest number of protein identifications for high-load (400 ng) samples and short gradients, outperforming variable slice configurations that, although reducing cycle time to 0.8 s, did not enhance proteome coverage. The optimized dia-PASEF method matched or exceeded identification metrics in some cases but did not surpass the static diagonal scheme under the tested conditions. These findings highlight the importance of window design and method refinement to fully leverage TIMS-based separations.
Benefits and Practical Applications of the Method
Static diagonal-PASEF acquisition provides a balanced approach to maximize ion sampling efficiency and selectivity without compromising throughput, making it suitable for high-throughput proteomic studies where rapid and reproducible results are essential. This method can be readily implemented in routine analysis pipelines for biological research, clinical proteomics and industrial quality control.
Future Trends and Possibilities
Further developments in adaptive window placement, guided by real-time data analytics or machine learning, may enhance dynamic coverage of peptide distributions. Expansion of diagonal-PASEF strategies to a wider range of sample matrices and integration with emerging ion mobility techniques will broaden its applicability. Optimizing software workflows and hardware capabilities will continue to drive improvements in speed and depth of proteomic analyses.
Conclusion
Optimized static diagonal-PASEF acquisition offers a robust and efficient platform for high-load, short-gradient proteomics, outperforming variable slice approaches under the investigated conditions. Ongoing refinements in window configuration and data processing will further elevate its performance and utility in diverse analytical settings.
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