A Study of Ion Statistics and Optimized Data Treatment for HRIM-MS and LC-HRIM-MS Data

Significance of the Topic

The integration of high resolution ion mobility separations with liquid chromatography and mass spectrometry addresses critical challenges in the analysis of isomeric and low abundance species. Structures for Lossless Ion Manipulation (SLIM) offers extended path lengths and enhanced resolving power without increasing analysis time, making it a promising dimension for complex sample characterization.

Objectives and Study Overview

This study evaluated the impact of ion statistics and optimized data treatment workflows for both standalone SLIM-HRIM-MS and combined LC-SLIM-HRIM-MS analyses. The goals were to determine how preprocessing strategies influence resolving power, sensitivity, and feature detection in targeted and untargeted approaches.

Methodology

Data were acquired using various SLIM wave settings (wave speed, amplitude, frame length) and HILIC LC separations. A range of preprocessing parameters was tested with the PNNL Preprocessor, including:

• IM frame summing (1 to 55 frames)
• Drift bin summing (1 to 7 bins at 0.12 ms each)
• Drift and mass smoothing (3 or 5 point moving averages)
• Thresholding and spike removal

Processed data were analyzed with Skyline for targeted extraction and Agilent IM-MS Browser for untargeted feature finding.

Instrumentation

• SLIM-based high-resolution ion mobility device (MOBILion Systems)
• Agilent 6545 LC/Q-TOF mass spectrometer
• Agilent 1290 Infinity II LC system
• HILIC column (RX-Sil, 3.0×100 mm, 1.8 µm)

Main Findings and Discussion

Preprocessing dramatically improved peak shapes and sensitivity, particularly for low intensity lipids and limited IM frame counts. Key observations included:

• Drift bin summing and smoothing increased signal intensity but slightly reduced resolving power at extreme settings.
• Summing more IM frames enhanced targeted extraction but required balancing with acquisition speed.
• Untargeted feature detection rates remained high with preprocessing down to seven summed frames, whereas raw data suffered losses at lower frame counts.
• Longer frame lengths and refined wave parameters yielded improved detection of low abundance species in LC-IM-MS experiments.

Benefits and Practical Applications

The optimized data treatment workflow supports both targeted and untargeted lipidomics and metabolomics applications by:

• Enhancing detection limits for low abundance analytes
• Reducing data file sizes without compromising feature recovery
• Facilitating the use of SLIM-HRIM data in conventional LC-MS software tools

Future Trends and Opportunities

Emerging directions include:

• Integration of AI-driven preprocessing algorithms for adaptive smoothing and feature recognition
• Expansion to 4D LC-IM-MS workflows for deeper omics coverage
• Further optimization of calibration models for precise collision cross section to arrival time conversion

Conclusion

Data preprocessing through drift bin summing and smoothing is essential to realize the full potential of SLIM-HRIM-MS and LC-HRIM-MS platforms, enabling robust targeted and untargeted analyses in complex biological and industrial matrices.

References

1. J C May et al Resolving Power and Collision Cross Section Measurement Accuracy of a Prototype High-Resolution Ion Mobility Platform Incorporating Structures for Lossless Ion Manipulation Journal of the American Society for Mass Spectrometry 2021 32(4) 1126-1137
2. A Bilbao et al A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows Journal of Proteome Research 2021