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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
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A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution
Oskar Munk Kronik1, Jan H Christensen2, Nikoline Juul Nielsen2
1Department of Plant and Environmental Science, University of Copenhagen, Thorvaldsensvej 40, DK-1871, Frederiksberg, Denmark. omkr@plen.ku.dk.
Analytical and Bioanalytical Chemistry
|January 9, 2025
Summary
A new algorithm for processing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) data improves data compression and compound identification. This method enhances the accuracy of matching regions of interest (ROIs) across multiple samples, aiding complex mixture analysis.
Area of Science:
- Analytical Chemistry
- Chromatography
- Mass Spectrometry
Background:
- Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is crucial for identifying compounds in complex samples.
- Effective data processing is essential to maintain the high resolution provided by LC-HRMS.
- Existing "region of interest" (ROI) algorithms offer better data compression and resolution preservation than equidistant binning.
Purpose of the Study:
- To introduce a novel ROI algorithm for enhanced LC-HRMS data processing.
- To improve the selection of contiguous m/z traces and enable automated optimization of mass deviation.
- To facilitate the matching of ROIs across multiple samples for comprehensive analysis.
Main Methods:
- Development of a new ROI algorithm incorporating a chromatographic filter and automated mass deviation optimization.
- Testing the algorithm on a dataset of 21 replicate LC-HRMS injections of wastewater effluent extract.
- Assessment of the algorithm's ability to retrieve and match ROIs for 57 known compounds.
Main Results:
- Achieved a ten-fold compression rate in on-disk storage at a noise threshold of 200 counts.
- Median ROI length matched observed chromatographic peak widths (12-23 points).
- Successfully matched ROIs for 52 out of 57 compounds across all 21 injections with 9 ppm mass accuracy.
Conclusions:
- The new ROI algorithm offers significant improvements in data compression and ROI matching for LC-HRMS data.
- It performs favorably compared to existing ROI algorithms within the ROI-MCR workflow.
- This advancement aids in the accurate deconvolution of complex HRMS chromatographic data.
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