Molecular Structure and Mass Spectral Data Quality-Driven Processing of High-Resolution Mass Spectrometry for Quantitative Analysis.

Abstract

Rationale: LC-MS-based quantification is traditionally performed using selected or multiple reaction monitoring (SRM/MRM) acquisition functions on triple quadrupole (QQQ) instruments resulting in both high sensitivity and selectivity. This workflow requires a previously identified reaction or transition from a precursor ion to a fragment ion to be monitored to obtain the needed selectivity for the compound of interest. High-resolution mass spectrometry (HRMS) has long sought to be a viable alternative for quantitatipve workflows but has been unable to broadly compete, mainly due to the lack of suitable data processing software.

Methods: The approach we developed agnostically and automatically identifies all ions related to the compound being analyzed in both the MS and MSMS data, acquired with data-dependent or data-independent methods. The algorithm automatically selects optimal parameters (ion extraction window, ions to sum, etc.) to provide the best overall method to meet the acceptance criteria defined by the user (accuracy/precision).

Results: The results obtained are directly compared to QQQ data collected from the same set of samples and show that the automated HRMS approach is as good as and, in some cases, better than the traditional QQQ approach in terms of selectivity, sensitivity, and dynamic range.

Conclusions: This new methodology enables the use of generic methods for data collection for quantitative analysis using high-resolution mass spectrometry. With this approach, data collection is faster, and the processing algorithm provides quality equal to or better than the current QQQ methodology. This enables an overall reduction in cycle time and improved assay performance versus current HRMS-based quantitative analysis as well as traditional QQQ workflows.