Signal Processing Workflow for Suspect Screening in LC × LC-HRMS: Efficient Extraction of Pure Mass Spectra for Identification of Suspects in Complex Samples Using a Mass Filtering Algorithm

Abstract

The data processing workflows for comprehensive two-dimensional liquid chromatography (LC × LC) hyphenated to high-resolution mass spectrometry (HRMS) operated in data-independent acquisition (DIA) are limited compared to their one-dimensional counterparts. A two-step workflow is proposed to extract pure mass spectra from LC × LC-HRMS. First, a mass filtering (MF) algorithm groups ions belonging to the same compound based on their elution profile similarity in the first (¹D) and second dimension (²D). Second, the filtered data are deconvoluted using multivariate curve resolution (MCR) to address potential coelution. The presented workflow is termed MF + MCR and was tested on pulsed elution-LC × LC-HRMS data from a wastewater effluent extract. The proposed workflow was benchmarked to the following three data processing strategies for mass spectra extraction: peak apex (PAM), using the MF approach alone, or using MCR without prior MF. The MF + MCR workflow identified 25 suspect compounds, compared to 23, 16, and 10 identified by MF, MCR, and PAM, respectively. The nine suspects that could not be identified using MCR compared to the MF + MCR all had low total signal contributions, i.e., low intensities compared to the TIC. This showed that adequate preprocessing prior to MCR is essential for trace level analysis. Additionally, it was shown that the MF + MCR workflow extracted statistically significantly purer mass spectra compared to PAM (p-value: 0.003) and MCR (p-value: 0.04) from a spiked blank sample. The results highlight that by utilizing the elution profiles in both chromatographic dimensions, clean mass spectra of analytes at trace levels measured in DIA can be extracted, allowing for more reliable compound identification compared to the workflows that were used for benchmarking.