Batchwise data analysis with inter-batch feature alignment in large scale platelet lipidomics study using UHPLC-ESI-QTOF-MS/MS by data-independent SWATH acquisition.

Abstract

Untargeted lipidomics by ultra-high-performance liquid chromatography (UHPLC) hyphenated with tandem mass spectrometry using data-independent acquisition (DIA) is a technique with increasing popularity for generating new hypotheses in support of clinical research. Its strength is its data comprehensiveness on both MS and MS/MS level. However, especially when applying SWATH acquisition for large-scale analysis, e.g. clinical studies with over 1000 s to 10,000 s of samples, simultaneous processing of acquired data in multiple batches over longer period of time may be challenging due to retention time and mass shifts as well as huge bulk of data, particularly when computer power is limited. This problem can be alleviated by a batchwise data processing strategy by inter-batch feature alignment of separately processed sample batches. After batchwise automated data processing in MS-DIAL, feature lists can be combined by aligning identical features from different batches attributed to similarity in precursor m/z and retention time, with the intention to generate a representative reference peak list for targeted data extraction. The workflow was established with detected features from three batches of platelet lipid extracts of coronary artery disease (CAD) patients (n = 120) and then applied on a clinical cohort with 1057 CAD patients measured in 22 batches. As a result, the lipidome coverage was significantly increased when several batches were used to create the target feature list compared to a single batch and the increase of annotated features levelled off with 7-8 batches. Further, the lipid identification was improved in terms of number of structurally annotated features.