Recent methodological developments in data-dependent analysis and data-independent analysis workflows for exhaustive lipidome coverage

Untargeted lipidomics applied to biological samples typically involves the coupling of separation methods to high-resolution mass spectrometry (HRMS). Getting an exhaustive coverage of the lipidome with a high confidence in structure identification is still highly challenging due to the wide concentration range of lipids in complex matrices and the presence of numerous isobaric and isomeric species. The development of innovative separation methods and HRMS(/MS) acquisition workflows helped improving the situation but issues still remain regarding confident structure characterization. To overcome these issues, thoroughly optimized MS/MS acquisition methods are needed. For this purpose, different methodologies have been developed to enable MS and MS/MS acquisition in parallel. Those methodologies, derived from the proteomics, are referred to Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). In this context, this perspective paper presents the latest developments of DDA- and DIA-based lipidomic workflows and lists available bioinformatic tools for the analysis of resulting spectral data.