LC-MS/DIA framework for comprehensive flavonoid profiling: An Ocotea sp. applicability case

Matheus Fernandes Alves^a, Albert Katchborian Neto^a, Paula Carolina Pires Bueno^b, Fausto Carnevale-Neto^c , Rosana Casoti^d, Miller Santos Ferreira^a, Michael Murgu^e, Danielle Ferreira Dias^a, Marisi Gomes Soares^a and Daniela Aparecida Chagas de Paula^a*

In the realm of natural product research, profiling glycosylated flavonoids, particularly the minor and less concentrated ones in complex matrices, remains challenging. Manual data inspection, although crucial for annotation accuracy, is time-consuming. This study introduces a Liquid Chromatography - Mass Spectrometry with Data-Independent Acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling.  This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of 6 different Ocotea plant species, known for their flavonoid content, as demonstrated by previous metabolomic and phytochemical studies in the literature, using a Quadrupole-Time of Flight analyzer operated in DIA/MS^e mode. This framework expanded flavonoid annotation beyond existing literature, revealing the distinct major and minor aglycone patterns. The most prevalent were the flavonol backbone of kaempferol, followed by quercetin, both with O-glycosylation patterns. These findings were corroborated by gas-phase fragmentation reactions, which were key in confirming the main product ions in the MS² spectra, along with their primary neutral losses. Apigenin flavone backbone was annotated in the majority of the examined Ocotea species for the first time in literature. The flavone apigenin derivatives were found mainly in the C-glycosides form, and interestingly, O. porosa displayed the highest flavone:flavonol ratio. Despite the current challenges in deconvoluting DIA-MS² spectra, this approach has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling.