New insights on the N-glycome landscape of thyroid lesions through MALDI mass spectrometry

N-glycosylation plays a crucial role in thyroid physiology, as functional glycoproteins such as thyroglobulin and thyroid receptors require N-glycan modifications for proper hormone synthesis. Normal thyroid tissue exhibits exceptionally high N-glycan relative abundance and a distinct glycan profile that is altered in cancer, marked by an overall reduction in total glycan levels alongside significant increases in multiantennary and polylactosamine-extended glycans [1]. The proteomic profile of thyroid nodules in post-thyroidectomy resections, particularly the non-invasive follicular thyroid neoplasms with papillary-like nuclear characteristics (NIFTPs), has been widely studied through matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) [2,3]. Given the importance of N-glycans in thyroid gland function, in this study we investigated the N-glycomic landscape of these lesions, using a sequential MALDI-MSI workflow with the timsTOF flex (Bruker Daltonics, Bremen, Germany) for N-glycans and tryptic peptides in five different classes of thyroid lesions, spacing from benign to intermediate (NIFTPs) and malignant tissues. Spatial N-glycan profiling revealed distinct glycomic patterns across the investigated histological subtypes, highlighting diseaseassociated differences in the thyroid glycome. Importantly, N-glycan analysis provided molecular information complementary to the proteomic profile and enabled the subsequent generation of deglycosylated tryptic peptides, improving their discriminatory performance. The integration of the two spatial molecular layers enhanced the molecular characterization of thyroid lesions and, in particular, improved the discrimination of NIFTPs from malignant thyroid neoplasms (Figure 1). To the best of our knowledge, this study provides the first NIFTPs spatial characterization of N-glycans using MALDI-MSI and demonstrates the potential of integrating spatial glycomics with proteomics within a sequential workflow. This integrated glycoproteomic approach may contribute to a more precise molecular classification of thyroid neoplasms and provide a basis for the discovery of novel biomarkers.