Multi-omic and multimodal characterization of thyroid nodules: novel interplay between molecular and morphological features

Introduction Thyroid nodules include indeterminate lesions, posing diagnostic challenges and exposing patients to overtreatment. Conventional histopathology is time-consuming and often inconclusive, failing to capture the underlying molecular heterogeneity [1]. Multi-omic and multimodal approaches integrating spatial and molecular information may improve lesion stratification and thyroid cancer diagnosis [2]. In this context, N-glycans and tryptic peptides were analysed on the same tissue section by MALDI mass spectrometry imaging. Methods Two thyroid nodules tissue microarrays (TMAs), comprising around 80 cores each and representing 80 patients in total, were analysed by MALDI mass spectrometry imaging (MALDI-MSI). Sequential analyses were performed on the same tissue sections, first targeting N-glycans and subsequently tryptic peptides. N-glycan digestion reduced signal interference associated with colloid-rich regions. Following MSI acquisition, slides were H&E stained and whole-slide images were used to automatically define regions of interest through a pixel-based classifier, as described in Coelho et al. [3]. Integrated multi-omic datasets were further investigated, and analytes were extracted from the MALDI matrix and identified by nanoLC-MS/MS. Results/Discussion The combined multi-omic and multimodal strategy enabled improved molecular characterization of thyroid lesions, particularly indeterminate forms. N-glycan digestion significantly enhanced spectral quality by minimizing colloid-derived interference, allowing more robust spatial molecular profiling. The tryptic peptides observed in the second molecular layer were more abundant in the surrounding connective regions. Automated ROI selection based on histological features ensured consistent and reproducible data extraction. Integrated analysis of N-glycans and tryptic peptides revealed distinct molecular signatures associated with different thyroid lesion types, highlighting alterations in glycosylation patterns alongside proteomic changes. Several candidate molecular features emerged as potential diagnostic markers, supporting the added value of spatially resolved glycoproteomic information in thyroid pathology. Conclusion This study demonstrates that integrating MALDI-MSI-based N-glycan and proteomic analyses with automated histology-guided ROI selection enhances the discrimination of thyroid lesions. The approach provides new insights into molecular mechanisms underlying indeterminate thyroid nodules and supports the development of more precise diagnostic strategies. Novelty This work integrates spatial multi-omics and histology to reveal glycosylation-related markers in indeterminate thyroid lesions. Impact The proposed workflow opens new avenues to explore molecular heterogeneity, enhancing the diagnostic potential of challenging thyroid lesions. Acknowledgment The team from the cancer molecular pathology unit at Fondazione IRCCS San Gerardo dei Tintori, guided by Prof. Fabio Pagni. Funding: Fondazione Cariplo (2023-1804). References [1] Nikiforov YE,, 'Nomenclature Revision for Encapsulated Follicular Variant of Papillary Thyroid Carcinoma: A Paradigm Shift to Reduce Overtreatment of Indolent Tumors'. JAMA Oncol. 2016;2(8):1023–1029; [2] Denti Vanna, ‘Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section’, Journal of Proteome Research 2022 21 (11), 2798-2809; [3] Coelho Vasco, 'Improving the Annotation for Spatial Proteomics: A Computational Approach to Enhance Molecular Characterization of Thyroid Nodules', Journal of Proteome Res. 2026 Jan 8.