Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision. Results: This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system’s ability to reliably detect tumor regions and showing high concordance with histopathological findings. Conclusion: The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.

Understanding host-pathogen interactions at the molecular level requires methods capable of linking spatial context with proteomic information. Here, we present an integrated workflow combining matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and laser microdissection (LMD)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate Aspergillus fumigatus infection in murine lung tissue. Consecutive formalin-fixed, paraffin-embedded (FFPE) tissue sections were used for spatially resolved MALDI-MSI and subsequent LC-MS/MS analysis of laser-microdissected fungal-infected and non-infected regions. MALDI-MSI revealed reproducible m/z features specifically associated with infected areas. Corresponding tissue microregions were microdissected and analyzed by proteomics to identify candidate proteins underlying these spatial signals. Comparative proteomics of fungal-infected with non-infected alveolar lung regions via LC-MS/MS identified host proteins involved in leukocyte recruitment, inflammatory signaling, and reactive oxygen species formation, including a 424-fold increase in formyl peptide receptor 2 (Fpr2) during fungal invasion of the lungs. Fungal regions were also enriched in proteins encoded by the gliotoxin biosynthetic gene cluster. Spatial and proteomic data were linked by matching theoretical peptide-adduct masses to MALDI-MSI features, using a semi-quantitative scoring system to prioritize protein assignments. Fungal regions showed contributions from both host and pathogen proteins. This workflow establishes a conceptual basis for spatial proteomics of host-pathogen-interactions in fungal infections and enables association of characteristic m/z signals with plausible protein candidates.