Abstract 1894: Correlative multiscale 3D spatial proteomics

Abstract Tissues and organisms are complex systems, characterized by 3D structures, interconnected distinct functional subregions, and cellular heterogeneity, particularly in diseased states. To comprehensively understand the cellular and molecular mechanisms underlying disease, like tumor or neurodegenerative disease, it is crucial to construct a complete 3D map of tissues that integrates microanatomical structures, spatial cell composition, and molecular expression landscape, providing a holistic view of tissue pathophysiology. Existing tissue imaging methods can provide 2D spatial cellular and molecular information in tissue slices; however, only a fraction of the full 3D tissue architecture, leaving critical spatial and contextual information about the tissue microenvironment unresolved. To address this gap, we developed a novel methodology that integrates spatial proteomics with multiscale 3D optical tissue microscopy. The workflow begins with an overall 3D imaging of the tissue by light-sheet microscopy (LSFM), utilizing immunofluorescence (IF) or autofluorescence (AF) to visualize whole tissue morphology, cells, and molecules, providing a preview to identify Regions of Interest (ROIs). Using a tracking-based approach, macrosections containing these ROIs are physically isolated for subsequent 3D multiplex IF staining by cyclic staining and confocal microscopy (CLSM), allowing detailed, high-resolution cellular-level image and analysis. These sections are then subjected to thin-slicing and Super-Resolution Microscopy or Laser Capture Microdissection (LCM), which precisely extracts tissue regions for spatial proteomics analysis. Finally, the spatial proteomics data are aligned with multiscale 3D imaging data to provide a comprehensive, integrated analysis of the tissue. This integrated pipeline not only bridges 3D tissue imaging with molecular profiling but also enables selective analysis of ROIs, significantly saving time and resources while maintaining spatial and molecular fidelity, offering new insights into complex biological systems and disease mechanisms. Citation Format: Jingtian Zheng, Seung Young Lee, Yi-Chien Wu. Correlative multiscale 3D spatial proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1894.