Three-dimensional (3D) visualization of tumor vasculature is a key factor in accurate evaluation of RNA interference (RNAi)-based antiangiogenic nanomedicine, a promising approach for cancer therapeutics. However, this remains challenging because there is not a physiologically relevant in vitro model or precise analytic methodology. To address this limitation, a strategy based on 3D microfluidic angiogenesis-on-a-chip and 3D tumor vascular mapping was developed for evaluating RNAi-based antiangiogenic nanomedicine. We developed a microfluidic model to recapitulate functional 3D angiogenic sprouting when co-cultured with various cancer cell types. This model enabled efficient and rapid assessment of antiangiogenic nanomedicine in treatment of hyper-angiogenic cancer. In addition, tissue-clearing-based whole vascular mapping of tumor xenograft allowed extraction of complex 3D morphological information in diverse quantitative parameters. Using this 3D imaging-based analysis, we observed tumor sub-regional differences in the antiangiogenic effect. Our systematic strategy can help in narrowing down the promising targets of antiangiogenic nanomedicine and then enables deep analysis of complex morphological changes in tumor vasculature, providing a powerful platform for the development of safe and effective nanomedicine for cancer therapeutics.

Two-dimensional (2D) histopathology based on the observation of thin tissue slides is the current paradigm in diagnosis and prognosis. However, labeling strategies in conventional histopathology are limited in compatibility with 3D imaging combined with tissue clearing techniques. Here, we present a rapid and efficient volumetric imaging technique of pathological tissues called 3D tissue imaging through de novo formation of fluorophores, or 3DNFC, which is the integration of citrate-based fluorogenic reaction DNFC and tissue clearing techniques. 3DNFC markedly increases the fluorescence intensity of tissues by generating fluorophores on nonfluorescent amino-terminal cysteine and visualizes the 3D structure of the tissues to provide their anatomical morphology and volumetric information. Furthermore, the application of 3DNFC to pathological tissue achieves the 3D reconstruction for the unbiased analysis of diverse features of the disorders in their natural context. We suggest that 3DNFC is a promising volumetric imaging method for the prognosis and diagnosis of pathological tissues.