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Expansion microscopy (ExM) enables sub-diffraction imaging by physically expanding labeled tissue samples. This increases the tissue volume relative to the instrument point spread function (PSF), thereby improving the effective resolution by reported factors of 4 - 20X. However, this volume increase dilutes the fluorescence signal, reducing both signal-to-noise ratio (SNR) and acquisition speed. This paper proposes and validates a method for mitigating these challenges. We overcame the limitations of ExM by developing a fast photo-stable protocol to enable scalable widefield three-dimensional imaging with ExM. We combined widefield imaging with quantum dots (QDots). Widefield imaging provides a significantly faster acquisition of a single field-of-view (FOV). However, the uncontrolled incoherent illumination induces photobleaching. We mitigated this challenge using QDots, which exhibit a long fluorescence lifetime and improved photostability. First, we developed a protocol for QDot labeling. Next, we utilized widefield imaging to obtain 3D image stacks and applied deconvolution, which is feasible due to reduced scattering in ExM samples. We show that increased transparency, which is a side-effect of ExM, enables widefield deconvolution, dramatically reducing the acquisition time for three-dimensional images compared to laser scanning microscopy. The proposed QDot labeling protocol is compatible with ExM and provides enhanced photostability compared to traditional fluorescent dyes. Widefield imaging significantly improves SNR and acquisition speed compared to conventional confocal microscopy. Combining widefield imaging with QDot labeling and deconvolution has the potential to be applied to ExM for faster imaging of large three-dimensional samples with improved SNR. 

Microvessels are frequent targets for research into tissue development and disease progression. These complex and subtle differences between networks are currently difficult to visualize, making sample comparisons subjective and difficult to quantify. These challenges are due to the structure of microvascular networks, which are sparse but space-filling. This results in a complex and interconnected mesh that is difficult to represent and impractical to interpret using conventional visualization techniques. We develop a bi-modal visualization framework, leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. This framework allows researchers to objectively interpret the complex and subtle variations that arise when comparing microvascular networks. 

Immunohistochemical techniques, such as immunofluorescence (IF) staining, enable microscopic imaging of local protein expression within tissue samples. Molecular profiling enabled by IF is critical to understanding pathogenesis and is often involved in complex diagnoses. A recent innovation, known asmicroscopy with ultraviolet surface excitation(MUSE), uses deep ultraviolet (≈280 nm) illumination to excite labels at the tissue surface, providing equivalent images without fixation, embedding, and sectioning. However, MUSE has not yet been integrated into traditional IF pipelines. This limits its application in more complex diagnoses that rely on protein-specific markers. This paper aims to broaden the applicability of MUSE to multiplex immunohistochemistry using quantum dot nanoparticles. We demonstrate the advantages of quantum dot labels for protein-specific MUSE imaging on both paraffin-embedded and intact tissue, significantly expanding MUSE applicability to protein-specific applications. Furthermore, with recent innovations in three-dimensional ultraviolet fluorescence microscopy, this opens the door to three-dimensional IF imaging with quantum dots using ultraviolet excitation.