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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. 

This study introduces label-free, automated ovarian tissue cell recognition using O-PTIR imaging, offering 10× better resolution than FTIR. It outperforms FTIR, achieving 0.98 classification accuracy. This work aids early ovarian cancer diagnosis. 

Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 μm to 12.5 μm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 μm to 12 μm), limiting the maximum resolving power to between 3 μm and 6 μm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy.