RESUMO
Super-resolution microscopy can capture spatiotemporal organizations of protein interactions with resolution down to 10 nm; however, the analyses of more than two proteins involving low-abundance protein are challenging because spectral crosstalk and heterogeneities of individual fluorescent labels result in molecular misidentification. Here we developed a deep learning-based imaging analysis method for spectroscopic single-molecule localization microscopy to minimize molecular misidentification in three-color super-resolution imaging. We characterized the 3-fold reduction of molecular misidentification in the new imaging method using pure samples of different photoswitchable fluorophores and visualized three distinct subcellular proteins in U2-OS cell lines. We further validated the protein counts and interactions of TOMM20, DRP1, and SUMO1 in a well-studied biological process, Staurosporine-induced apoptosis, by comparing the imaging results with Western-blot analyses of different subcellular portions.
Assuntos
Fenômenos Biológicos , Imagem Individual de Molécula , Corantes Fluorescentes/química , Microscopia de Fluorescência/métodos , Imagem Individual de Molécula/métodos , Estaurosporina/farmacologiaRESUMO
Imaging complex, non-planar anatomies with optical coherence tomography (OCT) is limited by the optical field of view (FOV) in a single volumetric acquisition. Combining linear mechanical translation with OCT extends the FOV but suffers from inflexibility in imaging non-planar anatomies. We report the freeform robotic OCT to fill this gap. To address challenges in volumetric reconstruction associated with the robotic movement accuracy being two orders of magnitudes worse than OCT imaging resolution, we developed a volumetric registration algorithm based on simultaneous localization and mapping (SLAM) to overcome this limitation. We imaged the entire aqueous humor outflow pathway, whose imaging has the potential to customize glaucoma surgeries but is typically constrained by the FOV, circumferentially in mice as a test. We acquired volumetric OCT data at different robotic poses and reconstructed the entire anterior segment of the eye. The reconstructed volumes showed heterogeneous Schlemm's canal (SC) morphology in the reconstructed anterior segment and revealed a segmental nature in the circumferential distribution of collector channels (CC) with spatial features as small as a few micrometers.