Browsing by Subject "Super-resolution"

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    Generating intravital super-resolution movies with conventional microscopy reveals actin dynamics that construct pioneer axons
    (The Company of Biologists, 2019-03-08) Zhang, Yide; Nichols, Evan L.; Zellmer, Abigail M.; Guldner, Ian H.; Kankel, Cody; Zhang, Siyuan; Howard, Scott S.; Smith, Cody J.; Medicine, School of Medicine
    Super-resolution microscopy is broadening our in-depth understanding of cellular structure. However, super-resolution approaches are limited, for numerous reasons, from utilization in longer-term intravital imaging. We devised a combinatorial imaging technique that combines deconvolution with stepwise optical saturation microscopy (DeSOS) to circumvent this issue and image cells in their native physiological environment. Other than a traditional confocal or two-photon microscope, this approach requires no additional hardware. Here, we provide an open-access application to obtain DeSOS images from conventional microscope images obtained at low excitation powers. We show that DeSOS can be used in time-lapse imaging to generate super-resolution movies in zebrafish. DeSOS was also validated in live mice. These movies uncover that actin structures dynamically remodel to produce a single pioneer axon in a 'top-down' scaffolding event. Further, we identify an F-actin population - stable base clusters - that orchestrate that scaffolding event. We then identify that activation of Rac1 in pioneer axons destabilizes stable base clusters and disrupts pioneer axon formation. The ease of acquisition and processing with this approach provides a universal technique for biologists to answer questions in living animals.
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    Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
    (Springer Nature, 2024) Zhang, Daiwei; Schroeder, Amelia; Yan, Hanying; Yang, Haochen; Hu, Jian; Lee, Michelle Y. Y.; Cho, Kyung S.; Susztak, Katalin; Xu, George X.; Feldman, Michael D.; Lee, Edward B.; Furth, Emma E.; Wang, Linghua; Li, Mingyao; Pathology and Laboratory Medicine, School of Medicine
    Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.