Technical Name High-throughput super-resolution 3D imaging for ultra-large tissues
Project Operator Brain Research Center, National Tsing Hua University
Project Host 江安世
This technology automatically converts an ultra-large biological tissue into three-dimensional digital information at nano-scale resolution. The obtained big data followed by AI analysis likely lead to a quantum leap forward for a wild spectrum of biological advances, such as brain connectomics, digital pathology,precision medical analysis.
Scientific Breakthrough
The ultra-expansion microscopy enables optical microscopy to achieve super-resolution imaging, close to the resolution capability of electron microscopy,can take images of specific structuresproperties. The technical completion level is already equivalent to TRL 7. System/process prototype demonstration in an operational environment. The appropriate microscopic imaging equipment can be used to obtain high-quality images of ultra-expanded samples. Currently, more image information is being accumulated intensively. By the iterative expansion method, ultra-expansion microscopy can improve the equivalent resolution by two orders of magnitude, reaching the resolution capability of several nanometers.
Industrial Applicability
This technology provides rapid acquisition of high-quality microscopic images of ultra-large tissues, which enables scientific research institutionspathology teams to simultaneously obtain large-scalecontinuous three-dimensional high-quality images for their researchdevelopment. The ultrasonic vibratome derived from it can provide serialflat sections of soft samples with different toughnesshardness. And an automated platformstorage system designed for biological samples can offer many biological samples scheduled operations on the production line.
Keyword Expansion microscopy Connectome Ultrasonic vibratome Fluorescence microscopy Super-resolved fluorescence microscopy Digital pathology Sample retrieval Fluorescent staining Integrated automation system 3D bioimaging
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  • Wei-Kun Chang
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