An integrated platform for high-throughput nanoscopy
| العنوان: | An integrated platform for high-throughput nanoscopy |
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| المؤلفون: | Andrew E S Barentine, Yu Lin, Edward M Courvan, Phylicia Kidd, Miao Liu, Leonhard Balduf, Timy Phan, Felix Rivera-Molina, Michael R Grace, Zach Marin, Mark Lessard, Juliana Rios Chen, Siyuan Wang, Karla M Neugebauer, Joerg Bewersdorf, David Baddeley |
| المصدر: | Nature Biotechnology. |
| بيانات النشر: | Springer Science and Business Media LLC, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | 0303 health sciences, Fluorescence-lifetime imaging microscopy, business.industry, Computer science, Pipeline (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Biomedical Engineering, Bioengineering, Applied Microbiology and Biotechnology, 03 medical and health sciences, 0302 clinical medicine, Data acquisition, Mammalian cell, Molecular Medicine, business, Throughput (business), 030217 neurology & neurosurgery, Computer hardware, 030304 developmental biology, Biotechnology |
| الوصف: | Diffraction-unlimited single-molecule techniques like STORM and (F)PALM enable three-dimensional (3D) fluorescence imaging at tens of nanometer resolution and are invaluable to investigate sub-cellular organization. The multitude of camera frames required to reconstruct a super-resolved image limits the typical throughput of these techniques to tens of cells per day, rendering these methods incompatible with large-scale cell biological or clinical application. STORM acquisition rates can be increased by over an order of magnitude, however the data volumes of about 40 TB a day and concomitant analysis burdens exceed the capacity of existing workflows. Here we present an integrated platform which transforms SMLM from a trick-pony technique into a work horse for cell biology. We leverage our developments in microscopy-specific data compression, distributed storage, and distributed analysis to automatically perform real-time localization analysis, which enable SMLM at throughputs of 10,000 cells a day. We implemented these advances in a fully-integrated environment that supports a highly-flexible architecture for distributed analysis, enabling quickly- and graphically-reconfigurable analyses to be automatically initiated from the microscope during acquisition, remotely executed, and even feedback and queue new acquisition tasks on the microscope. We demonstrate the utility of this framework by imaging hundreds of cells per well in multi-well sample formats. Our platform, the PYthon-Microscopy Environment (PYME), is easily configurable for hardware control on custom microscopes, and includes a plugin framework so users can readily extend all components of their imaging, visualization, and analysis pipeline. PYME is cross-platform, open source, and efficiently puts high-caliber visualization and analysis tools into the hands of both microscope developers and users. |
| تدمد: | 1546-1696 1087-0156 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cdd0517d4be9044eaab9ffc37a50ce5 https://doi.org/10.1038/s41587-023-01702-1 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....6cdd0517d4be9044eaab9ffc37a50ce5 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15461696 10870156 |
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