An integrated platform for high-throughput nanoscopy

• Published in: Nature biotechnology Vol. 41; no. 11; pp. 1549 - 1556
• Main Authors: Barentine, Andrew E. S., Lin, Yu, Courvan, Edward M., Kidd, Phylicia, Liu, Miao, Balduf, Leonhard, Phan, Timy, Rivera-Molina, Felix, Grace, Michael R., Marin, Zach, Lessard, Mark, Rios Chen, Juliana, Wang, Siyuan, Neugebauer, Karla M., Bewersdorf, Joerg, Baddeley, David
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2023; Nature Publishing Group
• Subjects: 631/1647/245; 631/1647/245/2225; 631/61; 631/80/2373/2238; Agriculture; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Data compression; Data processing; Image reconstruction; Imaging, Three-Dimensional; Life Sciences; Localization; Microscopes; Microscopy; Microscopy, Fluorescence - methods; Single Molecule Imaging - methods; Software
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-023-01702-1

Single-molecule localization microscopy enables three-dimensional fluorescence imaging at tens-of-nanometer resolution, but requires many camera frames to reconstruct a super-resolved image. This limits the typical throughput to tens of cells per day. While frame rates can now be increased by over an order of magnitude, the large data volumes become limiting in existing workflows. Here we present an integrated acquisition and analysis platform leveraging microscopy-specific data compression, distributed storage and distributed analysis to enable an acquisition and analysis throughput of 10,000 cells per day. The platform facilitates graphically reconfigurable analyses to be automatically initiated from the microscope during acquisition and remotely executed, and can even feed back 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, implemented within the PYthon-Microscopy Environment (PYME), is easily configurable to control custom microscopes, and includes a plugin framework for user-defined extensions. A fast data processing platform enables super-resolution microscopy with increased throughput.