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An Integrated Platform for High-Throughput Nanoscopy

View ORCID ProfileAndrew E S Barentine, View ORCID ProfileYu Lin, View ORCID ProfileEdward M Courvan, View ORCID ProfilePhylicia Kidd, Miao Liu, View ORCID ProfileLeonhard Balduf, View ORCID ProfileTimy Phan, Felix Rivera-Molina, Michael R Grace, View ORCID ProfileZach Marin, View ORCID ProfileMark Lessard, Juliana Rios Chen, Siyuan Wang, Karla M Neugebauer, View ORCID ProfileJoerg Bewersdorf, View ORCID ProfileDavid Baddeley
doi: https://doi.org/10.1101/606954
Andrew E S Barentine
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
2Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
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Yu Lin
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
2Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
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Edward M Courvan
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
3Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, Connecticut, USA
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Phylicia Kidd
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
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Miao Liu
4Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA
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Leonhard Balduf
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
5Department of Computer Science and Mathematics, University of Applied Sciences, Munich, Germany
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Timy Phan
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
5Department of Computer Science and Mathematics, University of Applied Sciences, Munich, Germany
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Felix Rivera-Molina
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
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Michael R Grace
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
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Zach Marin
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
2Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
6Auckland Bioengineering Institute at University of Auckland
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Mark Lessard
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
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Juliana Rios Chen
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
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Siyuan Wang
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
4Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA
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Karla M Neugebauer
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
3Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, Connecticut, USA
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Joerg Bewersdorf
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
2Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
7Department of Physics, Yale University, New Haven, Connecticut, USA
8Nanobiology Institute, Yale University, West Haven, Connecticut, USA
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  • For correspondence: joerg.bewersdorf@yale.edu d.baddeley@auckland.ac.nz
David Baddeley
1Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
6Auckland Bioengineering Institute at University of Auckland
8Nanobiology Institute, Yale University, West Haven, Connecticut, USA
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  • For correspondence: joerg.bewersdorf@yale.edu d.baddeley@auckland.ac.nz
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Abstract

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.

Competing Interest Statement

ificant financial interest in Bruker Corp. and Hamamatsu Photonics

Footnotes

  • Added new biological application of method.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 20, 2022.
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An Integrated Platform for High-Throughput Nanoscopy
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
bioRxiv 606954; doi: https://doi.org/10.1101/606954
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An Integrated Platform for High-Throughput Nanoscopy
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
bioRxiv 606954; doi: https://doi.org/10.1101/606954

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