Abstract
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.
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Data availability
The 3D localizations, calibration files and raw blinking videos for all series in Fig. 2, and Cell no. 1, no. 2,504, no. 5,735, no. 8,041, no. 9,577 and no. 11,160 from the lamin-NPM1 dataset in Fig. 4 (and 3D localizations for the remaining cells), are publicly available through the 4D Nucleome data portal at https://data.4dnucleome.org/publications/7d9fad19-54c4-419e-8d99-8157f5c1904b/. Any additional data from this work can be obtained through the authors upon request.
Code availability
The code for automated acquisition, distributed data storage and analysis is released under the GNU General Public License v.3 as part of the python-microscopy project and is available at github.com/python-microscopy/python-microscopy. The quantized compression software can be installed independently, with instructions for use with third-party software additionally available at github.com/python-microscopy/pymecompress. Code for GPU acceleration of single-molecule fitting is available under an academic use license from github.com/barentine/pyme-warp-drive. The LabVIEW acquisition software used in phase 1 can be obtained from the authors; however, it is not actively maintained. Please contact the authors for alternative licensing arrangements.
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Acknowledgements
We thank A. Wrogg for contributing the timeline in Fig. 1. We thank L. Schroeder and Y. Zhang for helpful discussions and technical assistance. This work was primarily supported by a 4D Nucleome grant from the National Institutes of Health (NIH) (grant no. U01 DA047734 to J.B. and D.B.). J.B. acknowledges support from NIH grant no. P30 DK045735 (to R. Sherwin). A.E.S.B. acknowledges support by an NIH training grant (no. T32 GM008283) and training on the Computational Image Analysis in Cellular and Developmental Biology Course of the Marine Biology Laboratory (which was supported by NIH grant no. R25 GM103792). We are also grateful for funding from NIH awards no. U01CA200147 TCPA-2017-Neugebauer and OPAS (to K.M.N.) and no. 1R01NS128358-1 (to K.M.N. and J.B.). This work is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Contributions
Y.L. and J.B. designed the optical hardware of the microscope which Y.L. built. A.E.S.B., Y.L., D.B., Z.M., T.P. and J.R.C. developed acquisition control software. M.R.G., A.E.S.B. and D.B. designed and implemented the computer cluster. D.B. designed the distributed the storage architecture and compression algorithm. D.B. and L.B. designed and implemented the cluster task distribution. A.E.S.B. and D.B. developed the GPU acceleration code. S.W. and M. Liu designed the FISH probes. E.M.C., P.K., M. Lessard, F.R.-M., M. Liu, S.W. and K.M.N. optimized sample preparation protocols and prepared samples. A.E.S.B., Y.L. and E.M.C. performed imaging experiments. A.E.S.B., Y.L. and D.B. performed post-localization analysis. All authors contributed to writing the manuscript.
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J.B. discloses a financial interest in Bruker Corp. and Hamamatsu Photonics. All other authors declare no competing interests.
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Nature Biotechnology thanks Bogdan Bintu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary Information
Supplementary Figs. 1–10, Notes 1–8 and Tables 1–4.
Video showing a reconstruction of each of the 11,145 nuclei contributing to Fig. 4.
Video showing an automated acquisition using simulated hardware in PYMEAcquire with a ‘cluster of one’ for automated localization and recipe-based analysis.
Supplementary Table 5
LAD probe design including sequences of PCR primers, reverse transcription primers and activator oligos. The worksheet lists the sequences of the forward and reverse PCR primers and the reverse transcription primers used in the synthesis of each LAD probe library. The /5Alex647N/ in the sequences indicates 5′ Alexa Fluor 647 modifications of the DNA oligos.
Supplementary Table 6
Template sequences for LAD probe library syntheses. All oligonulceotide sequences in each template LAD probe library are compiled as a list. Different LAD libraries are placed in different worksheets labeled with the names of the LADs that the libraries target.
Supplementary Table 7
TAD probe design information including sequences of PCR primers and reverse transcription primers. The worksheet lists the sequences of the forward and reverse PCR primers and the reverse transcription primers used in the synthesis of the Chr22 probe library. The /5Biosg/ in the sequence indicates 5′ biotin modifications of the DNA oligos.
Supplementary Table 8
Template sequences for TAD probe library syntheses. All oligonulceotide sequences in each template LAD probe library are compiled as a list. Different LAD libraries are placed in different worksheets labeled with the names of the LADs that the libraries target.
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Barentine, A.E.S., Lin, Y., Courvan, E.M. et al. An integrated platform for high-throughput nanoscopy. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01702-1
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DOI: https://doi.org/10.1038/s41587-023-01702-1
