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PYMEVisualize: an open-source tool for exploring 3D super-resolution data

Nature Methods volume 18, pages 582–584 (2021)Cite this article

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PYMEVisualize is the result of over 10 years of continuous development, evolving from a simple OpenGL-based point viewer into a complete platform for the analysis of localization microscopy point clouds, including modules for quality control, artifact correction, density image reconstruction and quantitative analysis. It has become an indispensable tool in our laboratories and those of several collaborators, facilitating widely varying workflows such as cluster analysis in multicolor ratiometric dSTORM data, correlative confocal and STORM imaging, PAINT imaging of DNA origami with fiducial correction, channel registration and z-mapping for multicolor biplanar astigmatism data, extraction of organelle surfaces from 4Pi-SMS images of the endoplasmic reticulum and Golgi, and generation of 3D animations for use in presentations8,9,10. Its configurable processing pipeline allows for flexibility and repeatability. Loading data from a wide selection of localization packages is possible through our CSV and Matlab importers (Supplementary Note 1), and a flexible plug-in system makes it easy to extend.

We believe that PYMEVisualize has the potential to become the go-to tool for the analysis and visualization of localization point datasets, akin to the status ImageJ has for pixel-based microscopy images, and that we have a duty to share it with the broader imaging community. In contrast to packages based on Matlab, PYMEVisualize and all its dependencies are freely available and modifiable. An added advantage is the ability to leverage the entire scientific Python ecosystem, containing a large volume of mature, well-tested algorithms for writing high-quality plug-ins with relative ease. PYMEVisualize is in active development, and we welcome contributions from everyone—be it to the core code, through the creation of custom plug-ins, or simply by providing feedback about your experience. Guidelines on how best to contribute are provided at https://python-microscopy.org/doc/Contributing.html. The source code is available on GitHub (https://github.com/python-microscopy/python-microscopy), and queries, feedback or suggestions can be made directly, on the GitHub issue tracker, or using the “pyme” tag in the image.sc forum. We have included parts of the PYMEVisualize documentation— a brief tutorial and the user guide—as Supplementary Notes 1 and 2, along with videos (Supplementary Videos 1 and 2) demonstrating visualization and quantification pipelines.

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Fig. 1: Overview of PYMEVisualize.

Data availability

The data used to generate the figures in this paper are available from the corresponding author on reasonable request.

Code availability

Source code for PYMEVisualize is available at https://github.com/python-microscopy/python-microscopy. Compiled packages can be downloaded from https://python-microscopy.org/downloads/. For installation guidance, see Supplementary Note 2.

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Acknowledgements

As a long-running project, this work has received funding from a range of sources including the NIH (R01 GM118486-02,U01EB021232), the Wellcome Trust (203285/B/16/Z), the Engineering and Physical Sciences Research Council of the UK (EP/N008235/1), the Health Research Council (New Zealand) and Marsden Fund (New Zealand). The authors thank Yongdeng Zhang and Lena Schroeder for generating the endoplasmic reticulum and mitochondrial and the microtubule and OMP25 datasets, and thank Yongdeng Zhang, Mengyuan Sun, Ben Rollins and Joerg Bewersdorf for discussions.

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Authors and Affiliations

  1. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

    Zach Marin & David Baddeley

  2. Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA

    Zach Marin, Michael Graff, Andrew E. S. Barentine, Kenny Kwok Hin Chung, Lukas A. Fuentes & David Baddeley

  3. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Zach Marin & Andrew E. S. Barentine

  4. Living Systems Institute and Biomedical Physics, University of Exeter, Exeter, UK

    Christian Soeller

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Contributions

D.B. created the PYME package. Z.M., M.G., K.K.H.C., C.S., A.E.S.B. and D.B. contributed to the codebase. L.A.F. suggested features and usability improvements, and tested the software extensively. All authors contributed to the writing of this manuscript.

Corresponding author

Correspondence to David Baddeley.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Dylan Owen and Nico Stuurman for their contribution to the peer review of this work.

Supplementary information

Supplementary Information

Supplementary Notes 1–3

Reporting Summary

Supplementary Video 1

A pipeline for visualizing mesh representations of super-resolution data.

Supplementary Video 2

A pipeline for quantifying super-resolution data of RyR and junctophilin.

Supplementary Data

Example endoplasmic reticulum super-resolution data for Supplementary Note 1.

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Marin, Z., Graff, M., Barentine, A.E.S. et al. PYMEVisualize: an open-source tool for exploring 3D super-resolution data. Nat Methods 18, 582–584 (2021). https://doi.org/10.1038/s41592-021-01165-9

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