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scNodes: a correlation and processing toolkit for super-resolution fluorescence and electron microscopy

Nature Methods volume 20, pages 1445–1446 (2023)Cite this article

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Correlative cryo-super resolution light and cryo-electron microscopy (SR-cryoCLEM) is an upcoming method that promises to enable accurately targeted, high-resolution in situ structure determination by combining the specificity of fluorescent labels with the high resolution of cryo-EM1,2,3,4. As it is a novel and rapidly developing method, there has been no dedicated open-source software available for data processing, and current implementations rely instead on numerous disjointed software packages for processing and visualization. Here we present a solution: an open-source, standalone processing suite called scNodes that is specialized for (but not limited to) SR-cryoCLEM and can be used for super-resolution fluorescence data processing, image correlation and fluorescence-guided particle picking for cryo-EM. The project is available at http://github.com/bionanopatterning/scNodes.

scNodes also contains a ‘correlation editor’ for correlation of light microscopy images with electron microscopy images and tomographic volumes. With this editor users can manually or semi-automatically perform rigid alignment and correlate, visualize and export fluorescence and electron microscopy datasets (Fig. 1b). While the editor was created to aid manual correlation of light to electron microscope images, a semi-automated procedure to align images of the same content type, based on TurboReg6, is also implemented. This procedure is highly suited for, for example, cryo-electron tomography datasets, which typically contain multiple views of one sample at various magnifications: overview, search, exposure and tomographic volume. The numerous alignment steps that are typically required in a CLEM experiment are facilitated by a number of internal plug-ins, including the aforementioned registration method as well as a hierarchical arrangement of the data in the scene that helps organize the entire correlation train in a straightforward way.

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Fig. 1: An overview of scNodes.

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Acknowledgements

We thank J. Slotman, M. van Klaveren and A. M. So for feedback and testing. This work was supported by the following grants to T.H.S.: ERC 759517, NWO OCENW.KLEIN.291 and NWO VI.Vidi.193.014.

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Authors and Affiliations

  1. Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands

    Mart G. F. Last, Lenard M. Voortman & Thomas H. Sharp

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  1. Mart G. F. Last
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  2. Lenard M. Voortman
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  3. Thomas H. Sharp
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M.G.F.L. wrote the software; T.H.S. and L.M.V. supervised the project.

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Correspondence to Thomas H. Sharp.

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The authors declare no competing interests.

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Nature Methods thanks Wei Ji and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Last, M.G.F., Voortman, L.M. & Sharp, T.H. scNodes: a correlation and processing toolkit for super-resolution fluorescence and electron microscopy. Nat Methods 20, 1445–1446 (2023). https://doi.org/10.1038/s41592-023-01991-z

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