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Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Nature Protocols (2024)Cite this article

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Abstract

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Key points

  • This protocol provides a pipeline for analyzing volume electron microscopy datasets covering the preparation of raw data, the segmentation of specific organelles, their spatial analysis and three-dimensional visualization of the segmentation maps.

  • The protocol demonstrates the use of tools such as Microscopy Image Browser, ilastik, Labkit and Album, which facilitates the installation of Python-based software (CSBDeep, CellSketch, StarDist, Blender and Jupyter notebooks).

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Fig. 1: Overview of the protocol.
Fig. 2: Overview of segmentation approaches on beta cell FIB–SEM datasets.
Fig. 3: Preparation of raw data for segmentation.
Fig. 4: Local thresholding with MIB.
Fig. 5: Autocontext segmentation in ilastik.
Fig. 6: Segmentation of crowded insulin SGs with StarDist.
Fig. 7: Skeleton tracing with Knossos.
Fig. 8: Types of data to be imported into the analysis project.
Fig. 9: CellSketch viewer.
Fig. 10: Plots generated from the spatial analysis results.
Fig. 11: Exported meshes displayed in VTK.
Fig. 12: 3D visualization of vEM data.
Fig. 13: Results of segmentation, spatial analysis and 3D rendering of a vEM dataset.

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Data availability

All raw datasets at their original resolution are available at OpenOrganelle (https://openorganelle.janelia.org/). Demo data of raw data, segmentation masks, deep learning training data and spatial analysis are available at https://zenodo.org/record/8114392. Further information on the demo data can be found in the Supplementary Information.

Code availability

The code for Album is available at https://gitlab.com/album-app/album. The code for the Album solutions is available at https://github.com/betaseg/solutions. The code for the CellSketch viewer is available at https://github.com/betaseg/cellsketch. The Jupyter Notebooks for demo workflows can be found at https://github.com/betaseg/protocol-notebooks.

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Acknowledgements

We thank K. Pfriem for administrative assistance. We thank members of the PLID for valuable feedback. We thank S. Pang and C. Shan Xu (Yale University) as well as H. F. Hess (Janelia Research Campus) for FIB–SEM. We thank S. Kretschmar and T. Kurth from the Center for Molecular and Cellular Bioengineering Dresden (CMCB) for initial sample preparation. We thank all further authors of the original Journal of Cell Biology publication, J. Verner D’Costa, C. Münster (both PLID), and F. Jug (Human Technopole) for their support. This work was supported by the Electron Microscopy and Histology Facility, a Core Facility of the CMCB Technology Platform at TU Dresden. We thank B. Busselman (Universtiy of South Dakota) for testing Album installation. We thank the EM facility of the Max Planck Institute of Molecular Cell Biology and Genetics for their services. This work was supported with funds to M.S. from the German Center for Diabetes Research by the German Ministry for Education and Research (BMBF) and from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115881 (RHAPSODY) and 115797 (INNODIA). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). This work is further supported by the Swiss State Secretariat for Education‚ Research and Innovation under contract no. 16.0097-2. A.M. was the recipient of a MeDDrive grant from the Carl Gustav Carus Faculty of Medicine at TU Dresden. M.W. was supported by the ELISIR program of the École Polytechnique Fédérale de Lausanne School of Life Sciences and by generous funding from CARIGEST SA. D.S. and L.R. were funded by Helmholtz Imaging, a platform of the Helmholtz Information & Data Science Incubator.

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Author notes

  1. These authors contributed equally: Andreas Müller, Deborah Schmidt.

Authors and Affiliations

  1. Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany

    Andreas Müller, Leticia Elizabeth Galicia Garcia & Michele Solimena

  2. Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany

    Andreas Müller, Leticia Elizabeth Galicia Garcia & Michele Solimena

  3. German Center for Diabetes Research, Neuherberg, Germany

    Andreas Müller, Leticia Elizabeth Galicia Garcia & Michele Solimena

  4. HELMHOLTZ IMAGING, Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany

    Deborah Schmidt, Jan Philipp Albrecht, Lucas Rieckert & Maximilian Otto

  5. Humboldt-Universität zu Berlin, Faculty of Mathematics and Natural Sciences, Berlin, Germany

    Jan Philipp Albrecht

  6. DFG Cluster of Excellence ‘Physics of Life’, TU Dresden, Dresden, Germany

    Leticia Elizabeth Galicia Garcia & Michele Solimena

  7. Experimental Center, Faculty of Medicine Carl Gustav Carus, Dresden, Dresden, Germany

    Gunar Fabig

  8. Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Martin Weigert

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Contributions

A.M., D.S., M.S. and M.W. wrote the manuscript. D.S., J.P.A. and M.W. wrote the workflow implementations. L.R., M.O., G.F. and L.E.G.G. tested the workflows and provided feedback on the manuscript.

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Correspondence to Andreas Müller, Deborah Schmidt or Martin Weigert.

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Nature Protocols thanks Kedar Narayan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

Müller, A. et al. J. Cell Biol. 220, e202010039 (2021): https://doi.org/10.1083/jcb.202010039

Supplementary information

Supplementary Information

Supplementary workflows and Table 1.

Reporting Summary

Supplementary Video 1

Album/CSBDeep training. How to use the Album GUI to install and execute the CSBDeep Album solution that trains a U-Net for semantic segmentation of Golgi from FIB–SEM volumes. This includes visualization of the loss and intermediate results via tensorboard.

Supplementary Video 2

Album/StarDist training. How to use the Album GUI to install and execute the StarDist Album solution that trains a StarDist network for instance segmentation of SGs from FIB–SEM volumes. It additionally shows how to set and change training parameters and how to visualize the loss and intermediate results via tensorboard.

Supplementary Video 3

Album/CellSketch project creation. How to install the protocol solutions catalog and how to create a CellSketch project via the Album GUI. In the video, several cell component annotations are added to the project.

Supplementary Video 4

Album/CellSketch spatial analysis. How to run the automated spatial analysis routine on an existing CellSketch project via the Album GUI. We also demonstrate how to visualize the analysis results in BigDataViewer and as Jupyter Notebook plots via Album solutions.

Supplementary Video 5

Album/CellSketch 3D rendering with Blender. How to convert pixel-based datasets from an existing CellSketch project into meshes via the Album GUI. We demonstrate how to visualize these meshes in VTK and how to automatically create a Blender scene including these meshes. Finally, the scene is rendered in Blender.

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Müller, A., Schmidt, D., Albrecht, J.P. et al. Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00957-5

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  • Published:

  • DOI: https://doi.org/10.1038/s41596-024-00957-5

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