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

Andreas Müller, Deborah Schmidt, Jan Philipp Albrecht, Lucas Rieckert, Maximilian Otto, Leticia Elizabeth Galicia Garcia, Gunar Fabig, Michele Solimena, Martin Weigert

Published: 2024-02-29 DOI: 10.1038/s41596-024-00957-5

Volume Electron Microscopy

Organelle Segmentation

Spatial Analysis

3D Visualization

Computational Pipeline

AI 解读

The GFP thermal shift assay for screening ligand and lipid interactions to solute carrier transporters

Co-fractionation–mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Simultaneously measuring the methylation of parent and daughter strands of replicated DNA at the single-molecule level by Hammer-seq

The Nodewalk assay to quantitate chromatin fiber interactomes in very small cell populations

Nondestructive production of exosomes loaded with ultrathin palladium nanosheets for targeted bio-orthogonal catalysis

An optimized culture system for efficient derivation of porcine expanded potential stem cells from preimplantation embryos and by reprogramming somatic cells

Engineering SARS-CoV-2 using a reverse genetic system

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Growing and making nano- and microcrystals

Composable microfluidic spinning platforms for facile production of biomimetic perfusable hydrogel microtubes

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Sections

Figures

References

Introduction
Materials
- Equipment
- Software
Procedure 1: preparation of raw data
Procedure 2: segmentation
Procedure 3: volume and spatial analysis
Procedure 4: visualization of vEM segmentation data
- Visualization with VTK and Blender
- Check colors for color-blind friendliness
Troubleshooting
Timing
Anticipated results
- Reporting summary
Acknowledgements

Schroff, F., Criminisi, A. & Zisserman, A. in Procedings of the British Machine Vision Conference 2008 54.1–54.10 (British Machine Vision Association, 2008).
Vergara, H. M. et al. Whole-body integration of gene expression and single-cell morphology. Cell 184, 4819–4837.e22 (2021).
Peddie, C. J. & Collinson, L. M. Exploring the third dimension: volume electron microscopy comes of age. Micron 61, 9–19 (2014).
Peddie, C. J. et al. Volume electron microscopy. Nat. Rev. Methods Prim. 2, 51 (2022).
Hua, Y., Laserstein, P. & Helmstaedter, M. Large-volume en-bloc staining for electron microscopy-based connectomics. Nat. Commun. 6, 7923 (2015).
Kievits, A. J., Lane, R., Carroll, E. C. & Hoogenboom, J. P. How innovations in methodology offer new prospects for volume electron microscopy. J. Microsc. 287, 114–137 (2022).
Graham, B. J. et al. High-throughput transmission electron microscopy with automated serial sectioning. Preprint at bioRxiv https://doi.org/10.1101/657346 (2019).
Yin, W. et al. A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy. Nat. Commun. 11, 4949 (2020).
Phelps, J. S. et al. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 184, 759–774.e18 (2021).
Xu, C. S. et al. Enhanced FIB-SEM systems for large-volume 3D imaging. eLife 6, e25916 (2017).
Motta, A. et al. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 366, eaay3134 (2019).
Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).
Müller, A. et al. 3D FIB-SEM reconstruction of microtubule-organelle interaction in whole primary mouse β. cells J. Cell Biol. 220, e202010039 (2021).
Parlakgül, G. et al. Regulation of liver subcellular architecture controls metabolic homeostasis. Nature 603, 736–742 (2022).
Sheu, S.-H. et al. A serotonergic axon-cilium synapse drives nuclear signaling to alter chromatin accessibility. Cell 185, 3390–3407.e18 (2022).
Weigel, A. V. et al. ER-to-Golgi protein delivery through an interwoven, tubular network extending from ER. Cell 184, 2412–2429.e16 (2021).
Uwizeye, C. et al. Morphological bases of phytoplankton energy management and physiological responses unveiled by 3D subcellular imaging. Nat. Commun. 12, 1049 (2021).
Musser, J. M. et al. Profiling cellular diversity in sponges informs animal cell type and nervous system evolution. Science 374, 717–723 (2021).
Bharathan, N. K. et al. Architecture and dynamics of a desmosome–endoplasmic reticulum complex. Nat. Cell Biol. 25, 823–835 (2023).
Malong, L. et al. Characterization of the structure and control of the blood-nerve barrier identifies avenues for therapeutic delivery. Dev. Cell 58, 174–191.e8 (2023).
Cortese, M. et al. Integrative imaging reveals SARS-CoV-2-induced reshaping of subcellular morphologies. Cell Host Microbe 28, 853–866.e5 (2020).
Iudin, A., Korir, P. K., Salavert-Torres, J., Kleywegt, G. J. & Patwardhan, A. EMPIAR: a public archive for raw electron microscopy image data. Nat. Methods 13, 387–388 (2016).
Conrad, R. & Narayan, K. CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning. eLife 10, e65894 (2021).
Xu, C. S. et al. An open-access volume electron microscopy atlas of whole cells and tissues. Nature 599, 147–151 (2021).
Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).
Noske, A. B., Costin, A. J., Morgan, G. P. & Marsh, B. J. Expedited approaches to whole cell electron tomography and organelle mark-up in situ in high-pressure frozen pancreatic islets. J. Struct. Biol. 161, 298–313 (2008).
Wu, Y. et al. Contacts between the endoplasmic reticulum and other membranes in neurons. Proc. Natl Acad. Sci. USA 114, E4859–E4867 (2017).
Kaynig, V. et al. Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med. Image Anal. 22, 77–88 (2015).
Dorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat. Methods 14, 435–442 (2017).
Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).
Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature 599, 141–146 (2021).
Spiers, H. et al. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations. Traffic 22, 240–253 (2021).
Gallusser, B. et al. Deep neural network automated segmentation of cellular structures in volume electron microscopy. J. Cell Biol. 222, e202208005 (2022).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
Belevich, I., Joensuu, M., Kumar, D., Vihinen, H. & Jokitalo, E. Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biol. 14, e1002340 (2016).
Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Kreshuk, A. & Zhang, C. in Computer Optimized Microscopy: Methods and Protocols (eds Rebollo, E. & Bosch, M.) 449–463 (Springer, 2019).
Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090–1097 (2018).
Weigert, M., Schmidt, U., Haase, R., Sugawara, K. & Myers, G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) https://doi.org/10.1109/WACV45572.2020.9093435 (2020).
Helmstaedter, M., Briggman, K. L. & Denk, W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat. Neurosci. 14, 1081–1088 (2011).
Arzt, M. et al. LABKIT: labeling and segmentation toolkit for big image data. Front. Comput. Sci. https://doi.org/10.3389/fcomp.2022.777728 (2022).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Bolte, S. & Cordelières, F. P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).
Shrestha, N. et al. Integration of ER protein quality control mechanisms defines β-cell function and ER architecture. J. Clin. Invest. https://doi.org/10.1172/JCI163584 (2022).
Schmid, B. et al. 3Dscript: animating 3D/4D microscopy data using a natural-language-based syntax. Nat. Methods 16, 278–280 (2019).
Albrecht, J. P., Schmidt, D. & Harrington, K. Album: a framework for scientific data processing with software solutions of heterogeneous tools. Preprint at https://doi.org/10.48550/arXiv.2110.00601 (2021).
Conrad, R. & Narayan, K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset. Cell Syst. 14, 58–71.e5 (2023).
von Chamier, L. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12, 2276 (2021).
Ouyang, W. et al. BioImage Model Zoo: a community-driven resource for accessible deep learning in bioimage analysis. Preprint at bioRxiv https://doi.org/10.1101/2022.06.07.495102 (2022).
Weber, B. et al. Automated tracing of microtubules in electron tomograms of plastic embedded samples of Caenorhabditis elegans embryos. J. Struct. Biol. 178, 129–138 (2012).
Eckstein, N., Buhmann, J., Cook, M. & Funke, J. Microtubule tracking in electron microscopy volumes. In Medical Image Computing and Computer Assisted Intervention (MICCAI) Part V 99–108 (Springer, 2020).
Kaltdorf, K. V. et al. Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning. PLoS ONE 13, e0205348 (2018).
Haberl, M. G. et al. CDeep3M—plug-and-play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680 (2018).
Koranne, S. in Handbook of Open Source Tools (ed. Koranne, S.) 191–200 (Springer, 2011).
Saalfeld, S. et al. saalfeldlab/n5: n5-2.5.1 https://doi.org/10.5281/zenodo.6578232 (2022).
Miles, A. et al. zarr-developers/zarr-python: v2.4.0 https://doi.org/10.5281/zenodo.3773450 (2020).
Luengo, I. et al. SuRVoS: super-region volume segmentation workbench. J. Struct. Biol. 198, 43–53 (2017).
Pennington, A. et al. SuRVoS 2: accelerating annotation and segmentation for large volumetric bioimage workflows across modalities and scales. Front. Cell Dev. Biol. 10, 842342 (2022).
Belevich, I. & Jokitalo, E. DeepMIB: user-friendly and open-source software for training of deep learning network for biological image segmentation. PLoS Comput. Biol. 17, e1008374 (2021).
Hennies, J. et al. CebraEM: a practical workflow to segment cellular organelles in volume SEM datasets using a transferable CNN-based membrane prediction. Preprint at bioRxiv https://doi.org/10.1101/2023.04.06.535829 (2023).
Smith, P. et al. Online citizen science with the Zooniverse for analysis of biological volumetric data. Histochem. Cell Biol. https://doi.org/10.1007/s00418-023-02204-6 (2023).
Jorstad, A. et al. NeuroMorph: a toolset for the morphometric analysis and visualization of 3D models derived from electron microscopy image stacks. Neuroinformatics 13, 83–92 (2015).
Jorstad, A., Blanc, J. & Knott, G. NeuroMorph: a software toolset for 3D analysis of neurite morphology and connectivity. Front. Neuroanat. 12, 59 (2018).
Troidl, J. et al. Barrio: customizable spatial neighborhood analysis and comparison for nanoscale brain structures. Comput. Graph. Forum 41, 183–194 (2022).
Arganda-Carreras, I. et al. Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).
Hallou, A., Yevick, H. G., Dumitrascu, B. & Uhlmann, V. Deep learning for bioimage analysis in developmental biology. Development 148, dev199616 (2021).
Shaga Devan, K., Kestler, H. A., Read, C. & Walther, P. Weighted average ensemble-based semantic segmentation in biological electron microscopy images. Histochem. Cell Biol. 158, 447–462 (2022).
Mandal, S. & Uhlmann, V. Splinedist: automated cell segmentation with spline curves. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) https://doi.org/10.1109/ISBI48211.2021.9433928 (IEEE, 2021).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Sheridan, A. et al. Local shape descriptors for neuron segmentation. Nat. Methods 20, 295–303 (2023).
McDonald, K. L., O’Toole, E. T., Mastronarde, D. N. & McIntosh, J. R. Kinetochore microtubules in PTK cells. J. Cell Biol. 118, 369–383 (1992).
Marsh, B. J., Mastronarde, D. N., Buttle, K. F., Howell, K. E. & McIntosh, J. R. Organellar relationships in the Golgi region of the pancreatic beta cell line, HIT-T15, visualized by high resolution electron tomography. Proc. Natl Acad. Sci. USA 98, 2399–2406 (2001).
Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinforma. 18, 529 (2017).
Pietzsch, T., Preibisch, S., Tomančák, P. & Saalfeld, S. ImgLib2—generic image processing in Java. Bioinformatics 28, 3009–3011 (2012).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
McKinney, W. Data structures for statistical computing in Python. In Proc. of the 9th Python in Science Conference. https://doi.org/10.25080/Majora-92bf1922-00a (2010).
Schmid, B., Schindelin, J., Cardona, A., Longair, M. & Heisenberg, M. A high-level 3D visualization API for Java and ImageJ. BMC Bioinforma. 11, 274 (2010).
Cardona, A. et al. TrakEM2 software for neural circuit reconstruction. PLoS ONE 7, e38011 (2012).
Hennies, J. et al. AMST: alignment to median smoothed template for focused ion beam scanning electron microscopy image stacks. Sci. Rep. 10, 2004 (2020).
Hanslovsky, P., Bogovic, J. A. & Saalfeld, S. Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy. Bioinformatics 33, 1379–1386 (2017).
Roels, J. et al. An interactive ImageJ plugin for semi-automated image denoising in electron microscopy. Nat. Commun. 11, 771 (2020).
Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void—learning denoising from single noisy images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2129–2137 (2019).
Perez, A. J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. 8, 126 (2014).
Hoffman, D. P. et al. Correlative three-dimensional super-resolution and block-face electron microscopy of whole vitreously frozen cells. Science 367, eaaz5357 (2020).
Müller, A. et al. Structure, interaction, and nervous connectivity of beta cell primary cilia. Preprint at bioRxriv https://doi.org/10.1101/2023.12.01.568979 (2024).
Park, G. et al. Amira annotation protocol. protocols.io https://www.protocols.io/view/amira-annotation-protocol-b834ryqw (2022).

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

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