SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
Abstract
Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. Here we present a segmentation framework based on Voronoï tessellation constructed from the coordinates of localized molecules, implemented in freely available and open-source SR-Tesseler software. This method allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. We validated our method on simulated data and on various biological experimental data of proteins labeled with genetically encoded fluorescent proteins or organic fluorophores. In addition to providing insight into complex protein organization, this polygon-based method should serve as a reference for the development of new types of quantifications, as well as for the optimization of existing ones.
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The RIM1-mEos3 distribution was nonuniform and contained nanoclusters with an average diameter of 80.95 ± 5.34 nm using adapted SR-Tesseler analysis and 78.93 ± 5.85 nm using nearest neighbor distance analysis.
“The distribution of RIM1-mEos3 was nonuniform and contained NCs with an average diameter of 80.95 ± 5.34 nm and 78.93 ± 5.85 nm using either an adapted SR-Tesseler analysis15 or nearest neighbor distance analysis4, respectively (Extended Data Fig. 6f), consistent with our 3D-STORM results (Fig. 1).”
From A trans-synaptic nanocolumn aligns neurotransmitter release to receptors (2016) · cites this paper as [15]
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A trans-synaptic nanocolumn aligns neurotransmitter release to receptors
Tang, A.-H., Chen, H., Li, T. P., Metzbower, S. R., MacGillavry, H. D., & Blanpied, T. A. (2016) · Nature · doi:10.1038/nature19058 · Cited by 677 · see citing works
CaMKII activation persistently segregates postsynaptic proteins via liquid phase separation
Hosokawa, T., Liu, P.-W., Cai, Q., Ferreira, J. S., Levet, F., Butler, C., Sibarita, J.-B., Choquet, D., Groc, L., Hosy, E., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00843-3 · Cited by 146 · see citing works
Self-citationLive-cell imaging powered by computation
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Kæstel-Hansen, J., de Sautu, M., Saminathan, A., Scanavachi, G., Bango Da Cunha Correia, R. F., Juma Nielsen, A., Bleshøy, S. V., Tsolakidis, K., Boomsma, W., Kirchhausen, T., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02665-8 · Cited by 51 · see citing works
- Authors
- Levet, F., Hosy, E., Kechkar, A., Butler, C., Beghin, A., Choquet, D., & Sibarita, J.-B.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Sibarita, Jean-Baptiste
- Venue
- Nature Methods
- Published
- 2015-09-07
- Volume
- 12
- Issue
- 11
- Pages
- 1065-1071
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1833667256
- Cited by
- 502
- References
- 1
- Citations by year
- 2026: 27 · 2025: 48 · 2024: 59 · 2023: 51 · 2022: 59 · 2021: 64 · 2020: 48 · 2019: 53 · 2018: 35 · 2017: 31 · 2016: 24 · 2015: 2
- FWCI
- 18.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-10-INBS-0004)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- Voronoi tessellation, image segmentation, organic fluorophores, molecular organization
- MeSH terms
- Algorithms, Animals, Chlorocebus aethiops, Computer Simulation, Fluorescent Dyes, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Neurons, Oocytes, Pattern Recognition, Automated, Software, Xenopus laevis, Cluster Analysis, Receptors, Glycine, Computational Biology, COS Cells
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