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SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data

Nature Methods volume 12, pages 1065–1071 (2015)Cite this article

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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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Figure 1: Voronoï-based segmentation.
Figure 2: Automatic segmentation of various proteins and cell types.
Figure 3: Segmentation and quantification of simulations and well-characterized nanotemplates.
Figure 4: Segmentation and quantification of experimental PALM data.
Figure 5: Segmentation and quantification of experimental dSTORM data.

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Acknowledgements

We thank D. Nair and K. Haas for the GluA1-mEOS2 and GPI-mEOS2 acquisitions, G. Giannone and O. Rossier for the integrin-β3−mEOS2 PALM data and M. Lakadamyali for providing GlyR data and feedback on the manuscript. This work was supported by the Ministère de l'Enseignement Supérieur et de la Recherche (ANR NanoDom, Labex BRAIN and ANR-10-INBS-04 France-BioImaging), the European Research Council (ERC; grant nano-dyn-syn to D.C.), the Centre National de la Recherche Scientifique, the Conseil Régional d'Aquitaine and the Institut National de la Santé et de la Recherche Médicale.

Author information

Authors and Affiliations

  1. Interdisciplinary Institute for Neuroscience, University of Bordeaux, Bordeaux, France

    Florian Levet, Eric Hosy, Adel Kechkar, Corey Butler, Anne Beghin, Daniel Choquet & Jean-Baptiste Sibarita

  2. Interdisciplinary Institute for Neuroscience, Centre National de la Recherche Scientifique (CNRS) UMR 5297, Bordeaux, France

    Florian Levet, Eric Hosy, Adel Kechkar, Corey Butler, Anne Beghin, Daniel Choquet & Jean-Baptiste Sibarita

  3. Bordeaux Imaging Center, University of Bordeaux, Bordeaux, France

    Florian Levet & Daniel Choquet

  4. Bordeaux Imaging Center, CNRS UMS 3420, Bordeaux, France

    Florian Levet & Daniel Choquet

  5. Bordeaux Imaging Center, INSERM US04, Bordeaux, France

    Florian Levet & Daniel Choquet

  6. Ecole Nationale Supérieure de Biotechnologie, Constantine, Algeria

    Adel Kechkar

  7. Imagine Optic, Orsay, France

    Corey Butler

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  1. Florian Levet
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  7. Jean-Baptiste Sibarita
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Contributions

F.L. developed the software and carried out the simulations. F.L. and J.-B. S. designed the analysis method. E.H. and D.C. designed the biological experiments. A.K. developed the single-molecule localization software. C.B. and A.B. worked on the quantitative analysis and the molecular counting. All the authors contributed to the manuscript. J.-B.S. came up with the original idea and supervised the work.

Corresponding author

Correspondence to Jean-Baptiste Sibarita.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Segmentation of various proteins and cell types using SR-Tesseler and several intensity-based thresholding methods.

(a–d) Automatic segmentation obtained with SR-Tesseler using the local 1st rank order density feature > 2δ (left column) and four intensity-based thresholding techniques (Isodata=cyan, Intermode=green, Huang=magenta and Percentile=red, see http://fiji.sc/Auto_Threshold#Default) implemented in ImageJ (right column). In each case, SR-Tesseler identified the different levels of molecular organization. Using the same automatic density parameters, we could successfully segmented small clusters, non-isotropic structures such as microtubules or adhesion sites, and the cell contour. This illustrates the versatility and robustness to molecular organization of our method. On the contrary, results obtained with the four intensity-based thresholding techniques provided much more arbitrary and variable results. For example, while the Isodata method shows good segmentation performance on microtubules, it failed segmenting correctly any organization level in neurons and fibroblast cells. Percentile and Huang gave very similar segmentations on the microtubule data but very different results on neuronal and fibroblast data. The Intermode method identified efficiently some nanoclusters in neuronal data but failed to capture any other structure on fibroblast and microtubule data.

Supplementary Figure 2 Comparison between density and cutting distance and DBSCAN.

(a) Segmentation comparison between density ( > 2δ, top-red outlines) and cutting distance ( < 30 nm bottom-magenta outlines) based analysis performed on simulated data. Simulated clusters (cyan outlines) have a density of 0.01 mol/nm², a radius of 50 nm and an enrichment factor R of ∞ (left), 20 (center) and 10 (right). In absence of background, the density criterion tends to underestimate the cluster size, while the use of the cutting distance defined from the localization accuracy is more accurate. When R decreases, the cutting distance criterion tends to fuse the clusters with the background localizations, quickly overestimating their sizes, while the density criterion performs robustly. (b) Segmentation comparison between the density-based spatial clustering analysis with noise (DBSCAN) (left) and SR-Tesseler (right) methods performed on 50 nm radius clusters of 0.01 mol/nm² density. Data with R = 2.5 (top) could be segmented using a set of (ε, Nε) parameters ((40, 80), (30, 40) and (20, 20) from left to right), where ε and Nε denote the neighborhood’s radius (in nm) and the minimum number of localizations in the neighborhood ε, respectively, two parameters of the DBSCAN method12. Using the same set of parameters on data with lower background (R = 20), all the segmentation failed (bottom). In comparison, SR-Tesseler analysis, performed with a unique density parameter > 2δ, could successfully segment all the clusters (right).

Supplementary Figure 3 Comparison with the Ripley function and benchmarking.

(a) Degradation of the simulations by randomizing the localization positions with various accuracies (0, 5, 10 and 20 nm) for different R ratios of 25, 10 and 5 (left to right). (b) H-Ripley functions for various simulation and experimental datasets. While a peak of maximal aggregation is observed on simulations data containing only sparse clusters, no clustering is visible in any experimental data of GPI::mEOS2 and GluA1::mEOS2 expressed in neurons. (c) On experimental data, when performed on ROIs centered on spines, Ripley function could measure clusters of 91.2 nm (IQR 83.2-112 nm). (d) Computation time of the H-Ripley function for different searching radii and different implementations. Since the Voronoï diagram is a space partitioning technique, it can be used to speed-up the H-Ripley function brute-force implementation. The Quadtree space-partitioning technique performs even faster.

Source data

Supplementary Figure 4 Multiple-localization correction.

(a–e) Multiple detections occurring in a vicinity of space (ω) and time (τ) are identified as the same molecule and merged. (a) Fluorophore photophysical parameters, such as the number of blinks undergone by the molecules (Nblinks), the fluorescent on-time (Ton) before a blinking or photobleaching event and the fluorescent off-time (Toff), are determined for each localization. (b) Four detections (3 squares and 1 triangle) are located within their respective space vicinity ω but only 3 of them (squares) are within the blinking tolerance time interval τ(for clarity, the neighborhood radius of the triangle was not displayed). Their corresponding Voronoï diagram is represented in red. Square localizations are then identified as the same molecule, merged together and replaced by a new detection (circle) at a location corresponding to their barycenter. The new Voronoï diagram is displayed in black. (c–e) To determine the correct time interval τ, the photophysics of the fluorophore is analyzed. From the initial dataset, the off-time (c), number of blinks per molecule (d) and on-time (e) distributions are computed. For a dataset composed of 473,983 localizations, the average number of blinks per molecule was 1.4, and the number of molecules after cleaning was 220,775. As a control, the number of emission bursts (319,473), counted with τ= 0, divided by the average number of blinks per molecule (1.4) was only 3.36% different.

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SR-Tesseler software (ZIP 20052 kb)

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Levet, F., Hosy, E., Kechkar, A. et al. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Nat Methods 12, 1065–1071 (2015). https://doi.org/10.1038/nmeth.3579

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