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A Bayesian cluster analysis method for single-molecule localization microscopy data

Abstract

Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)—for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is 18 h; user input takes 1 h.

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Figure 1: Schematic of the Bayesian cluster analysis workflow.
Figure 2: Schematic of the ImageJ interface while displaying the output of ThunderSTORM.
Figure 3: Schematic of the RStudio interface.
Figure 4: Format of the folder structure ready for the Bayesian cluster analysis algorithm.
Figure 5: Format of the folder and file structure after processing and postprocessing are complete.
Figure 6: Schematic of the MATLAB interface for generating optional histograms of the output data.
Figure 7: Expected outcome of the analysis algorithm when used on experimental dSTORM data.

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Acknowledgements

D.M.O. acknowledges funding from the European Research Council (FP7 starter grant 337187) and Marie Curie Career Integration Grant 334303. A.P.C. is funded by Arthritis Research UK grants 19652 and 20525. M.S. was supported by the King's Bioscience Institute and the Guy's and St. Thomas' Charity Prize PhD Programme in Biomedical and Translational Science. We acknowledge the use of the King's College Nikon Imaging Centre (NIC).

Author information

Authors and Affiliations

Authors

Contributions

J.G., M.S., C.L.B., L.B., N.A.H., D.M.O. and P.R.-D. developed and tested the protocol. J.G. and P.R.-D. wrote the analysis code. G.L.B., D.J.W. and A.P.C. provided samples. J.G., M.S., G.L.B., D.M.O. and P.R.-D. performed imaging, simulations and analysis. J.G., D.M.O. and P.R.-D. wrote the manuscript.

Corresponding authors

Correspondence to Dylan M Owen or Patrick Rubin-Delanchy.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Performance of the algorithm when applied to simulated data with hard edge clusters

a) Spatial distribution over the 3 x 3 μm ROI. b) Heat map of the scores generated. c) Histogram of the estimated number of clusters for each ROI. d) Histogram of the estimated percentage of localisations found in clusters for each ROI. e) Histogram of the estimated number localisations per cluster. f) Histogram of the standard deviation of the detected clusters. n = 30 simulated ROIs.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 256 kb)

Supplementary Software

Implementation of the cluster analysis algorithm, including preprocessing steps, simulation, analysis and postprocessing. (ZIP 10 kb)

Supplementary Data

.csv file containing experimental data from a dSTORM acquisition of ZAP-70 at a T-cell immunological synapse. The file can be opened in ThunderSTORM and used to test the protocol. A 3 x 3 μm region in the center of this cell, containing 4,200 localizations, has a processing time of approximately 2 h and 30 min on a standard desktop computer. (CSV 12482 kb)

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Griffié, J., Shannon, M., Bromley, C. et al. A Bayesian cluster analysis method for single-molecule localization microscopy data. Nat Protoc 11, 2499–2514 (2016). https://doi.org/10.1038/nprot.2016.149

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