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

Research output: Contribution to journalArticlepeer-review

Juliette Griffié, Michael Shannon, Claire L. Bromley, Lies Boelen, Garth L. Burn, David J. Williamson, Nicholas A. Heard, Andrew P. Cope, Dylan M. Owen, Patrick Rubin-Delanchy

Original languageEnglish
Pages (from-to)2499-2514
Number of pages16
JournalNature Protocols
Issue number12
Early online date17 Nov 2016
Accepted/In press1 Nov 2016
E-pub ahead of print17 Nov 2016
Published1 Dec 2016


King's Authors


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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