3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse

Juliette Griffié*, Leigh Shlomovich, David J. Williamson, Michael Shannon, Jesse Aaron, Satya Khuon, Garth L. Burn, Lies Boelen, Ruby Peters, Andrew P. Cope, Edward A.K. Cohen, Patrick Rubin-Delanchy, Dylan M. Owen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)
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Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell's nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D. This lack of quantification tools can be explained by the relative novelty of imaging techniques such as interferometric photo-activated localisation microscopy (iPALM). Also, existing methods that could be extended to 3D SMLM are usually subject to user defined analysis parameters, which remains a major drawback. Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable parameters, relying on a model-based Bayesian approach which takes full account of the individual localisation precisions in all three dimensions. The accuracy and reliability of the method is validated using simulated data sets. This tool is then deployed on novel experimental data as a proof of concept, illustrating the recruitment of LAT to the T-cell immunological synapse in data acquired by iPALM providing ~10 nm isotropic resolution.

Original languageEnglish
Article number4077
JournalScientific Reports
Issue number1
Early online date22 Jun 2017
Publication statusPublished - 1 Dec 2017


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