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

Juliette Griffié, Leigh Shlomovich, David Williamson, Michael Shannon, Jesse Aaron, Satya Khuon, Garth Burn, Lies Boelen, Ruby Peters, Andrew Cope, Edward Cohen, Dylan Owen, Patrick Rubin-Delanchy

Research output: Contribution to journalArticle (Academic Journal)peer-review

389 Downloads (Pure)

Abstract

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
Number of pages9
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 22 Jun 2017

Fingerprint Dive into the research topics of '3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse'. Together they form a unique fingerprint.

Cite this