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3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse
File | Description | Size | Format | |
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s41598-017-04450-w.pdf | Published version | 2.67 MB | Adobe PDF | View/Open |
Title: | 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse |
Authors: | Griffie, J Shlomovich, L Williamson, D Shannon, M Aarons, J Khuon, S Burn, G Boelen, L Peters, R Cope, A Cohen, E Rubin-Delanchy, P Owen, D |
Item Type: | Journal Article |
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. Introduction. |
Issue Date: | 22-Jun-2017 |
Date of Acceptance: | 12-May-2017 |
URI: | http://hdl.handle.net/10044/1/49681 |
DOI: | https://dx.doi.org/10.1038/s41598-017-04450-w |
ISSN: | 2045-2322 |
Publisher: | Nature Publishing Group |
Journal / Book Title: | Scientific Reports |
Volume: | 7 |
Copyright Statement: | © The Author(s) 2017. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
Publication Status: | Published |
Open Access location: | https://www.nature.com/articles/s41598-017-04450-w |
Article Number: | 4077 |
Appears in Collections: | Statistics Mathematics |