@article {65127,
	title = {Differential nanoscale organisation of LFA-1 modulates T cell migration.},
	journal = {Journal of Cell Science},
	volume = {132},
	year = {2019},
	month = {2019 Aug 29},
	pages = {1-28},
	issn = {0021-9533},
	doi = {10.1242/jcs.232991},
	url = {http://jcs.biologists.org/lookup/doi/10.1242/jcs.23299},
	author = {Shannon, Michael J. and Pineau, Judith and Griffi{\'e}, Juliette and Aaron, Jesse and Peel, Tamlyn and Williamson, David J. and Zamoyska, Rose and Cope, Andrew P. and Cornish, Georgina H. and Owen, Dylan M.}
}
@article {49467,
	title = {Development of 2-colour and 3D SMLM data analysis methods for fibrous spatial point patterns.},
	journal = {Journal of Physics D: Applied Physics},
	volume = {52},
	year = {2018},
	month = {2018 Oct 11},
	pages = {1},
	abstract = {<p>Abstract ingle molecule localisation microscopy (SMLM), experimentally achieved over a decade ago, has become a routinely used analytical tool across the life sciences. Synergistic advances in probe chemistry, optical physics and data analysis has propelled SMLM into the quantitative realm, enabling unprecedented access to the cellular machinery at the nanoscale. In its early years, SMLM primarily served as a platform for impressive rendered images of sub diffraction scale structures, however more recently a shift towards interrogating SMLM point pattern data in a robust mathematical framework has occurred. A prevalent theme in the SMLM field is the need for quantitative analytical methods, to better understand the underlying processes on which SMLM reports and to extract statistically valid biological insights. Whilst some forms of post processing analytics, for example cluster analysis, have been widely studied, others such as fibre analysis remain in their infancy. Here, we review the current state of the art of cluster analysis and fibre analysis and present new methods for their implementation in both 3D SMLM data sets and multi-colour data.</p>
},
	doi = {10.1088/1361-6463/aae7ac},
	author = {Peters, Ruby and Griffi{\'e}, Juliette and Williamson, David and Aaron, Jesse and Khuon, Satya and Owen, Dylan}
}
@article {48621,
	title = {3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse.},
	journal = {Scientific Reports},
	volume = {7},
	year = {2017},
	month = {2017 Jun 22},
	pages = {4077},
	abstract = {<p>Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell{\textquoteright}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.</p>
},
	issn = {2045-2322},
	doi = {10.1038/s41598-017-04450-w},
	author = {Griffi{\'e}, Juliette and Shlomovich, Leigh and Williamson, David J and Shannon, Michael and Aaron, Jesse and Khuon, Satya and L Burn, Garth and Boelen, Lies and Peters, Ruby and Cope, Andrew P and Cohen, Edward A K and Rubin-Delanchy, Patrick and Owen, Dylan M}
}
