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Dynamic Bayesian Cluster Analysis of Live‐Cell Single Molecule Localization Microscopy Datasets
Author(s) -
Griffié Juliette,
Burn Garth L.,
Williamson David J.,
Peters Ruby,
RubinDelanchy Patrick,
Owen Dylan M.
Publication year - 2018
Publication title -
small methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.66
H-Index - 46
ISSN - 2366-9608
DOI - 10.1002/smtd.201800008
Subject(s) - visualization , cluster (spacecraft) , computer science , nanoscopic scale , limiting , microscopy , characterization (materials science) , bayesian probability , artificial intelligence , biological system , nanotechnology , physics , materials science , biology , optics , mechanical engineering , engineering , programming language
Until recently, single‐molecule localization microscopy (SMLM) was constrained to the study of fixed cells, limiting analysis to the structural characterization of cell anatomy. The extension of SMLM to live‐cell imaging enables the dynamic visualization of molecular organization, paving the way for more functional studies. If associated with novel quantification tools such as presented here, it has the potential to provide a unique insight into cellular machinery at the nanoscale. While cluster analysis for conventional SMLM data sets is relatively well established, the extension of SMLM to live‐cell imaging lacks the required analytical tools. Here, a Bayesian‐based cluster analysis strategy is presented for live‐cell SMLM that allows the dynamics of nanoscale molecular clusters to be analyzed for the first time, generating functional information otherwise lost in fixed cell studies. The method is validated on simulations as well as on experimental data sets derived from naive CD4 + T‐cell synapses.