3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse
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 analys...
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Published in | Scientific reports Vol. 7; no. 1; pp. 4077 - 9 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Nature Publishing Group UK
22.06.2017
Nature Publishing Group Nature Portfolio |
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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. |
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AbstractList | 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.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. 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. 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. |
ArticleNumber | 4077 |
Author | Cope, Andrew P. Rubin-Delanchy, Patrick Shannon, Michael Peters, Ruby Shlomovich, Leigh Aaron, Jesse Boelen, Lies L. Burn, Garth Owen, Dylan M. Cohen, Edward A. K. Griffié, Juliette Williamson, David J. Khuon, Satya |
Author_xml | – sequence: 1 givenname: Juliette surname: Griffié fullname: Griffié, Juliette email: juliette.griffie@kcl.ac.uk organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London – sequence: 2 givenname: Leigh surname: Shlomovich fullname: Shlomovich, Leigh organization: Department of Mathematics, Imperial College London – sequence: 3 givenname: David J. surname: Williamson fullname: Williamson, David J. organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London – sequence: 4 givenname: Michael surname: Shannon fullname: Shannon, Michael organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London – sequence: 5 givenname: Jesse surname: Aaron fullname: Aaron, Jesse organization: Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus – sequence: 6 givenname: Satya surname: Khuon fullname: Khuon, Satya organization: Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus – sequence: 7 givenname: Garth surname: L. Burn fullname: L. Burn, Garth organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London – sequence: 8 givenname: Lies surname: Boelen fullname: Boelen, Lies organization: Department of Medicine, Imperial College London – sequence: 9 givenname: Ruby surname: Peters fullname: Peters, Ruby organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London – sequence: 10 givenname: Andrew P. orcidid: 0000-0003-0470-1827 surname: Cope fullname: Cope, Andrew P. organization: Department of Immunology, Infection and Inflammatory Disease, King’s College London – sequence: 11 givenname: Edward A. K. surname: Cohen fullname: Cohen, Edward A. K. organization: Department of Mathematics, Imperial College London – sequence: 12 givenname: Patrick surname: Rubin-Delanchy fullname: Rubin-Delanchy, Patrick organization: Department of Statistics, University of Oxford – sequence: 13 givenname: Dylan M. surname: Owen fullname: Owen, Dylan M. organization: Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London |
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Snippet | Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10–30 nm, revealing the cell’s nanoscale... Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell's nanoscale... Abstract Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10–30 nm, revealing the cell’s nanoscale... |
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SubjectTerms | 14/35 14/63 631/1647/328/2238 631/250/2503 631/57/2265 Adaptor Proteins, Signal Transducing - metabolism Bayes Theorem Bayesian analysis Cell Line Cluster Analysis Data processing Fluorophores Humanities and Social Sciences Humans Immunological synapses Immunological Synapses - metabolism Localization Lymphocyte Activation - immunology Lymphocytes T Membrane Proteins - metabolism Microscopy Models, Biological multidisciplinary Science Science (multidisciplinary) Synapses T-Lymphocytes - immunology T-Lymphocytes - metabolism |
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Title | 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse |
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