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Saavedra LA, Mosqueira A, Barrantes FJ. A supervised graph-based deep learning algorithm to detect and quantify clustered particles. NANOSCALE 2024; 16:15308-15318. [PMID: 39082742 DOI: 10.1039/d4nr01944j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (i.e., human intervention-independent) algorithm consisting of two concatenated binary Graph Neural Network (GNNs) classifiers with the aim of detecting and quantifying dynamic clustering of particles. As the algorithm only needs simulated data to train the GNNs, it is parameter-independent. The GNN-based algorithm is first tested on datasets based on simulated, albeit biologically realistic data, and validated on actual fluorescence microscopy experimental data. Application of the new GNN method is shown to be faster than other currently used approaches for high-dimensional SMLM datasets, with the additional advantage that it can be implemented on standard desktop computers. Furthermore, GNN models obtained via training procedures are reusable. To the best of our knowledge, this is the first application of GNN-based approaches to the analysis of particle aggregation, with potential applications to the study of nanoscopic particles like the nanoclusters of membrane-associated proteins in live cells.
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Affiliation(s)
- Lucas A Saavedra
- Laboratory of Molecular Neurobiology, Biomedical Research institute (BIOMED), UCA-CONICET, Av. Alicia Moreau de Justo 1600, C1107AFF Buenos Aires, Argentina.
| | - Alejo Mosqueira
- Laboratory of Molecular Neurobiology, Biomedical Research institute (BIOMED), UCA-CONICET, Av. Alicia Moreau de Justo 1600, C1107AFF Buenos Aires, Argentina.
| | - Francisco J Barrantes
- Laboratory of Molecular Neurobiology, Biomedical Research institute (BIOMED), UCA-CONICET, Av. Alicia Moreau de Justo 1600, C1107AFF Buenos Aires, Argentina.
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2
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Wu X, Jia W. Multilayer Annotation Strategy AnnoSePS: Disentangling the Intricate Structure of Selenium-Containing Polysaccharides Based on Preferential Fragmentation Patterns. Anal Chem 2024; 96:10696-10704. [PMID: 38904260 DOI: 10.1021/acs.analchem.4c01576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Precision mapping of selenium at structural and position levels poses significant challenges in selenium-containing polysaccharide identification. Due to the absence of reference spectra, database-centric approaches are still limited in the discovery of selenium binding sites and distinction among different isomeric structures. A multilayer annotation strategy, AnnoSePS, is proposed for achieving the identification of seleno-substituent and the unbiased profiling of polysaccharides. Applying Snoop-triggered multiple reaction monitoring (Snoop-MRM) identified multidimensional monosaccharides in selenium-containing polysaccharides. Galactose, galacturonic acid, and glucose were the predominant monosaccharides with a molar ratio of 25.19, 19.45, and 11.72, respectively. Selenium present in seleno-rhamnose was found to substitute the hydroxyl group located at C-1 positions through the formation of a Se-H bond. Ions C6H9O3Se-, C6H7O3Se-, C5H5O3Se-, C4H5O2Se-, C3H5O2Se-, C2H3O2Se-, and CHOSe- were defined as the characteristic fragments of seleno-rhamnose. The agglomerative hierarchical clustering algorithm is applied to group spectra from each run based on the characteristic information. Preferential fragmentation patterns in mass spectrometry are revealed by training a probabilistic model. A list of candidate oligosaccharides is generated by step-by-step browsing through the transition pairs for all reference spectra and applying the transitions (addition, insertion, removal, and substitution) to reference structures. Combining time course analyses revealed the linkage composition of selenium-containing oligosaccharides. Glycosidic linkages were annotated based on a synthesis-driven approach. T-Galactose (16.67 ± 5.23%) and T-Galacturonic acid (11.54 ± 4.66%) were the predominant linkage residues. As the database-independent mapping strategy, AnnoSePS makes it possible to comprehensively interrogate spectral data and dissect the fine structure of selenium-containing polysaccharides.
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Affiliation(s)
- Xixuan Wu
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
- Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China
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3
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Nguyen TD, Chen YI, Chen LH, Yeh HC. Recent Advances in Single-Molecule Tracking and Imaging Techniques. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:253-284. [PMID: 37314878 DOI: 10.1146/annurev-anchem-091922-073057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Since the early 1990s, single-molecule detection in solution at room temperature has enabled direct observation of single biomolecules at work in real time and under physiological conditions, providing insights into complex biological systems that the traditional ensemble methods cannot offer. In particular, recent advances in single-molecule tracking techniques allow researchers to follow individual biomolecules in their native environments for a timescale of seconds to minutes, revealing not only the distinct pathways these biomolecules take for downstream signaling but also their roles in supporting life. In this review, we discuss various single-molecule tracking and imaging techniques developed to date, with an emphasis on advanced three-dimensional (3D) tracking systems that not only achieve ultrahigh spatiotemporal resolution but also provide sufficient working depths suitable for tracking single molecules in 3D tissue models. We then summarize the observables that can be extracted from the trajectory data. Methods to perform single-molecule clustering analysis and future directions are also discussed.
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Affiliation(s)
- Trung Duc Nguyen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Yuan-I Chen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Limin H Chen
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Hsin-Chih Yeh
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA;
- Texas Materials Institute, University of Texas at Austin, Austin, Texas, USA
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4
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Abstract
Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.
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Affiliation(s)
- Siewert Hugelier
- Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; , ,
| | - P L Colosi
- Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; , ,
| | - Melike Lakadamyali
- Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; , ,
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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5
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Ball ML, Koestler SA, Muresan L, Rehman SA, O’Holleran K, White R. The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy. PLoS Genet 2023; 19:e1010654. [PMID: 36867662 PMCID: PMC10016678 DOI: 10.1371/journal.pgen.1010654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/15/2023] [Accepted: 02/04/2023] [Indexed: 03/04/2023] Open
Abstract
While the biochemistry of gene transcription has been well studied, our understanding of how this process is organised in 3D within the intact nucleus is less well understood. Here we investigate the structure of actively transcribed chromatin and the architecture of its interaction with active RNA polymerase. For this analysis, we have used super-resolution microscopy to image the Drosophila melanogaster Y loops which represent huge, several megabases long, single transcription units. The Y loops provide a particularly amenable model system for transcriptionally active chromatin. We find that, although these transcribed loops are decondensed they are not organised as extended 10nm fibres, but rather they largely consist of chains of nucleosome clusters. The average width of each cluster is around 50nm. We find that foci of active RNA polymerase are generally located off the main fibre axis on the periphery of the nucleosome clusters. Foci of RNA polymerase and nascent transcripts are distributed around the Y loops rather than being clustered in individual transcription factories. However, as the RNA polymerase foci are considerably less prevalent than the nucleosome clusters, the organisation of this active chromatin into chains of nucleosome clusters is unlikely to be determined by the activity of the polymerases transcribing the Y loops. These results provide a foundation for understanding the topological relationship between chromatin and the process of gene transcription.
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Affiliation(s)
- Madeleine L. Ball
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Site, Cambridge, United Kingdom
| | - Stefan A. Koestler
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Site, Cambridge, United Kingdom
| | - Leila Muresan
- Cambridge Advanced Imaging Centre, University of Cambridge, Downing Site, Cambridge, United Kingdom
| | - Sohaib Abdul Rehman
- Cambridge Advanced Imaging Centre, University of Cambridge, Downing Site, Cambridge, United Kingdom
| | - Kevin O’Holleran
- Cambridge Advanced Imaging Centre, University of Cambridge, Downing Site, Cambridge, United Kingdom
| | - Robert White
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Site, Cambridge, United Kingdom
- * E-mail:
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6
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Nieves DJ, Pike JA, Levet F, Williamson DJ, Baragilly M, Oloketuyi S, de Marco A, Griffié J, Sage D, Cohen EAK, Sibarita JB, Heilemann M, Owen DM. A framework for evaluating the performance of SMLM cluster analysis algorithms. Nat Methods 2023; 20:259-267. [PMID: 36765136 DOI: 10.1038/s41592-022-01750-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/06/2022] [Indexed: 02/12/2023]
Abstract
Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.
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Affiliation(s)
- Daniel J Nieves
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Jeremy A Pike
- Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.,Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Florian Levet
- Interdisciplinary Institute for Neuroscience, CNRS, IINS, UMR 5297, Université de Bordeaux, Bordeaux, France.,Bordeaux Imaging Center, CNRS, INSERM, BIC, UMS 3420, US 4, Université de Bordeaux, Bordeaux, France
| | - David J Williamson
- Department of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Mohammed Baragilly
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Department of Mathematics, Insurance and Applied Statistics, Helwan University, Helwan, Egypt
| | - Sandra Oloketuyi
- Laboratory of Environmental and Life Sciences, University of Nova Gorica, Rožna Dolina, Slovenia
| | - Ario de Marco
- Laboratory of Environmental and Life Sciences, University of Nova Gorica, Rožna Dolina, Slovenia
| | - Juliette Griffié
- Laboratory of Experimental Biophysics, Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Daniel Sage
- Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Jean-Baptiste Sibarita
- Interdisciplinary Institute for Neuroscience, CNRS, IINS, UMR 5297, Université de Bordeaux, Bordeaux, France
| | - Mike Heilemann
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany
| | - Dylan M Owen
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK. .,Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK. .,School of Mathematics, University of Birmingham, Birmingham, UK.
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7
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Scalisi S, Ahmad A, D'Annunzio S, Rousseau D, Zippo A. Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM). Methods Mol Biol 2023; 2655:183-200. [PMID: 37212997 DOI: 10.1007/978-1-0716-3143-0_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The polycomb group (PcG) proteins play a central role in the maintenance of a repressive state of gene expression. Recent findings demonstrate that PcG components are organized into nuclear condensates, contributing to the reshaping of chromatin architecture in physiological and pathological conditions, thus affecting the nuclear mechanics. In this context, direct stochastic optical reconstruction microscopy (dSTORM) provides an effective tool to achieve a detailed characterization of PcG condensates by visualizing them at a nanometric level. Furthermore, by analyzing dSTORM datasets with cluster analysis algorithms, quantitative information can be yielded regarding protein numbers, grouping, and spatial organization. Here, we describe how to set up a dSTORM experiment and perform the data analysis to study PcG complexes' components in adhesion cells quantitatively.
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Affiliation(s)
- Silvia Scalisi
- Chromatin Biology & Epigenetics Lab, Department of Cellular, Computational, and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France
| | - Sarah D'Annunzio
- Chromatin Biology & Epigenetics Lab, Department of Cellular, Computational, and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France
| | - Alessio Zippo
- Chromatin Biology & Epigenetics Lab, Department of Cellular, Computational, and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
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8
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Verzelli P, Nold A, Sun C, Heilemann M, Schuman EM, Tchumatchenko T. Unbiased choice of global clustering parameters for single-molecule localization microscopy. Sci Rep 2022. [PMID: 36581654 DOI: 10.1101/2021.02.22.432198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.
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Affiliation(s)
- Pietro Verzelli
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Andreas Nold
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
- Theory of Neural Dynamics Group, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Chao Sun
- Department of Synaptic Plasticity, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Mike Heilemann
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany
| | - Erin M Schuman
- Department of Synaptic Plasticity, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Tatjana Tchumatchenko
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany.
- Institute for Physiological Chemistry, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
- Theory of Neural Dynamics Group, Max Planck Institute for Brain Research, Frankfurt, Germany.
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9
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Verzelli P, Nold A, Sun C, Heilemann M, Schuman EM, Tchumatchenko T. Unbiased choice of global clustering parameters for single-molecule localization microscopy. Sci Rep 2022; 12:22561. [PMID: 36581654 PMCID: PMC9800574 DOI: 10.1038/s41598-022-27074-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 12/31/2022] Open
Abstract
Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.
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Affiliation(s)
- Pietro Verzelli
- grid.15090.3d0000 0000 8786 803XInstitute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Andreas Nold
- grid.15090.3d0000 0000 8786 803XInstitute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany ,grid.419505.c0000 0004 0491 3878Theory of Neural Dynamics Group, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Chao Sun
- grid.419505.c0000 0004 0491 3878Department of Synaptic Plasticity, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Mike Heilemann
- grid.7839.50000 0004 1936 9721Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany
| | - Erin M. Schuman
- grid.419505.c0000 0004 0491 3878Department of Synaptic Plasticity, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Tatjana Tchumatchenko
- grid.15090.3d0000 0000 8786 803XInstitute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany ,grid.410607.4Institute for Physiological Chemistry, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany ,grid.419505.c0000 0004 0491 3878Theory of Neural Dynamics Group, Max Planck Institute for Brain Research, Frankfurt, Germany
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10
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Scotti A, Schulte MF, Lopez CG, Crassous JJ, Bochenek S, Richtering W. How Softness Matters in Soft Nanogels and Nanogel Assemblies. Chem Rev 2022; 122:11675-11700. [PMID: 35671377 DOI: 10.1021/acs.chemrev.2c00035] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Softness plays a key role in determining the macroscopic properties of colloidal systems, from synthetic nanogels to biological macromolecules, from viruses to star polymers. However, we are missing a way to quantify what the term "softness" means in nanoscience. Having quantitative parameters is fundamental to compare different systems and understand what the consequences of softness on the macroscopic properties are. Here, we propose different quantities that can be measured using scattering methods and microscopy experiments. On the basis of these quantities, we review the recent literature on micro- and nanogels, i.e. cross-linked polymer networks swollen in water, a widely used model system for soft colloids. Applying our criteria, we address the question what makes a nanomaterial soft? We discuss and introduce general criteria to quantify the different definitions of softness for an individual compressible colloid. This is done in terms of the energetic cost associated with the deformation and the capability of the colloid to isotropically deswell. Then, concentrated solutions of soft colloids are considered. New definitions of softness and new parameters, which depend on the particle-to-particle interactions, are introduced in terms of faceting and interpenetration. The influence of the different synthetic routes on the softness of nanogels is discussed. Concentrated solutions of nanogels are considered and we review the recent results in the literature concerning the phase behavior and flow properties of nanogels both in three and two dimensions, in the light of the different parameters we defined. The aim of this review is to look at the results on micro- and nanogels in a more quantitative way that allow us to explain the reported properties in terms of differences in colloidal softness. Furthermore, this review can give researchers dealing with soft colloids quantitative methods to define unambiguously which softness matters in their compound.
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Affiliation(s)
- Andrea Scotti
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
| | - M Friederike Schulte
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
| | - Carlos G Lopez
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
| | - Jérôme J Crassous
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
| | - Steffen Bochenek
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
| | - Walter Richtering
- Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, 52056 Aachen, Germany, European Union
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11
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Jensen LG, Hoh TY, Williamson DJ, Griffié J, Sage D, Rubin-Delanchy P, Owen DM. Correction of multiple-blinking artifacts in photoactivated localization microscopy. Nat Methods 2022; 19:594-602. [PMID: 35545712 DOI: 10.1038/s41592-022-01463-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 03/18/2022] [Indexed: 11/09/2022]
Abstract
Photoactivated localization microscopy (PALM) produces an array of localization coordinates by means of photoactivatable fluorescent proteins. However, observations are subject to fluorophore multiple blinking and each protein is included in the dataset an unknown number of times at different positions, due to localization error. This causes artificial clustering to be observed in the data. We present a 'model-based correction' (MBC) workflow using calibration-free estimation of blinking dynamics and model-based clustering to produce a corrected set of localization coordinates representing the true underlying fluorophore locations with enhanced localization precision, outperforming the state of the art. The corrected data can be reliably tested for spatial randomness or analyzed by other clustering approaches, and descriptors such as the absolute number of fluorophores per cluster are now quantifiable, which we validate with simulated data and experimental data with known ground truth. Using MBC, we confirm that the adapter protein, the linker for activation of T cells, is clustered at the T cell immunological synapse.
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Affiliation(s)
- Louis G Jensen
- Department of Mathematics, Aarhus University, Aarhus, Denmark.
| | - Tjun Yee Hoh
- Institute for Statistical Science, School of Mathematics, University of Bristol, Bristol, UK
| | - David J Williamson
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK
| | - Juliette Griffié
- Laboratory of Experimental Biophysics, Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Sage
- Biomedical Imaging Group, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Patrick Rubin-Delanchy
- Institute for Statistical Science, School of Mathematics, University of Bristol, Bristol, UK.
| | - Dylan M Owen
- Institute of Immunology and Immunotherapy, School of Mathematics and Centre of Membrane Proteins and Receptors, University of Birmingham, Birmingham, UK.
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12
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Wang Z, Deng W. Dynamic transcription regulation at the single-molecule level. Dev Biol 2021; 482:67-81. [PMID: 34896367 DOI: 10.1016/j.ydbio.2021.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 11/14/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Cell fate changes during development, differentiation, and reprogramming are largely controlled at the transcription level. The DNA-binding transcription factors (TFs) often act in a combinatorial fashion to alter chromatin states and drive cell type-specific gene expression. Recent advances in fluorescent microscopy technologies have enabled direct visualization of biomolecules involved in the process of transcription and its regulatory events at the single-molecule level in living cells. Remarkably, imaging and tracking individual TF molecules at high temporal and spatial resolution revealed that they are highly dynamic in searching and binding cognate targets, rather than static and binding constantly. In combination with investigation using techniques from biochemistry, structure biology, genetics, and genomics, a more well-rounded view of transcription regulation is emerging. In this review, we briefly cover the technical aspects of live-cell single-molecule imaging and focus on the biological relevance and interpretation of the single-molecule dynamic features of transcription regulatory events observed in the native chromatin environment of living eukaryotic cells. We also discuss how these dynamic features might shed light on mechanistic understanding of transcription regulation.
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Affiliation(s)
- Zuhui Wang
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Wulan Deng
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, 100871, China; Peking-Tsinghua Center for Life Sciences (CLS), Peking University, Beijing, 100871, China; School of Life Sciences, Peking University, Beijing, 100871, China.
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13
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Mancebo A, Mehra D, Banerjee C, Kim DH, Puchner EM. Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data. FRONTIERS IN BIOINFORMATICS 2021; 1:739769. [PMID: 36303727 PMCID: PMC9581065 DOI: 10.3389/fbinf.2021.739769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 10/14/2021] [Indexed: 11/25/2022] Open
Abstract
Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.
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Affiliation(s)
- Angel Mancebo
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, United States
| | - Dushyant Mehra
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Chiranjib Banerjee
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, United States
| | - Do-Hyung Kim
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, United States
| | - Elias M. Puchner
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, United States
- *Correspondence: Elias M. Puchner,
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14
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Kutz S, Zehrer AC, Svetlitckii R, Gülcüler Balta GS, Galli L, Kleber S, Rentsch J, Martin-Villalba A, Ewers H. An Efficient GUI-Based Clustering Software for Simulation and Bayesian Cluster Analysis of Single-Molecule Localization Microscopy Data. FRONTIERS IN BIOINFORMATICS 2021; 1:723915. [PMID: 36303736 PMCID: PMC9581037 DOI: 10.3389/fbinf.2021.723915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/17/2021] [Indexed: 11/13/2022] Open
Abstract
Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.
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Affiliation(s)
- Saskia Kutz
- Institut für Biochemie, Freie Universität Berlin, Berlin, Germany
| | - Ando C. Zehrer
- Institut für Biochemie, Freie Universität Berlin, Berlin, Germany
| | | | - Gülce S. Gülcüler Balta
- Department of Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lucrezia Galli
- Department of Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Susanne Kleber
- Department of Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Rentsch
- Institut für Biochemie, Freie Universität Berlin, Berlin, Germany
| | - Ana Martin-Villalba
- Department of Molecular Neurobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Helge Ewers
- Institut für Biochemie, Freie Universität Berlin, Berlin, Germany
- *Correspondence: Helge Ewers,
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15
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Marenda M, Lazarova E, van de Linde S, Gilbert N, Michieletto D. Parameter-free molecular super-structures quantification in single-molecule localization microscopy. J Cell Biol 2021; 220:211893. [PMID: 33734291 PMCID: PMC7980255 DOI: 10.1083/jcb.202010003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/06/2021] [Accepted: 02/23/2021] [Indexed: 12/18/2022] Open
Abstract
Understanding biological function requires the identification and characterization of complex patterns of molecules. Single-molecule localization microscopy (SMLM) can quantitatively measure molecular components and interactions at resolutions far beyond the diffraction limit, but this information is only useful if these patterns can be quantified and interpreted. We provide a new approach for the analysis of SMLM data that develops the concept of structures and super-structures formed by interconnected elements, such as smaller protein clusters. Using a formal framework and a parameter-free algorithm, (super-)structures formed from smaller components are found to be abundant in classes of nuclear proteins, such as heterogeneous nuclear ribonucleoprotein particles (hnRNPs), but are absent from ceramides located in the plasma membrane. We suggest that mesoscopic structures formed by interconnected protein clusters are common within the nucleus and have an important role in the organization and function of the genome. Our algorithm, SuperStructure, can be used to analyze and explore complex SMLM data and extract functionally relevant information.
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Affiliation(s)
- Mattia Marenda
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
| | - Elena Lazarova
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sebastian van de Linde
- Scottish Universities Physics Alliance, Department of Physics, University of Strathclyde, Glasgow, UK
| | - Nick Gilbert
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Davide Michieletto
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
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16
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Marenda M, Lazarova E, van de Linde S, Gilbert N, Michieletto D. Parameter-free molecular super-structures quantification in single-molecule localization microscopy. J Cell Biol 2021. [PMID: 33734291 DOI: 10.1101/2020.08.19.254540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Understanding biological function requires the identification and characterization of complex patterns of molecules. Single-molecule localization microscopy (SMLM) can quantitatively measure molecular components and interactions at resolutions far beyond the diffraction limit, but this information is only useful if these patterns can be quantified and interpreted. We provide a new approach for the analysis of SMLM data that develops the concept of structures and super-structures formed by interconnected elements, such as smaller protein clusters. Using a formal framework and a parameter-free algorithm, (super-)structures formed from smaller components are found to be abundant in classes of nuclear proteins, such as heterogeneous nuclear ribonucleoprotein particles (hnRNPs), but are absent from ceramides located in the plasma membrane. We suggest that mesoscopic structures formed by interconnected protein clusters are common within the nucleus and have an important role in the organization and function of the genome. Our algorithm, SuperStructure, can be used to analyze and explore complex SMLM data and extract functionally relevant information.
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Affiliation(s)
- Mattia Marenda
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
| | - Elena Lazarova
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sebastian van de Linde
- Scottish Universities Physics Alliance, Department of Physics, University of Strathclyde, Glasgow, UK
| | - Nick Gilbert
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Davide Michieletto
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
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17
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Ahmad A, Frindel C, Rousseau D. Detecting Differences of Fluorescent Markers Distribution in Single Cell Microscopy: Textural or Pointillist Feature Space? Front Robot AI 2021; 7:39. [PMID: 33501207 PMCID: PMC7805927 DOI: 10.3389/frobt.2020.00039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 03/09/2020] [Indexed: 12/22/2022] Open
Abstract
We consider the detection of change in spatial distribution of fluorescent markers inside cells imaged by single cell microscopy. Such problems are important in bioimaging since the density of these markers can reflect the healthy or pathological state of cells, the spatial organization of DNA, or cell cycle stage. With the new super-resolved microscopes and associated microfluidic devices, bio-markers can be detected in single cells individually or collectively as a texture depending on the quality of the microscope impulse response. In this work, we propose, via numerical simulations, to address detection of changes in spatial density or in spatial clustering with an individual (pointillist) or collective (textural) approach by comparing their performances according to the size of the impulse response of the microscope. Pointillist approaches show good performances for small impulse response sizes only, while all textural approaches are found to overcome pointillist approaches with small as well as with large impulse response sizes. These results are validated with real fluorescence microscopy images with conventional resolution. This, a priori non-intuitive result in the perspective of the quest of super-resolution, demonstrates that, for difference detection tasks in single cell microscopy, super-resolved microscopes may not be mandatory and that lower cost, sub-resolved, microscopes can be sufficient.
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Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France.,Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, CNRS UMR 5220-INSERM U1206, Université Lyon 1, INSA de Lyon, Lyon, France
| | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé, CNRS UMR 5220-INSERM U1206, Université Lyon 1, INSA de Lyon, Lyon, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes, UMR INRAE IRHS, Université d'Angers, Angers, France
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18
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Multi-color Molecular Visualization of Signaling Proteins Reveals How C-Terminal Src Kinase Nanoclusters Regulate T Cell Receptor Activation. Cell Rep 2020; 33:108523. [PMID: 33357425 DOI: 10.1016/j.celrep.2020.108523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 07/07/2020] [Accepted: 11/24/2020] [Indexed: 11/22/2022] Open
Abstract
Elucidating the mechanisms that controlled T cell activation requires visualization of the spatial organization of multiple proteins on the submicron scale. Here, we use stoichiometrically accurate, multiplexed, single-molecule super-resolution microscopy (DNA-PAINT) to image the nanoscale spatial architecture of the primary inhibitor of the T cell signaling pathway, Csk, and two binding partners implicated in its membrane association, PAG and TRAF3. Combined with a newly developed co-clustering analysis framework, we find that Csk forms nanoscale clusters proximal to the plasma membrane that are lost post-stimulation and are re-recruited at later time points. Unexpectedly, these clusters do not co-localize with PAG at the membrane but instead provide a ready pool of monomers to downregulate signaling. By generating CRISPR-Cas9 knockout T cells, our data also identify that a major risk factor for autoimmune diseases, the protein tyrosine phosphatase non-receptor type 22 (PTPN22) locus, is essential for Csk nanocluster re-recruitment and for maintenance of the synaptic PAG population.
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19
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Nino DF, Djayakarsana D, Milstein JN. FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy. PLoS Comput Biol 2020; 16:e1008479. [PMID: 33290385 PMCID: PMC7748281 DOI: 10.1371/journal.pcbi.1008479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/18/2020] [Accepted: 10/30/2020] [Indexed: 12/03/2022] Open
Abstract
Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python. We have developed an accurate, highly-efficient and flexible algorithm for quantifying spatial clustering in large, 3-dimensional single-molecule localization microscopy (SMLM) datasets. Our method, FOCAL3D, is provided as an open-source software package written in Python. FOCAL3D scales linearly with the number of localizations and the algorithmic parameters may be systematically optimized so that the resulting analysis is insensitive to variation over a range of parameter choices. We initially validate the performance and parametric insensitivity of FOCAL3D on simulated datasets, then apply the algorithm to 3-dimensional, astigmatic dSTORM images of the nuclear pore complex in human osteosarcoma cells.
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Affiliation(s)
- Daniel F. Nino
- Department of Physics, University of Toronto, Toronto, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada
| | - Daniel Djayakarsana
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada
| | - Joshua N. Milstein
- Department of Physics, University of Toronto, Toronto, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada
- * E-mail:
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20
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Peters DK, Erickson KD, Garcea RL. Live Cell Microscopy of Murine Polyomavirus Subnuclear Replication Centers. Viruses 2020; 12:v12101123. [PMID: 33023278 PMCID: PMC7650712 DOI: 10.3390/v12101123] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 09/22/2020] [Accepted: 09/30/2020] [Indexed: 01/24/2023] Open
Abstract
During polyomavirus (PyV) infection, host proteins localize to subnuclear domains, termed viral replication centers (VRCs), to mediate viral genome replication. Although the protein composition and spatial organization of VRCs have been described using high-resolution immunofluorescence microscopy, little is known about the temporal dynamics of VRC formation over the course of infection. We used live cell fluorescence microscopy to analyze VRC formation during murine PyV (MuPyV) infection of a mouse fibroblast cell line that constitutively expresses a GFP-tagged replication protein A complex subunit (GFP-RPA32). The RPA complex forms a heterotrimer (RPA70/32/14) that regulates cellular DNA replication and repair and is a known VRC component. We validated previous observations that GFP-RPA32 relocalized to sites of cellular DNA damage in uninfected cells and to VRCs in MuPyV-infected cells. We then used GFP-RPA32 as a marker of VRC formation and expansion during live cell microscopy of infected cells. VRC formation occurred at variable times post-infection, but the rate of VRC expansion was similar between cells. Additionally, we found that the early viral protein, small TAg (ST), was required for VRC expansion but not VRC formation, consistent with the role of ST in promoting efficient vDNA replication. These results demonstrate the dynamic nature of VRCs over the course of infection and establish an approach for analyzing viral replication in live cells.
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Affiliation(s)
- Douglas K. Peters
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA; (D.K.P.); (K.D.E.)
| | - Kimberly D. Erickson
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA; (D.K.P.); (K.D.E.)
| | - Robert L. Garcea
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA; (D.K.P.); (K.D.E.)
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, USA
- Correspondence:
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21
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Nosov G, Kahms M, Klingauf J. The Decade of Super-Resolution Microscopy of the Presynapse. Front Synaptic Neurosci 2020; 12:32. [PMID: 32848695 PMCID: PMC7433402 DOI: 10.3389/fnsyn.2020.00032] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 07/21/2020] [Indexed: 01/05/2023] Open
Abstract
The presynaptic compartment of the chemical synapse is a small, yet extremely complex structure. Considering its size, most methods of optical microscopy are not able to resolve its nanoarchitecture and dynamics. Thus, its ultrastructure could only be studied by electron microscopy. In the last decade, new methods of optical superresolution microscopy have emerged allowing the study of cellular structures and processes at the nanometer scale. While this is a welcome addition to the experimental arsenal, it has necessitated careful analysis and interpretation to ensure the data obtained remains artifact-free. In this article we review the application of nanoscopic techniques to the study of the synapse and the progress made over the last decade with a particular focus on the presynapse. We find to our surprise that progress has been limited, calling for imaging techniques and probes that allow dense labeling, multiplexing, longer imaging times, higher temporal resolution, while at least maintaining the spatial resolution achieved thus far.
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Affiliation(s)
- Georgii Nosov
- Institute of Medical Physics and Biophysics, University of Münster, Münster, Germany.,CIM-IMPRS Graduate Program in Münster, Münster, Germany
| | - Martin Kahms
- Institute of Medical Physics and Biophysics, University of Münster, Münster, Germany
| | - Jurgen Klingauf
- Institute of Medical Physics and Biophysics, University of Münster, Münster, Germany
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22
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Khater IM, Nabi IR, Hamarneh G. A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods. PATTERNS (NEW YORK, N.Y.) 2020; 1:100038. [PMID: 33205106 PMCID: PMC7660399 DOI: 10.1016/j.patter.2020.100038] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.
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Affiliation(s)
- Ismail M. Khater
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Ivan Robert Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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23
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Nieves DJ, Owen DM. Analysis methods for interrogating spatial organisation of single molecule localisation microscopy data. Int J Biochem Cell Biol 2020; 123:105749. [PMID: 32325279 DOI: 10.1016/j.biocel.2020.105749] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/06/2020] [Accepted: 04/16/2020] [Indexed: 01/01/2023]
Abstract
Single-molecule localisation microscopy (SMLM) gives access to biological information below the diffraction limit, allowing nanoscale cellular structures to be probed. The data output is unlike that of conventional microscopy images, instead consisting of an array of molecular coordinates. These represent a spatial point pattern that attempts to approximate, as closely as possible, the underlying positions of the molecules of interest. Here, we review the analysis methods that can be used to extract biological insight from SMLM data, in particular for the application of quantifying nanoscale molecular clustering. We review how some of the common artefacts inherent in SMLM can corrupt the acquired data, and therefore, how the output of SMLM cluster analysis should be interpreted.
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Affiliation(s)
- Daniel J Nieves
- Institute of Immunology and Immunotherapy, School of Medical and Dental Sciences and Department of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK; Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, B15 2TT, UK
| | - Dylan M Owen
- Institute of Immunology and Immunotherapy, School of Medical and Dental Sciences and Department of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK; Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, B15 2TT, UK.
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24
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Machine learning for cluster analysis of localization microscopy data. Nat Commun 2020; 11:1493. [PMID: 32198352 PMCID: PMC7083906 DOI: 10.1038/s41467-020-15293-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 02/27/2020] [Indexed: 12/29/2022] Open
Abstract
Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in their ability to process large-scale data sets, to deal effectively with sample heterogeneity, or require subjective user-defined analysis parameters. Here, we develop a supervised machine-learning approach to cluster analysis which is fast and accurate. Trained on a variety of simulated clustered data, the neural network can classify millions of points from a typical single-molecule localization microscopy data set, with the potential to include additional classifiers to describe different subtypes of clusters. The output can be further refined for the measurement of cluster area, shape, and point-density. We demonstrate this approach on simulated data and experimental data of the kinase Csk and the adaptor PAG in primary human T cell immunological synapses. The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.
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25
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Griffié J, Peters R, Owen DM. An agent-based model of molecular aggregation at the cell membrane. PLoS One 2020; 15:e0226825. [PMID: 32032349 PMCID: PMC7006917 DOI: 10.1371/journal.pone.0226825] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 12/04/2019] [Indexed: 12/22/2022] Open
Abstract
Molecular clustering at the plasma membrane has long been identified as a key process and is associated with regulating signalling pathways across cell types. Recent advances in microscopy, in particular the rise of super-resolution, have allowed the experimental observation of nanoscale molecular clusters in the plasma membrane. However, modelling approaches capable of recapitulating these observations are in their infancy, partly because of the extremely complex array of biophysical factors which influence molecular distributions and dynamics in the plasma membrane. We propose here a highly abstracted approach: an agent-based model dedicated to the study of molecular aggregation at the plasma membrane. We show that when molecules are modelled as though they can act (diffuse) in a manner which is influenced by their molecular neighbourhood, many of the distributions observed in cells can be recapitulated, even though such sensing and response is not possible for real membrane molecules. As such, agent-based offers a unique platform which may lead to a new understanding of how molecular clustering in extremely complex molecular environments can be abstracted, simulated and interpreted using simple rules.
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Affiliation(s)
- Juliette Griffié
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London, London, England, United Kingdom
- * E-mail: (JG); (DO)
| | - Ruby Peters
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London, London, England, United Kingdom
| | - Dylan M. Owen
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London, London, England, United Kingdom
- * E-mail: (JG); (DO)
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Košuta T, Cullell-Dalmau M, Cella Zanacchi F, Manzo C. Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering. Phys Chem Chem Phys 2020; 22:1107-1114. [PMID: 31895350 DOI: 10.1039/c9cp05616e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further permits the quantitation relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately estimate these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular aggregates of different stoichiometry from segmented images. The distribution of proxies for the number of molecules in a cluster, such as the number of localizations or the fluorescence intensity, is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and thus determine the optimum number of mixture components and their weights. We test the performance of the algorithm on in silico data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling an accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmic single motors and higher-order structures.
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Affiliation(s)
- Tina Košuta
- The Quantitative BioImaging lab, Facultat de Ciències i Tecnologia, Universitat de Vic - Universitat Central de Catalunya, Vic, Spain. and University of Ljubljana, Ljubljana, Slovenia
| | - Marta Cullell-Dalmau
- The Quantitative BioImaging lab, Facultat de Ciències i Tecnologia, Universitat de Vic - Universitat Central de Catalunya, Vic, Spain.
| | - Francesca Cella Zanacchi
- Nanoscopy and NIC@IIT, Istituto Italiano di Tecnologia, Genoa, Italy and Biophysics Institute (IBF), National Research Council (CNR), Genoa, Italy
| | - Carlo Manzo
- The Quantitative BioImaging lab, Facultat de Ciències i Tecnologia, Universitat de Vic - Universitat Central de Catalunya, Vic, Spain.
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27
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Kennedy PR, Barthen C, Williamson DJ, Pitkeathly WTE, Hazime KS, Cumming J, Stacey KB, Hilton HG, Carrington M, Parham P, Davis DM. Genetic diversity affects the nanoscale membrane organization and signaling of natural killer cell receptors. Sci Signal 2019; 12:eaaw9252. [PMID: 31848320 PMCID: PMC6944503 DOI: 10.1126/scisignal.aaw9252] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genetic diversity in human natural killer (NK) cell receptors is linked to resistance and susceptibility to many diseases. Here, we tested the effect of this diversity on the nanoscale organization of killer cell immunoglobulin-like receptors (KIRs). Using superresolution microscopy, we found that inhibitory KIRs encoded by different genes and alleles were organized differently at the surface of primary human NK cells. KIRs that were found at low abundance assembled into smaller clusters than those formed by KIRs that were more highly abundant, and at low abundance, there was a greater proportion of KIRs in clusters. Upon receptor triggering, a structured interface called the immune synapse assembles, which facilitates signal integration and controls NK cell responses. Here, triggering of low-abundance receptors resulted in less phosphorylation of the downstream phosphatase SHP-1 but more phosphorylation of the adaptor protein Crk than did triggering of high-abundance receptors. In cells with greater KIR abundance, SHP-1 dephosphorylated Crk, which potentiated NK cell spreading during activation. Thus, genetic variation modulates both the abundance and nanoscale organization of inhibitory KIRs. That is, as well as the number of receptors at the cell surface varying with genotype, the way in which these receptors are organized in the membrane also varies. Essentially, a change in the average surface abundance of a protein at the cell surface is a coarse descriptor entwined with changes in local nanoscale clustering. Together, our data indicate that genetic diversity in inhibitory KIRs affects membrane-proximal signaling and, unexpectedly, the formation of activating immune synapses.
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Affiliation(s)
- Philippa R Kennedy
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Charlotte Barthen
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - David J Williamson
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - William T E Pitkeathly
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Khodor S Hazime
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Joshua Cumming
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Kevin B Stacey
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - Hugo G Hilton
- Department of Structural Biology, Stanford University School of Medicine, D159, Sherman Fairchild Science Building, 299 Campus Drive West, Stanford, CA 94305, USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, Building 560, Room 21-89, Frederick, MD 21702, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Peter Parham
- Department of Structural Biology, Stanford University School of Medicine, D159, Sherman Fairchild Science Building, 299 Campus Drive West, Stanford, CA 94305, USA
| | - Daniel M Davis
- Manchester Collaborative Centre for Inflammation Research, University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK.
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28
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Shannon MJ, Pineau J, Griffié J, Aaron J, Peel T, Williamson DJ, Zamoyska R, Cope AP, Cornish GH, Owen DM. Differential nanoscale organisation of LFA-1 modulates T-cell migration. J Cell Sci 2019; 133:jcs.232991. [PMID: 31471459 PMCID: PMC7614863 DOI: 10.1242/jcs.232991] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/21/2019] [Indexed: 11/20/2022] Open
Abstract
Effector T-cells rely on integrins to drive adhesion and migration to facilitate their immune function. The heterodimeric transmembrane integrin LFA-1 (αLβ2 integrin) regulates adhesion and migration of effector T-cells through linkage of the extracellular matrix with the intracellular actin treadmill machinery. Here, we quantified the velocity and direction of F-actin flow in migrating T-cells alongside single-molecule localisation of transmembrane and intracellular LFA-1. Results showed that actin retrograde flow positively correlated and immobile actin negatively correlated with T-cell velocity. Plasma membrane-localised LFA-1 forms unique nano-clustering patterns in the leading edge, compared to the mid-focal zone, of migrating T-cells. Deleting the cytosolic phosphatase PTPN22, loss-of-function mutations of which have been linked to autoimmune disease, increased T-cell velocity, and leading-edge co-clustering of pY397 FAK, pY416 Src family kinases and LFA-1. These data suggest that differential nanoclustering patterns of LFA-1 in migrating T-cells may instruct intracellular signalling. Our data presents a paradigm where T-cells modulate the nanoscale organisation of adhesion and signalling molecules to fine tune their migration speed, with implications for the regulation of immune and inflammatory responses.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Michael J Shannon
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King's College London, London WC2R 2LS, UK
| | - Judith Pineau
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King's College London, London WC2R 2LS, UK
| | - Juliette Griffié
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King's College London, London WC2R 2LS, UK
| | - Jesse Aaron
- Advanced Imaging Center, HHMI Janelia Research Campus, Ashburn, VA 20147, USA
| | - Tamlyn Peel
- Centre for Inflammation Biology and Cancer Immunology, School of Immunology and Microbiological Sciences, King's College London, London SE1 1UL, UK
| | - David J Williamson
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King's College London, London WC2R 2LS, UK
| | - Rose Zamoyska
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Andrew P Cope
- Centre for Inflammation Biology and Cancer Immunology, School of Immunology and Microbiological Sciences, King's College London, London SE1 1UL, UK
| | - Georgina H Cornish
- Centre for Inflammation Biology and Cancer Immunology, School of Immunology and Microbiological Sciences, King's College London, London SE1 1UL, UK
| | - Dylan M Owen
- Department of Physics and Randall Centre for Cell and Molecular Biophysics, King's College London, London WC2R 2LS, UK .,Institute of Immunology and Immunotherapy and Department of Mathematics and Centre for Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham B15 2TQ, UK
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29
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Xu J, Liu Y. A guide to visualizing the spatial epigenome with super-resolution microscopy. FEBS J 2019; 286:3095-3109. [PMID: 31127980 DOI: 10.1111/febs.14938] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/24/2019] [Accepted: 05/23/2019] [Indexed: 12/28/2022]
Abstract
Genomic DNA in eukaryotic cells is tightly compacted with histone proteins into nucleosomes, which are further packaged into the higher-order chromatin structure. The physical structuring of chromatin is highly dynamic and regulated by a large number of epigenetic modifications in response to various environmental exposures, both in normal development and pathological processes such as aging and cancer. Higher-order chromatin structure has been indirectly inferred by conventional bulk biochemical assays on cell populations, which do not allow direct visualization of the spatial information of epigenomics (referred to as spatial epigenomics). With recent advances in super-resolution microscopy, the higher-order chromatin structure can now be visualized in vivo at an unprecedent resolution. This opens up new opportunities to study physical compaction of 3D chromatin structure in single cells, maintaining a well-preserved spatial context of tissue microenvironment. This review discusses the recent application of super-resolution fluorescence microscopy to investigate the higher-order chromatin structure of different epigenomic states. We also envision the synergistic integration of super-resolution microscopy and high-throughput genomic technologies for the analysis of spatial epigenomics to fully understand the genome function in normal biological processes and diseases.
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Affiliation(s)
- Jianquan Xu
- Biomedical Optical Imaging Laboratory, Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yang Liu
- Biomedical Optical Imaging Laboratory, Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
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Roberts SK, Hirsch M, McStea A, Zanetti-Domingues LC, Clarke DT, Claus J, Parker PJ, Wang L, Martin-Fernandez AML. Cluster Analysis of Endogenous HER2 and HER3 Receptors in SKBR3 Cells. Bio Protoc 2018; 8:e3096. [PMID: 34532543 DOI: 10.21769/bioprotoc.3096] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 11/30/2018] [Accepted: 12/03/2018] [Indexed: 01/11/2023] Open
Abstract
The Human Epidermal Growth Factor Receptor (HER) family of receptor tyrosine kinases consists of four, single pass, transmembrane receptor homologs (HER1-4) that act to regulate many critical processes in normal and tumor cells. HER2 is overexpressed in many tumors, and the deregulated proliferation of cancerous cells is driven by cooperation with its preferred receptor partner, HER3. The assessment of the in-situ organization of tagged HER2 and HER3 using super-resolution microscopy reveals quantitative Single Molecule Localization Microscopy (SMLM) as an ideal bioanalytical tool to characterize receptor clusters. Clustering of receptors is an important regulatory mechanism to prime cells to respond to stimuli so, to understand these processes, it is necessary to measure parameters such as numbers of clusters, cluster radii and the number of localizations per cluster for different perturbations. Previously, Fluorescence Localization Imaging with Photobleaching (FLImP), another nanoscale, single-molecule technique, characterized the oligomerization state of HER1 [or Epidermal Growth Factor Receptors (EGFR)] in cell membranes. To achieve an unprecedented resolution (< 5 nm) for inter-molecular separations in EGFR oligomers using FLImP, very few receptors are tagged, and so this method is unsuitable for measurements of whole receptor populations in cancer cells where receptors are frequently upregulated. Here, in order to detect all receptors involved in cluster formation, we saturate endogenous HER2 and HER3 membrane receptors with ligands at a 1:1 dye to protein ratio, in the presence or absence of therapeutic drugs (lapatinib or bosutinib). This is performed in the commonly used breast cancer cell line model SKBR3 cells, where there are ~1.6 million HER2 receptors/cell and 10,000-40,000 HER3 receptors/cell. The basal state of these receptors is studied using HER2- or HER3-specific Affibodies, and likewise, the active state is probed using the natural HER3 ligand, Neuregulin-beta1 (NRGβ1). Stochastic Optical Reconstruction Microscopy (STORM), one form of SMLM, was used here to image cells, which were chemically fixed to minimize image blurring and provide data (x and y coordinates and standard deviation of the measured localizations) for cluster analysis. Further analysis can also determine proportions of receptor colocalizations. Our findings show that lapatinib-bound HER2, complexed with HER3 via a non-canonical kinase dimer structure, induces higher order oligomers. We hypothesized that nucleation of receptors creates signaling platforms that explain the counterintuitive, increase in cell proliferation upon ligand binding, in the presence of the HER2-inhibitor lapatinib.
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Affiliation(s)
- Selene K Roberts
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - Michael Hirsch
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - Alexandra McStea
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - Laura C Zanetti-Domingues
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - David T Clarke
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - Jeroen Claus
- Protein Phosphorylation Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Peter J Parker
- Protein Phosphorylation Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK.,School of Cancer and Pharmaceutical Sciences, New Hunt's House, King's College London, Guy's Campus, London, SE1 1UL, UK
| | - Lin Wang
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK
| | - And Marisa L Martin-Fernandez
- Central Laser Facility, Research Complex at Harwell, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell, Didcot, Oxford, OX11 0QX, UK.,School of Cancer and Pharmaceutical Sciences, New Hunt's House, King's College London, Guy's Campus, London, SE1 1UL, UK
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31
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Visualisation and analysis of hepatitis C virus non-structural proteins using super-resolution microscopy. Sci Rep 2018; 8:13604. [PMID: 30206266 PMCID: PMC6134135 DOI: 10.1038/s41598-018-31861-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 08/29/2018] [Indexed: 01/17/2023] Open
Abstract
Hepatitis C virus (HCV) RNA replication occurs in the cytosol of infected cells within a specialised membranous compartment. How the viral non-structural (NS) proteins are associated and organised within these structures remains poorly defined. We employed a super-resolution microscopy approach to visualise NS3 and NS5A in HCV infected cells. Using single molecule localisation microscopy, both NS proteins were resolved as clusters of localisations smaller than the diffraction-limited volume observed by wide-field. Analysis of the protein clusters identified a significant difference in size between the NS proteins. We also observed a reduction in NS5A cluster size following inhibition of RNA replication using daclatasvir, a phenotype which was maintained in the presence of the Y93H resistance associated substitution and not observed for NS3 clusters. These results provide insight into the NS protein organisation within hepatitis C virus RNA replication complexes and the mode of action of NS5A inhibitors.
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32
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Abstract
The galectin family of secreted lectins have emerged as important regulators of immune cell function; however, their role in B-cell responses is poorly understood. Here we identify IgM-BCR as a ligand for galectin-9. Furthermore, we show enhanced BCR microcluster formation and signaling in galectin-9-deficient B cells. Notably, treatment with exogenous recombinant galectin-9 nearly completely abolishes BCR signaling. We investigated the molecular mechanism for galectin-9-mediated inhibition of BCR signaling using super-resolution imaging and single-particle tracking. We show that galectin-9 merges pre-existing nanoclusters of IgM-BCR, immobilizes IgM-BCR, and relocalizes IgM-BCR together with the inhibitory molecules CD45 and CD22. In resting naive cells, we use dual-color super-resolution imaging to demonstrate that galectin-9 mediates the close association of IgM and CD22, and propose that the loss of this association provides a mechanism for enhanced activation of galectin-9-deficient B cells. The galectin family of secreted lectins are important regulators of immune cell function; however, their role in B cell responses is poorly understood. Here, the authors identify IgM-BCR as a ligand for galectin-9. In resting naive cells, they show that galectin-9 mediates a close association between IgM and CD22.
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33
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Khater IM, Meng F, Wong TH, Nabi IR, Hamarneh G. Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds. Sci Rep 2018; 8:9009. [PMID: 29899348 PMCID: PMC5998020 DOI: 10.1038/s41598-018-27216-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 05/24/2018] [Indexed: 12/04/2022] Open
Abstract
Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.
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Affiliation(s)
- Ismail M Khater
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Fanrui Meng
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Timothy H Wong
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Ivan Robert Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
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34
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Lagache T, Grassart A, Dallongeville S, Faklaris O, Sauvonnet N, Dufour A, Danglot L, Olivo-Marin JC. Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics. Nat Commun 2018; 9:698. [PMID: 29449608 PMCID: PMC5814551 DOI: 10.1038/s41467-018-03053-x] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 01/17/2018] [Indexed: 12/20/2022] Open
Abstract
Elucidating protein functions and molecular organisation requires to localise precisely single or aggregated molecules and analyse their spatial distributions. We develop a statistical method SODA (Statistical Object Distance Analysis) that uses either micro- or nanoscopy to significantly improve on standard co-localisation techniques. Our method considers cellular geometry and densities of molecules to provide statistical maps of isolated and associated (coupled) molecules. We use SODA with three-colour structured-illumination microscopy (SIM) images of hippocampal neurons, and statistically characterise spatial organisation of thousands of synapses. We show that presynaptic synapsin is arranged in asymmetric triangle with the 2 postsynaptic markers homer and PSD95, indicating a deeper localisation of homer. We then determine stoichiometry and distance between localisations of two synaptic vesicle proteins with 3D-STORM. These findings give insights into the protein organisation at the synapse, and prove the efficiency of SODA to quantitatively assess the geometry of molecular assemblies.
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Affiliation(s)
- Thibault Lagache
- Institut Pasteur, BioImage Analysis Unit. CNRS UMR 3691. 25 rue du Docteur Roux, 75724, Paris Cedex 15, France
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Alexandre Grassart
- Institut Pasteur, Molecular Microbial Pathogenesis Unit. INSERM U1202. 28 rue du Docteur Roux, 75724, Paris Cedex 15, France
| | - Stéphane Dallongeville
- Institut Pasteur, BioImage Analysis Unit. CNRS UMR 3691. 25 rue du Docteur Roux, 75724, Paris Cedex 15, France
| | - Orestis Faklaris
- CNRS UMR7592, Institut Jacques Monod, Université Paris Diderot, 15 rue Hélène Brion, 75013, Paris, France
| | - Nathalie Sauvonnet
- Institut Pasteur, Molecular Microbial Pathogenesis Unit. INSERM U1202. 28 rue du Docteur Roux, 75724, Paris Cedex 15, France
| | - Alexandre Dufour
- Institut Pasteur, BioImage Analysis Unit. CNRS UMR 3691. 25 rue du Docteur Roux, 75724, Paris Cedex 15, France
| | - Lydia Danglot
- Inserm U894 Center for Psychiatry and Neuroscience, Team Membrane traffic in healthy and diseased brain, 102-108 rue de la Santé, 75014, Paris, France.
| | - Jean-Christophe Olivo-Marin
- Institut Pasteur, BioImage Analysis Unit. CNRS UMR 3691. 25 rue du Docteur Roux, 75724, Paris Cedex 15, France.
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Robinson GA, Waddington KE, Pineda-Torra I, Jury EC. Transcriptional Regulation of T-Cell Lipid Metabolism: Implications for Plasma Membrane Lipid Rafts and T-Cell Function. Front Immunol 2017; 8:1636. [PMID: 29225604 PMCID: PMC5705553 DOI: 10.3389/fimmu.2017.01636] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 11/09/2017] [Indexed: 01/10/2023] Open
Abstract
It is well established that cholesterol and glycosphingolipids are enriched in the plasma membrane (PM) and form signaling platforms called lipid rafts, essential for T-cell activation and function. Moreover, changes in PM lipid composition affect the biophysical properties of lipid rafts and have a role in defining functional T-cell phenotypes. Here, we review the role of transcriptional regulators of lipid metabolism including liver X receptors α/β, peroxisome proliferator-activated receptor γ, estrogen receptors α/β (ERα/β), and sterol regulatory element-binding proteins in T-cells. These receptors lie at the interface between lipid metabolism and immune cell function and are endogenously activated by lipids and/or hormones. Importantly, they regulate cellular cholesterol, fatty acid, glycosphingolipid, and phospholipid levels but are also known to modulate a broad spectrum of immune responses. The current evidence supporting a role for lipid metabolism pathways in controlling immune cell activation by influencing PM lipid raft composition in health and disease, and the potential for targeting lipid biosynthesis pathways to control unwanted T-cell activation in autoimmunity is reviewed.
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Affiliation(s)
- George A. Robinson
- Centre of Rheumatology, Division of Medicine, University College London, London, United Kingdom
| | - Kirsty E. Waddington
- Centre of Rheumatology, Division of Medicine, University College London, London, United Kingdom
- Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
| | - Ines Pineda-Torra
- Clinical Pharmacology, Division of Medicine, University College London, London, United Kingdom
| | - Elizabeth C. Jury
- Centre of Rheumatology, Division of Medicine, University College London, London, United Kingdom
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36
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Tiede C, Bedford R, Heseltine SJ, Smith G, Wijetunga I, Ross R, AlQallaf D, Roberts APE, Balls A, Curd A, Hughes RE, Martin H, Needham SR, Zanetti-Domingues LC, Sadigh Y, Peacock TP, Tang AA, Gibson N, Kyle H, Platt GW, Ingram N, Taylor T, Coletta LP, Manfield I, Knowles M, Bell S, Esteves F, Maqbool A, Prasad RK, Drinkhill M, Bon RS, Patel V, Goodchild SA, Martin-Fernandez M, Owens RJ, Nettleship JE, Webb ME, Harrison M, Lippiat JD, Ponnambalam S, Peckham M, Smith A, Ferrigno PK, Johnson M, McPherson MJ, Tomlinson DC. Affimer proteins are versatile and renewable affinity reagents. eLife 2017; 6:e24903. [PMID: 28654419 PMCID: PMC5487212 DOI: 10.7554/elife.24903] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 06/07/2017] [Indexed: 12/11/2022] Open
Abstract
Molecular recognition reagents are key tools for understanding biological processes and are used universally by scientists to study protein expression, localisation and interactions. Antibodies remain the most widely used of such reagents and many show excellent performance, although some are poorly characterised or have stability or batch variability issues, supporting the use of alternative binding proteins as complementary reagents for many applications. Here we report on the use of Affimer proteins as research reagents. We selected 12 diverse molecular targets for Affimer selection to exemplify their use in common molecular and cellular applications including the (a) selection against various target molecules; (b) modulation of protein function in vitro and in vivo; (c) labelling of tumour antigens in mouse models; and (d) use in affinity fluorescence and super-resolution microscopy. This work shows that Affimer proteins, as is the case for other alternative binding scaffolds, represent complementary affinity reagents to antibodies for various molecular and cell biology applications.
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Affiliation(s)
- Christian Tiede
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Robert Bedford
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Sophie J Heseltine
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Gina Smith
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Imeshi Wijetunga
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Rebecca Ross
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Danah AlQallaf
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | | | - Alexander Balls
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Alistair Curd
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Ruth E Hughes
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Heather Martin
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Sarah R Needham
- Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Laura C Zanetti-Domingues
- Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
| | | | | | - Anna A Tang
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Naomi Gibson
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Hannah Kyle
- Avacta Life Sciences, Wetherby, United Kingdom
| | | | - Nicola Ingram
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Thomas Taylor
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Louise P Coletta
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Iain Manfield
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Margaret Knowles
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Sandra Bell
- Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, Leeds, United Kingdom
| | - Filomena Esteves
- Leeds Institute of Cancer Studies and Pathology, University of Leeds, Leeds, United Kingdom
| | - Azhar Maqbool
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Raj K Prasad
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Mark Drinkhill
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Robin S Bon
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | | | | | - Marisa Martin-Fernandez
- Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Ray J Owens
- Oxford Protein Production Facility UK, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Joanne E Nettleship
- Oxford Protein Production Facility UK, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
| | - Michael E Webb
- School of Chemistry, University of Leeds, Leeds, United Kingdom
| | - Michael Harrison
- School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
| | - Jonathan D Lippiat
- School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
| | - Sreenivasan Ponnambalam
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Michelle Peckham
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | | | | | | | - Michael J McPherson
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | - Darren Charles Tomlinson
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
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Griffié J, Shlomovich L, Williamson DJ, Shannon M, Aaron J, Khuon S, L Burn G, Boelen L, Peters R, Cope AP, Cohen EAK, Rubin-Delanchy P, Owen DM. 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse. Sci Rep 2017; 7:4077. [PMID: 28642595 PMCID: PMC5481387 DOI: 10.1038/s41598-017-04450-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 05/16/2017] [Indexed: 12/03/2022] Open
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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Affiliation(s)
- Juliette Griffié
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK.
| | | | - David J Williamson
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK
| | - Michael Shannon
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK
| | - Jesse Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, USA
| | - Garth L Burn
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK
| | - Lies Boelen
- Department of Medicine, Imperial College London, London, UK
| | - Ruby Peters
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK
| | - Andrew P Cope
- Department of Immunology, Infection and Inflammatory Disease, King's College London, London, UK
| | | | | | - Dylan M Owen
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK
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