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Umney O, Leng J, Canettieri G, Galdo NARD, Slaney H, Quirke P, Peckham M, Curd A. Annotation and automated segmentation of single-molecule localisation microscopy data. J Microsc 2024. [PMID: 39092628 DOI: 10.1111/jmi.13349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 07/05/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.
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Affiliation(s)
- Oliver Umney
- Faculty of Engineering and Physical Sciences, School of Computing, University of Leeds, Leeds, UK
| | - Joanna Leng
- Faculty of Engineering and Physical Sciences, School of Computing, University of Leeds, Leeds, UK
| | - Gianluca Canettieri
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
- Institute Pasteur Italy - Cenci Bolognetti Foundation, Sapienza University of Rome, Rome, Italy
| | - Natalia A Riobo-Del Galdo
- Faculty of Biological Sciences, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- School of Medicine, Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
| | - Hayley Slaney
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Michelle Peckham
- Faculty of Biological Sciences, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
| | - Alistair Curd
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
- Faculty of Engineering and Physical Sciences, School of Physics, University of Leeds, Leeds, UK
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2
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Adler J, Bernhem K, Parmryd I. Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction. Commun Biol 2024; 7:791. [PMID: 38951588 PMCID: PMC11217499 DOI: 10.1038/s42003-024-06472-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/19/2024] [Indexed: 07/03/2024] Open
Abstract
According to single-molecule localisation microscopy almost all plasma membrane proteins are clustered. We demonstrate that clusters can arise from variations in membrane topography where the local density of a randomly distributed membrane molecule to a degree matches the variations in the local amount of membrane. Further, we demonstrate that this false clustering can be differentiated from genuine clustering by using a membrane marker to report on local variations in the amount of membrane. In dual colour live cell single molecule localisation microscopy using the membrane probe DiI alongside either the transferrin receptor or the GPI-anchored protein CD59, we found that pair correlation analysis reported both proteins and DiI as being clustered, as did its derivative pair correlation-photoactivation localisation microscopy and nearest neighbour analyses. After converting the localisations into images and using the DiI image to factor out topography variations, no CD59 clusters were visible, suggesting that the clustering reported by the other methods is an artefact. However, the TfR clusters persisted after topography variations were factored out. We demonstrate that membrane topography variations can make membrane molecules appear clustered and present a straightforward remedy suitable as the first step in the cluster analysis pipeline.
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Affiliation(s)
- Jeremy Adler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Kristoffer Bernhem
- Science for Life Laboratory, Department of Applied Physics, Royal Institute of Technology, Stockholm, Sweden
| | - Ingela Parmryd
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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3
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Weidner J, Neitzel C, Gote M, Deck J, Küntzelmann K, Pilarczyk G, Falk M, Hausmann M. Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM). Comput Struct Biotechnol J 2023; 21:2018-2034. [PMID: 36968017 PMCID: PMC10030913 DOI: 10.1016/j.csbj.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/11/2023] Open
Abstract
The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.
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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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Hyun Y, Kim D. Recent development of computational cluster analysis methods for single-molecule localization microscopy images. Comput Struct Biotechnol J 2023; 21:879-888. [PMID: 36698968 PMCID: PMC9860261 DOI: 10.1016/j.csbj.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/07/2023] [Accepted: 01/07/2023] [Indexed: 01/11/2023] Open
Abstract
With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.
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Affiliation(s)
- Yoonsuk Hyun
- Department of Mathematics, Inha University, Republic of Korea
| | - Doory Kim
- Department of Chemistry, Hanyang University, Republic of Korea
- Research Institute for Convergence of Basic Science, Hanyang University, Republic of Korea
- Institute of Nano Science and Technology, Hanyang University, Republic of Korea
- Research Institute for Natural Sciences, Hanyang University, Republic of Korea
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Steindel M, Orsine de Almeida I, Strawbridge S, Chernova V, Holcman D, Ponjavic A, Basu S. Studying the Dynamics of Chromatin-Binding Proteins in Mammalian Cells Using Single-Molecule Localization Microscopy. Methods Mol Biol 2022; 2476:209-247. [PMID: 35635707 DOI: 10.1007/978-1-0716-2221-6_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Single-molecule localization microscopy (SMLM) allows the super-resolved imaging of proteins within mammalian nuclei at spatial resolutions comparable to that of a nucleosome itself (~20 nm). The technique is therefore well suited to the study of chromatin structure. Fixed-cell SMLM has already allowed temporal "snapshots" of how proteins are arranged on chromatin within mammalian nuclei. In this chapter, we focus on how recent developments, for example in selective plane illumination, 3D SMLM, and protein labeling, have led to a range of live-cell SMLM studies. We describe how to carry out single-particle tracking (SPT) of single proteins and, by analyzing their diffusion parameters, how to determine whether proteins interact with chromatin, diffuse freely, or do both. We can study the numbers of proteins that interact with chromatin and also determine their residence time on chromatin. We can determine whether these proteins form functional clusters within the nucleus as well as whether they form specific nuclear structures.
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Affiliation(s)
- Maike Steindel
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | | | - Stanley Strawbridge
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Valentyna Chernova
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - David Holcman
- Group of Computational Biology and Applied Mathematics, Institute of Biology, Ecole Normale Supérieure, Paris, France
| | - Aleks Ponjavic
- School of Physics and Astronomy and School of Food Science and Nutrition, University of Leeds, Leeds, UK.
| | - Srinjan Basu
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
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Hummert J, Tashev SA, Herten DP. An update on molecular counting in fluorescence microscopy. Int J Biochem Cell Biol 2021; 135:105978. [PMID: 33865985 DOI: 10.1016/j.biocel.2021.105978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/14/2021] [Accepted: 04/08/2021] [Indexed: 01/18/2023]
Abstract
Quantitative assessment of protein complexes, such as receptor clusters in the context of cellular signalling, has become a pressing objective in cell biology. The advancements in the field of single molecule fluorescence microscopy have led to different approaches for counting protein copy numbers in various cellular structures. This has resulted in an increasing interest in robust calibration protocols addressing photophysical properties of fluorescent labels and the effect of labelling efficiencies. Here, we want to give an update on recent methods for protein counting with a focus on novel calibration protocols. In this context, we discuss different types of calibration samples and identify some of the challenges arising in molecular counting experiments. Some recently published applications offer potential approaches to tackle these challenges.
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Affiliation(s)
- Johan Hummert
- College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, UK; Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, UK
| | - Stanimir Asenov Tashev
- College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, UK; Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, UK
| | - Dirk-Peter Herten
- College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, UK; Centre of Membrane Proteins and Receptors (COMPARE), Universities of Birmingham and Nottingham, UK.
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8
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Henning Stumpf B, Ambriović-Ristov A, Radenovic A, Smith AS. Recent Advances and Prospects in the Research of Nascent Adhesions. Front Physiol 2020; 11:574371. [PMID: 33343382 PMCID: PMC7746844 DOI: 10.3389/fphys.2020.574371] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 11/09/2020] [Indexed: 01/08/2023] Open
Abstract
Nascent adhesions are submicron transient structures promoting the early adhesion of cells to the extracellular matrix. Nascent adhesions typically consist of several tens of integrins, and serve as platforms for the recruitment and activation of proteins to build mature focal adhesions. They are also associated with early stage signaling and the mechanoresponse. Despite their crucial role in sampling the local extracellular matrix, very little is known about the mechanism of their formation. Consequently, there is a strong scientific activity focused on elucidating the physical and biochemical foundation of their development and function. Precisely the results of this effort will be summarized in this article.
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Affiliation(s)
- Bernd Henning Stumpf
- PULS Group, Institute for Theoretical Physics, Interdisciplinary Center for Nanostructured Films, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreja Ambriović-Ristov
- Laboratory for Cell Biology and Signalling, Division of Molecular Biology, Ruđer Bošković Institute, Zagreb, Croatia
| | - Aleksandra Radenovic
- Laboratory of Nanoscale Biology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ana-Sunčana Smith
- PULS Group, Institute for Theoretical Physics, Interdisciplinary Center for Nanostructured Films, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Group for Computational Life Sciences, Division of Physical Chemistry, Ruđer Bošković Institute, Zagreb, Croatia
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