PALM imaging and cluster analysis of protein heterogeneity at the cell surface

Owen, Dylan M.; Rentero, Carles; Rossy, Jérémie; Magenau, Astrid; Williamson, David J.; Rodríguez, Macarena; Gaus, Katharina

doi:10.1002/jbio.200900089

Cited by 244 publications

(256 citation statements)

References 25 publications

(25 reference statements)

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“…Unlike previously demonstrated methods based on the generation of cluster maps which involve an interpolation algorithm to generate the surface 19 , the new method is not prone to artefacts in sparse data sets, e.g. from low copy-number proteins.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian cluster identification in single-molecule localization microscopy data

et al. 2015

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Correspondence should be addressed to patrick. rubin-delanchy@bristol.ac.uk and dylan.owen@kcl.ac.uk Single-molecule identification-based super-resolution microscopy techniques such as photo-activated localisation microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. While many algorithms exist for the identification and localisation of molecules from the raw image data, methods for analysing the resulting point patterns for properties such as clustering have remained relatively under-studied. Here, we present the first model-based Bayesian approach to evaluate molecular cluster assignment proposals which, in this article, are generated by analysis based on Ripley's Kfunction. The method is also the first to take full account of the individual localisation precisions calculated for each emitter. The technique is validated using simulated and experimental data from which we characterise the clustering behaviour of CD3ζ, an important subunit of the CD3-T cell receptor complex required for T cell function, in resting and activated primary human T cells.Conventional fluorescence microscopes produce images of the distribution of fluorophores in the sample convolved with the microscope Point Spread Function (PSF). Due to diffraction, this PSF typically has a width of hundreds of nanometres meaning the resulting image has a resolution, as assessed by the Rayleigh criterion, of ~200 nm. Several strategies now exist to circumvent this resolution limit 1 . Some of these, such as Stimulated Emission Depletion (STED) microscopy, rely on narrowing the excitation spot of a confocal microscope by means of a toroidal depletion beam and the process of stimulated emission 2,3 . Despite the increased resolution, these produce conventional fluorescence images, i.e., arrays of pixels with values representing the fluorescence intensity at those locations. Quantification can be performed in the same way as for conventional microscopes.Another strategy is based on Single-Molecule Localisation Microscopy (SMLM) [4][5][6][7] . This relies on the temporal separation of the excitation of fluorophores in the sample whose PSFs would otherwise overlap at the detector.The position of each fluorophore can then be estimated from the centres of the PSFs. Many algorithms are available to extract the x-y coordinates of the molecules [8][9][10] . Each emitter can be localised to a precision between 10 and 30 nm. Common strategies for the temporal separation of molecules involve intra-molecular rearrangements to switch from dark to fluorescent states or the exploitation of non-emitting molecular radicals 11,12 . These strategies are typically pursued using photoactivatable or photoconvertible fluorescent proteins or small molecule probes coupled with a reducing buffer and immunostaining protocols 13 . We refer to all such strategies as SMLM.Unlike non-pointillist microscopy methods, SMLM imaging does not produce a conventional image. Instead, the raw data is a list of the...

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Section: Discussionmentioning

confidence: 99%

“…These allow the degree of clustering at different spatial scales to be determined. In the case of the K-function, the values at each localisation can be interpolated to create cluster maps to which thresholds can then be applied 19 .…”

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confidence: 99%

Bayesian cluster identification in single-molecule localization microscopy data

et al. 2015

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“…Samples were drift corrected and rendered using Gaussian rendering. Cluster analysis was performed on the dSTORM data in MATLAB, using a custom-made algorithm based on Ripley’s K-function [40] with modifications as described [41]. For the analysis, 2 × 2 µm regions of interest were analysed for clustering with the spatial scale r = 50 nm used to generate the pseudo-coloured cluster heat map, where red indicates high degrees of clustering.…”

Section: Methodsmentioning

confidence: 99%

Tetraspanin Tspan9 regulates platelet collagen receptor GPVI lateral diffusion and activation

et al. 2016

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The tetraspanins are a superfamily of four-transmembrane proteins, which regulate the trafficking, lateral diffusion and clustering of the transmembrane proteins with which they interact. We have previously shown that tetraspanin Tspan9 is expressed on platelets. Here we have characterised gene-trap mice lacking Tspan9. The mice were viable with normal platelet numbers and size. Tspan9-deficient platelets were specifically defective in aggregation and secretion induced by the platelet collagen receptor GPVI, despite normal surface GPVI expression levels. A GPVI activation defect was suggested by partially impaired GPVI-induced protein tyrosine phosphorylation. In mechanistic experiments, Tspan9 and GPVI co-immunoprecipitated and co-localised, but super-resolution imaging revealed no defects in collagen-induced GPVI clustering on Tspan9-deficient platelets. However, single particle tracking using total internal reflection fluorescence microscopy showed that GPVI lateral diffusion was reduced by approximately 50% in the absence of Tspan9. Therefore, Tspan9 plays a fine-tuning role in platelet activation by regulating GPVI membrane dynamics.

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“…Quantifying the properties of the FA substructure with SMLM clustering methods is therefore challenging. Clusters in SMLM data are often characterized using the pair correlation function (27) or Ripley's K(r) or L(r) function (28). These functions describe the density around a certain point as a function of the distance r from that point.…”

Section: Emgmmentioning

confidence: 99%

Investigating Focal Adhesion Substructures by Localization Microscopy

Deschout

Platzman

Sage

et al. 2017

Biophysical Journal

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Cells rely on focal adhesions (FAs) to carry out a variety of important tasks, including motion, environmental sensing, and adhesion to the extracellular matrix. Although attaining a fundamental characterization of FAs is a compelling goal, their extensive complexity and small size, which can be below the diffraction limit, have hindered a full understanding. In this study we have used single-molecule localization microscopy (SMLM) to investigate integrin b3 and paxillin in rat embryonic fibroblasts growing on two different extracellular matrix-representing substrates (i.e., fibronectin-coated substrates and specifically biofunctionalized nanopatterned substrates). To quantify the substructure of FAs, we developed a clustering method based on expectation maximization of a Gaussian mixture that accounts for localization uncertainty and background. Analysis of our SMLM data indicates that the structures within FAs, characterized as a Gaussian mixture, typically have areas between 0.01 and 1 mm 2 , contain 10-100 localizations, and can exhibit substantial eccentricity. Our approach based on SMLM opens new avenues for studying structural and functional biology of molecular assemblies that display substantial varieties in size, shape, and density.

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PALM imaging and cluster analysis of protein heterogeneity at the cell surface

Cited by 244 publications

References 25 publications

Bayesian cluster identification in single-molecule localization microscopy data

Bayesian cluster identification in single-molecule localization microscopy data

Tetraspanin Tspan9 regulates platelet collagen receptor GPVI lateral diffusion and activation

Investigating Focal Adhesion Substructures by Localization Microscopy

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