Bayesian localization microscopy reveals nanoscale podosome dynamics

Cox, Susan; Rosten, Edward; Monypenny, James; Jovanovic-Talisman, Tijana; Burnette, Dylan T.; Lippincott‐Schwartz, Jennifer; Jones, Gareth E.; Heintzmann, Rainer

doi:10.1038/nmeth.1812

Cited by 402 publications

(414 citation statements)

References 29 publications

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“…While methods to localise molecules from the raw fluorescence data have been extensively analysed 8,10,33 , the subsequent interrogation of the point pattern data has been relatively under-studied. We have demonstrated a new, Bayesian cluster analysis algorithm for SMLM data.…”

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%

“…The method also has the possibility to allow faster imaging as less localisations are required to accurately identify and characterise clustering. Increasing the speed of SMLM data acquisition and processing has been one of the major goals to move the technique into the domain of live cell imaging 33 .…”

Section: Discussionmentioning

confidence: 99%

Bayesian cluster identification in single-molecule localization microscopy data

et al. 2015

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“…A recent trend in the super-resolution localization microscopy is the development of methods to detect multiple overlapping emitters in high density data sets 31,41,[52][53][54][55] . To benchmark these methods, our generated HD are particularly suitable.…”

Section: Competing Financial Interestsmentioning

confidence: 99%

Quantitative evaluation of software packages for single-molecule localization microscopy

et al. 2015

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the quality of super-resolution images obtained by singlemolecule localization microscopy (smlm) depends largely on the software used to detect and accurately localize point sources. in this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. these metrics reflect the various tradeoffs of smlm software packages and help users to choose the software that fits their needs.We have conducted a large-scale comparative study of software packages developed in the context of SMLM, including recently developed algorithms. We designed realistic data that are generic and cover a broad range of experimental conditions and compared the software packages using a multiple-criterion quantitative assessment that is based on a known ground truth.Our study is based on the active participation of developers of SMLM software. More than 30 groups have participated so far, and the study is still under way. We provide participants access to our benchmark data as an ongoing public challenge. Participants run their own software on our data and report their list of localized particles for evaluation. The results of the challenge are accessible online and updated regularly.SMLM was demonstrated in 2006, independently by three research groups 1-3 , and has enabled subsequent breakthroughs in diverse fields 4,5 . SMLM can resolve biological structures at the nanometer scale (typically 20 nm lateral resolution), circumventing Abbe's diffraction limit. At the cost of a relatively simple setup 6,7 , it has opened exciting new opportunities in life science research 8,9 .The underlying principle of SMLM is the sequential imaging of sparse subsets of fluorophores distributed over thousands of frames, to populate a high-density map of fluorophore positions. Such large data sets require automated image-analysis algorithms to detect and precisely infer the position of individual fluorophore, taking advantage of their separation in space and time.The acquired data cannot be visualized directly; further computerized image-reconstruction methods are required. These typically comprise four steps: preprocessing, detection, localization and rendering. Preprocessing reduces the effects of the background and noise; detection identifies potential molecule candidates in each frame; localization performs a subpixel refine...

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“…3). [90,92,94] SIM SIM Patterned excitation allows information on fine details in the sample to be modulated onto the emitted pattern. Upon detection, the pattern is demodulated and the fine details are recovered.…”

Section: Stedmentioning

confidence: 99%

“…Methods based on the statistical analysis of fluorophore blinking have also proved very successful for molecular localisation in high molecular density images with improved temporal resolution [89][90][91][92]. These methods are described below as they make use of different spectroscopic properties.…”

Section: Temporal Resolution and Live-cell Stochastic Lmmentioning

confidence: 99%

Fluorescence nanoscopy. Methods and applications

Requejo-Isidro

2013

J Chem Biol

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Fluorescence nanoscopy refers to the experimental techniques and analytical methods used for fluorescence imaging at a resolution higher than conventional, diffractionlimited, microscopy. This review explains the concepts behind fluorescence nanoscopy and focuses on the latest and promising developments in acquisition techniques, labelling strategies to obtain highly detailed super-resolved images and in the quantitative methods to extract meaningful information from them.

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Bayesian localization microscopy reveals nanoscale podosome dynamics

Cited by 402 publications

References 29 publications

Bayesian cluster identification in single-molecule localization microscopy data

Bayesian cluster identification in single-molecule localization microscopy data

Quantitative evaluation of software packages for single-molecule localization microscopy

Fluorescence nanoscopy. Methods and applications

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