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      Varying label density allows artifact-free analysis of membrane-protein nanoclusters.

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          Abstract

          We present a method to robustly discriminate clustered from randomly distributed molecules detected with techniques based on single-molecule localization microscopy, such as PALM and STORM. The approach is based on deliberate variation of labeling density, such as titration of fluorescent antibody, combined with quantitative cluster analysis, and it thereby circumvents the problem of cluster artifacts generated by overcounting of blinking fluorophores. The method was used to analyze nanocluster formation in resting and activated immune cells.

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          Direct stochastic optical reconstruction microscopy with standard fluorescent probes.

          Sebastian van de Linde, Anna Löschberger, Teresa Klein … (2011)
          Direct stochastic optical reconstruction microscopy (dSTORM) uses conventional fluorescent probes such as labeled antibodies or chemical tags for subdiffraction resolution fluorescence imaging with a lateral resolution of ∼20 nm. In contrast to photoactivated localization microscopy (PALM) with photoactivatable fluorescent proteins, dSTORM experiments start with bright fluorescent samples in which the fluorophores have to be transferred to a stable and reversible OFF state. The OFF state has a lifetime in the range of 100 milliseconds to several seconds after irradiation with light intensities low enough to ensure minimal photodestruction. Either spontaneously or photoinduced on irradiation with a second laser wavelength, a sparse subset of fluorophores is reactivated and their positions are precisely determined. Repetitive activation, localization and deactivation allow a temporal separation of spatially unresolved structures in a reconstructed image. Here we present a step-by-step protocol for dSTORM imaging in fixed and living cells on a wide-field fluorescence microscope, with standard fluorescent probes focusing especially on the photoinduced fine adjustment of the ratio of fluorophores residing in the ON and OFF states. Furthermore, we discuss labeling strategies, acquisition parameters, and temporal and spatial resolution. The ultimate step of data acquisition and data processing can be performed in seconds to minutes.
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            Modelling Spatial Patterns

            B. Ripley (1977)
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              Quantitative evaluation of software packages for single-molecule localization microscopy.

              Daniel P. Sage, Hagai Kirshner, Thomas Pengo … (2015)
              The quality of super-resolution images obtained by single-molecule 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.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                Publisher: Springer Nature
                ISSN (Electronic): 1548-7105
                ISSN (Print): 1548-7091
                Publication date (Electronic): Aug 2016
                Volume: 13
                Issue: 8
                Affiliations
                [1 ] Institute of Applied Physics, TU Wien, Vienna, Austria.
                [2 ] School of Engineering and Environmental Sciences, University of Applied Sciences Upper Austria, Wels, Austria.
                [3 ] Institute for Hygiene and Applied Immunology, Medical University of Vienna, Vienna, Austria.
                Article
                Publisher Item ID: nmeth.3897
                DOI: 10.1038/nmeth.3897
                PubMed ID: 27295310
                SO-VID: c8c125bb-67d9-43a9-8873-bac68d2a44d8
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