Abstract
BackgroundSuper resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations.ResultsHere, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210.ConclusionsOur pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings.
Highlights
Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution
We introduce and test a novel image analysis procedure for SR microscopy, which depends on Gaussian blurring to merge super-resolved structural details in SR images into biologically meaningful objects
We suggest a more global experimental and analytical scheme (Fig. 2e and f) that takes into account the following: Fig. 1 Pipeline and analysis tested on Single Molecule Localization Microscopy (SMLM) simulation image. a Pipeline design in CellProfiler window. b Simulation image blurring, segmentation and de clumping by shape algorithm. c Simulation image segmentation and de clumping by intensity watersheds. d Relating segmented images before and after blurring with number of virtual bruchpilot clusters per virtual active zone indicated
Summary
Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution This breakthrough in spatial resolution made image analysis a challenging procedure. Image segmentation algorithms that use shape or intensity data will identify single super resolved clusters, SR microscopy techniques provide a much narrower Gaussian point spread function (PSF) of the focused scanning area, enabling us to resolve features that are distant below diffraction limit of light, which is approx. Structures and groups of molecules clusters can be merged when distance separating them is below Gaussian width of the applied filter. Once this cluster grouping is achieved, segmentation remains to be done. It is logical to use algorithms that use shape information to segment clumped objects, avoiding by that over-segmentation problems [7]
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