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In the presented work we aimed at improving confocal imaging to obtain highest possible resolution in thick biological samples, such as the mouse oocyte. We therefore developed an image processing workflow that allows improving the lateral and axial resolution of a standard confocal microscope. Our workflow comprises refractive index matching, the optimization of microscope hardware parameters and image restoration by deconvolution. We compare two different deconvolution algorithms, evaluate the necessity of denoising and establish the optimal image restoration procedure. We validate our workflow by imaging sub resolution fluorescent beads and measuring the maximum lateral and axial resolution of the confocal system. Subsequently, we apply the parameters to the imaging and data restoration of fluorescently labelled meiotic spindles of mouse oocytes. We measure a resolution increase of approximately 2-fold in the lateral and 3-fold in the axial direction throughout a depth of 60μm. This demonstrates that with our optimized workflow we reach a resolution that is comparable to 3D-SIM-imaging, but with better depth penetration for confocal images of beads and the biological sample., (Copyright © 2016 Elsevier Inc. All rights reserved.)

We present a new plugin for ImageJ called DiAna, for Distance Analysis, which comes with a user-friendly interface. DiAna proposes robust and accurate 3D segmentation for object extraction. The plugin performs automated object-based co-localization and distance analysis. DiAna offers an in-depth analysis of co-localization between objects and retrieves 3D measurements including co-localizing volumes and surfaces of contact. It also computes the distribution of distances between objects in 3D. With DiAna, we furthermore introduce an original method, which allows for estimating the statistical significance of object co-localization. DiAna offers a complete and intuitive 3D image analysis tool for biologists., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Multicolor fluorescence microscopy helps to define the local interplay of subcellular components in cell biological experiments. The analysis of spatial coincidence of two or more markers is a first step in investigating the potential interactions of molecular actors. Colocalization studies rely on image preprocessing and further analysis; however, they are limited by optical resolution. Once those limitations are taken into account, characterization might be performed. In this review, we discuss two types of parameters that are aimed at evaluating colocalization, which are indicators and quantifiers. Indicators evaluate signal coincidence over a predefined scale, while quantifiers provide an absolute measurement. As the image is both a collection of intensities and a collection of objects, both approaches are applicable. Most of the available image processing software include various colocalization options; however, guidance for the choice of the appropriate method is rarely proposed. In this review, we provide the reader with a basic description of the available colocalization approaches, proposing a guideline for their use, either alone or in combination., (© 2014 Elsevier Inc. All rights reserved.)