![]() ![]() Here, we extent the application range of map projections to quantitative analyses of commonly used (and often smaller-scale) translucent biological samples like cells and subcellular components. A similar approach has recently been published in order to reduce data rate in storage-demanding selective-plane illumination microscopy of Zebrafish embryos 2. We implemented map projection algorithms used in cartography to unfold spatial fluorescence image data onto a single structurally connected map ( Fig. We developed a new software application in MATLAB (MathWorks), called ‘Map3-2D’, to visualize surface data from 3D fluorescence microscopy. ![]() Since this is presenting a particular challenge for qualitative and quantitative analyses of structurally connected, multiple images-spanning signals, we sought to overcome such limitations. As a consequence, there is always some image content that remains absent, lost or altered with any of these display options. While all of the abovementioned presentation methods have their advantages, they all fail to display the full 3D surface information content as a single, structurally connected 2D image ( Supplementary Fig. A third option is the use of image galleries, which put a collection of images next to each other in form of a single rather disconnected montage. from confocal Z-series), stacks of 2D images that show one image at a time, or 2D projections (of the maximum, sum, mean intensities, or texture-based volume renderings, surfaces or orthoslices) are usually presented. ![]() In order to perform spatial imaging in light microscopy three dimensions need to be considered, which are the lateral (X and Y) and axial (Z) dimensions 1. ![]()
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