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Videos related to ERC application

Video 1. Spatial analysis of 3D images of BM parenchyma. Immunostained BM sinusoidal vessels are segmented using customized deep learning algorithms. The DAPI signal is employed to additional segment tissue boundaries and the extravascular space is extracted by substracting the sinusoidal volume from the total tissue volume. The empty space distance (ESD) transform is then calculated by measuring the distance of every voxel in the extravascular portion of the tissue to the nearest sinusoid. The ESD, which  is shown in grayscale in the last frames of the video, can be employed using spatial points processes to analyze spatial dependencies of cellular objects with respect to sinusoids as a benchmark for randomness.

Video 2. Deep learning segmentation and classification of different BM vascular structures. Convolutional neural netrowks were used to   segment and classify sinusoids (red), arteries (magenta) and transitional vessels (blue). CXCL12 abundant reticular cells (green) are shown, detected and marked as yellow spheres.

Video 3. 3D Imaging of global distribution of a-catulin-GFP+ Hematopoietic Stem cells in BM slices. Tissues were stained for a sinusoidal marker (red), anti c-kit (blue) and anti-GFP orange. Sinusoidal endothelial cells express GFP (as reported. HSCs are identified as c-kit+ a-catulinGFP+ and marked with cyan spheres.

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