Fig. 1: End-to-end workflow for generation of a 3D skin map of cell types and spatial distance analysis visualization tools. | Communications Biology

Fig. 1: End-to-end workflow for generation of a 3D skin map of cell types and spatial distance analysis visualization tools.

From: 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging

Fig. 1

(1) Healthy skin biopsies were embedded into a single formalin-fixed and paraffin-embedded (FFPE) tissue block. The human male and female skin 3D reference organ was used to spatially register and semantically annotate the biopsies via the HuBMAP Registration User Interface; (2) Skin biomarkers were identified using the skin ASCT + B tables and corresponding antibodies were validated; (3) The tissue block underwent micro CT imaging and was then sectioned into 26 serial sections for highly multiplexed immunofluorescence imaging using 18 protein and cell type markers; (4) Cell classification was conducted for each section using a hybrid supervised (deep learning-based) and unsupervised (probability-based) GMM workflow; 2D serial sections and segmented cells then underwent 3D reconstruction. The 3D spatial ___location of cells was used to compute immune cell cluster density, immune cell distributions from endothelial cells, and distribution and distances of p53, DDB2, and Ki67 positive cells from the skin surface. Created using BioRender.com.

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