ABSTRACT
The periosteum is critical for bone maintenance and healing. However, the in vivo identity and specific regulatory mechanisms of adult periosteum-resident skeletal stem cells are unknown. Here, we report animal models that selectively and durably label postnatal Mx1+αSMA+ periosteal stem cells (P-SSCs) and establish that P-SSCs are a long-term repopulating, functionally distinct SSC subset responsible for lifelong generation of periosteal osteoblasts. P-SSCs rapidly migrate toward an injury site, supply osteoblasts and chondrocytes, and recover new periosteum. Notably, P-SSCs specifically express CCL5 receptors, CCR3 and CCR5. Real-time intravital imaging revealed that the treatment with CCL5 induces P-SSC migration in vivo and bone healing, while CCL5/CCR5 deletion, CCR5 inhibition, or local P-SSC ablation reduces osteoblast number and delays bone healing. Human periosteal cells express CCR5 and undergo CCL5-mediated migration. Thus, the adult periosteum maintains genetically distinct SSC subsets with a CCL5-dependent migratory mechanism required for bone maintenance and injury repair.
Subject(s)
Actins/metabolism , Myxovirus Resistance Proteins/metabolism , Periosteum/cytology , Periosteum/metabolism , Stem Cells/metabolism , Actins/genetics , Adolescent , Adult , Animals , Cell Movement/physiology , Child , Female , Flow Cytometry , Fluorescent Antibody Technique , Humans , Immunohistochemistry , Male , Mice, Inbred C57BL , Microarray Analysis , Myxovirus Resistance Proteins/genetics , Reverse Transcriptase Polymerase Chain Reaction , Stem Cells/cytology , Young AdultABSTRACT
Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange ( http://orange.biolab.si ) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.
