Characterization of Nanoscale Organization of F-Actin in Morphologically Distinct Dendritic Spines In Vitro Using Supervised Learning.


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The cytoarchitecture of a neuron is very important in defining morphology and ultrastructure. Though there is a wealth of information on the molecular components that make and regulate these ultrastructures, there is a dearth of understanding of how these changes occur or how they affect neurons in health and disease. Recent advances in nanoscale imaging which resolve cellular structures at the scale of tens of nanometres below the limit of diffraction enable us to understand these structures in fine detail. However, automated analysis of these images is still in its infancy. Towards this goal, attempts have been made to automate the detection and analysis of the cytoskeletal organization of microtubules. To date, evaluation of the nanoscale organization of filamentous actin (F-actin) in neuronal compartments remains challenging. Here, we present an objective paradigm for analysis which adopts supervised learning of nanoscale images of F-actin network in excitatory synapses, obtained by single molecule based super-resolution light microscopy. We so have used the proposed analysis to understand the heterogeneity in the organization of F-actin in dendritic spines of primary neuronal cultures from rodents. Our results were validated using ultrastructural data obtained from Platinum Replica Electron Microscopy. The automated analysis approach was used to differentiate the heterogeneity in the nanoscale organization of F-actin in primary neuronal cultures from wild type and a transgenic mouse model of Alzheimer's Disease (APP(swe) /PS1 Delta E9).
Alzheimer’s Disease,Dendritic Spines,F-actin,Segmentation and Pattern Recognition,Super-resolution,Supervised Learning
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