Retinoid receptors trigger neuritogenesis in retinal degenerations.

Anomalous neuritogenesis is a hallmark of neurodegenerative disorders, including retinal degenerations, epilepsy, and Alzheimer's disease. The neuritogenesis processes result in a partial reinnervation, new circuitry, and functional changes within the deafferented retina and brain regions. Using the light-induced retinal degeneration (LIRD) mouse model, which provides a unique platform for exploring the mechanisms underlying neuritogenesis, we found that retinoid X receptors (RXRs) control neuritogenesis. LIRD rapidly triggered retinal neuron neuritogenesis and up-regulated several key elements of retinoic acid (RA) signaling, including retinoid X receptors (RXRs). Exogenous RA initiated neuritogenesis in normal adult retinas and primary retinal cultures and exacerbated it in LIRD retinas. However, LIRD-induced neuritogenesis was partly attenuated in retinol dehydrogenase knockout (Rdh12(-/-)) mice and by aldehyde dehydrogenase inhibitors. We further found that LIRD rapidly increased the expression of glutamate receptor 2 and ß Ca(2+)/calmodulin-dependent protein kinase II (ßCaMKII). Pulldown assays demonstrated interaction between ßCaMKII and RXRs, suggesting that CaMKII pathway regulates the activities of RXRs. RXR antagonists completely prevented and RXR agonists were more effective than RA in inducing neuritogenesis. Thus, RXRs are in the final common path and may be therapeutic targets to attenuate retinal remodeling and facilitate global intervention methods in blinding diseases and other neurodegenerative disorders.

A computational framework for ultrastructural mapping of neural circuitry.

Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists.