Assembling a functional neural circuit requires guiding axons and dendrites to appropriate regions to form synaptic connections during development. Neurodevelopmental disorders, such as Fragile X syndrome and Rett syndrome, have distinct defects in dendritic morphologies, including alteration of branching patterns, loss of branches, and changes in spine shapes and numbers. Many mental diseases, such as mental retardation, schizophrenia and Autism spectrum disorder, have hidden developmental origins, and their neurological and cognitive deficits, characteristic of neural miscommunication, are likely caused by dendritic defects. It is unclear however, how genetic defects cause dendritic patterning defects, resulting in erroneous connections and functional deficit. We use the Drosophila visual neurons as a model to study dendrite development in the central nervous system. Like vertebrate cortex and retina, the Drosophila optic lobe is organized in columns and layers, suggesting that the fly visual neurons and vertebrate cortex neurons face similar challenges in routing their dendrites to specific layers and columns during development. In addition, the Drosophila visual system has several unique advantages: (i) the medulla neurons extend dendritic arbors in a lattice-like structure, facilitating morphometric analysis; (ii) the synaptic partnership is known; (iii) genetic tools for labeling specific classes of medulla neurons and determining their connectivity are available; (iv) sensitive behavioral assays are available for quantifying functional deficits. To analyze the dendritic patterns of the medulla neurons, we developed a number of new techniques: (i) a dual-imaging technique for high-resolution imaging; (ii) a registration technique to standardize and to compare dendritic patterns; (iv) a modified GRASP method for visualize bona fide synaptic connections at the light-microscopic level. Using these techniques, we first analyzed the dendritic morphologies of four types of medulla neurons, Tm1, Tm2, Tm9 and Tm20. We identified four dendritic attributes: (i) over 80% of dendritic branches arise from one or two primary branching nodes in the medulla M2-3 layers; (ii) dendrites project from the primary branching points in either anterior or posterior direction (planar directions) in a type-specific fashion; (iii) dendrites terminate in specific layers (layer-specific distribution) in a type-specific fashion; (iv) the dendrites of four types of Tm neurons are largely confined in single medulla columns. By clustering analyses using either PCA (principle component analysis) or information-theory-based t-SNE (t-distributed stochastic neighboring embedding) algorithm, we found that layer-specific distribution of dendritic terminals and planar projection directions are the most important type-specific attributes and they are sufficient to differentiate the Tm neurons. In sharp contrast, standard morphometric parameters, such as branch numbers and bifurcation topologies, are similar among these Tm neurons and these parameters are incapable of categorizing Tm neurons dendritic patterns. Furthermore, the other two attributes, layer-specific location of primary branching nodes and the dendritic field sizes differentiate Tm1/2/9/20 from other Tm neurons and Dm8 neurons. To determine the molecular mechanisms controlling dendritic patterning during development, we screened loss-of-function mutations for morphological defects in Tm20 and Dm8 dendrites. We identified mutations that specifically affect distinct aspects of dendritic patterning, including layer-specific location of primary branching nodes, planar projection directions, and dendritic field sizes. We focused on the components of the TGF-beta/Activin signaling pathway, which specifically affect the size of the dendritic fields of Tm20 and Dm8. Single-cell mosaic analyses revealed that mutant Tm20 lacking Activin signaling components, such as the receptor Baboon and the downstream transcription factor Smad2, elaborated an expanded dendritic tree, spanning several medulla columns. Morphometric analyses based on a Kaplan-Meier non-parametric estimator further showed that baboon and smad2 mutations significantly reduced dendritic termination frequency. Using a modified GRASP method, we found that the expanded dendritic tree of mutant Tm20 forms aberrant synaptic contacts with several neighboring R8 photoreceptors. In contrast, wild-type Tm20 forms synaptic connections with single R8 photoreceptors in its cognate column. Thus, the loss of Activin signaling in Tm20 neurons not only affects their sizes of dendritic fields but also results in the formation of incorrect synaptic connections. Similarly, removing Baboon or Smad2 in Dm8 neurons resulted in expanded dendritic fields as compared to the wild-type. Conversely, overexpressing a dominant active form of Baboon in developing Dm8 resulted in reduced dendritic field sizes. Thus, Activin signaling negatively regulates the sizes of dendritic fields of Tm20 and Dm8. To search for the source of TGF-beta ligands for Tm20 and Dm8, we carried out in situ hybridization and RT-PCR for all four potential ligands, Activin, Dawdle, Myoglianin, and Maverick and found Activin expression in developing R7 and R8 photoreceptor neurons. RNAi-mediated knockdown of Activin in R7s and R8s caused abnormal expansion of dendritic fields of Dm8 and Tm20, respectively. These results indicate that photoreceptors R7 and R8 provide Activin specifically for their respective synaptic targets, Dm8 and Tm20. Interestingly, while the R7 and R8 growth cones were only a few micrometer apart, the Activin they provide is incapable of replacing one another, suggesting that Activin acts in a very short range. In summary, we found that photoreceptor-derived Activin controls the dendritic development of their respective synaptic target neurons, suggesting that anterograde Activin signaling coordinates afferent-target development.

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5
Fiscal Year
2015
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U.S. National Inst/Child Hlth/Human Dev
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Ting, Chun-Yuan; McQueen, Philip G; Pandya, Nishith et al. (2017) Analyzing Dendritic Morphology in Columns and Layers. J Vis Exp :
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