Piwi-interacting RNAs (piRNAs) are small noncoding RNAs generated by a conserved pathway. Their most widely studied function involves restricting transposable elements, particularly in the germline, where piRNAs are highly abundant. Increasingly, another set of piRNAs derived from intergenic regions appears to have a role in the regulation of mRNA from early embryos and gonads. We report a more widespread expression of a limited set of piRNAs and particularly focus on their expression in the hippocampus. Deep sequencing of extracted RNA from the mouse hippocampus revealed a set of small RNAs in the size range of piRNAs. These were confirmed by their presence in the piRNA database as well as coimmunoprecipitation with MIWI. Their expression was validated by Northern blot and in situ hybridization in cultured hippocampal neurons, where signal from one piRNA extended to the dendritic compartment. Antisense suppression of this piRNA suggested a role in spine morphogenesis. Possible targets include genes, which control spine shape by a distinctive mechanism in comparison to microRNAs.
Summary
We develop methods for analysing the spatial pattern of events, classified into several types, that occur on a network of lines. The motivation is the study of small protrusions called ‘spines’ which occur on the dendrite network of a neuron. The spatially varying density of spines is modelled by using relative distributions and regression trees. Spatial correlations are investigated by using counterparts of the K‐function and pair correlation function, where the main problem is to compensate for the network geometry. This application illustrates the need for careful analysis of spatial variation in the intensity of points, before assessing any evidence of clustering.
BackgroundDendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley’s K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites.ResultsOur study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type.ConclusionsAlthough these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.
A 1/8th size down-sampled version of the seven retinal image mosaics described in this article can be found on BISQUE (Kvilekval et al., 2010) at http://bisque.ece.ucsb.edu/client_service/view?resource=http://bisque.ece.ucsb.edu/data_service/dataset/6566968.
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