mRNP granules at adult central synapses are postulated to regulate local mRNA translation and synapse plasticity. However, they are very poorly characterized in vivo. Here, in Drosophila olfactory synapses, we present early observations and characterization of candidate synaptic mRNP particles, one of which contains a widely conserved, DEAD-box helicase, Me31B. In Drosophila, Me31B is required for translational repression of maternal and miRNA-target mRNAs. A role in neuronal translational control is primarily suggested by Me31B's localization, in cultured primary neurons, to neuritic mRNP granules that contain: (i) various translational regulators; (ii) CaMKII mRNA; and (iii) several P-body markers including the mRNA hydrolases, Dcp1, and Pcm/Xrn-1. In adult neurons, Me31B localizes to P-body like cytoplasmic foci/particles in neuronal soma. In addition it is present to synaptic foci that may lack RNA degradative enzymes and localize predominantly to dendritic elements of olfactory sensory and projection neurons (PNs). MARCM clones of PNs mutant for Me31B show loss of both Me31B and Dcp1-positive dendritic puncta, suggesting potential interactions between these granule types. In PNs, expression of validated hairpin-RNAi constructs against Me31B causes visible knockdown of endogenous protein, as assessed by the brightness and number of Me31B puncta. Knockdown of Me31B also causes a substantial elevation in observed levels of a translational reporter of CaMKII, a postsynaptic protein whose mRNA has been shown to be localized to PN dendrites and to be translationally regulated, at least in part through the miRNA pathway. Thus, neuronal Me31B is present in dendritic particles in vivo and is required for repression of a translationally regulated synaptic mRNA.
In confocal microscopy imaging, the target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and analysis is an important task for the biological studies from the observed images. However, while the spots have irregular sizes and positions due to the variant amount of objects on each spot, the quantitative interpretation of the labeled objects is still heavily reliant on manual evaluation. In this paper, a novel shape modeling algorithm is proposed for automating the detection and analysis of the spots of interest. The algorithm exploits a Gaussian mixture model to characterize the spatial intensity distribution of the spots, and optimizes the model parameters using split-and-merge expectation maximization (SMEM) algorithm. As a result, a large amount of target objects with uncertain shapes can be analyzed in a systematic way.
In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and analysis is therefore an important task but it is still heavily reliant on manual analysis. In this paper, a novel shape modeling algorithm is proposed for automating the detection and analysis of the spots of interest. The algorithm exploits a Gaussian mixture model to characterize the spatial intensity distribution of the spots, and estimates parameters using a novel split-and-merge expectation maximization (SMEM) algorithm. In previous work the split step is random which is an issue for biological analysis where repeatability is important. The new split/merge steps are deterministic, hence more useful, and further do not impact adversely on the optimality of the final result.
Active contours are well known for object segmentation and widely adopted in various forms for biological image analysis. Most of the techniques are commonly based on object geometry but overlapping regions cause severe problems to contour propagation. In this paper, we propose a novel active contour technique ("cellsnake") for solving this problem with an application to cell and fibre segmentation. Given that the transparency of overlapped objects is unavailable, we present a new set of contour forces derived from a-priori knowledge of cell geometry that allows the contour to deform correctly in those regions. We have combined these terms with other existing forces and we show that cellsnake gives appropriate shape estimation of the objects especially in the overlapped area in the observed images.
In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.
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