Detecting Stress Granules in Drosophila Neurons.

Stress granules (SGs) are cytoplasmic ribonucleoprotein condensates that dynamically and reversibly assemble in response to stress. They are thought to contribute to the adaptive stress response by storing translationally inactive mRNAs as well as signaling molecules. Recent work has shown that SG composition and properties depend on both stress and cell types, and that neurons exhibit a complex SG proteome and a strong vulnerability to mutations in SG proteins. Drosophila has emerged as a powerful genetically tractable organism where to study the physiological regulation and functions of SGs in normal and pathological contexts. In this chapter, we describe a protocol enabling quantitative analysis of SG properties in both larval and adult Drosophila CNS samples. In this protocol, fluorescently tagged SGs are induced upon acute ex vivo stress or chronic in vivo stress, imaged at high-resolution via confocal microscopy and detected automatically, using a dedicated software.

Detecting and quantifying stress granules in tissues of multicellular organisms with the Obj.MPP analysis tool.

Stress granules (SGs) are macromolecular assemblies induced by stress and composed of proteins and mRNAs stalled in translation initiation. SGs play an important role in the response to stress and in the modulation of signaling pathways. Furthermore, these structures are related to the pathological ribonucleoprotein (RNP) aggregates found in neurodegenerative disease contexts, highlighting the need to understand how they are formed and recycled in normal and pathological contexts. Although genetically tractable multicellular organisms have been key in identifying modifiers of RNP aggregate toxicity, in vivo analysis of SG properties and regulation has lagged behind, largely due to the difficulty of detecting SG from images of intact tissues. Here, we describe the object detector software Obj.MPP and show how it overcomes the limits of classical object analyzers to extract the properties of SGs from wide-field and confocal images of Caenorhabditis elegans and Drosophila tissues, respectively. We demonstrate that Obj.MPP enables the identification of genes modulating the assembly of endogenous and pathological SGs, and thus that it will be useful in the context of future genetic screens and in vivo studies.

Morphological analysis and feature extraction of neurons from mouse cortices multiscale 3D microscopic images.

In this paper, we propose a framework to analyze the morphology of mouse neurons in the layer V of the cortex from 3D microscopic images. We are given 8 sets of images, each of which is composed of a 10x image showing the whole neurons, and a few (2 to 5) 40x images focusing on the somas. The framework consists in segmenting the neurons on both types of images to compute a set of specific morphological features, and in matching the neurons in the 40x images to their counterparts in the 10x images to combine the features we obtained, in a fully automatic fashion.

High-dimensional statistical measure for region-of-interest tracking.

This paper deals with region-of-interest (ROI) tracking in video sequences. The goal is to determine in successive frames the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. Some tracking methods define similarity measures which efficiently combine several visual features into a probability density function (PDF) representation, thus building a discriminative model of the ROI. This approach implies dealing with PDFs with domains of definition of high dimension. To overcome this obstacle, a standard solution is to assume independence between the different features in order to bring out low-dimension marginal laws and/or to make some parametric assumptions on the PDFs at the cost of generality. We discard these assumptions by proposing to compute the Kullback-Leibler divergence between high-dimensional PDFs using the k th nearest neighbor framework. In consequence, the divergence is expressed directly from the samples, i.e., without explicit estimation of the underlying PDFs. As an application, we defined 5, 7, and 13-dimensional feature vectors containing color information (including pixel-based, gradient-based and patch-based) and spatial layout. The proposed procedure performs tracking allowing for translation and scaling of the ROI. Experiments show its efficiency on a movie excerpt and standard test sequences selected for the specific conditions they exhibit: partial occlusions, variations of luminance, noise, and complex motion.

From snakes to region-based active contours defined by region-dependent parameters.

Image and sequence segmentation of a the segmentation task are discussed from the point of view of optimizing the segmentation criterion. Such a segmentation criterion involves so-called (boundary and region) descriptors, which, in general, may depend on their respective boundaries or regions. This dependency must be taken into account when one is computing the criterion derivative with respect to the unknown object domain (defined by its boundary). If this dependency not considered, some correctional terms may be omitted. Computing the derivative of the segmentation criterion with a dynamic scheme is described. The scheme is general enough to provide a framework for a wide variety of applications in segmentation. It also provides a theoretical meaning to the philosophy of active contours.