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.
Keywords: detection algorithm; image analysis; marked point process; stress granules.
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.