We describe the first comprehensive analysis of the midgut metabolome of Aedes aegypti, the primary mosquito vector for arboviruses such as dengue, Zika, chikungunya and yellow fever viruses. Transmission of these viruses depends on their ability to infect, replicate and disseminate from several tissues in the mosquito vector. The metabolic environments within these tissues play crucial roles in these processes. Since these viruses are enveloped, viral replication, assembly and release occur on cellular membranes primed through the manipulation of host metabolism. Interference with this virus infection-induced metabolic environment is detrimental to viral replication in human and mosquito cell culture models. Here we present the first insight into the metabolic environment induced during arbovirus replication in Aedes aegypti. Using high-resolution mass spectrometry, we have analyzed the temporal metabolic perturbations that occur following dengue virus infection of the midgut tissue. This is the primary site of infection and replication, preceding systemic viral dissemination and transmission. We identified metabolites that exhibited a dynamic-profile across early-, mid- and late-infection time points. We observed a marked increase in the lipid content. An increase in glycerophospholipids, sphingolipids and fatty acyls was coincident with the kinetics of viral replication. Elevation of glycerolipid levels suggested a diversion of resources during infection from energy storage to synthetic pathways. Elevated levels of acyl-carnitines were observed, signaling disruptions in mitochondrial function and possible diversion of energy production. A central hub in the sphingolipid pathway that influenced dihydroceramide to ceramide ratios was identified as critical for the virus life cycle. This study also resulted in the first reconstruction of the sphingolipid pathway in Aedes aegypti. Given conservation in the replication mechanisms of several flaviviruses transmitted by this vector, our results highlight biochemical choke points that could be targeted to disrupt transmission of multiple pathogens by these mosquitoes.

Digital PCR is an exciting new field for molecular analysis, allowing unprecedented precision in the quantification of nucleic acids, as well as the fine discrimination of rare molecular events in complex samples. We here present a novel technology for digital PCR, Crystal Digital PCR™, which relies on the use of a single chip to partition samples into 2D droplet arrays, which are then subjected to thermal cycling and finally read using a three-color fluorescence scanning device. This novel technology thus allows three-color multiplexing, which entails a different approach to data analysis. In the present publication, we present this innovative workflow, which is both fast and user-friendly, and discuss associated data analysis issue, such as fluorescence spillover compensation and data representation. Lastly, we also present proof-of-concept of this three-color detection system, using a quadriplex assay for the detection of EGFR mutations L858R, L861Q and T790M.

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To support the challenging task of early epithelial cancer diagnosis from in vivo endomicroscopy, we propose a content-based video retrieval method that uses an expert-annotated database. Motivated by the recent successes of non-medical contentbased image retrieval, we first adjust the standard Bag-of-Visual-Words method to handle single endomicroscopic images. A local dense multi-scale description is proposed to keep the proper level of invariance, in our case to translations, in-plane rotations and affine transformations of the intensities. Since single images may have an insufficient field-of-view to make a robust diagnosis, we introduce a video-mosaicing technique that provides large field-of-view mosaic images. To remove outliers, retrieval is followed by a geometrical approach that captures a statistical description of the spatial relationships between the local features. Building on image retrieval, we then focus on efficient video retrieval. Our approach avoids the time-consuming parts of the video-mosaicing by relying on coarse registration results only to account for spatial overlap between images taken at different times. To evaluate the retrieval, we perform a simple nearest neighbors classification with leave-one-patient-out crossvalidation. From the results of binary and multi-class classification, we show that our approach outperforms, with statistical significance, several state-of-the art methods. We obtain a binary classification accuracy of 94.2%, which is quite close to clinical expectations.