A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator

International audienceMiRNAs are key regulators of gene expression. By binding to many genes, they create a complex network of gene co-regulation. Here, using a network-based approach, we identified miRNA hub groups by their close connections and common targets. In one cluster containing three miRNAs, miR-612, miR-661 and miR-940, the annotated functions of the co-regulated genes suggested a role in small GTPase signalling. Although the three members of this cluster targeted the same subset of predicted genes, we showed that their overexpression impacted cell fates differently. miR-661 demonstrated enhanced phosphorylation of myosin II and an increase in cell invasion, indicating a possible oncogenic miRNA. On the contrary, miR-612 and miR-940 inhibit phosphorylation of myosin II and cell invasion. Finally, expression profiling in human breast tissues showed that miR-940 was consistently downregulated in breast cancer tissues M icroRNAs are a class of endogenous, small (19–25 nucleotides), single-stranded non-coding RNAs that regulate gene expression in all eukaryotic organisms. In metazoans, microRNAs most commonly bind to the 39 untranslated region (39UTR) of their mRNA target transcript and cause translational repression and/or mRNA degradation. Every microRNA is predicted to regulate from a dozen to thousands of genes, including transcription factors. This fine-tuning of protein expression is known to be involved in many physiological processes, such as development, apoptosis, signal transduction and even cancer progression 1,2. More than 2,000 mature human microRNAs are listed in the 20 th release of miRBase: http://www.mirbase.org (2014) (Date of access:19/08/2013), and some authors hypothesise that the majority of human genes are regulated by microRNAs 3. Since their discovery in 1993 4 , a fair understanding of their role in animal development and in the onset and progression of diseases 2 , as well as of their potential use in therapies 5 , has been gathered. However, the cooperative behaviour of microRNAs is still under investigation. A growing body of experimental evidence suggests that microRNAs can regulate genes through complementarity, meaning that microRNAs can act together to regulate individual genes or groups of genes involved in similar processes 6. For example, Hu and co-workers demonstrated that transducing a cocktail of precursor microRNAs (miR-21, miR-24 and miR-221) can result in more effective engraftment of transplanted cardiac progenitor cells 7. Consistent with these discoveries, Zhu et al. demonstrated that miR-21 and miR-221 coregulate 56 gene ontology (GO) processes 8. In the same study, the authors also showed that cotransfection of miR-1 and miR-21 increases H 2 O 2-induced myocardial apoptosis and oxidative stress. These recent findings support the idea of microRNA-mediated cooperative regulation but also argue for the use of systemic approaches, notably based on graph theory, to decipher individual and complementary roles of microRNAs. Some work has been conducted to use recent high-throughput experiment-derived data sets to infer microRNA synergistic relationships 9–12. Herein, we present a microRNA network based on target similarities among microRNAs to infer clusters of microRNAs. Clusters are defined as groups of microRNAs sharing a set of common targets, predicted by either DIANA-microT v3 13 or TargetScan v6.2 14. Some authors have used GO enrichment analysis as a confirmatory tool for their clustering approach 11. In our case, GO enrichment is not used to infer networks but as a way to estimate the probable metabolic pathway(s) a cluster of microRNAs could co-regulate. Moreover, the novelty of our approach is to consider not only clusters of microRNAs but also OPE

Segmentation et quantification des couches rétiniennes dans des images de tomographie de cohérence optique, dans le cas de sujets sains et pathologiques

Optical coherence tomography (OCT) is a non-invasive imaging technique, based on the principle of interferometry. Thus, OCT is now a standard examination for the detection and the monitoring of retinal diseases including macular degeneration. In this context, the first objective of this thesis is to propose a new method for the segmentation of OCT images of healthy subjects. The proposed method exploits prior knowledge on the structure and the appearearance of the retinal layers. It is based on a combination of local and global segmentation algorithms, including active contours, k-means and Markov random fields. Thus, eight retinal layers can be detected, including the inner segments (IS) of photoreceptors. However, the slow evolution of this disease makes the evaluation of these therapies difficult. The second objective of this thesis is then to extend the scope of the method developed for healthy subjects to retinitis pigmentosa subjects. We have developed a new parametric deformable model that incorporates a priori information by adding a constraint of approximate parallelism, which is more robust in the presence of pathologies. In both healthy and pathological study cases, we performed a comprehensive qualitative and quantitative assessrnent of the proposed methods. We evaluated the accuracv of the segmentation of interfaces between layers, and, in the case of healthy subjects, the accuracy of the segmentation of interfaces between layers, and, in the case of healthy subjects, the precision of thickness measurements derived from the segmentation. This study was conducted on a large image database. These evaluations show a very good agreement anda strong correlation between automatic segmentation and segmentation done manually by an expert.La tomographie de cohérence optique (OCT) est une technique d'imagerie non invasive, fondée sur le principe de I'interférométrie. Elle est maintenant un examen classique pour le dépistage et le suivi des affections rétiniennes, notamment des dégénérescences maculaires. C'est dans ce cadre que s'inscrit le premier objectif de ces travaux de thèse, où nous proposons une nouvelle méthode de segmentation des images OCT de sujets sains. Les principales difficultés sont liées au bruit des images, à la variabilité de la morphologie d’un patient à l’autre et aux interfaces mal définies entre les différentes couches. Notre nouvelle approche est fondée sur des algorithmes de segmentation plus globaux. Ainsi. huit couches rétiniennes peuvent étre détectées, y compris les segments internes (IS) des photorécepteurs. L’évolution lente des maladies rétiniennes pose le problème de l’évaluation de ces thérapeutiques. C’est dans ce cadre que s’inscrit le second objectif de cette thèse, où nous étendons le champ d’application des méthodes développées pour les sujets sains aux sujets atteints de rétinopathie pigmentaire. Nous avons ainsi développé un nouveau modèle paramétrique déformable qui intègre les informations a priori en ajoutant une contrainte de parallélisme. Dans les cas sains et pathologiques, nous avons réalisé une évaluation exhaustive qualitative et quantitative. Les résultats de segmentation automatique ont été comparés avec les segmentations manuelles réalisées par différents experts.Ces évaluations montrent une très bonne concordance et une forte corrélation entre les segmentations automatiques et les segmentations faites manuellement par un expert

Segmentation et quantification des couches rétiniennes dans des images de tomographie de cohérence optique, dans le cas de sujets sains et pathologiques

Optical coherence tomography (OCT) is a non-invasive imaging technique, based on the principle of interferometry. Thus, OCT is now a standard examination for the detection and the monitoring of retinal diseases including macular degeneration. In this context, the first objective of this thesis is to propose a new method for the segmentation of OCT images of healthy subjects. The proposed method exploits prior knowledge on the structure and the appearearance of the retinal layers. It is based on a combination of local and global segmentation algorithms, including active contours, k-means and Markov random fields. Thus, eight retinal layers can be detected, including the inner segments (IS) of photoreceptors. However, the slow evolution of this disease makes the evaluation of these therapies difficult. The second objective of this thesis is then to extend the scope of the method developed for healthy subjects to retinitis pigmentosa subjects. We have developed a new parametric deformable model that incorporates a priori information by adding a constraint of approximate parallelism, which is more robust in the presence of pathologies. In both healthy and pathological study cases, we performed a comprehensive qualitative and quantitative assessrnent of the proposed methods. We evaluated the accuracv of the segmentation of interfaces between layers, and, in the case of healthy subjects, the accuracy of the segmentation of interfaces between layers, and, in the case of healthy subjects, the precision of thickness measurements derived from the segmentation. This study was conducted on a large image database. These evaluations show a very good agreement anda strong correlation between automatic segmentation and segmentation done manually by an expert.La tomographie de cohérence optique (OCT) est une technique d'imagerie non invasive, fondée sur le principe de I'interférométrie. Elle est maintenant un examen classique pour le dépistage et le suivi des affections rétiniennes, notamment des dégénérescences maculaires. C'est dans ce cadre que s'inscrit le premier objectif de ces travaux de thèse, où nous proposons une nouvelle méthode de segmentation des images OCT de sujets sains. Les principales difficultés sont liées au bruit des images, à la variabilité de la morphologie d'un patient à l'autre et aux interfaces mal définies entre les différentes couches. Notre nouvelle approche est fondée sur des algorithmes de segmentation plus globaux. Ainsi. huit couches rétiniennes peuvent étre détectées, y compris les segments internes (IS) des photorécepteurs. L'évolution lente des maladies rétiniennes pose le problème de l'évaluation de ces thérapeutiques. C'est dans ce cadre que s'inscrit le second objectif de cette thèse, où nous étendons le champ d'application des méthodes développées pour les sujets sains aux sujets atteints de rétinopathie pigmentaire. Nous avons ainsi développé un nouveau modèle paramétrique déformable qui intègre les informations a priori en ajoutant une contrainte de parallélisme. Dans les cas sains et pathologiques, nous avons réalisé une évaluation exhaustive qualitative et quantitative. Les résultats de segmentation automatique ont été comparés avec les segmentations manuelles réalisées par différents experts.Ces évaluations montrent une très bonne concordance et une forte corrélation entre les segmentations automatiques et les segmentations faites manuellement par un expert

Automated segmentation of macular layers in optical coherence tomography images and quantitative evaluation of performances in healthy and pathological subjects

La tomographie de cohérence optique (OCT) est une technique d'imagerie non invasive, fondée sur le principe de I'interférométrie. Elle est maintenant un examen classique pour le dépistage et le suivi des affections rétiniennes, notamment des dégénérescences maculaires. C'est dans ce cadre que s'inscrit le premier objectif de ces travaux de thèse, où nous proposons une nouvelle méthode de segmentation des images OCT de sujets sains. Les principales difficultés sont liées au bruit des images, à la variabilité de la morphologie d’un patient à l’autre et aux interfaces mal définies entre les différentes couches. Notre nouvelle approche est fondée sur des algorithmes de segmentation plus globaux. Ainsi. huit couches rétiniennes peuvent étre détectées, y compris les segments internes (IS) des photorécepteurs. L’évolution lente des maladies rétiniennes pose le problème de l’évaluation de ces thérapeutiques. C’est dans ce cadre que s’inscrit le second objectif de cette thèse, où nous étendons le champ d’application des méthodes développées pour les sujets sains aux sujets atteints de rétinopathie pigmentaire. Nous avons ainsi développé un nouveau modèle paramétrique déformable qui intègre les informations a priori en ajoutant une contrainte de parallélisme. Dans les cas sains et pathologiques, nous avons réalisé une évaluation exhaustive qualitative et quantitative. Les résultats de segmentation automatique ont été comparés avec les segmentations manuelles réalisées par différents experts.Ces évaluations montrent une très bonne concordance et une forte corrélation entre les segmentations automatiques et les segmentations faites manuellement par un expert.Optical coherence tomography (OCT) is a non-invasive imaging technique, based on the principle of interferometry. Thus, OCT is now a standard examination for the detection and the monitoring of retinal diseases including macular degeneration. In this context, the first objective of this thesis is to propose a new method for the segmentation of OCT images of healthy subjects. The proposed method exploits prior knowledge on the structure and the appearearance of the retinal layers. It is based on a combination of local and global segmentation algorithms, including active contours, k-means and Markov random fields. Thus, eight retinal layers can be detected, including the inner segments (IS) of photoreceptors. However, the slow evolution of this disease makes the evaluation of these therapies difficult. The second objective of this thesis is then to extend the scope of the method developed for healthy subjects to retinitis pigmentosa subjects. We have developed a new parametric deformable model that incorporates a priori information by adding a constraint of approximate parallelism, which is more robust in the presence of pathologies. In both healthy and pathological study cases, we performed a comprehensive qualitative and quantitative assessrnent of the proposed methods. We evaluated the accuracv of the segmentation of interfaces between layers, and, in the case of healthy subjects, the accuracy of the segmentation of interfaces between layers, and, in the case of healthy subjects, the precision of thickness measurements derived from the segmentation. This study was conducted on a large image database. These evaluations show a very good agreement anda strong correlation between automatic segmentation and segmentation done manually by an expert

Segmentation et quantification des couches rétiniennes dans des images de tomographie de cohérence optique, dans le cas de sujets sains et pathologiques

La tomographie de cohérence optique (OCT) est une technique d'imagerie non invasive, fondée sur le principe de I'interférométrie. Elle est maintenant un examen classique pour le dépistage et le suivi des affections rétiniennes, notamment des dégénérescences maculaires. C'est dans ce cadre que s'inscrit le premier objectif de ces travaux de thèse, où nous proposons une nouvelle méthode de segmentation des images OCT de sujets sains. Les principales difficultés sont liées au bruit des images, à la variabilité de la morphologie d un patient à l autre et aux interfaces mal définies entre les différentes couches. Notre nouvelle approche est fondée sur des algorithmes de segmentation plus globaux. Ainsi. huit couches rétiniennes peuvent étre détectées, y compris les segments internes (IS) des photorécepteurs. L évolution lente des maladies rétiniennes pose le problème de l évaluation de ces thérapeutiques. C est dans ce cadre que s inscrit le second objectif de cette thèse, où nous étendons le champ d application des méthodes développées pour les sujets sains aux sujets atteints de rétinopathie pigmentaire. Nous avons ainsi développé un nouveau modèle paramétrique déformable qui intègre les informations a priori en ajoutant une contrainte de parallélisme. Dans les cas sains et pathologiques, nous avons réalisé une évaluation exhaustive qualitative et quantitative. Les résultats de segmentation automatique ont été comparés avec les segmentations manuelles réalisées par différents experts.Ces évaluations montrent une très bonne concordance et une forte corrélation entre les segmentations automatiques et les segmentations faites manuellement par un expert.Optical coherence tomography (OCT) is a non-invasive imaging technique, based on the principle of interferometry. Thus, OCT is now a standard examination for the detection and the monitoring of retinal diseases including macular degeneration. In this context, the first objective of this thesis is to propose a new method for the segmentation of OCT images of healthy subjects. The proposed method exploits prior knowledge on the structure and the appearearance of the retinal layers. It is based on a combination of local and global segmentation algorithms, including active contours, k-means and Markov random fields. Thus, eight retinal layers can be detected, including the inner segments (IS) of photoreceptors. However, the slow evolution of this disease makes the evaluation of these therapies difficult. The second objective of this thesis is then to extend the scope of the method developed for healthy subjects to retinitis pigmentosa subjects. We have developed a new parametric deformable model that incorporates a priori information by adding a constraint of approximate parallelism, which is more robust in the presence of pathologies. In both healthy and pathological study cases, we performed a comprehensive qualitative and quantitative assessrnent of the proposed methods. We evaluated the accuracv of the segmentation of interfaces between layers, and, in the case of healthy subjects, the accuracy of the segmentation of interfaces between layers, and, in the case of healthy subjects, the precision of thickness measurements derived from the segmentation. This study was conducted on a large image database. These evaluations show a very good agreement anda strong correlation between automatic segmentation and segmentation done manually by an expert.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Quantitative imaging of cell dynamics Parallelized contact imaging and automated analysis of cell migration dynamics

International audienceWe show the capacity to carry out screens for cell migrations using 96 wound healing assays achieved in 96-well microtiter plates based on a new optical technique developed by our group. Dynamics of the wound closures were obtained by parallelized time lapse contact imaging microscopy and dedicated automated image analysis. Our plate reader relies on an array of 96 image sensors, namely the planar arrangement of 12×8 image sensors, placed under the transparent flat-bottomed 96-well microtiter plate so that each well can be imaged by the image sensor placed underneath. Week-long monitoring of live cell populations showed long-term imaging position stability and no focus drift in any image series, which makes our time-lapse plate reader very competitive in comparison to conventional video microscopy equipment. The 96 wound closure dynamics were extracted from the images using a specifically developed automated segmentation method. Robust localization of the wound edges in low contrast images was achieved by global segmentation algorithms based on Markov random fields and active contours even with non-uniform illumination conditions. A parallel double snake was used to model the approximate parallelism between the two edges of a wound. The performance of global segmentation was validated on a set of images showing wounds in confluent epithelial cell cultures. Automated wound localization was compared with manual segmentation performed by seven cell biology experts by determining the root-mean-square error between the segmented interfaces and region-oriented analysis. Evaluations of intra and inter-biologist variabilities showed that automated segmentations are as accurate and robust as the cell biologist's ones. Wound closure dynamics was applied to measure and compare the motility of four affiliated prostate cell lines representing various grades of prostate cancer development

Parallel Double Snakes. Application to the segmentation of retinal layers in 2D-OCT for pathological subjects

International audienceIn order to segment elongated structures, we propose a new approach for integrating an approximate parallelism constraint in deformable models. The proposed Parallel Double Snakes evolve simultaneously two contours, in order to minimize an energy functional which attracts these contours towards high image gradients and enforces the approximate parallelism between them by controlling their distance to a centerline under regularity constraints of this line. The proposed approach is applied on retina images, for segmenting retinal layers in optical coherence tomography images of pathological subjects (and it applies to healthy subjects as well). Results are evaluated by comparing with manual segmentations for three retinal layers, and provide a similarity index above 0.87, sensitivity between 0.85 and 0.93, and specificity between 0.84 and 0.94. These results are within the range of intra and inter-expert variability. Moreover, quantitative studies demonstrate that, in our application, our Parallel Double Snake (PDS) model outperforms other parametric active contour algorithms integrating parallelism information

MODELING A PARALLELISM CONSTRAINT IN ACTIVE CONTOURS. APPLICATION TO THE SEGMENTATION OF EYE VESSELS AND RETINAL LAYERS

International audienceParametric deformable models are an important technique for image segmentation. In order to improve the robustness of the model, it may be interesting to incorporate a priori information about the shape of the objects to be segmented. In this paper, we propose to add a parallelism constraint. Such a model is relevant in many applications where elongated structures have to be detected. One main advantage of our formulation is that it only needs few parameters to be adjusted in addition to those of traditional snakes. The proposed model has been applied for the segmentation of OCT images of the retina and for the segmentation of retinal vessels. Experimental results, obtained on 25 OCT images and 30 eye fundus images, demonstrated the robustness, flexibility and large potential applicability of this new formulation. The accuracy of the method has been assessed by comparing manual segmentations, made by experts, with the automatic ones

Segmentation of OCT images of retina for the quantitative study of retinal variability

Nous proposons une méthode de segmentation d'images de la rétine, acquises par tomographie de cohérence optique (OCT), en haute résolution. Elle permet d'extraire automatiquement huit couches rétiniennes, avec une bonne précision autour de la fovéola. Les résultats ont été évalués et validés par comparaison avec les segmentations manuelles faites par cinq médecins différents. Les mesures effectuées à partir des segmentations automatiques ont également été comparées aux mesures faites manuellement par les experts, pour validation. Ainsi, des études quantitatives de variabilités rétiniennes ont pu être menées, sur une base de données de 72 images segmentées automatiquement par la méthode proposée

AUTOMATED SEGMENTATION OF RETINAL LAYERS IN OCT IMAGING AND DERIVED OPHTHALMIC MEASURES

International audienceThis paper proposes an automated method for the segmentation of eight retinal layers in high resolution OCT images. It has been evaluated based on comparison with manual segmentation performed by five different experts. The method has been successfully applied on a database of 72 images. Quantitative measures are then derived as an aid to ophthalmic diagnosis. A good agreement with measures derived from manual segmentation is obtained which allows us to use the proposed method for retinal variability studies