Genome Sequences of Two Tunisian Field Strains of Avian <I>Mycoplasma, M. meleagridis<I> and <I>M. gallinarum<I>

International audienceMycoplasma meleagridis and Mycoplasma gallinarum are bacteria that affect birds, but little is known about the genetic basis of their interaction with chickens and other poultry. Here, we sequenced the genomes of M. meleagridis strain MM_26B8_IPT and M. gallinarum strain Mgn_IPT, both isolated from chickens showing respiratory symptoms, poor growth, reduction in hatchability, and loss of production

Neural network image processing and analysis methods for fluorescence microscopy

Le développement des méthodes de microscopie de fluorescence permet aujourd'hui l'acquisition de données d'imagerie cellulaire à haut débit et à une résolution moléculaire. Ces techniques sont désormais un élément essentiel pour faire la lumière sur les processus cellulaires. Ainsi, la microscopie rend possible l'étude de phénotypes cellulaires couvrant les phénomènes globaux tels que les modifications morphologiques de la cellule entière et de ses composants, ou encore la détection de l'altération des processus cellulaires grâce à la quantification et localisation des molécules. Dans cette thèse, nous nous intéresserons particulièrement à l'emploi de méthodes informatiques et mathématiques pour analyser de manière quantitative les données d'imagerie de microscopie de fluorescence. Dans le premier chapitre, il s'agit d'étudier comment l'utilisation de réseaux de neurones à convolution dédiés au rehaussement de l'intensité du signal de fluorescence des spots d'ARNm dans les images de microscopie permet d'automatiser leur détection en aval par des outils conventionnels largement utilisés par la communauté d'analyse d'images de microscopie. Nous présentons dans ce chapitre la méthode DeepSpot, et nous montrons qu'en renforçant le signal de tous les spots de manière à ce qu'ils aient la même intensité sur toutes les images, indépendamment du contraste, du bruit ou de la taille des spots, il devient possible d'automatiser leur détection en aval.Dans le second chapitre, nous proposons une approche de deep learning non supervisée de rehaussement du signal des noyaux cellulaires. Plus précisément, nous appliquons le transfert de style entre des images d'organoïdes acquises avec une faible intensité lumineuse vers des images sur lesquelles les noyaux sont visiblement saillants, sans qu'une annotation préalable des images ne soit requise. Pour cela, nous avons implémenté un CycleGAN avec une fonction de coût sur mesure dédiée à l'augmentation du signal des noyaux. Nous avons entraîné ce réseau de neurones et évalué l'impact du rehaussement du signal des noyaux sur leur segmentation en aval, à l'aide de méthodes conventionnelles ainsi qu'avec des méthodes basées sur l'apprentissage.Dans le troisième chapitre, nous présentons DypFISH, une librairie d'analyse de données spatiales, qui propose des outils d'analyse quantitative pour étudier les localisations subcellulaires des ARNm et des protéines de manière statistiquement robuste. Entre autres, DypFISH introduit des techniques qui permettent l'évaluation conjointe des données ponctuelles d'ARNm dans les images smFISH et des données continues de protéines par immunofluorescence (IF).The advent of fluorescence microscopy methods make it currently possible to acquire cellular imaging data on a molecular scale at that in high-throughput fashion. These techniques now constitute an essential tool for unraveling cellular processes. Thus, microscopy makes it possible to study cellular phenotypes covering global phenomena such as morphological modifications of the whole cell and of its components, or the detection of alterations of cellular processes thanks to the quantification and localization of molecules. In this thesis, we will focus on the use of computational and mathematical methods to quantitatively analyse fluorescence microscopy imaging data. In the first chapter, we study how the use of convolutional neural networks dedicated to the enhancement of the intensity of fluorescence signal of mRNA spots in microscopy images allows to automate their downstream detection by conventional tools widely used by the microscopy image analysis community. In this chapter, we present the DeepSpot deep learning method, and show that by enhancing the signal of all spots up to the same intensity in all images, regardless of the contrast, noise or size of the spots, it enables seamless automation of their downstream detection.In the second chapter, we propose an unsupervised deep learning approach for cell nuclei signal enhancement. More precisely, we apply style transfer between images of organoids acquired with low light intensity to images where the nuclei are visibly prominent, without requiring prior annotation of the images. For this purpose, we implemented a CycleGAN with a custom cost function dedicated to the signal enhancement of nuclei. We trained this neural network and evaluated the impact of nuclei signal enhancement on their downstream segmentation, using conventional methods as well as learning-based methods.In the third chapter, we present DypFISH, a spatial data analysis library, which provides quantitative analysis tools to study subcellular localizations of mRNAs and proteins in a statistically robust manner. In particular, DypFISH introduces techniques that allow joint evaluation of point data of mRNA in smFISH images and continuous immunofluorescence (IF) protein dat

Méthodes de traitement et d’analyse d’image par réseaux de neurones pour la microscopie de fluorescence

The advent of fluorescence microscopy methods make it currently possible to acquire cellular imaging data on a molecular scale at that in high-throughput fashion. These techniques now constitute an essential tool for unraveling cellular processes. Thus, microscopy makes it possible to study cellular phenotypes covering global phenomena such as morphological modifications of the whole cell and of its components, or the detection of alterations of cellular processes thanks to the quantification and localization of molecules. In this thesis, we will focus on the use of computational and mathematical methods to quantitatively analyse fluorescence microscopy imaging data. In the first chapter, we study how the use of convolutional neural networks dedicated to the enhancement of the intensity of fluorescence signal of mRNA spots in microscopy images allows to automate their downstream detection by conventional tools widely used by the microscopy image analysis community. In this chapter, we present the DeepSpot deep learning method, and show that by enhancing the signal of all spots up to the same intensity in all images, regardless of the contrast, noise or size of the spots, it enables seamless automation of their downstream detection.In the second chapter, we propose an unsupervised deep learning approach for cell nuclei signal enhancement. More precisely, we apply style transfer between images of organoids acquired with low light intensity to images where the nuclei are visibly prominent, without requiring prior annotation of the images. For this purpose, we implemented a CycleGAN with a custom cost function dedicated to the signal enhancement of nuclei. We trained this neural network and evaluated the impact of nuclei signal enhancement on their downstream segmentation, using conventional methods as well as learning-based methods.In the third chapter, we present DypFISH, a spatial data analysis library, which provides quantitative analysis tools to study subcellular localizations of mRNAs and proteins in a statistically robust manner. In particular, DypFISH introduces techniques that allow joint evaluation of point data of mRNA in smFISH images and continuous immunofluorescence (IF) protein dataLe développement des méthodes de microscopie de fluorescence permet aujourd'hui l'acquisition de données d'imagerie cellulaire à haut débit et à une résolution moléculaire. Ces techniques sont désormais un élément essentiel pour faire la lumière sur les processus cellulaires. Ainsi, la microscopie rend possible l'étude de phénotypes cellulaires couvrant les phénomènes globaux tels que les modifications morphologiques de la cellule entière et de ses composants, ou encore la détection de l'altération des processus cellulaires grâce à la quantification et localisation des molécules. Dans cette thèse, nous nous intéresserons particulièrement à l'emploi de méthodes informatiques et mathématiques pour analyser de manière quantitative les données d'imagerie de microscopie de fluorescence. Dans le premier chapitre, il s'agit d'étudier comment l'utilisation de réseaux de neurones à convolution dédiés au rehaussement de l'intensité du signal de fluorescence des spots d'ARNm dans les images de microscopie permet d'automatiser leur détection en aval par des outils conventionnels largement utilisés par la communauté d'analyse d'images de microscopie. Nous présentons dans ce chapitre la méthode DeepSpot, et nous montrons qu'en renforçant le signal de tous les spots de manière à ce qu'ils aient la même intensité sur toutes les images, indépendamment du contraste, du bruit ou de la taille des spots, il devient possible d'automatiser leur détection en aval.Dans le second chapitre, nous proposons une approche de deep learning non supervisée de rehaussement du signal des noyaux cellulaires. Plus précisément, nous appliquons le transfert de style entre des images d'organoïdes acquises avec une faible intensité lumineuse vers des images sur lesquelles les noyaux sont visiblement saillants, sans qu'une annotation préalable des images ne soit requise. Pour cela, nous avons implémenté un CycleGAN avec une fonction de coût sur mesure dédiée à l'augmentation du signal des noyaux. Nous avons entraîné ce réseau de neurones et évalué l'impact du rehaussement du signal des noyaux sur leur segmentation en aval, à l'aide de méthodes conventionnelles ainsi qu'avec des méthodes basées sur l'apprentissage.Dans le troisième chapitre, nous présentons DypFISH, une librairie d'analyse de données spatiales, qui propose des outils d'analyse quantitative pour étudier les localisations subcellulaires des ARNm et des protéines de manière statistiquement robuste. Entre autres, DypFISH introduit des techniques qui permettent l'évaluation conjointe des données ponctuelles d'ARNm dans les images smFISH et des données continues de protéines par immunofluorescence (IF)

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone

DypFISH: Dynamic Patterned FISH to Interrogate RNA and Protein Spatial and Temporal Subcellular Distribution

Advances in single cell RNA sequencing have allowed for the identification and characterization of cellular subtypes based on quantification of the number of transcripts in each cell. However, cells may differ not only in the number of mRNA transcripts that they exhibit, but also in their spatial and temporal distribution, intrinsic to the definition of their cellular state. Here we describe DypFISH, an approach to quantitatively investigate the spatial and temporal subcellular localization of RNA and protein, by combining micropatterning of cells with fluorescence microscopy at high resolution. We introduce a range of analytical techniques for quantitatively interrogating single molecule RNA FISH data in combination with protein immunolabeling over time. Strikingly, our results show that constraining cellular architecture reduces variation in subcellular mRNA and protein distributions, allowing the characterization of their localization and dynamics with high reproducibility. Many tissues contain cells that exist in similar constrained architectures. Thus DypFISH reveals reproducible patterns of clustering, strong correlative influences of mRNA-protein localization on MTOC orientation when they are present and interdependent dynamics globally and at specific subcellular locations which can be extended to physiological systems

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

International audienceAdvances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers