Cytométrie et ses applications en immunologie clinique / Cytometry and its clinical applications in immunology

National audienceLa cytométrie (cyto = cellule ; métrie = mesure) consiste en l'analyse objective, quantitative et multiparamétrique des cellules. Elle utilise la fluorescence, des moyens fluidiques, optiques et le soutien informatique pour le traitement des signaux ou des images. Ses performances sont exceptionnelles (analyse 4, 6, 8, à très grande vitesse (de 500 à 10 000 cellules par seconde). La cytométrie en image est très utilisée en recherche. En analyse médicale, la cytométrie en flux est de plus en plus utilisées, pour le typage des leucémies, la numération des très nombreux sous-types cellulaires par exemple pour le suivi du SIDA ou des traitements immunosuppresseurs et greffes. De plus, l'état d'activation, de maturation et de prolifération des cellules peuvent être mesurés. La haute précision et large utilisation de la cytométrie en routine a déjà permis de décrire des nouveaux sous-types cellulaires tels que les lymphocytes T gamma delta, muqueux, les cellules TNK, les lymphocytes régulateurs dont l'action est ciblée au sein de la cellule immune. Des proliférations monoclonales de signification indéterminées mais potentiellement évolutives ont été observées. Les désordres prolifératifs et des hétéroploidies peuvent être rapidement analysés. D'autres applications en cancérologie et microbiologie sont en cours de développement. En conclusion, l'apport majeur de la cytométrie est de pouvoir aborder les populations cellulaires dans leur grande diversité et complexité. Il serait en effet ridicule de limiter ces systèmes à des ensembles homogènes et uniformes. La « sociologie » des populations cellulaires est un nouveau champ encore à défricher. ================================================== Cytometry (cyto = cell; metry = measuring) consist in an objective, quantitative et multiparametric analysis of cells. It is based on fluorescence, fluidic and optical tools with the help of signal or image computer treatment. It is a high performance system allowing simultaneously analysis 4, 6, 8 or more parameters, at very high speed from 500 to 10 000 cells per second). Image cytometry is largely used in research. In medical analysis, Flow-cytometry is more and more used for leukaemia typing, lymphocyte counting, among them HIV monitoring or immunological treatments follow-up. Identification of numerous cell subtypes and their activation, maturation or proliferation status is now possible. Its high precision and wide use have lead to the description of new cell subtypes (e.g. gamma delta, mucosal, T.NK or regulatory T cells...) that have targeted activity. Recently, oligoclonal clonopathies of undetermined significance but with risks for pathological development have been described. Proliferative or heteroploidy disorders can also be evaluated. Applications in solid tumor and microbiology are under development. In conclusion, the major point of cytometry is to bring tools to approach cell populations in their diversity and complexity. It would be ridiculous to consider them as homogeneous, uniform systems. In other words, cell « sociology » is a wide new field that remains to be explored

Genetic differentiation and gene flow between the Tunisian ovine breeds Barbarine and Western thin tail using random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) analysis

Sheep is an important livestock species of Tunisia. They contribute greatly to the food safety of the country and in the livelihood of a large number of small and marginal farmers and landless labourers engaged in sheep rearing. In this study, random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) analysis was used to assess the genetic difference and gene flow among two Tunisian sheep breeds (the Barbarine and the Western thin tail). A total of 62 bands were detected with an average of 7.75 bands per primer. The unweighted pair-group method with arithmetic average (UPGMA) and principal component analysis (PCA) showed a clear differentiation between the two studied breeds. Genetic differentiation coefficient (Gst) over all loci was 0.1922, the fixation index [Fst by Analysis of molecular variance (AMOVA)] was 0.308 (P<0.001), and the gene flow value (Nm) was 1.3102. It is clear from this study that Barbarine and Western thin tail breeds are completely distinct and that they show high and significant genetic differentiation. However, the level of gene migration between them is quite high and may lead eventually to the loss of breed’s purity.Key words: Sheep, genetic differentiation, gene flow, RAPD-PCR

Modèle théorique d’évaluation de l’apport des systèmes d’information à la performance organisationnelle

La problématique liée à l’évaluation de l’apport des systèmes d’information à la performance organisationnelle est l’une des questions les plus soulevées par la communauté scientifique en management des systèmes d’information et en pilotage de la performance. Le présent travail porte sur la proposition d’un modèle théorique d’évaluation de la contribution des systèmes d’information à la performance, qui s’inscrit directement dans une perspective processuelle et qui se penche plus particulièrement sur la théorie des ressources. Ainsi l’objectif de cette recherche est d’expliquer le processus par lequel les systèmes d’information contribuent à la performance organisationnelle

Population balances in case of crossing characteristic curves: Application to T-cells immune response

The progression of a cell population where each individual is characterized by the value of an internal variable varying with time (e.g. size, weight, and protein concentration) is typically modeled by a Population Balance Equation, a first order linear hyperbolic partial differential equation. The characteristics described by internal variables usually vary monotonically with the passage of time. A particular difficulty appears when the characteristic curves exhibit different slopes from each other and therefore cross each other at certain times. In particular such crossing phenomenon occurs during T-cells immune response when the concentrations of protein expressions depend upon each other and also when some global protein (e.g. Interleukin signals) is also involved which is shared by all T-cells. At these crossing points, the linear advection equation is not possible by using the classical way of hyperbolic conservation laws. Therefore, a new Transport Method is introduced in this article which allowed us to find the population density function for such processes. The newly developed Transport method (TM) is shown to work in the case of crossing and to provide a smooth solution at the crossing points in contrast to the classical PDF techniques.Comment: 18 pages, 10 figure

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

Etude expérimentale et modélisation mathématique de la réponse lymphocytaire T

302 pagesThe specific T Cell response is complex, because of the participation of many actors and it depends on several external and internal parameters that are difficult to dread. This make very difficult to anticipate the reaction, especially from the dynamic point of view, and to analyze immune disorders or monitor immuno-interventions (vaccines). The numeric modeling and the simulation would allow to test hypotheses, to draw new experimental conditions and in fine to optimize therapeutic protocols. To this purpose, a mathematical model mimicking the dynamics of the Specific T cell response was recently proposed by our team (Bidot et al. 2008). The model uses published parameters and its confrontations with experimental data that were validated in the literature, has shown that how it is realistic. However, it still lack of precision because certain parameters arte still not determined. In order to complete, correct and validate our model, we started a set of experiments to analyse the dynamic of the T cell response in details under various aspects. First, we studied the high variability of CD3 membrane expression and its relation to the level of T cell activation. Then, study the behavior of lymphocytes T for specific stimulations (peptide) in a TCR transgenic murine model. Finally, we realized a new mathematical model to overcome the lacks of the statistical treatments of cytometry data in commercial software using a dynamic of population approach.La réponse lymphocytaire T, spécifique, est complexe, car elle fait intervenir plusieurs acteurs et dépend de paramètres externes et internes difficiles Cette multitude de paramètres rend difficile d'appréhender la réaction, surtout du point de vue dynamique. Une meilleure connaissance de ce mécanisme permettrait d'analyser et si possible anticiper les désordres immunitaires ou d'immuno-intervention (vaccins, immunothérapies). La modélisation et la simulation numériques permettraient de tester des hypothèses, de dessiner de nouvelles conditions expérimentales et in fine d'optimiser des protocoles thérapeutiques. Dans ce but, Un modèle mathématique mimant la réponse adaptative T a été proposé par notre équipe (Bidot et al. 2008). Le modèle utilise des paramètres publiés et les confrontations avec des données expérimentales validées dans la littérature montrent qu'il est réaliste. Cependant, il reste encore imprécis car certains paramètres n'ont pas été déterminés. Dans le but de compléter, corriger et valider notre modèle, nous nous sommes intéressés à l'étude détaillée du lymphocyte T sous différents aspects. Dans un premier temps, nous avons étudié expérimentalement la variabilité d'expression des CD3 membranaires et ses conséquences dans l'activation des lymphocytes. Puis, nous avons cherché à étudier le comportement des lymphocytes T pour des stimulations spécifiques (selon le peptide) à l'aide d'un modèle murin spécifique d'un antigène par transfert de gène du récepteur T. Enfin, nous avons réalisé un modèle mathématique dans le but de surmonter les ambigüités des traitements statistiques des résultats de cytométrie en flux en utilisant les principes de dynamique de population

Etude expérimentale et modélisation mathématique de la réponse lymphocytaire t

La réponse lymphocytaire T, spécifique, est complexe, car elle fait intervenir plusieurs acteurs et dépend de paramètres externes et internes difficiles Cette multitude de paramètres rend difficile d appréhender la réaction, surtout du point de vue dynamique. Une meilleure connaissance de ce mécanisme permettrait d analyser et si possible anticiper les désordres immunitaires ou d'immuno-intervention (vaccins, immunothérapies). La modélisation et la simulation numériques permettraient de tester des hypothèses, de dessiner de nouvelles conditions expérimentales et in fine d'optimiser des protocoles thérapeutiques. Dans ce but, Un modèle mathématique mimant la réponse adaptative T a été proposé par notre équipe (Bidot et al. 2008). Le modèle utilise des paramètres publiés et les confrontations avec des données expérimentales validées dans la littérature montrent qu il est réaliste. Cependant, il reste encore imprécis car certains paramètres n ont pas été déterminés. Dans le but de compléter, corriger et valider notre modèle, nous nous sommes intéressés à l'étude détaillée du lymphocyte T sous différents aspects. Dans un premier temps, nous avons étudié expérimentalement la variabilité d expression des CD3 membranaires et ses conséquences dans l activation des lymphocytes. Puis, nous avons cherché à étudier le comportement des lymphocytes T pour des stimulations spécifiques (selon le peptide) à l aide d un modèle murin spécifique d un antigène par transfert de gène du récepteur T. Enfin, nous avons réalisé un modèle mathématique dans le but de surmonter les ambigüités des traitements statistiques des résultats de cytométrie en flux en utilisant les principes de dynamique de population.The specific T Cell response is complex, because of the participation of many actors and it depends on several external and internal parameters that are difficult to dread. This make very difficult to anticipate the reaction, especially from the dynamic point of view, and to analyze immune disorders or monitor immuno-interventions (vaccines). The numeric modeling and the simulation would allow to test hypotheses, to draw new experimental conditions and in fine to optimize therapeutic protocols. To this purpose, a mathematical model mimicking the dynamics of the Specific T cell response was recently proposed by our team (Bidot et al. 2008). The model uses published parameters and its confrontations with experimental data that were validated in the literature, has shown that how it is realistic. However, it still lack of precision because certain parameters arte still not determined. In order to complete, correct and validate our model, we started a set of experiments to analyse the dynamic of the T cell response in details under various aspects. First, we studied the high variability of CD3 membrane expression and its relation to the level of T cell activation. Then, study the behavior of lymphocytes T for specific stimulations (peptide) in a TCR transgenic murine model. Finally, we realized a new mathematical model to overcome the lacks of the statistical treatments of cytometry data in commercial software using a dynamic of population approach.ST ETIENNE-ENS des Mines (422182304) / SudocSudocFranceF

Mathematical modeling of T-cell activation kinetic

International audienceT-cell activation is a crucial step in mounting of the immune response. The dynamics of T-cell receptor (TCR) specific recognition of peptide presented by major histocompatibility complex (MHC) molecule decides the fate of the T cell. Several biochemical interactions interfere resulting in a highly complex mechanism that would be difficult to understand without computer help. The aim of the present study was to define a mathematical model in order to approach the kinetics of monoclonal T-cell-specific activation. The reaction scheme was first described and the model was tested using experimental parameters from the published data. Simulations were concordant with experimental data showing proportional decrease of membrane TCR and production of interleukin-2 (IL-2). Agonist and antagonist peptides induce different levels of intracellular signal that could make the yes or no decision for entry to cell cycle. Different conditions (peptide concentrations, initial TCR density and exogenous IL-2 levels) can be tested. Several parameters are missing for parameters estimation and adjustment before it could be adapted for a polyclonal T-cell reaction model. However, the model should be of interest in setting experiments, simulation of clinical responses and optimization of preventive or therapeutic immunotherapy