VIDÉO-MICROSCOPIE SANS LENTILLE POUR LA BIOLOGIE CELLULAIRE 2D ET 3D

International audienceL'étude de l'évolution et de l'organisation de populations de cellules cultivées in vitro intéresse les biologistes depuis plusieurs dizaines d'années. À ces fins, d'importants progrès ont été réalisés dans les méthodes d'imagerie à l'échelle microscopique. Cependant, certaines informations demeurent inaccessibles, notamment à l'échelle mésoscopique, en raison du champ de vue réduit, ainsi que la complexité et le coût pour réaliser des acquisitions hors incubateur en temps réel sur de longues périodes. En réponse à ces limitations, nous avons développé la vidéo-microscopie sans lentille, en plaçant directement les cellules vivantes sur un capteur numérique en regard d'une illumination cohérente selon le principe de l'holographie en ligne. Cette technique permet l'observation d'une culture cellulaire sur un large champ de vue (24 mm² soit plusieurs dizaines de milliers de cellules), et ce à l'intérieur même de l'incubateur, autorisant de surcroît des acquisitions dynamiques couvrant des périodes allant de quelques jours à plusieurs semaines. À partir des images holographiques brutes acquises, nous pouvons remonter aux images refocalisées par reconstruction numérique jusqu'à une résolution de 2µm. Le traitement de ces images donne accès à des niveaux d'information quantifiables allant de la cellule unique à l'organisation inter-individus de la population. Avec des premières études sur des cultures standard de cellules sur substrat 2D, nous sommes aujourd'hui en mesure, avec notre dispositif et la force de l'imagerie holographique, d'explorer et d'étudier la vie cellulaire en 3D, nous rapprochant un peu plus de la réalité physiologique des phénomènes biologiques

Confinement-Induced Transition between Wavelike Collective Cell Migration Modes

International audienceThe structural and functional organization of biological tissues relies on the intricate interplay between chemical and mechanical signaling. Whereas the role of constant and transient mechanical perturbations is generally accepted, several studies recently highlighted the existence of longrange mechanical excitations (i.e., waves) at the supracellular level. Here, we confine epithelial cell mono-layers to quasi-one dimensional geometries, to force the establishment of tissue-level waves of well-defined wavelength and period. Numerical simulations based on a self-propelled Voronoi model reproduce the observed waves and exhibit a phase transition between a global and a multi-nodal wave, controlled by the confinement size. We conrm experimentally the existence of such a phasetransition, and show that wavelength and period are independent of the confinement length. Together, these results demonstrate the intrinsic origin of tissue oscillations, which could provide cells with a mechanism to accurately measure distances at the supracellular level

Curvature-dependent constraints drive remodeling of epithelia

Epithelial tissues function as barriers that separate the organism from the environment. They usually have highly curved shapes, such as tubules or cysts. However, the processes by which the geometry of the environment and the cell's mechanical properties set the epithelium shape are not yet known. In this study, we encapsulated two epithelial cell lines, MDCK and J3B1A, into hollow alginate tubes and grew them under cylindrical confinement forming a complete monolayer. MDCK monolayers detached from the alginate shell at a constant rate, whereas J3B1A monolayers detached at a low rate unless the tube radius was reduced. We showed that this detachment is driven by contractile stresses in the epithelium and can be enhanced by local curvature. This allows us to conclude that J3B1A cells exhibit smaller contractility than MDCK cells. Monolayers inside curved tubes detach at a higher rate on the outside of a curve, confirming that detachment is driven by contraction

Aménagement du territoire et observatoires multi-sites pour mieux comprendre le comportement des colonies d'abeilles mellifères

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Comparaison des capacités prédictives de réseaux de neurones, application à la masse sèche de cellules

International audienceDepuis une dizaine d'années, de nombreuses architectures de réseaux de neurones ont été proposées pour résoudre des problèmes complexes, jusqu'alors insolubles. Bien que chaque architecture tente d'augmenter les performances de ses prédécesseurs, nous constatons aujourd'hui un manque d'évaluation comparée de leurs performances.Nous présentons dans ce papier une étude comparative de différentes architectures des réseaux de neurones pour la prédiction de séries temporelles. En particulier, celle de masse sèche de cellules.Quatre types d'architectures (Perceptrons Multicouches, CNN-1D, LSTM et réseaux à connexions résiduelles) sont comparées selon leurs capacités prédictives, leur nombre de paramètres, leur temps d'entraînement et leur temps d'inférence.Les expériences réalisées mettent en avant une prédominance des perceptrons multicouches à extraire une représentation utile pour la prédiction de la masse sèche de cellule par un réseau totalement connecté, et ce, sur toutes les métriques étudiées

Self-supervised learning for anomaly detection on time series: application to cellular data

International audienceThis paper presents a new method for anomaly detec-tion in time series and its application to cellular data.These time series are computed from cell images ac-quired thanks to lens-free microscopy. In the context ofcellular biology, detecting abnormal cells is interestingfor any further analysis. Indeed, cells that deviate fromhealthy trajectories can further drive tissues towarddiseases [RAG+20]. It would be both time-consumingand costly to manually analyse each cell in a dataset often thoudands cells. To overcome this human process,we present a deep self-supervised approach to automat-ically detect abnormal cells from their dry mass timeseries. A 1D-convolutio nal neural network is trained topredict the dry mass of cells. An anomaly is detected ifthe mean squared error (MSE) between prediction andground truth is above a fixed threshold. This processbased on self-supervised learning is tested on a datasetof 9,100 time series of dry mass. The method succeedsin detecting abnormal time series with a precision of 96.6%

Deep anomaly detection using self-supervised learning: application to time series of cellular data

International audienceWe present a deep self-supervised method for anomaly detection on time series. We apply this methodology to detect anomalies from cellular time series. In particular, this study focuses on cell dry mass, obtained in the context of lensfree microscopy. The method we propose is an innovative two-step pipeline using self-supervised learning. As a first step, a representation of the time series is learned thanks to a 1D-convolutional neural network without any labels. Then, the learned representation is used to feed a threshold anomaly detector. This new self-supervised learning method is tested on an unlabelled dataset of 9100 time series of dry mass and succeeded in detecting abnormal time series with a precision of 96.6%

Self-supervised learning for anomaly detection on time series: application to cellular data

International audienceThis paper presents a new method for anomaly detec-tion in time series and its application to cellular data.These time series are computed from cell images ac-quired thanks to lens-free microscopy. In the context ofcellular biology, detecting abnormal cells is interestingfor any further analysis. Indeed, cells that deviate fromhealthy trajectories can further drive tissues towarddiseases [RAG+20]. It would be both time-consumingand costly to manually analyse each cell in a dataset often thoudands cells. To overcome this human process,we present a deep self-supervised approach to automat-ically detect abnormal cells from their dry mass timeseries. A 1D-convolutio nal neural network is trained topredict the dry mass of cells. An anomaly is detected ifthe mean squared error (MSE) between prediction andground truth is above a fixed threshold. This processbased on self-supervised learning is tested on a datasetof 9,100 time series of dry mass. The method succeedsin detecting abnormal time series with a precision of 96.6%

Culture Conditions can Counterbalance the Thermosensitive Properties of Poly (N-Isopropylacrylamide) during Cell Harvesting

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