Biology subjects

Ostvar, S.

Publications and source records attributed to Ostvar, S..

4 recordsLinked to original sources

bioRxiv2024

E-cadherin tunes tissue mechanical behavior before and during morphogenetic tissue flows

Adhesion between epithelial cells enables the remarkable mechanical behavior of epithelial tissues during morphogenesis. However, it remains unclear how cell-cell adhesion influences mechanics in static as well as in dynamically flowing epithelial tissues. Here, we systematically modulate E-cadherin-mediated adhesion in the Drosophila embryo and study the effects on the mechanical behavior of the germband epithelium before and during dramatic tissue remodeling and flow associated with body axis elongation. Before axis elongation, we find that increasing E-cadherin levels produces tissue comprising more elongated cells and predicted to be more fluid-like, providing reduced resistance to tissue flow. During axis elongation, we find that the dominant effect of E-cadherin is tuning the speed at which cells proceed through rearrangement events, revealing potential roles for E-cadherin in generating friction between cells. Before and during axis elongation, E-cadherin levels influence patterns of actomyosin-dependent forces, supporting the notion that E-cadherin tunes tissue mechanics in part through effects on actomyosin. Taken together, these findings reveal dual--and sometimes opposing--roles for E-cadherin-mediated adhesion in controlling tissue structure and dynamics in vivo that result in unexpected relationships between adhesion and flow.

developmental biology↗

bioRxiv2024

Cell rearrangement progression along the apical-basal axis is linked with 3D epithelial tissue structure

Epithelial tissues undergo extensive structural remodeling during embryonic development. Tissue remodeling is often enabled by oriented cell rearrangements that are linked with patterns of mechanical stress in the tissue and with tissue mechanical properties. Cell rearrangements and their links to tissue structure have largely been studied at the apical side of tissues at the level of adherens junctions. Less is known about the involvement of basolateral domains in cell rearrangements. Here we use live confocal imaging to quantify cell rearrangements, cell packing structure, and cell morphology in 3D in the converging and extending Drosophila germband epithelium. We report gradients in cell shapes and tissue structure along the apical-basal axis of the germband, suggesting that the apical and basolateral domains display distinct behaviors. Cell rearrangements initiate at apical as well as basolateral positions, with initiation frequencies also displaying a gradient along the apical-basal axis. Following initiation, rearrangements propagate across the apical-basal axis and lateral cell contacts remodel; these events involve scutoids and other complex 3D cell shapes as intermediate states. These findings uncover novel aspects of the cell rearrangements that drive dynamic remodeling of epithelia and reveal links between rearrangements and gradients in tissue structure along the apical-basal axis.

developmental biology↗

bioRxiv2023

Mechanical control of neural plate folding by apical domain alteration

Vertebrate neural tube closure is associated with complex changes in cell shape and behavior, however, the relative contribution of these processes to tissue folding is not well understood. In this study, we evaluated morphology of the superficial cell layer in the Xenopus neural plate. At the stages corresponding to the onset of tissue folding, we observed the alternation of cells with apically constricting and apically expanding apical domains. The cells had a biased orientation along the anteroposterior (AP) axis. This apical domain heterogeneity required planar cell polarity (PCP) signaling and was especially pronounced at neural plate hinges. Vertex model simulations suggested that spatially dispersed isotropically constricting cells cause the elongation of their non-constricting counterparts along the AP axis. Consistent with this hypothesis, cell-autonomous induction of apical constriction in Xenopus ectoderm cells was accompanied by the expansion of adjacent non-constricting cells. Our observations indicate that a subset of isotropically constricting cells can initiate neural plate bending, whereas a tug-of-war contest between the force-generating and responding cells reduces its shrinking along the AP axis. This mechanism is an alternative to anisotropic shrinking of cell junctions that are perpendicular to the body axis. We propose that neural folding relies on PCP-dependent transduction of mechanical signals between neuroepithelial cells.

developmental biology↗

bioRxiv2020

Transfer learning from simulations improves the classification of OCT images of glandular epithelia

Esophageal adenocarcinoma (EAC) is a rare but lethal cancer with rising incidence in several global hotspots including the United States. The five-year survival rate for patients diagnosed with advanced disease can be as low as 5% in EAC, making early detection and preventive intervention crucial. The current standard of care for EAC targets patients with Barretts esophagus (BE), the main precursor to EAC and a relatively common condition in adults with chronic acid reflux disease. Preventive care for EAC requires repeated surveillance endoscopies of BE patients with biopsy sampling, and can be intrusive, error-prone, and costly. The integration of minimally-invasive subsurface tissue imaging in the current standard of care can reduce the need for exhaustive tissue sampling and improve the quality of life in BE patients. Effective adoption of subsurface imaging in EAC care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Despite their recent successes in lung and breast cancer imaging, the development of deep neural networks for rare conditions like EAC remains challenging due to data scarcity, heavy bias in existing datasets toward non-cases, and uncertainty in image labels. Here we explore the use of synthetic datasets-specifically data derived from simulations of optical back-scattering during imaging- in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of BE. We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.

bioinformatics↗