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Abstract The human brain microvasculature is constantly exposed to variable fluid flow regimes and their influence on the endothelium depends in part on the synchronous cooperative behavior between cell–cell junctions and the cytoskeleton. In this study, we exposed human cerebral microvascular endothelial cells to a low laminar flow (1 dyne⋅cm⁻²), high laminar flow (10 dyne⋅cm⁻²), low oscillatory flow (±1 dyne⋅cm⁻²), or high oscillatory flow (±10 dyne⋅cm⁻²) for 24 hr. After this time, endothelial cell–cell junction and cytoskeletal structural response was characterized through observation of zonula occludens‐1 (ZO‐1), claudin‐5, junctional adhesion molecule‐A (JAM‐A), vascular endothelial cadherin (VE‐Cad), and F‐actin. In addition, we also characterized cell morphology through measurement of cell area and cell eccentricity. Our results revealed the greatest change in junctional structure reorganization for ZO‐1 and JAM‐A to be observed under low laminar flow conditions while claudin‐5 exhibited the greatest change in structural reorganization under both low and high laminar flow conditions. However, VE‐Cad displayed the greatest structural response under a high laminar flow, reflecting the unique responses each cell–cell junction protein had to each fluid flow regime. In addition, cell area and cell eccentricity displayed most significant changes under the high laminar flow and low oscillatory flow, respectively. We believe this study will be useful to the field of cell mechanics and mechanobiology. 

Disturbed fluid flow is well understood to have significant ramifications on endothelial function, but the impact disturbed flow has on endothelial biomechanics is not well understood. In this study, we measured tractions, intercellular stresses, and cell velocity of endothelial cells exposed to disturbed flow using a custom-fabricated f low chamber. Our flow chamber exposed cells to disturbed fluid flow within the following spatial zones: zone 1 (inlet; length 0.676–2.027 cm): 0.0037 ± 0.0001 Pa; zone 2 (middle; length 2.027–3.716 cm): 0.0059 ± 0.0005 Pa; and zone 3 (outlet; length 3.716–5.405 cm): 0.0051 ± 0.0025 Pa. Tractions and intercellular stresses were observed to be highest in the middle of the chamber (zone 2) and lowest at the chamber outlet (zone 3), while cell velocity was highest near the chamber inlet (zone 1), and lowest near the middle of the chamber (zone 2). Our findings suggest endothelial biomechanical response to disturbed fluid flow to be dependent on not only shear stress magnitude, but the spatial shear stress gradient as well. We believe our results will be useful to a host of fields including endothelial cell biology, the cardiovascular field, and cellular biomechanics in general. 

Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechan- ical stresses mentioned above is poorly understood, hindering the discovery of novel therapeutics, and representing a barrier in the field. To overcome this barrier, we implemented two approaches: 1) monolayer boundary predictor and 2) discretized window predictor utilizing either stepwise linear regression or quadratic support vector machine machine learning model to predict the dose-dependent response of tractions and intercellular stresses to chemical perturbation. We used experimental traction and intercellular stress data gathered from samples subject to 0.2 or 2 mg/mL drug concentrations along with cell morphological prop- erties extracted from the bright-field images as predictors to train our model. To demonstrate the predictive capability of our ma- chine learning models, we predicted tractions and intercellular stresses in response to 0 and 1 mg/mL drug concentrations which were not utilized in the training sets. Results revealed the discretized window predictor trained just with four samples (292 im- ages) to best predict both intercellular stresses and tractions using the quadratic support vector machine and stepwise linear regression models, respectively, for the unseen sample images. 

The endothelium constitutes the innermost layer of all blood vessels and plays an important role in vascular physiology and pathology. During certain vascular diseases, the extracellular matrix has been suggested to transition from a collagen-rich matrix to a fibronectin-rich matrix. In this study, we demonstrate the impact various collagen and fibronectin ratios have on endothelial biomechanical and morphological response.