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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.
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A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation
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.
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- Award ID(s):
- 2045750
- PAR ID:
- 10492893
- Publisher / Repository:
- Elsivier
- Date Published:
- Journal Name:
- Biophysical Journal
- Volume:
- 122
- Issue:
- 17
- ISSN:
- 0006-3495
- Page Range / eLocation ID:
- 3413 to 3424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.bpj.2023.07.016
