DPG Phi
Verhandlungen
Verhandlungen
DPG

Berlin 2024 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

BP: Fachverband Biologische Physik

BP 20: Poster IIIa

BP 20.13: Poster

Mittwoch, 20. März 2024, 11:00–14:30, Poster B

Quantification and model-based classification of the aging dynamics of single endothelial cells under confluent conditions — •Anselm Hohlstamm, Andreas Deussen, Stephan Speier, and Peter Dieterich — Institut für Physiologie, TU Dresden

Endothelial cells, which are grown into a two-dimensional, confluent layer, exhibit intricate movement patterns and aging processes. While maintaining dynamic cell-cell contacts, individual cells perform a continuous, correlated motion. It is the objective of this study to quantify and classify these dynamics. Therefore, we studied the migration of human umbilical vein endothelial cells. Their nuclei were marked using a fluorescent dye and observed for 48 hours, with data collected at 10-minute intervals. We were able to monitor several 10.000 cells in each of the 10 experiments. The mean squared velocity of the cells decreased as a function of time, which could be characterized with two temporal scales. In addition, the mean squared displacement revealed a temporal transition of scaling ∼ tα from more directional movements with α ∼ 1.6 for short times towards a subdiffusive behavior with α ∼ 0.4 for long times. Based on the analysis of the temporal velocity autocorrelation, we constructed different stochastic models as combinations of Ornstein-Uhlenbeck and fractional processes that were supplemented by aging contributions. Bayesian inference allowed selecting the best model given the experimental data. In summary, the movement of cells under confluent conditions can be characterized as an aging dynamics in a non-thermal environment.

Keywords: endothelium; cell migration; Bayesian inference

100% | Mobil-Ansicht | English Version | Kontakt/Impressum/Datenschutz
DPG-Physik > DPG-Verhandlungen > 2024 > Berlin