Calcium Signaling Dynamics in MDCK II Cell Clusters

Sammanfattning

Calcium signaling plays an essential role in communication between cells inmulticellular systems. In epithelial tissues, coordinated calcium dynamicshave been proposed to arise from local interactions between neighboring cells,yet the extent to which such coordination depends on spatial organization,direct cell-cell connectivity, or other environmental factors still remainsunclear. This thesis investigates the dynamics of calcium signaling in smallMDCK II cell clusters, in particular how the signaling depends on spatialpositioning and metabolic conditions.Fluorescence microscopy was used in order to image MDCK II cellclusters expressing the genetically encoded calcium indicator GCaMP6m,enabling the extraction of fluorescence intensity signals corresponding tocalcium activity. Cells within a cluster were individually tracked usingthe deep-learning based segmentation method CellPose, allowing individualintensity signals to be extracted from every cell. The data was analyzed usingmethods such as Pearson correlation and Cross correlation analysis, in orderto quantify the temporal relationships between cells. Spatial relationshipswere assessed by calculating pairwise distances and classifying cell pairs asneighboring or non-neighboring, as well as edge or interior cells.The results show that calcium signaling exhibits varying correlationsacross cell pairs. Correlation shows a weak decrease with distance, whileneighboring cells consistently display higher correlation than non-neighboringcells. Furthermore, the influence of metabolic conditions was assessed bycomparing clusters under varying glucose concentrations. While glucoselevels did not affect signal intensity, they influenced cellular coordination,with high-glucose clusters showing markedly reduced coordination.Overall, the findings demonstrate that the dynamics of calcium signalingcan be explained not solely based on spatial proximity, but also cell-cellconnectivity. Future work could expand on these findings by using largerdatasets, improving tracking accuracy, and using more advanced models ofintercellular signaling.