Cell division is crucial for tissue growth and repair responses. Deep learning models can automate the analysis of cell divisions in complex tissues, providing valuable insights into spatial and temporal patterns. The study developed deep learning models to detect dividing cells, determine their orientation, and analyze post-division behaviors. These models were trained on time-lapse imaging data of Drosophila pupal wing epithelium. The results show that cell divisions are influenced by tissue tension lines and exhibit spatial synchronicity following wounding. The study also highlights the importance of multiple fluorescent channels for accurate model performance. Overall, the deep learning approach offers a powerful tool for studying dynamic cell behaviors in living tissues.
To Another Language
from source content
biorxiv.org
Key Insights Distilled From
by Turley,J. M.... at www.biorxiv.org 03-23-2023
https://www.biorxiv.org/content/10.1101/2023.03.20.533343v3Deeper Inquiries