PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

This study presents a comprehensive dataset that captures the phagocytosis process in wild-type and FTD microglial cells, with each acquisition comprising 20 distinct scenes and seven hours of time-lapse video microscopy. The dataset has undergone rigorous validation and data quality correction techniques, ensuring its reliability for further analysis. The dataset includes 36,496 normalized cell and aggregate images, 1,306,131 individual instance masks for cells, 1,553,036 for aggregates, and 36,496 registered aggregates, all generated using the PhagoStat algorithm. The dataset also provides extensive biological features such as area, position, and speed, presented frame-by-frame for in-depth analysis. To enhance its value, 226 manually annotated images containing 6,430 individual cell masks were incorporated, covering different conditions and scenes. In total, the dataset comprises 235,288 files (94GB), offering researchers a valuable resource for investigating various cellular and aggregate properties.

By providing this comprehensive dataset, the study aims to facilitate the development and application of novel methodologies in bioinformatics, computational modeling, and cellular biology. This initiative promotes innovation and collaboration among researchers in these interconnected disciplines, ultimately contributing to the understanding of cellular dynamics and the development of novel therapeutic strategies for neurodegenerative diseases like FTD.

For more infomration please check:

GitHub: https://github.com/ounissimehdi/PhagoStat

Paper:   https://www.nature.com/articles/s41598-024-56081-7

DOI:       10.1038/s41598-024-56081-7