Speaker
Description
Ionizing radiation induces a variety of DNA lesions, including DNA double-strand breaks (DSBs), which are among the most challenging to repair. The formation of these lesions initiates a cascade of DNA repair protein recruitment, leading to the formation of radiation-induced foci (RIF). To automate the RIF analysis, we have followed a deep learning approach which consists of two stages; first a pretrained neural network called SAM2 is used to detect the cell nuclei in the fluorescent image, then the trained neural network YOLO on our foci-annotated data is used to detect RIF within each nucleus. Following RIF detection and individual image preprocessing on each focus location, the colocalized number of the two foci are calculated. Based on this model, we developed a web service on WRITER Framework for the automated RIF detection and quantification of object-based colocalization between two RIF populations. The web service MOSTLIT allows the user to observe the identified cell nuclei in an uploaded fluorescent image, choose the desired nuclei, automatically get the marked foci and obtain the numerical characteristics such as the number of RIF per cell, RIF area and colocalizations.
