DeepFoci: Deep Learning-Based Algorithm for Fast Automatic Analysis of DNA Double Strand Break Ionizing Radiation-Induced Foci

Abstract

DNA double-strand breaks, marked by Ionizing Radiation-Induced (Repair) Foci (IRIF), are the most serious DNA lesions, dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold standard method in radiation biodosimetry and allows research of DSB induction and repair at the molecular and a single cell level. In this study, we introduce DeepFoci - a deep learning-based fully-automatic method for IRIF counting and its morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and γH2AX) and uses U-Net for the nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation.The proposed method was trained and tested on challenging datasets consisting of mixtures of non-irradiated and irradiated cells of different types and IRIF characteristics - permanent cell lines (NHDF, U-87) and cell primary cultures prepared from tumors and adjacent normal tissues of head and neck cancer patients. The cells were dosed with 1-4 Gy gamma-rays and fixed at multiple (0-24 h) post-irradiation times. Upon all circumstances, DeepFoci was able to quantify the number of IRIF foci with the highest accuracy among current advanced algorithms. Moreover, while the detection error of DeepFoci remained comparable to the variability between two experienced experts, the software kept its sensitivity and fidelity across dramatically different IRIF counts per nucleus. In addition, information was extracted on IRIF 3D morphometric features and repair protein colocalization within IRIFs. This allowed multiparameter IRIF categorization, thereby refining the analysis of DSB repair processes and classification of patient tumors with a potential to identify specific cell subclones.The developed software improves IRIF quantification for various practical applications (radiotherapy monitoring, biodosimetry, etc.) and opens the door to an advanced DSB focus analysis and, in turn, a better understanding of (radiation) DNA damaging and repair.HighlightsNew method for DSB repair focus (IRIF) detection and multi-parameter analysisTrainable deep learning-based methodFully automated analysis of multichannel 3D datasetsTrained and tested on extremely challenging datasets (tumor primary cultures)Comparable to an expert analysis and superb to available methodsGraphical Abstract

HARLEY: A Semi-Automated detection of foci in fluorescence images of yeast

“…Many architectures have been proposed for various tasks, with CNNs being commonly used. DeepFoci (Vicar et al, 2020) utilizes U-Net, a specific CNN architecture to detect and segment foci and nuclei with promising accuracy in a fully unsupervised manner. A combined approach in (Hohmann et al, 2020) uses classical techniques to detect regions of interest, extracts an over abundant set of features using filters and feeds these results into a classifier.…”

“…First, excluding manual classification (e.g., in Fiji) or generic approaches (e.g., CellProfiler), the identification of SGs, or other cytoplasmic foci with significant cytoplasmic background signal has seen very few tailored automated approaches. Second, as reported in an exhaustive study on bioimaging informatics tools (Schneider et al, 2019), overall usability is a significant hurdle for the use of automated methods, making CellProfiler one of most popular, yet not optimal (Vicar et al, 2020) methods. Finally, in biological publications the issue of human variability and error is often not addressed.…”