DeepFoci: Deep Learning-Based Algorithm for Fast Automatic Analysis of DNA Double-Strand Break Ionizing Radiation-Induced Foci
| Autor: | Tomas Vicar, Radim Kolar, Eva Pagáčová, Olga Kopečná, Iva Falková, Jaromír Gumulec, Martin Falk |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
CNN
convolutional neural network DSB DNA double-strand break Confocal Microscopy Focus (geometry) Computer science DNA damage Biophysics IRIF ionizing radiation-induced (repair) foci DNA Damage and Repair Convolutional Neural Network γH2AX histone H2AX phosphorylated at serine 139 Image Analysis Biochemistry FOV field of view Ionizing radiation 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Deep Learning Biodosimetry Structural Biology Genetics GUI graphical user interface ComputingMethodologies_COMPUTERGRAPHICS 030304 developmental biology Double strand 0303 health sciences business.industry Deep learning Morphometry NHDFs normal human dermal fibroblasts 3. Good health Computer Science Applications chemistry Ionizing radiation-induced foci U-87 U-87 glioblastoma cell line 030220 oncology & carcinogenesis Ionizing Radiation-Induced Foci (IRIFs) 53BP1 P53-binding protein 1 MSER maximally stable extremal region (algorithm) Artificial intelligence business Algorithm TP248.13-248.65 DNA Research Article RAD51 DNA repair protein RAD51 homolog 1 Biotechnology |
| Zdroj: | Computational and Structural Biotechnology Journal. 2021, vol. 19, issue 1, p. 1-16. Computational and Structural Biotechnology Journal Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 6465-6480 (2021) |
| Popis: | Graphical abstract Highlights • New method for DSB repair focus (IRIF) detection and multiparameter analysis. • Trainable deep learning-based method. • Fully automated analysis of multichannel 3D datasets. • Trained and tested on realistic and challenging datasets. • Comparable to expert analysis and superior to available methods. DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci – a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and γH2AX) and uses U-Net for 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 nonirradiated and irradiated cells of different types and IRIF characteristics – permanent cell lines (NHDFs, U-87) and primary cell cultures prepared from tumors and adjacent normal tissues of head and neck cancer patients. The cells were dosed with 0.5–8 Gy γ-rays and fixed at multiple (0–24 h) postirradiation times. Under all circumstances, DeepFoci quantified the number of IRIFs 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 maintained 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 approach allowed multiparameter IRIF categorization of single- or multichannel data, thereby refining the analysis of DSB repair processes and classification of patient tumors, with the 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 advanced DSB focus analysis and, in turn, a better understanding of (radiation-induced) DNA damage and repair. |
| Databáze: | OpenAIRE |
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