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

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dc.contributor.authorVičar, Tomášcs
dc.contributor.authorGumulec, Jaromírcs
dc.contributor.authorKolář, Radimcs
dc.contributor.authorKopečná, Olgacs
dc.contributor.authorPagáčová, Evacs
dc.contributor.authorFalková, Ivacs
dc.contributor.authorFalk, Martincs
dc.coverage.issue1cs
dc.coverage.volume19cs
dc.date.accessioned2021-12-16T15:56:00Z
dc.date.available2021-12-16T15:56:00Z
dc.date.issued2021-09-24cs
dc.description.abstractDNA 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 H2AX) 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 -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.en
dc.formattextcs
dc.format.extent1-16cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputational and Structural Biotechnology Journal. 2021, vol. 19, issue 1, p. 1-16.en
dc.identifier.doi10.1016/j.csbj.2021.11.019cs
dc.identifier.issn2001-0370cs
dc.identifier.other173240cs
dc.identifier.urihttp://hdl.handle.net/11012/203247
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofComputational and Structural Biotechnology Journalcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2001037021004840cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2001-0370/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectDNA Damage and Repairen
dc.subjectIonizing Radiation-Induced Foci (IRIFs)en
dc.subjectBiodosimetryen
dc.subjectDeep Learningen
dc.subjectConvolutional Neural Networken
dc.subjectMorphometryen
dc.subjectConfocal Microscopyen
dc.subjectImage Analysisen
dc.titleDeepFoci: Deep Learning-Based Algorithm for Fast Automatic Analysis of DNA Double-Strand Break Ionizing Radiation-Induced Focien
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-173240en
sync.item.dbtypeVAVen
sync.item.insts2022.03.23 20:55:12en
sync.item.modts2022.03.23 20:16:06en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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