Deep-Learning-Assisted Assessment of DNA Damage Based on Foci Images and Its Application in High-Content Screening of Lead Compounds

Multi-scale cellular imaging of DNA double strand break repair

Live-cell and high-resolution fluorescence microscopy are powerful tools to study the organization and dynamics of DNA double-strand break repair foci and specific repair proteins in single cells. This requires specific induction of DNA double-strand breaks and fluorescent markers to follow the DNA lesions in living cells. In this review, where we focused on mammalian cell studies, we discuss different methods to induce DNA double-strand breaks, how to visualize and quantify repair foci in living cells., We describe different (live-cell) imaging modalities that can reveal details of the DNA double-strand break repair process across multiple time and spatial scales. In addition, recent developments are discussed in super-resolution imaging and single-molecule tracking, and how these technologies can be applied to elucidate details on structural compositions or dynamics of DNA double-strand break repair.

Deep learning-assisted high-content screening identifies isoliquiritigenin as an inhibitor of DNA double-strand breaks for preventing doxorubicin-induced cardiotoxicity

BACKGROUND

Anthracyclines including doxorubicin are essential components of many cancer chemotherapy regimens, but their cardiotoxicity severely limits their use. New strategies for treating anthracycline-induced cardiotoxicity (AIC) are still needed. Anthracycline-induced DNA double-strand break (DSB) is the major cause of its cardiotoxicity. However, DSB-based drug screening for AIC has not been performed possibly due to the limited throughput of common assays for detecting DSB. To discover new therapeutic candidates for AIC, here we established a method to rapidly visualize and accurately evaluate the intranuclear anthracycline-induced DSB, and performed a screening for DSB inhibitors.

RESULTS

First, we constructed a cardiomyocyte cell line stably expressing EGFP-53BP1, in which the formation of EGFP-53BP1 foci faithfully marked the doxorubicin-induced DSB, providing a faster and visible approach to detecting DSB. To quantify the DSB, we used a deep learning-based image analysis method, which showed the better ability to distinguish different cell populations undergoing different treatments of doxorubicin or reference compounds, compared with the traditional threshold-based method. Subsequently, we applied the deep learning-assisted high-content screening method to 315 compounds and found three compounds (kaempferol, kaempferide, and isoliquiritigenin) that exert cardioprotective effects in vitro. Among them, the protective effect of isoliquiritigenin is accompanied by the up-regulation of HO-1, down-regulation of peroxynitrite and topo II, and the alleviation of doxorubicin-induced DSB and apoptosis. The results of animal experiments also showed that isoliquiritigenin maintained the myocardial tissue structure and cardiac function in vivo. Moreover, isoliquiritigenin did not affect the killing of HeLa and MDA-MB-436 cancer cells by doxorubicin and thus has the potential to be a lead compound to exert cardioprotective effects without affecting the antitumor effect of doxorubicin.

CONCLUSIONS

Our findings provided a new method for the drug discovery for AIC, which combines phenotypic screening with artificial intelligence. The results suggested that isoliquiritigenin as an inhibitor of DSB may be a promising drug candidate for AIC.

Use of Deep-Learning Assisted Assessment of Cardiac Parameters in Zebrafish to Discover Cyanidin Chloride as a Novel Keap1 Inhibitor Against Doxorubicin-Induced Cardiotoxicity

Doxorubicin-induced cardiomyopathy (DIC) brings tough clinical challenges as well as continued demand in developing agents for adjuvant cardioprotective therapies. Here, a zebrafish phenotypic screening with deep-learning assisted multiplex cardiac functional analysis using motion videos of larval hearts is established. Through training the model on a dataset of 2125 labeled ventricular images, ZVSegNet and HRNet exhibit superior performance over previous methods. As a result of high-content phenotypic screening, cyanidin chloride (CyCl) is identified as a potent suppressor of DIC. CyCl effectively rescues cardiac cell death and improves heart function in both in vitro and in vivo models of Doxorubicin (Dox) exposure. CyCl shows strong inhibitory effects on lipid peroxidation and mitochondrial damage and prevents ferroptosis and apoptosis-related cell death. Molecular docking and thermal shift assay further suggest a direct binding between CyCl and Keap1, which may compete for the Keap1-Nrf2 interaction, promote nuclear accumulation of Nrf2, and subsequentially transactivate Gpx4 and other antioxidant factors. Site-specific mutation of R415A in Keap1 significantly attenuates the protective effects of CyCl against Dox-induced cardiotoxicity. Taken together, the capability of deep-learning-assisted phenotypic screening in identifying promising lead compounds against DIC is exhibited, and new perspectives into drug discovery in the era of artificial intelligence are provided.

Multiplexed-Based Assessment of DNA Damage Response to Chemotherapies Using Cell Imaging Cytometry

The current methods for measuring the DNA damage response (DDR) are relatively labor-intensive and usually based on Western blotting, flow cytometry, and/or confocal immunofluorescence analyses. They require many cells and are often limited to the assessment of a single or few proteins. Here, we used the Celigo^® image cytometer to evaluate the cell response to DNA-damaging agents based on a panel of biomarkers associated with the main DDR signaling pathways. We investigated the cytostatic or/and the cytotoxic effects of these drugs using simultaneous propidium iodide and calcein-AM staining. We also describe new dedicated multiplexed protocols to investigate the qualitative (phosphorylation) or the quantitative changes of eleven DDR markers (H2AX, DNA-PKcs, ATR, ATM, CHK1, CHK2, 53BP1, NBS1, RAD51, P53, P21). The results of our study clearly show the advantage of using this methodology because the multiplexed-based evaluation of these markers can be performed in a single experiment using the standard 384-well plate format. The analyses of multiple DDR markers together with the cell cycle status provide valuable insights into the mechanism of action of investigational drugs that induce DNA damage in a time- and cost-effective manner due to the low amounts of antibodies and reagents required.

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose-response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0-4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.

60-nt DNA Direct Detection without Pretreatment by Surface-Enhanced Raman Scattering with Polycationic Modified Ag Microcrystal Derived from AgCl Cube

Direct detection of long-strand DNA by surface-enhanced Raman scattering (SERS) is a valuable method for diagnosis of hereditary diseases, but it is currently limited to less than 25-nt DNA strand in pure water, which makes this approach unsuitable for many real-life applications. Here, we report a 60-nt DNA label-free detection strategy without pretreatment by SERS with polyquaternium-modified Ag microcrystals derived from an AgCl cube. Through the reduction-induced decomposition, the size of the about 3 × 3 × 3 μm³ AgCl cube is reduced to Ag, and the surface is distributed with the uniform size of 63 nm silver nanoparticles, providing a large area of a robust and highly electromagnetic enhancement region. The modified polycationic molecule enhances the non-specific electrostatic interaction with the phosphate group, thereby anchoring DNA strands firmly to the SERS enhanced region intactly. As a result, the single-base recognition ability of this strategy reaches 60-nt and is successfully applied to detect thalassemia-related mutation genes.

Engineering Bacteria to Monitor the Bleeding of Animals Using Far-Red Fluorescence

Microorganisms living in animals can function as drug delivery systems or as detectors for some diseases. Here, we developed a biosensor constructed by the deletion of hemF and harboring ho1, chuA, and bdfp1.6 in Escherichia coli. HemF is an enzyme involved in heme synthesis in E. coli. ChuA and HO1 can transfer extracellular heme into cells and generate biliverdin (BV). BDFP1.6 can bind BV autocatalytically, and it emits a far-red fluorescence signal at 667 nm. Therefore, we named this biosensor as the far-red light for bleeding detector (FRLBD). Our results indicated that the FRLBD was highly efficient and specific for detecting heme or blood in vitro. Moreover, the FRLBD could be used to detect bleeding in the zebrafish induced by aspirin, and a convolutional neural network was an appropriate model to identify the fluorescence features in the images.