Analysis of Ionizing Radiation Induced DNA Damage by Superresolution dSTORM Microscopy

Kovács BB, Novák T, Erdélyi M. Cluster parameter-based DBSCAN maps for image characterization

Single-molecule localization microscopy techniques are one of the most powerful methods in biological studies, allowing the visualization of nanoclusters. Cluster analysis algorithms are used for quantitative evaluation, with DBSCAN being one of the most widely used. Clustering results are extremely sensitive to the initial parameters; thus, several methods including DBSCAN maps, have been developed for parameter optimization. Here, we introduce cluster parameter-based DBSCAN maps, which are directly applicable to measured datasets. These maps can be used for image characterization and parameter optimization through sensitivity studies. We show the applicability of these maps to simulated and measured datasets and compare our results with the recently implemented lacunarity analysis for SMLM measurements.

Specific spectral sub-images for machine learning evaluation of optical differences between carbon ion and X ray radiation effects

Advances in radiotherapy, particularly the exploration of alternative radiation types such as carbon ions have updated our understanding of its effects and applicability on chondrosarcoma cells. Here we compare the optical effects produced by carbon ions (CI) and X-rays (XR) radiations on chondrosarcoma cells nuclei and set an automated method for evaluating the radiation-induced alterations without the need of chemical marking. Hyperspectral images (HSI) of SW1353 chondrosarcoma line carry detectable optical changes of the cells irradiated either with CI or XR compared to non-irradiated ones (REF). The differences between the spectral profiles of CI, XR and REF nuclei classes led to partitioning the HSIs into spectral sub-images. The changes are detected by support vector machine (SVM) classifiers whose performances are evaluated by the most used point metrics: sensitivity (SEN), accuracy (ACC), and precision (PREC), applied on spatial feature values. Specific interaction mechanisms by radiation type reveal distinct subintervals where HSIs changes are more prominent, and the classifiers perform at best. For CI the best classifiers are obtained for sub-images in the interval (424-436 nm), while for XR the best classifiers are obtained for sub-images in the interval (436-445 nm). The classifiers work better with texture features than roughness features in both cases. The classifier with the best SEN point metric in the testing phase is the most suitable to measure the irradiation efficiency irrespective of the radiation type. The altered nuclei are easier to discriminate when irradiated with CI than with XR. The study proves that SVM with optical data offers a rapid, automated, and label-free method for evaluating radiation-induced alterations in chondrosarcoma nuclei, thereby enabling effective analysis of extensive data.

Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images

The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm’s efficiency.

Profiling DNA damage in 3D Histology Samples

The morphology of individual cells can reveal much about the underlying states and mechanisms in biology. In tumor environments, the interplay among different cell morphologies in local neighborhoods can further improve this characterization. In this paper, we present an approach based on representation learning to capture similarities and subtle differences in cells positive for γH2AX, a common marker for DNA damage. We demonstrate that texture representations using GLCM and VAE-GAN enable profiling of cells in both singular and local neighborhood contexts. Additionally, we investigate a possible quantification of immune and DNA damage response interplay by enumerating CD8+ and γH2AX+ on different scales. Using our profiling approach, regions in treated tissues can be differentiated from control tissue regions, demonstrating its potential in aiding quantitative measurements of DNA damage and repair in tumor contexts.

In situ hybridization to detect DNA amplification in extracellular vesicles

EVs have emerged as an important component in tumour initiation, progression and metastasis. Although notable progresses have been made, the detection of EV cargoes remain significantly challenging for researchers to practically use; faster and more convenient methods are required to validate the EV cargoes, especially as biomarkers. Here we show, the possibility of examining embedded EVs as substrates to be used for detecting DNA amplification through ultrasensitive in situ hybridization (ISH). This methodology allows the visualization of DNA targets in a more direct manner, without time consuming optimization steps or particular expertise. Additionally, formalin-fixed paraffin-embedded (FFPE) blocks of EVs allows long-term preservation of samples, permitting future studies. We report here: (i) the successful isolation of EVs from liposarcoma tissues; (ii) the EV embedding in FFPE blocks (iii) the successful selective, specific ultrasensitive ISH examination of EVs derived from tissues, cell line, and sera; (iv) and the detection of MDM2 DNA amplification in EVs from liposarcoma tissues, cell lines and sera. Ultrasensitive ISH on EVs would enable cargo study while the application of ISH to serum EVs, could represent a possible novel methodology for diagnostic confirmation. Modification of probes may enable researchers to detect targets and specific DNA alterations directly in tumour EVs, thereby facilitating detection, diagnosis, and improved understanding of tumour biology relevant to many cancer types.