RESUMEN
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.
RESUMEN
We present a method that integrates the standard imaging tools for locating and detecting unlabeled nanoparticles (NPs) with computational tools for partitioning cell volumes and NPs counting within specified regions to evaluate their internal traffic. The method uses enhanced dark field CytoViva optical system and combines 3D reconstructions of double fluorescently labeled cells with hyperspectral images. The method allows the partitioning of each cell image into four regions: nucleus, cytoplasm, and two neighboring shells, as well as investigations across thin layers adjacent to the plasma membrane. MATLAB scripts were developed to process the images and to localize NPs in each region. Specific parameters were computed to assess the uptake efficiency: regional densities of NPs, flow densities, relative accumulation indices, and uptake ratios. The results of the method are in line with biochemical analyses. It was shown that a sort of saturation limit for intracellular NPs density is reached at high extracellular NPs concentrations. Higher NPs densities were found in the proximity of the plasma membranes. A decrease of the cell viability with increasing extracellular NPs concentration was observed and explained the negative correlation of the cell eccentricity with NPs number.