Grading of glioma tumors using digital holographic microscopy.

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Method for nanoparticles uptake evaluation based on double labeled fluorescent cells scanned in enhanced darkfield microscopy.

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.

Evaluation of intracellular distribution of folate functionalized silica nanoparticles using fluorescence and hyperspectral enhanced dark field microscopy.

Using nanoparticles as carriers for drug delivery systems has become a widely applied strategy in therapeutics and diagnostics. However, the pattern of their intracellular distribution is yet to be clarified. Here we present an in vitro study on the incorporation of mesoporous silica nanoparticles conjugated with folate and loaded with a cytotoxic drug, Irinotecan. The nanoparticles count and distribution within the cell frame were evaluated by means of enhanced dark field microscopy combined with hyperspectral imagery and 3D reconstructions from double-labeled fluorescent samples. An original post-processing procedure was developed to emphasize the nanoparticles' localization in 3D reconstruction of cellular compartments. By these means, it has been shown that the conjugation of mesoporous silica nanoparticles with folate increases the efficiency of nanoparticles entering the cell and their preferential localization in the close vicinity of the nucleus. As revealed by metabolic viability assays, the nanoparticles functionalized with folate enhance the cytotoxic efficiency of Irinotecan.

Digital holographic microscopy evaluation of dynamic cell response to electroporation.

Phase-derived parameters and time autocorrelation functions were used to analyze the behavior of murine B16 cells exposed to different amplitudes of electroporation pulses. Cells were observed using an off-axis digital holographic microscope equipped with a fast camera. Series of quantitative phase images of cells were reconstructed and further processed using MATLAB codes. Projected area, dry mass density, and entropy proved to be predictors for permeabilized cells that swell or collapse. Autocorrelation functions of phase fluctuations in different regions of the cell showed a good correlation with the local effectiveness of permeabilization.