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Digital holographic microscopy; Glioma grading; Quantitative phase images; Image processing; Supervised classification

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.

The therapeutic use of mesenchymal stem cells (MSCs) becomes more and more important due to their potential for cell replacement procedures as well as due to their immunomodulatory properties. However, protocols for MSCs differentiation can be lengthy and may result in incomplete or asynchronous differentiation. To ensure homogeneous populations for therapeutic purposes, it is crucial to develop protocols for separation of the different cell types after differentiation. In this article we show that, when MSCs start to differentiate towards adipogenic or osteogenic progenies, their dielectrophoretic behavior changes. The values of cell electric parameters which can be obtained by dielectrophoretic measurements (membrane permittivity, conductivity, and cytoplasm conductivity) change before the morphological features of differentiation become microscopically visible. We further demonstrate, by simulation, that these electric modifications make possible to separate cells in their early stages of differentiation by using the dielectrophoretic separation technique. A label free method which allows obtaining cultures of homogenously differentiated cells is thus offered.

Dielectrophoretic (DEP) cell separation, which utilizes electric fields to selectively manipulate and separate cells based on their electrical properties, has emerged as a cutting-edge label-free technique. DEP separation techniques rely on differences in the electrical and morphological properties of cells, which can be obtained by a thorough analysis of DEP spectra. This article presents a novel platform, named OpenDEP, for acquiring and processing DEP spectra of suspended cells. The platform consists of lab-on-a-chip and open-source software that enables the determination of DEP spectra and electric parameters. The performance of OpenDEP was validated by comparing the results obtained using this platform with the results obtained using a commercially available device, 3DEP from DEPtech. The lab-on-a-chip design features two indium tin oxide-coated slides with a specific geometry, forming a chamber where cells are exposed to an inhomogeneous alternating electric field with different frequencies, and microscopic images of cell distributions are acquired. A custom-built software written in the Python programing language was developed to convert the acquired images into DEP spectra, allowing for the estimation of membrane and cytoplasm conductivities and permittivities. The platform was validated using two cell lines, DC3F and NIH 3T3. The OpenDEP platform offers several advantages, including easy manufacturing, statistically robust computations due to large cell population analysis, and a closed environment for sterile work. Furthermore, continuous observation using any microscope allows for integration with other techniques.

Gold Nanoparticles, Resonance, Media

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.& COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement