Improving the Stability and Effectiveness of Immunotropic Squalene Nanoemulsion by Adding Turpentine Oil.

Turpentine oil, owing to the presence of 7-50 terpenes, has analgesic, anti-inflammatory, immunomodulatory, antibacterial, anticoagulant, antioxidant, and antitumor properties, which are important for medical emulsion preparation. The addition of turpentine oil to squalene emulsions can increase their effectiveness, thereby reducing the concentration of expensive and possibly deficient squalene, and increasing its stability and shelf life. In this study, squalene emulsions were obtained by adding various concentrations of turpentine oil via high-pressure homogenization, and the safety and effectiveness of the obtained emulsions were studied in vitro and in vivo. All emulsions showed high safety profiles, regardless of the concentration of turpentine oil used. However, these emulsions exhibited dose-dependent effects in terms of both efficiency and storage stability, and the squalene emulsion with 1.0% turpentine oil had the most pronounced adjuvant and cytokine-stimulating activity as well as the most pronounced stability indicators when stored at room temperature. Thus, it can be concluded that the squalene emulsion with 1% turpentine oil is a stable, monomodal, and reliably safe ultradispersed emulsion and may have pleiotropic effects with pronounced immunopotentiating properties.

Essential Role of Adhesion GPCR, GPR123, for Human Pluripotent Stem Cells and Reprogramming towards Pluripotency.

G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors. They modulate key physiological functions and are required in diverse developmental processes including embryogenesis, but their role in pluripotency maintenance and acquisition during the reprogramming towards hiPSCs draws little attention. Meanwhile, it is known that more than 106 GPCRs are overexpressed in human pluripotent stem cells (hPSCs). Previously, to identify novel effectors of reprogramming, we performed a high-throughput RNA interference (RNAi) screening assay and identified adhesion GPCR, GPR123, as a potential reprogramming effector. Its role has not been explored before. Herein, by employing GPR123 RNAi we addressed the role of GPR123 for hPSCs. The suppression of GPR123 in hPSCs leads to the loss of pluripotency and differentiation, impacted colony morphology, accumulation of cells at the G2 phase of the cell cycle, and absence of the scratch closure. Application of the GPR123 RNAi at the initiation stage of reprogramming leads to a decrease in the percentage of the "true" hiPSC colonies, a drop in E-cadherin expression, a decrease in the percentage of NANOG+ nuclei, and the absence of actin cytoskeleton remodeling. Together this leads to the absence of the alkaline-phosphatase-positive hiPSCs colonies on the 18th day of the reprogramming process. Overall, these data indicate for the first time the essential role of GPR123 in the maintenance and acquisition of pluripotency.

Prognostic Analysis of Human Pluripotent Stem Cells Based on Their Morphological Portrait and Expression of Pluripotent Markers.

The ability of human pluripotent stem cells for unlimited proliferation and self-renewal promotes their application in the fields of regenerative medicine. The morphological assessment of growing colonies and cells, as a non-invasive method, allows the best clones for further clinical applications to be safely selected. For this purpose, we analyzed seven morphological parameters of both colonies and cells extracted from the phase-contrast images of human embryonic stem cell line H9, control human induced pluripotent stem cell (hiPSC) line AD3, and hiPSC line HPCASRi002-A (CaSR) in various passages during their growth for 120 h. The morphological phenotype of each colony was classified using a visual analysis and associated with its potential for pluripotency and clonality maintenance, thus defining the colony phenotype as the control parameter. Using the analysis of variance for the morphological parameters of each line, we showed that selected parameters carried information about different cell lines and different phenotypes within each line. We demonstrated that a model of classification of colonies and cells by phenotype, built on the selected parameters as predictors, recognized the phenotype with an accuracy of 70-75%. In addition, we performed a qRT-PCR analysis of eleven pluripotency markers genes. By analyzing the variance of their expression in samples from different lines and with different phenotypes, we identified group-specific sets of genes that could be used as the most informative ones for the separation of the best clones. Our results indicated the fundamental possibility of constructing a morphological portrait of a colony informative for the automatic identification of the phenotype and for linking this portrait to the expression of pluripotency markers.

Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks.

Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with "good" and "bad" morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 µm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 µm) and the entire colony (~540 µm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.