Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.

The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ambiguity in their in vitro behavior. Intra-clonal population heterogeneity has also been identified and pre-clinical mechanistic studies suggest that these cumulatively depreciate the therapeutic effects of hMSC transplantation. Although various biomarkers identify these specific stem cell populations, recent artificial intelligence-based methods have capitalized on the cellular morphologies of hMSCs, opening a new approach to understand their attributes. A robust and rapid platform is required to accommodate and eliminate the heterogeneity observed in the cell population, to standardize the quality of hMSC therapeutics globally. Here, we report our primary findings of morphological heterogeneity observed within and across two sources of hMSCs namely, stem cells from human exfoliated deciduous teeth (SHEDs) and human Wharton jelly mesenchymal stem cells (hWJ MSCs), using real-time single-cell images generated on immunophenotyping by imaging flow cytometry (IFC). We used the ImageJ software for identification and comparison between the two types of hMSCs using statistically significant morphometric descriptors that are biologically relevant. To expand on these insights, we have further applied deep learning methods and successfully report the development of a Convolutional Neural Network-based image classifier. In our research, we introduced a machine learning methodology to streamline the entire procedure, utilizing convolutional neural networks and transfer learning for binary classification, achieving an accuracy rate of 97.54%. We have also critically discussed the challenges, comparisons between solutions and future directions of machine learning in hMSC classification in biotherapeutics.

Single-Cell Sorting of Immunophenotyped Mesenchymal Stem Cells from Human Exfoliated Deciduous Teeth.

The mesenchymal stem cells (MSCs) of an organism possess an extraordinary capacity to differentiate into multiple lineages of adult cells in the body and are known for their immunomodulatory and anti-inflammatory properties. The use of these stem cells is a boon to the field of regenerative biology, but at the same time, a bane to regenerative medicine and therapeutics owing to the multiple cellular ambiguities associated with them. These ambiguities may arise from the diversity in the source of these stem cells and from their in vitro growth conditions, both of which reflect upon their functional heterogeneity. This warrants methodologies to provide purified, homogeneous populations of MSCs for therapeutic applications. Advances in the field of flow cytometry have enabled the detection of single-cell populations using a multiparametric approach. This protocol outlines a way to identify and purify stem cells from human exfoliated deciduous teeth (SHEDs) through fluorescence-assisted single-cell sorting. Simultaneous expression of surface markers, namely, CD90-fluorescein isothiocyanate (FITC), CD73-peridinin-chlorophyll-protein (PerCP-Cy5.5), CD105-allophycocyanin (APC), and CD44-V450, identified the "bright," positive-expressors of MSCs using multiparametric flow cytometry. However, a significant drop was observed in percentages of quadruple expressors of these positive markers from passage 7 onwards to the later passages. The immunophenotyped subpopulations were sorted using the single-cell sort mode where only two positive and one negative marker constituted the inclusion criteria. This methodology ensured the cell viability of the sorted populations and maintained cell proliferation post sorting. The downstream application for such sorting can be used to evaluate lineage-specific differentiation for the gated subpopulations. This approach can be applied to other single-cell systems to improve isolation conditions and for acquiring multiple cell surface marker information.

Review on bisphenol A and the risk of polycystic ovarian syndrome: an insight from endocrine and gene expression.

Bisphenol A (BPA) is one of the most widely studied endocrine disrupting chemicals because of its structural similarity to 17-ß estradiol; its ability to bind as an agonist/antagonist to estrogen receptors elicits adverse effects on the functioning of the metabolic and endocrinal system. Therefore, BPA has been thoroughly scrutinized concerning its disruption of pathways like lipid metabolism, steroidogenesis, insulin signaling, and inflammation. This has resulted in reports of its correlation with various aspects of cardiovascular diseases, obesity, diabetes, male and female reproductive disorders, and dysfunctions. Among these, the occurrence of the polycystic ovarian syndrome (PCOS) in premenopausal women is of great concern. PCOS is a highly prevalent disorder affecting women in their reproductive age and is clinically characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, along with metabolism-related dysfunctions like hyperinsulinemia, obesity, and insulin resistance. In this review, we analyzed certain researched effects of BPA, while focusing on its ability to alter the expression of various significant genes like GnRH, AdipoQ, ESR1, StAR, CYP11A1, CYP19A1, and many more involved in the pathways and endocrine regulation, whose disruption is commonly associated with the clinical manifestations of PCOS.