Capturing Mesenchymal Stem Cell Heterogeneity during Osteogenic Differentiation: An Experimental–Modeling Approach

Early differentiation of mesenchymal stem cells is reflected in their dielectrophoretic behavior

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.

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Mesenchymal stem cells (MSCs) are proliferative and multipotent cells that play a key role in the bone regeneration process. Empirical data have repeatedly shown the bioregulatory importance of magnesium (Mg) ions in MSC growth and osteogenesis. In this study, we propose an agent-based model to predict the spatiotemporal dynamics of the MSC population and osteogenic differentiation in response to Mg2+ ions. A fuzzy-logic controller was designed to govern the decision-making process of cells by predicting four cellular processes of proliferation, differentiation, migration, and mortality in response to several important bioregulatory factors such as Mg2+ ions, pH, BMP2, and TGF-β1. The model was calibrated using the empirical data obtained from three sets of cell culture experiments. The model successfully reproduced the empirical observations regarding live cell count, viability, DNA content, and the differentiation-related markers of alkaline phosphate (ALP) and osteocalcin (OC). The simulation results, in agreement with the empirical data, showed that Mg2+ ions within 3-6 mM concentration have the highest stimulation effect on cell population growth. The model also correctly reproduced the stimulatory effect of Mg2+ ions on ALP and its inhibitory effect on OC as the early and late differentiation markers, respectively. Besides, the numerical simulation shed light on the innate cellular differences of the cells cultured in different experiments in terms of the proliferative capacity as well as sensitivity to Mg2+ ions. The proposed model can be adopted in the study of the osteogenesis around Mg-based implants where ions released due to degradation interact with local cells and regulate bone regeneration.

Single-Cell Infrared Microspectroscopy Quantifies Dynamic Heterogeneity of Mesenchymal Stem Cells during Adipogenic Differentiation

The central relevance of cellular heterogeneity to biological phenomena raises the rational needs for analytical techniques with single-cell resolution. Here, we developed a single-cell FTIR microspectroscopy-based method for the quantitative evaluation of cellular heterogeneity by calculating the cell-to-cell similarity distance of the infrared spectral data. Based on this method, we revealed the infrared phenotypes might reflect the dynamic heterogeneity changes in the cell population during the adipogenic differentiation of the human mesenchymal stem cells. These findings provide an alternative label-free optical approach for quantifying the cellular heterogeneity, and the combination with other single-cell analysis tools will be very helpful for understanding the genotype-to-phenotype relationship in cellular populations.