A Neural Network Model for Cell Classification Based on Single-Cell Biomechanical Properties

Abstract

The potential success of tissue engineering or other cell-based therapies is dependent on factors such as the purity and homogeneity of the source cell populations. The ability to enrich cell harvests for specific phenotypes can have significant effects on the overall success of such therapies. While most techniques for cell sorting or enrichment have relied on cell surface markers, recent studies have shown that single-cell mechanical properties can serve as identifying markers of phenotype. In this study, a neural network modeling approach was developed to classify mesenchymal-derived primary and stem cells based on their biomechanical properties. Cell sorting was simulated using previously published data characterizing the mechanical properties of several different cell types as measured by atomic force microscopy. Neural networks were trained using combined data sets, with the resultant groupings analyzed for their purity, efficiency, and enrichment. Heterogeneous populations of zonal chondrocytes, chondrosarcoma cells, and mesenchymal-lineage cells, respectively, could all be classified into enriched subpopulations. Additionally, adult stem cells (adipose-derived or bone marrow-derived) separated disproportionately into nodes associated with the three primary mesenchymal lineages examined. These findings suggest that mathematical approaches such as neural network modeling, in combination with novel measures of cell properties, may provide a means of classifying and eventually sorting mixed populations of cells that are otherwise difficult to identify using more established techniques. In this respect, the identification of biomechanically based cell properties that increase the percentage of stem cells capable of differentiating into predictable lineages may improve the overall success of cell-based therapies.