Progenitor and Stem Cell Heterogeneity

Abstract Single cell technologies have made it possible to study individual cells with comprehensiveness. These evolving technologies have demonstrated that cellular populations previously considered similar are heterogeneous. Heterogeneity can be due to intrinsic factors such as transcriptional expression, translational modification, cellular epigenetics and metabolism, or extrinsic factors such as the tissue microenvironment. Using mathematical modeling and statistical analysis on single cell datasets, seemingly similar cells can be clustered into subpopulations revealing previously undiscovered or rare cell types. Further, intracellular genes defining the cell can be correlated to surface markers to identify unique surface marker combinations for isolating cellular subpopulations. However, single cell technologies generate large datasets from individual cells that may vary due to confounding factors such as the cell cycle or technical noise, making it important to determine the accuracy of the biologic data generated. It is also necessary to understand whether the cellular subpopulations discovered are simply a descriptive curiosity, or if they are functionally distinct with implications for human health. Ultimately, an accurate and precise analysis of cellular heterogeneity is important to identify cellular subpopulations that initiate or worsen disease, those that persist following drug treatment, and subpopulations that will be useful to cellular therapies.