Abstract: Understanding the heterogeneity within a stem cell population remains a major impediment to the development of clinically effective cell-based therapies. Gene expression patterns exhibited by individual cells are a crucial component of this heterogeneity, yet transcriptional events within a single cell are inherently stochastic and can produce tremendous variability, even among genetically identical cells. It remains unclear how mammalian cellular systems overcome this intrinsic noisiness of gene expression to produce consequential variations in function. To address these questions, we utilized a novel single cell analysis method to characterize transcriptional programs across hundreds of individual murine long-term hematopoietic stem cells (LT-SCs). We demonstrate that multiple subpopulations exist within this putatively homogeneous stem cell population, defined by nonrandom patterns that are distinguishable from noise and can predict functional properties of these cells.

Abstract: Understanding the heterogeneity within a stem cell population remains a major impediment to the development of clinically effective cell-based therapies. Gene expression patterns exhibited by individual cells are a crucial component of this heterogeneity, yet transcriptional events within a single cell are inherently stochastic and can produce tremendous variability, even among genetically identical cells. It remains unclear how mammalian cellular systems overcome this intrinsic noisiness of gene expression to produce consequential variations in function. To address these questions, we utilized a novel single cell analysis method to characterize transcriptional programs across hundreds of individual murine long-term hematopoietic stem cells (LT-SCs). We demonstrate that multiple subpopulations exist within this putatively homogeneous stem cell population, defined by nonrandom patterns that are distinguishable from noise and can predict functional properties of these cells.