Abstract: Aspects of the present disclosure include methods of treating a subject to reduce adhesion formation.

Abstract: Methods of promoting healing of a wound in a dermal location of a subject are provided. Aspects of the methods may include administering an effective amount of a Piezo inhibitor composition to the wound to promote healing of the wound, e.g., by reducing transition of adipocytes to fibroblasts in the wound. Also provided are methods of preventing or reversing scarring during healing of a wound in a subject. Aspects of the methods may include forming a wound in a dermal location of a subject and administering an effective amount of a Piezo inhibitor composition to the wound to promote regenerative healing or regenerative remodeling of the wound. Also provided are methods of ameliorating, e.g., reducing or inhibiting, organ fibrosis, e.g., liver fibrosis, heart fibrosis, inflammatory bowel fibrosis, muscle fibrosis, kidney fibrosis, etc., in a subject by administering to the subject an effective amount of a Piezo inhibitor composition. Also provided are kits including an amount of a Piezo inhibitor composition.

Abstract: Aspects of the present disclosure include methods of treating a subject to reduce adhesion formation.

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.