Project description:Multipotent and pluripotent stem cells have significant potential as sources for cell replacement therapies. However, the low yield and quality of in vitro differentiated cells produced from various stem cell sources presents a significant limitation for therapeutic applications. The most mature use of these stem cell products is in the field of transfusion medicine, where stem cell-derived red blood cells (RBCs) have clinically-proven potential as alternative transfusion products. To improve upon current approaches for RBC production, we used insight from both common and rare human genetic variation of blood counts to focus on the SH2B3 gene. By producing loss of function of SH2B3 using targeted knockdown and genome editing approaches in human hematopoietic stem and progenitor cells, as well as human pluripotent stem cells, we are able to significantly improve both the quality and yield of in vitro derived RBCs. We illustrate how insight from human genetic variation can assist in the development of broadly applicable approaches that have tremendous value for regenerative medicine.
Project description:Cellular identity in complex multicellular organisms is controlled in part by the physical organization of cells. However, large-scale investigation of the cellular interactome has been technically challenging. Here we develop Cell Interaction by Multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell types presented in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types using machine learning. Contrary to previous deconvolution-based methods, CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing the data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium, or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results from different tissue types demonstrate that CIM-seq is broadly applicable to profile cell type interactions in different tissue types using in an unsupervised manner.