Project Abstract Cell fate engineering, for example the directed differentiation of pluripotent stem cells or the direct conversion among somatic cell types, holds great promise to improve disease modeling, drug screening, and to lead to regenerative medicine therapies. I recently developed a novel analytical tool, CellNet, which assesses how well engineered cells approach their in vivo target cell types based on cell and tissue specific gene regulatory networks (GRNs). By applying CellNet to all cell fate engineering studies for which compatible data was available, I discovered several issues that were common to virtually all methods and target lineages. First, I found that the only robustly faithful fate engineering was that of reprogramming to pluripotency. Second, I found that the GRN of the starting cell type (e.g. pluripotent stem cells in cases of directed differentiation or often fibroblasts in cases of direct conversion) is often partially retained in engineered cells. Third, I found that GRNs of alternate lineages (i.e. those not associated with the starting cell type or the target lineage) are frequently established in engineered cells. Finally, I found that the complex signaling milieu of the mouse microenvironment to which engineered cells are transplanted potently represses alternate/aberrant lineages and induces the target cell type GRNs in directly converted cells. These observations have revealed several fundamental barriers to faithful cell fate engineering and they define opportunities for progress. My current research program, which I seek to fund through this MIRA opportunity, is to develop novel theoretical and computational methods to define cell type identity from single cell RNA-Seq (scRNA-Seq) data with an emphasis on developmental cell types that emerge during mesoderm development and subsequent commitment to chondrocyte fate. As part of this work, we will generate scRNA-Seq data of the developing and adult synovial joint, we will harvest and incorporate publicly available and collaborator-provided scRNA-Seq data of other lineages to make a generally applicable platform for assessing cell type identity at the single cell level of resolution, and we will make the resulting methods, software, and data freely available. Finally, we will use the system to determine the extent to which the cell types and compositions of directly differentiating mouse ESCs match their in vivo counterparts.