In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how singlecell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

SummaryPluripotency represents a cell state comprising a fine-tuned pattern of transcription factor activity required for embryonic stem cell (ESC) self-renewal. TBX3 is the earliest expressed member of the T-box transcription factor family and is involved in maintenance and induction of pluripotency. Hence, TBX3 is believed to be a key member of the pluripotency circuitry, with loss of TBX3 coinciding with loss of pluripotency. We report a dynamic expression of TBX3 in vitro and in vivo using genetic reporter tools tracking TBX3 expression in mouse ESCs (mESCs). Low TBX3 levels are associated with reduced pluripotency, resembling the more mature epiblast. Notably, TBX3-low cells maintain the intrinsic capability to switch to a TBX3-high state and vice versa. Additionally, we show TBX3 to be dispensable for induction and maintenance of naive pluripotency as well as for germ cell development. These data highlight novel facets of TBX3 action in mESCs.

Background Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. Methods We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. Results We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. Conclusions CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package (https://github.com/pcahan1/cancerCellNet) and as a web application (http://www.cahanlab.org/resources/cancerCellNet_web) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.