Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

Defects in somitogenesis result in vertebral malformations at birth known as spondylocostal dysostosis (SCDO). Somites are formed with a species-specific periodicity controlled by the "segmentation clock," which comprises a group of oscillatory genes in the presomitic mesoderm. Here, we report that a segmentation clock model derived from human embryonic stem cells shows many hallmarks of the mammalian segmentation clock in vivo, including a dependence on the NOTCH and WNT signaling pathways. The gene expression oscillations are highly synchronized, displaying a periodicity specific to the human clock. Introduction of a point of mutation into HES7, a specific mutation previously associated with clinical SCDO, eliminated clock gene oscillations, successfully reproducing the defects in the segmentation clock. Thus, we provide a model for studying the previously inaccessible human segmentation clock to better understand the mechanisms contributing to congenital skeletal defects.

Metabolic flexibility allows cells to adapt to various environments and limits the efficacy of metabolic drugs. Therapeutic targeting of cancer metabolism relies on defining limiting requirements and vulnerabilities in the highly dynamic metabolic network. Here, we characterize the metabolic reprogramming and identify cancer-specific metabolic vulnerabilities in response to the pharmacological inhibition of mitochondrial complex I. Our work reveals the adaptation mechanism in malignant lymphocytes providing resistance against the biguanides phenformin and metformin by transcriptionally reprogramming glucose metabolism. Metabolic adaptation to complex I inhibition is mediated by mitochondrial reactive oxygen species (mROS) serving as a mitochondrial stress signal activating hypoxia-inducible factor-1a (HIF-1a). Inhibition of the mROS/HIF-1a axis through antioxidants or direct suppression of HIF-1a selectively disrupts metabolic adaptation and survival during complex I dysfunction in malignant lymphocytes. Our results identify HIF-1a signaling as a critical factor in resistance against biguanide-induced mitochondrial dysfunction, allowing selective targeting of metabolic pathways in leukemia and lymphoma.

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

Hemogenic endothelium (HE) has been recognized as a source of hematopoietic stem cells (HSCs) in the embryo. Access to human HE progenitors (HEPs) is essential for enabling the investigation of the molecular determinants of HSC specification. Here, we show that HEPs capable of generating definitive hematopoietic cells can be obtained from human pluripotent stem cells (hPSCs) and identified precisely by a VE-cadherin(+)CD73(-)CD235a/CD43(-) phenotype. This phenotype discriminates true HEPs from VE-cadherin(+)CD73(+) non-HEPs and VE-cadherin(+)CD235a(+)CD41a(-) early hematopoietic cells with endothelial and FGF2-dependent hematopoietic colony-forming potential. We found that HEPs arise at the post-primitive-streak stage of differentiation directly from VE-cadherin-negative KDR(bright)APLNR(+)PDGFRα(low/-) hematovascular mesodermal precursors (HVMPs). In contrast, hemangioblasts, which are capable of forming endothelium and primitive blood cells, originate from more immature APLNR(+)PDGFRα(+) mesoderm. The demarcation of HEPs and HVMPs provides a platform for modeling blood development from endothelium with a goal of facilitating the generation of HSCs from hPSCs.