Integrated pseudotime analysis of human preimplantation embryo single-cell transcriptomes reveals the dynamics of lineage specification Graphical abstract Highlights d Distinct trophectoderm/epiblast signatures arise at the B2-B3 blastocyst stages d IFI16 is broadly expressed in the ICM and then restricted to epiblast after implantation d NR2F2 arises from the polar TE in late blastocysts and then spreads to all TE cells

Highlights d Reprogramming of patient somatic cells to induced hTSCs with OSKM d Conversion of naive and extended hPSCs to hTSCs d Comparison of models of the human trophoblast lineage d h(i/c)TSCs are akin to day 8 trophoblasts of the human embryo

Induced pluripotent stem cells (iPSCs) have considerably impacted human developmental biology and regenerative medicine, notably because they circumvent the use of cells of embryonic origin and offer the potential to generate patient-specific pluripotent stem cells. However, conventional reprogramming protocols produce developmentally advanced, or primed, human iPSCs (hiPSCs), restricting their use to post-implantation human development modeling. Hence, there is a need for hiPSCs resembling preimplantation naive epiblast. Here, we develop a method to generate naive hiPSCs directly from somatic cells, using OKMS overexpression and specific culture conditions, further enabling parallel generation of their isogenic primed counterparts. We benchmark naive hiPSCs against human preimplantation epiblast and reveal remarkable concordance in their transcriptome, dependency on mitochondrial respiration and X-chromosome status. Collectively, our results are essential for the understanding of pluripotency regulation throughout preimplantation development and generate new opportunities for disease modeling and regenerative medicine.

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce , a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.