ETV4 and ETV5 Orchestrate FGF-Mediated Lineage Specification and Epiblast Maturation during Early Mouse Development.

Cell fate decisions in early mammalian embryos are tightly regulated processes crucial for proper development. While FGF signaling plays key roles in early embryo patterning, its downstream effectors remain poorly understood. Our study demonstrates that the transcription factors Etv4 and Etv5 are critical mediators of FGF signaling in cell lineage specification and maturation in mouse embryos. We show that loss of Etv5 compromises primitive endoderm formation at pre-implantation stages. Furthermore, Etv4/5 deficiency delays naïve pluripotency exit and epiblast maturation, leading to elevated NANOG and reduced OTX2 expression within the blastocyst epiblast. As a consequence of delayed pluripotency progression, Etv4/5 deficient embryos exhibit anterior visceral endoderm migration defects post-implantation, a process essential for coordinated embryonic patterning and gastrulation initiation. Our results demonstrate the successive roles of these FGF signaling effectors in early lineage specification and embryonic body plan establishment, providing new insights into the molecular control of mammalian development.

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.