We investigate the mechanisms that direct ‘cell fate’ in human embryos and stem cells. This means studying the different factors that tell embryonic cells which type of cell to become.
After a human egg is fertilised, the cells multiply as the embryo grows. After five days there are around 100 cells, under 10 of which are embryonic epiblast cells that go on to form the foetus – these are pluripotent cells, as they are capable of becoming any type of cell in the body. The remaining 90 or so cells will go on to form either the placenta or the yolk sac.
We’re trying to understand how these early human pluripotent embryonic cells are established, how they remain pluripotent and how this process if turned off when the cells specialise. We’re trying to map out the complex hierarchy of different genes that control cell activity in early development, determine the influence of factors outside of the cells and understand the similarities and differences between human and mouse development.
The processes that underpin early development and stem cell pluripotency are fundamental to human biology. If we knew how these processes worked, this knowledge could inform the understanding and treatment of infertility and developmental disorders. We could also use this knowledge to improve our use of stem cells in both science and medicine.
The allocation of cells to a specific lineage is regulated by the activities of key signalling pathways and developmentally regulated transcription factors. The focus of our research is to understand the influence of signalling and transcription factors on differentiation during early human development. During preimplantation development, totipotent human zygotes undergo subsequent rounds of mitotic cell divisions leading to the divergence of pluripotent embryonic cells, which form the foetus, and extra-embryonic cells, which contribute to the placenta and yolk sac.
The central question we are addressing is what are the molecular mechanisms that regulate embryonic pluripotency and how is it disengaged during cellular differentiation? We seek to define the genetic hierarchy acting during differentiation, the influence of extracellular signalling and the extent to which these mechanisms are conserved between humans and mice.
Bone Morphogenic Protein (BMP) signaling plays an essential and highly conserved role in embryo axial patterning in animal species. However, in mammalian embryos, which develop inside the mother, early development includes a preimplantation stage, which does not occur in externally developing embryos. During preimplantation, the epiblast is segregated from extraembryonic lineages that enable implantation and development in utero. Yet, the requirement for BMP signaling in is imprecisely defined in mouse early embryos. Here, we show that, in contrast to prior reports, BMP signaling (SMAD1/5/9 phosphorylation) is not detectable until implantation when it is detected in the primitive endoderm - an extraembryonic lineage. Moreover, preimplantation development appears normal following deletion of maternal and zygotic Smad4, an essential effector of canonical BMP signaling. In fact, mice lacking maternal Smad4 are viable. Finally, we uncover a new requirement for zygotic Smad4 in epiblast scaling and cavitation immediately after implantation, via a mechanism involving FGFR/ERK attenuation. Altogether, our results demonstrate no role for BMP4/SMAD4 in the first lineage decisions during mouse development. Rather, multi-pathway signaling among embryonic and extraembryonic cell types drives epiblast morphogenesis post-implantation.
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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.