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.
There is no abstract provided for this publication
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.
The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here we describe GIANI (General Image Analysis of Nuclei-based Images; https://djpbarry.github.io/Giani), new software for the analysis of 3D images. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images and comes with scripting and command line tools. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.