Bayesian inference of transcriptional branching identifies regulators of early germ cell development in humans

Abstract

During embryonic development, cells undertake a series of fate decisions to form a complete organism comprised of various cell types, epitomising a branching process. A striking example of branching occurs in humans around the time of implantation, when primordial germ cells (PGCs), precursors of sperm and eggs, and somatic lineages are specified. Due to inaccessibility of human embryos at this stage of development, understanding the mechanisms of PGC specification remains difficult. The integrative modelling of single cell transcriptomics data from embryos and appropriate in vitro models should prove to be a useful resource for investigating this system, provided that the cells can be suitably ordered over a developmental axis. Unfortunately, most methods for inferring cell ordering were not designed with structured (time series) data in mind. Although some probabilistic approaches address these limitations by incorporating prior information about the developmental stage (capture time) of the cell, they do not allow the ordering of cells over processes with more than one terminal cell fate. To investigate the mechanisms of PGC specification, we develop a probabilistic pseudotime approach, branch-recombinant Gaussian process latent variable models (B-RGPLVMs), that use an explicit model of transcriptional branching in individual marker genes, allowing the ordering of cells over developmental trajectories with arbitrary numbers of branches. We use first demonstrate the advantage of our approach over existing pseudotime algorithms and subsequently use it to investigate early human development, as primordial germ cells (PGCs) and somatic cells diverge. We identify known master regulators of human PGCs, and predict roles for a variety of signalling pathways, transcription factors, and epigenetic modifiers. By concentrating on the earliest branched signalling events, we identified an antagonistic role for FGF receptor (FGFR) signalling pathway in the acquisition of competence for human PGC fate, and identify putative roles for PRC1 and PRC2 in PGC specification. We experimentally validate our predictions using pharmacological blocking of FGFR or its downstream effectors (MEK, PI3K and JAK), and demonstrate enhanced competency for PGC fate in vitro, whilst small molecule inhibition of the enzymatic component of PRC1/PRC2 reveals reduced capacity of cells to form PGCs in vitro. Thus, B-RGPLVMs represent a powerful and flexible data-driven approach for dissecting the temporal dynamics of cell fate decisions, providing unique insights into the mechanisms of early embryogenesis. Scripts relating to this analysis are available from: https://github.com/cap76/PGCPseudotime