Characterization of the progression of cellular states during human embryogenesis can provide insights into the origin of pediatric diseases. We examined the transcriptional states of neural crest- and mesoderm-derived lineages differentiating into adrenal glands, kidneys, endothelium, and hematopoietic tissue between post-conception weeks 6 and 14 of human development. Our results reveal transitions connecting intermediate mesoderm and progenitors of organ primordia, the hematopoietic system, and endothelial subtypes. Unexpectedly, by using a combination of single cell transcriptomics and lineage tracing, we found that intra-adrenal sympathoblasts at that stage are directly derived from the nerve-associated Schwann cell precursors similarly to local chromaffin cells, whereas the majority of extra-adrenal sympathoblasts arise from the migratory neural crest. In humans, this process persists during several weeks of development within the large intra-adrenal ganglia-like structures, which may also serve as reservoirs of originating cells in neuroblastoma.

179)Large datasets represented by multidimensional data point clouds often possess nontrivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples. Reducing the complexity and producing compact and interpretable representations of such data remains a challenging task. Most of the existing computational methods are based on exploring the local data point neighbourhood relations, a step that can perform poorly in the case of multidimensional and noisy data. Here we present ElPiGraph, a scalable and robust method for approximation of datasets with complex structures which does not require computing the complete data distance matrix or the data point neighbourhood graph. This method is able to withstand high levels of noise and is capable of approximating complex topologies via principal graph ensembles that can be combined into a consensus principal graph. ElPiGraph deals efficiently with large and complex datasets in various fields from biology, where it can be used to infer gene dynamics from single-cell RNA-Seq, to astronomy, where it can be used to explore complex structures in the distribution of galaxies.