An interactive resource of molecular signalling in the developing human haematopoietic stem cell (HSC) niche [scRNA-seq]
The emergence of definitive human haematopoietic stem cells (HSCs) during Carnegie Stages (CS) 14-17 in the aorta-gonad-mesonephros (AGM) region is a complex and tightly regulated process. In a previous study we conducted spatial transcriptomic analysis of the human AGM region at the end of this period (CS16/17) and identified secreted factors involved in HSC development. Here, we extend our analysis to investigate the progression of dorso-ventral polarized signalling around the dorsal aorta over the entire period of HSC emergence. Our results reveal a dramatic increase in signalling complexity from the CS13 to CS14 transition, coinciding with the activation of endothelial-to-haematopoietic transition (EHT) in the ventral domain of the dorsal aorta. We further observe stage-specific changes in signalling complexity up to CS17. Signalling progression described here may underpin step-wise maturation of HSCs described in the mouse model. A data-rich bioinformatics resource presented here along with an interactive online interface enables in silico analysis of molecular interactions between spatially defined domains of the AGM region. This resource will be of particular interest for researchers studying mechanisms underlying human HSC development as well as those developing in vitro methods for the generation of clinically relevant HSCs from pluripotent stem cells. Overall design: 10X single cell analysis of a carnegie stage (CS13) dorsal aorta from a human embryo. Third-party reanalysis: CS13 dorsal aorta single cell data was processed using the Cell Ranger 2.1.0 (10x Genomics) analysis pipeline by aligning reads to GRCh38 human transcriptome (Ensembl). The ScanPy pipeline (Wolf, et al. 2018) was used to explore and integrate the dataset with other dorsal aorta single cell datasets spanning stages CS13 - CS15 from a publicly available resource (Zeng, et al. 2019, accession GSE135202 (including samples GSM3993422, GSM3993424, GSM3993425 and GSM3993426)). Cells with less than 200 genes and genes that were in less than 3 cells were filtered out. Cells with a percentage of mitochondrial genes >1 were also filtered out. Contaminating HB genes were also removed from the CS13 dorsal aorta dataset. The reads per cell were normalized and logarithmised. The variance effects of total counts per cell, cell cycle and mitochondrial genes were regressed out. Each dataset was then subset to include only genes with highly variable expression. The tool Scanorama (Hie, et al. 2019) was used to integrate datasets. Then nearest neighbours was computed using 10 principal components and the neighbourhood graph was embedded in two dimensions in a Uniform Manifold Approximation and Projection (UMAP) (Becht, et al. 2018). Clustering of sub-populations within the UMAP were made using the Leiden algorithm (Traag, et al. 2019). Following cluster identification by gene signature and any known identities from prior sorting strategies, the data was then subset into two parts to include hematopoietic and endothelial populations in one subset and stromal and epithelial populations in the second. Partition-based graph abstraction (PAGA) (Wolf, et al. 2019) was used to make lineage inferences from the neighbourhood graphs.