Development of the dorsal aorta is a key step in the establishment of the adult blood-forming system since hematopoietic stem and progenitor cells (HSPCs) arise from ventral aortic endothelium in all vertebrate animals studied. Work in zebrafish has demonstrated that arterial and venous endothelial precursors arise from distinct subsets of lateral plate mesoderm. Here, we profile the transcriptome of the earliest detectable endothelial cells (ECs) during zebrafish embryogenesis to demonstrate that tissue-specific EC programs initiate much earlier than previously appreciated, by the end of gastrulation. Classic studies in the chick embryo showed that paraxial mesoderm generates a subset of somite-derived endothelial cells (SDECs) that incorporate into the dorsal aorta to replace HSPCs as they exit the aorta and enter circulation. We describe a conserved program in the zebrafish, where a rare population of endothelial precursors delaminates from the dermomyotome to incorporate exclusively into the developing dorsal aorta. Although SDECs lack hematopoietic potential, they act as a local niche to support the emergence of HSPCs from neighboring hemogenic endothelium. Thus, at least three subsets of ECs contribute to the developing dorsal aorta: vascular ECs, hemogenic ECs, and SDECs. Taken together, our findings indicate that the distinct spatial origins of endothelial precursors dictate different cellular potentials within the developing dorsal aorta.

Single-cell RNA sample preparation

After FACS, total cell concentration and viability were ascertained using a TC20 Automated Cell Counter (Bio-Rad). Samples were then resuspended in 1XPBS with 10% BSA at a concentration between 800-3000 per ml. Samples were loaded on the 10X Chromium system and processed as per manufacturer’s instructions (10X Genomics). Single cell libraries were prepared as per the manufacturer’s instructions using the Single Cell 3’ Reagent Kit v2 (10X Genomics). Single cell RNA-seq libraries and barcode amplicons were sequenced on an Illumina HiSeq platform.

Single-cell RNA sequencing analysis

The Chromium 3’ sequencing libraries were generated using Chromium Single Cell 3’ Chip kit v3 and sequenced with (actually, I don’t know:( what instrument was used?). The Ilumina FASTQ files were used to generate filtered matrices using CellRanger (10X Genomics) with default parameters and imported into R for exploration and statistical analysis using a Seurat package (La Manno et al., 2018). Counts were normalized according to total expression, multiplied by a scale factor (10,000), and log-transformed. For cell cluster identification and visualization, gene expression values were also scaled according to highly variable genes after controlling for unwanted variation generated by sample identity. Cell clusters were identified based on UMAP of the first 14 principal components of PCA using Seurat’s method, Find Clusters, with an original Louvain algorithm and resolution parameter value 0.5. To find cluster marker genes, Seurat’s method, FindAllMarkers. Only genes exhibiting significant (adjusted p-value < 0.05) a minimal average absolute log2-fold change of 0.2 between each of the clusters and the rest of the dataset were considered as differentially expressed. To merge individual datasets and to remove batch effects, Seurat v3 Integration and Label Transfer standard workflow (Stuart et al., 2019)