KIT is dispensable for physiological organ vascularisation in the embryo

Blood vessels form vast networks in all vertebrate organs to sustain tissue growth, repair and homeostatic metabolism, but they also contribute to a range of diseases with neovascularisation. It is, therefore, important to define the molecular mechanisms that underpin blood vessel growth. The receptor tyrosine kinase KIT is required for the normal expansion of hematopoietic progenitors that arise during embryogenesis from hemogenic endothelium in the yolk sac and dorsal aorta. Additionally, KIT has been reported to be expressed in endothelial cells during embryonic brain vascularisation and has been implicated in pathological angiogenesis. However, it is neither known whether KIT expression is widespread in normal organ endothelium nor whether it promotes blood vessel growth in developing organs. Here, we have used single-cell analyses to show that KIT is expressed in endothelial cell subsets of several organs, both in the adult and in the developing embryo. Knockout mouse analyses revealed that KIT is dispensable for vascularisation of growing organs in the midgestation embryo, including the lung, liver and brain. By contrast, vascular changes emerged during late-stage embryogenesis in these organs from KIT-deficient embryos, concurrent with severe erythrocyte deficiency and growth retardation. These findings suggest that KIT is not required for developmental tissue vascularisation in physiological conditions, but that KIT deficiency causes foetal anaemia at late gestation and thereby pathological vascular remodelling. Supplementary Information The online version contains supplementary material available at 10.1007/s10456-022-09837-6.

. Endothelial cluster identification and Runx1 expression in embryonic mouse midbrain, liver and lung.
scRNA-seq analysis of E12.5 mouse midbrain (a-f), liver (g-j) and E12.0 mouse lung (k-n), including schematic representations of each organ. UMAP plots visualise clusters of distinct cell types (a,g,k), as well as Cdh5 (c,h,l), Cldn5 (e,i,m) and Runx1 (f,j,n) transcript levels in each cell cluster, whereby the colour intensity represents the average transcript level; boxes indicate EC clusters. Key marker genes for the indicated cell types are shown as bubble plots (b), whereby the dot size corresponds to the percentage of cells in which that marker was detected. Violin plots (d) illustrate transcript levels for the indicated genes, with the cell number in each cluster indicated to the right. Abbreviations:  Gating strategy with the indicated markers to FACS-isolate the following cell populations: ECs (PECAM1 + CD45 -F4/80 -), microglia (F4/80 + CD45 + ) and neural parenchyma (PECAM1 -CD45 -F4/80 -) from brains (including both midbrain and hindbrain, top); ECs (PECAM1 + CD45 -F4/80 -) and immune cells (CD45 + ) from livers (middle); and ECs (PECAM1 + CD45 -F4/80 -) from lungs (bottom). Figure S4. Endothelial cell cluster identification in late gestation mouse brain, liver and lung.
(f) Quantification of phosphorylated histone H3 (pHH3)-positive ECs in E16.5 and E18.5 organs from wild type and Kit-null mutants from 1 litter for each time point; n = 5 wild type brains, n = 7 wild type livers and lungs and 3 KIT-null mutant brain, livers and lungs each.

Mouse strains
To obtain mouse embryos of defined gestational age, mice were paired in the evening and the presence of a vaginal plug the following morning was defined as E0.5. Embryos lacking Kit expression were generated by mating Kit CreERT2:IRES mice (MGI:5543260) [7] or by mating

FACS followed by quantitative RT-PCR (qRT-PCR) analysis
For FACS sorting, brains, livers and lungs from embryos were dissected, minced and digested in

Statistical analysis
Tissues for analysis were allocated to experimental groups according to genotype, gestational age, organ or cell type, rather than being randomised. To ensure the unbiased interpretation of results, the genotype and gestational age were disclosed only after data collection was complete, but the investigators knew the sample origin (i.e., organ or cell type). No statistical methods were used to predetermine sample size. For hindbrain experiments, the F4/80 + , tdTomato + and IB4 + volumes were determined from confocal z-stacks of four randomly chosen 0.25 mm 2 regions on the lateral side of each hindbrain. The z-stacks were surface rendered with Imaris (Bitplane) to obtain the F4/80 + , tdTom + and IB4 + volumes, and the F4/80 + volume was then subtracted from both the IB4 + and tdTom + total volumes to obtain the IB4 + EC and tdTom + EC volumes before calculating the ratio of tdTom + to IB4 + EC volume. The same confocal z-stacks were analysed with Angiotool [14] as maximum intensity projections to determine the percentage of vascular area relative to total tissue area and the number of vascular intersections. All counts obtained from one hindbrain were averaged to yield the value for that hindbrain. We used Angiotool to quantify vascular complexity (branchpoints) and area in maximum intensity projections of the E12.5 and E18.5 liver and lung (whole left lobe), E12.5 forelimb (0.15 mm 2 rectangular area corresponding to the middle digit) and E18.5 brain striatum. A MATLAB (2021b, MathWorks) code was used to adjust confocal 2D maximum intensity projection images, segment, skeletonise and measure the vessel diameter at several cross-sections perpendicular to the centre skeletonised lines. For all experiments, the error bars represent the standard deviation of the mean. Comparison of medians against means justified the use of a parametric test; to determine whether two datasets were significantly different, we therefore calculated P values with a two-tailed unpaired Student's t-test; P < 0.05 was considered significant.
Statistical analyses were performed with Excel 12.2.6 (Microsoft Office) or Prism 7 (GraphPad Software).