SRA

We report the generation of single-cell RNAseq data obtained using Drop-seq from endothelial-enriched retinal cell suspension. Single-cell suspension were enriched using CD-31 antibody coated magnetic beads, and processed for encapsulation and library preparation as in Macosko et al (Cell 2015) based on the Drop-seq procedure. Overall design: Retinas were digested as in Macosko et al (Cell 2015), and single-cell suspensions were prepared from P14 OIR eNOS+/+ (C57BL/6) and eNOS-/- mouse retinas (n=3 replicates per genotype, with 3-5 retina pooled per replicate). The final EC suspension was obtained by positive enrichment using beads coated with CD31 antibodies and magnetic columns. For single-cell RNA-seq of ECsendothelial-enriched retinal cell suspension, droplet generation and cDNA libraries were performed as described in the Drop-seq procedure (http://mccarrolllab.org/dropseq/), and sequencing was carried out on an Illumina NextSeq 500 at an estimated read depth/cell similar to that used by Macosko et al. (i.e., 50,000 reads/cell). Unique molecular identifier (UMI) counts associated with aligned reads (kb-python, GRCm38 reference genome) from the scRNA-seq replicates of eNOS+/+ and eNOS-/- retina were merged into one single digital gene expression (DGE) matrix and processed using the “Seurat” package (spatial reconstruction of single-cell gene expression data [28]). Cells expressing fewer than 100 genes and more than 10% of mitochondrial genes were filtered out. Single-cell transcriptomes were normalized by dividing by the total number of UMIs per cell and then multiplying by 10,000. All calculations and data were then performed in log space [i.e., ln(transcripts per 10,000 + 1)]. After integrating of the different biological replicates using the Seurat anchor integration algorithm, the 20 most significant components were used as input for dimensionality reduction and clustering. To identify putative cell types from the Uniform Manifold Approximation and Projection (UMAP) dimensional reductions, a graph-based clustering approach using K-nearest neighbor graph and the Louvain algorithm was used to define clusters and average gene expression was computed for each of the identified cluster based on Euclidean distances. Marker genes that were significantly enriched for each cluster were then identified, allowing cluster annotation to specific cell types. A similar computational approach was performed on non-integrated RNA data to sub-cluster the population of ECs, allowing us to define 3 cell subtypes based on marker genes. After removing the contaminant cell cluster (i.e., red blood cells and retinal pigmented epithelium), a total of 3031 cells were obtained from eNOS+/+ retina (including 2044 ECs) and 5913 cells from eNOS-/- retina (including 3334 ECs).