Purpose: Molecular profiling of human retinal endothelial cells provides opportuni-ties to understand the roles of this cell population in maintenance of the blood-ocular barrier, and its involvements in diverse retinal vasculopathies. We aimed to generate a transcriptome of human retinal endothelial cells in the unstimulated state, and following treatment with inflammatory cytokines linked to cell dysfunction.
Methods: Endothelial cells were isolated from retinae of five human cadaveric donors, and treated for 60 minutes and 24 hours with interleukin-1β or tumor necrosis factor-α, or exposed to medium alone for the same intervals. Expression of intercellular adhesion molecule-1 was measured by RT-qPCR to confirm cytokine-induced activation of the cells. RNA was sequenced on the Illumina NovaSeq 6000 platform. Reads were aligned to the human GRCh38 genome, and reads that aligned to Ensembl-annotated genes were counted. Quality control of sequencing was performed with FastQC, and sequences were classified by Kraken.
Results: A human retinal endothelial cell RNA-sequencing dataset with mean of 99% reads aligned to the human genome was produced as raw RNA sequence data (FASTQ files) and processed read data (XLSX files). Multidimensional scaling analysis showed a strong donor effect, which was readily controlled by ComBat.
Conclusions: Our dataset may be useful for human retinal endothelial cell transcrip-tomic assemblies, functional gene annotating and/or gene expression and enrichment analyses, as well as cross-dataset harmonization.
Translational Relevance: The molecular profile of the human retinal endothelium is a source of candidate biologic targets for retinal vasculopathies.