A Novel Approach to Single Cell RNA‐Sequence Analysis Facilitates In Silico Gene Reporting of Human Pluripotent Stem Cell‐Derived Retinal Cell Types

Cell type‐specific investigations commonly use gene reporters or single‐cell analytical techniques. However, reporter line development is arduous and generally limited to a single gene of interest, while single‐cell RNA (scRNA)‐sequencing (seq) frequently yields equivocal results that preclude definitive cell identification. To examine gene expression profiles of multiple retinal cell types derived from human pluripotent stem cells (hPSCs), we performed scRNA‐seq on optic vesicle (OV)‐like structures cultured under cGMP‐compatible conditions. However, efforts to apply traditional scRNA‐seq analytical methods based on unbiased algorithms were unrevealing. Therefore, we developed a simple, versatile, and universally applicable approach that generates gene expression data akin to those obtained from reporter lines. This method ranks single cells by expression level of a bait gene and searches the transcriptome for genes whose cell‐to‐cell rank order expression most closely matches that of the bait. Moreover, multiple bait genes can be combined to refine datasets. Using this approach, we provide further evidence for the authenticity of hPSC‐derived retinal cell types. StemCells2018;36:313–324 Schematic of a novel single cell RNA‐seq analysis method using Spearman's rank correlation coefficient analysis (SRCCA). By selecting “bait” genes indicative of individual cell types, subsequent application of SRCCA allows rapid, in‐depth examination of stem cell‐derived retinal cell progeny and gene profiling of individual retinal cell types. This simple and versatile analytical method is applicable to any complex culture or tissue.