Abstract
Purpose :
Photoreceptors cells (PRC) are highly specialized cells with high metabolic requirements. The homeostasis maintenance of these cells relies on a plethora of genes involved in different functions converging into pathways that ultimately are involved in the PRC survival. Mutations in these genes, leading to PRC degeneration, have been documented; what is missing is to decipher the molecular principles underlying the gene disruption. The retina is a heterogeneous tissue composed of six types of neurons and one type of glia. Traditional qPCR analysis of the whole retina mask the true signature of differentially expressed genes in specific cell types. The purpose of this study is to investigate transcriptomics, specifically in PRC, and to define networks and pathways linked to retina functions and diseases at the single cell level.
Methods :
Wild type mouse retinas were dissociated using papain and trituration to obtain an enriched supernatant fraction composed of isolated PRC. Cells captured in microfluidic chambers were lysed. Their content was then reverse transcribed and subsequent cDNA was pre-amplified for qPCR processing. Primer sets targeting specific biomarkers were used to confirm the cells identity; primer sets for candidates and known targets for retinal functions and diseases were tested.
Results :
Genes such as Nrf2, Elovl4, Peripherin2 and Tulp1 were found to be amongst the highly expressed genes in our dataset. Since this analysis was performed in wild type PRC, the other genes linked to cell stress, such as Bad, TNF-alpha and IL-6, were found at low abundance or near undetectable levels.
Conclusions :
We were able to positively detect differential expression of genes in relatively high level in isolated PRC. Amongst these genes, AdipoR1 was highly expressed. This gene encodes a protein that when genetically ablated , leads to a “flecked form” of retinal degeneration in mouse (Rice et al 2015) and is involved in human autosomal dominant retinitis pigmentosa (Zhang, J. Human Genetics 2016). This approach compares, defines and analyzes gene expression and gene networks in isolated photoreceptor cells and offers an innovative method for identifying the genes signatures in a given cell type.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.