Abstract
Purpose :
Retinal ganglion cells (RGCs) in primate have been classified into at least 19 distinct subtypes based on physiological and anatomical criteria, however, molecular correlates of these distinctions are lacking. In order to identify genes that define RGC classes we performed a transcriptional analysis using RNA-seq on physiologically and anatomically characterized single neurons.
Methods :
Whole retinas from macaque monkeys and humans were maintained in vitro in oxygenated culture medium for approximately 48 hrs. Using differential interference contrast optics at high magnification, cells (20 monkey, 22 human) within the RGC layer were characterized using morphological and/or physiological criteria. Following classification, cellular contents (nucleus and somatic cytoplasm) were micro- aspirated into cell lysis buffer, flash frozen and maintained at -80oC. Transcriptional profiling of isolated RGCs was performed by high-throughput RNA sequencing (RNA-seq). Samples were processed for RNA-seq library construction using the Ovation Single Cell RNA-seq System (Nugen Inc.). Raw paired sequence reads were mapped to the macaque and/or human genome builds using the Tophat/Cufflinks pipeline and HTSeq was used to obtain raw counts of reads. Non-dirceted hierarchical clustering analysis was used to group transcriptionally related cells followed by assignment of physiological and anatomical characteristics to determine if resulting clusters were functionally relevant.
Results :
Thousands of genes showing differential cellular expression were identified. Non-directed hierarchical clustering analysis of normalized read counts (expressed as reads per kilobase of exon per million reads (RPKM)) segregated cells into biologically relevant groups. In particular, cells classified as OFF or ON RGCs mapped to distinct clusters. Ingenuity pathway analysis identified genes related to development of cell morphology and signaling. Additional clusters are emerging as diversity of cell types increases.
Conclusions :
Transcriptional profiling primate RGCs at the single-cell level provides an unprecedented insight into the genetic diversity of such cells and has the potential to identify novel genes specific to functionally distinct RGC cell types. Such cellular markers will enhance our understanding of neuronal organization within the visual system and provide novel means with which to assess cellular loss in retinal disease.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.