Network-based bioinformatics analysis of spatio-temporal RNA-Seq data reveals transcriptional programs underpinning normal and aberrant retinal development

Abstract

BackgroundThe retina as a model system with extensive information on genes involved in development/maintenance is of great value for investigations employing deep sequencing to capture transcriptome change over time. This in turn could enable us to find patterns in gene expression across time to reveal transition in biological processes.MethodsWe developed a bioinformatics pipeline to categorize genes based on their differential expression and their alternative splicing status across time by binning genes based on their transcriptional kinetics. Genes within same bins were then leveraged to query gene annotation databases to discover molecular programs employed by the developing retina.ResultsUsing our pipeline on RNA-Seq data obtained from fractionated (nucleus/cytoplasm) developing retina at embryonic day (E) 16 and postnatal day (P) 0, we captured high-resolution as in the difference between the cytoplasm and the nucleus at the same developmental time. We found de novo transcription of genes whose transcripts were exclusively found in the nuclear transcriptome at P0. Further analysis showed that these genes enriched for functions that are known to be executed during postnatal development, thus showing that the P0 nuclear transcriptome is temporally ahead of that of its cytoplasm. We extended our strategy to perform temporal analysis comparing P0 data to either P21-Nrl-wildtype (WT) or P21-Nrl-knockout (KO) retinae, which predicted that the KO retina would have compromised vasculature. Indeed, histological manifestation of vasodilation has been reported at a later time point (P60).ConclusionsThus, our approach was predictive of a phenotype before it presented histologically. Our strategy can be extended to investigating the development and/or disease progression of other tissue types.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2822-z) contains supplementary material, which is available to authorized users.

Investigating cone photoreceptor development using patient-derived NRL null retinal organoids

“…While previous studies have utilized scRNAseq to identify cell types of developing retinal organoids, they have not discerned distinct photoreceptor sub-populations [26][27][28] . Additionally, while transcriptomics have been used to characterize Nrl loss in murine photoreceptors, these analyses were not performed at a single-cell level, thus limiting the potential to identify and characterize sub-populations [29][30][31] .…”

GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish

“…GRIK5 is expressed in endothelial cells ( Figure S3C) 38,53,54 but has also been shown to be expressed in mice in the photoreceptor cells of the retina. 56 We observed GRIK5 to be highly expressed in the eye of 3-day-old zebrafish embryos ( Figure S3). Thus, additional early or later biological consequences of reduced expression of GRIK5 might also contribute to the development of eye disease.…”

Transcriptome Analysis of Novosphingobium pentaromativorans US6-1 Reveals the Rsh Regulon and Potential Molecular Mechanisms of N-acyl-l-homoserine Lactone Accumulation

“…KEGG-based annotation and pathway enrichment analysis was performed using the KEGG pathway database ( , 25 September 2017). All these KEGG terms were decided by a Fisher’s exact test with phyper function in R language [ 42 ]. An adjust p value of 0.01 was used as the threshold to obtain significantly over-represented KEGG terms.…”

“…KEGG annotation and pathway enrichment analysis was performed using the KEGG pathway database ( ). The p values denote the enriched levels in a KEGG pathway, which was calculated using a Fisher’s exact test [ 42 ]. Up- and down-regulated genes (∆ rsh vs. US6-1) are represented by red and blue bars respectively, representing the number of genes per functional category.…”