Optimization of circular RNA enrichment in control RNA
Circular RNAs (circRNAs) are a class of novel, non-coding RNA species that were first observed in the 1970s and thought to be a byproduct of atypical splicing, with no biological significance. However, with the advent of next-generation sequencing and advanced computational methods for sequencing data analysis, numerous circRNAs, some of which are evolutionarily conserved, have been detected across various tissue types and have been shown to be involved in transcriptional regulation. In the current study, our goal is to optimize enrichment of circRNAs by evaluating multiple parameters including using different input amounts (1 and 2 µg), tissue types (testis, brain, universal, cerebellum, and liver), and enrichment methods in control RNA samples (n=8). To enrich for circRNAs, we first depleted ribosomal RNA (rRNA) in six of the eight samples. We then depleted linear RNA in all samples using RNase R treatment. The remaining RNA was converted into a complementary DNA (cDNA) library, quantified, sequenced, and analyzed for the presence of circRNAs. We observed that depleting rRNA enhances our ability to detect circRNAs, larger input amounts of RNA yield higher numbers of unique circRNAs, and neurological tissues contain an abundance of circRNAs compared to other tissue types. Optimizing circRNA enrichment, and understanding the impact of different parameters on data analysis, are both key in streamlining the process of comparing these novel RNA species between healthy and disease affected tissues and in the interpretation of sequencing data. We plan to extend our analysis to evaluate circRNA detection when using degraded RNA samples, larger input amounts, and biological replicates. Additionally, we will investigate circRNA abundance and expression in disease affected tissues, as well as other tissue types.