Enhanced transcriptome-wide RNA G-quadruplex sequencing for low RNA input samples with rG4-seq 2.0

Background RNA G-quadruplexes (rG4s) are non-canonical structural motifs that have diverse functional and regulatory roles, for instance in transcription termination, alternative splicing, mRNA localization and stabilization, and translational process. We recently developed the RNA G-quadruplex structure sequencing (rG4-seq) technique and described rG4s in both eukaryotic and prokaryotic transcriptomes. However, rG4-seq suffers from a complicated gel purification step and limited PCR product yield, thus requiring a high amount of RNA input, which limits its applicability in more physiologically or clinically relevant studies often characterized by the limited availability of biological material and low RNA abundance. Here, we redesign and enhance the workflow of rG4-seq to address this issue. Results We developed rG4-seq 2.0 by introducing a new ssDNA adapter containing deoxyuridine during library preparation to enhance library quality with no gel purification step, less PCR amplification cycles and higher yield of PCR products. We demonstrate that rG4-seq 2.0 produces high-quality cDNA libraries that support reliable and reproducible rG4 identification at varying RNA inputs, including RNA mounts as low as 10 ng. rG4-seq 2.0 also improved the rG4-seq calling outcome and nucleotide bias in rG4 detection persistent in rG4-seq 1.0. We further provide in vitro mapping of rG4 in the HEK293T cell line, and recommendations for assessing RNA input and sequencing depth for individual rG4 studies based on transcript abundance. Conclusions rG4-seq 2.0 can improve the identification and study of rG4s in low abundance transcripts, and our findings can provide insights to optimize cDNA library preparation in other related methods.

Automated summary

RNA G-quadruplex structure sequencing (rG4-seq) technique has been enhanced in version 2.0 to eliminate the need for gel purification, reduce PCR cycles, and increase PCR product yield, allowing for reliable identification of rG4s in low abundance transcripts. The improvements in rG4-seq 2.0 also address nucleotide biases and enhance the quality of cDNA libraries for further optimization in related methods.