Adaptive Sampling for Nanopore direct RNA sequencing

Abstract

Nanopore long-read sequencing enables real-time monitoring and controlling of individual nanopores. This allows to enrich or deplete specific sequences in DNA sequencing in a process called “adaptive sampling”. So far, adaptive sampling was not applicable to direct sequencing of RNA. Here, we show that adaptive sampling is feasible and useful for direct RNA sequencing, which has its specific technical and biological challenges. Employing a well-controlled in vitro transcript-based model system, we identify essential characteristics and parameter settings for adaptive sampling in direct RNA sequencing, as the superior performance of depletion over enrichment. Here, the efficiency of depletion is close to the theoretical maximum. Additionally, we demonstrate that adaptive sampling efficiently depletes specific transcripts in transcriptome-wide sequencing applications. Specifically, we applied our adaptive sampling approach to polyA-enriched RNA samples from human induced pluripotent stem cell-derived cardiomyocytes and mouse whole heart tissue and show efficient 2.5 to 2.8 fold depletion of highly abundant mitochondrial-encoded transcripts. Finally, we characterize depletion and enrichment performance for complex transcriptome subsets i.e. at the level of the entire Chromosome 11, proving the general applicability of direct RNA adaptive sampling. Our analyses provide evidence that adaptive sampling is especially useful to enable detection of lowly expressed transcripts and reduce the sequencing of highly abundant disturbing transcripts. Workflow and sequencing data are provided on Zenodo: https://doi.org/10.5281/zenodo.7701823