Improved nanopore direct RNA sequencing of cardiac myocyte samples by selective mt-RNA depletion

Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduces their respective read counts by more than 85%, resulting in an increase in sequencing depth of 47% on average for long non-coding RNAs. We also apply RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as an increase in non-globin reads by 16% on average. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER's modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser .

Blocking Abundant RNA Transcripts by High-Affinity Oligonucleotides during Transcriptome Library Preparation

BACKGROUND

RNA sequencing has become the gold standard for transcriptome analysis but has an inherent limitation of challenging quantification of low-abundant transcripts. In contrast to microarray technology, RNA sequencing reads are proportionally divided in function of transcript abundance. Therefore, low-abundant RNAs compete against highly abundant - and sometimes non-informative - RNA species.

RESULTS

We developed an easy-to-use strategy based on high-affinity RNA-binding oligonucleotides to block reverse transcription and PCR amplification of specific RNA transcripts, thereby substantially reducing their abundance in the final sequencing library. To demonstrate the broad application potential of our method, we applied it to different transcripts and library preparation strategies, including YRNAs in small RNA sequencing of human blood plasma, mitochondrial rRNAs in both 3' end sequencing and long-read sequencing, and MALAT1 in single-cell 3' end sequencing. We demonstrate that the blocking strategy is highly efficient, reproducible, specific, and generally results in better transcriptome coverage and complexity.

CONCLUSION

Our method does not require modifications of the library preparation procedure apart from simply adding blocking oligonucleotides to the RT reaction and can thus be easily integrated into virtually any RNA sequencing library preparation protocol.

Targeted Analysis of circRNA Expression in Patient Samples by Lexo-circSeq

Recently, circular RNAs (circRNAs) have been extensively studied in animals and plants. circRNAs are generated by backsplicing from the same linear transcripts that are canonically spliced to produce, for example, mature mRNAs. circRNAs exhibit tissue-specific expression and are potentially involved in many diseases, among them cardiovascular diseases. The comprehensive analysis of circRNA expression patterns across larger patient cohorts requires a streamlined and cost-effective workflow designed to meet small input requirements. In this article, we present Lexo-circSeq, a targeted RNA sequencing approach that can profile up to 110 circRNAs and their corresponding linear transcripts in one experiment. We established Lexo-circSeq employing total human heart RNA and show that our protocol can detect depletion of a specific circRNA in hiPSC-derived cardiomyocytes. Finally, Lexo-circSeq was applied to biopsies from patients diagnosed with dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM), respectively. Interestingly, our results indicate that circular-to-linear-ratios for circSLC8A1 and circRBM33 are deregulated in cardiomyopathy.