Direct detection of RNA modifications and structure using single-molecule nanopore sequencing

The RMaP challenge of predicting RNA modifications by nanopore sequencing

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data. Consequently, the field's cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RNA Modification and Processing (RMaP) challenge to advance solutions for RNA modification detection and discuss ideas, problems and approaches. We show several computational methods to detect the most researched mRNA modifications (m6A, ψ, and m5C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms' comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.

Probing nanopores: molecular dynamics insights into the mechanisms of DNA and protein translocation through solid-state and biological nanopores

Nanopore sequencing technology has revolutionized single-molecule analysis through its unique capability to detect and characterize individual biomolecules with unprecedented precision. This perspective provides a comprehensive analysis of molecular dynamics (MD) simulations in nanopore research, with particular emphasis on comparing molecular transport mechanisms between biological and solid-state platforms. We first examine how MD simulations at atomic resolution reveal distinct characteristics: biological nanopores exhibit sophisticated molecular recognition through specific amino acid interactions, while solid-state nanopores demonstrate advantages in structural stability and geometric control. Through detailed analysis of simulation methodologies and their applications, we show how computational approaches have advanced our understanding of critical phenomena such as ion selectivity, conformational dynamics, and surface effects in both nanopore types. Despite computational challenges including limited simulation timescales and force field accuracy constraints, recent advances in high-performance computing and artificial intelligence integration have significantly improved simulation capabilities. By synthesizing perspectives from physics, chemistry, biology, and computational science, this perspective provides both theoretical insights and practical guidelines for developing next-generation nanopore platforms. The integration of computational and experimental approaches discussed here offers promising directions for advancing nanopore technology in applications ranging from DNA/RNA sequencing and protein post-translational modification analysis to disease diagnosis and drug screening.

A novel photoelectrochemical strategy for ultrasensitive and simultaneous detection of 5-methylcytosine and N6-methyladenosine based on proximity binding-triggered assembly MNAzyme -mediated HRCA

The preparation of UiO-66@CdTe@AuNPs composites is presented for the first time, which function as a photoelectrochemical (PEC) sensing matrix and are conjugated to a three-way junction (TWJ). We propose an antibody-based specific recognition-induced neighbor-joining reaction that initiates the assembly of two molecularly designed nucleic acid enzymes (MNAzymes), to release an oligonucleotide that hydrolyzes TWJ through the mechanism of a toehold-mediated strand displacement reaction (TSDR). Subsequently, a hybridization chain reaction application (HRCA)-based dendrimer is formed, which immobilizes a large number of quantum dots to generate a burst effect that reduces the photocurrent signal. As anticipated, the developed PEC biosensor showed excellent analytical performance for both m6A-RNA and m5C-RNA, with detection limits of 0.309 fM and 6.918 aM, respectively. The successful fabrication of this ultrasensitive and simultaneous PEC biosensor provides new insights for epigenetic research and the bioassay, mechanism investigation and clinical diagnosis of diseases associated with RNA methylation.

The Impact of Viral Concentration Method on Quantification and Long Amplicon Nanopore Sequencing of SARS-CoV-2 and Noroviruses in Wastewater

Wastewater-based surveillance has gained attention in the four years following the start of the COVID-19 pandemic. Accurate pathogen detection, quantification and characterisation rely on the selection of appropriate methodologies. Here, we explore the impact of viral concentration method on RT-qPCR inhibition and quantification of norovirus genogroups I and II (GI and GII), crAssphage, phi6 and SARS-CoV-2. Additionally, their impact on long amplicon sequencing for typing noroviruses and whole-genome sequencing (WGS) SARS-CoV-2 was explored. RT-qPCR inhibition for each viral concentration method was significantly different apart from the two ultrafiltration methods, InnovaPrep® concentrating pipette (IP) and Vivaspin® (VS) centrifugal concentrators. Using an ultrafiltration method reduced inhibition by 62.0% to 96.0% compared to the ammonium sulphate (AS) and polyethylene glycol (PEG) precipitation-based methods. Viral quantification was significantly impacted by concentration method with the highest concentrations (copies/L) observed for VS with 7.2- to 83.2-fold differences from AS depending on the target. Norovirus long amplicon sequencing showed genotype-dependent differences with IP performing best for GI and VS for GII although IP performance gains for GI were relatively small. VS outperformed AS and IP across all metrics during SARS-CoV-2 WGS. Overall, VS performed the best when considering all the areas of investigation.

High-throughput sequencing: a breakthrough in molecular diagnosis for precision medicine

High-resolution insights into the nucleotide arrangement within an organism's genome are pivotal for deciphering its genetic composition, function, and evolutionary trajectory. Over the years, nucleic acid sequencing has been instrumental in driving significant advancements in genomics and molecular biology. The advent of high-throughput or next-generation sequencing (NGS) technologies has revolutionized whole genome sequencing, revealing novel and intriguing features of genomes, such as single nucleotide polymorphisms and lethal mutations in both coding and non-coding regions. These platforms provide a practical approach to comprehensively identifying and analyzing whole genomes with remarkable throughput, accuracy, and scalability within a short time frame. The resulting data holds immense potential for enhancing healthcare systems, developing novel and personalized therapies, and preparing for future pandemics and outbreaks. Given the wide array of available high-throughput sequencing platforms, selecting the appropriate technology based on specific needs is crucial. However, there is limited information regarding sample preparation, sequencing principles, and output data to facilitate a comparative evaluation of these platforms. This review details various NGS technologies and approaches, examining their advantages, limitations, and future potential. Despite being in their early stages and facing challenges, ongoing advancements in NGS are expected to yield significant future benefits.

Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

To date, over 40 epigenetic and 300 epitranscriptomic modifications have been identified. However, current short-read sequencing-based experimental methods can detect <10% of these modifications. Integrating long-read sequencing technologies with advanced computational approaches, including statistical analysis and machine learning, offers a promising new frontier to address this challenge. While supervised machine learning methods have achieved some success, their usefulness is restricted to a limited number of well-characterized modifications. Here, we introduce Modena, an innovative unsupervised learning approach utilizing long-read nanopore sequencing capable of detecting a broad range of modifications. Modena outperformed other methods in five out of six benchmark datasets, in some cases by a wide margin, while being equally competitive with the second best method on one dataset. Uniquely, Modena also demonstrates consistent accuracy on a DNA dataset, distinguishing it from other approaches. A key feature of Modena is its use of 'dynamic thresholding', an approach based on 1D score-clustering. This methodology differs substantially from the traditional statistics-based 'hard-thresholds.' We show that this approach is not limited to Modena but has broader applicability. Specifically, when combined with two existing algorithms, 'dynamic thresholding' significantly enhances their performance, resulting in up to a threefold improvement in F1-scores.

The detection, function, and therapeutic potential of RNA 2'-O-methylation

RNA modifications play crucial roles in shaping RNA structure, function, and metabolism. Their dysregulation has been associated with many diseases, including cancer, developmental disorders, cardiovascular diseases, as well as neurological and immune-related conditions. A particular type of RNA modification, 2'-O-methylation (Nm) stands out due to its widespread occurrence on all four types of nucleotides (A, U, G, C) and in most RNA categories, e.g., mRNA, rRNA, tRNA, miRNA, snRNA, snoRNA, and viral RNA. Nm is the addition of a methyl group to the 2' hydroxyl of the ribose moiety of a nucleoside. Given its great biological significance and reported association with many diseases, we first reviewed the occurrences and functional implications of Nm in various RNA species. We then summarized the reported Nm detection methods, ranging from biochemical techniques in the 70's and 80's to recent methods based on Illumina RNA sequencing, artificial intelligence (AI) models for computational prediction, and the latest nanopore sequencing methods currently under active development. Moreover, we discussed the applications of Nm in the realm of RNA medicine, highlighting its therapeutic potential. At last, we present perspectives on potential research directions, aiming to offer insights for future investigations on Nm modification.

RNA methylation and breast cancer: insights into m6A, m7G and m5C

Breast cancer remains the most commonly diagnosed cancer in female worldwide, marked by its molecular diversity and complex subtypes. Despite progress in targeted therapies, tumor heterogeneity and treatment resistance continue to present major challenges. Recent studies emphasize the crucial role of RNA modifications in cancer biology, with nearly 200 distinct modifications identified. Among these, methylation is particularly significant, with methylation-related factors emerging as key regulators of RNA metabolism, influencing cancer progression, metastasis, and treatment resistance. This review focuses on the roles of key RNA methylation in breast cancer, particularly N6-methyladenosine (m6A), N7-methylguanosine (m7G), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N3-methylcytidine (m3C). We examine the functions of m6A "writers" like METTL3 and METTL14, and "readers" such as the YTH domain family in modulating tumor behavior. Dysregulation of m6A "erasers" like FTO and ALKBH5 are noticed too, highlighting their impact on cancer stem cell phenotypes, chemoresistance, and immune evasion. Additionally, the role of m7G modifications in mRNA stability and translation, facilitated by METTL1/WDR4 and RNMT, is discussed as a potential therapeutic target. The involvement of m5C, m1A, and m3C modifications, particularly those mediated by NSUN2 and NSUN6, in breast cancer tumorigenesis and prognosis is also reviewed. Despite coding RNAs, the interplay between these RNA methylations and non-coding RNAs, such as lncRNAs and miRNAs, is explored, shedding light on their roles in cancer cell proliferation, invasion, and immune response modulation. This review highlights the potential of RNA methylations as novel therapeutic targets in breast cancer, offering insights for precision medicine and improved patient outcomes.

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

The biological relevance and dynamics of mRNA modifications have been extensively studied; however, whether rRNA modifications are dynamically regulated, and under which conditions, remains unclear. Here, we systematically characterize bacterial rRNA modifications upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we develop NanoConsensus, a novel pipeline that is robust across RNA modification types, stoichiometries and coverage, with very low false positive rates, outperforming all individual algorithms tested. We then apply NanoConsensus to characterize the rRNA modification landscape upon antibiotic exposure, finding that rRNA modification profiles are altered in the vicinity of A and P-sites of the ribosome, in an antibiotic-specific manner, possibly contributing to antibiotic resistance. Our work demonstrates that rRNA modification profiles can be rapidly altered in response to environmental exposures, and provides a robust workflow to study rRNA modification dynamics in any species, in a scalable and reproducible manner.

Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models

In recent years, nanopore direct RNA sequencing (DRS) became a valuable tool for studying the epitranscriptome, owing to its ability to detect multiple modifications within the same full-length native RNA molecules. Although RNA modifications can be identified in the form of systematic basecalling "errors" in DRS data sets, N6-methyladenosine (m6A) modifications produce relatively low "errors" compared with other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the "error" signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads-especially in shorter RNA fractions-and increased basecalling error signatures at pseudouridine (Ψ)- and N1-methylpseudouridine (m1Ψ)-modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS data sets.

Nanopore sequencing of intact aminoacylated tRNAs

Transfer RNAs (tRNA) are decorated during biogenesis with a variety of modifications that modulate their stability, aminoacylation, and decoding potential during translation. The complex landscape of tRNA modification presents significant analysis challenges and to date no single approach enables the simultaneous measurement of important but disparate chemical properties of individual, mature tRNA molecules. We developed a new, integrated approach to analyze the sequence, modification, and aminoacylation state of tRNA molecules in a high throughput nanopore sequencing experiment, leveraging a chemical ligation that embeds the charged amino acid in an adapted tRNA molecule. During nanopore sequencing, the embedded amino acid generates unique distortions in ionic current and translocation speed, enabling application of machine learning approaches to classify charging status and amino acid identity. Specific applications of the method indicate it will be broadly useful for examining relationships and dependencies between tRNA sequence, modification, and aminoacylation.

Nanopore-based detection of periodontitis biomarker miR31 in saliva samples

MicroRNAs (miRNAs) play important roles in posttranscriptional gene regulation. Aberrations in the miRNA levels have been the cause behind various diseases, including periodontitis. Therefore, sensitive, specific, and accurate detection of disease-associated miRNAs is vital to early diagnosis and can facilitate inhibitor screening and drug design. In this study, we developed a label-free, real-time sensing method for the detection of miR31, which has been frequently linked to periodontitis, using an engineered protein nanopore and in the presence of a complementary ssDNA as a molecular probe. Our method is rapid and highly sensitive with nanomolar concentration of miR31 that could be determined in minutes. Furthermore, our sensor showed high selectivity toward the target miR31 sequence even in the presence of interfering nucleic acids. In addition, artificial saliva and human saliva samples were successfully analyzed. Our developed nanopore sensing platform could be used to detect other miRNAs and offers a potential application for the clinical diagnosis of disease biomarkers.

Unraveling Clinical Glycoproteome by Integrating Affinity Enrichment with Nanopore Sequencing

This prospect explores the integration of enrichment strategies with nanopore detection to advance clinical glycoproteomics. Glycoproteins, crucial for understanding biological processes, pose challenges due to their low abundance and structural diversity. Enrichment techniques using lectin affinity, boronate affinity, and hydrazide chemistry and especially molecular imprinted polymers may selectively and specifically isolate glycoproteins from complex samples, while nanopore technology enables label-free, real-time, and single-molecule analysis. This approach holds promise for disease-related glycosylation studies, biomarker discovery, personalized medicine, and streamlined clinical analysis. Standardization, optimization, and data analysis remain challenges, requiring interdisciplinary collaborations and technological advancements. Overall, this integration may offer transformative potential for clinical glycoproteomics and innovative diagnostic and therapeutic strategies.

Advances in long-read single-cell transcriptomics

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Nanopore signal deviations from pseudouridine modifications in RNA are sequence-specific: quantification requires dedicated synthetic controls

Chemical modifications to mRNA respond dynamically to environmental cues and are important modulators of gene expression. Nanopore direct RNA sequencing has been applied for assessing the presence of pseudouridine (ψ) modifications through basecalling errors and signal analysis. These approaches strongly depend on the sequence context around the modification, and the occupancies derived from these measurements are not quantitative. In this work, we combine direct RNA sequencing of synthetic RNAs bearing site-specific modifications and supervised machine learning models (ModQuant) to achieve near-analytical, site-specific ψ quantification. Our models demonstrate that the ionic current signal features important for accurate ψ classification are sequence dependent and encompass information extending beyond n + 2 and n - 2 nucleotides from the ψ site. This is contradictory to current models, which assume that accurate ψ classification can be achieved with signal information confined to the 5-nucleotide k-mer window (n + 2 and n - 2 nucleotides from the ψ site). We applied our models to quantitatively profile ψ occupancy in five mRNA sites in datasets from seven human cell lines, demonstrating conserved and variable sites. Our study motivates a wider pipeline that uses ground-truth RNA control sets with site-specific modifications for quantitative profiling of RNA modifications. The ModQuant pipeline and guide are freely available at https://github.com/wanunulab/ModQuant .

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coli and Staphylococcus aureus and extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.

R2Dtool: integration and visualization of isoform-resolved RNA features

MOTIVATION

Long-read RNA sequencing enables the mapping of RNA modifications, structures, and protein-interaction sites at the resolution of individual transcript isoforms. To understand the functions of these RNA features, it is critical to analyze them in the context of transcriptomic and genomic annotations, such as open reading frames and splice junctions.

RESULTS

We have developed R2Dtool, a bioinformatics tool that integrates transcript-mapped information with transcript and genome annotations, allowing for the isoform-resolved analytics and graphical representation of RNA features in their genomic context. We illustrate R2Dtool's capability to integrate and expedite RNA feature analysis using epitranscriptomics data. R2Dtool facilitates the comprehensive analysis and interpretation of alternative transcript isoforms.

AVAILABILITY AND IMPLEMENTATION

R2Dtool is freely available under the MIT license at github.com/comprna/R2Dtool.

Interactive visualization of nanopore sequencing signal data with Squigualiser

MOTIVATION

Nanopore sequencing current signal data can be 'basecalled' into sequence information or analysed directly, with the capacity to identify diverse molecular features, such as DNA/RNA base modifications and secondary structures. However, raw signal data is large and complex, and there is a need for improved visualization strategies to facilitate signal analysis, exploration and tool development.

RESULTS

Squigualiser (Squiggle visualiser) is a toolkit for intuitive, interactive visualization of sequence-aligned signal data, which currently supports both DNA and RNA sequencing data from Oxford Nanopore Technologies instruments. Squigualiser is compatible with a wide range of alternative signal-alignment software packages and enables visualization of both signal-to-read and signal-to-reference aligned data at single-base resolution. Squigualiser generates an interactive signal browser view (HTML file), in which the user can navigate across a genome/transcriptome region and customize the display. Multiple independent reads are integrated into a 'signal pileup' format and different datasets can be displayed as parallel tracks. Although other methods exist, Squigualiser provides the community with a software package purpose-built for raw signal data visualization, incorporating a range of new and existing features into a unified platform.

AVAILABILITY AND IMPLEMENTATION

Squigualiser is an open-source package under an MIT licence: https://github.com/hiruna72/squigualiser. The software was developed using Python 3.8 and can be installed with pip or bioconda or executed directly using prebuilt binaries provided with each release.

Comparative analysis of 43 distinct RNA modifications by nanopore tRNA sequencing

Transfer RNAs are the fundamental adapter molecules of protein synthesis and the most abundant and heterogeneous class of noncoding RNA molecules in cells. The study of tRNA repertoires remains challenging, complicated by the presence of dozens of post transcriptional modifications. Nanopore sequencing is an emerging technology with promise for both tRNA sequencing and the detection of RNA modifications; however, such studies have been limited by the throughput and accuracy of direct RNA sequencing methods. Moreover, detection of the complete set of tRNA modifications by nanopore sequencing remains challenging. Here we show that recent updates to nanopore direct RNA sequencing chemistry (RNA004) combined with our own optimizations to tRNA sequencing protocols and analysis workflows enable high throughput coverage of tRNA molecules and characterization of nanopore signals produced by 43 distinct RNA modifications. We share best practices and protocols for nanopore sequencing of tRNA and further report successful detection of low abundance mitochondrial and viral tRNAs, providing proof of concept for use of nanopore sequencing to study tRNA populations in the context of infection and organelle biology. This work provides a roadmap to guide future efforts towards de novo detection of RNA modifications across multiple organisms using nanopore sequencing.

Beta-Barrel Nanopores as Diagnostic Sensors: An Engineering Perspective

Biological nanopores are ultrasensitive and highly attractive platforms for disease diagnostics, including the sequencing of viral and microbial genes and the detection of biomarkers and pathogens. To utilize biological nanopores as diagnostic sensors, they have been engineered through various methods resulting in the accurate and highly sensitive detection of biomarkers and disease-related biomolecules. Among diverse biological nanopores, the β-barrel-containing nanopores have advantages in nanopore engineering because of their robust structure, making them well-suited for modifications. In this review, we highlight the engineering approaches for β-barrel-containing nanopores used in single-molecule sensing for applications in early diagnosis and prognosis. In the highlighted studies, β-barrel nanopores can be modified by genetic mutation to change the structure; alter charge distributions; or add enzymes, aptamers, and protein probes to enhance sensitivity and accuracy. Furthermore, this review discusses challenges and future perspectives for advancing nanopore-based diagnostic sensors.

Nanopore tweezers show fractional-nucleotide translocation in sequence-dependent pausing by RNA polymerase

RNA polymerases (RNAPs) carry out the first step in the central dogma of molecular biology by transcribing DNA into RNA. Despite their importance, much about how RNAPs work remains unclear, in part because the small (3.4 Angstrom) and fast (~40 ms/nt) steps during transcription were difficult to resolve. Here, we used high-resolution nanopore tweezers to observe the motion of single Escherichia coli RNAP molecules as it transcribes DNA ~1,000 times improved temporal resolution, resolving single-nucleotide and fractional-nucleotide steps of individual RNAPs at saturating nucleoside triphosphate concentrations. We analyzed RNAP during processive transcription elongation and sequence-dependent pausing at the yrbL elemental pause sequence. Each time RNAP encounters the yrbL elemental pause sequence, it rapidly interconverts between five translocational states, residing predominantly in a half-translocated state. The kinetics and force-dependence of this half-translocated state indicate it is a functional intermediate between pre- and post-translocated states. Using structural and kinetics data, we show that, in the half-translocated and post-translocated states, sequence-specific protein-DNA interaction occurs between RNAP and a guanine base at the downstream end of the transcription bubble (core recognition element). Kinetic data show that this interaction stabilizes the half-translocated and post-translocated states relative to the pre-translocated state. We develop a kinetic model for RNAP at the yrbL pause and discuss this in the context of key structural features.

Co-transcriptional gene regulation in eukaryotes and prokaryotes

Many steps of RNA processing occur during transcription by RNA polymerases. Co-transcriptional activities are deemed commonplace in prokaryotes, in which the lack of membrane barriers allows mixing of all gene expression steps, from transcription to translation. In the past decade, an extraordinary level of coordination between transcription and RNA processing has emerged in eukaryotes. In this Review, we discuss recent developments in our understanding of co-transcriptional gene regulation in both eukaryotes and prokaryotes, comparing methodologies and mechanisms, and highlight striking parallels in how RNA polymerases interact with the machineries that act on nascent RNA. The development of RNA sequencing and imaging techniques that detect transient transcription and RNA processing intermediates has facilitated discoveries of transcription coordination with splicing, 3'-end cleavage and dynamic RNA folding and revealed physical contacts between processing machineries and RNA polymerases. Such studies indicate that intron retention in a given nascent transcript can prevent 3'-end cleavage and cause transcriptional readthrough, which is a hallmark of eukaryotic cellular stress responses. We also discuss how coordination between nascent RNA biogenesis and transcription drives fundamental aspects of gene expression in both prokaryotes and eukaryotes.

Isoform-specific RNA structure determination using Nano-DMS-MaP

RNA structure determination is essential to understand how RNA carries out its diverse biological functions. In cells, RNA isoforms are readily expressed with partial variations within their sequences due, for example, to alternative splicing, heterogeneity in the transcription start site, RNA processing or differential termination/polyadenylation. Nanopore dimethyl sulfate mutational profiling (Nano-DMS-MaP) is a method for in situ isoform-specific RNA structure determination. Unlike similar methods that rely on short sequencing reads, Nano-DMS-MaP employs nanopore sequencing to resolve the structures of long and highly similar RNA molecules to reveal their previously hidden structural differences. This Protocol describes the development and applications of Nano-DMS-MaP and outlines the main considerations for designing and implementing a successful experiment: from bench to data analysis. In cell probing experiments can be carried out by an experienced molecular biologist in 3-4 d. Data analysis requires good knowledge of command line tools and Python scripts and requires a further 3-5 d.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Single-molecule epitranscriptomic analysis of full-length HIV-1 RNAs reveals functional roles of site-specific m6As

Although the significance of chemical modifications on RNA is acknowledged, the evolutionary benefits and specific roles in human immunodeficiency virus (HIV-1) replication remain elusive. Most studies have provided only population-averaged values of modifications for fragmented RNAs at low resolution and have relied on indirect analyses of phenotypic effects by perturbing host effectors. Here we analysed chemical modifications on HIV-1 RNAs at the full-length, single RNA level and nucleotide resolution using direct RNA sequencing methods. Our data reveal an unexpectedly simple HIV-1 modification landscape, highlighting three predominant N6-methyladenosine (m6A) modifications near the 3' end. More densely installed in spliced viral messenger RNAs than in genomic RNAs, these m6As play a crucial role in maintaining normal levels of HIV-1 RNA splicing and translation. HIV-1 generates diverse RNA subspecies with distinct m6A ensembles, and maintaining multiple of these m6As on its RNAs provides additional stability and resilience to HIV-1 replication, suggesting an unexplored viral RNA-level evolutionary strategy.

Detection of ribonucleotides embedded in DNA by Nanopore sequencing

Ribonucleotides represent the most common non-canonical nucleotides found in eukaryotic genomes. The sources of chromosome-embedded ribonucleotides and the mechanisms by which unrepaired rNMPs trigger genome instability and human pathologies are not fully understood. The available sequencing technologies only allow to indirectly deduce the genomic location of rNMPs. Oxford Nanopore Technologies (ONT) may overcome such limitation, revealing the sites of rNMPs incorporation in genomic DNA directly from raw sequencing signals. We synthesized two types of DNA molecules containing rNMPs at known or random positions and we developed data analysis pipelines for DNA-embedded ribonucleotides detection by ONT. We report that ONT can identify all four ribonucleotides incorporated in DNA by capturing rNMPs-specific alterations in nucleotide alignment features, current intensity, and dwell time. We propose that ONT may be successfully employed to directly map rNMPs in genomic DNA and we suggest a strategy to build an ad hoc basecaller to analyse native genomes.

2'-O-methylation (Nm) in RNA: progress, challenges, and future directions

RNA 2'-O-methylation (Nm) is highly abundant in noncoding RNAs including ribosomal RNA (rRNA), transfer RNA (tRNA), and small nuclear RNA (snRNA), and occurs in the 5' cap of virtually all messenger RNAs (mRNAs) in higher eukaryotes. More recently, Nm has also been reported to occur at internal sites in mRNA. High-throughput methods have been developed for the transcriptome-wide detection of Nm. However, these methods have mostly been applied to abundant RNAs such as rRNA, and the validity of the internal mRNA Nm sites detected with these approaches remains controversial. Nonetheless, Nm in both coding and noncoding RNAs has been demonstrated to impact cellular processes, including translation and splicing. In addition, Nm modifications at the 5' cap and possibly at internal sites in mRNA serve to prevent the binding of nucleic acid sensors, thus preventing the activation of the innate immune response by self-mRNAs. Finally, Nm has been implicated in a variety of diseases including cancer, cardiovascular diseases, and neurologic syndromes. In this review, we discuss current challenges in determining the distribution, regulation, function, and disease relevance of Nm, as well as potential future directions for the field.

Nanopore Direct RNA Sequencing for Modified Uridine Nucleotides Yields Signals Dependent on the Physical Properties of the Modified Base

Sequencing for RNA modifications with the nanopore direct RNA sequencing platform provides ionic current levels, helicase dwell times, and base call data that differentiate the modifications from the canonical form. Herein, model RNAs were synthesized with site-specific uridine (U) base modifications that enable the study of increasing an alkyl group size, halogen identity, or a change in base acidity to impact the nanopore data. The analysis concluded that increases in alkyl size trend with greater current blockage but a similar change in base-call error was not found. The addition of a halogen series to C5 of U revealed that the current levels recorded a trend with the water-octanol partition coefficient of the base, as well as the base call error. Studies with U modifications that are deprotonated (i.e., anionic) under the sequencing conditions gave broad current levels that influenced the base call error. Some modifications led to helicase dwell time changes. These insights provide design parameters for modification-specific chemical reagents that can shift nanopore signatures to minimize false positive reads, a known issue with this sequencing approach.

Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Modifications on viral RNAs (vRNAs), either genomic RNAs or RNA transcripts, have complex effects on the viral life cycle and cellular responses to viral infection. The advent of Oxford Nanopore Technologies Direct RNA Sequencing provides a new strategy for studying RNA modifications. To this end, multiple computational tools have been developed, but a systemic evaluation of their performance in mapping vRNA modifications is lacking. Here, 10 computational tools were tested using the Sindbis virus (SINV) RNAs isolated from infected mammalian (BHK-21) or mosquito (C6/36) cells, with in vitro-transcribed RNAs serving as modification-free control. Three single-mode approaches were shown to be inapplicable in the viral context, and three out of seven comparative methods required cutoff adjustments to reduce false-positive predictions. Utilizing optimized cutoffs, an integrated analysis of comparative tools suggested that the intersected predictions of Tombo_com and xPore were significantly enriched compared with the background. Consequently, a pipeline integrating Tombo_com and xPore was proposed for vRNA modification detection; the performance of which was supported by N6-methyladenosine prediction in severe acute respiratory syndrome coronavirus 2 RNAs using publicly available data. When applied to SINV RNAs, this pipeline revealed more intensive modifications in subgenomic RNAs than in genomic RNAs. Modified uridines were frequently identified, exhibiting substantive overlapping between vRNAs generated in different cell lines. On the other hand, the interpretation of other modifications remained unclear, underlining the limitations of the current computational tools despite their notable potential.IMPORTANCEComputational approaches utilizing Oxford Nanopore Technologies Direct RNA Sequencing data were almost exclusively designed to map eukaryotic epitranscriptomes. Therefore, extra caution must be exercised when using these tools to detect vRNA modifications, as in most cases, vRNA modification profiles should be regarded as unknown epitranscriptomes without prior knowledge. Here, we comprehensively evaluated the performance of 10 computational tools in detecting vRNA modification sites. All tested single-mode methods failed to differentiate native and in vitro-transcribed samples. Using optimized cutoff values, seven tested comparative tools generated very different predictions. An integrated analysis showed significant enrichment of Tombo_com and xPore predictions against the background. A pipeline for vRNA modification detection was proposed accordingly and applied to Sindbis virus RNAs. In conclusion, our study underscores the need for the careful application of computational tools to analyze viral epitranscriptomics. It also offers insights into alphaviral RNA modifications, although further validation is required.

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification

Next-generation sequencing has revolutionized cancer genomics by enabling high-throughput mutation screening yet detecting fusion genes reliably remains challenging. Long-read sequencing offers potential for accurate fusion transcript identification, though challenges persist. In this study, we present an optimized workflow using nanopore sequencing technology to precisely identify fusion transcripts. Our approach encompasses a tailored library preparation protocol, data processing, and fusion gene analysis pipeline. We evaluated the performance using Universal Human Reference RNA and human adenocarcinoma cell lines. Our optimized nanopore sequencing workflow generated high-quality full-length transcriptome data characterized by an extended length distribution and comprehensive transcript coverage. Validation experiments confirmed novel fusion events with potential clinical relevance. Our protocol aims to mitigate biases and enhance accuracy, facilitating increased adoption in clinical diagnostics. Continued advancements in long-read sequencing promise deeper insights into fusion gene biology and improved cancer diagnostics.

Current technical advancements in plant epitranscriptomic studies

The growth and development of plants are the result of the interplay between the internal developmental programming and plant-environment interactions. Gene expression regulations in plants are made up of multi-level networks. In the past few years, many studies were carried out on co- and post-transcriptional RNA modifications, which, together with the RNA community, are collectively known as the "epitranscriptome." The epitranscriptomic machineries were identified and their functional impacts characterized in a broad range of physiological processes in diverse plant species. There is mounting evidence to suggest that the epitranscriptome provides an additional layer in the gene regulatory network for plant development and stress responses. In the present review, we summarized the epitranscriptomic modifications found so far in plants, including chemical modifications, RNA editing, and transcript isoforms. The various approaches to RNA modification detection were described, with special emphasis on the recent development and application potential of third-generation sequencing. The roles of epitranscriptomic changes in gene regulation during plant-environment interactions were discussed in case studies. This review aims to highlight the importance of epitranscriptomics in the study of gene regulatory networks in plants and to encourage multi-omics investigations using the recent technical advancements.

Analysis approaches for the identification and prediction of N6-methyladenosine sites

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m⁶A sites, in particular full-transcriptome m⁶A. And individual m⁶A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m⁶A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m⁶A methylation, including the experimental detection of m⁶A methylation sites, techniques of data analysis, the way of predicting m⁶A methylation sites, m⁶A methylation databases, and detection of m⁶A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

Advantages and challenges associated with bisulfite-assisted nanopore direct RNA sequencing for modifications

Nanopore direct RNA sequencing is a technology that allows sequencing for epitranscriptomic modifications with the possibility of a quantitative assessment. In the present work, pseudouridine (Ψ) was sequenced with the nanopore before and after the pH 7 bisulfite reaction that yields stable ribose adducts at C1' of Ψ. The adducted sites produced greater base call errors in the form of deletion signatures compared to Ψ. Sequencing studies on E. coli rRNA and tmRNA before and after the pH 7 bisulfite reaction demonstrated that using chemically-assisted nanopore sequencing has distinct advantages for minimization of false positives and false negatives in the data. The rRNA from E. coli has 19 known U/C sequence variations that give similar base call signatures as Ψ, and therefore, are false positives when inspecting base call data; however, these sites are refractory to reacting with bisulfite as is easily observed in nanopore data. The E. coli tmRNA has a low occupancy Ψ in a pyrimidine-rich sequence context that is called a U representing a false negative; partial occupancy by Ψ is revealed after the bisulfite reaction. In a final study, 5-methylcytidine (m5C) in RNA can readily be observed after the pH 5 bisulfite reaction in which the parent C deaminates to U and the modified site does not react. This locates m5C when using bisulfite-assisted nanopore direct RNA sequencing, which is otherwise challenging to observe. The advantages and challenges of the overall approach are discussed.

Nanopore molecular trajectories of a eukaryotic reverse transcriptase reveal a long-range RNA structure sensing mechanism

Eukaryotic reverse transcriptases (RTs) can have essential or deleterious roles in normal human physiology and disease. Compared to well-studied helicases, it remains unclear how RTs overcome the ubiquitous RNA structural barriers during reverse transcription. Herein, we describe the development of a Mycobacterium smegmatis porin A (MspA) nanopore technique to sequence RNA to quantify the single-molecule kinetics of an RT from Bombyx mori with single-nucleotide resolution. By establishing a quadromer map that correlates RNA sequence and MspA ion current, we were able to quantify the RT's dwell time at every single nucleotide step along its RNA template. By challenging the enzyme with various RNA structures, we found that during cDNA synthesis the RT can sense and actively destabilize RNA structures 11-12 nt downstream of its front boundary. The ability to sequence single molecules of RNA with nanopores paves the way to investigate the single-nucleotide activity of other processive RNA translocases.

Nanopore-based direct sequencing of RNA transcripts with 10 different modified nucleotides reveals gaps in existing technology

RNA undergoes complex posttranscriptional processing including chemical modifications of the nucleotides. The resultant-modified nucleotides are an integral part of RNA sequences that must be considered in studying the biology of RNA and in the design of RNA therapeutics. However, the current "RNA-sequencing" methods primarily sequence complementary DNA rather than RNA itself, which means that the modifications present in RNA are not captured in the sequencing results. Emerging direct RNA-sequencing technologies, such as those offered by Oxford Nanopore, aim to address this limitation. In this study, we synthesized and used Nanopore technology to sequence RNA transcripts consisting of canonical nucleotides and 10 different modifications in various concentrations. The results show that direct RNA sequencing still has a baseline error rate of >10%, and although some modifications can be detected, many remain unidentified. Thus, there is a need to develop sequencing technologies and analysis methods that can comprehensively capture the total complexity of RNA. The RNA sequences obtained through this project are made available for benchmarking analysis methods.

Exploring the brain epitranscriptome: perspectives from the NSAS summit

Increasing evidence reinforces the essential function of RNA modifications in development and diseases, especially in the nervous system. RNA modifications impact various processes in the brain, including neurodevelopment, neurogenesis, neuroplasticity, learning and memory, neural regeneration, neurodegeneration, and brain tumorigenesis, leading to the emergence of a new field termed neuroepitranscriptomics. Deficiency in machineries modulating RNA modifications has been implicated in a range of brain disorders from microcephaly, intellectual disability, seizures, and psychiatric disorders to brain cancers such as glioblastoma. The inaugural NSAS Challenge Workshop on Brain Epitranscriptomics hosted in Crans-Montana, Switzerland in 2023 assembled a group of experts from the field, to discuss the current state of the field and provide novel translational perspectives. A summary of the discussions at the workshop is presented here to simulate broader engagement from the general neuroscience field.

Direct Nanopore Sequencing for the 17 RNA Modification Types in 36 Locations in the E. coli Ribosome Enables Monitoring of Stress-Dependent Changes

The bacterium Escherichia coli possesses 16S and 23S rRNA strands that have 36 chemical modification sites with 17 different structures. Nanopore direct RNA sequencing using a protein nanopore sensor and helicase brake, which is also a sensor, was applied to the rRNAs. Nanopore current levels, base calling profile, and helicase dwell times for the modifications relative to unmodified synthetic rRNA controls found signatures for nearly all modifications. Signatures for clustered modifications were determined by selective sequencing of writer knockout E. coli and sequencing of synthetic RNAs utilizing some custom-synthesized nucleotide triphosphates for their preparation. The knowledge of each modification's signature, apart from 5-methylcytidine, was used to determine how metabolic and cold-shock stress impact rRNA modifications. Metabolic stress resulted in either no change or a decrease, and one site increased in modification occupancy, while cold-shock stress led to either no change or a decrease. The double modification m4Cm1402 resides in 16S rRNA, and it decreased with both stressors. Using the helicase dwell time, it was determined that the N4 methyl group is lost during both stressors, and the 2'-OMe group remained. In the ribosome, this modification stabilizes binding to the mRNA codon at the P-site resulting in increased translational fidelity that is lost during stress. The E. coli genome has seven rRNA operons (rrn), and the earlier studies aligned the nanopore reads to a single operon (rrnA). Here, the reads were aligned to all seven operons to identify operon-specific changes in the 11 pseudouridines. This study demonstrates that direct sequencing for >16 different RNA modifications in a strand is achievable.

RNA Infrastructure Profiling Illuminates Transcriptome Structure in Crowded Spaces

RNAs can fold into compact three-dimensional structures, and most RNAs undergo protein interactions in the cell. These compact and occluded environments can block the ability of structure-probing agents to provide useful data about the folding and modification of the underlying RNA. The development of probes that can analyze structure in crowded settings, and differentiate the proximity of interactions, can shed new light on RNA biology. To this end, here we employ 2'-OH-reactive probes that are small enough to access folded RNA structure underlying many close molecular contacts within cells, providing considerably broader coverage for intracellular RNA structural analysis. We compare reverse transcriptase stops in RNA-Seq data from probes of small and standard size to assess RNA-protein proximity and evaluate solvent-exposed tunnels adjacent to RNA. The data are analyzed first with structurally characterized complexes (human 18S and 28S RNA), and then applied transcriptome-wide to polyadenylated transcripts in HEK293 cells. In our transcriptome profile, the smallest probe acetylimidazole (AcIm) yields 80% greater structural coverage than larger conventional reagent NAIN3, providing enhanced structural information in hundreds of transcripts. We further show that acetyl probes provide superior signals for identifying m6A modification sites in transcripts, and provide information regarding methylation sites that are inaccessible to a larger standard probe. RNA infrastructure profiling (RISP) enables enhanced analysis of transcriptome structure, modification, and interactions in living cells, especially in spatially crowded settings.

Bisulfite and Nanopore Sequencing for Pseudouridine in RNA

Nucleophilic addition of bisulfite to pyrimidine bases has been known for a half century, and the reaction has been in use for at least a quarter of a century for identifying 5-methylcytidine in DNA. This account focuses on the chemistry of bisulfite with pseudouridine, an isomer of the RNA nucleoside uridine in which the uracil base is connected to C1' of ribose via C5 instead of N1. Pseudouridine, Ψ, is the most common nucleotide modification found in cellular RNA overall, in part due to its abundance in rRNAs and tRNAs. It has a stabilizing influence on RNA structure because N1 is now available for additional hydrogen bonding and because the heterocycle is slightly better at π stacking. The isomerization of U to Ψ in RNA strands is catalyzed by 13 different enzymes in humans and 11 in E. coli; some of these enzymes are implicated in disease states which is testament to the biological importance of pseudouridine in cells. Recently, pseudouridine came into the limelight as the key modification that, after N1 methylation, enables mRNA vaccines to be delivered efficiently into human tissue with minimal generation of a deleterious immunogenic response. Here we describe the bisulfite reaction with pseudouridine which gives rise to a chemical sequencing method to map the modified base in the epitranscriptome. Unlike the reaction with cytidine, the addition of bisulfite to Ψ leads irreversibly to form an adduct that is bypassed during cDNA synthesis by reverse transcriptases yielding a characteristic deletion signature. Although there were hints to the structure of the bisulfite adduct(s) 30 to 50 years ago, it took modern spectroscopic and computational methods to solve the mystery. Raman spectroscopy along with extensive NMR, ECD, and computational work led to the assignment of the major product as the (R) diastereomer of an oxygen adduct at C1' of a ring-opened pseudouridine. Mechanistically, this arose from a succession of conjugate addition, E2 elimination, and a [2,3] sigmatropic rearrangement, all of which are stereodefined reactions. A minor reaction with excess bisulfite led to the (S) isomer of a S-adducted SO3- group. Understanding structure and mechanism aided the design of a Ψ-specific sequencing reaction and guided attempts to improve the utility and specificity of the method. Separately, we have been investigating the use of nanopore direct RNA sequencing, a single-molecule method that directly analyzes RNA strands isolated from cells after end-ligation of adaptor sequences. By combining the electrical current and base-calling data from the nanopore with dwell-time analysis from the helicase employed to deliver RNA to the nanopore, we were able to map Ψ sites in nearly all sequence contexts. This analysis was employed to find Ψ residues in the SARS-CoV-2 vRNA, to analyze the sequence context effects of mRNA vaccine synthesis via in vitro transcription, and to evaluate the impact of stress on chemical modifications in the E. coli ribosome. Most recently, we found that bisulfite treatment of RNA leading to Ψ adducts could modulate the nanopore signal to help in mapping modifications of low occupancy.

Profiling the polyadenylated transcriptome of extracellular vesicles with long-read nanopore sequencing

BACKGROUND

While numerous studies have described the transcriptomes of extracellular vesicles (EVs) in different cellular contexts, these efforts have typically relied on sequencing methods requiring RNA fragmentation, which limits interpretations on the integrity and isoform diversity of EV-targeted RNA populations. It has been assumed that mRNA signatures in EVs are likely to be fragmentation products of the cellular mRNA material, and the extent to which full-length mRNAs are present within EVs remains to be clarified.

RESULTS

Using long-read nanopore RNA sequencing, we sought to characterize the full-length polyadenylated (poly-A) transcriptome of EVs released by human chronic myelogenous leukemia K562 cells. We detected 443 and 280 RNAs that were respectively enriched or depleted in EVs. EV-enriched poly-A transcripts consist of a variety of biotypes, including mRNAs, long non-coding RNAs, and pseudogenes. Our analysis revealed that 10.58% of all EV reads, and 18.67% of all cellular (WC) reads, corresponded to known full-length transcripts, with mRNAs representing the largest biotype for each group (EV = 58.13%, WC = 43.93%). We also observed that for many well-represented coding and non-coding genes, diverse full-length transcript isoforms were present in EV specimens, and these isoforms were reflective-of but often in different ratio compared to cellular samples.

CONCLUSION

This work provides novel insights into the compositional diversity of poly-A transcript isoforms enriched within EVs, while also underscoring the potential usefulness of nanopore sequencing to interrogate secreted RNA transcriptomes.

Coordination of RNA modifications in the brain and beyond

Gene expression regulation is a critical process throughout the body, especially in the nervous system. One mechanism by which biological systems regulate gene expression is via enzyme-mediated RNA modifications, also known as epitranscriptomic regulation. RNA modifications, which have been found on nearly all RNA species across all domains of life, are chemically diverse covalent modifications of RNA nucleotides and represent a robust and rapid mechanism for the regulation of gene expression. Although numerous studies have been conducted regarding the impact that single modifications in single RNA molecules have on gene expression, emerging evidence highlights potential crosstalk between and coordination of modifications across RNA species. These potential coordination axes of RNA modifications have emerged as a new direction in the field of epitranscriptomic research. In this review, we will highlight several examples of gene regulation via RNA modification in the nervous system, followed by a summary of the current state of the field of RNA modification coordination axes. In doing so, we aim to inspire the field to gain a deeper understanding of the roles of RNA modifications and coordination of these modifications in the nervous system.

Nanopore sequencing of internal 2'-PO4 modifications installed by RNA repair

Ligation by plant and fungal RNA ligases yields an internal 2'-phosphate group on each RNA ligation product. In budding yeast, this covalent mark occurs at the splice junction of two targets of ligation: intron-containing tRNAs and the messenger RNA HAC1 The repertoire of RNA molecules repaired by RNA ligation has not been explored due to a lack of unbiased approaches for identifying RNA ligation products. Here, we define several unique signals produced by 2'-phosphorylated RNAs during nanopore sequencing. A 2'-phosphate at the splice junction of HAC1 mRNA inhibits 5' → 3' degradation, enabling detection of decay intermediates in yeast RNA repair mutants by nanopore sequencing. During direct RNA sequencing, intact 2'-phosphorylated RNAs on HAC1 and tRNAs produce diagnostic changes in nanopore current properties and base calling features, including stalls produced as the modified RNA translocates through the nanopore motor protein. These approaches enable directed studies to identify novel RNA repair events in other contexts.

Navigating the pitfalls of mapping DNA and RNA modifications

Chemical modifications to nucleic acids occur across the kingdoms of life and carry important regulatory information. Reliable high-resolution mapping of these modifications is the foundation of functional and mechanistic studies, and recent methodological advances based on next-generation sequencing and long-read sequencing platforms are critical to achieving this aim. However, mapping technologies may have limitations that sometimes lead to inconsistent results. Some of these limitations are technical in nature and specific to certain types of technology. Here, however, we focus on common (yet not always widely recognized) pitfalls that are shared among frequently used mapping technologies and discuss strategies to help technology developers and users mitigate their effects. Although the emphasis is primarily on DNA modifications, RNA modifications are also discussed.

Nano-DMS-MaP allows isoform-specific RNA structure determination

Genome-wide measurements of RNA structure can be obtained using reagents that react with unpaired bases, leading to adducts that can be identified by mutational profiling on next-generation sequencing machines. One drawback of these experiments is that short sequencing reads can rarely be mapped to specific transcript isoforms. Consequently, information is acquired as a population average in regions that are shared between transcripts, thus blurring the underlying structural landscape. Here, we present nanopore dimethylsulfate mutational profiling (Nano-DMS-MaP)-a method that exploits long-read sequencing to provide isoform-resolved structural information of highly similar RNA molecules. We demonstrate the value of Nano-DMS-MaP by resolving the complex structural landscape of human immunodeficiency virus-1 transcripts in infected cells. We show that unspliced and spliced transcripts have distinct structures at the packaging site within the common 5' untranslated region, likely explaining why spliced viral RNAs are excluded from viral particles. Thus, Nano-DMS-MaP is a straightforward method to resolve biologically important transcript-specific RNA structures that were previously hidden in short-read ensemble analyses.

Recent Advances in Nanopore Technology for Copper Detection and Their Potential Applications

Recently, nanopore technology has emerged as a promising technique for the rapid, sensitive, and selective detection of various analytes. In particular, the use of nanopores for the detection of copper ions has attracted considerable attention due to their high sensitivity and selectivity. This review discusses the principles of nanopore technology and its advantages over conventional techniques for copper detection. It covers the different types of nanopores used for copper detection, including biological and synthetic nanopores, and the various mechanisms used to detect copper ions. Furthermore, this review provides an overview of the recent advancements in nanopore technology for copper detection, including the development of new nanopore materials, improvements in signal amplification, and the integration of nanopore technology with other analytical methods for enhanced detection sensitivity and accuracy. Finally, we summarize the extensive applications, current challenges, and future perspectives of using nanopore technology for copper detection, highlighting the need for further research in the field to optimize the performance and applicability of the technique.

Single-Molecule Correlated Chemical Probing: A Revolution in RNA Structure Analysis

RNA molecules convey biological information both in their linear sequence and in their base-paired secondary and tertiary structures. Chemical probing experiments, which involve treating an RNA with a reagent that modifies conformationally dynamic nucleotides, have broadly enabled examination of short- and long-range RNA structure in diverse contexts, including in living cells. For decades, chemical probing experiments have been interpreted in a per-nucleotide way, such that the reactivity measured at each nucleotide reports the average structure at a position over all RNA molecules within a sample. However, there are numerous important cases where per-nucleotide chemical probing falls short, including for RNAs that are bound by proteins, RNAs that form complex higher order structures, and RNAs that sample multiple conformations.Recent experimental and computational innovations have started a revolution in RNA structure analysis by transforming chemical probing into a massively parallel, single-molecule experiment. Enabled by a specialized reverse transcription strategy called mutational profiling (MaP), multiple chemical modification events can be measured within individual RNA molecules. Nucleotides that communicate structurally through direct base pairing or large-scale folding-unfolding transitions will react with chemical probes in a correlated manner, thereby revealing structural complexity hidden to conventional approaches. These single-molecule correlated chemical probing (smCCP) experiments can be interpreted to directly identify nucleotides that base pair (the PAIR-MaP strategy) and to reveal long-range, through-space structural communication (RING-MaP). Correlated probing can also define the thermodynamic populations of complex RNA ensembles (DANCE-MaP). Complex RNA-protein networks can be interrogated by cross-linking proteins to RNA and measuring correlations between cross-linked positions (RNP-MaP).smCCP thus visualizes RNA secondary and higher-order structure with unprecedented accuracy, defining novel structures, RNA-protein interaction networks, time-resolved dynamics, and allosteric structural switches. These strategies are not mutually exclusive; in favorable cases, multiple levels of RNA structure ─ base pairing, through-space structural communication, and equilibrium ensembles ─ can be resolved concurrently. The physical experimentation required for smCCP is profoundly simple, and experiments are readily performed in cells on RNAs of any size, including large noncoding RNAs and mRNAs. Single-molecule correlated chemical probing is paving the way for a new generation of biophysical studies on RNA in living systems.

Magnipore: Prediction of differential single nucleotide changes in the Oxford Nanopore Technologies sequencing signal of SARS-CoV-2 samples

Oxford Nanopore Technologies (ONT) allows direct sequencing of ribonucleic acids (RNA) and, in addition, detection of possible RNA modifications due to deviations from the expected ONT signal. The software available so far for this purpose can only detect a small number of modifications. Alternatively, two samples can be compared for different RNA modifications. We present Magnipore, a novel tool to search for significant signal shifts between samples of Oxford Nanopore data from similar or related species. Magnipore classifies them into mutations and potential modifications. We use Magnipore to compare SARS-CoV-2 samples. Included were representatives of the early 2020s Pango lineages (n=6), samples from Pango lineages B.1.1.7 (n=2, Alpha), B.1.617.2 (n=1, Delta), and B.1.529 (n=7, Omicron). Magnipore utilizes position-wise Gaussian distribution models and a comprehensible significance threshold to find differential signals. In the case of Alpha and Delta, Magnipore identifies 55 detected mutations and 15 sites that hint at differential modifications. We predicted potential virus-variant and variant-group-specific differential modifications. Magnipore contributes to advancing RNA modification analysis in the context of viruses and virus variants.