Artifacts and biases of the reverse transcription reaction in RNA sequencing

Re-analysis of mobile mRNA datasets raises questions about the extent of long-distance mRNA communication

Short-read RNA-seq studies of grafted plants have led to the proposal that thousands of messenger RNAs (mRNAs) move over long distances between plant tissues1-7, potentially acting as signals8-12. Transport of mRNAs between cells and tissues has been shown to play a role in several physiological and developmental processes in plants, such as tuberization13, leaf development14 and meristem maintenance15; yet for most mobile mRNAs, the biological relevance of transport remains to be determined16-19. Here we perform a meta-analysis of existing mobile mRNA datasets and examine the associated bioinformatic pipelines. Taking technological noise, biological variation, potential contamination and incomplete genome assemblies into account, we find that a high percentage of currently annotated graft-mobile transcripts are left without statistical support from available RNA-seq data. This meta-analysis challenges the findings of previous studies and current views on mRNA communication.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation

Long-read sequencing (LRS) technologies have revolutionized transcriptomic research by enabling the comprehensive sequencing of full-length transcripts. Using these technologies, researchers have reported tens of thousands of novel transcripts, even in well-annotated genomes, while developing new algorithms and experimental approaches to handle the noisy data. The Long-read RNA-seq Genome Annotation Assessment Project community effort benchmarked LRS methods in transcriptomics and validated many novel, lowly expressed, often times sample-specific transcripts identified by long reads. These molecules represent deviations of the major transcriptional program that were overlooked by short-read sequencing methods but are now captured by the full-length, single-molecule approach. This Perspective discusses the challenges and opportunities associated with LRS' capacity to unravel this fraction of the transcriptome, in terms of both transcriptome biology and genome annotation. For transcriptome biology, we need to develop novel experimental and computational methods to effectively differentiate technology errors from rare but real molecules. For genome annotation, we must agree on the strategy to capture molecular variability while still defining reference annotations that are useful for the genomics community.

Single-step discovery of high-affinity RNA ligands by UltraSelex

Aptamers, nucleic acid ligands targeting specific molecules, have emerged as drug candidates, sensors, imaging tools and nanotechnology building blocks. The predominant method for their discovery, systematic evolution of ligands by exponential enrichment, while successful, is laborious, time-consuming and often results in candidates enriched for unintended criteria. Here we present UltraSelex, a noniterative method that combines biochemical partitioning, high-throughput sequencing and computational signal-to-background rank modeling for discovering RNA aptamers in about 1 day. UltraSelex identified high-affinity RNA aptamers capable of binding a fluorogenic silicon rhodamine dye and two protein targets, the SARS-CoV-2 RNA-dependent RNA polymerase and HIV reverse transcriptase, enabling live-cell RNA imaging and efficient enzyme inhibition, respectively. From the ranked sequences, minimal aptamer motifs could be easily inferred. UltraSelex provides a rapid route to reveal new drug candidates and diagnostic tools.

Transcriptomics in the era of long-read sequencing

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

Detection of mRNA Transcript Variants

Most eukaryotic genes express more than one mature mRNA, defined as transcript variants. This complex phenomenon arises from various mechanisms, such as using alternative transcription start sites and alternative post-transcriptional processing events. The resulting transcript variants can lead to synthesizing proteins that possess distinct functional domains or may even generate noncoding RNAs, each with unique roles in cellular processes. The generation of these transcript variants is not merely a random occurrence; it is cell-type specific and varies with developmental stages, aging processes, or pathogenesis of diseases. This highlights the biological significance of transcript variants in regulating gene expression and their potential impact on cellular functionality. Despite the biological importance, investigating transcript variants has been hampered by challenges associated with detecting their expression. This review article addresses the advancements in molecular techniques in detecting transcript variants. Traditional methods such as RT-PCR and RT-qPCR can easily detect known transcript variants using primers that target unique exons associated with the variants. Other techniques like RACE-PCR and hybridization-based methods, including Northern blotting, RNase protection assays, and microarrays, have also been utilized to detect transcript variants. Nevertheless, RNA sequencing (RNA-Seq) has emerged as a powerful technique for identifying transcript variants, especially those with previously unknown sequences. The effectiveness of RNA sequencing in transcript variant detection depends on the specific sequencing approach and the precision of data analysis. By understanding the strengths and weaknesses of each laboratory technique, researchers can develop more effective strategies for detecting mRNA transcript variants. This ability will be crucial for our comprehensive understanding of gene regulation and the implications of transcript diversity in various biological contexts.

Systematic analysis of cotranscriptional RNA folding using transcription elongation complex display

RNA can fold into structures that mediate diverse cellular functions. Understanding how RNA primary sequence directs the formation of functional structures requires methods that can comprehensively assess how changes in an RNA sequence affect its structure and function. Here we have developed a platform for performing high-throughput cotranscriptional RNA biochemical assays, called Transcription Elongation Complex display (TECdisplay). TECdisplay measures RNA function by fractionating a TEC library based on the activity of cotranscriptionally displayed nascent RNA. In this way, RNA function is measured as the distribution of template DNA molecules between fractions of the transcription reaction. This approach circumvents typical RNA sequencing library preparation steps that can cause technical bias. We used TECdisplay to characterize the transcription antitermination activity of >1 million variants of the Clostridium beijerinckii pfl ZTP riboswitch designed to perturb steps within its cotranscriptional folding pathway. Our findings establish TECdisplay as an accessible platform for high-throughput RNA biochemical assays.

Therapeutic Effects of Liposomal Resveratrol in the Mitigation of Diabetic Nephropathy via Modulating Inflammatory Response, Oxidative Stress, and Apoptosis

An important factor in the development of diabetes and its associated consequences is prolonged chronic hyperglycemia, which weakens the antioxidant defense system and produces reactive oxygen species. Phytochemicals have been found to scavenge free radicals and exhibit antioxidant effects necessary to increase insulin sensitivity and reduce the development of diabetes-related complications. Current treatments for managing diabetes and diabetic nephropathy are often not very effective and come with several limitations and side effects. Resveratrol, for example, has shown therapeutic potential in mitigating kidney damage induced by high glucose levels, but its short bioavailability is a significant limitation. This accentuates the need for alternatives that not only improve the disease but also reduce the side effects associated with treatment. To enhance the therapeutic efficacy of resveratrol, we investigated the protective effects of liposomal resveratrol (LR) in a streptozotocin-induced diabetic rat model at doses of 20 and 40 mg/kg. We compared the impact of LR to that of resveratrol alone (at a dose of 40 mg/kg) on various parameters, including serum levels of biochemical markers, tissue levels of pro-inflammatory cytokines, nuclear transcription factor, oxidative stress indices, and apoptotic markers. LR, as a highly absorbable and metabolized form of resveratrol, has demonstrated beneficial effects in diabetic rats. Administered at both 20 mg/kg and 40 mg/kg dosages over a 5-week period, it demonstrated notable efficacy in alleviating inflammation. This was accomplished by diminishing the levels of pro-inflammatory mediators, TNF-α and IL-6, through the inhibition of NF-κB translocation. Additionally, LR influenced apoptotic markers, specifically caspase, BCL-2, and BAX. Furthermore, it enhanced the expression of key antioxidant enzymes such as catalase and glutathione peroxidase while significantly lowering malondialdehyde levels. These significant biochemical and immunological protective effects correlated with improved histological integrity and overall kidney architecture. Notably, resveratrol alone was not as effective as LR in restoring kidney function, highlighting its potential as a therapeutic candidate for the treatment of diabetic nephropathy. However, more in-depth studies are needed to explore its mechanism of action and improved bioavailability.

IsoTools 2.0: Software for Comprehensive Analysis of Long-read Transcriptome Sequencing Data

Direct, single molecule measurement of RNA by long-read transcriptome sequencing (LRTS) enables the reliable detection of transcripts and alternative splicing events, thus contributing to the identification of splicing mechanisms, improvement of current gene models, as well as to the prediction of more reliable protein isoforms. LRTS data comes from either PacBio's single-molecule real time sequencing or from Oxford Nanopore's nanopore sequencing. Previously, we developed IsoTools, a software originally designed for processing and analyzing PacBio data. IsoTools copes with the complexity of LRTS data and offers multiple functionality for transcript identification and quantification as well as the analysis of differential isoform usage and local differential splicing events. Here, we report an update of the software, IsoTools 2.0, and demonstrate its additional performance on Oxford Nanopore data from multiple experimental protocols. We present the IsoTools 2.0 workflow, highlighting novel functionalities with respect to reliable transcript detection as well as transcription start site prediction. Additionally, we show novel metrics for structural description and quantification of gene model variability based on the gene's transcripts. We demonstrate the performance of IsoTools 2.0 on a variety of experimental protocols for library construction from a recent LRTS challenge. We show that IsoTools 2.0 is able to cope with the inherent complexity of LRTS data and that the workflow generates meaningful hypotheses on biomarkers for alternative splicing. The software is available from https://github.com/HerwigLab/IsoTools2/.

Sources of non-uniform coverage in short-read RNA-Seq data

The origin of several normal cellular functions and related abnormalities can be traced back to RNA splicing. As such, RNA splicing is currently the focus of a vast array of studies. To quantify the transcriptome, short-read RNA-Seq remains the standard assay. The primary technical artifact of RNASeq library prep, which severely interferes with analysis, is extreme non-uniformity in coverage across transcripts. This non-uniformity is present in both bulk and single-cell RNA-Seq and is observed even when the sample contains only full-length transcripts. This issue dramatically affects the accuracy of isoform-level quantification of multi-isoform genes. Understanding the sources of this non-uniformity is critical to developing improved protocols and analysis methods. Here, we explore eight potential sources of non-uniformity. We demonstrate that it cannot be explained by one factor alone. We performed targeted experiments to investigate the effect of fragment length, PCR ramp rate, and ribosomal depletion. We assessed existing data sets with varying sample quality, PCR cycle number, reverse transcriptase, and technical or biological replicates. We found evidence that interference of reverse transcription by secondary structure is unlikely to be the major contributing factor, that rRNA pull-down methods do not cause non-uniformity, that PCR ramp rate does not substantially impact non-uniformity, and that shorter fragments do not reduce non-uniformity. All these findings contradict prior publications or recommendations.

Machine learning-optimized targeted detection of alternative splicing

RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

Charting the epitranscriptomic landscape across RNA biotypes using native RNA nanopore sequencing

RNA modifications are conserved chemical features found in all domains of life and across diverse RNA biotypes, shaping gene expression profiles and enabling rapid responses to environmental changes. Their broad chemical diversity and dynamic nature pose significant challenges for studying them comprehensively. These limitations can now be addressed through direct RNA nanopore sequencing (DRS), which allows simultaneous identification of diverse RNA modification types at single-molecule and single-nucleotide resolution. Here, we review recent efforts pioneering the use of DRS to better understand the epitranscriptomic landscape. We highlight how DRS can be applied to investigate different RNA biotypes, emphasizing the use of specialized library preparation protocols and downstream bioinformatic workflows to detect both natural and synthetic RNA modifications. Finally, we provide a perspective on the future role of DRS in epitranscriptomic research, highlighting remaining challenges and emerging opportunities from improved sequencing yields and accuracy enabled by the latest DRS chemistry.

A probe-based capture enrichment method for detection of A-to-I editing in low abundance transcripts

Exactly two decades ago, the ability to use high-throughput RNA sequencing technology to identify sites of editing by ADARs was employed for the first time. Since that time, RNA sequencing has become a standard tool for researchers studying RNA biology and led to the discovery of RNA editing sites present in a multitude of organisms, across tissue types, and in disease. However, transcriptome-wide sequencing is not without limitations. Most notably, RNA sequencing depth of a given transcript is correlated with expression, and sequencing depth impacts the ability to robustly detect RNA editing events. This chapter focuses on a method for enrichment of low-abundance transcripts that can facilitate more efficient sequencing and detection of RNA editing events. An important note is that while we describe aspects of the protocol important for capturing intron-containing transcripts, this probe-based enrichment method could be easily modified to assess editing within any low-abundance transcript. We also provide some perspectives on the current limitations as well as important future directions for expanding this technology to gain more insights into how RNA editing can impact transcript diversity.

En masse evaluation of RNA guides (EMERGe) for ADARs

Adenosine Deaminases Acting on RNA (ADARs) convert adenosine to inosine in duplex RNA, and through the delivery of guide RNAs, can be directed to edit specific adenosine sites. As ADARs are endogenously expressed in humans, their editing capacities hold therapeutic potential and allow us to target disease-relevant sequences in RNA through the rationale design of guide RNAs. However, current design principles are not suitable for difficult-to-edit target sites, posing challenges to unlocking the full therapeutic potential of this approach. This chapter discusses how we circumvent this barrier through an in vitro screening method, En Masse Evaluation of RNA Guides (EMERGe), which enables comprehensive screening of ADAR substrate libraries and facilitates the identification of editing-enabling guide strands for specific adenosines. From library generation and screening to next generation sequencing (NGS) data analysis to verification experiments, we describe how a sequence of interest can be identified through this high-throughput screening method. Furthermore, we discuss downstream applications of selected guide sequences, challenges in maximizing library coverage, and potential to couple the screen with machine learning or deep learning models.

Single-cell RNA-sequencing of BK polyomavirus replication in primary human renal proximal tubular epithelial cells identifies specific transcriptome signatures and a novel mitochondrial stress pattern

BK polyomavirus (BKPyV) contributes to premature renal failure in 10%-20% of kidney transplant recipients. Current treatment relies on reducing immunosuppression to regain BKPyV-specific immune control. Subsequently, declining allograft function may result from persisting viral cytopathology, BKPyV-specific immune reconstitution, or alloimmunity/rejection, all being poorly distinguishable by current histological or molecular approaches. To reduce the complexity encountered in BKPyV-replicating kidneys, we analyzed differentially expressed genes (DEGs) in primary human renal proximal tubular epithelial cells at 24 and 48 h post-infection (hpi) using single-cell RNA-sequencing (10x-Genomics-3´ kit). At 24 hpi, viral transcript reads predominantly mapped to the early viral gene region (EVGR) and shifted to >100-fold higher late viral gene region (LVGR) levels at 48 hpi, matching the sequential bi-directional viral protein expression from the circular double-stranded BKPyV-DNA genome. Besides expected coverage "hills" at viral 3´-poly-A sites, unexpected "spike" and "pulse" reads resulted from off-target TSO priming. "Spike" and "pulse" patterns were rare for the mostly unidirectional reads mapping to the circular mitochondrial genome. Bioinformatic curation removed "spikes" and "pulses" and reclassified 10% of DEGs in renal proximal tubular epithelial cells (RPTECs). Up-regulated gene ontologies included S and G2/M phase, double-stranded DNA repair, proximal tubulopathy, and renal tubular dysfunction, whereas allograft rejection, antigen presentation, innate immunity, translation, and autophagy were down-regulated. BKPyV-LVGR expression induced a novel mitochondrial cell stress pattern consisting of discordant up-regulation and down-regulation of mitochondria-encoded and nucleus-encoded mitochondrial genes, respectively. We explored which top-scoring gene sets of late-phase BKPyV-replicating RPTECs can identify BKPyV-associated nephropathy in kidney transplant biopsies. The results should facilitate distinguishing BKPyV-associated pathology from other entities in kidney transplant biopsies.IMPORTANCEBK polyomavirus (BKPyV) infects more than 90% of the general population and then persists in the reno-urinary tract. Subsequently, low-level urinary shedding is seen in 10% of healthy BKPyV-seropositive persons, indicating that BKPyV replication occurs despite the presence of virus-specific cellular and humoral immunity. Notably, transplantation of donor kidneys with low-level BKPyV replication is a risk factor for progression to high-level BKPyV viruria, new-onset BKPyV-DNAemia and biopsy-proven BKPyV nephropathy. Here, we identify a short list of robust up- and down-regulated nucleus-encoded differentially expressed genes potentially allowing to discriminate viral from allograft immune damage. By carefully curating viral and mitochondrial transcriptomes, we identify a novel virus-associated mitochondrial stress pattern of up-regulated mitochondria-encoded and down-regulated nucleus-encoded mitochondrial transcripts which heralds the BKPyV-agnoprotein-mediated immune escape by breakdown of the mitochondrial membrane potential and network and mitophagy. The results may prove useful when assessing the role of BKPyV replication in kidney transplant patients with suspected acute rejection and/or BKPyV nephropathy.

Challenges in identifying mRNA transcript starts and ends from long-read sequencing data

Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology through the comprehensive identification and quantification of full-length mRNA isoforms. Despite great promise, challenges remain in the widespread implementation of LRS technologies for RNA-based applications, including concerns about low coverage, high sequencing error, and robust computational pipelines. Although much focus has been placed on defining mRNA exon composition and structure with LRS data, less careful characterization has been done of the ability to assess the terminal ends of isoforms, specifically, transcription start and end sites. Such characterization is crucial for completely delineating full mRNA molecules and regulatory consequences. However, there are substantial inconsistencies in both start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. Here, we describe the specific challenges of identifying and quantifying mRNA terminal ends with LRS technologies and how these issues influence biological interpretations of LRS data. We then review recent experimental and computational advances designed to alleviate these problems, with ideal use cases for each approach. Finally, we outline anticipated developments and necessary improvements for the characterization of terminal ends from LRS data.

Current Understandings and Open Hypotheses on Extracellular Circular RNAs

Circular RNAs (circRNAs) are closed RNA loops present in humans and other organisms. Various circRNAs have an essential role in diseases, including cancer. Cells can release circRNAs into the extracellular space of adjacent biofluids and can be present in extracellular vesicles. Due to their circular nature, extracellular circRNAs (excircRNAs) are more stable than their linear counterparts and are abundant in many biofluids, such as blood plasma and urine. circRNAs' link with disease suggests their extracellular counterparts have high biomarker potential. However, circRNAs and the extracellular space are challenging research domains, as they consist of complex biological systems plagued with nomenclature issues and a wide variety of protocols with different advantages and disadvantages. Here, we summarize what is known about excircRNAs, the current challenges in the field, and what is needed to improve extracellular circRNA research.

Is it worth the extra mile? Comparing environmental DNA and RNA metabarcoding for vertebrate and invertebrate biodiversity surveys in a lowland stream

Environmental DNA (eDNA) metabarcoding has emerged as a promising approach to assess biodiversity and derive ecological status classes from water samples. However, a limitation of eDNA surveys is that detected DNA molecules may originate from other places or even dead organisms, distorting local biodiversity assessments. Environmental RNA (eRNA) metabarcoding has recently been proposed as a complementary tool for more localized assessments of the biological community. In this study, we evaluated the effectiveness of eDNA and eRNA metabarcoding for inferring the richness and species distribution patterns of vertebrates and invertebrates in a Central European lowland river. We collected water samples and analyzed them using a 12S marker for vertebrates and a COI marker for invertebrates. We detected 31 fish, 16 mammal, 10 bird and one lamprey species in the vertebrate dataset. While results were largely consistent, we detected a higher number of species when analysing eRNA (mean = 30.89) than eDNA (mean = 26.16). Also, eRNA detections had a stronger local signature than eDNA detections when compared against species distribution patterns from traditional fish monitoring data. For invertebrates, we detected 109 arthropod, 22 annelid, 12 rotiferan, eight molluscan and four cnidarian species. In contrast to the pattern of vertebrate richness, we detected a higher richness using eDNA (mean = 41.37) compared to eRNA (mean = 22.42). Our findings primarily show that eDNA and eRNA-based detections are comparable for vertebrate and invertebrate taxa. Biological replication was important for both template molecules studied. Signal detections for vertebrates were more localized for eRNA compared to eDNA. Overall, the advantages of the extra steps needed for eRNA analyses depend on the study question but both methods provide important data for biodiversity monitoring and research.

Alu insertion-mediated dsRNA structure formation with pre-existing Alu elements as a disease-causing mechanism

We previously identified a homozygous Alu insertion variant (Alu_Ins) in the 3'-untranslated region (3'-UTR) of SPINK1 as the cause of severe infantile isolated exocrine pancreatic insufficiency. Although we established that Alu_Ins leads to the complete loss of SPINK1 mRNA expression, the precise mechanisms remained elusive. Here, we aimed to elucidate these mechanisms through a hypothesis-driven approach. Initially, we speculated that, owing to its particular location, Alu_Ins could independently disrupt mRNA 3' end formation and/or affect other post-transcriptional processes such as nuclear export and translation. However, employing a 3'-UTR luciferase reporter assay, Alu_Ins was found to result in only an ∼50% reduction in luciferase activity compared to wild type, which is insufficient to account for the severe pancreatic deficiency in the Alu_Ins homozygote. We then postulated that double-stranded RNA (dsRNA) structures formed between Alu elements, an upstream mechanism regulating gene expression, might be responsible. Using RepeatMasker, we identified two Alu elements within SPINK1's third intron, both oriented oppositely to Alu_Ins. Through RNAfold predictions and full-length gene expression assays, we investigated orientation-dependent interactions between these Alu repeats. We provide compelling evidence to link the detrimental effect of Alu_Ins to extensive dsRNA structures formed between Alu_Ins and pre-existing intronic Alu sequences, including the restoration of SPINK1 mRNA expression by aligning all three Alu elements in the same orientation. Given the widespread presence of Alu elements in the human genome and the potential for new Alu insertions at almost any locus, our findings have important implications for detecting and interpreting Alu insertions in disease genes.

Machine learning-optimized targeted detection of alternative splicing

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

Mitochondrial transcriptome of Candida albicans in flagranti - direct RNA sequencing reveals a new layer of information

BACKGROUND

Organellar transcriptomes are relatively under-studied systems, with data related to full-length transcripts and posttranscriptional modifications remaining sparse. Direct RNA sequencing presents the possibility of accessing a previously unavailable layer of information pertaining to transcriptomic data, as well as circumventing the biases introduced by second-generation RNA-seq platforms. Direct long-read ONT sequencing allows for the isoform analysis of full-length transcripts and the detection of posttranscriptional modifications. However, there are still relatively few projects employing this method specifically for studying organellar transcriptomes.

RESULTS

Candida albicans is a promising model for investigating nucleo-mitochondrial interactions. This work comprises ONT sequencing of the Candida albicans mitochondrial transcriptome along with the development of a dedicated data analysis pipeline. This approach allowed for the detection of complete transcript isoforms and posttranslational RNA modifications, as well as an analysis of C. albicans deletion mutants in genes coding for the 5' and 3' mitochondrial RNA exonucleases CaPET127 and CaDSS1. It also enabled for corrections to previous studies in terms of 3' and 5' transcript ends. A number of intermediate splicing isoforms was also discovered, along with mature and unspliced transcripts and changes in their abundances resulting from disruption of both 5' and 3' exonucleolytic processing. Multiple putative posttranscriptional modification sites have also been detected.

CONCLUSIONS

This preliminary work demonstrates the suitability of direct RNA sequencing for studying yeast mitochondrial transcriptomes in general and provides new insights into the workings of the C. albicans mitochondrial transcriptome in particular. It also provides a general roadmap for analyzing mitochondrial transcriptomic data from other organisms.

Validation of one-step reverse transcription digital PCR assays for Norovirus GI

Regular monitoring of Norovirus presence in environmental and food samples is crucial due to its high transmission rates and outbreak potential. For detecting Norovirus GI, reverse transcription qPCR method is commonly used, but its sensitivity can be affected by assay performance. This study shows significantly reduced assay performance in digital PCR or qPCR when using primers targeting Norovirus GI genome 5291-5319 (NC_001959), located on the hairpin of the predicted RNA structure. It is highly recommended to avoid this region in commercial kit development or diagnosis to minimizing potential risk of false negatives.

Temperature-Dependent tRNA Modifications in Bacillales

Transfer RNA (tRNA) modifications are essential for the temperature adaptation of thermophilic and psychrophilic organisms as they control the rigidity and flexibility of transcripts. To further understand how specific tRNA modifications are adjusted to maintain functionality in response to temperature fluctuations, we investigated whether tRNA modifications represent an adaptation of bacteria to different growth temperatures (minimal, optimal, and maximal), focusing on closely related psychrophilic (P. halocryophilus and E. sibiricum), mesophilic (B. subtilis), and thermophilic (G. stearothermophilus) Bacillales. Utilizing an RNA sequencing approach combined with chemical pre-treatment of tRNA samples, we systematically profiled dihydrouridine (D), 4-thiouridine (s4U), 7-methyl-guanosine (m7G), and pseudouridine (Ψ) modifications at single-nucleotide resolution. Despite their close relationship, each bacterium exhibited a unique tRNA modification profile. Our findings revealed increased tRNA modifications in the thermophilic bacterium at its optimal growth temperature, particularly showing elevated levels of s4U8 and Ψ55 modifications compared to non-thermophilic bacteria, indicating a temperature-dependent regulation that may contribute to thermotolerance. Furthermore, we observed higher levels of D modifications in psychrophilic and mesophilic bacteria, indicating an adaptive strategy for cold environments by enhancing local flexibility in tRNAs. Our method demonstrated high effectiveness in identifying tRNA modifications compared to an established tool, highlighting its potential for precise tRNA profiling studies.

Exploring the impact of primer length on efficient gene detection via high-throughput sequencing

Reverse transcription (RT) is a crucial step in most RNA analysis methods. Optimizing protocols for this initial stage is critical for effective target detection, particularly when working with limited input RNA. Several factors, such as the input material quality and reaction conditions, influence RT efficiency. However, the effect of RT primer length on gene detection efficiency remains largely unknown. Thus, we investigate its impact by generating RNA-seq libraries with random RT primers of 6, 12, 18, or 24 nucleotides. To our surprise, the 18mer primer shows superior efficiency in overall transcript detection compared to the commonly used 6mer primer, especially in detecting longer RNA transcripts in complex human tissue samples. This study highlights the critical role of primer length in RT efficiency, which has significant potential to benefit various transcriptomic assays, from basic research to clinical diagnostics, given the central role of RT in RNA-related analyses.

Uncovering the dynamics and consequences of RNA isoform changes during neuronal differentiation

Static gene expression programs have been extensively characterized in stem cells and mature human cells. However, the dynamics of RNA isoform changes upon cell-state-transitions during cell differentiation, the determinants and functional consequences have largely remained unclear. Here, we established an improved model for human neurogenesis in vitro that is amenable for systems-wide analyses of gene expression. Our multi-omics analysis reveals that the pronounced alterations in cell morphology correlate strongly with widespread changes in RNA isoform expression. Our approach identifies thousands of new RNA isoforms that are expressed at distinct differentiation stages. RNA isoforms mainly arise from exon skipping and the alternative usage of transcription start and polyadenylation sites during human neurogenesis. The transcript isoform changes can remodel the identity and functions of protein isoforms. Finally, our study identifies a set of RNA binding proteins as a potential determinant of differentiation stage-specific global isoform changes. This work supports the view of regulated isoform changes that underlie state-transitions during neurogenesis.

The Current Situation and Development Prospect of Whole-Genome Screening

High-throughput genetic screening is useful for discovering critical genes or gene sequences that trigger specific cell functions and/or phenotypes. Loss-of-function genetic screening is mainly achieved through RNA interference (RNAi), CRISPR knock-out (CRISPRko), and CRISPR interference (CRISPRi) technologies. Gain-of-function genetic screening mainly depends on the overexpression of a cDNA library and CRISPR activation (CRISPRa). Base editing can perform both gain- and loss-of-function genetic screening. This review discusses genetic screening techniques based on Cas9 nuclease, including Cas9-mediated genome knock-out and dCas9-based gene activation and interference. We compare these methods with previous genetic screening techniques based on RNAi and cDNA library overexpression and propose future prospects and applications for CRISPR screening.

The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions

The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.

Systematic analysis of cotranscriptional RNA folding using transcription elongation complex display

RNA can fold into structures that mediate diverse cellular functions. Understanding how RNA primary sequence directs the formation of functional structures requires methods that can comprehensively assess how changes in an RNA sequence affect its structure and function. Here we have developed a platform for performing high-throughput cotranscriptional RNA biochemical assays, called Transcription Elongation Complex display (TECdisplay). TECdisplay measures RNA function by fractionating a TEC library based on the activity of cotranscriptionally displayed nascent RNA. In this way, RNA function is measured as the distribution of template DNA molecules between fractions of the transcription reaction. This approach circumvents typical RNA sequencing library preparation steps that can cause technical bias. We used TECdisplay to characterize the transcription antitermination activity of 32,768 variants of the Clostridium beijerinckii pfl ZTP riboswitch designed to perturb steps within its cotranscriptional folding pathway. Our findings establish TECdisplay as an accessible platform for high-throughput RNA biochemical assays.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.