Massive RNA sequencing effort proposed.

U.S. plan would harness the "RNome" for medicine and more-but funding is uncertain.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials.

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Cost and time-efficient construction of a 3'-end mRNA library from unpurified bulk RNA in a single tube.

The major drawbacks of RNA sequencing (RNA-seq), a remarkably accurate transcriptome profiling method, is its high cost and poor scalability. Here, we report a highly scalable and cost-effective method for transcriptomics profiling called Bulk transcriptOme profiling of cell Lysate in a single poT (BOLT-seq), which is performed using unpurified bulk 3'-end mRNA in crude cell lysates. During BOLT-seq, RNA/DNA hybrids are directly subjected to tagmentation, and second-strand cDNA synthesis and RNA purification are omitted, allowing libraries to be constructed in 2 h of hands-on time. BOLT-seq was successfully used to cluster small molecule drugs based on their mechanisms of action and intended targets. BOLT-seq competes effectively with alternative library construction and transcriptome profiling methods.

A comprehensive assessment of exome capture methods for RNA sequencing of formalin-fixed and paraffin-embedded samples.

RNA-Seq analysis of Formalin-Fixed and Paraffin-Embedded (FFPE) samples has emerged as a highly effective approach and is increasingly being used in clinical research and drug development. However, the processing and storage of FFPE samples are known to cause extensive degradation of RNAs, which limits the discovery of gene expression or gene fusion-based biomarkers using RNA sequencing, particularly methods reliant on Poly(A) enrichment. Recently, researchers have developed an exome targeted RNA-Seq methodology that utilizes biotinylated oligonucleotide probes to enrich RNA transcripts of interest, which could overcome these limitations. Nevertheless, the standardization of this experimental framework, including probe designs, sample multiplexing, sequencing read length, and bioinformatic pipelines, remains an essential requirement. In this study, we conducted a comprehensive comparison of three main commercially available exome capture kits and evaluated key experimental parameters, to provide the overview of the advantages and limitations associated with the selection of library preparation protocols and sequencing platforms. The results provide valuable insights into the best practices for obtaining high-quality data from FFPE samples.

Emerging Roles of RNA Methylation in Development.

More than 170 different types of chemical modifications have been identified on diverse types of RNA, collectively known as the epitranscriptome. Among them, N6-methyladenine (m6A), 5-methylcytosine (m5C), N1-methyladenine (m1A), and N7-methylguanosine (m7G) as the ubiquitous post-transcriptional modification are widely involved in regulating the metabolic processes such as RNA degradation, translation, stability, and export, mediating important physiological and pathological processes such as stress regulation, immune response, development, and tumorigenesis. Recently, the regulatory role of RNA modification during developmental processes is getting more attention. Therefore, the development of low-input even single-cell and high-resolution sequencing technologies is crucial for the exploration of the regulatory roles of RNA modifications in these important biological events of trace samples.This account focuses on the roles of RNA modifications in various developmental processes. We describe the distribution characteristics of various RNA modifications, catalytic enzymes, binding proteins, and the development of sequencing technologies. RNA modification is dynamically reversible, which can be catalyzed by methyltransferases and eliminated by demethylases. RNA m6A is the most abundant post-transcriptional modification on eukaryote mRNA, which is mainly concentrated near the stop codon, and involves in RNA metabolism regulation. RNA m5C, another most studied RNA modification, has been identified in a various of organisms and RNA species, mainly enriched in the regions downstream of translation initiation sites and broadly distributes across the whole coding sequence (CDS) in mammalian mRNAs. RNA m1A, with a lower abundance than m6A, is widely distributed in various RNA types, mainly locates in the 5' untranslated region (5'UTR) of mRNA and regulates translation. RNA m7G, one of the most common RNA modifications in eukaryotes, has been identified at cap regions and internal positions of RNAs and recently gained considerable attention.Thanks to the development of sequencing technology, m6A has been found to regulate the tumorigenic process, including tumor proliferation, invasion, and metastasis by modulating oncogenes and tumor suppressor genes, and affect oocyte maturation and embryonic development through regulating maternal and zygotic genes. m5C related proteins have been identified to participate in embryonic development, plant growth, and neural stem cell differentiation in a m5C dependent manner. m1A also has been revealed to be involved in these developmental processes. m7G dysregulation mainly involves in neurodevelopmental disorders and neurodegenerative diseases.Collectively, we summarized the gradually exhibited roles of RNA methylation during development, and discussed the possibility of RNA modifications as candidate biomarkers and potential therapeutic targets. The technological development is anticipated as the major driving force to expand our knowledge in this field.

TEQUILA-seq: a versatile and low-cost method for targeted long-read RNA sequencing.

Long-read RNA sequencing (RNA-seq) is a powerful technology for transcriptome analysis, but the relatively low throughput of current long-read sequencing platforms limits transcript coverage. One strategy for overcoming this bottleneck is targeted long-read RNA-seq for preselected gene panels. We present TEQUILA-seq, a versatile, easy-to-implement, and low-cost method for targeted long-read RNA-seq utilizing isothermally linear-amplified capture probes. When performed on the Oxford nanopore platform with multiple gene panels of varying sizes, TEQUILA-seq consistently and substantially enriches transcript coverage while preserving transcript quantification. We profile full-length transcript isoforms of 468 actionable cancer genes across 40 representative breast cancer cell lines. We identify transcript isoforms enriched in specific subtypes and discover novel transcript isoforms in extensively studied cancer genes such as TP53. Among cancer genes, tumor suppressor genes (TSGs) are significantly enriched for aberrant transcript isoforms targeted for degradation via mRNA nonsense-mediated decay, revealing a common RNA-associated mechanism for TSG inactivation. TEQUILA-seq reduces the per-reaction cost of targeted capture by 2-3 orders of magnitude, as compared to a standard commercial solution. TEQUILA-seq can be broadly used for targeted sequencing of full-length transcripts in diverse biomedical research settings.

Monte Carlo samplers for efficient network inference.

Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data's structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.

Odd-even disparity in the population of slipped hairpins in RNA repeat sequences with implications for phase separation.

Low-complexity nucleotide repeat sequences, which are implicated in several neurological disorders, undergo liquid-liquid phase separation (LLPS) provided the number of repeat units, n, exceeds a critical value. Here, we establish a link between the folding landscapes of the monomers of trinucleotide repeats and their propensity to self-associate. Simulations using a coarse-grained Self-Organized Polymer (SOP) model for (CAG)n repeats in monovalent salt solutions reproduce experimentally measured melting temperatures, which are available only for small n. By extending the simulations to large n, we show that the free-energy gap, ΔGS, between the ground state (GS) and slipped hairpin (SH) states is a predictor of aggregation propensity. The GS for even n is a perfect hairpin (PH), whereas it is a SH when n is odd. The value of ΔGS (zero for odd n) is larger for even n than for odd n. As a result, the rate of dimer formation is slower in (CAG)30 relative to (CAG)31, thus linking ΔGS to RNA-RNA association. The yield of the dimer decreases dramatically, compared to the wild type, in mutant sequences in which the population of the SH decreases substantially. Association between RNA chains is preceded by a transition to the SH even if the GS is a PH. The finding that the excitation spectrum-which depends on the exact sequence, n, and ionic conditions-is a predictor of self-association should also hold for other RNAs (mRNA for example) that undergo LLPS.

Assessment of stained direct cytology smears of breast cancer for whole transcriptome and targeted messenger RNA sequencing.

BACKGROUND: Rather than surgical resection, cytologic specimens are often used as first-line clinical diagnostic procedures due to higher safety, speed, and cost-effectiveness. Archival diagnostic cytology slides containing cancer can be equivalent to tissue biopsies for DNA mutation testing, but the accuracy of transcriptomic profiling by RNA sequencing (RNA-seq) is less understood. METHODS: This study compares the results from whole transcriptome RNA-seq and a targeted RNA-seq assay of stained cytology smears (CS) versus matched tumor tissue samples preserved fresh-frozen (FF) and processed as formalin-fixed paraffin-embedded (FFPE) sections. Cellular cytology scrapes from all 11 breast cancers were fixed and stained using three common protocols: Carnoy's (CS_C) or 95% ethanol (CS_E) fixation and then Papanicolaou stain or air-dried then methanol fixation and DiffQuik stain (CS_DQ). Agreement between samples was assessed using Lin's concordance correlation coefficient. RESULTS: Library yield for CS_DQ was too low, therefore it was not sequenced. The distributions of concordance correlation coefficient of gene expression levels in comparison to FF were comparable between CS_C and CS_E, but expression of genes enriched in stroma was lower in cytosmear samples than in FF or FFPE. Six signatures showed similar concordance to FF for all methods and two were slightly worse in CS_C and CS_E. Genomic signatures were highly concordant using targeted RNA-seq. The allele fraction of selected mutations calculated on cytosmear specimens was highly correlated with FF tissues using both RNA-seq methods. CONCLUSION: RNA can be reliably extracted from cytology smears and is suitable for transcriptome profiling or mutation detection, except for signatures of tumor stroma.

HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging.

The ability to visualize RNA in its native subcellular environment by using single-molecule fluorescence in situ hybridization (smFISH) has reshaped our understanding of gene expression and cellular functions. A major hindrance of smFISH is the difficulty to perform systematic experiments in medium- or high-throughput formats, principally because of the high cost of generating the individual fluorescent probe sets. Here, we present high-throughput smFISH (HT-smFISH), a simple and cost-efficient method for imaging hundreds to thousands of single endogenous RNA molecules in 96-well plates. HT-smFISH uses RNA probes transcribed in vitro from a large pool of unlabeled oligonucleotides. This allows the generation of individual probes for many RNA species, replacing commercial DNA probe sets. HT-smFISH thus reduces costs per targeted RNA compared with many smFISH methods and is easily scalable and flexible in design. We provide a protocol that combines oligo pool design, probe set generation, optimized hybridization conditions and guidelines for image acquisition and analysis. The pipeline requires knowledge of standard molecular biology tools, cell culture and fluorescence microscopy. It is achievable in ~20 d. In brief, HT-smFISH is tailored for medium- to high-throughput screens that image RNAs at single-molecule sensitivity.

The emerging role of circRNAs on skeletal muscle development in economical animals.

CircRNAs are a novel type of closed circular molecules formed through a covalent bond lacking a 5'cap and 3' end tail, which mainly arise from mRNA precursor. They are widely distributed in plants and animals and are characterized by stable structure, high conservativeness in cells or tissues, and showed the expression specificity at different stages of development in different tissues. CircRNAs have been gradually attracted wide attention with the development of RNA sequencing, which become a new research hotspot in the field of RNA. CircRNAs play an important role in gene expression regulation. Presently, the related circRNAs research in the regulation of animal muscle development is still at the initial stage. In this review, the formation, properties, biological functions of circRNAs were summarized. The recent research progresses of circRNAs in skeletal muscle growth and development from economic animals including livestock, poultry and fishes were introduced. Finally, we proposed a prospective for further studies of circRNAs in muscle development, and we hope our research could provide new ideas, some theoretical supports and helps for new molecular genetic markers exploitation and animal genetic breeding in future.

Comparative environmental RNA and DNA metabarcoding analysis of river algae and arthropods for ecological surveys and water quality assessment.

Environmental DNA (eDNA) metabarcoding is widely used for species analysis, while the use of environmental RNA (eRNA) metabarcoding is more limited. We conducted comparative eDNA/eRNA metabarcoding of the algae and arthropods (aquatic insects) in water samples from Naka River, Japan, to evaluate their potential for biological monitoring and water quality assessment. Both methods detected various algae and arthropod species; however, their compositions were remarkably different from those in traditional field surveys (TFSs), indicating low sensitivity. For algae, the species composition derived from eDNA and eRNA metabarcoding was equivalent. While TFSs focus on attached algae, metabarcoding analysis theoretically detects both planktonic and attached algae. A recently expanded genomic database for aquatic insects significantly contributed to the sensitivity and positive predictivity for arthropods. While the sensitivity of eRNA was lower than that of eDNA, the positive predictivity of eRNA was higher. The eRNA of terrestrial arthropods indicated extremely high or low read numbers when compared with eDNA, suggesting that eRNA could be an effective indicator of false positives. Arthropod and algae eDNA/eRNA metabarcoding analysis enabled water quality estimates from TFSs. The eRNA of algae and arthropods could thus be used to evaluate biodiversity and water quality and provide insights from ecological surveys.

Assessment of transcription inhibition as a characteristic of immunogenic cell death.

Anticancer drugs that suppress DNA-to-RNA transcription are particularly efficient in stimulating immunogenic cell death and hence eradicate malignant cells in a way that they will ignite an antitumor immune response. This is therapeutically relevant as it allows treatment response to last beyond drug discontinuation. For this reason, it is important to measure transcription inhibition in a precise fashion. Here, we detail two complementary assays for the assessment of transcription inhibition, one that detects the physical separation of fibrillarin and nucleolin by two-color immunofluorescence and another that measures the diminution of incorporated 5-ethynyl uridine (EU) into RNA, as revealed by click chemistry and the per-cell-intensity of a fluorescent signal.

Reducing costs for DNA and RNA sequencing by sample pooling using a metagenomic approach.

DNA and RNA sequencing are widely used techniques to investigate genomic modifications and gene expression. The costs for sequencing dropped dramatically in the last decade. However, due to material and labor intense steps, the sample preparation costs could not keep up with that pace. About 80% of the total costs occur prior to sequencing during DNA/RNA extraction, enrichment steps and subsequent library preparation. In this study, we investigate the potential of pooling different organisms samples prior to DNA/RNA extraction to significantly reduce costs in preparative steps. Similar to the common procedure of ligated DNA tags to pool (c)DNA samples, sequence diversity of different organisms intrinsically provide unique sequences that allow separation of reads after sequencing. With this approach, sample pooling can occur before DNA/RNA isolation and library preparation. We show that pooled sequencing of three related bacterial organisms is possible without loss of data quality at a cost reduction of approx. 50% in DNA- and RNA-seq approaches. Furthermore, we show that this approach is highly efficient down to the level of a shared genus and is, therefore, widely applicable in sequencing facilities and companies with diverse sample pools.

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.

The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson's disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.

Assessment of RNA extraction protocols from cladocerans.

The usage of cladocerans as non-model organisms in ecotoxicological and risk assessment studies has intensified in recent years due to their ecological importance in aquatic ecosystems. The molecular assessment such as gene expression analysis has been introduced in ecotoxicological and risk assessment to link the expression of specific genes to a biological process in the cladocerans. The validity and accuracy of gene expression analysis depends on the quantity, quality and integrity of extracted ribonucleic acid (RNA) of the sample. However, the standard methods of RNA extraction from the cladocerans are still lacking. This study evaluates the extraction of RNA from tropical freshwater cladocerans Moina micrura using two methods: the phenol-chloroform extraction method (QIAzol) and a column-based kit (Qiagen Micro Kit). Glycogen was introduced in both approaches to enhance the recovery of extracted RNA and the extracted RNA was characterised using spectrophotometric analysis (NanoDrop), capillary electrophoresis (Bioanalyzer). Then, the extracted RNA was analysed with reverse transcription polymerase chain reaction (RT-PCR) to validate the RNA extraction method towards downstream gene expression analysis. The results indicate that the column-based kit is most suitable for the extraction of RNA from M. micrura, with the quantity (RNA concentration = 26.90 ± 6.89 ng/µl), quality (A260:230 = 1.95 ± 0.15, A280:230 = 1.85 ± 0.09) and integrity (RNA integrity number, RIN = 7.20 ± 0.16). The RT-PCR analysis shows that the method successfully amplified both alpha tubulin and actin gene at 33-35 cycles (i.e. Ct = 32.64 to 33.48). The results demonstrate that the addition of glycogen is only suitable for the phenol-chloroform extraction method. RNA extraction with high and comprehensive quality control assessment will increase the accuracy and reliability of downstream gene expression, thus providing more ecotoxicological data at the molecular biological level on other freshwater zooplankton species.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129.

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

FISHing on a Budget.

Sensitive quantification of RNA transcripts via fluorescence in situ hybridization (FISH) is a ubiquitous part of understanding quantitative gene expression in single cells. Many techniques exist to identify and localize transcripts inside the cell, but often they are costly and labor intensive. Here we present a method to use a singly labeled short DNA oligo probe to perform FISH in yeast cells. This method is effective for highly constrained FISH applications where the target length is limited (<200 nucleotides). This method can quantify different RNA isoforms or enable the use of fluorescence resonance energy transfer (FRET) to detect co-transcription of neighboring sequence blocks. Since this method relies on a single probe, it is also more cost-effective than a multiple probe labeling strategy.

Simulation-based inference of differentiation trajectories from RNA velocity fields.

We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis, we include differentiation trajectories with varying bifurcated, circular, convergent, and mixed topologies studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate the capability to reconstruct differentiation trajectories, assess the association of RNA velocity-based pseudotime with actually elapsed process time, and identify drawbacks in current state-of-the art trajectory inference approaches.