Expanding the Nucleoside Recoding Toolkit: Revealing RNA Population Dynamics with 6-Thioguanosine

Fragile X messenger ribonucleoprotein 1 (FMRP) regulates glycolytic gene expression under chronic hypoxia in HCT116 cells

Oxygen shortage, known as hypoxia, occurs commonly in both physiological and pathological conditions. Transcriptional regulation by hypoxia-inducible factors is a dominant regulatory mechanism controlling hypoxia-responsive genes during acute hypoxia; however, recent studies suggest that post-transcriptional regulation, including RNA degradation, also involves hypoxia-induced gene expression during the chronic hypoxia. In this study, we developed a method to quantify the contributions of RNA synthesis and degradation to differential gene expression, and identified 102 genes mainly regulated via RNA degradation under chronic hypoxia in HCT116 cells. Bioinformatics analysis showed that the genes mainly regulated by RNA degradation were involved in glycolysis. We examined changes in the RNA-binding ability of RNA-binding proteins by RNA interactome capture and statistical analysis using public databases. We identified fragile X messenger ribonucleoprotein 1 (FMRP) as an RNA-binding protein involved in the chronic hypoxia-induced increase in mRNAs encoding rate-limiting enzymes. This study emphasizes the importance of post-transcriptional gene regulation under chronic hypoxia in HCT116 cells.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR, an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR, a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions, etc.), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.

Splice_sim: a nucleotide conversion-enabled RNA-seq simulation and evaluation framework

Nucleotide conversion RNA sequencing techniques interrogate chemical RNA modifications in cellular transcripts, resulting in mismatch-containing reads. Biases in mapping the resulting reads to reference genomes remain poorly understood. We present splice_sim, a splice-aware RNA-seq simulation and evaluation pipeline that introduces user-defined nucleotide conversions at set frequencies, creates mixture models of converted and unconverted reads, and calculates mapping accuracies per genomic annotation. By simulating nucleotide conversion RNA-seq datasets under realistic experimental conditions, including metabolic RNA labeling and RNA bisulfite sequencing, we measure mapping accuracies of state-of-the-art spliced-read mappers for mouse and human transcripts and derive strategies to prevent biases in the data interpretation.

Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.

N4-Allylcytidine: a new nucleoside analogue for RNA labelling and chemical sequencing

RNA labelling has become indispensable in studying RNA biology. Nucleoside analogues with a chemical sequencing power represent desirable RNA labelling molecules because precise labelling information at base resolution can be obtained. Here, we report a new nucleoside analogue, N4-allylcytidine (a4C), which is able to tag RNA through both in vitro and in vivo pathways and further specifically reacts with iodine to form 3, N4-cyclized cytidine (cyc-C) in a catalyst-free, fast and complete manner. Full spectroscopic characterization concluded that cyc-C consisted of paired diastereoisomers with opposite chiral carbon centers in the fused 3, N4-five-membered ring. During RNA reverse transcription into complementary DNA, cyc-C induces base misincorporation due to the disruption of canonical hydrogen bonding by the cyclized structure and thus can be accurately identified by sequencing at single base resolution. With the chemical sequencing rationale of a4C, successful applications have been performed including pinpointing N4-methylcytidine methyltransferases' substrate modification sites, metabolically labelling mammalian cellular RNAs, and mapping active cellular RNA polymerase locations with the chromatin run-on RNA sequencing technique. Collectively, our work demonstrates that a4C is a promising molecule for RNA labelling and chemical sequencing and expands the toolkit for studying sophisticated RNA biology.

Development and Comparison of 4-Thiouridine to Cytidine Base Conversion Reaction

To study transcriptome dynamics without harming cells, it is essential to convert chemical bases. 4-Thiouridine (4sU) is a biocompatible uridine analogue that can be converted into a cytidine analogue. Although several reactions can convert 4sU into a cytidine analogue, few studies have compared the features of these reactions. In this study, we performed three reported base conversion reactions, including osmium tetroxide, iodoacetamide, and sodium periodate treatment, as well as a new reaction using 2,4-dinitrofluorobenzene. We compared the reaction time, conversion efficacy, and effects on reverse transcription. These reactions successfully converted 4sU into a cytidine analogue quantitatively using trinucleotides. However, the conversion efficacy and effect on reverse transcription vary depending on the reaction with the RNA transcript. OsO4 treatment followed by NH4Cl treatment showed the best base-conversion efficiency. Nevertheless, each reaction has its own advantages and disadvantages as a tool for studying the transcriptome. Therefore, it is crucial to select the appropriate reaction for the target of interest.

a6A-seq: N 6-allyladenosine-based cellular messenger RNA metabolic labelling and sequencing

The integration of RNA metabolic labelling by nucleoside analogues with high-throughput RNA sequencing has been harnessed to study RNA dynamics. The immunoprecipitation purification or chemical pulldown technique is generally required to enrich the analogue-labelled RNAs. Here we developed an a6A-seq method, which takes advantage of N6-allyladenosine (a6A) metabolic labelling on cellular mRNAs and profiles them in an immunoprecipitation-free and mutation-based manner. a6A plays a role as a chemical sequencing tag in that the iodination of a6A in mRNAs results in 1,N 6-cyclized adenosine (cyc-A), which induces base misincorporation during RNA reverse transcription, thus making a6A-labelled mRNAs detectable by sequencing. A nucleic acid melting assay was utilized to investigate why cyc-A prefers to be paired with guanine. a6A-seq was utilized to study cellular gene expression changes under a methionine-free stress condition. Compared with regular RNA-seq, a6A-seq could more sensitively detect the change of mRNA production over a time scale. The experiment of a6A-containing mRNA immunoprecipitation followed by qPCR successfully validated the high-throughput a6A-seq data. Together, our results show a6A-seq is an effective tool to study RNA dynamics.

Simultaneous measurement of nascent transcriptome and translatome using 4-thiouridine metabolic RNA labeling and translating ribosome affinity purification

Regulation of gene expression in response to various biological processes, including extracellular stimulation and environmental adaptation requires nascent RNA synthesis and translation. Analysis of the coordinated regulation of dynamic RNA synthesis and translation is required to determine functional protein production. However, reliable methods for the simultaneous measurement of nascent RNA synthesis and translation at the gene level are limited. Here, we developed a novel method for the simultaneous assessment of nascent RNA synthesis and translation by combining 4-thiouridine (4sU) metabolic RNA labeling and translating ribosome affinity purification (TRAP) using a monoclonal antibody against evolutionarily conserved ribosomal P-stalk proteins. The P-stalk-mediated TRAP (P-TRAP) technique recovered endogenous translating ribosomes, allowing easy translatome analysis of various eukaryotes. We validated this method in mammalian cells by demonstrating that acute unfolded protein response (UPR) in the endoplasmic reticulum (ER) induces dynamic reprogramming of nascent RNA synthesis and translation. Our nascent P-TRAP (nP-TRAP) method may serve as a simple and powerful tool for analyzing the coordinated regulation of transcription and translation of individual genes in various eukaryotes.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

Improving the study of RNA dynamics through advances in RNA-seq with metabolic labeling and nucleotide-recoding chemistry

RNA metabolic labeling using 4-thiouridine (s4U) captures the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s4U-labeled reads in a process we refer to as dropout. Here we show that s4U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotide recoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s4U from a uridine analog to a cytidine analog and using the apparent T-to-C mutations to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibility of NR-seq experiments.

Determining RNA Natural Modifications and Nucleoside Analog-Labeled Sites by a Chemical/Enzyme-Induced Base Mutation Principle

The natural chemical modifications of messenger RNA (mRNA) in living organisms have shown essential roles in both physiology and pathology. The mapping of mRNA modifications is critical for interpreting their biological functions. In another dimension, the synthesized nucleoside analogs can enable chemical labeling of cellular mRNA through a metabolic pathway, which facilitates the study of RNA dynamics in a pulse-chase manner. In this regard, the sequencing tools for mapping both natural modifications and nucleoside tags on mRNA at single base resolution are highly necessary. In this work, we review the progress of chemical sequencing technology for determining both a variety of naturally occurring base modifications mainly on mRNA and a few on transfer RNA and metabolically incorporated artificial base analogs on mRNA, and further discuss the problems and prospects in the field.

Internally controlled RNA sequencing comparisons using nucleoside recoding chemistry

Quantitative comparisons of RNA levels from different samples can lead to new biological understanding if they are able to distinguish biological variation from variable sample preparation. These challenges are pronounced in comparisons that require complex biochemical manipulations (e.g. isolating polysomes to study translation). Here, we present Transcript Regulation Identified by Labeling with Nucleoside Analogues in Cell Culture (TILAC), an internally controlled approach for quantitative comparisons of RNA content. TILAC uses two metabolic labels, 4-thiouridine (s4U) and 6-thioguanosine (s6G), to differentially label RNAs in cells, allowing experimental and control samples to be pooled prior to downstream biochemical manipulations. TILAC leverages nucleoside recoding chemistry to generate characteristic sequencing signatures for each label and uses statistical modeling to compare the abundance of RNA transcripts between samples. We verified the performance of TILAC in transcriptome-scale experiments involving RNA polymerase II inhibition and heat shock. We then applied TILAC to quantify changes in mRNA association with actively translating ribosomes during sodium arsenite stress and discovered a set of transcripts that are translationally upregulated, including MCM2 and DDX5. TILAC is broadly applicable to uncover differences between samples leading to improved biological insights.

Probing Nascent RNA with Metabolic Incorporation of Modified Nucleosides

The discovery of previously unknown functional roles of RNA in biological systems has led to increased interest in revealing novel RNA molecules as therapeutic targets and the development of tools to better understand the role of RNA in cells. RNA metabolic labeling broadens the scope of studying RNA by incorporating of unnatural nucleobases and nucleosides with bioorthogonal handles that can be utilized for chemical modification of newly synthesized cellular RNA. Such labeling of RNA provides access to applications including measurement of the rates of synthesis and decay of RNA, cellular imaging for RNA localization, and selective enrichment of nascent RNA from the total RNA pool. Several unnatural nucleosides and nucleobases have been shown to be incorporated into RNA by endogenous RNA synthesis machinery of the cells. RNA metabolic labeling can also be performed in a cell-specific manner, where only cells expressing an essential enzyme incorporate the unnatural nucleobase into their RNA. Although several discoveries have been enabled by the current RNA metabolic labeling methods, some key challenges still exist: (i) toxicity of unnatural analogues, (ii) lack of RNA-compatible conjugation chemistries, and (iii) background incorporation of modified analogues in cell-specific RNA metabolic labeling. In this Account, we showcase work done in our laboratory to overcome these challenges faced by RNA metabolic labeling.To begin, we discuss the cellular pathways that have been utilized to perform RNA metabolic labeling and study the interaction between nucleosides and nucleoside kinases. Then we discuss the use of vinyl nucleosides for metabolic labeling and demonstrate the low toxicity of 5-vinyluridine (5-VUrd) compared to other widely used nucleosides. Next, we discuss cell-specific RNA metabolic labeling with unnatural nucleobases, which requires the expression of a specific phosphoribosyl transferase (PRT) enzyme for incorporation of the nucleobase into RNA. In the course of this work, we discovered the enzyme uridine monophosphate synthase (UMPS), which is responsible for nonspecific labeling with modified uracil nucleobases. We were able to overcome this background labeling by discovering a mutant uracil PRT (UPRT) that demonstrates highly specific RNA metabolic labeling with 5-vinyluracil (5-VU). Furthermore, we discuss the optimization of inverse-electron-demand Diels-Alder (IEDDA) reactions for performing chemical modification of vinyl nucleosides to achieve covalent conjugation of RNA without transcript degradation. Finally, we highlight our latest endeavor: the development of mutually orthogonal chemical reactions for selective labeling of 5-VUrd and 2-vinyladenosine (2-VAdo), which allows for potential use of multiple vinyl nucleosides for simultaneous investigation of multiple cellular processes involving RNA. We hope that our methods and discoveries encourage scientists studying biological systems to include RNA metabolic labeling in their toolkit for studying RNA and its role in biological systems.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Metabolic incorporation of electron-rich ribonucleosides enhances APEX-seq for profiling spatially restricted nascent transcriptome

The spatial arrangement of newly synthesized transcriptome in eukaryotic cells underlies various biological processes including cell proliferation and differentiation. In this study, we combine metabolic incorporation of electron-rich ribonucleosides (e.g., 6-thioguanosine and 4-thiouridine) with a peroxidase-mediated proximity-dependent RNA labeling technique (APEX-seq) to develop a sensitive method, termed MERR APEX-seq, for selectively profiling newly transcribed RNAs at specific subcellular locations in live cells. We demonstrate that MERR APEX-seq is 20-fold more efficient than APEX-seq and offers both high spatial specificity and high coverage in mitochondrial matrix. At the ER membrane, 91% of the transcripts captured by MERR APEX-seq encode for secretory pathway proteins, thus demonstrating the high spatial specificity of MERR APEX-seq in open subcellular compartments. Application of MERR APEX-seq to the nuclear lamina of human cells reveals a local transcriptome of 1,012 RNAs, many of which encode for nuclear proteins involved in histone modification, chromosomal structure maintenance, and RNA processing.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Temporal modelling using single-cell transcriptomics

Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.

Non-radioactive In Vivo Labeling of RNA with 4-Thiouracil

RNA molecules and their expression dynamics play essential roles in the establishment of complex cellular phenotypes and/or in the rapid cellular adaption to environmental changes. Accordingly, analyzing RNA expression remains an important step to understand the molecular basis controlling the formation of cellular phenotypes, cellular homeostasis or disease progression. Steady-state RNA levels in the cells are controlled by the sum of highly dynamic molecular processes contributing to RNA expression and can be classified in transcription, maturation and degradation. The main goal of analyzing RNA dynamics is to disentangle the individual contribution of these molecular processes to the life cycle of a given RNA under different physiological conditions. In the recent years, the use of nonradioactive nucleotide/nucleoside analogs and improved chemistry, in combination with time-dependent and high-throughput analysis, have greatly expanded our understanding of RNA metabolism across various cell types, organisms, and growth conditions.In this chapter, we describe a step-by-step protocol allowing pulse labeling of RNA with the nonradioactive nucleotide analog, 4-thiouracil , in the eukaryotic model organism Saccharomyces cerevisiae and the model archaeon Haloferax volcanii .

Addition-Elimination Mechanism-Activated Nucleotide Transition Sequencing for RNA Dynamics Profiling

Dynamic information of intracellular transcripts is essential to understand their functional roles. Routine RNA-sequencing (RNA-seq) methods only measure RNA species at a steady state and do not provide RNA dynamic information. Here, we develop addition-elimination mechanism-activated nucleotide transition sequencing (AENT-seq) for transcriptome-wide profiling of RNA dynamics. In AENT-seq, nascent transcripts are metabolically labeled with 4-thiouridine (4sU). The total RNA is treated with N₂H₄·H₂O under aqueous conditions. N₂H₄·H₂O is demonstrated to convert 4sU to 4-hydrazino cytosine (C*) based on an addition-elimination chemistry. C* is regarded as cytosine (C) during the DNA extension process. This 4sU-to-C transition marks nascent transcripts, so it enables sequencing analysis of RNA dynamics. We apply our AENT-seq to investigate transcript dynamic information of several genes involved in cancer progression and metastasis. This method uses a simple chemical reaction in aqueous solutions and will be rapidly disseminated with extensive applications.

Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq

Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

Metabolic labeling of RNA using multiple ribonucleoside analogs enables the simultaneous evaluation of RNA synthesis and degradation rates

Gene expression is determined by a balance between RNA synthesis and RNA degradation. To elucidate the underlying regulatory mechanisms and principles of this, simultaneous measurements of RNA synthesis and degradation are required. Here, we report the development of “Dyrec-seq,” which uses 4-thiouridine and 5-bromouridine to simultaneously quantify RNA synthesis and degradation rates. Dyrec-seq enabled the quantification of RNA synthesis and degradation rates of 4702 genes in HeLa cells. Functional enrichment analysis showed that the RNA synthesis and degradation rates of genes are actually determined by the genes’ biological functions. A comparison of theoretical and experimental analyses revealed that the amount of RNA is determined by the ratio of RNA synthesis to degradation rates, whereas the rapidity of responses to external stimuli is determined only by the degradation rate. This study emphasizes that not only RNA synthesis but also RNA degradation is important in shaping gene expression patterns.

Acrylonitrile-Mediated Nascent RNA Sequencing for Transcriptome-Wide Profiling of Cellular RNA Dynamics

RNA sequencing has greatly facilitated gene expression studies but is weak in studying temporal RNA dynamics; this issue can be addressed by analyzing nascent RNAs. A famous method for nascent RNA analysis is metabolic labeling with noncanonic nucleoside followed by affinity purification, however, purification processes can always introduce biases into data analysis. Here, a chemical method for nascent RNA sequencing that avoids affinity purification based on acrylonitrile-mediated uridine-to-cytidine (U-to-C) conversion (AMUC-seq) via 4-thiouridine (s4U) cyanoethylation is presented. This method converts s4U base-pairing with guanine through the nucleophilic addition of s4U to acrylonitrile. The high reaction efficiency permits AMUC-seq directly and efficiently to recover nascent RNA information from total RNAs. AMUC-seq is validated by being used to detect mRNA half-lives and investigating the direct gene targets of a G-quadruplex stabilizer, which can be regarded as potential anticancer drug, in human cells. Thousands of direct gene targets of this drug are verified (these genes are significantly enriched in cancer such as SRC and HRAS). AMUC-seq also confirms G-quadruplex stabilization that impacts RNA polyadenylation. These results show AMUC-seq is qualified for the study of temporal RNA dynamics, and it can be a promising strategy to study the therapeutic mechanism of transcription-modulating drugs.

Thioguanosine Conversion Enables mRNA-Lifetime Evaluation by RNA Sequencing Using Double Metabolic Labeling (TUC-seq DUAL)

Temporal information about cellular RNA populations is essential to understand the functional roles of RNA. We have developed the hydrazine/NH₄ Cl/OsO₄ -based conversion of 6-thioguanosine (6sG) into A', where A' constitutes a 6-hydrazino purine derivative. A' retains the Watson-Crick base-pair mode and is efficiently decoded as adenosine in primer extension assays and in RNA sequencing. Because 6sG is applicable to metabolic labeling of freshly synthesized RNA and because the conversion chemistry is fully compatible with the conversion of the frequently used metabolic label 4-thiouridine (4sU) into C, the combination of both modified nucleosides in dual-labeling setups enables high accuracy measurements of RNA decay. This approach, termed TUC-seq DUAL, uses the two modified nucleosides in subsequent pulses and their simultaneous detection, enabling mRNA-lifetime evaluation with unprecedented precision.

Human Long Noncoding RNA Interactome: Detection, Characterization and Function

The application of a new generation of sequencing techniques has revealed that most of the genome has already been transcribed. However, only a small part of the genome codes proteins. The rest of the genome "dark matter" belongs to divergent groups of non-coding RNA (ncRNA), that is not translated into proteins. There are two groups of ncRNAs, which include small and long non-coding RNAs (sncRNA and lncRNA respectively). Over the last decade, there has been an increased interest in lncRNAs and their interaction with cellular components. In this review, we presented the newest information about the human lncRNA interactome. The term lncRNA interactome refers to cellular biomolecules, such as nucleic acids, proteins, and peptides that interact with lncRNA. The lncRNA interactome was characterized in the last decade, however, understanding what role the biomolecules associated with lncRNA play and the nature of these interactions will allow us to better understand lncRNA's biological functions in the cell. We also describe a set of methods currently used for the detection of lncRNA interactome components and the analysis of their interactions. We think that such a holistic and integrated analysis of the lncRNA interactome will help to better understand its potential role in the development of organisms and cancers.

Chemo-enzymatic treatment of RNA to facilitate analyses

Labeling RNA is a recurring problem to make RNA compatible with state-of-the-art methodology and comes in many flavors. Considering only cellular applications, the spectrum still ranges from site-specific labeling of individual transcripts, for example, for live-cell imaging of mRNA trafficking, to metabolic labeling in combination with next generation sequencing to capture dynamic aspects of RNA metabolism on a transcriptome-wide scale. Combining the specificity of RNA-modifying enzymes with non-natural substrates has emerged as a valuable strategy to modify RNA site- or sequence-specifically with functional groups suitable for subsequent bioorthogonal reactions and thus label RNA with reporter moieties such as affinity or fluorescent tags. In this review article, we will cover chemo-enzymatic approaches (a) for in vitro labeling of RNA for application in cells, (b) for treatment of total RNA, and (c) for metabolic labeling of RNA. This article is categorized under: RNA Processing < RNA Editing and Modification RNA Methods < RNA Analyses in vitro and In Silico RNA Methods < RNA Analyses in Cells.

Thermodynamic and structural contributions of the 6-thioguanosine residue to helical properties of RNA

Thionucleotides, especially 4-thiouridine and 6-thioguanosine, are photosensitive molecules that photocrosslink to both proteins and nucleic acids, and this feature is a major reason for their application in various investigations. To get insight into the thermodynamic and structural contributions of 6-thioguanosine to the properties of RNA duplexes a systematic study was performed. In a series of RNA duplexes, selected guanosine residues located in G-C base pairs, mismatches (G-G, G-U, and G-A), or 5' and 3'-dangling ends were replaced with 6-thioguanosine. Generally, the presence of 6-thioguanosine diminishes the thermodynamic stability of RNA duplexes. This effect depends on its position within duplexes and the sequence of adjacent base pairs. However, when placed at a dangling end a 6-thioguanosine residue actually exerts a weak stabilizing effect. Furthermore, the structural effect of 6-thioguanosine substitution appears to be minimal based on NMR and Circular Dichroism (CD) data.