Gaining insight into transcriptome‐wide RNA population dynamics through the chemistry of 4‐thiouridine

Duffy, Erin E.; Schofield, J. A.; Simon, Matthew D.

doi:10.1002/wrna.1513

Cited by 48 publications

(50 citation statements)

References 79 publications

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“…We next sought to distinguish newly-transcribed from pre-existing RNAs by counting and statistically modeling T-to-C substitutions in transcripts (UMIs), an approach that overcomes the problem of incomplete 4sU labeling of new transcripts (up to 50% of all reads originated from new RNAs may not contain T-to-C substitutions) 17 . The quantification accuracy is further improved by UMI-based analysis, which substantially increases the number of uridines or T-to-C substitutions covered in each transcript compared to the analysis of individual sequencing reads (Supplementary Fig.…”

Section: Application Of Scnt-seq To Study Neuronal Activity-dependent Transcription and Regulatory Networkmentioning

confidence: 99%

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

et al. 2020

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Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms..

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Section: Application Of Scnt-seq To Study Neuronal Activity-dependent Transcription and Regulatory Networkmentioning

confidence: 99%

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

et al. 2020

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“…One of the methods exploits chemically active nucleoside analogs (i.e. noncanonical nucleosides) that can serve as surrogates of natural nucleosides and be used for RNA synthesis in living cells ( 5 ). The noncanonical nucleoside-incorporated RNAs are then chemically conjugated with fluorophores for imaging or affinity tags for enrichment and sequencing.…”

Section: Introductionmentioning

confidence: 99%

Metabolic RNA labeling for probing RNA dynamics in bacteria

Meng

Guo

Tang

et al. 2020

Nucleic Acids Research

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Metabolic labeling of RNAs with noncanonical nucleosides that are chemically active, followed by chemoselective conjugation with imaging probes or enrichment tags, has emerged as a powerful method for studying RNA transcription and degradation in eukaryotes. However, metabolic RNA labeling is not applicable for prokaryotes, in which the complexity and distinctness of gene regulation largely remain to be explored. Here, we report 2′-deoxy-2′-azidoguanosine (AzG) as a noncanonical nucleoside compatible with metabolic labeling of bacterial RNAs. With AzG, we develop AIR-seq (azidonucleoside-incorporated RNA sequencing), which enables genome-wide analysis of transcription upon heat stress in Escherichia coli. Furthermore, AIR-seq coupled with pulse-chase labeling allows for global analysis of bacterial RNA degradation. Finally, we demonstrate that RNAs of mouse gut microbiotas can be metabolically labeled with AzG in living animals. The AzG-enabled metabolic RNA labeling should find broad applications in studying RNA biology in various bacterial species.

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“…Single labeling reduces experimental cost and complexity, likely at the expense of the accuracy of the degradation rate estimates (Wada and Becskei, 2017). Alternatively, the dynamics of nucleotide incorporation can also be explored to increase rate inference accuracy using approach-to-equilibrium or pulse-chase designs (Duffy et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells

Biasini

Marques

2020

Front. Cell Dev. Biol.

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The relative ease of mouse Embryonic Stem Cells (mESCs) culture and the potential of these cells to differentiate into any of the three primary germ layers: ectoderm, endoderm and mesoderm (pluripotency), makes them an ideal and frequently used ex vivo system to dissect how gene expression changes impact cell state and differentiation. These efforts are further supported by the large number of constitutive and inducible mESC mutants established with the aim of assessing the contributions of different pathways and genes to cell homeostasis and gene regulation. Gene product abundance is controlled by the modulation of the rates of RNA synthesis, processing, and degradation. The ability to determine the relative contribution of these different RNA metabolic rates to gene expression control using standard RNA-sequencing approaches, which only capture steady state abundance of transcripts, is limited. In contrast, metabolic labeling of RNA with 4-thiouridine (4sU) coupled with RNAsequencing, allows simultaneous and reproducible inference of transcriptome wide synthesis, processing, and degradation rates. Here we describe, a detailed protocol for 4sU metabolic labeling in mESCs that requires short 4sU labeling times at low concentration and minimally impacts cellular homeostasis. This approach presents a versatile method for in-depth characterization of the gene regulatory strategies governing gene steady state abundance in mESC.

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Gaining insight into transcriptome‐wide RNA population dynamics through the chemistry of 4‐thiouridine

Cited by 48 publications

References 79 publications

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

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

Metabolic RNA labeling for probing RNA dynamics in bacteria

A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells

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