ArticleACS chemical neuroscience2018
Bioorthogonal Metabolic Labeling of Nascent RNA in Neurons Improves the Sensitivity of Transcriptome-Wide Profiling.
Article in ACS chemical neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Transcriptome-Wide Profiling of Nascent RNA in Neurons with Enriched H3K27ac Signal Elevates eRNA Identification Efficiency.ACS chemical neuroscience · 2024Article
- The Efficiency of Metabolic Labeling of DNA by Diels-Alder Reactions with Inverse Electron Demand: Correlation with the Size of Modified 2'-Deoxyuridines.ACS chemical biology · 2023Article
- An Optimized Enzyme-Nucleobase Pair EnablesBiochemistry · 2022Article
- Tag-Free Internal RNA Labeling and Photocaging Based on mRNA Methyltransferases.Angewandte Chemie (International ed. in English) · 2021Article
- Article
- An optimized chemical-genetic method for cell-specific metabolic labeling of RNA.Nature methods · 2020Article
- Identification of novel regulators of dendrite arborization using cell type-specific RNA metabolic labeling.PloS one · 2020Article
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
Transcriptome-wide expression profiling of neurons has provided important insights into the underlying molecular mechanisms and gene expression patterns that transpire during learning and memory formation. However, there is a paucity of tools for profiling stimulus-induced RNA within specific neuronal cell populations. A bioorthogonal method to chemically label nascent (i.e., newly transcribed) RNA in a cell-type-specific and temporally controlled manner, which is also amenable to bioconjugation via click chemistry, was recently developed and optimized within conventional immortalized cell lines. However, its value within a more fragile and complicated cellular system such as neurons, as well as for transcriptome-wide expression profiling, has yet to be demonstrated. Here, we report the visualization and sequencing of activity-dependent nascent RNA derived from neurons using this labeling method. This work has important implications for improving transcriptome-wide expression profiling and visualization of nascent RNA in neurons, which has the potential to provide valuable insights into the mechanisms underlying neural plasticity, learning, and memory.
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