Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

By M. Rabani, J. Levin, Lin Fan, Xian Adiconis, R. Raychowdhury, Manuel Garber, Andreas Gnirke, Chad Nusbaum, Nir Hacohen, Nir Friedman and 2 more. Nature Biotechnology, 2011

591 citations recorded by the source corpus.

Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd21a608cfd56f2d9329600b44d5215ca9d994f4

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Citations in the corpus

Massively-parallel single nucleus RNA-seq with DroNc-seq (2017, unknown): “We found only two genes up-regulated in the nuclei (lincRNAs Malat1 and Meg3), and 57 genes up regulated in cells, including many mitochondrial RNAs and ribosomal protein RNAs (known to be stable and thus enriched in cells compared to nuclei 13,14 ) (Supplementary Table 2).”
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, background): “RNA velocity, which relies on differences between unspliced and spliced mRNAs (Rabani et al., 2011), predicts the future of individual cells on a timescale of hours (La Manno et al., 2018).”
SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues (2020, unknown): “Moreover, the protein amounts of each might differ from their mRNA abundances (Genshaft et al., 2016; Jovanovic et al., 2015; Rabani et al., 2011; Shalek et al., 2013).”
Single cell RNA Seq reveals dynamic paracrine control of cellular variation (2014, background): “One involved component may be the RNA degradation factor ZFP36 (TTP), whose targets are enriched in the “peaked” inflammatory module 32 .”
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. (2016, methodology): “Bone marrow derived dendritic cells To obtain a sufficient number of cells, we implemented amodified version of the DCs isolation protocol as previously described (Amit et al., 2009; Chevrier et al., 2011; Garber et al., 2012; Lutz et al., 1999; Rabani et al., 2011).”