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Transcriptome profiling is widely used to infer functional states of specific cell types, as well as their responses to stimuli, to define contributions to physiology and pathophysiology. Focusing on microglia, the brain macrophages, we report here a side-by-side comparison of classical cell sort-based transcriptome sequencing and the ‘RiboTag’ method that avoids cell retrieval from tissue context and yields translatome sequencing information. Conventional whole cell microglia transcriptomes were found to be significantly tainted by artifacts induced by tissue-dissociation, cargo contaminations and transcripts sequestered from ribosomes. Conversely, our data highlight the added value of RiboTag profiling to assess the accuracy of Cre transgenic mice. Collectively, this study indicates method-based biases, reveals observer effects and establishes RiboTag-based translatome profiling as a valuable complement to standard sort-based profiling strategies.
The core components of the nuclear RNA export pathway are thought to be required for export of virtually all polyadenylated RNAs. Here, we depleted different proteins that act in nuclear export in human cells and quantified the transcriptome-wide consequences on RNA localization. Different genes exhibited substantially variable sensitivities, with depletion of NXF1 and TREX components causing some transcripts to become strongly retained in the nucleus while others were not affected. Specifically, NXF1 is preferentially required for export of single-or few-exon transcripts with long exons or high A/U content, whereas depletion of TREX complex components preferentially affects spliced and G/C-rich transcripts. Using massively parallel reporter assays, we identified short sequence elements that render transcripts dependent on NXF1 for their export and identified synergistic effects of splicing and NXF1. These results revise the current model of how nuclear export shapes the distribution of RNA within human cells.
Export to the cytoplasm is a key regulatory junction for both protein-coding mRNAs and long noncoding RNAs (lncRNAs), and cytoplasmic enrichment varies dramatically both within and between those groups. We used a new computational approach and RNA-seq data from human and mouse cells to quantify the genome-wide association between cytoplasmic/ nuclear ratios of both gene groups and various factors, including expression levels, splicing efficiency, gene architecture, chromatin marks, and sequence elements. Splicing efficiency emerged as the main predictive factor, explaining up to a third of the variability in localization. Combination with other features allowed predictive models that could explain up to 45% of the variance for protein-coding genes and up to 34% for lncRNAs. Factors associated with localization were similar between lncRNAs and mRNAs with some important differences. Readily accessible features can thus be used to predict RNA localization.
Highlights d Transcription strength impacts rates of mRNA decay d Transcription dynamics modulate poly(A) tail length via m 6 A and the CCR4-Not complex d IRES elements impede transcription, provoke m 6 A, and restrict mRNA stability d Global transcription changes impact the degradation machinery to buffer mRNA levels
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