High content genome-wide siRNA screen to investigate the coordination of cell size and RNA production

Coordination of RNA abundance and production rate with cell size has been observed in diverse organisms and cell populations. However, how cells achieve such ‘scaling’ of transcription with size is unknown. Here we describe a genome-wide siRNA screen to identify regulators of global RNA production r...

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Veröffentlicht in:Scientific data 2021-06, Vol.8 (1), p.162-162, Article 162
Hauptverfasser: Müller, Micha, Avar, Merve, Heinzer, Daniel, Emmenegger, Marc, Aguzzi, Adriano, Pelkmans, Lucas, Berry, Scott
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Sprache:eng
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Zusammenfassung:Coordination of RNA abundance and production rate with cell size has been observed in diverse organisms and cell populations. However, how cells achieve such ‘scaling’ of transcription with size is unknown. Here we describe a genome-wide siRNA screen to identify regulators of global RNA production rates in HeLa cells. We quantify the single-cell RNA production rate using metabolic pulse-labelling of RNA and subsequent high-content imaging. Our quantitative, single-cell measurements of DNA, nascent RNA, proliferating cell nuclear antigen (PCNA), and total protein, as well as cell morphology and population-context, capture a detailed cellular phenotype. This allows us to account for changes in cell size and cell-cycle distribution (G1/S/G2) in perturbation conditions, which indirectly affect global RNA production. We also take advantage of the subcellular information to distinguish between nascent RNA localised in the nucleolus and nucleoplasm, to approximate ribosomal and non-ribosomal RNA contributions to perturbation phenotypes. Perturbations uncovered through this screen provide a resource for exploring the mechanisms of regulation of global RNA metabolism and its coordination with cellular states. Measurement(s) nascent RNA • Image • S phase • nucleolus organization • Cellular Morphology • Cell Cycle Phase Technology Type(s) metabolic labelling: 5-ethynyl uridine • spinning-disk confocal microscope • supervised machine learning • Image Processing Factor Type(s) gene expression Sample Characteristic - Organism HeLa cell Sample Characteristic - Environment cell culture Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14332916
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-021-00944-5