Multivariate Control of Transcript to Protein Variability in Single Mammalian Cells

Published: 08-10-2018| Version 1 | DOI: 10.17632/5nwct8htjt.1
Doris Popovic,
Birgit Koch,
Moritz Kueblbeck,
Jan Ellenberg,
Lucas Pelkmans


Single-cell features measurements that accompany manuscript "Multivariate Control of Transcript to Protein Variability in Single Mammalian Cells" by D Popovic, B Koch, M Kueblbeck, J Ellenberg and L Pelkmans.


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Data were generated using image based analysis pipeline in CellProfiler, and computer vision algorithms as described in: Battich, N., Stoeger, T., and Pelkmans, L. (2013). Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133. Stoeger, T., Battich, N., Herrmann, M.D., Yakimovich, Y., and Pelkmans, L. (2015). Computer vision for image-based transcriptomics. Methods San Diego Calif 85, 44–53. Rämö, P., Sacher, R., Snijder, B., Begemann, B., and Pelkmans, L. (2009). CellClassifier: supervised learning of cellular phenotypes. Bioinforma. Oxf. Engl. 25, 3028–3030. Images were obtained using high-content screening microscope CellVoyager 7000 (Yokogawa) with the enhanced CSU-X1 spinning disc (Microlens enhanced dual Nipkow disc confocal scanner, wide view type) and a 40X Olympus objective of 0.95 NA and Neo sCMOS cameras (Andor, 2.560 x 2.560 pixels). 12 Z slices with the distance of 1uM were imaged and maximum projection images (MIP) used for the further image analysis.