A Computational Framework to Study Sub-Cellular RNA Localization
ARTICLE DOI: 10.1038/s41467-018-06868-w OPEN A computational framework to study sub-cellular RNA localization Aubin Samacoits1,2, Racha Chouaib3,4, Adham Safieddine3,4, Abdel-Meneem Traboulsi3,4, Wei Ouyang 1,2, Christophe Zimmer 1,2, Marion Peter3,4, Edouard Bertrand3,4, Thomas Walter 5,6,7 & Florian Mueller 1,2 RNA localization is a crucial process for cellular function and can be quantitatively studied by single molecule FISH (smFISH). Here, we present an integrated analysis framework to ana- 1234567890():,; lyze sub-cellular RNA localization. Using simulated images, we design and validate a set of features describing different RNA localization patterns including polarized distribution, accumulation in cell extensions or foci, at the cell membrane or nuclear envelope. These features are largely invariant to RNA levels, work in multiple cell lines, and can measure localization strength in perturbation experiments. Most importantly, they allow classification by supervised and unsupervised learning at unprecedented accuracy. We successfully vali- date our approach on representative experimental data. This analysis reveals a surprisingly high degree of localization heterogeneity at the single cell level, indicating a dynamic and plastic nature of RNA localization. 1 Unité Imagerie et Modélisation, Institut Pasteur and CNRS UMR 3691, 28 rue du Docteur Roux, 75015 Paris, France. 2 C3BI, USR 3756 IP CNRS, 28 rue du Docteur Roux, 75015 Paris, France. 3 Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France. 4 Equipe labellisée Ligue Nationale Contre le Cancer, Paris, France. 5 MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 75006 Paris, France. 6 Institut Curie, PSL Research University, 75005 Paris, France.
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