Co-translational targeting of transcripts to endosomes
Asymmetric localization and translation of mRNAs is used by single cells to sense their environment and integrate extrinsic cues with the appropriate cellular response. Here we investigate the extent to which endosomes impact subcellular patterning of transcripts and provide a platform for localized translation. Using image-based transcriptomics, indirect immunofluorescence, and RNAseq of isolated organelles, we discover mRNAs that associate with early endosomes in a translation-dependent and -independent manner. We explore this in more detail for the mRNA of a major endosomal tethering factor and fusogen, Early Endosomal Antigen 1, EEA1, which localises to early endosomes in a puromycin-sensitive manner. By reconstituting EEA1 knock-out cells with either the coding sequence or 3’UTR of EEA1, we show that the coding region is sufficient for endosomal localisation of mRNA. Finally, we use quantitative proteomics to discover proteins associated with EEA1 mRNA and identify CSRP1 as a factor that controls EEA1 translational efficiency. Our findings reveal that multiple transcripts associate with early endosomes in a translation-dependent manner and identify mRNA-binding proteins that may participate in controlling endosome-localised translation.
Steps to reproduce
These data accompany following preprint: https://doi.org/10.1101/2020.07.17.208652 RNA sequencing data are analysed on Sushi (https://fgcz-sushi.uzh.ch), where raw data can be requested. Hatakeyama, M, Opitz, L, Russo, G et al. SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis. BMC Bioinformatics 17, 228 (2016). https://doi.org/10.1186/s12859-016-1104-8 Raw Proteomics Data are available via ProteomeXchange with identifier PXD020531. (http://www.ebi.ac.uk/pride) Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA (2019). The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 47(D1):D442-D450 Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, García-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA(2020). The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics, Nucleic Acids Res 48(D1):D1145-D1152 Perez-Riverol Y, Xu QW, Wang R, Uszkoreit J, Griss J, Sanchez A, Reisinger F, Csordas A, Ternent T, del Toro N, Dianes JA, Eisenacher M, Hermjakob H, Vizcaíno JA (2016). PRIDE Inspector Toolsuite: moving towards a universal visualization tool for proteomics data standard formats and quality assessment of ProteomeXchange datasets. Mol Cell Proteomics 15(1):305-17 Imaging 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, MD, 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.