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Systems survey of endocytosis by multiparametric image analysis

Nature volume 464, pages 243–249 (2010)Cite this article

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A Corrigendum to this article was published on 17 September 2014

This article has been updated

Abstract

Endocytosis is a complex process fulfilling many cellular and developmental functions. Understanding how it is regulated and integrated with other cellular processes requires a comprehensive analysis of its molecular constituents and general design principles. Here, we developed a new strategy to phenotypically profile the human genome with respect to transferrin (TF) and epidermal growth factor (EGF) endocytosis by combining RNA interference, automated high-resolution confocal microscopy, quantitative multiparametric image analysis and high-performance computing. We identified several novel components of endocytic trafficking, including genes implicated in human diseases. We found that signalling pathways such as Wnt, integrin/cell adhesion, transforming growth factor (TGF)-β and Notch regulate the endocytic system, and identified new genes involved in cargo sorting to a subset of signalling endosomes. A systems analysis by Bayesian networks further showed that the number, size, concentration of cargo and intracellular position of endosomes are not determined randomly but are subject to specific regulation, thus uncovering novel properties of the endocytic system.

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Figure 1: Multiparametric image analysis.
Figure 2: Reproducibility of multiparametric profiles by the same siRNA and different siRNAs/gene.
Figure 3: Phenotypic cluster groups profiles.
Figure 4: Regulators of transport of cargo to EEA1 vs APPL endosomes.
Figure 5: Design principles of the endocytic system.

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Change history

  • 11 March 2009

    The b and c labels on Fig. 4 had been misplaced and were transposed on 11 March 2010.

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Acknowledgements

We acknowledge T. Galvez and G. Marsico, members of the Zerial group for discussions and scientific support. We are particularly indebted to K. Korn who developed transfection protocols and organized various preparatory steps and logistics before the screen and E. Krausz for the management of the HT-TDS, the MPI-CBG screening facility. We thank J. Schmitt, A. Lohmann, S. Christ, N. Tomschke, A. Niederlein, J. Wagner, M. Gierth, E. Krausz for technical, robotics and computational assistance, and M. Boes and J. Oegema for IT support that made the large-scale computational analysis possible. We acknowledge I. C. Baines, T. Galvez, J. Howard, T. Hyman, M. McShane, G. O’Sullivan and P. Tomancak for comments on the manuscript. This work was financially supported by the Max Planck Society (MPG) and by the systems biology network HepatoSys of the German Ministry for Education and Research BMBF, the DFG and the Gottlieb Daimler und Karl Benz Stiftung. This work is also part of the project ‘Endotrack’, which received research funding from the European Community’s Sixth Framework Programme.

Author Contributions C.C., Y.K. and M.Z. conceived the project and M.Z. directed it. C.C. and D.K. developed the endocytosis assay under the guidance of M.Z. and E.F.; M.S. performed the screen and image acquisition. Y.K. developed the QMPIA with the help of C.C. and J.C.R. and performed all computational analysis of the screening data. N.S. developed the clustering and pheno-score algorithms and together with C.C. performed the analysis of the clusters. C.R.B., under the supervision of B.H., performed the si/esiRNAs remapping and bioinformatics analysis. CC. and Y.K performed the Bayesian Network analysis. F.B. provided the esiRNA library. R.H., under the supervision of M.S.M. and W.E.N., provided IT support for the use of the computer cluster located at the TUD. C.C.,Y.K. and M.Z. wrote the manuscript.

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Author notes

  1. Jochen C. Rink & Denise Kenski

    Present address: Present addresses: University of Utah School of Medicine, 401 MREB, 20 North 1900 East, Salt Lake City, Utah 84132-3401, USA (J.C.R.); Sirna Therapeutics Inc., San Francisco, California 94158, USA (D.K.).,

Authors and Affiliations

  1. Max Planck Institute for Molecular Cell Biology and Genetics,,

    Claudio Collinet, Charles R. Bradshaw, Nikolay Samusik, Bianca Habermann, Frank Buchholz, Yannis Kalaidzidis & Marino Zerial

  2. High-Throughput Technology Development Studio, MPI-CBG, Pfotenhauerstrasse 108, 01307 Dresden, Germany ,

    Martin Stöter & Eugenio Fava

  3. Center for Information Services and High Performance Computing (ZIH), Dresden University of Technology, D-01062 Dresden, Germany

    Robert Henschel, Matthias S. Mueller & Wolfgang E. Nagel

  4. Belozersky Institute of Physico-Chemical Biology, Moscow State University, 119899, Moscow, Russia ,

    Yannis Kalaidzidis

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Supplementary information

Supplementary Information

This file contains Supplementary Materials and Methods with Supplementary Figures 11-12 with Legends, Supplementary Data and Supplementary References. (PDF 812 kb)

Supplementary Figures

This file contains Supplementary Figures 1-10 with Legends. (PDF 9116 kb)

Supplementary Table 1

This table contains a list of parameters used in the phenotype profiling. (XLS 30 kb)

Supplementary Table 2

This table contains a list of known genes involved in endocytic trafficking. (XLS 20 kb)

Supplementary Table 3

The table contains a list of kinases having an effect on VSV virus infection. (XLS 23 kb)

Supplementary Table 4

This table contains the 4609 genes that were considered for further analysis. (XLS 9732 kb)

Supplementary Table 5

This table includes the sequences of all si/esiRNAs targeting the 4609 positive genes. (XLS 5056 kb)

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Collinet, C., Stöter, M., Bradshaw, C. et al. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243–249 (2010). https://doi.org/10.1038/nature08779

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