Journal article

Development of a kinetic assay for late endosome movement

Université de Fribourg

  • Esner, Milan Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic - Max-Planck Institute of Cell Biology and Genetics, Dresden, Germany
  • Meyenhofer, Felix University of Fribourg, Department of Medicine–Anatomy, Switzerland - Max-Planck Institute of Cell Biology and Genetics, Dresden, Germany
  • Kuhn, Michael Biotechnology Center, TU Dresden, Dresden, Germany
  • Thomas, Melissa St Vincent’s Centre for Applied Medical Research, Sydney, Australia - Max-Planck Institute of Cell Biology and Genetics, Dresden, Germany
  • Kalaidzidis, Yannis Max-Planck Institute of Cell Biology and Genetics, Dresden, Germany
  • Bickle, Marc Max-Planck Institute of Cell Biology and Genetics, Dresden, Germany
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    01.08.2014
Published in:
  • Journal of Biomolecular Screening. - 2014, vol. 19, no. 7, p. 1070–1078
English Automated imaging screens are performed mostly on fixed and stained samples to simplify the workflow and increase throughput. Some processes, such as the movement of cells and organelles or measuring membrane integrity and potential, can be measured only in living cells. Developing such assays to screen large compound or RNAi collections is challenging in many respects. Here, we develop a live-cell high-content assay for tracking endocytic organelles in medium throughput. We evaluate the added value of measuring kinetic parameters compared with measuring static parameters solely. We screened 2000 compounds in U-2 OS cells expressing Lamp1-GFP to label late endosomes. All hits have phenotypes in both static and kinetic parameters. However, we show that the kinetic parameters enable better discrimination of the mechanisms of action. Most of the compounds cause a decrease of motility of endosomes, but we identify several compounds that increase endosomal motility. In summary, we show that kinetic data help to better discriminate phenotypes and thereby obtain more subtle phenotypic clustering.
Faculty
Faculté des sciences et de médecine
Department
Département de Médecine
Language
  • English
Classification
Biological sciences
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/303654
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