2014
Development of a Kinetic Assay for Late Endosome Movement
EŠNER, Milan; Felix MEYENHOFER; Michael KUHN; Melissa THOMAS; Yannis KALAIDZIDIS et al.Základní údaje
Originální název
Development of a Kinetic Assay for Late Endosome Movement
Autoři
EŠNER, Milan ORCID; Felix MEYENHOFER; Michael KUHN; Melissa THOMAS; Yannis KALAIDZIDIS a Marc BICKLE
Vydání
Journal of Biomolecular Screening, Thousand Oaks, SAGE Publications Inc. 2014, 1087-0571
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10600 1.6 Biological sciences
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Impakt faktor
Impact factor: 2.423
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14110/14:00077529
Organizační jednotka
Lékařská fakulta
UT WoS
EID Scopus
Klíčová slova anglicky
live cell; tracking; high-content imaging; Lamp1; cardiac glycoside
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 28. 11. 2014 13:19, Soňa Böhmová
Anotace
V originále
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.