3D Cell Phenotyping Data

Datacite citation style:
J.H.N. (John) Meerman (2014): 3D Cell Phenotyping Data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:d5b91e46-07e7-4077-bd63-3fa2b82c847f
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
The publication describes how, in many situations, 3D cell cultures mimic the natural organization of tissues more closely than 2D cultures. Conventional methods for phenotyping such 3D cultures use either single or multiple simple parameters based on morphology and fluorescence staining intensity. However, due to their simplicity many details are not taken into account which limits system-level study of phenotype characteristics. The authors have developed a new image analysis platform to automatically profile 3D cell phenotypes with 598 parameters including morphology, topology, and texture parameters such as wavelet and image moments. As proof of concept, they analyzed mouse breast cancer cells (4T1 cells) in a 384-well plate format following exposure to a diverse set of compounds at different concentrations. The result showed concentration dependent phenotypic trajectories for different biologically active compounds that could be used to classify compounds based on their biological target. To demonstrate the wider applicability of our method, they analyzed the phenotypes of a collection of 44 human breast cancer cell lines cultured in 3D and showed that their method correctly distinguished basal-A, basal-B, luminal and ERBB2+ cell lines in a supervised nearest neighbor classification method.
  • 2014-08-20 first online, published, posted
Leiden University
media types: application/vnd.ms-excel, application/x-rar-compressed, image/tiff
  • Price, L.S. (Leo)
  • Zi, Di (Research Associate)


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