Abstract
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. Here, we 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, we 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, we analyzed the phenotypes of a collection of 44 human breast cancer cell lines cultured in 3D and showed that our method correctly distinguished basal-A, basal-B, luminal and ERBB2+ cell lines in a supervised nearest neighbor classification method.
Highlights
Over the past decade, in vivo models and 2D cell cultures represented the two principle approaches used to study cellular processes
One potential application of 3D cultures is for the highthroughput screening (HTS) and high content analysis (HCA) of pharmacologically active compounds [8]
4T1 breast cancer cells acquire a complex phenotype in 3D culture, which is perturbed by biologically active compounds
Summary
In vivo models and 2D cell cultures represented the two principle approaches used to study cellular processes. One potential application of 3D cultures is for the highthroughput screening (HTS) and high content analysis (HCA) of pharmacologically active compounds [8]. For such purposes, 3D cultures are treated with compound libraries in 96- or 384-wells micro plates. Many methodologies have been established for the image analysis of HTS [9], the quantification of phenotypes is mostly performed with single or multiple simple parameters. This is not sufficient to study of the full range of effects of test compounds
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