Application of 3D Cell Culture Models to Evaluating Cancer Drug Efficacy for Precision Medicine
The Society of Biomolecular Imaging and Informatics (SBI2) annual conference is a great place to see the world’s cutting edge imaging and analysis techniques for use in drug discovery. The conference is focused broadly on how best to use advanced in vitro models, high content imaging, novel imaging approaches and a wide array of data processing and evaluation techniques (AI, deep learning, machine learning). This year at SBI2, Dr. Tom Villani from Visikol gave a presentation on new approaches for analyzing 3D cell culture model high content confocal imaging data.
After researchers have addressed the problem of 3D cell culture model tissue clearing and imaging, the next obstacle is how to process the data that is generated. One of the challenges with 3D cell culture model imaging data is that cells are closely packed to one another in 3D and thus accurate segmentation can be a challenge. While many researchers have used CellProfiler for their 2D in vitro studies, segmentation in CellProfiler leads a lot to be desired as thresholding and segmentation methods are somewhat limited.
Typically, most segmentation algorithms rely upon watershed gradient segmentation which works very well for many uses but is limited in the context of 3D cell culture models due to their close proximity to one another and overlapping nature. To address this segmentation problem, Visikol has developed a fast radial symmetry transform segmentation approach which distinguishes objects based on radial symmetry. This approach is extremely rapid and has been shown to be substantially more reliable and accurate than current segmentation approaches for use with 3D cell culture models.
Visikol currently leverages this segmentation approach within its 3D cell culture assay services where confocal imaging and analysis are required. Reach out directly to Dr. Erin Edwards (Director of In Vitro Studies) for more info.