Abstract
AIMS
A faithful representation of real tumour conditions in vivo such as a 3D glioma spheroid in a 3D experimental setting has become highly desirable for brain tumour research as it aids the study of tumour behaviour such as cell invasion and initiation of metastasis. However, data resulting from such studies need to be accurately analysed to correctly assess, for example, inhibitor effects on spheroid morphology and cell migration. To aid 3D data analysis we aimed to develop a reliable system for quantification of observed morphological changes such as number, morphology and size of invasive cells and spheroid extensions.
METHOD
We established a novel workflow that reconstructs a 3D entity from, for example, microtome sectioned glioma spheroids or confocal image z-stacks into pointclouds and subsequent comparison of basic readout parameters. The workflow requires little computational effort. Sliced image stacks are successfully scaled down towards a 1x1x1 ratio at threshold with subsequent edge detection and transformed into a pointcloud and analysed within minutes on a standard workstation /computer, e.g. shared graphics (4x 1.80 GHz CPU, 15 GB RAM).
RESULTS
We were able to validate the usefulness of this workflow using 3D data generated by various means including z-stacks from confocal microscopy and immunohistochemistry sectioning and demonstrate the possibility to accurately characterize inhibitor effects in depth.
CONCLUSION
We developed and validated ‘Cloudbuster’ as a 3D quantification tool to accurately assess structural changes detected in brain tumour cells after drug treatment, resulting in a versatile and adaptable 3D morphology analysis tool.