‘Cloudbuster’: a Python-based open source application for three-dimensional reconstruction and quantification of stacked biological imaging samples

TL;DR: This work provides a novel open-source workflow to reconstruct a 3D entity from slice-recorded microscopical images with or without treatment with anti-migratory small molecule inhibitors, and produces distinct point clouds as basis for subsequent comparison of basic readout parameters.

Abstract: Three-dimensional (3D) spheroid cultures are generating increasing interest in cancer research, e.g. for the evaluation of pharmacological effects of novel small molecule inhibitors. This is mainly due to the fact that such 3D structures reflect physiological characteristics of tumours and the cellular microenvironments they reside in more faithfully than two-dimensional (2D) cell cultures; in addition, they allow the reduction of animal experiments while providing significantly relevant human-based models. Quantification of such organoid structures as well as the mainly slice-based acquisition and thus forced 2D representation of 3D spheroids provide a challenge for the interpretation of the associated generated data. Here, we provide a novel open-source workflow to reconstruct a 3D entity from slice-recorded microscopical images with or without treatment with anti-migratory small molecule inhibitors. This reconstruction produces distinct point clouds as basis for subsequent comparison of basic readout parameters using average computer processor, memory and graphics resources within an acceptable time frame. We were able to validate the usefulness of this workflow using 3D data generated by various imaging techniques, including z-stacks from confocal microscopy and histochemically labelled spheroid sectioning, and demonstrate the possibility to accurately characterize inhibitor effects in great detail.