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

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.

In vitro and in silico approaches to engineering three-dimensional biological tissues and organoids

“…A natural next step for exploiting further the information that can be extracted from 3D organoids could involve integrating the imaging methods developed in [5] and [6] with mathematical and computational models that describe the biophysical processes that underpin the growth and response to treatment of 3D organoids [8,9]. By fitting the theoretical models to data extracted from 3D reconstructions of the organoids, estimates of biophysical parameters can be obtained and the impact of different interventions quantified.…”

“…The principles for analysing 3D organoids introduced in [5] are reinforced in the article by Temple et al [6]; there the attention focuses on the need for robust and reproducible methods to quantify cell numbers and gene expression levels in 3D culture systems so that accurate comparisons can be made between the performance of different 3D systems. The authors start by reviewing existing methods for culturing 3D tissue constructs, focusing on the pros and cons of each method, with particular emphasis on the challenges associated with collecting data from each system.…”

“…The contributions come from members of an MRC-funded network 3DBioNet which aims to establish multi-disciplinary teams of scientists from industry and academia who together possess the diverse skills needed to develop 3D microtissues. As explained below, the five, featured articles span experimental methods for organoid development [3], the use of 3D scaffolds to promote wound healing [4], software for reconstructing and analysing 3D structures from 2D microscopy images [5], methods for quantifying cell numbers in 3D organoids [6] and mathematical and computational modelling [7].…”

“…The principles for analysing 3D organoids introduced in [5] are reinforced in the article by Temple et al . [6]; there the attention focuses on the need for robust and reproducible methods to quantify cell numbers and gene expression levels in 3D culture systems so that accurate comparisons can be made between the performance of different 3D systems.…”