Empowering high-throughput high-content analysis of 3D tumor models: Open-source software for automated non-confocal image analysis

Purpose The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in three-dimensional tumor models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of cancer and endothelial cell dynamics in vitro , facilitating the rapid assessment of drug sensitivity. Methods Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a deep neural network binary classification model, measuring the distance between Z-planes with invaded cells. Lastly, microvessel dynamics were assessed through a U-Net Xception-style deep learning model for segmentation, a disperse algorithm for network graph representation, then persistent homology to quantify microvessel length and connectivity. Finally, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Results The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC 50 values with a 95% confidence level compared to manual calculations. Additionally, it significantly reduced the image processing time from weeks down to hours. Conclusions Our free and open source software offers an automated solution for quantifying 3D cell behavior in tumor models using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.