SImBA-SiQuAl: advancing high-content high-throughput phenotypic profiling of 3D microtumours

Three-dimensional microtumour models such as spheroids are increasingly used in cancer research as they better capture tumour architecture, growth and invasion than conventional two-dimensional cultures. However, robust and accessible tools for quantitative analysis remain limited. Here we present SImBA-SiQuAl, an integrated open-source workflow for high-throughput quantitative phenotyping of 3D spheroids and organoids. The pipeline combines SImBA, an automated image-analysis framework for performant quality-controlled image segmentation and multi-feature extraction from spheroid assays, with SiQuAl, a downstream analysis platform that automatically performs comprehensive statistical and multivariate analyses to reveal phenotypic differences between experimental conditions. In a first case study, we showcase how SImBA-SiQuAl resolves intrinsic invasion phenotypes between cancer cell lines. In a second case study, it quantifies heterogeneous responses in a spheroid drug screening assay. Together, SImBA-SiQuAl provides an effective timely tool for high-throughput, high-content microtumour phenomics in cancer research. MOTIVATION3D-microtumour assays such as spheroids and organoids are increasingly used in preclinical research. These assays generate rich phenotypic imaging data, but automated quantitative analysis remains a major bottleneck. This limits reproducibility, scalability, and broad adoption for large-scale, high-content phenomics studies. Moreover, it impedes comprehensively addressing biologically relevant phenotypic (heterogeneous) responses in e.g. perturbation studies. SImBA-SiQuAl (Spheroid Image Batch Analysis - Simba Quantitative output Analysis) is developed to address this gap by providing an open-source, integrated workflow offering solutions in both the image processing and downstream analysis. Together, this enables in-depth quantitative analysis of 3D microtumour phenotypes across experimental settings.