Visualization, Data Extraction, and Multiparametric Analysis of 3D Pancreatic and Colorectal Cancer Cell Lines for High-Throughput Screening

Background: Three-dimensional (3D) cancer models are currently essential tools in high-throughput screening (HTS), serving as a bridge between in vitro and in vivo approaches during drug development. Even though spheroids offer many advantages over 2D cultures, analyzing 3D cultures with heterogeneous morphology remains challenging due to the lack of standardized visualization techniques and multiparameter analysis. Methods: In this work, an optimized CellProfiler pipeline and a Python algorithm for weighting morphological features are used to visualize, extract, and analyze morphological data from spheroids derived from colorectal and pancreatic cancer cell lines with diverse morphologies (HCT116, LoVo, PANC-1, and CFPAC-1). Results: We developed a feature weighting process that combines multiple morphological parameters into a single metric using principal component analysis (PCA). There is a strong correlation between this process and a standard Alamar Blue proliferation assay (r = 0.89, ρ = 0.91, p < 0.001). Using this method, we were able to ascertain the IC50 values of substances that did not produce results in cell lines with heterogeneous morphology (LoVo and CFPAC-1) using a standard proliferation assay. Conclusions: By removing the need for tracer dyes, the resulting methodology may lower costs while accelerating preclinical drug development through informative multiparameter analysis of compound efficacy.