Automated Quality Control of Time-Course Imaging from 3D in vitro cultures

Abstract

Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. Existing computational methods struggle to address this issue. We present a new algorithm to evaluate data integrity and rectify mislabeling in longitudinal imaging of 3D cell culture. Our algorithm integrates permutation-based optimization with Procrustes analysis. By using X and Y coordinates of images, it accurately reorders, matches, and aligns object positions across time points, correcting for rotation, translation, and small movements. Validation with simulated data confirmed its accuracy and robustness. Applied to longitudinal imaging of tumor spheroids, our algorithm revealed frequent displacement amongst the spheroids between time points and corrected many mislabeled images. This computationally efficient and adaptable method needs no experimental adjustments and presents a readily accessible solution for data quality control. MotivationThree-dimensional (3D) in vitro models, such as tumor organoids and spheroids embedded in an extracellular matrix, are increasingly vital for studying normal and disease biology, including drug responses.1-3 A key advantage of these models is that imaging platforms can perform continuous longitudinal imaging to track phenotypic changes. However, common issues in 3D techniques, such as matrix shifts during experimental setup or image capture, can introduce technical artifacts that affect downstream analyses. Currently, no automated analytical approaches exist for assessing or correcting technical artifacts. Here, we introduce a robust, automated algorithm for assessing the quality of time-course image data and, in some cases, correcting object mislabeling to enable accurate tracking of individual spheroids over time. This approach relies only on image metadata, requiring no experimental modifications. It offers a readily implementable solution for improving data integrity and reproducibility and enhancing the reliability of longitudinal 3D cell culture studies.