Cell Invasion Analysis of Tumor Spheroids Using 2D Image Data

Metastatic disease is the most severe complication in oncological patients. The quantification of cellular invasion into the surrounding tissue is crucial for the identification of strategies to suppress this process. Extracellular matrix-embedded 3D cancer models, such as spheroids and organoids, are commonly used to mimic tumor progression under in vitro conditions. However, robust and widely used algorithms to detect and quantify spheroid growth and invasion into the surrounding matrix are still lacking. In this study, we use fluorescently labeled 3D models, as fluorescence images are generally of higher quality than bright-field images. We present a methodology to compute the mask of the spheroid core and to detect and characterize cells outside this mask. We have developed two strategies for mask computation, one for compact spheroids and another for models that lose their boundaries soon after insertion into the extracellular matrix. In both modes, masks can be created for spheroids of various shapes. Cells or their clusters outside the mask are recognized on the basis of filtered local maxima. This method enables the analysis of images with a nonconstant background, which is often found in real fluorescence images. The evaluation is largely automated but allows visual inspection based on the overlay of the objects detected by the algorithm with the original fluorescence signal of the spheroid core and the invading cells. A user-friendly manual adjustment of the parameters for mask fitting and cell detection is implemented.