Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model

The invasion of cancer cells from the primary tumor into the adjacent healthy tissues is an early step in metastasis. Invasive cancer cells pose a major clinical challenge because no efficient method exist for their elimination once their dissemination is underway. A better understanding of the mechanisms regulating cancer cell invasion may lead to the development of novel potent therapies. Due to their physiological resemblance to tumors, spheroids embedded in collagen I have been extensively utilized by researchers to study the mechanisms governing cancer cell invasion into the extracellular matrix (ECM). However, this assay is limited by (1) a lack of control over the embedding of spheroids into the ECM; (2) high cost of collagen I and glass bottom dishes, (3) unreliable immunofluorescent labeling, due to the inefficient penetration of antibodies and fluorescent dyes and (4) time-consuming image processing and quantification of the data. To address these challenges, we optimized the three-dimensional (3D) spheroid protocol to image fluorescently labeled cancer cells embedded in collagen I, either using time-lapse videos or longitudinal imaging, and analyze cancer cell invasion. First, we describe the fabrication of a spheroid imaging device (SID) to embed spheroids reliably and in a minimal collagen I volume, reducing the assay cost. Next, we delineate the steps for robust fluorescence labeling of live and fixed spheroids. Finally, we offer an easy-to-use Fiji macro for image processing and data quantification. Altogether, this simple methodology provides a reliable and affordable platform to monitor cancer cell invasion in collagen I. Furthermore, this protocol can be easily modified to fit the users' needs.

Automated summary

The study introduces an optimized method for studying cancer cell invasion mechanisms by embedding fluorescently labeled cancer cells in collagen I using three-dimensional spheroid protocol, addressing the challenges of control over embedding, cost, labeling efficiency, and data processing efficiency.