Three-dimensional computer vision for exploring heterogeneity in collective Cancer Invasion

Abstract

Collective cancer invasion exhibits a hierarchical structure characterized by leader-follower organization. Dynamic gene expression analysis of invading cells using nanobiosensors within 3D microenvironments provides a valuable means to explore the regulation of leader cells during collective cancer invasion. Nonetheless, the analysis of time-lapse, multimodal images that capture the intricacies of complex invading structures and gene expression profiles in 3D tumor spheroids poses a significant technological challenge. Here, we present a computer vision-based workflow that streamlines the identification of protrusions and detached clusters from 3D tumor spheroids. This methodology not only discerns invading multicellular structures and quantifies their physical properties, but also captures gene expression patterns associated with these invasive mechanisms using an intracellular nanobiosensor. Consequently, it empowers a systematic exploration of the genotypic and phenotypic heterogeneities inherent in cancer invasion. To illustrate the effectiveness of this approach, we applied it to the analysis of a long noncoding RNA, MALAT1, in tumor spheroids derived from patients with muscle-invasive bladder cancer. Our investigation delved into the heterogeneity of cancer invasion and its relationship to MALAT1 expression. Overall, this workflow represents a valuable tool for gaining insights into the complexities of cancer invasion.