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

Area of Science:

• Oncology
• Biotechnology
• Computer Vision

Background:

• Collective cancer invasion is a hierarchical process involving leader-follower cells.
• Analyzing dynamic gene expression in 3D microenvironments is crucial for understanding invasion regulation.
• Technological challenges exist in analyzing complex 3D tumor invasion dynamics and gene expression.

Purpose of the Study:

• To develop a computer vision-based workflow for analyzing collective cancer invasion in 3D.
• To streamline the identification and quantification of invading multicellular structures and their physical properties.
• To capture gene expression patterns associated with cancer invasion mechanisms using intracellular nanobiosensors.

Main Methods:

• A computer vision workflow was developed to process time-lapse, multimodal images of 3D tumor spheroids.
• The methodology identifies protrusions and detached clusters, quantifying physical properties of invading structures.
• Intracellular nanobiosensors were used to capture gene expression patterns linked to invasion.

Main Results:

• The workflow successfully identified invading multicellular structures and quantified their physical properties.
• Gene expression patterns associated with invasive mechanisms were captured.
• The approach was applied to study MALAT1 expression heterogeneity in muscle-invasive bladder cancer spheroids.

Conclusions:

• The developed workflow enables systematic exploration of genotypic and phenotypic heterogeneities in cancer invasion.
• It provides valuable insights into the complexities of collective cancer invasion and the role of specific genes like MALAT1.
• This tool aids in understanding cancer invasion mechanisms and heterogeneity.