The Beginner’s Guide to Spatial Image Data Science

All you need to know about the AI-driven image analysis unlocking the complexity of disease

Here, Gourab Chatterjee, Director, Product Strategy and Management, and Lorenz Rognoni, Senior Director, Image Data Science, at Vizgen, guide us through the fundamentals.

What is spatial image data science – and why is it becoming increasingly important in modern pathology?

Spatial image data science uses advanced imaging combined with computational analytics to map and quantify the locations, interactions, and heterogeneity of cells and molecules in tissue.

Its rise in importance is driven by the increasing need to understand complex tissue architecture in diseases. It can provide context that traditional single-marker or bulk measurements can’t offer, ultimately leading to more informed decisions and strategies in therapeutic development.

What unique insights does spatial biology provide over traditional histopathology and genomic approaches in understanding tissue microenvironments?

Spatial biology presents a high-resolution view of tissue architecture and cell-to-cell relationships. Unlike traditional histopathology approaches that predominantly provide morphological details or genomics approaches that mostly examine molecular averages, spatial biology provides quantitative data on cell types, their locations, and interactions.

This spatial image data is particularly valuable in understanding complex diseases like cancer, where the tumor microenvironment plays a critical role in disease progression and treatment response.