StrataQuest computes Euclidean distances from each detected cell centroid to defined reference populations or tissue boundaries. Results can be displayed as distance histograms, color-coded overlays on the tissue image, or exported as per-cell measurements. The engine supports nearest-neighbor distance (minimum distance to closest member of reference population), average distance to k-nearest neighbors, and distance to tissue boundaries or manually defined regions of interest. Statistical analysis includes comparison of observed distance distributions against random spatial models.
Spatial Tool
Distance Maps
Measuring spatial relationships between cells
Definition
A spatial analysis tool in StrataQuest that calculates and visualizes the physical distances between cell populations, tissue landmarks, or regions of interest. Distance maps transform complex spatial patterns into quantitative measurements that reveal how cell types organize relative to each other within tissue architecture.
Technical Detail
In Simple Terms
Imagine you want to know how close immune cells are to tumor cells in a tissue sample. Distance maps measure the actual physical distance between every cell and its neighbors, giving you a precise picture of which cells cluster together and which stay apart.
Why It Matters
Spatial proximity between cell types is a key indicator of biological interaction. Immune cells infiltrating a tumor versus remaining at the periphery represent fundamentally different states. Distance maps quantify these patterns objectively, enabling comparison across patients and treatment conditions.
Practical Example
In immuno-oncology research, distance maps can quantify CD8+ T cell proximity to PD-L1+ tumor cells, revealing whether checkpoint-positive tumor cells successfully exclude cytotoxic immune cells or whether immune infiltration is occurring despite checkpoint expression.