MoSAIC: Multi-Resolution Spatial Regression Analysis of Cellular Colocalizations in Cancer Imaging

2026-05-28

AI Summary

Existing methods struggle to effectively decipher the multi-level spatial architecture of cellular co-localization within multi-resolution cancer imaging data and its dynamic variation along biological gradients such as epithelial–mesenchymal transition (EMT). To address this, this work proposes MoSAIC—a hierarchical Bayesian regression model that, for the first time, jointly integrates global tumor gradient effects, patient-specific variability, and inter-field local spatial dependencies to enable interpretable quantification of cellular interactions in the tumor microenvironment. By combining Gaussian processes with multi-scale single-cell spatial data, MoSAIC reveals in a renal cell carcinoma cohort that macrophage–tumor cell co-localization increases while cytotoxic T cell co-localization decreases along the EMT gradient, corroborating EMT-associated immune suppression. Simulation studies further demonstrate its superior predictive accuracy and model fit compared to existing approaches.

Abstract

Hierarchical multiplex imaging approaches generate spatially resolved single-cell measurements across multiple, spatially organized fields of view (FOVs) within patient tumor specimens, thereby enabling systematic investigation of how the organization of the tumor microenvironment varies along biologically meaningful intratumoral gradients. Existing approaches fail to jointly address this multi-resolution data structure needed to recover true biological signals. We propose MoSAIC: multi-resolution spatial regression analysis of cell colocalizations, a hierarchical Bayesian spatial regression model designed for multi-resolution spatial data. MoSAIC decomposes the joint variation into three model components: (i) global tumor-gradient effects, (ii) patient-specific effects to capture inter-patient variability, and (iii) Gaussian process models to account for spatial dependence between FOVs within each patient tumor tissue. Simulations demonstrate MoSAIC has improved prediction and model fit compared to existing spatial and non-spatial model alternatives. Our method is motivated by and applied to a renal cell carcinoma multiplex imaging cohort to investigate immune-tumor colocalization patterns across the epithelial-to-mesenchymal transition (EMT) gradient. MoSAIC identifies increased macrophage-tumor colocalization and decreased cytotoxic T-tumor colocalization progressing across the increasing EMT gradient, consistent with EMT-associated immune suppression and spatially varying immune engagement. Overall, MoSAIC provides an interpretable, multi-resolution framework for quantifying spatial tumor-gradient effects in cancer imaging studies. Software is available on GitHub at jcaldous/MoSAIC.

Problem

• multi-resolution
• spatial regression
• cellular colocalization
• tumor microenvironment
• intratumoral gradients

Innovation

• multi-resolution spatial modeling
• hierarchical Bayesian regression
• cellular colocalization
• tumor microenvironment
• Gaussian process