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.

翻译：分层多重成像方法可在患者肿瘤样本中跨多个空间组织的视野（FOVs）生成空间分辨的单细胞测量数据，从而系统研究肿瘤微环境组织沿具有生物学意义的瘤内梯度的变化规律。现有方法无法联合处理恢复真实生物信号所需的多分辨率数据结构。我们提出MoSAIC：细胞共定位的多分辨率空间回归分析，这是一种专为多分辨率空间数据设计的层次贝叶斯空间回归模型。MoSAIC将联合变异分解为三个模型组件：（i）全局肿瘤梯度效应，（ii）捕获患者间变异性的患者特异性效应，以及（iii）用于解释每个患者肿瘤组织内FOVs间空间依赖关系的高斯过程模型。模拟实验表明，与现有空间及非空间模型替代方案相比，MoSAIC在预测准确性和模型拟合度上均有提升。我们的方法受肾细胞癌多重成像队列的驱动，并应用于该队列以研究跨上皮-间充质转化（EMT）梯度的免疫-肿瘤共定位模式。MoSAIC发现随着EMT梯度递增，巨噬细胞-肿瘤共定位增加而细胞毒性T细胞-肿瘤共定位减少，这与EMT相关的免疫抑制及空间变化的免疫参与相一致。总体而言，MoSAIC为量化癌症影像研究中的空间肿瘤梯度效应提供了一个可解释的多分辨率框架。软件可从GitHub上的jcaldous/MoSAIC获取。