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Counterfactual Diffusion Modeling Enables Spatially Targeted Reprogramming of Tissue Microenvironments
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DOI：10.3390/biology15141097.png)
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Abstract
En 中文
Spatially resolved single-cell technologies can provide deep insights into cellular heterogeneity and tissue structural characteristics. However, the data obtained are purely observational and cannot reveal the specific mechanisms by which tissues respond to particular perturbations. Most computational models of single-cell perturbations either operate in a non-spatial latent space or fix tissue geometry within a static spatial structure, thereby limiting their ability to integrate molecular profiles with tissue topological remodeling. We propose SPAD-CFR (Spatial Point-cloud Attention-based Diffusion for CounterFactual Reprogramming). Each tissue is treated as a spatial point cloud containing cellular molecular profiles and physical coordinates. We implement Pearl’s three-step workflow for causal inference through deterministic diffusion inversion and sampling. This model can apply interventions to individual cells and generate counterfactual-style tissues in which molecular profiles and spatial coordinates change together. In validation across three datasets, SPAD-CFR reproduces the hierarchical structure of the mouse cerebral cortex, simulates phenotypic distribution differences across different histological grades of breast cancer, and reconstructs hypoxia-associated mesenchymal phenotypes at the invasion margins of triple-negative tumors. In melanoma, activation interventions targeting PD-1+ CD8+ T cells produce spatially confined, distance-dependent bystander cytotoxic effects. Based on these findings, we propose SPAD-CFR, a biologically informed generative framework for conducting counterfactual-style spatial simulations to validate hypotheses regarding microenvironment reprogramming.
Keywords:
generative model
in silico simulation
spatial transcriptomics
spatial proteomics
tumor microenvironment
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