Generative modeling of spatially resolved perturbation responses with CONCERT

Spatial perturbation transcriptomics measures gene expression responses to genetic or chemical perturbations in intact tissue. Each response depends on the intrinsic state of a cell and on its niche, the neighboring cells through which perturbation effects propagate. Here we present CONCERT a niche-aware Gaussian process variational autoencoder that embeds each perturbation with the niche of the receiving cell and learns a spatial kernel for each perturbation, whose lengthscale sets how far the effect extends in tissue. We evaluate CONCERT in within-niche prediction, for held-out cells in the niche where a perturbation was measured, and in cross-slide prediction, for a slide from which the perturbation was withheld during training. On a SPAC-seq screen of hundreds of knockouts at single-cell resolution and on Perturb-map lung tumors at spot resolution, CONCERT predicts response magnitude and gene ranking more accurately than counterfactual models trained on dissociated cells, spatial perturbation models and spatial nearest neighbor aggregation. In dextran sodium sulfate colitis, where each mouse is sampled once, CONCERT reconstructs spatial gene expression at unmeasured time points, enabling longitudinal comparison that separates changes over time from differences between mice. The predictions recover the nonmonotonic course of injury, which peaks at day 9 and resolves by day 21. In ischemic stroke, CONCERT predicts responses to lesions of different sizes and locations, in single sections and across serial sections in three dimensions, and reproduces the expression patterns of the lesion core and perilesional tissue. Across lung tumor, colon and brain tissue, CONCERT predicts niche-aware counterfactual responses at locations where a perturbation was not measured and reconstructs missing spatial data.