Anni Wang

Cells don't live by themselves. In a healthy organ or a diseased one, they sit in a sophisticated spatial arrangement and constantly talk to whoever is next to them . Yet almost every perturbation tool we have is cell-autonomous: CRISPR screens, knockdowns, drug assays all ask what a cell does when its own genes change. They study the cell; they don't study the tissue. NicheOut perturbs the neighbourhood instead. It learns how a cell's transcriptional state depends on the cells physically packed around it, then performs a counterfactual — remove one neighbour in silico, hold everything else fixed, and measure how far the state moves. Neighbours whose removal collapses the state are load-bearing; those whose removal changes nothing are incidental. I asked it a specific question in pre-cancerous lung (637K Visium spots, 25 patients): is the IL1B-high macrophage the cell holding the inflammatory fibroblast niche together? It is — but as an activation state, not a headcount. How activated the neighbouring macrophages are doubles held-out predictive power and ranks first of all neighbours; how many there are is uncorrelated. The dependency matches untouched tissue at r = 0.99, replicates in a second cohort, conserves in gastric and bladder cancer, and narrows to IL1B alone. Even a purely in-silico model of tissue synergy generates ideas a single-cell view cannot. And deleting a neighbour outright is only the crudest move available — the same framework extends to dialling a ligand's dose, gating on its receptor, or perturbing whole programs rather than whole cells. That's where this goes next!

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