Wenbin Mei

Squidiff: Generative diffusion models for predicting cell fate and perturbation responses Single-cell sequencing lets us observe how individual cells differ, evolve, and respond to their environment. But while we can measure these transcriptomic states, predicting how a cell will change under a new stimulus — a gene knockout, a drug, or even something as complex as radiation exposure — remains extremely difficult. Experiments are slow, expensive, and often impossible to run at the scale needed for mechanistic insight. Squidiff proposes a different path. This framework combines a semantic encoder with a conditional diffusion model, enabling the generation of new transcriptomic profiles by iteratively denoising from latent space. The key idea is that cellular identity and environmental cues can be captured as smooth, manipulable vectors in a shared latent representation. By shifting these vectors, Squidiff can navigate cellular state trajectories: from pluripotent stem cells into differentiated lineages, across gene perturbations, or along drug-response gradients. What makes this interesting is not just that Squidiff generates realistic single-cell data — many generative models attempt that — but that it captures transient cell states and developmental trajectories that are often inaccessible experimentally, including intermediate stages and nonlinear responses. The diffusion process encodes the underlying stochasticity, while the semantic space carries the structured biological signal, enabling controlled interpolation across time and condition. The authors demonstrate this on multiple fronts: predicting iPSC differentiation into germ layer lineages; modeling non-additive gene perturbations without graph priors; reconstructing cell-type-specific drug responses; and, remarkably, predicting the effects of neutron irradiation and the protective effects of G-CSF in blood vessel organoids. In the organoid system, Squidiff recovered not only cell-type-specific damage signatures but also the dynamic progression of vascular disruption, and how G-CSF shifts these trajectories toward recovery — all from sparse experimental sampling. This suggests something important. Instead of treating single-cell sequencing as a static snapshot technology, we may be moving toward generative, predictive models of cellular development, where experiments guide the model, and the model guides the next experiment. Squidiff does not replace data — it amplifies the value of each dataset, enabling in silico hypothesis generation and perturbation screening before wet-lab validation. Paper: nature.com/articles/s41592-0…