New AI Framework Puts Tissue Geography Into RNA Velocity and Steers Cell Fates In Silico

Single-cell biology has long promised a movie of development but delivered only still frames. RNA velocity, first introduced in 2018, brought the field closer to motion by comparing unspliced and spliced messenger RNA inside each cell to predict where that cell is heading next. Yet the technique has a stubborn blind spot: it ignores geography. Cells are inferred to travel through abstract expression space, even when the predicted journeys cross anatomical boundaries that no real cell could traverse. A team led by Vishvak Raghavan, Brent Yoon, and Jun Ding at McGill University, with Gregory J. Fonseca at Khalifa University and Yue Li, now reports in Molecular Systems Biology a framework called veloAgent that welds spatial coordinates directly into velocity inference, and then goes a step further by letting researchers virtually knock out genes and watch trajectories bend.

The problem veloAgent tackles is well documented. Existing tools such as scVelo, cellDancer, DeepVelo, veloVI, VeloVAE, UniTVelo, and SIRV each improve some aspect of velocity estimation, but most assume transcriptional kinetics that are uniform across cells, an oversimplification that erases the heterogeneity driving development and disease. More sophisticated models that estimate gene- and cell-specific rates, like cellDancer, become computationally prohibitive on atlas-scale datasets. And nearly all methods rely on embeddings of transcriptional similarity alone, discarding the tissue microenvironment. The result is velocity fields that contradict known tissue structure, misdirect flows from tumor cells toward healthy neighbors, or push neurons toward neuroblasts they can never become.

veloAgent is a hybrid of three components. First, a variational autoencoder compresses noisy spliced and unspliced count matrices from each cell into a shared latent representation, denoising the data while preserving the features that mark dynamic cellular states. Second, a deep neural network constrained by protein–protein interactions from the STRING database takes that latent vector and infers transcription, splicing, and degradation rates—α, β, and γ—for every gene in every cell. The biologically informed sparsity matters: each gene’s output depends only on its annotated interactors, grounding the learned kinetics in known regulatory relationships. These parameters feed ordinary differential equations that yield initial velocity estimates, which are then refined by aligning each cell’s vector with those of its transcriptionally similar neighbors.

The third component is the distinctive one. An agent-based model treats every cell as an autonomous agent sitting at its true position in the tissue, and updates its velocity using local interaction rules. Three factors shape each update: expression similarity between neighbors, local cell density, and inverse spatial distance, with closer and more transcriptionally alike cells exerting stronger influence. The result is a velocity field that respects tissue architecture rather than floating free of it. Crucially, the authors emphasize that the projected arrows represent inferred transcriptional state transitions, not physical cell migration—a distinction that keeps the spatial maps honest while still revealing how tissue organization constrains and guides cellular trajectories.

The benchmarking is extensive. The team applied veloAgent to four spatial transcriptomics datasets spanning different platforms and resolutions: a Stereo-seq mouse brain, a human breast cancer Visium sample, a developing chicken heart, and a HybISS mouse brain. In the Stereo-seq brain, competing methods produced unclear or contradictory velocities around fiber-tract cells, while veloAgent captured coherent transitions from hippocampal, thalamic sensory-motor, and polymodal association regions consistent with known neuronal projections. In breast cancer, SIRV incorrectly directed flow from tumor cells toward normal cells; veloAgent corrected the misprediction. In the chicken heart, it fixed reversed trajectories at the ventricle–atrium boundary, and in the HybISS brain it correctly modeled neurons as terminal states rather than steering them toward neuroblasts.

Quantitatively, veloAgent was evaluated against seven leading methods on three metrics: cross-boundary direction correctness, fate probability, and velocity confidence. It achieved the highest or near-highest scores across all datasets. For cross-boundary direction, it outperformed all competitors by an average of 92.03 percent on the Stereo-seq brain, 92.02 percent on breast cancer, 55.52 percent on the chicken heart, and 12.15 percent on the HybISS brain. Fate probability gains averaged between 33 and 66 percent, and velocity confidence remained near-perfect, at 0.998 or above, across every dataset. Marker-gene analyses reinforced the picture: Mbp velocity stayed confined to myelinating fiber tracts, ERBB2 velocity localized to tumor regions rather than macrophages, NPPA velocity remained restricted to the atria, and Nrg1 velocity marked neurons specifically.

The improved velocities also sharpen downstream biology. Feeding veloAgent’s output into CellRank, the researchers recovered lineage-specific driver genes ordered correctly along pseudotime. In the developing mouse brain, early En1 expression aligned with progenitor states, Dlk1 peaked mid-trajectory alongside pro-neural factors, and late genes such as Lhx5 and Klhl14 marked terminal neuronal populations, with Pax8 emerging as a key mid-hindbrain regulator. In the chicken heart, transient valve progenitors enriched for DICER1 were distinguished from terminal valve cells expressing extracellular matrix genes like COL1A1 and SPARC. Gene Ontology enrichment confirmed the programs: circulatory-system development and mesenchymal development in the heart, neuron differentiation and nervous-system development in the brain. SCENIC analysis further recovered known transcription factors, including Foxj1, Lmx1a, Msx1, and Msx2 in choroid-plexus patterning, Nfia and Tcf12 in fiber-tract formation, and Foxf1 and Gata4 in endoderm and cardiac lineages.

The most provocative feature, however, is the in silico perturbation module—something no prior velocity framework offers. Because veloAgent explicitly parameterizes α, β, and γ for each gene, researchers can set a gene’s transcription rate to zero after training, without retraining, and recompute the entire velocity field. The change in cross-boundary directionality then quantifies that gene’s causal influence on fate progression. Applied to the mouse brain, silencing top-ranked genes reversed differentiation toward the fiber-tract fate, and the perturbed genes were enriched for neurogenesis, axonogenesis, and synaptic organization. Applied to breast cancer, the top hits included CARD14 and ERBB2, both established oncogenes whose virtual silencing redirected velocity vectors away from malignant clusters. Kaplan–Meier analyses showed that patients with high expression of these genes had significantly poorer overall survival, and ERBB2 is already the target of FDA-approved drugs including trastuzumab and trastuzumab deruxtecan—a striking validation of the computational predictions.

Scalability is the other headline advance. veloAgent’s architecture scales with the number of genes, which is essentially fixed per organism, rather than with cell count, which grows exponentially with sequencing throughput. When the team duplicated the HybISS dataset from 50,000 up to one million cells, cellDancer’s memory usage climbed near-cubically to roughly 120 gigabytes, while veloAgent required only about 37 gigabytes with near-constant per-cell runtime. The agent-based module is also fully modular: applied post hoc to scVelo’s velocity estimates, it lifted velocity confidence dramatically across all four datasets—for example from 0.0677 to 0.9943 on the Stereo-seq brain—without retraining the underlying model. Ablation experiments confirmed that removing any component, especially the spatial ABM, significantly degraded performance.

The authors are candid about limitations. Visium spots can capture transcripts from multiple cells, so inferred dynamics represent averaged states, though consistent results on higher-resolution platforms suggest the signals are robust. The perturbation framework currently handles single genes rather than combinatorial interventions, interaction databases like STRING remain incomplete, and the model operates at gene rather than exon level. It has also not yet been run on a true million-cell atlas. Even so, veloAgent marks a conceptual shift: RNA velocity becomes a spatially coherent, causally probeable model of tissue dynamics rather than an abstract trajectory plot. For developmental biologists mapping organogenesis, oncologists tracing tumor evolution, and regenerative medicine researchers seeking to steer stem cells toward desired fates, the ability to simulate a gene knockout in seconds and watch the cellular movie change direction could reshape how hypotheses are generated—and which experiments get run first.

Subject of Research: A spatially informed RNA velocity framework combining deep generative modeling and agent-based simulation to infer and perturb cell state transitions in tissues

Article Title: Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent

Article References: Raghavan, V., Yoon, B., Fonseca, G. J., Li, Y., & Ding, J. (2026). Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent. Molecular Systems Biology, 22(7), 1180-1200. https://doi.org/10.1038/s44320-026-00213-w

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00213-w

Keywords: RNA velocity, spatial transcriptomics, single-cell RNA sequencing, agent-based modeling, variational autoencoder, computational biology, cell fate, in silico perturbation, breast cancer, ERBB2, developmental biology, therapeutic target discovery