TEMPUS: TEmporal Modeling Platform for Unified Spatial Omics

Tissues evolve over time through development, homeostasis, and disease, and understanding these dynamics requires measurements that are spatially resolved and temporally informative. Spatial transcriptomic studies increasingly profile tissues at multiple time points alongside hematoxylin and eosin (H&E) histology, but the cost of the two modalities is disproportionate: H&E is acquired routinely while spatial transcriptomics remains expensive and is profiled at only a few time points. We formulate tissue dynamics prediction as a multi-modal cross-time task: predict spatial gene expression at any time point given dense H&E images across all time points and sparse spatial transcriptomics at few. We introduce TEMPUS (Temporal Modeling Platform for Unified Spatial Omics), the first model for this task. TEMPUS combines three design choices: (i) a learned per-query top-$K$ selection over a two-dimensional grid of past observations indexed by time point and tissue coordinate, compressed into one per-query, per-time-point token consumed by a cross-attention decoder; (ii) a chained cross-modal and temporal objective with cycle-consistency, where an auxiliary decoder reconstructs each pre-fusion history token from the prediction-time latent, preventing collapse onto the most recent observation; and (iii) operation in frozen foundation-model embedding space, decoded to counts only at evaluation, which keeps the model gene-panel and platform agnostic and scales to whole-tissue in Xenium resolution. On two synthetic benchmarks where the target provably cannot be predicted from any single time point, and four experimental datasets, the mean Pearson correlation improves over the strongest baselines from non-temporal histology-to-expression methods and temporal models. Ablations validate that learned top-$K$ outperforms $k$-nearest-neighbor retrieval, and that per-query attention is interpretable against ground-truth niche identity.