Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.

Current spatial and single-cell technologies capture complementary but incomplete views of cellular state, with transcriptomic, proteomic, and spatial information distributed across distinct platforms. Integration is challenged by unpaired measurements, mismatched feature spaces, and modality-specific biases. We present MultiTME, a multimodal framework that integrates heterogeneous spatial and single-cell data using a spatially-regularized, cycle-consistent deep generative model. By enforcing consistency of bidirectional mappings, MultiTME learns a shared latent representation that enables translation between modalities without requiring paired observations or shared features. Across benchmarks, MultiTME outperforms existing methods, produces accurate cross-modal cell typing, improves spatial transcriptomic panel completion, and transfers whole-transcriptome information to generate spatially resolved maps at cellular resolution. Applied to a multimodal colorectal cancer dataset, we demonstrate that MultiTME integration reveals a spatially coherent proliferative-invasive tumor axis not directly observable within single modalities. Across five multimodal spatial datasets, we show MultiTME can correct for platform-specific biases between Xenium and CosMx, thereby facilitating cross-dataset harmonization and enabling pan-cancer spatial studies. Code for MultiTME is available at https://github.com/tansey-lab/multitme.