NeighborMIF: Reference-Guided Cross-Section Molecular Transfer for Multiplex Immunofluorescence Recovery

In spatial multi-omics studies, different molecular modalities are often measured on separate tissue sections. Existing approaches mainly infer molecular expression from H&E morphology or learn cross-omics mappings, but their performance remains limited for protein markers that are weakly associated with morphology or other omics features. To address this, we propose NeighborMIF, a reference-guided framework for cross-section molecular information transfer. NeighborMIF performs cell-level matching to transfer experimentally measured protein signals from reference sections, reconstructs the transferred molecular representations in the spatial feature space of the target section, and integrates them with H&E features for missing mIF recovery. On the consecutive-section dataset, NeighborMIF achieves an average cell-level Pearson correlation of 82.83% (r × 100), outperforming the best external method by 6.11 percentage points. On ORION CRC under the cross-sample non-contiguous-reference setting, reference molecular information consistently improves the same-architecture H&E-only baseline across all 15 proteins. These results demonstrate that experimentally measured reference molecular signals provide effective complementary evidence beyond H&E morphology, offering a new computational pathway for cross-section molecular information transfer and spatial multi-omics integration.