Mass spectrometry imaging (MSI) enables unlabeled, untargeted mapping of biomolecular distributions in tissue but is limited by low spatial resolution and high data complexity. We address these challenges with a generative model for MSI peak learning that produces compact, chemically meaningful embeddings, and a transformer-based framework for multimodal fusion of low-resolution MSI with high-resolution H&E microscopy across serial sections. The model tokenizes MSI and H&E patches and employs deformable cross-attention to transfer histological structure while preserving MSI chemistry, yielding slice-consistent high-resolution 3D reconstructions. Experiments on 3D DESI Orbitrap MSI/H&E data from human colorectal adenocarcinoma demonstrate anatomically accurate and chemicallyfaithful reconstruction without manual intervention. Code is available at https://github.com/H-deep/TransUniFERE-3D.
Imani et al. (Thu,) studied this question.