Abstract A comprehensive understanding of cancer progression requires integrating tissue morphology with spatial molecular profiles. We present SHEST, a multi-task profiling framework that leverages haematoxylin and eosin morphology to predict cellular composition and reconstruct spatial gene expression at single-cell resolution. SHEST employs a quadruple-tile input capturing nuclear and contextual information, combined with a neighbourhood-informed clustering algorithm to filter ambiguous cellular signals. It comprises a shared morphological encoder with two task-specific heads: a classifier for cell-type prediction and a reconstructor for gene expression. Multi-task optimization uses cross-entropy and zero-inflated negative binomial losses, specifically addressing the sparsity of spatial transcriptomic data. Evaluation on human lung adenocarcinoma datasets demonstrated high accuracy for the principal reciprocal constituents of the tumour–immune axis (F₁: 0. 97 for tumour cells and 0. 91 for lymphocytes). External validation confirmed its generalizability, revealing alveolar cells and their early neoplastic transitions. Reconstructed gene expression reproduced spatially resolved, cell-type-specific marker patterns—EPCAM in tumour cells, LTBP2 in fibroblasts, and CD3E in lymphocytes—recovering biologically coherent transcriptional architecture. SHEST also preserved distance-dependent spatial relationships and gene-level autocorrelation, reflecting the multicellular niche structure of the tumour microenvironment. By unifying cell-type identification, gene expression reconstruction, and spatial mapping within a single interpretable framework, SHEST provides a synergistic and cost-efficient bridge between histopathology and spatial transcriptomics. This approach facilitates comprehensive tissue characterization and forms a foundation for precision oncology through spatially informed, cell-level insights into tumour–immune ecosystems.
Jeong et al. (Thu,) studied this question.