SHEST: Single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell type prediction and spatial transcriptomics reconstruction

A comprehensive understanding of cancer progression requires integrating tissue morphol-ogy with spatial molecular profiles. We present SHEST, a multi-task profiling framework that leverages haematoxylin and eosin morphology to predict cellular composition and re-construct 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 reconstruc-tor for gene expression. Multi-task optimisation uses cross-entropy and zero-inflated negative binomial losses, specifically addressing the sparsity of spatial transcriptomic data. Evalua-tion on human lung adenocarcinoma datasets demonstrated high accuracy for the principal reciprocal constituents of the tumour–immune axis ( F ₁ : 0.97 for tumour cells and 0.91 for lym-phocytes). External validation confirmed its generalisability, 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 pre-served distance-dependent spatial relationships and gene-level autocorrelation, reflecting the multicellular niche structure of the tumour microenvironment. By unifying cell type iden-tification, 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 characterisation and forms a foundation for precision oncology through spatially informed, cell-level insights into tumour–immune ecosystems.