Que, N. | Search bioRxiv

bioRxiv2026

Histology-Aware Graph for Modeling Intercellular Communication in Spatial Transcriptomics

Cell-cell communication (CCC) is essential to how life forms and functions. Recent tools achieve single-cell-resolved CCC inference utilizing spatial transcriptomics (ST). However, most ignore the modeling of tissue contexts surrounding cells, causing high false-positive/negative rates. Here, we propose HARMONIC, a CCC inference method integrating multimodal ST and hematoxylin and eosin (H&E)-stained images. HARMONIC causally modeling the transcriptomic-to-contextual relationships for CCC inference. The state-of-the-art performance was verified across ST platforms, species and healthy/diseased status, on both synthetic and biological samples. HARMONIC was applied in various real-world scenarios, especially on tissues with clear morphological boundaries, including cortical layers in mouse brain, medullary-cortex structures in mouse kidney, as well as tumor-stromal/immune interface. Significant refinement of false-positive/negative predictions was observed compared to ST-only CCC tools.

bioinformatics↗

bioRxiv2025

A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics

Spatial transcriptomics (ST) platforms are limited by spatial resolution, sensitivity to low expression levels, alignment with tissue structures, and the balance across tissue complexity. Computational enhancement typically targets a single challenge, e.g., super-resolution using hematoxylin and eosin (H&E) images or sensitivity enhancement with single-cell RNA sequencing (scRNA-seq). However, most ignore the interdependence across challenges, yielding biologically inconsistent enhancement. Here we introduce FOCUS, a foundational generative model for unified ST enhancement, conditioned on H&E images, scRNA-seq references, and spatial co-expression priors. With large-scale pretrained encoders, FOCUS uses a modular design for multimodal integration and a cross-challenge coordination strategy to target co-occurring challenges, enabling joint optimization. FOCUS was trained and comprehensively benchmarked on >1.7 million H&E-ST pairs and >5.8 million single-cell profiles, demonstrating state-of-the-art performance across ten ST platforms, on both individual and coupled challenges. The real-world utility and generalizability were validated on a rare suprasellar tumor, papillary craniopharyngioma, and an unseen ST platform (Open-ST) for primary and metastatic head and neck squamous cell carcinoma.

bioengineering↗