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bioRxiv · 10.64898/2026.01.22.701166

Histology-Aware Graph for Modeling Intercellular Communication in Spatial Transcriptomics

Wang, X.·Tao, C.·Jiang, Y.·Liu, H.·Jiang, Z.·Zhu, P.·Que, N.·Xi, J.·Price, S.·Mou, Y.·Li, C.
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Abstract

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.

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Wang, X., Tao, C., Jiang, Y., Liu, H., Jiang, Z., Zhu, P., Que, N., Xi, J., Price, S., Mou, Y., Li, C.. 2026-01-23. Histology-Aware Graph for Modeling Intercellular Communication in Spatial Transcriptomics. https://doi.org/10.64898/2026.01.22.701166

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