Quantifying Cross-Modal Shared Information Between Histomorphology and Spatial Transcriptomics via Spatiotemporal Trajectory Correlation

Abstract

Histopathological imaging and spatial transcriptomics (ST) provide synergistic morphological and molecular insights into tissue architecture. While conventional downstream analyses predominantly adopt a discrete paradigm, such as segmenting histological images or identifying spatial domains, emerging trajectory reconstruction methods offer a continuous perspective for analyzing these modalities. However, most current studies are confined to single-modality or single-organ analyses, lacking systematic integration across modalities and multiple organs. To address this limitation, we performed trajectory reconstruction on ST-derived gene expression data and on histopathological morphological features extracted using ten widely used pathology pretrained models across multiple cancer samples from six organs. The results demonstrate that trajectory reconstruction, as a continuous analytical framework, effectively bridges spatial transcriptomics and histopathological imaging. More importantly, we propose an innovative framework that uses trajectory pseudotime as a mediating variable to quantify the extent of information sharing between molecular and morphological features. This framework not only provides a new perspective for understanding the intrinsic links between modalities but also establishes a solid theoretical and methodological foundation for future cross-modal translation studies.