Bridging cell morphological behaviors and molecular dynamics in multi-modal spatial omics with MorphLink

Jing Huang, Chenyang Yuan, Jiahui Jiang, Jianfeng Chen, Sunil S. Badve, Yesim Gokmen-Polar, Rossana L. Segura, Xinmiao Yan, Alexander Lazar, Jianjun Gao, Bing Yao, Michael Epstein, Linghua Wang () and Jian Hu ()
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Jing Huang: Emory University
Chenyang Yuan: Emory University
Jiahui Jiang: The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS)
Jianfeng Chen: The University of Texas MD Anderson Cancer Center
Sunil S. Badve: Emory University
Yesim Gokmen-Polar: Emory University
Rossana L. Segura: The University of Texas MD Anderson Cancer Center
Xinmiao Yan: The University of Texas MD Anderson Cancer Center
Alexander Lazar: The University of Texas MD Anderson Cancer Center
Jianjun Gao: The University of Texas MD Anderson Cancer Center
Bing Yao: Emory University
Michael Epstein: Emory University
Linghua Wang: The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS)
Jian Hu: Emory University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in spatial omics analyses. These linkages provide a transparent view of cellular behavior heterogeneity within tissue regions with similar cell type compositions, characterizing tumor subtypes and immune diversity across different organs. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

Date: 2025
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DOI: 10.1038/s41467-025-61142-0

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