MorphLink: Bridging Cellular Morphological Behaviors and Molecular Dynamics in Spatial Omics

Jian Hu, PhD, is an Assistant Professor at the Department of Human Genetics with a joint appointment in the Department of Biostatistics and Bioinformatics at Emory University. He is also a member of Emory’s AI Humanity initiative. Hu’s research program involves two synergistic components: 1) a methodological component focuses on developing novel statistical methods for biomedical data, including single-cell RNA sequencing, spatial transcriptomics, protein omics, and metabolomics; 2) an applied component focuses on applying these methods for the clinical and biological studies. Specifically, he takes a multidisciplinary approach that integrates methods drawn from statistics, machine learning, bioinformatics, and computational biology for multi-omics data analysis. Hu has also extended his expertise to digital pathology as the cell morphology from histology image data is critical in understanding the tissue microenvironment. Hu works closely with biomedical researchers in order to elucidate the structure and function of genomes and to translate genetics and genomics knowledge into human health. Hu holds a PhD in Biostatistics from the University of Pennsylvania.
The increasing generation of multi-modal spatial omics data is invaluable for exploring aberrant cellular behavior in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on predictive tasks, such as clustering, 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 bi- and tri-modality spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive tumor heterogeneity and immune diversity across different cancers. Additionally, MorphLink is scalable and robust against cross-sample batch effects. We envision MorphLink becoming a significant tool for multi-sample, multi-modality spatial omics data analysis, facilitating atlas usage, and enhancing the interpretation of analyses.