Huang Jing, Yuan Chenyang, Jiang Jiahui, Chen Jianfeng, Badve Sunil S, Gokmen-Polar Yesim, Segura Rossana L, Yan Xinmiao, Lazar Alexander, Gao Jianjun, Yao Bing, Epstein Michael, Wang Linghua, Hu Jian
Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA.
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Nat Commun. 2025 Jul 1;16(1):5878. doi: 10.1038/s41467-025-61142-0.
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.
多模态空间组学数据对于从形态学和分子学角度探索疾病中的复杂细胞行为非常宝贵。当前的分析方法主要集中在聚类和分类上,没有充分研究细胞形态与分子动力学之间的关系。在这里,我们提出了MorphLink,这是一个旨在系统识别疾病相关形态-分子相互作用的框架。MorphLink已在大量数据集上进行了评估,展示了其在空间组学分析中提取可解释的形态特征并将其与各种分子测量联系起来的有效性。这些联系提供了具有相似细胞类型组成的组织区域内细胞行为异质性的清晰视图,表征了不同器官中的肿瘤亚型和免疫多样性。此外,MorphLink具有可扩展性,并且对跨样本批次效应具有鲁棒性,使其成为跨样本、队列和模态进行综合空间组学数据分析的有效方法,并增强了大规模研究结果的解释力。