Resolving tissue complexity by multimodal spatial omics modeling with MISO

• Published in: Nature methods Vol. 22; no. 3; pp. 530 - 538
• Main Authors: Coleman, Kyle, Schroeder, Amelia, Loth, Melanie, Zhang, Daiwei, Park, Jeong Hwan, Sung, Ji-Youn, Blank, Niklas, Cowan, Alexis J., Qian, Xuyu, Chen, Jianfeng, Jiang, Jiahui, Yan, Hanying, Samarah, Laith Z., Clemenceau, Jean R., Jang, Inyeop, Kim, Minji, Barnfather, Isabel, Rabinowitz, Joshua D., Deng, Yanxiang, Lee, Edward B., Lazar, Alexander, Gao, Jianjun, Furth, Emma E., Hwang, Tae Hyun, Wang, Linghua, Thaiss, Christoph A., Hu, Jian, Li, Mingyao
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2025; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2415; Algorithms; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cluster Analysis; Clustering; Complexity; Computational Biology - methods; Computational efficiency; Datasets; Epigenetics; Feature extraction; Gene expression; Gene Expression Profiling - methods; Genomics - methods; Histology; Humans; Life Sciences; Localization; Medical research; Metabolomics; Metabolomics - methods; Modelling; Proteomics; Software; Spatial analysis; Spatial data; Spatial discrimination; Spatial resolution; Tissues
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-024-02574-2

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies. MISO (MultI-modal Spatial Omics) integrates two or more spatial omics modalities, despite differences in data quality and spatial resolution for improved feature extraction and clustering to reveal biologically meaningful tissue organization.