美国宾夕法尼亚大学Mingyao Li等利用MISO进行多模态空间组学建模，以解析组织复杂性。相关论文发表在2025年1月15日出版的《自然—方法学》杂志上。

MISO（MultI-modal Spatial Omics，多模态空间组学）是一种多功能算法，专注于特征提取与聚类，能够将来自多种高空间分辨率的空间组学实验的多种模态（包括基因表达、蛋白表达、表观遗传学、代谢组学及组织组织学模态）进行整合。

MISO在多个数据集上表现出色，能够准确识别具有生物学意义的空间域，比现有方法表现更优，代表了多模态空间组学分析的重大进步。此外，MISO 的计算效率确保其具备可扩展性，可处理由亚细胞分辨率空间组学技术生成的大规模数据集。

空间分子分型为生物医学研究人员提供了宝贵的机会，能够更好地理解细胞定位与组织功能之间的关系。高效建模多模态空间组学数据对于解析组织复杂性及其潜在生物学机制至关重要。此外，空间分辨率的提升催生了能够生成亚细胞分辨率空间分子数据的技术，这要求开发能够处理大规模数据集的高效计算方法。

附：英文原文

Title: Resolving tissue complexity by multimodal spatial omics modeling with MISO

Author: 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

Issue&Volume: 2025-01-15

Abstract: 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.

DOI: 10.1038/s41592-024-02574-2

Source: https://www.nature.com/articles/s41592-024-02574-2