Yang Ching-Chia, Lin Ching-Ya, Yuan Hsin-Yo, Huang Hsuan-Cheng, Juan Hsueh-Fen
Department of Life Science, National Taiwan University, Taipei, 106, Taiwan.
Center for Computational and Systems Biology, National Taiwan University, Taipei, 106, Taiwan.
J Biomed Sci. 2026 Feb 6;33(1):16. doi: 10.1186/s12929-026-01219-0.
Mass spectrometry-based spatial omics is a powerful approach for visualizing the spatial organization of proteins, metabolites, lipids, and other biomolecules in situ, combining the molecular depth of mass spectrometry with spatially resolved imaging. This systematic review traces the rapid technological and computational evolution of this field, including innovations in mass spectrometry imaging (MSI), labeling-based approaches, and proximity labeling techniques. It also highlights recent advances that enhance spatial resolution, expand molecular coverage, and enable deep molecular characterization and review analytical pipelines that integrate deep learning, cross-modality registration, and cloud-optimized data formats. From the multimodal and practical perspective, the integration of MSI with other spatial omics platforms and its transformative applications in tumor microenvironment profiling, neurodegenerative disease, developmental biology, biomarker discovery, and precision medicine are discussed. Finally, this review outlines challenges and opportunities, emphasizing the need for standardization, clinical validation, and interpretable artificial intelligence to enable broader adoption. These advances position MS-based spatial omics as a foundational pillar for multimodal spatial biology and personalized healthcare.
基于质谱的空间组学是一种强大的方法，可在原位可视化蛋白质、代谢物、脂质和其他生物分子的空间组织，将质谱的分子深度与空间分辨成像相结合。本系统综述追溯了该领域快速的技术和计算发展，包括质谱成像（MSI）、基于标记的方法和邻近标记技术的创新。它还强调了最近在提高空间分辨率、扩大分子覆盖范围、实现深度分子表征方面的进展，并回顾了整合深度学习、跨模态配准和云优化数据格式的分析流程。从多模态和实际应用的角度，讨论了MSI与其他空间组学平台的整合及其在肿瘤微环境分析、神经退行性疾病、发育生物学、生物标志物发现和精准医学中的变革性应用。最后，本综述概述了挑战和机遇，强调了标准化、临床验证和可解释人工智能以实现更广泛应用的必要性。这些进展使基于质谱的空间组学成为多模态空间生物学和个性化医疗的基础支柱。
