空间多组学技术及其在癌症中的应用。

IF 1.9 4区生物学 Q2 BIOLOGY

Biosystems Pub Date : 2025-09-09 DOI:10.1016/j.biosystems.2025.105576

Lulin Ji

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引用次数: 0

摘要

空间多组学将个体基因组学技术与同时从多个基因组学获取数据的单一技术相结合，使平行甚至相同的组织切片能够用于组织中细胞的联合分析。它促进了细胞间相互作用的分析，并提供了组织的三维全景视图。因此，空间多组学特征的联合分析可能使我们能够重建肿瘤发生的关键过程。通过空间多组学技术，研究人员揭示了空间细胞相互作用、TLS鉴定、免疫功能变化，建立了人类肿瘤的空间图谱和空间基因数据库，促进了肿瘤个性化治疗的发展。在未来，可能需要进一步开发新的空间分析技术和工具，主要是在空间和时间分辨率，吞吐量和灵敏度方面，以帮助癌症诊断和治疗，并解码肿瘤发生和发展的新机制。本文通过阐述各种空间多组学技术的优缺点，为选择合适的空间多组学技术提供指导，并重点介绍了空间多组学技术在癌症领域的研究进展。

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Spatial multi-omics technologies and applications in cancer

Spatial multi-omics integrates individual genomics technologies with a single technology that simultaneously acquires data from multiple genomics, enabling parallel or even identical tissue sections for joint analysis of cells in tissues. It facilitates the analysis of cell-cell-interactions and provides a three-dimensional panoramic view of tissues. Thus, joint profiling of spatial multi-omics features may enable us to reconstruct key processes in tumorigenesis. Through spatial multi-omics technology, researchers have revealed spatial cellular interactions, TLS identification, changes in immune function, and established a spatial map of human tumors and a spatial gene database, which facilitates the development of personalized tumor therapy. In the future, there may be a need to further develop new spatial analysis techniques and tools, mainly in terms of spatial and temporal resolution, throughput, and sensitivity, to aid in cancer diagnosis and treatment and to decode novel mechanisms of tumorigenesis and development. In this review, we provide guidance for selecting appropriate spatial multi-omics techniques by elucidating the advantages and disadvantages of various spatial multi-omics and highlight advances in cancer field of spatial multi-omics technologies.

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来源期刊

Biosystems 生物-生物学

CiteScore

3.70

自引率

18.80%

发文量

129

审稿时长

34 days

期刊介绍： BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.