Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, Zhejiang 314100, China.
J Genet Genomics. 2023 Sep;50(9):641-651. doi: 10.1016/j.jgg.2023.07.011. Epub 2023 Aug 5.
Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.
空间组学技术已成为提供复杂背景下细胞和组织有价值见解的强大方法,极大地增强了我们对复杂多样的生物系统的理解。随着对空间异质性的关注度不断提高,人们越来越需要无偏倚的、空间分辨的组学技术。激光捕获显微切割(LCM)是一种获取空间信息的尖端方法,可以从异质组织中快速收集感兴趣区域(ROIs),分辨率从单细胞到细胞群体不等。因此,LCM 已被广泛用于研究疾病的细胞和分子机制。本综述重点介绍了四种常用 LCM 技术之间的差异及其在组学和疾病研究中的应用。还强调了应用案例的关键属性,例如通量和空间分辨率。此外,我们从高通量、多组学和单细胞分辨率的角度全面讨论了 LCM 在生物医学研究、疾病诊断和靶向治疗中的现有挑战和巨大潜力。