Linbu Liao, Patrick C N Martin, Hyobin Kim, Sanaz Panahandeh, Kyoung Jae Won
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引用次数: 0
Abstract
Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.
揭示细胞在其原生环境中错综复杂的相互作用是了解基本生物过程和揭示疾病机制的核心,尤其是在癌症等复杂疾病中。空间转录组学(ST)为研究组织内基因表达的空间组织提供了一个革命性的视角,使研究人员有能力研究健康和疾病中的细胞异质性和微环境。然而,目前的表观基因组学技术往往在分辨率或同时分析的基因数量方面受到限制。将 ST 数据与单细胞转录组学和详细的组织染色图像等补充来源进行整合,是克服这些局限性的强大解决方案。本综述深入探讨了推动空间转录组学与其他数据类型整合的计算方法。通过阐明关键挑战和概述当前的算法解决方案,我们旨在强调这些方法在彻底改变我们对癌症生物学的理解方面所具有的巨大潜力。