空间转录组学:理解生物复杂性的新维度。

Zhuxia Li, Guangdun Peng
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引用次数: 2

摘要

细胞和组织以复杂而有序的方式精心组织,形成器官和身体,使个体能够正常运作。空间组织和组织结构是所有生物的基本属性。完整组织内的分子结构和细胞组成在多种生物过程中起着至关重要的作用,例如形成复杂的组织功能,在所有生命活动中精确调节细胞转变,中枢神经系统的巩固,细胞对免疫和病理信号的反应。为了在大尺度和精细分辨率上探索这些生物事件,对空间细胞变化的全基因组理解是必不可少的。然而,以往的大体积RNA测序和单细胞RNA测序技术虽然能够检测到高含量的转录变化,但却无法获得组织和细胞的重要空间信息。这些限制促使了许多空间分辨率技术的发展,这些技术为研究区域基因表达、细胞微环境、解剖异质性和细胞-细胞相互作用提供了一个新的维度。自空间转录组学出现以来,使用这些技术的相关工作迅速增加,具有更高通量和分辨率的新方法迅速发展,所有这些都为加速理解生物复杂性的新发现提供了巨大的希望。在这篇综述中,我们简要讨论了空间分解转录组的历史演变。我们对有代表性的方法进行了广泛的调查。此外,我们还总结了空间基因表达数据的一般计算分析管道。最后,对空间多组学的技术发展前景进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Spatial transcriptomics: new dimension of understanding biological complexity.

Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.

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CiteScore
1.30
自引率
0.00%
发文量
117
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