Volume 8 Issue 3
Jun.  2022
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Zhuxia Li, Guangdun Peng. Spatial transcriptomics: new dimension of understanding biological complexity[J]. Biophysics Reports, 2022, 8(3): 119-135. doi: 10.52601/bpr.2021.210037
Citation: Zhuxia Li, Guangdun Peng. Spatial transcriptomics: new dimension of understanding biological complexity[J]. Biophysics Reports, 2022, 8(3): 119-135. doi: 10.52601/bpr.2021.210037

Spatial transcriptomics: new dimension of understanding biological complexity

doi: 10.52601/bpr.2021.210037
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  • Corresponding author: peng_guangdun@gibh.ac.cn
  • Received Date: 01 August 2021
  • Accepted Date: 18 October 2021
  • Available Online: 21 January 2022
  • Publish Date: 22 June 2022
  • 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.

  • Guangdun Peng and Zhuxia Li declare that they have no conflict of interest.
    This article does not contain any studies with human or animal subjects performed by any of the authors.

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