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. 2023 Dec 20;14:379. doi: 10.1186/s13287-023-03616-w

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — A comprehensive depiction of spatial transcriptomic analysis. Spatial Transcriptomics represents a pioneering approach that facilitates a meticulous exploration of gene expression patterns within the context of tissue sections. By capturing high-resolution gene expression data while preserving the intricate tissue architecture, spatial transcriptomic datasets not only provide precise gene expression measurements but also impart a profound understanding of gene activity within its native tissue microenvironment. The figures presented in this article were crafted by our group