Visualization and Analysis of Gene Expression in Stanford Type A Aortic Dissection Tissue Section by Spatial Transcriptomics

Yan-Hong Li; Ying Cao; Fen Liu; Qian Zhao; Dilare Adi; Qiang Huo; Zheng Liu; Jun-Yi Luo; Bin-Bin Fang; Ting Tian; Xiao-Mei Li; Di Liu; Yi-Ning Yang

doi:10.3389/fgene.2021.698124

Visualization and Analysis of Gene Expression in Stanford Type A Aortic Dissection Tissue Section by Spatial Transcriptomics

Front Genet. 2021 Jun 28:12:698124. doi: 10.3389/fgene.2021.698124. eCollection 2021.

Authors

Yan-Hong Li^{1

2

3}, Ying Cao^{4

5

6

7}, Fen Liu^{3

8}, Qian Zhao^{1

3}, Dilare Adi^{3

8}, Qiang Huo⁹, Zheng Liu⁹, Jun-Yi Luo^{1

3}, Bin-Bin Fang^{3

8}, Ting Tian³, Xiao-Mei Li^{1

3}, Di Liu^{1

4

6

10}, Yi-Ning Yang^{1

3

11}

Affiliations

¹ Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
² Department of Clinical Laboratory, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
³ State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asian, Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
⁴ Computational Virology Group, Center for Bacteria and Virus Resources and Application, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China.
⁵ CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
⁶ University of Chinese Academy of Sciences, Beijing, China.
⁷ College of Animal Sciences and Veterinary Medicine, Guangxi University, Nanning, China.
⁸ State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
⁹ Department of Cardiac Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
¹⁰ Xinjiang Medical University, Urumqi, China.
¹¹ People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.

Abstract

Background: Spatial transcriptomics enables gene expression events to be pinpointed to a specific location in biological tissues. We developed a molecular approach for low-cell and high-fiber Stanford type A aortic dissection and preliminarily explored and visualized the heterogeneity of ascending aortic types and mapping cell-type-specific gene expression to specific anatomical domains. Methods: We collected aortic samples from 15 patients with Stanford type A aortic dissection and a case of ascending aorta was randomly selected followed by 10x Genomics and spatial transcriptomics sequencing. In data processing of normalization, component analysis and dimensionality reduction analysis, different algorithms were compared to establish the pipeline suitable for human aortic tissue. Results: We identified 19,879 genes based on the count level of gene expression at different locations and they were divided into seven groups based on gene expression trends. Major cell that the population may contain are indicated, and we can find different main distribution of different cell types, among which the tearing sites were mainly macrophages and stem cells. The gene expression of these different locations and the cell types they may contain are correlated and discussed in terms of their involvement in immunity, regulation of oxygen homeostasis, regulation of cell structure and basic function. Conclusion: This approach provides a spatially resolved transcriptome- and tissue-wide perspective of the adult human aorta and will allow the application of human fibrous aortic tissues without any effect on genes in different layers with low RNA expression levels. Our findings will pave the way toward both a better understanding of Stanford type A aortic dissection pathogenesis and heterogeneity and the implementation of more effective personalized therapeutic approaches.

Keywords: Stanford type A aortic dissection; aortic; bioinformatics; gene expression; spatial transcriptomics.