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Integrating microarray-based spatial transcriptomics and single-cell RNA-sequencing reveals tissue architecture in esophageal squamous cell carcinoma
- Source :
- EBioMedicine, Vol 84, Iss , Pp 104281- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- Summary: Background: The tumor microenvironment (TME) serves as an important factor in tumorigenesis and metastasis. Although distinct cell subsets can be identified via single-cell RNA sequencing (scRNA-seq), the spatial composition of cells within the TME is difficult to characterise. Methods: Tissue samples were collected from three patients with esophageal squamous cell carcinoma (ESCC), and scRNA-seq was performed to identify distinct cell subsets. In addition, a microarray-based spatial transcriptomics (ST) method was used to characterise the spatial landscape of expression data via an array of spots. Using multimodal intersection analysis (MIA) to integrate scRNA-seq and ST, the exact cellular components of the tumor and stromal regions were annotated. Findings: The subpopulations of seven stromal cells were identified within the TME of ESCC, and the architecture of scRNA-seq-determined subsets was mapped in cancer and stromal regions. The distribution of various stromal cells and their subpopulations was heterogeneous. Compared with immune cells, non-immune stromal cells were significantly enriched in the TME. Most subsets of epithelial cells were enriched in the cancer regions, whereas inflammatory cancer-associated fibroblasts were correlated with the stromal regions. Furthermore, TME features were different between metastatic and non-metastatic samples and between the primary and metastatic sites of the metastatic sample. Interpretation: This study revealed the spatial landscape of various cell subsets within the TME and the potential cross-talk among diverse cells, which provides novel insights into cancer intervention. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
Details
- Language :
- English
- ISSN :
- 23523964
- Volume :
- 84
- Issue :
- 104281-
- Database :
- Directory of Open Access Journals
- Journal :
- EBioMedicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.80e29d77aefa49ab82c243d0859cf106
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.ebiom.2022.104281