Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas 9606
Dataset ID: STDS0000073
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PMID:31932730
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2,997 Spots
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19,738 Genes
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Dataset information
Summary:
Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues.
Overall design:
Single-cell RNA-sequencing and spatial transcriptomics of primary pancreatic cancer tissue from six patients. 2018-03-12
Technology:
Spatial Transcriptomics
Platform:
Illumina NextSeq 500
Species:
Homo sapiens(hg38)
Tissues:
Pancreas
Organ parts:
Pancreatic adenocarcinoma
Citation:
Moncada, Reuben et al. “Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.” Nature biotechnology vol. 38,3 (2020): 333-342. doi:10.1038/s41587-019-0392-8
Submission date: 2018-03-02Update date: 2020-11-05
Sample number: 9
Contributors
Reuben Moncada
Contact: itai.yanai@nyumc.org
Accessions
GEO Series Accessions: GSE111672