Dataset Information

ScRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

ABSTRACT: The tumor microenvironment (TME) in gastric cancer (GC) has been shown to be important for tumor control but the specific characteristics for GC are not fully appreciated. We generated an atlas of 166,533 cells from 10 GC patients with matched paratumor tissues and blood. Our results show tumor-associated stromal cells (TASCs) have upregulated activity of Wnt signaling and angiogenesis, and are negatively correlated with survival. Tumor-associated macrophages and LAMP3⁺ DCs are involved in mediating T cell activity and form intercellular interaction hubs with TASCs. Clonotype and trajectory analysis demonstrates that Tc17 (IL-17⁺CD8⁺ T cells) originate from tissue-resident memory T cells and can subsequently differentiate into exhausted T cells, suggesting an alternative pathway for T cell exhaustion. Our results indicate that IL17⁺ cells may promote tumor progression through IL17, IL22, and IL26 signaling, highlighting the possibility of targeting IL17⁺ cells and associated signaling pathways as a therapeutic strategy to treat GC.

SUBMITTER: Sun K

PROVIDER: S-EPMC9399107 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

Sun Keyong K Xu Runda R Ma Fuhai F Yang Naixue N Li Yang Y Sun Xiaofeng X Jin Peng P Kang Wenzhe W Jia Lemei L Xiong Jianping J Hu Haitao H Tian Yantao Y Lan Xun X

Nature communications 20220823 1

The tumor microenvironment (TME) in gastric cancer (GC) has been shown to be important for tumor control but the specific characteristics for GC are not fully appreciated. We generated an atlas of 166,533 cells from 10 GC patients with matched paratumor tissues and blood. Our results show tumor-associated stromal cells (TASCs) have upregulated activity of Wnt signaling and angiogenesis, and are negatively correlated with survival. Tumor-associated macrophages and LAMP3<sup>+</sup> DCs are involv ...[more]

PMID: 35999201

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Project description:The emergence of single-cell RNA-seq (scRNA-seq) technology has made it possible to measure gene expression variations at cellular level. This breakthrough enables the investigation of a wider range of problems including analysis of splicing heterogeneity among individual cells. However, compared to bulk RNA-seq, scRNA-seq data are much noisier due to high technical variability and low sequencing depth. Here we propose SCATS (Single-Cell Analysis of Transcript Splicing) for differential splicing analysis in scRNA-seq, which achieves high sensitivity at low coverage by accounting for technical noise. SCATS models scRNA-seq data either with or without Unique Molecular Identifiers (UMIs). For non-UMI data, SCATS explicitly models technical noise by accounting for capture efficiency and amplification bias through the use of external spike-ins; for UMI data, SCATS models capture efficiency and further accounts for transcriptional burstiness. A key aspect of SCATS lies in its ability to group "exons" that originate from the same isoform(s). Grouping exons is essential in splicing analysis of scRNA-seq data as it naturally aggregates spliced reads across different exons, making it possible to detect splicing events even when sequencing depth is low. To evaluate the performance of SCATS, we analyzed both simulated and real scRNA-seq datasets and compared with existing methods including Census and DEXSeq. We show that SCATS has well controlled type I error rate, and is more powerful than existing methods, especially when splicing difference is small. In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservative. When applied to mouse brain scRNA-seq datasets, SCATS identified more differential splicing events with subtle difference across cell types compared to Census and DEXSeq. With the increasing adoption of scRNA-seq, we believe SCATS will be well-suited for various splicing studies. The implementation of SCATS can be downloaded from https://github.com/huyustats/SCATS.

Dataset Information

ScRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

Publications

scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

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