Single-cell atlas of lineage states, tumor microenvironment and subtype-specific expression programs in gastric cancer
Gastric cancer heterogeneity represents a barrier to disease management. We generated a comprehensive single-cell atlas of gastric cancer (>200,000 cells) comprising 48 samples from 31 patients across clinical stages and histologic subtypes. We identified 34 distinct cell-lineage states including novel rare cell populations. Many lineage states exhibited distinct cancer-associated expression profiles, individually contributing to a combined tumor-wide molecular collage. We observed increased plasma cell proportions in diffuse-type tumors associated with epithelial-resident KLF2 and stage-wise accrual of cancer-associated fibroblast subpopulations marked by high INHBA and FAP coexpression. Single-cell comparisons between patient-derived organoids (PDO) and primary tumors highlighted inter- and intralineage similarities and differences, demarcating molecular boundaries of PDOs as experimental models. We complemented these findings by spatial transcriptomics, orthogonal validation in independent bulk RNA-sequencing cohorts, and functional demonstration using in vitro and in vivo models. Our results provide a high-resolution molecular resource of intra- and interpatient lineage states across distinct gastric cancer subtypes. Significance: We profiled gastric malignancies at single-cell resolution and identified increased plasma cell proportions as a novel feature of diffuse-type tumors. We also uncovered distinct cancer-associated fibroblast subtypes with INHBA-FAP-high cell populations as predictors of poor clinical prognosis. Our findings highlight potential origins of deregulated cell states in the gastric tumor ecosystem. This article is highlighted in the In This Issue feature, p. 587.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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