Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with ‘normal’ stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.

胰腺导管腺癌 (PDAC) 样品的批量分析因肿瘤微环境 (TME)（即来自成纤维细胞、内分泌、外分泌和免疫细胞的信号）而变得复杂。尽管如此，我们和其他人已经建立了具有预后意义的肿瘤和基质亚型。然而，对驱动不同免疫和基质景观的潜在信号的理解仍然不完整。在这里，我们整合了来自 7 项独立研究的 92 个单细胞 RNA-seq 样本，以构建可重复的 PDAC 图谱，重点关注肿瘤与 TME 的相互依赖性。基质活化的患者具有较高的肌成纤维细胞和免疫原性成纤维细胞，并且还显示出增加的 M2 样巨噬细胞和调节性 T 细胞。相比之下，具有“正常”基质的患者表现出类似 M1 的募集、效应细胞升高和 T 细胞耗尽。为了帮助未来研究的互操作性，我们提供了预训练的细胞类型分类器和基于亚型的信号因子图集，我们也在小鼠数据中验证了这些因子。最终，这项工作利用单细胞研究之间的异质性来创建控制 PDAC 的信号相互作用的整体视图。