胰腺导管腺癌（PDAC）样本的大规模分析受到肿瘤微环境（TME）的复杂影响，即纤维母细胞、内分泌细胞、外分泌细胞和免疫细胞发出的信号。尽管如此，我们和其他人已经确定了具有预后重要性的肿瘤和基质亚型。然而，对于驱动不同免疫和基质景观的潜在信号的理解仍然不完整。在本研究中，我们整合了来自七个独立研究的92个单细胞RNA测序样本，构建了一个可重复的PDAC图谱，重点研究肿瘤-TME的相互关系。激活基质的患者与更高水平的肌纤维母细胞和免疫活性纤维母细胞同义，并且显示出增加的M2样巨噬细胞和调节性T细胞。相反，正常基质的患者显示M1样招募，增加的效应细胞和耗竭T细胞。为了帮助未来研究的互操作性，我们提供了一个预先训练的细胞类型分类器和一个基于亚型的信号因子图谱，我们还在小鼠数据中对其进行了验证。最终，本研究利用单细胞研究的多样性，创建了一个综合性的肿瘤信号相互作用乐团的全面视图。© 2023. Springer Nature Limited.

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.© 2023. Springer Nature Limited.