这项研究引入了一种新的成像、空间转录组学 (ST) 和单细胞 RNA 测序整合流程来表征肿瘤发生过程中的肿瘤细胞状态转变。我们应用半监督分析流程来检查可发展为胰腺导管腺癌 (PDAC) 的癌前胰腺上皮内瘤变 (PanIN)。他们对福尔马林固定石蜡包埋 (FFPE) 样本的严格诊断限制了微环境中人类 PanIN 的单细胞表征。我们利用全转录组 FFPE ST 来研究一组罕见的匹配的低级别 (LG) 和高级别 (HG) PanIN 病变，以跟踪进展并绘制相对于单细胞 PDAC 数据集的细胞表型。我们证明癌症相关成纤维细胞 (CAF)，包括抗原呈递 CAF，位于 PanIN 附近。我们进一步观察到 PanIN 进展过程中从 CAF 相关炎症信号传导到细胞增殖的转变。我们通过单细胞高维成像蛋白质组学和转录组学技术验证了这些发现。总而言之，我们的空间多组学半监督学习框架在各种癌症类型中具有广泛的适用性，可破译癌症发生的时空动态。版权所有 © 2024 Elsevier Inc. 保留所有权利。

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.Copyright © 2024 Elsevier Inc. All rights reserved.