Chijimatsu Ryota, Kobayashi Shogo, Takeda Yu, Kitakaze Masatoshi, Tatekawa Shotaro, Arao Yasuko, Nakayama Mika, Tachibana Naohiro, Saito Taku, Ennishi Daisuke, Tomida Shuta, Sasaki Kazuki, Yamada Daisaku, Tomimaru Yoshito, Takahashi Hidenori, Okuzaki Daisuke, Motooka Daisuke, Ohshiro Takahito, Taniguchi Masateru, Suzuki Yutaka, Ogawa Kazuhiko, Mori Masaki, Doki Yuichiro, Eguchi Hidetoshi, Ishii Hideshi
Department of Medical Data Science, Center of Medical Innovation and Translational Research, Osaka University Graduate School of Medicine, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.
Center for Comprehensive Genomic Medicine, Okayama University Hospital, Shikata-cho, Kita-ku, Okayama 700-8558, Japan.
iScience. 2022 Jun 22;25(8):104659. doi: 10.1016/j.isci.2022.104659. eCollection 2022 Aug 19.
Single-cell RNA sequencing (scRNAseq) has been used to assess the intra-tumor heterogeneity and microenvironment of pancreatic ductal adenocarcinoma (PDAC). However, previous knowledge is not fully universalized. Here, we built a single cell atlas of PDAC from six datasets containing over 70 samples and >130,000 cells, and demonstrated its application to the reanalysis of the previous bulk transcriptomic cohorts and inferring cell-cell communications. The cell decomposition of bulk transcriptomics using scRNAseq data showed the cellular heterogeneity of PDAC; moreover, high levels of tumor cells and fibroblasts were indicative of poor-prognosis. Refined tumor subtypes signature indicated the tumor cell dynamics in intra-tumor and their specific regulatory network. We further identified functionally distinct tumor clusters that had close interaction with fibroblast subtypes via different signaling pathways dependent on subtypes. Our analysis provided a reference dataset for PDAC and showed its utility in research on the microenvironment of intra-tumor heterogeneity.
单细胞RNA测序（scRNAseq）已被用于评估胰腺导管腺癌（PDAC）的肿瘤内异质性和微环境。然而，先前的知识尚未完全普及。在这里，我们从六个包含70多个样本和超过130,000个细胞的数据集构建了一个PDAC单细胞图谱，并展示了其在先前批量转录组队列的重新分析和推断细胞间通讯中的应用。使用scRNAseq数据对批量转录组学进行细胞分解显示了PDAC的细胞异质性；此外，高水平的肿瘤细胞和成纤维细胞表明预后不良。精细的肿瘤亚型特征表明了肿瘤内肿瘤细胞的动态及其特定的调控网络。我们进一步鉴定了功能不同的肿瘤簇，这些肿瘤簇通过依赖于亚型的不同信号通路与成纤维细胞亚型密切相互作用。我们的分析为PDAC提供了一个参考数据集，并展示了其在肿瘤内异质性微环境研究中的效用。
