Project description:Pancreatic islets consist of several cell types, including alpha, beta, delta, epsilon, and PP cells. Due to cellular heterogeneity, it is challenging to interpret whole-islet transcriptome data. Single-cell transcriptomics offers a powerful method for investigating gene expression at the single-cell level and identifying cellular heterogeneity and subpopulations. Here, we describe a protocol for mouse pancreatic islet isolation, culturing, and dissociation into a single-cell suspension. This protocol yields highly viable cells for successful library preparation and single-cell RNA sequencing. For complete details on the use and execution of this protocol, please refer to Lee et al. (2020).
Project description:Pancreatic cancer (PaC) is resistant to immune checkpoint therapy, but the underlying mechanisms are largely unknown. In this study, we have established four orthotopic PaC murine models with different PaC cell lines by intra-pancreatic inoculation. Therapeutic examinations demonstrate that only tumors induced with Panc02-H7 cells respond to αPD-1 antibody treatment, leading to significantly reduced tumor growth and increased survival in the recipient mice. Transcriptomic profiling at a single-cell resolution characterizes the molecular activity of different cells within tumors. Comparative analysis and validated experiments demonstrate that αPD-1-sensitive and -resistant tumors differently shape the immune landscape in the tumor microenvironment (TME) and markedly altering effector CD8+ T cells and tumor-associated macrophages (TAMs) in their number, frequency, and gene profile. More exhausted effector CD8+ T cells and increased M2-like TAMs with a reduced capacity of antigen presentation are detected in resistant Panc02-formed tumors versus responsive Panc02-H7-formed tumors. Together, our data highlight the correlation of tumor-induced imbalance of macrophages with the fate of tumor-resident effector CD8+ T cells and PaC response to αPD-1 immunotherapy. TAMs as a critical regulator of tumor immunity and immunotherapy contribute to PaC resistance to immune checkpoint blockade.
Project description:Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.
Project description:Tumor-associated macrophages M2 (TAM2), which are highly prevalent infiltrating immune cells in the stroma of pancreatic cancer (PC), have been found to induce an immunosuppressive tumor microenvironment, thus enhancing tumor initiation and progression. However, the immune therapy response and prognostic significance of regulatory genes associated with TAM2 in PC are currently unknown. Based on TCGA transcriptomic data and single-cell sequencing data from the GEO database, we identified TAM2-driven genes using the WGCNA algorithm. Molecular subtypes based on TAM2-driven genes were clustered using the ConsensusClusterPlus algorithm. The study constructed a prognostic model based on TAM2-driven genes through Lasso-COX regression analysis. A total of 178 samples obtained by accessing TCGA were accurately categorized into two molecular subtypes, including the high-TAM2 infiltration (HMI) cluster and the low-TAM2 infiltration (LMI) cluster. The HMI cluster exhibits a poor prognosis, a malignant tumor phenotype, immune-suppressive immune cell infiltration, resistance to immunotherapy, and a high number of genetic mutations, while the LMI cluster is the opposite. The prognostic model composed of six hub genes from TAM2-driven genes exhibits a high degree of accuracy in predicting the prognosis of patients with PC and serves as an independent risk factor. The induction of TAM2 was employed as a means of verifying these six gene expressions, revealing the significant up-regulation of BCAT1, BST2, and MERTK in TAM2 cells. In summary, the immunophenotype and prognostic model based on TAM2-driven genes offers a foundation for the clinical management of PC. The core TAM2-driven genes, including BCAT1, BST2, and MERTK, are involved in regulating tumor progression and TAM2 polarization, which are potential targets for PC therapy.
Project description: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.
Project description:Pancreatic cancer (PC) is known for its high degree of heterogeneity and exceptionally adverse outcome. While disulfidptosis is the most recently identified form of cell death, the predictive and therapeutic value of disulfidptosis-related genes (DRGs) for PC remains unknown. RNA sequencing data with the follow-up information, were retrieved from the TCGA and ICGC databases. Consensus clustering analysis was conducted on patient data using R software. Subsequently, the LASSO regression analysis was conducted to create a prognostic signature for foreseeing the outcome of PC. Differences in relevant pathways, mutational landscape, and tumor immune microenvironment were compared between PC samples with different risk levels. Finally, we experimentally confirmed the impact of DSG3 on the invasion and migration abilities of PC cells. All twenty DRGs were found to be hyperexpressed in PC tissues, and fourteen of them significantly associated with PC survival. Using consensus clustering analysis based on these DRGs, four DRclusters were identified. Additionally, altogether 223 differential genes were evaluated between clusters, indicating potential biological differences between them. Four gene clusters (geneClusters) were recognized according to these genes, and a 10-gene prognostic signature was created. High-risk patients were found to be primarily enriched in signaling pathways related to the cell cycle and p53. Furthermore, the rate of mutations was markedly higher in high-risk patients, besides important variations were present in terms of immune microenvironment and chemotherapy sensitivity among patients with different risk levels. DSG3 could appreciably enhance the invasion and migration of PC cells. This work, based on disulfidoptosis-related genes (DRGs), holds the promise of classifying PC patients and predicting their prognosis, mutational landscape, immune microenvironment, and drug therapy. These insights could boost an improvement in a better comprehension of the role of DRGs in PC as well as provide new opportunities for prognostic prediction and more effective treatment strategies.
Project description:Circulating tumor cells (CTCs) are shed from primary tumors into the bloodstream, mediating the hematogenous spread of cancer to distant organs. To define their composition, we compared genome-wide expression profiles of CTCs with matched primary tumors in a mouse model of pancreatic cancer, isolating individual CTCs using epitope-independent microfluidic capture, followed by single-cell RNA sequencing. CTCs clustered separately from primary tumors and tumor-derived cell lines, showing low-proliferative signatures, enrichment for the stem-cell-associated gene Aldh1a2, biphenotypic expression of epithelial and mesenchymal markers, and expression of Igfbp5, a gene transcript enriched at the epithelial-stromal interface. Mouse as well as human pancreatic CTCs exhibit a very high expression of stromal-derived extracellular matrix (ECM) proteins, including SPARC, whose knockdown in cancer cells suppresses cell migration and invasiveness. The aberrant expression by CTCs of stromal ECM genes points to their contribution of microenvironmental signals for the spread of cancer to distant organs.
Project description:Pancreatic cancer, one of the most prevalent tumors of the digestive system, has a dismal prognosis. Cancer of the pancreas is distinguished by an inflammatory tumor microenvironment rich in fibroblasts and different immune cells. Neutrophils are important immune cells that infiltrate the microenvironment of pancreatic cancer tumors. The purpose of this work was to examine the complex mechanism by which neutrophils influence the carcinogenesis and development of pancreatic cancer and to construct a survival prediction model based on neutrophil marker genes. We incorporated the GSE111672 dataset, comprising RNA expression data from 27,000 cells obtained from 3 patients with PC, and conducted single-cell data analysis. Thorough investigation of pancreatic cancer single-cell RNA sequencing data found 350 neutrophil marker genes. Using The Cancer Genome Atlas (TCGA), GSE28735, GSE62452, GSE57495, and GSE85916 datasets to gather pancreatic cancer tissue transcriptome data, and consistent clustering was used to identify two categories for analyzing the influence of neutrophils on pancreatic cancer. Using the Random Forest algorithm and Cox regression analysis, a survival prediction model for pancreatic cancer was developed, the model showed independent performance for survival prognosis, clinic pathological features, immune infiltration, and drug sensitivity. Multivariate Cox analysis findings revealed that the risk scores derived from predictive models is independent prognostic markers for pancreatic patients. In conclusion, based on neutrophil marker genes, this research created a molecular typing and prognostic grading system for pancreatic cancer, this system was very accurate in predicting the prognosis, tumor immune microenvironment status, and pharmacological treatment responsiveness of pancreatic cancer patients.
Project description:Preparation of single-cell suspension from primary tumor tissue can provide a valuable resource for functional, genetic, proteomic, and tumor microenvironment studies. Here, we describe an effective protocol for mouse pancreatic tumor dissociation with further processing of tumor suspension for single-cell RNA sequencing analysis of cellular populations. We further provide an outline of the bioinformatics processing of the data and clustering of heterogeneous cellular populations comprising pancreatic tumors using Common Workflow Language (CWL) pipelines within user-friendly Scientific Data Analysis Platform (https://SciDAP.com). For complete details on the use and execution of this protocol, please refer to Gabitova-Cornell et al. (2020).