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: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: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).
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes.MethodsWe used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR.ResultsIn this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue.ConclusionsIn summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.
Project description:BACKGROUND:Human pancreatic ductal adenocarcinoma (PDAC) responds poorly to immune checkpoint inhibitor (ICPi). While the mechanism is not completely clear, it has been recognized that tumor microenvironment (TME) plays key roles. We investigated if targeting CD47 with a monoclonal antibody could enhance the response of PDAC to ICPi by altering the TME. METHODS:Using immunohistochemistry, we examined tumor-infiltrating CD68+ pan-macrophages (CD68+ M) and CD163+ M2 macrophages (CD163+ M2) and tumor expression of CD47 and PD-L1 proteins in 106 cases of PDAC. The efficacy of CD47 blockade was examined in xenograft models. CD45+ immune cells from syngeneic tumor models were subjected to single-cell RNA-sequencing (scRNA-seq) by using the 10x Genomics pipeline. RESULTS:We found that CD47 expression correlated with the level of CD68+ M but not CD163+ M2. High levels of tumor-infiltrating CD68+ M, CD163+ M2, and CD47 expression were significantly associated with worse survival. CD47high/CD68+ Mhigh and CD47high/CD163+ M2high correlated significantly with shorter survival, whereas CD47low/CD68+ Mlow and CD47low/CD163+ M2low correlated with longer survival. Intriguingly, CD47 blockade decreased the tumor burden in the Panc02 but not in the MPC-83 syngeneic mouse model. Using scRNA-seq, we showed that anti-CD47 treatment significantly remodeled the intratumoral lymphocyte and macrophage compartments in Panc02 tumor-bearing mice by increasing the pro-inflammatory macrophages that exhibit anti-tumor function, while reducing the anti-inflammatory macrophages. Moreover, CD47 blockade not only increased the number of intratumoral CD8+ T cells, but also remodeled the T cell cluster toward a more activated one. Further, combination therapy targeting both CD47 and PD-L1 resulted in synergistic inhibition of PDAC growth in the MPC-83 but not in Panc02 model. MPC-83 but not Panc02 mice treated with both anti-CD47 and anti-PD-L1 showed increased number of PD-1+CD8+ T cells and enhanced expression of key immune activating genes. CONCLUSION:Our data indicate that CD47 targeting induces compartmental remodeling of tumor-infiltrating immune cells of the TME in PDAC. Different PDAC mouse models exhibited differential response to the anti-CD47 and anti-PD-L1 blockade due to the differential effect of this combination treatment on the infiltrating immune cells and key immune activating genes in the TME established by the different PDAC cell lines.