Project description:As pancreatic ductal adenocarcinoma (PDAC) is the deadliest solid cancer, and new immunological therapies have only marginally improved patient survival, we aimed at better understainding the immunological fingerprint of this disease. PDAC development involves inflammatory reprogramming and bacterial colonization, conditions that influence the adaptation of mucosa-associated invariant T cells (MAIT). We investigated MAIT signatures in patients with PDAC, chronic pancreatitis (CP), a risk factor for PDAC, and healthy donors (HD) using high-dimensional single-cell techniques, complemented by serum proteomics and multicolor histology. In our manuscript, we identify for the first-time human effector MAIT1 cells likely involved in protecting against PDAC. We characterize the transcriptional, phenotypic, and metabolic signatures of human MAIT1 compared to MAIT17-like cells, their adaptation to the PDAC inflammatory microenvironment, and provide insights into mechanisms underlying MAIT1-mediated protection against PDAC.
Project description:Mice were infected with the pancreatic cancer cell line Pan02 (into their pancreas). 30 days later, pancreas was extracted, cells digested and BD Rhapsody RNAseq targeted against the MM immune panel was performed.
Project description:scRNAseq of HPMCs isolated ex vivo from 3 Patients. Targeted scRNASeq was applied using BD onco Panel with a additional genes. Purpose of the experiment was to characterize HPMC phenotype ex vivo and compare with the phenotype observed in RNAseq of HPMCs treated with IL17A and TNF.
Project description:We conducted single-cell gene expression analysis of neutrophils from mouse tumors with and without microbial treatment to investigate neutrophil response in the tumor microenvironment (TME). Briefly, tumor-infiltrating Ly6G+CD11b+ neutrophils were isolated from unmanipulated tumors (resting), tumors treated with a vehicle control (control), and tumors treated with S. aureus bioparticles (stimulated) 24 hours after treatment. To survey the whole spectrum of the TME, we also isolated and sequenced non-neutrophil leukocytes in each sample.
Project description:This study investigates the role of dendritic cells (DCs) in anti-tumor immunity, focusing on the phosphatase SHP-1 and its regulation of signaling pathways and interactions with CD8+ T cells. Using conditional knockout mouse models and single-cell transcriptomics, we show that SHP-1 loss in conventional type 1 dendritic cells (cDC1s) and macrophages disrupts interferon responses, antigen presentation, and migratory programs, leading to impaired tumor rejection and reduced efficacy of PD-1 blockade. These findings provide mechanistic insights into dendritic cell biology within the tumor microenvironment (TME) and identify SHP-1 as a key regulator with implications for the design of DC-based cancer immunotherapies.
Project description:Microglia are brain-resident macrophages critical for cerebral development, function and homeostasis. During development, yolk-sac-derived microglial progenitors colonize and populate the brain following a well-defined spatiotemporal pattern. However, the mechanisms driving microglial colonization and proliferation are largely unknown. Herein, using scRNA-seq of conditional inactivation of Colony Stimulating Factor 1 (Csf1), we revealed that embryonic cortical microglia critically rely on neural Csf1, mainly produced by cortical progenitors but also by post-mitotic neurons, and that the action of Csf1 is local, dose-dependent and transient. Alongside, intrinsic Csf1 expressed by ATM contributed to their sustained proliferation at developmental hotspots.
Project description:scRNA-seq data from human eosinophils purified from blood samples. Samples include three healthy patients and three asthmatic donors.
Project description:4 groups of mice : Control, antibiotics, antibiotics + 4days of recolonization, antibiotics + 4days of recolonization + Enterocloster clostridioformis. Tumor draining lymph node were harvested after CFSE injection were harvested and CFSE+ cells were sorted and proccessed in order to generate single cell RNA-sequencing using BD Rhapsody mouse immune response targeted panel. Groups were barcoded using BD Rhapsody Multiplexing Kit.
Project description:SPP1 stimulation of human leukocytes drives proinflammatory monocyte activation and differentiation of dysregulated CD274(PDL-1)pos neutrophil phenotype.
Project description:Immunotherapy using CD19-directed chimeric antigen receptor (CAR)-T cells has shown excellent results for treatment of B-cell leukaemia and lymphoma. To produce CAR-T cells, the patient’s own T cells are isolated from the blood and modified in a laboratory with a genetic vector to express a tumor antigen-directed CAR on its surface. The CAR-T cells are then expanded in numbers and given back to the patient with the aim to eradicate the tumors. However, some patients display primary resistance to CAR-T treatment while others relapse quickly after CAR-T treatment. In this experiment, we seek to understand whether the quality of the individual CAR-T cell product the patients were given can predict outcome to the therapy. We investigate the transcriptional profile of the individual CAR-T infusion products using single-cell RNA sequencing. In this dataset, we identified a T cell subset correlating with response that could be used as an indicator for clinical outcome. Targeted RNA and protein single-cell libraries were obtained using the BD Rhapsody platform (BD Biosciences). In total four separate targeted libraries were produced with 6 patients per library. Sequencing was performed on NovaSeq 6000 S1 sequencer at the SNP&SEQ Technology Platform (Uppsala, Sweden). The raw scRNA-seq data was pre-processed by BD Biosciences using the Rhapsody Analysis pipeline to convert the raw reads into Unique Molecular Identifier (UMI) counts. UMIs are further adjusted within Rhapsody by applying BD’s Recursive Substitution Error Correction (RSEC) and Distribution-Based Error Correction (DBEC) in order to remove false UMIs caused by sequencing or library preparation errors. Pooled samples were deconvoluted using Sample-tag reads. The scRNA-seq and AbSeq counts were loaded, processed and used for clustering and differential gene expression with Seurat v. 4.0.0.