Single-cell RNA sequencing of human IL-18R supported CAR T cells targeting oncofetal Tenascin C
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE282046 | GEO | 2024/11/20
REPOSITORIES: GEO
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Project description:Single-cell RNA sequencing of human IL-18R supported CAR T cells targeting oncofetal Tenascin C
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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.
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Project description:We established a differentiation strategy to generate endocardial-like cells from hPSCs. Endocardial-like cells generated from our protocol display major functional properties of endocardium in developing embryo: they can induce trabecular markers in early hPSC-derived ventricular cardiomyocytes and they can undergo endothelial-to-mesenchymal-trasition under appropriate conditions. Furthermore, the transcriptome of our in vitro generated endocardial cells showed a very high level of correlation to that of primary fetal endocardial cells. Functional properties and transcriptional profile of hPSC-derived endocardial cells were also compared to those of control (non-endocardial) endothelial cells. For scRNA-seq analysis live cells from bulk endocardial and control endothelial differentiation cultures were labeled with two different sets of anti-human Hashtag antibodies before sequencing. Single cell RNA sequencing was carried out on a sample consisting of 85% bulk endocardial cells (TotalSeq-A0255 anti-human Hashtag 5 antibody, BioLegend) and 15% bulk control endothelial cells (TotalSeq-A0254 anti-human Hashtag 4, Biolegend).
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Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas
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