{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Tom Cupedo"],"organism":["Homo sapiens"],"software":["CellRanger (10x Genomics)","Seurat"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-12760"],"description":["Neutrophils are the most abundant nucleated cell type in the bone marrow. A pro-tumor bias in this cell type may have implications for bone-marrow residing malignancies, such as multiple myeloma. Here, we generated single cell transcriptomic overviews of the entire myeloid compartment, including the entire neutrophilic lineage, of the bone marrow of 6 newly diagnosed myeloma patients, 5 treated myeloma patients and 4 non-cancer controls. We find dat mature neutrophils in myeloma patients, both newly diagnosed and treated, have an activated and pro-inflammatory phenotype, accompanied by increased transcription of pro-inflammatory cytokines, such as IL-1β, and myeloma cell survival factors, such as BCMA-ligand BAFF/TNFSF13B. Moreover, inflammatory stromal cells can activate naive neutrophils to acquire an inflammatory phenotype as is seen in patients. Previously, we have shown that inflammatory stromal cells characterized the bone marrow of newly diagnosed myeloma patients. Here, we generate single cell RNA sequencing dataset of non-hematopoietic bone marrow cells of patients after induction treatment, high-dose melphalan, stem cell transplantation and consolidation treatment. We show that this intensive treatment reduced, but did not normalize, stromal inflammation."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Library Construction - Libraries were constructed according to the manufacturer’s recommendations (10X Genomics)","Sample Collection - To remove erythrocytes, unfractionated BM samples were incubated with red blood cell (RBC) lysis buffer (0.15M NH4Cl, 10mM KHCO3, 0.1mM EDTA in miliQ water) at a ratio of 1:4 for 5min at room temperature. Fc receptors were blocked with 10% normal human AB serum (Sigma-Aldrich). After blocking, cells were stained for sorting in PBS containing 2% FCS at 4°C with the following antibodies: CD138-FITC (MI15, BioLegend), CD45-PerCP-Cy5.5 (2D1, eBioscience), CD11b-PE (ICRF44, eBioscience), CD14-PECy7 (HCD14, BioLegend), CD163-PE-Dazzle594 (GHI/61, BioLegend), CD3-APCCy7 (HIT3a, BioLegend), CD71-Alexa Fluor 700 (Mem-75, ExBio), CD235a-APC (GA-R2, BD Biosciences), CD16-BV510 (3G8, BioLegend). DAPI (Life Technologies) was used for dead cell exclusion. Granulocytes were sorted as CD45mid/hiCD138-CD235a-CD71-CD3- cells within the granulocytic scatter. Monocytes/macrophages were sorted as CD45mid/hiCD138-CD235a-CD71-CD3- cells within the monocytic scatter. Cells were sorted in DMEM + 10% FCS using a FACSAria III (BD Biosciences) and BD FACSDiva version 5.0 (BD Biosciences).","Sequencing - Libraries were sequenced on the NovaSeq 6000 platform (Illumina), paired-end mode, at a sequencing depth of ~20.000 reads/cell.","Sample Collection - Viably frozen bone marrow aspirates were thawed in DMEM + 10% FCS according to the 10X Genomics protocol ‘Fresh Frozen Human Peripheral Blood Mononuclear Cells for Single Cell RNA sequencing’. Only samples containing >200x10E6 MNCs were included. Fc receptors were blocked in 10% normal human AB serum (Sigma-Aldrich). To enrich for stromal fibroblasts, samples were incubated with biotinylated antibodies against CD45 (clone HI30, BioLegend), CD235a (clone HIR2, BioLegend) and CD38 (from human CD38 MicroBead Kit, Miltenyi Biotec) followed by depletion using magnetic anti-biotin beads (Miltenyi Biotec) and the iMag (BD Biosciences). After depletion, cells were stained for sorting in PBS containing 2% FCS at 4°C with the following antibodies: CD271-PE (ME20.4), CD235a-PECy7 (HI264), CD31-APCCy7 (WM59), CD44-BV711 (IM7), streptavidin-BV421 (all BioLegend), CD34-PECF610 (4H11, eBioscience), CD45-APC (2D1, eBioscience), CD38-FITC (MHCD3801, Life Technologies), CD71-Alexa Fluor 700 (MEM-75, ExBio) and CD105-BV510 (266, BD Biosciences). 7AAD (Beckman Coulter) or DAPI (Life Technologies) were used for dead cell exclusion. Cells were sorted in DMEM + 10% FCS using a FACSAria III (BD Biosciences). Prior to depletion, 10x106 cells were used to sort CD45+CD38- and CD38+ hematopoietic populations using the same antibody panel. Sorted CD45- cells were combined with sorted CD45+CD38- cells and processed as one sample.","Nucleic Acid Extraction - Single cells were encapsulated for cDNA synthesis and barcoded using the Chromium Single Cell 3' Reagent Kit v3 (10X Genomics)"],"figure_sub":["Organization","MINSEQE Score","Assays and Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - After alignment, datasets were subjected to quality control steps using Seurat (R package, v 3.1.0) (Satija et al., 2015) that included selecting cells with a library complexity of more than 200 features, removing doublets, and filtering out cells with high percentages of mitochondrial genes (>0.1%) which are considered low in viability. Non-hematopoietic cells and CD45+CD38- cells were separated in silico using the CellSelector tool. SDC+ plasma cells were removed from the CD38+ dataset using the subset() function. For identification of cell subsets, data from samples of 10 myeloma patients and if appropriate, 2 control patients were merged by integration and label transfer (Stuart et al., 2019), normalized and then analyzed by principal-component analysis (PCA) on the most variable genes (k = 2,000) across all cells to implement dimensionality reduction. With the linearly uncorrelated principal components (PCs) (k = 40), unsupervised clustering was performed using a shared nearest neighbor (SNN) modularity optimization based clustering algorithm (resolution 0.3-1)(Waltman and van Eck, 2013) and cells were projected in two dimensions using Uniform Manifold Approximation and Projection (UMAP) (McInnes et al., 2018).","Sequence Alignment - Computational alignment was carried out using CellRanger (v 3.0.2, 10x Genomics)."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["Illumina NovaSeq 6000"],"study_type":["RNA-seq of coding RNA from single cells"],"species":["Homo sapiens"],"pubmed_title":["An IL-1β driven neutrophil-stromal cell axis fosters a BAFF-rich microenvironment in multiple myeloma"],"pubmed_authors":["Madelon ME de Jong, Tom Cupedo","Tom Cupedo","Remco Hoogenboezem","Madelon de Jong"],"additional_accession":[]},"is_claimable":false,"name":"An IL-1β driven neutrophil-stromal cell axis fosters a BAFF-rich microenvironment in multiple myeloma","description":"Neutrophils are the most abundant nucleated cell type in the bone marrow. A pro-tumor bias in this cell type may have implications for bone-marrow residing malignancies, such as multiple myeloma. Here, we generated single cell transcriptomic overviews of the entire myeloid compartment, including the entire neutrophilic lineage, of the bone marrow of 6 newly diagnosed myeloma patients, 5 treated myeloma patients and 4 non-cancer controls. We find dat mature neutrophils in myeloma patients, both newly diagnosed and treated, have an activated and pro-inflammatory phenotype, accompanied by increased transcription of pro-inflammatory cytokines, such as IL-1β, and myeloma cell survival factors, such as BCMA-ligand BAFF/TNFSF13B. Moreover, inflammatory stromal cells can activate naive neutrophils to acquire an inflammatory phenotype as is seen in patients. Previously, we have shown that inflammatory stromal cells characterized the bone marrow of newly diagnosed myeloma patients. Here, we generate single cell RNA sequencing dataset of non-hematopoietic bone marrow cells of patients after induction treatment, high-dose melphalan, stem cell transplantation and consolidation treatment. We show that this intensive treatment reduced, but did not normalize, stromal inflammation.","dates":{"release":"2023-03-31T00:00:00Z","modification":"2023-11-07T13:00:21.997Z","creation":"2023-03-10T11:59:18.853Z"},"accession":"E-MTAB-12760","cross_references":{"ENA":["ERP145591"],"EFO":["EFO_0002944","EFO_0004170","EFO_0005684","EFO_0004917","EFO_0005518","EFO_0003816","EFO_0004184"]}}