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3,134 results for “bone marrow”
Megakaryocyte volume modulates bone marrow niche properties and cell migration dynamics
<p>Supplementary videos showing raw time and z-stacks as well a final, processed result for Neutrophil tracking in naive and platelet depleted mouse.</p> <p>Matlab scripts to run simulation of megakaryocytes, neutrophils and hematopoetic stem cell in a vessel environment.</p> <p>Ilastik training data set used in segmentation of bone and bone marrow.</p> <p> </p>
Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation
<p>Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives.</p> <p>Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2 based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020) (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used.</p> <p>Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done. </p> <p>To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used. </p> <p>We have curated two datasets</p> <p>Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation"<br>Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation"</p> <p>*Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"</p>
Expression of terminal deoxynucleotidyl transferase (TdT) identifies lymphoid-primed progenitors in human bone marrow
<p><span>With emerging single-cell techniques in the human hematopoiesis, discrepancies between the traditional cell-surface-marker-based cell-type identification and their single-cell level molecular phenotype are observed. To better associate lymphoid identity with protein-level cell features, we examined the protein expression of terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, and detected TdT expression within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincided with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions showed enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs was inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrated robust lymphoid potential ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies previously unappreciated lymphoid-primed progenitors, redefining the lympho-myeloid axis in human hematopoiesis.</span></p>
Human CD34 bone marrow SCE data set to reproduce Totem protocols
<p>The data set <code>human_cd34_bm_rep1.rds</code> was parsed with the R script <code>download_h5ad_to_SCE_rds_script.R</code> (see github repository <a href="https://github.com/elolab/Totem-protocol">elolab/Totem-protocol</a>). It is a parsed <code>SingleCellExperiment</code> <code>RDS</code> object corresponding to the anndata h5ad <code>human_cd34_bm_rep1.h5ad</code> available on <a href="https://github.com/elolab/Totem-protocol/blob/main">HCA Portal</a> and published by <a href="https://www.nature.com/articles/s41587-019-0068-4">Setty et al., 2019</a>.</p>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
MarrowDLD: a microfluidic method for label-free retrieval of fragile bone marrow cells
<p>We introduce here a label-free cytometry microsystem, MarrowDLD, based on deterministic lateral displacement. MarrowDLD enables the isolation of fragile cells based on intrinsic size properties while preserving their viability and functionality. Bone marrow adipocytes, obtained from mouse and human stromal line differentiation, as well as megakaryocytes, from primary human CD34+ hematopoietic stem and progenitor cells, were used for validation.</p>
A Study of E7820 in People With Bone Marrow (Myeloid) Cancers
ClinicalTrials.gov study NCT05024994. IPD Sharing: YES. Countries: 1. Publications: 1.
Expression of terminal deoxynucleotidyl transferase (TdT) identifies lymphoid-primed progenitors in human bone marrow
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IFN-γ primes bone marrow neutrophils to acquire regulatory functions in severe viral respiratory infections
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Bulk RNA sequencing of human mesenchymal stromal cells derived from labial salivary glands, bone marrow, and adipose
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Global Characterization of Megakaryocytes in Bone Marrow, Peripheral Blood, and Cord Blood by Single-cell RNA Sequencing
<p><span><span>Megakaryocytes (MK) are mainly derived from bone marrow (BM) and are mainly involved in platelet production. Recent studies have shown that MK derived from BM may have immune function, and that MK from peripheral blood (PB) are associated with prostate cancer. We analyzed more than 1.2 million single-cell transcriptome data from 132 samples of PB, BM, and cord blood (CB) from healthy individuals and patients, and obtained 4474 MK single cell and 14 MK subtypes. We found that MK were widely distributed and the amount of MK in PB was more than that in BM and there were specificity MK subtypes in PB. We found classical MK1 with typical MK characteristics and non-classical MK2 closely related to immunity which was the most common subtype in BM and CB. Classical MK1 was closely related to Non-Small Cell Lung Cancer (NSCLC) and has diagnostic ability. MK2 may have potential adaptive immune function and play a role in tumor NSCLC and autoimmune diseases Systemic Lupus Erythematosus. This study deepened our understanding of MK and suggested that MK had potential immune functions and was involved in various diseases.</span></span></p>
Comparing the effect of TGF-β receptor inhibition on human mesenchymal stem/stromal cells derived from endometrium, bone marrow and adipose tissues
<p><strong>Figure S1: Differences between bmMSC donors. A)</strong> Graph showing two groups of bmMSCs with and without effect of A83-01 treatment on % SUSD2<sup>+</sup> cells. <strong>B)</strong> Graph showing no difference in the number of cells following A83-01 treatment in the two groups of donor cells from <strong>A</strong>. Plots are median for n=3 biological samples per treatment group.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - final volumes
<p>These are the registered and filtered volumes used to generate the meshes that we then show in videos.</p>
Human bone marrow assessment by single-cell RNA sequencing
<p>Seurat objects and CyTOF data for <a href="https://doi.org/10.1172/jci.insight.124928">10.1172/jci.insight.124928</a></p> <p>Please run UpdateSeuratObject() after loading. Small object contains annotated metadata with cca and tsne analyses. Large object contains additional reductions (eg umap). </p>
The bone marrow endothelial progenitor cell response to septic infection: Dataset
<p>An early increase in the level of endothelial progenitor cells (EPCs) in the systemic circulation occurs in patients with septic infection/sepsis. The significance and underlying mechanisms of this response remain unclear. This study investigated the bone marrow EPC response in adult mice with septic infection induced by intravenous injection (i.v.) of <em>Escherichia coli</em>. For <em>in vitro</em> experiments, sorted marrow stem/progenitor cells (SPCs) including lineage(lin)<sup>-</sup>stem cell growth factor receptor(c-kit)<sup>+</sup>stem cell antigen-1(Sca-1)<sup>-</sup>, lin<sup>-</sup>c-kit<sup>+</sup>, and lin<sup>-</sup> cells were cultured with or without lipopolysaccharides (LPS) and recombinant murine vascular endothelial growth factor (VEGF) in the absence and presence of anti-Sca-1 crosslinking antibodies. In a separate set of experiments, marrow lin<sup>-</sup>c-kit<sup>+</sup> cells from green fluorescence protein (GFP)<sup>+</sup> mice, i.v. challenged with heat-inactivated <em>E. coli</em> or saline for 24 h were subcutaneously implanted in Matrigel plugs for 5 weeks. Marrow lin<sup>-</sup>c-kit<sup>+</sup> cells from Sca-1 knockout (KO) mice challenged with heat-inactivated <em>E. coli</em> for 24 h were cultured in the Matrigel medium for 8 weeks. The marrow pool of EPCs bearing the lin<sup>-</sup>c-kit<sup>+</sup>Sca-1<sup>+</sup>VEGF receptor 2 (VEGFR2)<sup>+</sup> (LKS VEGFR2<sup>+</sup>) and LKS CD133<sup>+</sup>VEGFR2<sup>+</sup> surface markers expanded rapidly following septic infection, which was supported by both proliferative activation and phenotypic conversion of marrow stem/progenitor cells. An increase in marrow EPCs and their reprogramming for enhancing angiogenic activity correlated with cell-marked upregulation of Sca-1 expression. Sca-1 coupled with ras-related C3 botulinum toxin substrate 2 (Rac2) in signaling the marrow EPC response. Septic infection caused a substantial increase in plasma levels of IFN-γ, VEGF, G-CSF, and SDF-1. The early increase in circulating EPCs was accompanied by their active homing and incorporation into pulmonary microvasculature. These results demonstrate that the marrow EPC response is a critical component of the host defense system. Sca-1 signaling plays a pivotal role in the regulation of EPC response in mice with septic infection.</p>
Engineering of Fully Humanized and Vascularized 3D Bone Marrow Niches Sustaining Undifferentiated Human Cord Blood Hematopoietic Stem and Progenitor Cells
<p>Data underlying the figures in the publication “Engineering of fully humanized and vascularized 3D bone marrow niches sustaining undifferentiated human cord blood hematopoietic stem and progenitor cells”, published in <em>J Tissue Eng., </em><strong>2021</strong>, 12, 2041731421044855.</p> <p>DOI:10.1177/20417314211044855</p> <p> </p> <p>Table of Contents:</p> <p><strong>1. P12-03_TP01_Suppernatant</strong>: Enzyme-linked immunosorbent assay of VEGF concentration in vascularized and not vascularized BM niches.</p> <p><strong>2. P12_WholeNiche</strong>: qPCR data of vascular and osteogenic markers in vascularized and not vascularized BM niches.</p> <p><strong>3. FACs-Overview-SN_relativ</strong>: FACS data showing haematopoietic cell populations in the supernatant after HSC cocoulture in vascularized and not vascularized BM niches.</p> <p><strong>4. FACs-Overview-niche_Relativ</strong>: FACS data showing haematopoietic cell populations in the niche after HSC cocoulture in vascularized and not vascularized BM niches.</p> <p><strong>5. CFU</strong>: Colony formation statistics after HSC cocoulture from vascularized and not vascularized BM niches.</p> <p><strong>6. Cell cycle</strong>: Cell cycle statistics of HSPCs after HSC cocoulture from vascularized and not vascularized BM niches.</p> <p><strong>7. P12_03_AfterHSPC-vs-BeforeHSPCs</strong>: qPCR data of vascular and osteogenic markers in vascularized and not vascularized BM niches after and before HSC cocoulture.</p> <p><strong>8. Scheme</strong>: Vector graphic of experimental setup.</p> <p><strong>9. Images engineered niches</strong>: Folder containing images of engineered niches.</p>
Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape
<p>This repository contains all necessary scRNA-seq inputs to reproduce the results described in "Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape" by Schmidt et al. (Cancer Research 2024). </p> <p>Content:</p> <ol> <li>"MPN_calreticulin_bm.R" --> R script containing all code</li> <li>"cells_table.RDS" --> cells table containing, cell_id, UMAP coordinates, complexity, cell type annotation and metadata</li> <li>"normalized_matrix.RDS" --> quality control filtered, log2-normalized and centered expression matrix</li> <li>"reference_signatures.RDS" --> all external signatures used for this study</li> <li>"EV2_*", "EV5_*", "MPN2_*", "MPN5_*", --> cellranger outputs</li> </ol>
Single-cell profiling of human bone marrow progenitors reveals mechanisms of failing erythropoiesis in Diamond-Blackfan anemia
<p>Ribosome dysfunction underlies the pathogenesis of many cancers and heritable ribosomopathies. Here we investigate how mutations in either ribosomal protein large (RPL) or ribosomal protein small (RPS) subunit genes selectively affect erythroid progenitor development and clinical phenotypes in Diamond-Blackfan anemia (DBA), a rare ribosomopathy with limited therapeutic options. Using single-cell assays of patient-derived bone marrow, we delineated two distinct cellular trajectories segregating with ribosomal protein genotypes: almost complete loss of erythroid specification were observed in <em>RPS</em>-DBA. In contrast, we observed relative preservation of qualitatively abnormal erythroid progenitors and precursors in <em>RPL</em>-DBA. Although both DBA genotypes exhibited a pro-inflammatory bone marrow milieu, <em>RPS</em>-DBA was characterized by erythroid differentiation arrest, whereas <em>RPL</em>-DBA was characterized by preserved GATA1 expression and activity. Compensatory stress erythropoiesis in <em>RPL</em>-DBA exhibited disordered differentiation underpinned by an altered glucocorticoid molecular signature, including reduced <em>ZFP36L2</em> expression<em>,</em> leading to milder anemia and improved corticosteroid response. This integrative analysis approach identified distinct pathways of erythroid failure and defined genotype-phenotype correlations in DBA. These findings may help facilitate therapeutic target discovery.</p> <p> </p>
Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
<p>In contrast to the rich single-cell transcriptomic and epigenetic data, the corresponding protein level information of human hematopoietic stem and progenitor cell (HSPC) populations is still missing. We used a highly-multiplexed single-cell screen to quantify the protein expression of 353 surface molecules and 79 functional intracellular molecules (TFs, chromatin regulators, and metabolic enzymes) with mass cytometry. In doing this, we created a core panel with probes against functional protein molecules associated with specific lineage potentials to better illuminate the differentiation potentials of the progenitors. In total, we analyzed 556,226 CD34+ bone marrow HSPCs across three individuals. Our analysis identified ten distinct clusters among HSPCs by unsupervised method and defined their unique proteomic composition. We compare our data-driven populations to the canonical HSPC cell types identified by cell surface proteins and observe discrepancies, especially in the lympho-myeloid axis. Overall, we supply a quantified summary of the proteomes of human HSPCs and create a framework to redefine progenitor populations with unique functional states along hematopoiesis. </p>
Ki-67 and Bcl-2 data by flow cytometry in non-malignant bone marrow aspirates and aspirates from patients with myeloid malignancies.
<p>This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferation and anti-apoptosis in hematopoietic cells. This dataset includes analysis of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell population in non-malignant BM, and the BM disorders myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. As gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 50 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research about the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations based on cell biological characteristics were identified, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells to identify minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.