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218 results for “hematopoietic differentiation”
Epigenome-wide analysis reveals specific DNA hypermethylation of T cells during human hematopoietic differentiation
<p>Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. In this study we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages.</p>
Discrete regulatory modules instruct hematopoietic lineage commitment and differentiation
<p>bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2020.04.02.022566v4</p> <p><strong>Contact:</strong> Grigorios Georgolopoulos (<a href="mailto:ggeorgol@altius.org">ggeorgol@altius.org</a>); Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Lineage commitment and differentiation is driven by the concerted action of master transcriptional regulators at their target chromatin sites. Multiple efforts have characterized the key transcription factors (TFs) that determine the various hematopoietic lineages. However, the temporal interactions between individual TFs and their chromatin targets during differentiation and how these interactions dictate lineage commitment remains poorly understood. Here we delineate the temporal interplay between the <em>cis</em>- and the <em>trans</em>-regulatory landscape in establishing lineage commitment and differentiation in human hematopoiesis by performing a dense timecourse of chromatin accessibility (DNase I-seq), and gene expression (total and single cell RNA-seq).</p> <p>All data uploaded correspond to human genome build version GRCh38.</p> <p><strong>Contents</strong></p> <ol> <li><strong>DNase I Hotspot (DHS) metadata: </strong>Supplementary_Data_1.txt</li> <li><strong>DNase I Hotspot quantile-normalized counts:</strong> A tab-separated matrix with quantile-normalized DNase I density counts from 79,085 FDR 5% hotspots, across 12 erythroid differentiation timepoints from 3 donors, present in at least n=2 samples. Rows correspond to DHS information in Supplementary_Data_1.txt (hotspots.fdr.0.05.qnorm.counts.tsv.gz)</li> <li><strong>Column information for DNase I Hotspot quantile-normalized counts: </strong>hotspots.fdr.0.05.qnorm.counts.info.tsv</li> <li><strong>Developmentally regulated gene metadata (erythroid): </strong>Supplementary_Data_2.csv</li> <li><strong>Gene matrix of quantile-normalized FPKM values (erythroid): </strong>A tab-separated matrix with the quantile-normalized FPKM values of all detected genes, across 13 erythroid differentiation timepoints from 3 donors. (fpkm_erythroid_qnorm.tsv.gz)</li> <li><strong>Column information for the quantile-normalized FPKM gene matrix (erythroid): </strong>A tab-separated table (fpkm_erythroid_qnorm.info.tsv)</li> <li><strong>CD34+ HSPC TADs at 10kb resolution: </strong>Supplementary_Data_3.bed</li> <li><strong>Day 11 <em>ex vivo</em> erythroid progenitor TADs at 10kb resolution: </strong>Supplementary_Data_4.bed</li> <li><strong>Transcription factor motif enrichment per DHS cluster: </strong>Supplementary_Data_5.csv</li> <li><strong>Correlation information (links) between developmentally regulated DHS and target genes: </strong>Supplementary_Data_6.csv</li> <li><strong>Chromatin anchor loops called from 10kb resolution Hi-C data: </strong>Supplementary_Data_7.bedgraph</li> <li><strong>Developmentally regulated gene metadata (megakaryocytic): </strong>Supplementary_Data_8.csv</li> <li><strong>Gene matrix of quantile-normalized FPKM values (megakaryocytic): </strong>A tab-separated matrix with the quantile-normalized FPKM values of all detected genes, across 13 megakaryocytic differentiation timepoints from 3 donors. (fpkm_megakaryocyte_qnorm.tsv.gz)</li> <li><strong>Column information for the quantile-normalized FPKM gene matrix (megakaryocytic): </strong>A tab-separated table (fpkm_megakaryocyte_qnorm.info.tsv)</li> <li><strong>Marker (differentially expressed) genes per single cell population: </strong>Supplementary_Data_9.csv</li> <li><strong>A SCANPY h5ad Annotated DataFrame object: </strong>Annotated Data frame `anndata` in h5ad format including the gene-by-cell count matrix, Velocyto splicing kinetics (RNA velocity) information layer, along with obs, obsm, var, varm, and uns layers. (SCANPY_anndata_object.h5ad)</li> </ol>
Flow Cytometry data from: "The EMT transcription factor Zeb1 is essential for HSPC differentiation that acts synergistically with Zeb2 in fine-tuning hematopoietic lineage fidelity"
<p>Abstract:</p> <p>The Zeb2 transcription factor has been demonstrated to play important roles in hematopoiesis and leukemic transformation. Zeb1 is a close family member of Zeb2 but has remained more enigmatic concerning its roles in hematopoiesis. Here we show using conditional loss of function approaches and bone marrow reconstitution experiments that Zeb1 plays cell autonomous role in hematopoietic lineage differentiation, particularly as a positive regulator of monocyte development in addition to its previously reported important role in T-cell differentiation. Analysis of existing single cell RNAseq data of early hematopoiesis has revealed distinctive expression differences between Zeb1 and Zeb2 in HSPC differentiation with Zeb2 being more highly and broadly expressed that Zeb1 except at a key transition point (ST-HSCàMPP1) whereby Zeb1 appears to be the dominantly expressed family member. Inducible deletion of both Zeb1 and Zeb2 using a tamoxifen inducible Cre-mediated approach leads to acute bone marrow failure at this transition point with increased long-term and shortterm hematopoietic stem cell numbers and an accompanying decrease in all hematopoietic lineage differentiation. Bioinformatics analysis of RNAseq data has revealed that Zeb2 acts predominantly as a transcriptional repressor involved in restraining mature hematopoietic lineage gene expression programs from being expressed too early in hematopoietic stem and progenitor cells (HSPCs). Zeb1 appears to fine tune this repressive role during hematopoiesis to ensure hematopoietic lineage fidelity. Analysis of ROSA26 locus based transgenic models has revealed that Zeb1 as well as Zeb2 overexpression within the hematopoietic system can drive extramedullary hematopoiesis/splenomegaly and enhanced monocyte development. Finally, deletion of Zeb2 alone or Zeb1/2 together was found to enhance survival in secondary MLL-AF9 AML models attesting to the oncogenic role of Zeb1/2 in AML.</p> <p> </p> <p>Flow cytometric and Hematocrit analysis methods: </p> <p><br> Cells were stained with antibodies listed in the provided Supplemental Table (Antibodies.xlsx) according to the manufacturer guidelines. Flow cytometric analyses were performed on the LSRII and Fortessa X-20 cytometer (BD Biosciences) and the results were analysed by FACSDiva or FlowJo software (BD Biosciences). Cells for MLL-AF9 experiments and RNA-seq were stained and sorted on Influx or FACSAria Fusion sorters (BD Biosciences) at AMREP Flow Cytometry Core Facility and FlowCore, Monash University. <br> Submandibular blood samples were collected into EDTA-coated tubes, and hematology parameters were measured using a HemaVet 950FS automated blood analysis machine (Drew Scientific).</p>
An Efficacy and Safety Study of Autologous Cluster of Differentiation 34 (CD34+) Hematopoietic Progenitor Cells Transduced With Placebo or an Anti- Human Immunodeficiency Virus Type 1 (HIV-1) Ribozyme
ClinicalTrials.gov study NCT00074997. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dataset and link to dataset related to article "MicroRNA-127-3p controls murine hematopoietic stem cell maintenance by limiting differentiation."
<p>The balance between self-renewal and differentiation is crucial to ensure the homeostasis of the hematopoietic system, and is a hallmark of hematopoietic stem cells. However, the underlying molecular pathways, including the role of micro-RNA, are not completely understood. To assess the contribution of micro-RNA, we performed micro-RNA profiling of hematopoietic stem cells and their immediate downstream progeny multi-potent progenitors from wild-type control and Pbx1-conditional knockout mice, whose stem cells display a profound self-renewal defect. Unsupervised hierarchical cluster analysis separated stem cells from multi-potent progenitors, suggesting that micro-RNA might regulate the first transition step in the adult hematopoietic development. Notably, Pbx1-deficient and wild-type cells clustered separately, linking micro-RNAs to self-renewal impairment. Differential expression analysis of micro-RNA in the physiological stem cell-to-multi-potent progenitor transition and in Pbx1-deficient stem cells compared to control stem cells revealed miR-127-3p as the most differentially expressed. Furthermore, miR-127-3p was strongly stem cell-specific, being quickly down-regulated upon differentiation and not re-expressed further downstream in the bone marrow hematopoietic hierarchy. Inhibition of miR-127-3p function in Lineage-negative cells, achieved through a lentiviral-sponge vector, led to severe stem cell depletion, as assessed with serial transplantation assays. miR-127-3p-sponged stem cells displayed accelerated differentiation, which was uncoupled from proliferation, accounting for the observed stem cell reduction. miR-127-3p overexpression in Lineage-negative cells did not alter stem cell pool size, but gave rise to lymphopenia, likely due to lack of miR-127-3p physiological downregulation beyond the stem cell stage. Thus, tight regulation of miR-127-3p is crucial to preserve the self-renewing stem cell pool and homeostasis of the hematopoietic system.</p> <p> </p> <p>Raw data relative to Figure 1 have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE113062, <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113062">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113062</a></p>
Reprogramming-associated aberrant DNA methylation determines hematopoietic differentiation capacity of human induced pluripotent stem cells [PSCderived_HPCs_methylation]
GEO Series GSE60811. Homo sapiens. 10 samples. Type: Methylation profiling by genome tiling array.
Differential gene expression analysis of trisomy 21 and euploid hematopoietic progenitor cells derived from human pluripotent stem cells.
GEO Series GSE238115. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
TCF7 is a key regulator of the switch of self-renewal and differentiation in a multipotential hematopoietic cell line
GEO Series GSE30068. Mus musculus. 3 samples. Type: Expression profiling by array.
Reprogramming-associated aberrant DNA methylation determines hematopoietic differentiation capacity of human induced pluripotent stem cells
GEO Series GSE60924. Homo sapiens. 181 samples. Type: Expression profiling by array; Methylation profiling by genome tiling array.
5-hydroxymethylcytosine poises expression of genes involved in hematopoietic differentiation
GEO Series GSE69905. Homo sapiens. 14 samples. Type: Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
ID2 and HIF-1α Collaborate to Protect Quiescent Hematopoietic Stem Cells from Activation, Differentiation and Exhaustion
GEO Series GSE198599. Mus musculus; Mus. 6 samples. Type: Expression profiling by high throughput sequencing.
Setd2 regulates quiescence and differentiation of adult hematopoietic stem cells by restricting RNA polymerase II elongation
GEO Series GSE112550. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
The transcriptional coactivator Cbp regulates self-renewal and differentiation in adult hematopoietic stem cells
GEO Series GSE25274. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array.
Engraftable multilineage hematopoietic cells differentiated from induced human pluripotent stem cells
GEO Series GSE232710. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Metabolic adaptation pilots the differentiation of human hematopoietic cells (scRNA-Seq and CITE-Seq)
GEO Series GSE243005. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
Genome-wide definition of regulatory elements in hematopoietic stem cell differentiation [RNA-seq (CAGE)]
GEO Series GSE70674. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
GATA2 promotes hematopoietic development and represses cardiac differentiation of human mesoderm
GEO Series GSE118980. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
RNA Sequencing Facilitates Quantitative Analysis of Transcriptomes of H1 derived cells and H1 after MSX2-knockout derived cells at Day8 after human early hematopoietic differentiation .
GEO Series GSE135171. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Universal fibroblasts across tissues can differentiate into niche cells for hematopoietic stem cells
GEO Series GSE237272. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Differentially expressed genes in hematopoietic stem cells after loss of Mettl3
GEO Series GSE123526. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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