cd45  (Elabscience Biotechnology)

Journal: Nucleic Acids Research

Article Title: chromIDEAS reveals epigenetic dynamics via multi-dimensional clustering of chromatin states

doi: 10.1093/nar/gkag176

Figure Lengend Snippet: chromIDEAS identifies five conserved CSCs across cell types. ( A ) Characterization of 37 CSs jointly identified from CD34+ cells and THP1 cells. The left panel shows the emission probability of each epigenetic signal within each CS, where darker shades indicate higher probabilities. The middle panel shows the enrichment of each CS in different genomic annotations derived from the GTF file, with red intensity representing the degree of enrichment. The right panel depicts the average genomic coverage of each CS in the two cell types. CSs (rows) are ordered by hierarchical clustering based on the similarity of their epigenetic signal composition. The CS segmentations are accessible at the UCSC Genome Browser: https://www.genome.ucsc.edu/cgi-bin/hgTracks?db=hg38&hubUrl=https://raw.githubusercontent.com/fatyang799/chromIDEAS-Paper-code-and-materias/main/hub.txt . ( B ) Clustering of CSs under increasing resolution using chromIDEAS with 2000 HITs per cell type. Left, CD34+ specific clustering; middle, joint clustering across both cell types; right, THP1-specific clustering. Each node represents a CSC, where node size scales with the number of CSs within cluster and numeric labels denote cluster IDs. Arrows trace cluster transitions with increasing resolution, and their transparency indicates the fraction of CSs transferred. The solid line highlights the final resolution used for downstream analysis. ( C ) Robustness assessment of CSs clustering with varying numbers of HITs. Heatmap showing ARI scores comparing clustering results using different numbers (100–4000) of HITs with the reference clustering based on 2000 HITs, across all two modes. The color represents the similarity, with more intense red indicating higher similarity. ( D ) UMAP visualization of CSs clustering under different conditions. The left, middle, and right panels show CSs clustering results when performed separately for CD34+ cells, jointly for both CD34+ and THP1 cells, and separately for THP1 cells, respectively. Each point represents a CS, and colors indicate its assigned CSC identified by chromIDEAS. ( E ) Sankey diagram showing the correspondence of CS assignments across CD34+ specific, joint, and THP1-specific clustering modes, revealing strong cross-mode consistency. The orange connections represent CSs that remain in the same cluster across conditions, while the gray connections indicate CSs that transition into different clusters. ( F ) Distinctive epigenetic signatures for each CSC. The heatmap displays the log2-fold enrichment of each signal in one CSC compared to all others, with color intensity reflecting the effect size. Red labels on the x -axis indicate significantly enriched (cluster-specific) markers. Statistically significant differences (one-sided Wilcoxon test, P < 0.05) are color-coded, while white indicates non-significant differences.

Article Snippet: The CD34+ cells were enriched using a CD34 MicroBead Kit (Miltenyi Biotec, 130-046-702).

Techniques: Derivative Assay

Journal: Nucleic Acids Research

Article Title: chromIDEAS reveals epigenetic dynamics via multi-dimensional clustering of chromatin states

doi: 10.1093/nar/gkag176

Figure Lengend Snippet: chromIDEAS reveals derepression of Wnt signaling related genes in THP1. ( A ) A schematic example illustrating the principle of DCSCGs identification. When using individual CSs as the comparison unit, both Gene1 and Gene2 are identified as DCSGs. However, under the CSC-based comparison, only Gene2 is identified as a DCSCG due to a functionally meaningful CSC2-to-CSC3 transition, while Gene1 is considered a nDCSCG. ( B ) Comparison of the number of DCSCGs and nDCSCGs identified using CSC-based methods. Within the nDCSCGs group, genes are further categorized into DCSGs and nDCSGs based on CS-based level (red, DCSGs; green, nDCSGs). ( C ) Distribution of the proportion of differential CS regions within DCSCGs and nDCSCGs. Each point represents a single gene. Statistical significance is evaluated using the Wilcoxon test (****: P ≤ 0.0001). ( D ) Heatmap visualization of CSC transitions between THP1 and CD34+ cells, measured by the number of differential CSC bins. Color intensity indicates the frequency of transitions. The upper number in each cell denotes the absolute number of differential bins; the lower number indicates their percentage among all differential bins. ( E ) Positional distribution of differential CSC bins within DCSCGs. The x -axis represents the relative positions from gene start to end, and the y -axis indicates the normalized frequency of observing a differential CSC bin at that position. ( F ) Heatmap summarizing the classification of DCSCGs based on their dominant CSC transition patterns. Each cell displays the number (top) and proportion (bottom) of DCSCGs associated with a given CSC transition. Color intensity reflects gene abundance. ( G ) GO enrichment analysis of DCSCGs classified by CSC transition patterns. The bar plot shows the merged top 20 most significant GO ancestors of GO terms across all individual CSC transition types for DCSCGs (see Materials and methods). ( H ) GO enrichment analysis of DCSCGs undergoing CSC3-to-CSC1 or CSC3-to-CSC4 in THP1 cells. The top 10 GO terms are shown, ranked by gene ratio. ( I ) Venn diagram illustrating the overlap between DEGs and DCSCGs.

Article Snippet: The CD34+ cells were enriched using a CD34 MicroBead Kit (Miltenyi Biotec, 130-046-702).

Techniques: Comparison

Journal: Nucleic Acids Research

Article Title: chromIDEAS reveals epigenetic dynamics via multi-dimensional clustering of chromatin states

doi: 10.1093/nar/gkag176

Figure Lengend Snippet: KDM4A-mediated epigenetic derepression drives WNT10B activation in THP1 cells. ( A ) Gene expression levels (CPM) of H3K27me3 and H3K9me3 related writers and erasers enzymes in THP1 cells. Red bars indicate expression levels in CD34+ cells, while blue bars represent expression in THP1 cells. ( B ) Western blot of KDM4A protein in THP1 cells empty vector controls (EV), and three independent KDM4A-knockdown variants (KD1-3). ( C ) Cell growth kinetics were compared between wild-type THP1 cells (WT), empty vector controls (EV, reference group), and three independent KDM4A-knockdown variants (KD rep1-3). Statistical significance was determined by t -test comparing each group to the EV control, using final-day cell counts (ns, not significant; * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001). ( D ) Volcano plot displaying DEGs after KDM4A knockdown (KD) in THP1 cells. Each point represents a gene. Significantly upregulated and downregulated genes are marked in red and blue, respectively. Key genes involved in Wnt signaling are labeled. ( E ) Classification of down-regulated genes upon KDM4A KD. Genes are grouped into three categories: directly regulated (KDM4A-bound and downregulated, n = 1079), indirectly regulated (downregulated without KDM4A-bound, n = 211), and background genes (no expression changes and no KDM4A binding, n = 36 871). The histone modification signal distribution (H3K27me3 and H3K9me3) at the TSSs ± 2kb region for each group is shown. Red and blue curves represent wild-type (WT) and KDM4A KD THP1 cells, respectively. The shadow region represents the signal difference between KD and WT. ( F ) Violin plots showing the difference in AUC values for H3K27me3 and H3K9me3 ChIP-seq signal between KDM4A KD and WT THP1 cells. Each dot represents a gene, and statistical significance is assessed using a Wilcoxon test (****: P ≤ 0.0001). ( G ) GO enrichment analysis of the directly regulated genes from panel (E). ( H ) Venn diagram illustrating the overlap among direct KDM4A targets, leukemia-upregulated genes (relative to CD34+), and Wnt family genes. ( I ) Genome browser displays the genomic distribution of sequencing signals across the WNT10B locus. ( J ) Cell growth kinetics were compared between THP1 EV control cells (reference group), WNT10B-overexpressing cells generated from EV background (EV + WNT10B), three independent KDM4A-knockdown variants (KD rep1-3), and WNT10B-overexpressing cells derived from KDM4A-knockdown background (KD + WNT10B). Statistical significance was determined by t -test comparing each group to the EV control, using final-day cell counts (ns, not significant; * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001).

Article Snippet: The CD34+ cells were enriched using a CD34 MicroBead Kit (Miltenyi Biotec, 130-046-702).

Techniques: Activation Assay, Gene Expression, Expressing, Western Blot, Plasmid Preparation, Knockdown, Control, Labeling, Binding Assay, Modification, ChIP-sequencing, Sequencing, Generated, Derivative Assay

Journal: Nucleic Acids Research

Article Title: Alternative polyadenylation links RNA processing to iron metabolism in human erythropoiesis

doi: 10.1093/nar/gkag218

Figure Lengend Snippet: A global 3′UTR shortening occurs during erythropoiesis and contributes to erythroid cell identity establishment. (A) Bar plot and pie chart depicting the number and proportion of PAS per gene during erythropoiesis. The colors in the pie chart correspond to the number of PAS, as indicated on the x -axis of the bar plot. (B) Cumulative distribution of the pPUI in five cell clusters. The rightward shift reflects increased proximal PAS usage. The small windows on the right show the significant changes in basophilopoiesis and erythropoiesis, respectively ( P -value calculated by Kruskal-Wallis test). (C) Mean pPUI projected onto the single-cell UMAP relative to Fig. . The left panel represents the mean pPUI across all 2 259 3′UTRs with multiple PASs, while the right panel focuses on 665 significantly APA-altered 3′UTRs. (D) The last exon length detected by nanopore sequencing in CD34 + HSPCs and erythrocytes (CD71 + CD235a + ) from human bone marrow. Data are the mean ± SEM. Mann-Whitney test, *** P < 0.0001. (E) Cell cycle proportion statistics for each cell population. Differences in cell cycle distribution between differentiated cells and HSCs were assessed using the chi-square test. **** P < 0.0001. (F) Proliferation index in each cell cluster (see Materials and methods). (G) pPUI UMAP of four erythropoiesis-related cell clusters (Fig. ) (top), hereafter referred to as APA cluster, and the RNA clusters (Fig. ) projected in the bottom panel. (H) Proportion of RNA clusters within each APA cell cluster. (I) Venn diagram of the APA-genes used in APA cluster (G, top) and the differentially expressed genes (DEGs) across four RNA cell clusters. Due to batch effects, Seurat’s integrated model analysis was performed using common feature values as anchors, including only 129 genes with multiple PASs.

Article Snippet: Mononuclear cells (MNCs) were isolated, and CD34 + HSPCs were enriched using CD34 MicroBeads (Miltenyi Biotec, Cat # 130–046-702) f according to the manufacturer’s instructions.

Techniques: Single Cell, Nanopore Sequencing, MANN-WHITNEY

Journal: Nucleic Acids Research

Article Title: Alternative polyadenylation links RNA processing to iron metabolism in human erythropoiesis

doi: 10.1093/nar/gkag218

Figure Lengend Snippet: Functional relevance of APA in erythropoiesis. (A) pPUI heatmap for 665 genes with significant 3′UTR-APA changes during erythropoiesis and basophilopoiesis. APA-genes were clustered into seven patterns based on APA dynamics. Mitochondria-associated (blue), iron-related (orange), and erythroid-specific (magenta) genes are indicated. (B) Trend lines for pPUI of each APA-gene in each pattern are shown in (A) in gray. The median pPUI points and trend lines for all APA-genes in each pattern are colored. (C) GO enrichment analysis of APA-genes of Patterns 1, 2, 3, and 5. Each bubble represents an enriched GO term, with the P -value indicated by the color. The size of the bubble is proportional to the gene ratio. Other patterns of APA genes are too few for functional enrichment. (D) The IGV shows representative gene examples related to iron ions and mitochondria. P, proximal PAS; D, distal PAS. (E) RT-qPCR validation of 3′UTR length changes in human umbilical cord blood (UCB)-derived CD34 + HSPCs differentiated into erythroid cells on Day 4 and Day 11. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Mononuclear cells (MNCs) were isolated, and CD34 + HSPCs were enriched using CD34 MicroBeads (Miltenyi Biotec, Cat # 130–046-702) f according to the manufacturer’s instructions.

Techniques: Functional Assay, Quantitative RT-PCR, Biomarker Discovery, Derivative Assay

Journal: Nucleic Acids Research

Article Title: Alternative polyadenylation links RNA processing to iron metabolism in human erythropoiesis

doi: 10.1093/nar/gkag218

Figure Lengend Snippet: CPSF6 is essential for human erythropoiesis. (A) Schematic diagram of experimental design. Human UCB-derived CD34 + HSPCs were differentiated into erythroid cells in vitro , and infected with short hairpin RNA (shRNA) lentivirus on day 4 to knock down CPSF6. Cells on day 11 were subjected to a cell assay. (B) Western blot showing CPSF6 and β-actin protein in untreated, control-shRNA, or CPSF6-shRNA transduced erythroblasts cultured on day 11. (C and D) Colony-forming ability of cells cultured on days 7 and 14, which contain mixed populations of cells that include BFU-E and CFU-E. (C) shows the morphology of BFU-E and CFU-E colonies. (D) shows quantification of BFU-E and CFU-E colonies. ( E and F ) Representative flow cytometry plots of erythroblasts on day 11 and the percentage of CD235a + /CD71 + erythroblasts with CPSF6 knockdown. ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Mononuclear cells (MNCs) were isolated, and CD34 + HSPCs were enriched using CD34 MicroBeads (Miltenyi Biotec, Cat # 130–046-702) f according to the manufacturer’s instructions.

Techniques: Derivative Assay, In Vitro, Infection, shRNA, Knockdown, Western Blot, Control, Cell Culture, Flow Cytometry

Journal: Nucleic Acids Research

Article Title: Alternative polyadenylation links RNA processing to iron metabolism in human erythropoiesis

doi: 10.1093/nar/gkag218

Figure Lengend Snippet: CPSF6 knockdown alters 3′UTR-APA. (A) Schematic diagram of experimental design. Human UCB-derived CD34 + HSPCs were induced to erythroid cells in vitro , and infected with shRNA lentivirus to knock down CPSF6. Cells on day 11 (D11) and 13 (D13) were subjected to bulk RNA-seq and 3′-seq. Schematic read distributions from 3′-seq and RNA-seq illustrating 3′UTR APA changes are shown on the right. (B) Cumulative distribution of the pPUI in differentiated cells with control and CPSF6 knockdown. Every shCPSF6 sample was compared to its control by the Kruskal–Wallis test, and the P values were all less than 2.2e-16. The pie chart shows the number and proportion of differentially shortening and lengthening APA-genes. (C) Volcano plot of differential APA-genes of CPSF6 knockdown samples compared to control samples identified by bulk RNA-seq at D11. (D) Venn diagram showing genes with differential APA identified in normal erythropoiesis (scRNA-seq) and in CPSF6 knockdown cells (bulk RNA-seq and 3′-seq at days 11 and 13). See . ( E and F ) Genome browser tracks of 3′-seq and RT-qPCR validation of representative iron metabolism-related differential APA genes after CPSF6 knockdown. (G) GSEA plot of APA-gene pPUI in 3′-seq datasets on day 11.

Article Snippet: Mononuclear cells (MNCs) were isolated, and CD34 + HSPCs were enriched using CD34 MicroBeads (Miltenyi Biotec, Cat # 130–046-702) f according to the manufacturer’s instructions.

Techniques: Knockdown, Derivative Assay, In Vitro, Infection, shRNA, RNA Sequencing, Control, Quantitative RT-PCR, Biomarker Discovery

Journal: Nucleic Acids Research

Article Title: Alternative polyadenylation links RNA processing to iron metabolism in human erythropoiesis

doi: 10.1093/nar/gkag218

Figure Lengend Snippet: CPSF6 mediates FAM210B APA to support iron homeostasis during erythropoiesis. (A-B) Detection of intracellular Fe 2+ and Fe 3+ content on day 11. (C) Western blot analysis of FAM210B protein expression in CPSF6-knockdown cells compared with shCtrl on day 11. (D) Schematic representation of FAM210B long transcript (LT) and short transcript (ST). The amino acid (aa) positions are annotated in the CDS region. (E) Representative fluorescence images of the GFP protein expression with 3′UTR of FAM210B -LT and 3′UTR of FAM210B -ST in the HEK293FT cell line. (F-H) Western blot quality control of CPSF6 RNA immunoprecipitation (RIP) in K562 cells (F) , (G-H) RT-qPCR analysis of RIP enrichment for FAM210B LT and ST. (I) The representative image of cell pellets from cord blood–derived CD34 + HSCPCs induced toward erythroid differentiation for 8 days in vitro . Experimental groups included shCtrl, shCPSF6, and shCPSF6 + ST (FAM210B-ST overexpression upon CPSF6 knockdown). (J) Validation of manipulation efficiency by RT-qPCR. mRNA levels of CPSF6 (left) and reconstituted FAM210B isoforms (right) were measured (mean ± SD, n = 3). ** P < 0.01, *** P < 0.001, **** P < 0.0001. ( K and L ) Representative flow cytometry plots of erythroblasts (L) and quantification of CD235a + CD71 + erythroblasts (mean ± SD, n = 3) (M) . * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Mononuclear cells (MNCs) were isolated, and CD34 + HSPCs were enriched using CD34 MicroBeads (Miltenyi Biotec, Cat # 130–046-702) f according to the manufacturer’s instructions.

Techniques: Western Blot, Expressing, Knockdown, Fluorescence, Control, RNA Immunoprecipitation, Quantitative RT-PCR, Derivative Assay, In Vitro, Over Expression, Biomarker Discovery, Flow Cytometry

Journal: bioRxiv

Article Title: Oncodevelopmental plasticity of the skeleton in myeloid neoplasms

doi: 10.64898/2026.03.19.712939

Figure Lengend Snippet: a, Intercellular communication networks in paired maxillary jawbones comparing JAK2V617F versus JAK2WT conditions. b, Cell-cell communication depicting ligand (L)-receptor(R) interaction between maxillary stromal cells (sender) and myeloid cells (receiver). Interactions are represented based on the difference in delta probability of mean LR expression between JAK2V617F and JAK2WT. c, Thrombospondin 1 (Thbs1) expression across transcriptionally defined stromal subclusters in the maxilla. d, Schematic overview of the murine thrombopoietin (ThPO)–driven myelofibrosis model. c-Kit–enriched hematopoietic stem cells isolated from WT donors were lentivirally transduced with ThPO or empty vector (EV) and transplanted into bigenic CreER;tdTomato reporter recipients (Gli1;tdTomato, Pdgfrb;tdTomato and Grem1;tdTomato). Following tamoxifen induction and lethal irradiation, recipients received donor cells intravenously. TdTomato⁺ stromal cells were isolated from mesoderm-derived bones for single-cell RNA sequencing, and tibias from the same cohorts were used for Visium spatial transcriptomics. e, Thbs1 expression in 17 annotated stromal subclusters under fibrotic (ThPO) and control (EV) conditions in mesoderm-derived bones. f-g, UMAP representation of integrated Visium spatial transcriptomics data from ThPO and EV tibias (n = 3 mice per group), identifying five major spatial domains (f) and corresponding Thbs1 expression patterns (g) . h, Representative overlay image of H&E and spatial domains detected in tibias from Visium spatial transcriptomics, n=3 per group. i, Thbs1 expression from Visium spatial transcriptomics dataset in both fibrosis (ThPO) and control (EV) conditions. j-k, Representative images of Thbs1-stained tibias in fibrosis (ThPO) and control (EV) groups (j) and the quantification of Thbs1+ cells per tissue area (k) . Scale bars, 50µm. l-m, Representative images of Thbs1-stained maxillary bones in both JAK2 and JAK2V617F conditions (l) and the quantification of Thbs1+ cells per tissue area (m) . Scale bars, 50µm. n, Thbs1 expression across nine annotated periodontal stromal subclusters from human tooth biopsies of MPN-periodontitis (n = 3), periodontitis without MPN (n = 3), and healthy controls (n = 1). Stromal subclusters are ordered according to cell-state prioritization by Augur (from left to right). o, Cell-cell communication depicting ligand (L)-receptor(R) interactions between patients’ tooth fibroblast subpopulation (sender) and myeloid cells (receiver). Interactions are represented based on the difference in delta probability of mean LR expression between MPN-Periodontitis (N=3) and healthy control (N=1) patients. p-q, Representative images of Thbs1-stained BM biopsies from MPN (N=7) and healthy (N=3) patients (p) and the quantification of Thbs1+ cells per tissue area (q) . Scale bars, 50µm. r-s, ELISA of Thbs1 (r) and its interaction partner CD47 (s) in plasma of MPN (N=293) and healthy (N=74) individuals. t, Schematic overview of 3D in vitro experiment. JAK2V617F;RFP+ iPSCs were differentiated into CD34+ cells and engrafted into mature healthy iPSC-derived BM organoids. Organoids were harvested 7, 14 and 21 days post engraftment of exogenous cells. u, Flow cytometry analysis depicting the frequency of donor-derived (JAK2V617F;RFP+) red blood cells (RBCs), megakaryocytes and monocytes. Cells were isolated from BM organoids after 7, 14 and 21 days post engraftment of exogenous cells. Two-way-ANOVA with post hoc Tukey’s was used. v, qRT-PCR analysis from BM organoid-derived cells after 7, 14 and 21 days post engraftment of exogenous cells. One-way-ANOVA with post hoc Tukey’s was used.

Article Snippet: On day 14 of SpinEB protocol (see above), EB-derived cells from JAK2V617F;RFP and JAK2WT;GFP iPSC lines were harvested and CD34+ cells were enriched by magnetic-activated cell sorting using CD34 UltraPure MicroBeads (Miltenyi Biotec, #130-100-453) for subsequent use as donor cells for bone marrow organoids.

Techniques: Expressing, Isolation, Transduction, Plasmid Preparation, Irradiation, Derivative Assay, Single Cell, RNA Sequencing, Spatial Transcriptomics, Control, Staining, Enzyme-linked Immunosorbent Assay, Clinical Proteomics, In Vitro, Flow Cytometry, Quantitative RT-PCR

Journal: bioRxiv

Article Title: Oncodevelopmental plasticity of the skeleton in myeloid neoplasms

doi: 10.64898/2026.03.19.712939

Figure Lengend Snippet: a, Experimental design of the in vitro BM organoid treatment assay. Mature human iPSC-derived bone marrow organoids were competitively engrafted with JAK2V617F;RFP⁺ and isogenic JAK2WT;GFP⁺ differentiated hematopoietic stem and progenitor cells (iHSPCs) at a 1:1 ratio and treated statically with the THBS1 antagonist LSKL or vehicle control for 7 days. b, Flow cytometric analysis of donor-derived cells from BM organoids treated with vehicle or LSKL, showing frequencies of HSPCs (CD45+CD34+) and erythroid cells (CD45-CD235a+) derived from mutant and WT competitors. Two-way-ANOVA with post hoc Tukey’s was used. c, qRT-PCR of BM-derived cells following LSKL or vehicle control treatment. Statistical analysis was performed using two-way ANOVA with Sidak’s correction. d, Schematic overview of treatment effect on patient-derived primary cells engrafted into BM organoids. Peripheral blood mononuclear cell-derived CD34+ fraction from a patient diagnosed with post-ET MF were labelled with CellVue dye and engrafted into mature iPSC-derived BM organoids. BM organoids with patient cells were treated statically with the THBS1 antagonist LSKL or vehicle control for 7 days. e, Clinical hematological parameters showing blood counts of a post-ET MF patient at the time of PBMC isolation. f, Flow cytometric analysis of CellVue-labeled patient-derived cells following vehicle or LSKL treatment. g, Schematic overview of the preclinical organ-on-a-chip platform enabling dynamic drug perfusion. BM organoids engrafted with JAK2V617F;RFP and JAK2WT;GFP CD34+ cells were cultured under continuous flow, with LSKLor vehicle delivered exclusively through the microfluidic circulation channel. h, Flow cytometric analysis of the preclinical organ-on-a-chip model with vehicle or LSKL treatment groups, showing frequencies of immature erythrocytes (CD45+CD235a+) and monocytes (CD45+CD14+) derived from mutant and WT competitors. Two-way-ANOVA with post hoc Tukey’s was used. i, Experimental design of in vivo therapeutic intervention. Lethally irradiated mice were competitively transplanted with JAK2V617F/JAK2WT and WT competitor bone marrow cells at a ratio 1:1. Blood counts were performed once in 4 weeks starting from 4 weeks post transplant. JAK2V617F mice started receiving the treatment at 16 weeks post-transplantation (time point 0) with vehicle (n=6), Fedratinib (n=4), LSKL (n=5), or combined Fedratinib + LSKL (n=4) until 20 and 24 weeks post transplant (4 and 8 weeks of treatment, respectively). j, Blood counts over time showing white blood cells (WBC), red blood cells (RBCs) and platelets. Each dot represents the value from one mouse. Two-way-ANOVA with post hoc Tukey’s was used. k, Representative images of H&E and reticulin staining on tibial sections from mice treated for 8 weeks. Scale bars, 50µm. l, Reticulin staining and grading of bone marrow fibrosis from mice treated for 8 weeks with vehicle (n=6), Fedratinib (n=4), LSKL (n=5), or combined Fedratinib + LSKL (n=4). Kruskal-Wallis H test was used. m-n, Reconstruction of 3D µCT images of femoral cortical (M) and trabecular (N) bones from mice treated for 8 weeks. o, Quantification of trabecular (Tb.) thickness from the proximal part of the femur. Mice treated for 8 weeks with vehicle (n=6), Fedratinib (n=4), LSKL (n=5), or combined Fedratinib + LSKL (n=4). One-way-ANOVA with Holm-Sidak’s correction was used. p, Reconstruction of 3D µCT images of maxillary bones from mice treated for 8 weeks.

Article Snippet: On day 14 of SpinEB protocol (see above), EB-derived cells from JAK2V617F;RFP and JAK2WT;GFP iPSC lines were harvested and CD34+ cells were enriched by magnetic-activated cell sorting using CD34 UltraPure MicroBeads (Miltenyi Biotec, #130-100-453) for subsequent use as donor cells for bone marrow organoids.

Techniques: In Vitro, Derivative Assay, Control, Mutagenesis, Quantitative RT-PCR, Isolation, Labeling, Cell Culture, In Vivo, Irradiation, Transplantation Assay, Staining