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bmsc human cell line hs 5  (ATCC)


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    ATCC bmsc human cell line hs 5
    Bmsc Human Cell Line Hs 5, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 328 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+bmsc+cell+line+hs/HS-5/pmc12683941-219-1-7
    Average 97 stars, based on 328 article reviews
    bmsc human cell line hs 5 - by Bioz Stars, 2026-09
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    Ex Vivo:

    Article Title: A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML
    Article Snippet: After one washing step, cells were incubated with Streptavidin Pacific Orange (Life Technologies; 1:200), together with a combination of the following antibodies: Sca1 Pacific Blue E13161.7 (BioLegend; 1:100), CD48 AF700 HM48-1 (BioLegend; 1:100), CD150 PE-Cy7 TC15-12F12.2 (BioLegend; 1:100), CD127 (IL7RA) APC A7R34 (BioLegend; 1:100), cKit PE-CF594 2B8 (BD Biosciences; 1:100), CD16/32 APC-Cy7 2.4G2 (BD Biosciences; 1:100), FLT3 APC A2F10 (1:40), and 7-ADD (1:100). .. Cell Line and Ex Vivo Coculture The STR-authenticated human BMSC cell line HS-5 was purchased from ATCC (CRL 3611) in 2020 and maintained in RPMI-1640 medium (Thermo Fisher Scientific) supplemented with 10% FCS and 1% penicillin and streptomycin. ..



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    bmsc human cell line hs 5  (ATCC)
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    ATCC bmsc human cell line hs 5
    Bmsc Human Cell Line Hs 5, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    human bmsc cell line hs  (ATCC)
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    Figure 2. Transcriptional identification of <t>BMSC</t> heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.
    Human Bmsc Cell Line Hs, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Figure 2. Transcriptional identification of <t>BMSC</t> heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.
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    human bmsc cell line hs 5  (ATCC)
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    Figure 2. Transcriptional identification of <t>BMSC</t> heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.
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    human bone marrow stromal cell bmsc line hs 5  (ATCC)
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    Figure 2. Transcriptional identification of <t>BMSC</t> heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.
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    Figure 2. Transcriptional identification of <t>BMSC</t> heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.
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    human bone marrow stromal cell line bmscs hs27a  (ATCC)
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    ATCC human bone marrow stromal cell line bmscs hs27a
    (a) Low HGF gene levels in <t>HS27A-derived</t> pre-OBs . mRNA expression of HGF was analyzed by quantitative PCR and expressed as fold increase of undifferentiated cells. (b) CM of HS27A-derived pre-OBs do not induce BC cell migration . Wound healing assay was performed to assess migration of the MDA-MB231 BC cell line in the presence of CM from HS27A-derived osteolineage cells. Results are expressed as percentage of negative control.
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    Figure 2. Transcriptional identification of BMSC heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.

    Journal: Blood Cancer Discovery

    Article Title: A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

    doi: 10.1158/2643-3230.bcd-23-0043

    Figure Lengend Snippet: Figure 2. Transcriptional identification of BMSC heterogeneity in the human NBM. A, Predicted cellular interactions based on transcriptional network- ing by CellChat, identifying BMSCs as the dominant source of signaling to all other cells. In the circle plot, colors represent signal senders and width represents signal strength. In the heat map, signal strength is represented by the color scale. B, Predicted ligand–receptor interactions between BMSCs and other cell types. Color scale and dot size represent the probability and P value of interactions, respectively. C, Heterogeneity of the BMSC popula- tion reflected in the uniform manifold approximation and projection (UMAP) plot representing 3,236 cells. D, Differential expression of LEPR and genes encoding key HSPC regulatory factors in BMSC subset 0 represented by UMAP and Violin plots. ***, FDR-adjusted P value (Padj) < 0.001. ****, Padj < 0.0001. Differential gene-expression analysis is performed using the pseudoDE R package at the sample level (pair-wise comparison in individual samples). E, Relative strength of predicted HSPC-supportive signaling originating from distinct BMSC subsets, as assessed by CellphoneDB. Color scale and dot size represent the relative mean strength and P value of interactions, respectively.

    Article Snippet: Cell Line and Ex Vivo Coculture The STR-authenticated human BMSC cell line HS-5 was purchased from ATCC (CRL 3611) in 2020 and maintained in RPMI-1640 medium (Thermo Fisher Scientific) supplemented with 10% FCS and 1% penicillin and streptomycin.

    Techniques: Quantitative Proteomics, Gene Expression, Comparison

    Figure 3. Remodeling of BMSC in NPM1m AML. A, Uniform manifold approximation and projection (UMAP) distribution of BM cells in NBM and AML. For AML, 49,758 cells from 6 patients are presented. B, Volcano plot of differentially (Padj < 0.05) expressed genes in BMSCs in AML versus NBM. Dif- ferential expression gene analysis is performed at the sample level using the pseudoDE R package. C and D, Differentially expressed transcriptional programs in BMSCs from AML in comparison with NBM, as demonstrated by Hallmark analysis (C) and GO term (D). Positive NES (normalized enrichment score) reflects programs enriched in AML, whereas negative scores indicate enrichment in NBM. E and F, Distribution and frequencies of BMSC subsets in AML and NBM. (continued on next page)

    Journal: Blood Cancer Discovery

    Article Title: A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

    doi: 10.1158/2643-3230.bcd-23-0043

    Figure Lengend Snippet: Figure 3. Remodeling of BMSC in NPM1m AML. A, Uniform manifold approximation and projection (UMAP) distribution of BM cells in NBM and AML. For AML, 49,758 cells from 6 patients are presented. B, Volcano plot of differentially (Padj < 0.05) expressed genes in BMSCs in AML versus NBM. Dif- ferential expression gene analysis is performed at the sample level using the pseudoDE R package. C and D, Differentially expressed transcriptional programs in BMSCs from AML in comparison with NBM, as demonstrated by Hallmark analysis (C) and GO term (D). Positive NES (normalized enrichment score) reflects programs enriched in AML, whereas negative scores indicate enrichment in NBM. E and F, Distribution and frequencies of BMSC subsets in AML and NBM. (continued on next page)

    Article Snippet: Cell Line and Ex Vivo Coculture The STR-authenticated human BMSC cell line HS-5 was purchased from ATCC (CRL 3611) in 2020 and maintained in RPMI-1640 medium (Thermo Fisher Scientific) supplemented with 10% FCS and 1% penicillin and streptomycin.

    Techniques: Expressing, Comparison

    Figure 3. (Continued) G, Expression of genes encoding HSPC regulatory factors in BMSC subsets in NBM and AML. H and I, Activation of transcriptional programs related to inflammation and connective tissue development in BMSC cluster 2, as demonstrated by gene signature calculation (H) and expression of inflam- mation- and ECM remodeling-associated genes (I). J and K, Inflammation of BMSCs in AML as demonstrated by expression of CD44 using fluorescence IHC on bone marrow biopsies. Scale bar, 50 μm (J) and flow cytometry on BM aspirate (K). n = 2 for NBM and n = 5 for AML. Investigations were performed in patients not included in the scRNA-seq analyses. *, P < 0.05 by an unpaired t test. Error bar represents mean ± SE. CD44 CD271 0 104 105 0

    Journal: Blood Cancer Discovery

    Article Title: A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

    doi: 10.1158/2643-3230.bcd-23-0043

    Figure Lengend Snippet: Figure 3. (Continued) G, Expression of genes encoding HSPC regulatory factors in BMSC subsets in NBM and AML. H and I, Activation of transcriptional programs related to inflammation and connective tissue development in BMSC cluster 2, as demonstrated by gene signature calculation (H) and expression of inflam- mation- and ECM remodeling-associated genes (I). J and K, Inflammation of BMSCs in AML as demonstrated by expression of CD44 using fluorescence IHC on bone marrow biopsies. Scale bar, 50 μm (J) and flow cytometry on BM aspirate (K). n = 2 for NBM and n = 5 for AML. Investigations were performed in patients not included in the scRNA-seq analyses. *, P < 0.05 by an unpaired t test. Error bar represents mean ± SE. CD44 CD271 0 104 105 0

    Article Snippet: Cell Line and Ex Vivo Coculture The STR-authenticated human BMSC cell line HS-5 was purchased from ATCC (CRL 3611) in 2020 and maintained in RPMI-1640 medium (Thermo Fisher Scientific) supplemented with 10% FCS and 1% penicillin and streptomycin.

    Techniques: Expressing, Activation Assay, Fluorescence, Flow Cytometry

    Figure 7. Gene signatures reflective of inflammatory niche remodeling are associated with favorable clinical outcomes in AML. A, Predicted BMSC subset size in the larger cohort of patients with AML (n = 62). ****, P < 0.0001 by the Wilcoxon test. B, Kaplan–Meier curves for overall survival (OS) and relapse probability indicating improved outcome in patients with the predicted loss of BMSC cluster 0. Cutoff is median frequency. Statistical signifi- cance is determined by a log-rank test. C, Construction of a gene signature reflective of niche remodeling (inflammatory activation and relative loss of BMSC cluster 0) in AML. 189 genes at the intersection of the genes differentially expressed in the BMSC-0 vs. other clusters in NBM, the genes differ- entially expressed in the BMSC-2 vs. other clusters in AML, and the genes differentially expressed in BMSCs in human AML vs. NBM were selected and tested for their correlation with outcome by multivariate Cox regression survival analysis. Thirteen of 189 genes were identified that were associated with outcome. D, Coefficient plot (left) and heat map (right) of the 13-gene signature. The gene-expression profile (GEP) score in each patient was calcu- lated using the coefficient and z-scale of the genes. Based on the median GEP score, patients were stratified into two groups, with a high score reflecting relative preservation of niche integrity and a low score reflecting niche inflammatory disruption. E, Kaplan–Meier curves for overall survival (OS) and relapse probability indicating improved outcome in patients with niche inflammatory disruption. Cutoff is median score. Log-rank test is used for statisti- cal analysis. F, Kaplan–Meier curves OS and relapse probability indicating improved outcome in patients with lower KITLG expression in BMSCs. Cutoff is median TPM. Log-rank test is used for statistical analysis. G, Kaplan–Meier curves for OS indicating improved outcome in age-matched patients with AML with lower KITLG expression in whole BM in Bohlander AML (GSE37642) cohort (n = 284) and TCGA-AML cohort (n = 98). Cutoff is 75th percentile normalized counts. Log-rank test is used for statistical analysis.

    Journal: Blood Cancer Discovery

    Article Title: A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

    doi: 10.1158/2643-3230.bcd-23-0043

    Figure Lengend Snippet: Figure 7. Gene signatures reflective of inflammatory niche remodeling are associated with favorable clinical outcomes in AML. A, Predicted BMSC subset size in the larger cohort of patients with AML (n = 62). ****, P < 0.0001 by the Wilcoxon test. B, Kaplan–Meier curves for overall survival (OS) and relapse probability indicating improved outcome in patients with the predicted loss of BMSC cluster 0. Cutoff is median frequency. Statistical signifi- cance is determined by a log-rank test. C, Construction of a gene signature reflective of niche remodeling (inflammatory activation and relative loss of BMSC cluster 0) in AML. 189 genes at the intersection of the genes differentially expressed in the BMSC-0 vs. other clusters in NBM, the genes differ- entially expressed in the BMSC-2 vs. other clusters in AML, and the genes differentially expressed in BMSCs in human AML vs. NBM were selected and tested for their correlation with outcome by multivariate Cox regression survival analysis. Thirteen of 189 genes were identified that were associated with outcome. D, Coefficient plot (left) and heat map (right) of the 13-gene signature. The gene-expression profile (GEP) score in each patient was calcu- lated using the coefficient and z-scale of the genes. Based on the median GEP score, patients were stratified into two groups, with a high score reflecting relative preservation of niche integrity and a low score reflecting niche inflammatory disruption. E, Kaplan–Meier curves for overall survival (OS) and relapse probability indicating improved outcome in patients with niche inflammatory disruption. Cutoff is median score. Log-rank test is used for statisti- cal analysis. F, Kaplan–Meier curves OS and relapse probability indicating improved outcome in patients with lower KITLG expression in BMSCs. Cutoff is median TPM. Log-rank test is used for statistical analysis. G, Kaplan–Meier curves for OS indicating improved outcome in age-matched patients with AML with lower KITLG expression in whole BM in Bohlander AML (GSE37642) cohort (n = 284) and TCGA-AML cohort (n = 98). Cutoff is 75th percentile normalized counts. Log-rank test is used for statistical analysis.

    Article Snippet: Cell Line and Ex Vivo Coculture The STR-authenticated human BMSC cell line HS-5 was purchased from ATCC (CRL 3611) in 2020 and maintained in RPMI-1640 medium (Thermo Fisher Scientific) supplemented with 10% FCS and 1% penicillin and streptomycin.

    Techniques: Activation Assay, Gene Expression, Preserving, Disruption, Expressing

    (a) Low HGF gene levels in HS27A-derived pre-OBs . mRNA expression of HGF was analyzed by quantitative PCR and expressed as fold increase of undifferentiated cells. (b) CM of HS27A-derived pre-OBs do not induce BC cell migration . Wound healing assay was performed to assess migration of the MDA-MB231 BC cell line in the presence of CM from HS27A-derived osteolineage cells. Results are expressed as percentage of negative control.

    Journal: PLoS ONE

    Article Title: Pre-Osteoblasts Stimulate Migration of Breast Cancer Cells via the HGF/MET Pathway

    doi: 10.1371/journal.pone.0150507

    Figure Lengend Snippet: (a) Low HGF gene levels in HS27A-derived pre-OBs . mRNA expression of HGF was analyzed by quantitative PCR and expressed as fold increase of undifferentiated cells. (b) CM of HS27A-derived pre-OBs do not induce BC cell migration . Wound healing assay was performed to assess migration of the MDA-MB231 BC cell line in the presence of CM from HS27A-derived osteolineage cells. Results are expressed as percentage of negative control.

    Article Snippet: The human bone marrow stromal cell line (BMSCs) HS27A was purchased from the American Type Culture Collection (ATCC [ ]), KM105 cells were a kind gift of Dr. Kenichi (Chiba University Graduate School of Medicine, Chiba, Japan) [ ].

    Techniques: Derivative Assay, Expressing, Real-time Polymerase Chain Reaction, Migration, Wound Healing Assay, Negative Control