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mouse  (R&D Systems)


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    Structured Review

    R&D Systems mouse
    Mouse, supplied by R&D Systems, used in various techniques. Bioz Stars score: 93/100, based on 45 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/anti+arg1+pe/pm40580876-119-71-76?v=R%26D+Systems
    Average 93 stars, based on 45 article reviews
    mouse - by Bioz Stars, 2026-07
    93/100 stars

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    R&D Systems mouse
    Mouse, supplied by R&D Systems, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    R&D Systems arg1 pe
    Disruption of IL-6 signaling in the tumor results in a greater abundance of tumor-infiltrating myeloid cells, higher levels of tumor-associated macrophages, an increase in their suppressive phenotype, and a reduction in the proinflammatory TAM subset. ( A , left) Percentages of myeloid cells (gated as live, CD45 + CD3 − CD11b + cells) among CD45 + cells in the tumors of Pan02 tumor-bearing mice that were treated with PBS, the TGFβ vaccine, IL-6R blockade, or the combination of the TGFβ vaccine and IL-6R blockade, as described in Fig. , were assayed via flow cytometry. ( A , right) Representative contour plots for the data shown in ( A , left). ( B , left) Percentage of tumor-associated macrophages (TAMs) among CD45 + cells across treatment groups. TAMs were gated as live CD45 + CD3 − CD11b + F4/80 + cells. ( B , right) Representative contour plots for the data shown in ( B , left). ( C , left) Percentages of MHC-II + TAMs among total TAMs across treatment groups. ( C , right) Representative histograms for MHC-II expression for the data shown in ( C , left). For ( A – C ), the data are presented as the means ± SEMs. Dots represent individual mice. Data were collected at the endpoint (day 25 post-inoculation). ( D ) UMAP displaying the meta clusters identified via the FlowSOM unsupervised clustering algorithm in the Cytobank platform on the live CD45 + CD3 − population of 18 samples ( n = 4–6 mice per group) identified via flow cytometry and assessed at the endpoint (day 25 postinoculation). ( E ) Heatmap showing the normalized expression by column of CD11b, F4/80, mannose receptor (MR), arginase-1 <t>(ARG1),</t> MHC-II, programmed death-ligand 1 (PD-L1) and CD8a using the Z score across the six different metaclusters identified in the FlowSOM analysis shown in ( D ). ( F ) Frequencies of the six different metaclusters identified in the FlowSOM analysis across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( G ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, showing how the metaclusters identified via FlowSOM changed between the treatment groups. ( H ) ARG1 mean fluorescence intensity (MFI) in the six metaclusters identified via FlowSOM across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( I ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, displaying the ARG1 MFI for metacluster 2. ( J ) Correlation between the percentage of T cells of total CD45 + cells and the percentage of TAMs of total CD45 + cells in Pan02 tumors. ( K – M ) Correlations between the percentage of T cells of total CD45 + cells and the frequencies of ( K ) metacluster 1, ( L ) metacluster 4, and ( M ) metacluster 6 as a percentage of the total. For ( J – M ), the dots represent individual mice. n = 4–6 mice per group. The treatment groups are color-coded. Correlations were performed with data collected at the endpoint (day 25 postinoculation). ns, not significant; * p < 0.05 and ** p < 0.01 according to an unpaired two-tailed t- test for ( A – C , F, H ) and linear regression for ( J – M )
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    R&D Systems pe conjugated anti arg1

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    Image Search Results


    Disruption of IL-6 signaling in the tumor results in a greater abundance of tumor-infiltrating myeloid cells, higher levels of tumor-associated macrophages, an increase in their suppressive phenotype, and a reduction in the proinflammatory TAM subset. ( A , left) Percentages of myeloid cells (gated as live, CD45 + CD3 − CD11b + cells) among CD45 + cells in the tumors of Pan02 tumor-bearing mice that were treated with PBS, the TGFβ vaccine, IL-6R blockade, or the combination of the TGFβ vaccine and IL-6R blockade, as described in Fig. , were assayed via flow cytometry. ( A , right) Representative contour plots for the data shown in ( A , left). ( B , left) Percentage of tumor-associated macrophages (TAMs) among CD45 + cells across treatment groups. TAMs were gated as live CD45 + CD3 − CD11b + F4/80 + cells. ( B , right) Representative contour plots for the data shown in ( B , left). ( C , left) Percentages of MHC-II + TAMs among total TAMs across treatment groups. ( C , right) Representative histograms for MHC-II expression for the data shown in ( C , left). For ( A – C ), the data are presented as the means ± SEMs. Dots represent individual mice. Data were collected at the endpoint (day 25 post-inoculation). ( D ) UMAP displaying the meta clusters identified via the FlowSOM unsupervised clustering algorithm in the Cytobank platform on the live CD45 + CD3 − population of 18 samples ( n = 4–6 mice per group) identified via flow cytometry and assessed at the endpoint (day 25 postinoculation). ( E ) Heatmap showing the normalized expression by column of CD11b, F4/80, mannose receptor (MR), arginase-1 (ARG1), MHC-II, programmed death-ligand 1 (PD-L1) and CD8a using the Z score across the six different metaclusters identified in the FlowSOM analysis shown in ( D ). ( F ) Frequencies of the six different metaclusters identified in the FlowSOM analysis across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( G ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, showing how the metaclusters identified via FlowSOM changed between the treatment groups. ( H ) ARG1 mean fluorescence intensity (MFI) in the six metaclusters identified via FlowSOM across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( I ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, displaying the ARG1 MFI for metacluster 2. ( J ) Correlation between the percentage of T cells of total CD45 + cells and the percentage of TAMs of total CD45 + cells in Pan02 tumors. ( K – M ) Correlations between the percentage of T cells of total CD45 + cells and the frequencies of ( K ) metacluster 1, ( L ) metacluster 4, and ( M ) metacluster 6 as a percentage of the total. For ( J – M ), the dots represent individual mice. n = 4–6 mice per group. The treatment groups are color-coded. Correlations were performed with data collected at the endpoint (day 25 postinoculation). ns, not significant; * p < 0.05 and ** p < 0.01 according to an unpaired two-tailed t- test for ( A – C , F, H ) and linear regression for ( J – M )

    Journal: Cellular and Molecular Immunology

    Article Title: The antitumor activity of TGFβ-specific T cells is dependent on IL-6 signaling

    doi: 10.1038/s41423-024-01238-7

    Figure Lengend Snippet: Disruption of IL-6 signaling in the tumor results in a greater abundance of tumor-infiltrating myeloid cells, higher levels of tumor-associated macrophages, an increase in their suppressive phenotype, and a reduction in the proinflammatory TAM subset. ( A , left) Percentages of myeloid cells (gated as live, CD45 + CD3 − CD11b + cells) among CD45 + cells in the tumors of Pan02 tumor-bearing mice that were treated with PBS, the TGFβ vaccine, IL-6R blockade, or the combination of the TGFβ vaccine and IL-6R blockade, as described in Fig. , were assayed via flow cytometry. ( A , right) Representative contour plots for the data shown in ( A , left). ( B , left) Percentage of tumor-associated macrophages (TAMs) among CD45 + cells across treatment groups. TAMs were gated as live CD45 + CD3 − CD11b + F4/80 + cells. ( B , right) Representative contour plots for the data shown in ( B , left). ( C , left) Percentages of MHC-II + TAMs among total TAMs across treatment groups. ( C , right) Representative histograms for MHC-II expression for the data shown in ( C , left). For ( A – C ), the data are presented as the means ± SEMs. Dots represent individual mice. Data were collected at the endpoint (day 25 post-inoculation). ( D ) UMAP displaying the meta clusters identified via the FlowSOM unsupervised clustering algorithm in the Cytobank platform on the live CD45 + CD3 − population of 18 samples ( n = 4–6 mice per group) identified via flow cytometry and assessed at the endpoint (day 25 postinoculation). ( E ) Heatmap showing the normalized expression by column of CD11b, F4/80, mannose receptor (MR), arginase-1 (ARG1), MHC-II, programmed death-ligand 1 (PD-L1) and CD8a using the Z score across the six different metaclusters identified in the FlowSOM analysis shown in ( D ). ( F ) Frequencies of the six different metaclusters identified in the FlowSOM analysis across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( G ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, showing how the metaclusters identified via FlowSOM changed between the treatment groups. ( H ) ARG1 mean fluorescence intensity (MFI) in the six metaclusters identified via FlowSOM across treatment groups. The data are presented in a box-and-whisker plot. n = 4–6 mice per group. ( I ) Representative UMAPs of a sample derived from a mouse treated with the TGFβ vaccine and a sample derived from a mouse treated with both the TGFβ vaccine and an anti-IL-6R antibody, displaying the ARG1 MFI for metacluster 2. ( J ) Correlation between the percentage of T cells of total CD45 + cells and the percentage of TAMs of total CD45 + cells in Pan02 tumors. ( K – M ) Correlations between the percentage of T cells of total CD45 + cells and the frequencies of ( K ) metacluster 1, ( L ) metacluster 4, and ( M ) metacluster 6 as a percentage of the total. For ( J – M ), the dots represent individual mice. n = 4–6 mice per group. The treatment groups are color-coded. Correlations were performed with data collected at the endpoint (day 25 postinoculation). ns, not significant; * p < 0.05 and ** p < 0.01 according to an unpaired two-tailed t- test for ( A – C , F, H ) and linear regression for ( J – M )

    Article Snippet: The following antibodies were used to assess changes in the myeloid subset in Pan02 tumors across treatment groups and changes in the phenotype of BMDMs differentiated in the absence or presence of IL-6: CD45-BV605 (BioLegend, 103140), CD3-AF700 (BioLegend, 100216), CD11b-Pacific blue (BioLegend, 101223), F4/80-FITC (BioLegend, 123108), MR (CD206)-PE/Cy7 (BioLegend, 141719), MHC-II-APC/Cy7 (BioLegend, 107627), Arg1-PE (R&D Systems, IC5868P), PD-L1-APC (BD Biosciences, 564715) and CD8a-PerCP/Cy5.5 (Pharmingen, 551162).

    Techniques: Disruption, Flow Cytometry, Expressing, Whisker Assay, Derivative Assay, Fluorescence, Two Tailed Test

    Changes in intratumoral gene expression induced by IL-6R blockade are associated mainly with myeloid migration and immunity, as well as with T-cell inhibition. ( A ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with an anti-IL-6R blocking antibody compared with those in tumors from untreated mice ( n = 3–4 per group). n = 161 upregulated genes and n = 161 downregulated genes. ( B ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as monotherapy ( n = 3 per group). n = 134 upregulated genes and n = 246 downregulated genes. For ( A , B ) False discovery rate (FDR) < 0.05 and absolute log2-fold-change > 0.585. ( C ) Venn diagram showing the overlap in the lists of differentially upregulated genes described in ( A , B ). A total of 28 genes were identified as differentially upregulated genes in both comparisons. ( D ) Gene Ontology (GO) enrichment analysis for biological processes associated with the 28 significantly upregulated genes described in ( C ). The 66 GO terms related to cancer immunity, of a total of 93 identified GO terms, are shown. The GO terms are classified into 10 different categories. ( E ) Enrichment map of the 66 GO terms related to cancer immunity showing five functional modules: 1) myeloid and lymphoid cell chemotaxis and phagocytosis, 2) myeloid immunity, 3) immune activation and B-cell function, 4) the extracellular matrix, and 5) metabolism signaling. ( F ) Mean normalized enrichment scores for monocytes and macrophages across treatment groups inferred via cell type deconvolution analysis ( n = 3-4 mice per group). ( G ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as a monotherapy, where Ccl6 , Ccl8, Ccl9, and Pf4 are highlighted. ( H ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as a monotherapy, where Alox12e and Fcer1a are highlighted. I (left) Pie chart displaying the percentage of T-cell-related GO terms ( n = 3) of total cancer immunity-related GO terms ( n = 144) identified in the GO enrichment analysis for biological processes associated with the 161 upregulated genes identified in the differential gene expression analysis comparing tumors from mice that received IL-6R blockade to those from untreated mice. GO terms unrelated to cancer immunity ( n = 49) were excluded from the data visualization. ( I , right) T-cell-related GO terms identified in the GO enrichment analysis described in ( I , left). ( J , left) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with an anti-IL-6R blocking antibody compared with those in tumors from untreated mice, where Arg1 is highlighted. ( J , right) Expression levels were assessed by RNA-seq and are presented as VST-normalized counts of Arg1 in Pan02 tumors across treatment groups. The data are presented as the means ± SEMs. The dots represent individual mice ( n = 3–4 per group)

    Journal: Cellular and Molecular Immunology

    Article Title: The antitumor activity of TGFβ-specific T cells is dependent on IL-6 signaling

    doi: 10.1038/s41423-024-01238-7

    Figure Lengend Snippet: Changes in intratumoral gene expression induced by IL-6R blockade are associated mainly with myeloid migration and immunity, as well as with T-cell inhibition. ( A ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with an anti-IL-6R blocking antibody compared with those in tumors from untreated mice ( n = 3–4 per group). n = 161 upregulated genes and n = 161 downregulated genes. ( B ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as monotherapy ( n = 3 per group). n = 134 upregulated genes and n = 246 downregulated genes. For ( A , B ) False discovery rate (FDR) < 0.05 and absolute log2-fold-change > 0.585. ( C ) Venn diagram showing the overlap in the lists of differentially upregulated genes described in ( A , B ). A total of 28 genes were identified as differentially upregulated genes in both comparisons. ( D ) Gene Ontology (GO) enrichment analysis for biological processes associated with the 28 significantly upregulated genes described in ( C ). The 66 GO terms related to cancer immunity, of a total of 93 identified GO terms, are shown. The GO terms are classified into 10 different categories. ( E ) Enrichment map of the 66 GO terms related to cancer immunity showing five functional modules: 1) myeloid and lymphoid cell chemotaxis and phagocytosis, 2) myeloid immunity, 3) immune activation and B-cell function, 4) the extracellular matrix, and 5) metabolism signaling. ( F ) Mean normalized enrichment scores for monocytes and macrophages across treatment groups inferred via cell type deconvolution analysis ( n = 3-4 mice per group). ( G ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as a monotherapy, where Ccl6 , Ccl8, Ccl9, and Pf4 are highlighted. ( H ) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with the TGFβ vaccine in combination with an anti-IL-6R blocking antibody compared with those in tumors from mice treated with the TGFβ vaccine as a monotherapy, where Alox12e and Fcer1a are highlighted. I (left) Pie chart displaying the percentage of T-cell-related GO terms ( n = 3) of total cancer immunity-related GO terms ( n = 144) identified in the GO enrichment analysis for biological processes associated with the 161 upregulated genes identified in the differential gene expression analysis comparing tumors from mice that received IL-6R blockade to those from untreated mice. GO terms unrelated to cancer immunity ( n = 49) were excluded from the data visualization. ( I , right) T-cell-related GO terms identified in the GO enrichment analysis described in ( I , left). ( J , left) Volcano plot showing differentially expressed genes in Pan02 tumors from mice treated with an anti-IL-6R blocking antibody compared with those in tumors from untreated mice, where Arg1 is highlighted. ( J , right) Expression levels were assessed by RNA-seq and are presented as VST-normalized counts of Arg1 in Pan02 tumors across treatment groups. The data are presented as the means ± SEMs. The dots represent individual mice ( n = 3–4 per group)

    Article Snippet: The following antibodies were used to assess changes in the myeloid subset in Pan02 tumors across treatment groups and changes in the phenotype of BMDMs differentiated in the absence or presence of IL-6: CD45-BV605 (BioLegend, 103140), CD3-AF700 (BioLegend, 100216), CD11b-Pacific blue (BioLegend, 101223), F4/80-FITC (BioLegend, 123108), MR (CD206)-PE/Cy7 (BioLegend, 141719), MHC-II-APC/Cy7 (BioLegend, 107627), Arg1-PE (R&D Systems, IC5868P), PD-L1-APC (BD Biosciences, 564715) and CD8a-PerCP/Cy5.5 (Pharmingen, 551162).

    Techniques: Gene Expression, Migration, Inhibition, Blocking Assay, Functional Assay, Chemotaxis Assay, Activation Assay, Cell Function Assay, Expressing, RNA Sequencing

    Journal: Cell Reports Medicine

    Article Title: Macrophages are activated toward phagocytic lymphoma cell clearance by pentose phosphate pathway inhibition

    doi: 10.1016/j.xcrm.2024.101830

    Figure Lengend Snippet:

    Article Snippet: Sheep polyclonal anti-Arg1 , RnD Systems , Cat#IC5868P.

    Techniques: Staining, Virus, Recombinant, Bicinchoninic Acid Protein Assay, Cell Viability Assay, Enzyme-linked Immunosorbent Assay, Plasmid Preparation, Blocking Assay, Phospho-proteomics, Purification, Mass Spectrometry, Software, Modification

    Journal: STAR Protocols

    Article Title: Protocol for analyzing arginase I expression in tumor-associated myeloid-derived suppressor cells from murine colon cancer using flow cytometry

    doi: 10.1016/j.xpro.2024.103222

    Figure Lengend Snippet:

    Article Snippet: Anti-mouse/human Arg1 (1:200), clone AlexF5, rat IgG2a, PE , eBioscience , 12-3697-82.

    Techniques: Western Blot, Recombinant, Staining, Software, Control