prism software graphpad instat version 5.00 Search Results


software version 12.5.0.0  (MedCalc Software Ltd)
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MedCalc Software Ltd software version 12.5.0.0
Software Version 12.5.0.0, supplied by MedCalc Software Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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algorithms chemidoc imaging system biorad  (Bio-Rad)
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Bio-Rad algorithms chemidoc imaging system biorad
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2021 antibody anti iba1  (Bio-Rad)
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Bio-Rad 2021 antibody anti iba1
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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cycle pure kit  (Omega Bio Tek)
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Omega Bio Tek cycle pure kit
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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cycle pure kit - by Bioz Stars, 2026-09
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gel extraction kit  (Omega Bio Tek)
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Omega Bio Tek gel extraction kit
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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gel extraction kit - by Bioz Stars, 2026-09
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single cell 30 reagent kits v2 10xgenomics  (10X Genomics)
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10X Genomics single cell 30 reagent kits v2 10xgenomics
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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single cell 30 reagent kits v2 10xgenomics - by Bioz Stars, 2026-09
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ezna miniprep kit  (Omega Bio Tek)
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Omega Bio Tek ezna miniprep kit
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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ezna miniprep kit - by Bioz Stars, 2026-09
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treg selection kit  (STEMCELL Technologies Inc)
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STEMCELL Technologies Inc treg selection kit
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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treg selection kit - by Bioz Stars, 2026-09
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low density lipoprotein  (STEMCELL Technologies Inc)
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STEMCELL Technologies Inc low density lipoprotein
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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microdialysis probe, 500, 2mm  (Harvard Bioscience)
90
Harvard Bioscience microdialysis probe, 500, 2mm
Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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easysep human cd34+ positive selection kit  (STEMCELL Technologies Inc)
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Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in <t>IBA1</t> (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over
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Image Search Results


Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in IBA1 (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over

Journal: eLife

Article Title: Microglia aging in the hippocampus advances through intermediate states that drive activation and cognitive decline

doi: 10.7554/elife.97671.3

Figure Lengend Snippet: Figure 3. Intermediate states of microglia aging act as checkpoints on inflammatory progression. (A) Representation of the microglia aging trajectory over the UMAP plot highlighting the region of peak Tgfb1 expression. (B) Representative RNAscope images and quantification of Tgfb1 (red) expression in IBA1 (cyan) cells across ages (n=5 per group; one-way ANOVA with Dunnett’s post hoc test; *p<0.05). (C) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of aging hippocampal microglia (6-, 12-, 18-, and 24-month-old). Percent of cells expressing the gene and average normalized expression are represented. (D) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of Tgfb1 from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from Pálovics et al., 2022. (E) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following TGFB1 treatment compared to control (DMSO) in LPS-treated microglia (n=5 per group). (F) Heatmap of top 10 genes in each aging module following TGFB1 compared to DMSO in LPS- treated microglia. (G) Average gene expression changes for each aging module represented as log2 fold change of TGFB1 treatment over DMSO (one- sample t-test with the expected value of 0 [no change]; *p<0.05, ***p<0.001, ****p<0.0001). (H) Representation of the microglia aging trajectory over

Article Snippet: resource Designation Source or reference Identifiers Additional information Antibody CD48- Pacific Blue BioLegend 103417, RRID:AB_756139 FACS 1:200 Software, algorithm DeSeq2 (1.42.1) https://bioconductor.org/packages/release/ bioc/html/DESeq2.html RRID:SCR_015687 Commercial assay or kit Cd11b magnetic beads Miltenyi 130- 126- 725 Software, algorithm Cell Ranger (v5.0.1) 10x Genomics RRID:SCR_021160 Software, algorithm Seurat (v3.2.1) https://satijalab.org/seurat/articles/install_v5 RRID:SCR_007322 Software, algorithm Monocle3 (v1.3.4) https://cole-trapnell-lab.github.io/monocle3/ docs/installation/ RRID:SCR_018685 Software, algorithm Scorpius (v1.0.9) https://github.com/rcannood/SCORPIUS; Cannoodt, 2021 Antibody Anti- Iba1, rabbit Wako 019- 19741, RRID:AB_839504 1:1000 IHC Antibody Anti- CD68, rat Bio- Rad MCA1957, RRID:AB_322219 1:250 IHC Antibody Anti- NFKB p65, rabbit SantaCruz sc- 372, RRID:AB_632037 1:500 IHC Antibody Anti- Iba1, guinea pig Synaptic Systems 234- 004, RRID:AB_2493179 1:1000 IHC Antibody Anti- C1q, rabbit Abcam ab182451, RRID:AB_2732849 1:1000 IHC Antibody Anti- C3, rat Abcam ab11862, RRID:AB_2066623 1:1000 IHC Antibody Anti- S6, rabbit Cell Signaling 2217, RRID:AB_331355 1:500 IHC Antibody Anti- KLF2, rabbit Bioss bs- 2772R, RRID:AB_10857057 1:250 IHC Peptide, recombinant protein M- CSF Peprotech 315- 02 10 ng/mL Peptide, recombinant protein TGFβ1 Thermo Fisher Scientific PHG9204 10 ng/mL Chemical compound, drug CX- 5461 EMD Millipore 509265 100 nM Sequence- based reagent Tgfb1 RNAScope Probe ACD 443571C2 Software, algorithm ggplot2 https://ggplot2.tidyverse.org/ RRID:SCR_014601 Software, algorithm Prism 8.0 GraphPad RRID:SCR_002798 Continued

Techniques: Expressing, RNAscope, Derivative Assay, RNA Sequencing, Control, Gene Expression