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rabbit anti cd68 antibody (Bio-Rad)

Name: rabbit anti cd68 antibody
Brand: Bio-Rad
Rating: 96 (3115 reviews)

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Bio-Rad rabbit anti cd68 antibody
Rabbit Anti Cd68 Antibody, supplied by Bio-Rad, used in various techniques. Bioz Stars score: 96/100, based on 3115 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/rabbit anti cd68 antibody/product/Bio-Rad
Average 96 stars, based on 3115 article reviews

rabbit anti cd68 antibody - by Bioz Stars, 2026-03

96/100 stars

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Image Search Results

( A ) UMAP plot of microglia separated into transcriptional clusters (n=1 pool of five animals for each age). ( B ) Superimposition of ages onto the UMAP plot with dashed lines signifying relative cluster demarcation. ( C ) Percent composition of each cluster by age. ( D ) Dotplot of expression of cluster markers sorted by age. Percent of cells expressing the gene and average normalized expression are represented. ( E ) Violin plots of genes with dynamic age-related expression patterns. ( F ) Representative images and quantification of C1q (yellow), C3 (red), and Iba1 (cyan) staining in the hilus and molecular layer (ML) of 6- and 24-month-old mice (n=4–5 mice per group; mixed effects analysis followed by Dunnett’s multiple comparisons; *p<0.05, **p<0.01, ****p<0.0001). ( G ) Number of differentially expressed genes from the 6-month timepoint at each age. Bars above the intersect represent increased expression and those below represent decreased expression. ( H ) The average expression change at all ages for genes differentially regulated genes at individual ages represented by the color scheme in ( B, I, J, K ), Volcano plots of differentially expressed genes in microglia between 6 and 12 months ( I ), 6 and 18 months ( J ), and 6 and 24 months ( K ) and corresponding gene ontology analysis of genes with significantly increased (brown) or decreased (green) expression for each comparison. Data are shown as mean ± s.e.m.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) UMAP plot of microglia separated into transcriptional clusters (n=1 pool of five animals for each age). ( B ) Superimposition of ages onto the UMAP plot with dashed lines signifying relative cluster demarcation. ( C ) Percent composition of each cluster by age. ( D ) Dotplot of expression of cluster markers sorted by age. Percent of cells expressing the gene and average normalized expression are represented. ( E ) Violin plots of genes with dynamic age-related expression patterns. ( F ) Representative images and quantification of C1q (yellow), C3 (red), and Iba1 (cyan) staining in the hilus and molecular layer (ML) of 6- and 24-month-old mice (n=4–5 mice per group; mixed effects analysis followed by Dunnett’s multiple comparisons; *p<0.05, **p<0.01, ****p<0.0001). ( G ) Number of differentially expressed genes from the 6-month timepoint at each age. Bars above the intersect represent increased expression and those below represent decreased expression. ( H ) The average expression change at all ages for genes differentially regulated genes at individual ages represented by the color scheme in ( B, I, J, K ), Volcano plots of differentially expressed genes in microglia between 6 and 12 months ( I ), 6 and 18 months ( J ), and 6 and 24 months ( K ) and corresponding gene ontology analysis of genes with significantly increased (brown) or decreased (green) expression for each comparison. Data are shown as mean ± s.e.m.

Article Snippet: The following primary antibodies were used: rabbit anti-IBA1 (1:1000, Wako, Cat# 019-19741, RRID: AB_839504 ), rat anti-CD68 (1:250, Bio-Rad, Cat# MCA1957, RRID: AB_322219 ), rabbit anti-NFKB p65 (1:500, Santa Cruz, Cat# sc-372, RRID: AB_632037 ), guinea pig anti-Iba1 (1:1000, Synaptic Systems, Cat# 234-004, RRID: AB_2493179 ), rabbit anti-C1q (1:1000, Abcam, Cat# ab182451, RRID: AB_2732849 ), rat anti-C3 (1:1000, Abcam, Cat# ab11862, RRID: AB_2066623 ), rabbit anti-S6 (1:500, Cell Signaling, Cat# 2217, RRID: AB_331355 ), and rabbit anti-KLF2 (1:250, Bioss, Cat# bs-2772R, RRID: AB_10857057 ).

Techniques: Expressing, Staining, Comparison

( A ) Diagram depicting ages utilized for immunohistochemical analysis. ( B ) Diagram of the hippocampus labeled with the regions analyzed. ( C ) Illustration of subregions analyzed by immunohistochemistry. ( D ) Representative images and quantification of IBA1 (cyan)/CD68 (red)-positive microglia across hippocampal subregions in 3- and 24-month-old mice. Scale bars are 10 μM (n=5 per group; t -test with Holm–Sidak correction; *p<0.05, **p<0.01). ( E ) Heatmap of the quantification of activated microglia across ages and subregions. ( F ) Representative wide-field images of IBA1 (cyan)/CD68 (red) in the dentate gyrus in 3- and 24-month-old mice. Scale bars are 100 μM. ( G ) Representative wide field images of Iba1 (cyan)/NFKB p65 (yellow) in the dentate gyrus in 3- and 24-month-old mice. Scale bars are 100 μM. ( H ) Representative images and quantification of Iba1 (cyan)/NFKB p65 (yellow) staining across hippocampal subregions. Scale bars are 10 μM (n=5 per group; t -test with Holm–Sidak correction; *p<0.05, **p<0.01, ****p<0.0001). ( I ) Heatmap of the quantification of NFKB signal in microglia across ages and subregions. n=4–5 mice per condition.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) Diagram depicting ages utilized for immunohistochemical analysis. ( B ) Diagram of the hippocampus labeled with the regions analyzed. ( C ) Illustration of subregions analyzed by immunohistochemistry. ( D ) Representative images and quantification of IBA1 (cyan)/CD68 (red)-positive microglia across hippocampal subregions in 3- and 24-month-old mice. Scale bars are 10 μM (n=5 per group; t -test with Holm–Sidak correction; *p<0.05, **p<0.01). ( E ) Heatmap of the quantification of activated microglia across ages and subregions. ( F ) Representative wide-field images of IBA1 (cyan)/CD68 (red) in the dentate gyrus in 3- and 24-month-old mice. Scale bars are 100 μM. ( G ) Representative wide field images of Iba1 (cyan)/NFKB p65 (yellow) in the dentate gyrus in 3- and 24-month-old mice. Scale bars are 100 μM. ( H ) Representative images and quantification of Iba1 (cyan)/NFKB p65 (yellow) staining across hippocampal subregions. Scale bars are 10 μM (n=5 per group; t -test with Holm–Sidak correction; *p<0.05, **p<0.01, ****p<0.0001). ( I ) Heatmap of the quantification of NFKB signal in microglia across ages and subregions. n=4–5 mice per condition.

Techniques: Immunohistochemical staining, Labeling, Immunohistochemistry, Staining

( A ) Pseudotime trajectories of microglia from an anchor point located in 6-month microglia presented in a UMAP plot (n=1 pool of five animals for each age). ( B ) Microglia ages superimposed over pseudotime trajectories. ( C ) Gene expression modules representing sections of the inflammatory aging trajectory over the right half of the UMAP plot (left). Modules were discovered using Moran’s I autocorrelation test. Top gene ontology terms and representative genes in each module (right). ( D ) Dotplot of pseudotime modules sorted by age. Percent of cells expressing the gene and average normalized expression are represented. ( E–G ) Average gene expression changes for each aging module represented as log2 fold change of 12 months ( E ), 18 months ( F ), or 24 months ( G ) over 6 months. ( H ) Representative images and quantification of KLF2 (magenta) and IBA1 (cyan) staining in the hippocampus across ages (n=3 mice per group; one-way ANOVA with Tukey’s post-hoc test; *p<0.05). ( I ) Diagram of the heterochronic parabiosis model with the comparisons made in scRNA-Seq. ( J ) Representative images and quantification of CD68 (red) and IBA1 (cyan) staining in the hippocampus of isochronic young (IY) and heterochronic young (HY) (n=5 mice per group; unpaired Student’s t -test; ***p<0.001). ( K ) Average gene expression changes for each aging module represented as log2 fold change of heterochronic young (HY) over isochronic young (IY) adult parabionts. Data from . ( L ) Diagram of microglia surrounding an Aβ plaque. ( M ) Average gene expression changes for each aging module represented as log2 fold change of the App NL-G-F genotype (AD) over wildtype (WT). Data from (one-sample t -test with the expected value of 0 [no change]; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). Data are shown as mean ± s.e.m.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) Pseudotime trajectories of microglia from an anchor point located in 6-month microglia presented in a UMAP plot (n=1 pool of five animals for each age). ( B ) Microglia ages superimposed over pseudotime trajectories. ( C ) Gene expression modules representing sections of the inflammatory aging trajectory over the right half of the UMAP plot (left). Modules were discovered using Moran’s I autocorrelation test. Top gene ontology terms and representative genes in each module (right). ( D ) Dotplot of pseudotime modules sorted by age. Percent of cells expressing the gene and average normalized expression are represented. ( E–G ) Average gene expression changes for each aging module represented as log2 fold change of 12 months ( E ), 18 months ( F ), or 24 months ( G ) over 6 months. ( H ) Representative images and quantification of KLF2 (magenta) and IBA1 (cyan) staining in the hippocampus across ages (n=3 mice per group; one-way ANOVA with Tukey’s post-hoc test; *p<0.05). ( I ) Diagram of the heterochronic parabiosis model with the comparisons made in scRNA-Seq. ( J ) Representative images and quantification of CD68 (red) and IBA1 (cyan) staining in the hippocampus of isochronic young (IY) and heterochronic young (HY) (n=5 mice per group; unpaired Student’s t -test; ***p<0.001). ( K ) Average gene expression changes for each aging module represented as log2 fold change of heterochronic young (HY) over isochronic young (IY) adult parabionts. Data from . ( L ) Diagram of microglia surrounding an Aβ plaque. ( M ) Average gene expression changes for each aging module represented as log2 fold change of the App NL-G-F genotype (AD) over wildtype (WT). Data from (one-sample t -test with the expected value of 0 [no change]; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001). Data are shown as mean ± s.e.m.

Techniques: Gene Expression, Expressing, Staining

( A ) Volcano plot of differential gene expression of 12-month microglia versus all other ages with significant genes in teal. ( B ) Gene ontology analysis of biological processes enriched in those genes with increased expression at 12 months of age. ( C ) Representative images and quantification of CD68 (red) and IBA1 (cyan) staining in the hippocampus of isochronic young (IY) and heterochronic young (HY) along with a diagram of the comparisons (n=5 mice per group; unpaired Student’s t -test; ***p<0.001). Data are shown as mean ± s.e.m. ( D ) Dotplot of pseudotime modules in young (Y), isochronic young (IY), and heterochronic young (HY) parabiont microglia. Data is from . Percent of cells expressing the gene and average normalized expression are represented. ( E ) Volcano plots of differential gene expression of App NL-G-F genotype (AD) over wildtype (WT) microglia at 6 (left) and 12 months of age. Significant genes are in teal. Data is from . ( F ) Dotplot of pseudotime modules across ages (3, 6, 12, and 21 months old) and genotypes ( App NL-G-F genotype (AD) over C57Bl/6 (WT)). Data is from . (G) Percent of cells expressing the gene and average normalized expression are represented.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) Volcano plot of differential gene expression of 12-month microglia versus all other ages with significant genes in teal. ( B ) Gene ontology analysis of biological processes enriched in those genes with increased expression at 12 months of age. ( C ) Representative images and quantification of CD68 (red) and IBA1 (cyan) staining in the hippocampus of isochronic young (IY) and heterochronic young (HY) along with a diagram of the comparisons (n=5 mice per group; unpaired Student’s t -test; ***p<0.001). Data are shown as mean ± s.e.m. ( D ) Dotplot of pseudotime modules in young (Y), isochronic young (IY), and heterochronic young (HY) parabiont microglia. Data is from . Percent of cells expressing the gene and average normalized expression are represented. ( E ) Volcano plots of differential gene expression of App NL-G-F genotype (AD) over wildtype (WT) microglia at 6 (left) and 12 months of age. Significant genes are in teal. Data is from . ( F ) Dotplot of pseudotime modules across ages (3, 6, 12, and 21 months old) and genotypes ( App NL-G-F genotype (AD) over C57Bl/6 (WT)). Data is from . (G) Percent of cells expressing the gene and average normalized expression are represented.

Techniques: Gene Expression, Expressing, Staining

( 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 . ( 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 the UMAP plot highlighting the stage where CX-5461 modulates the trajectory. ( I ) Representative images of S6 (magenta) and Iba1 (cyan) staining in the hippocampus of 6- and 24-month-old mice and quantification across aging (n=3 mice per group; one-way ANOVA with Tukey’s post hoc test; *p<0.05, **p<0.005, ****p<0.0001). ( J ) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of translation module from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from (one-sample t -test with the expected value of 0 [no change]; **p<0.01). ( K ) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following CX-5461 treatment compared to control (DMSO) in LPS-treated microglia (n=3 per group) ( L ) Heatmap of top 10 genes in each aging module following CX-5461 compared to DMSO in LPS-treated microglia. ( M ) Average gene expression changes for each aging module represented as log2 fold change of CX-5461 treatment over DMSO (one-sample t -test with the expected value of 0 [no change]; *p<0.05, **p<0.01, ****p<0.0001).

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( 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 . ( 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 the UMAP plot highlighting the stage where CX-5461 modulates the trajectory. ( I ) Representative images of S6 (magenta) and Iba1 (cyan) staining in the hippocampus of 6- and 24-month-old mice and quantification across aging (n=3 mice per group; one-way ANOVA with Tukey’s post hoc test; *p<0.05, **p<0.005, ****p<0.0001). ( J ) Schematic of the heterochronic parabiosis model and quantification of hippocampal microglia expression of translation module from isochronic young (IY) and heterochronic young (HY) adult parabionts. Data derived from (one-sample t -test with the expected value of 0 [no change]; **p<0.01). ( K ) Top gene ontology terms for the set of genes with significantly decreased expression in bulk microglia RNA-Seq following CX-5461 treatment compared to control (DMSO) in LPS-treated microglia (n=3 per group) ( L ) Heatmap of top 10 genes in each aging module following CX-5461 compared to DMSO in LPS-treated microglia. ( M ) Average gene expression changes for each aging module represented as log2 fold change of CX-5461 treatment over DMSO (one-sample t -test with the expected value of 0 [no change]; *p<0.05, **p<0.01, ****p<0.0001).

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

( A ) Quantification of percentage of Tgfb1 RNAscope signal within IBA1 cells. ( B ) Quantification of IHC of pTGFBR1 signal in IBA1 cells across ages (n=4–5 mice per group; one-way ANOVA; *p<0.05). ( C ) Quantification of the expression of TGFB1 signaling components from . Expression values for the genes are represented as Z-scores (n=12 genes with reads in >25% of cells; Friedman test followed by Dunn’s multiple comparisons test; *p<0.05, ***p<0.001). ( D ) Dotplot of the expression values of TGFB1 signaling components in young (Y), aged (A), isochronic young (IY), and heterochronic young (HY) parabiont microglia. Data is from . Percent of cells expressing the gene and average normalized expression are represented. UMAP plot of Tgfb1 scRNA-Seq colored by genotype. ( E ) Dotplot of the expression values of TGFB1 signaling components in aging microglia categorized by the expression of Tgfb1 into quartiles with Q1 having the lowest expression of Tgfb1 and Q4 having the highest expression. ( F ) Quantification of the expression of TGFB1 signaling components from ( E ). Expression values for the genes are represented as Z-scores (n=12 genes with reads in >25% of cells; Friedman test followed by Dunn’s multiple comparisons test; *p<0.05, ***p<0.001). ( G ) Dotplot of the expression values of selected TGFB signaling targets (activated and repressed, genes found in both and ) in aging microglia categorized by the expression of Tgfb1 . ( H ) Heatmap of gene expression changes in the top 10 genes in each aging module induced by LPS. ( I ) Overlap between microglia gene expression changes induced by LPS and aging (χ 2 <2.2e-16). ( J ) PCA plot of pharmacological manipulations with or without LPS treatment.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) Quantification of percentage of Tgfb1 RNAscope signal within IBA1 cells. ( B ) Quantification of IHC of pTGFBR1 signal in IBA1 cells across ages (n=4–5 mice per group; one-way ANOVA; *p<0.05). ( C ) Quantification of the expression of TGFB1 signaling components from . Expression values for the genes are represented as Z-scores (n=12 genes with reads in >25% of cells; Friedman test followed by Dunn’s multiple comparisons test; *p<0.05, ***p<0.001). ( D ) Dotplot of the expression values of TGFB1 signaling components in young (Y), aged (A), isochronic young (IY), and heterochronic young (HY) parabiont microglia. Data is from . Percent of cells expressing the gene and average normalized expression are represented. UMAP plot of Tgfb1 scRNA-Seq colored by genotype. ( E ) Dotplot of the expression values of TGFB1 signaling components in aging microglia categorized by the expression of Tgfb1 into quartiles with Q1 having the lowest expression of Tgfb1 and Q4 having the highest expression. ( F ) Quantification of the expression of TGFB1 signaling components from ( E ). Expression values for the genes are represented as Z-scores (n=12 genes with reads in >25% of cells; Friedman test followed by Dunn’s multiple comparisons test; *p<0.05, ***p<0.001). ( G ) Dotplot of the expression values of selected TGFB signaling targets (activated and repressed, genes found in both and ) in aging microglia categorized by the expression of Tgfb1 . ( H ) Heatmap of gene expression changes in the top 10 genes in each aging module induced by LPS. ( I ) Overlap between microglia gene expression changes induced by LPS and aging (χ 2 <2.2e-16). ( J ) PCA plot of pharmacological manipulations with or without LPS treatment.

Techniques: RNAscope, Expressing, Gene Expression

( A ) Representative images and quantification of IBA1 (cyan)/CD68 (red)-positive microglia in wildtype, Tgfb1 cHet, and Tgfb1 cKO hippocampi (n=3–5; one way ANOVA with Tukey post hoc test; *p<0.05). ( B ) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of Tgfb1 WT, cHet, and cKO microglia (n=2 pools of three animals per genotype). ( C ) UMAP plot of Tgfb1 scRNA-Seq colored by genotype. ( D ) FACS plots of gating strategy for microglia isolation for RNA-Seq in cHet and cKO hippocampi. Quantification of the flow cytometry analysis of CD11b and CD45 (n=3–5; mixed effects analysis; *p<0.05, ****p<0.0001). ( E ) Example histogram and quantification of CD48 in control and Tgfb1 cKO FACS-sorted microglia (n=3–5 per group; t -test; ****p<0.0001). ( F ) Top gene ontology terms associated with genes significantly increased in microglia bulk RNA-Seq in Tgfb1 cKO samples (n=3–5 samples per genotype). ( G ) Top gene ontology terms associated with genes significantly decreased in microglia bulk RNA-Seq in Tgfb1 cKO samples. ( H ) Overlap between concordant microglia gene expression changes induced by Tgfb1 knockout and aging. (χ 2 <2.2e-16). ( I ) Heatmap of gene expression of the top 10 genes in each aging module in control and Tgfb1 cKO microglia. ( J ) Average gene expression changes for each aging module represented as log2 fold change of Tgfb1 cKO over control (one-sample t -test with the expected value of 0 [no change]; ****p<0.0001). Data are shown as means ± s.e.m.

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet: ( A ) Representative images and quantification of IBA1 (cyan)/CD68 (red)-positive microglia in wildtype, Tgfb1 cHet, and Tgfb1 cKO hippocampi (n=3–5; one way ANOVA with Tukey post hoc test; *p<0.05). ( B ) Dotplot of the expression values of TGFB1 signaling components from scRNA-Seq of Tgfb1 WT, cHet, and cKO microglia (n=2 pools of three animals per genotype). ( C ) UMAP plot of Tgfb1 scRNA-Seq colored by genotype. ( D ) FACS plots of gating strategy for microglia isolation for RNA-Seq in cHet and cKO hippocampi. Quantification of the flow cytometry analysis of CD11b and CD45 (n=3–5; mixed effects analysis; *p<0.05, ****p<0.0001). ( E ) Example histogram and quantification of CD48 in control and Tgfb1 cKO FACS-sorted microglia (n=3–5 per group; t -test; ****p<0.0001). ( F ) Top gene ontology terms associated with genes significantly increased in microglia bulk RNA-Seq in Tgfb1 cKO samples (n=3–5 samples per genotype). ( G ) Top gene ontology terms associated with genes significantly decreased in microglia bulk RNA-Seq in Tgfb1 cKO samples. ( H ) Overlap between concordant microglia gene expression changes induced by Tgfb1 knockout and aging. (χ 2 <2.2e-16). ( I ) Heatmap of gene expression of the top 10 genes in each aging module in control and Tgfb1 cKO microglia. ( J ) Average gene expression changes for each aging module represented as log2 fold change of Tgfb1 cKO over control (one-sample t -test with the expected value of 0 [no change]; ****p<0.0001). Data are shown as means ± s.e.m.

Techniques: Expressing, Isolation, RNA Sequencing, Flow Cytometry, Control, Gene Expression, Knock-Out

Journal: eLife

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

doi: 10.7554/eLife.97671

Figure Lengend Snippet:

Techniques: Software, Magnetic Beads, Recombinant, Sequencing, RNAscope

(A) Volcano plot of upregulated (yellow) and downregulated proteins (blue) in the thalamus of GFP- Grn −/− vs. GFP- Grn +/+ mice (fold change [FC] > 1.2, p < 0.05). (B) Bar graph of the most significantly enriched Gene Ontology (GO) terms describing the differentially expressed proteins in (A) (GFP- Grn −/− mice vs. GFP- Grn +/+ mice; FC = 1.2 and adjusted p value = 0.05). Displaying all significant changed modules ( p < 0.05). (C) Plot of PCs (PC1 vs. PC2) for GFP- Grn +/+ mice (blue), GFP- Grn −/− mice (gray), hPGRN- Grn −/− mice (purple), hGRN2- Grn −/− mice (green), and hGRN4-GFP- Grn −/− mice (yellow). Ellipses: 95% confidence interval. (D) Heatmap of top 140 proteins (rows) differentially expressed between GFP- Grn −/− and GFP- Grn +/+ and treatment groups (columns). Quantification of individual proteins shown (log 2 Z score transformed). Individual mouse numbers are below the column. (E) Bar plots comparing correction of elevated levels of LGALS3, CD68, GFAP, GPNMB, HEXB, LYZ2, MPEG1, SERPINA3N, and TPP1 in Grn −/− mice injected with GFP, hGRN2, hGRN4, or hPGRN. Mean (protein abundance) ± SD. One-way ANOVA with Tukey’s post hoc. n = 5–7 mice/group. * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001. (F) Correlation of hGRN2 (green) and hGRN4 (yellow) expression with galectin-3 (R = 0.78, p = 0.0011). (G) Correlation of hGRN2 (green) and hGRN4 (yellow) expression with P2RY12 (R = 0.77, p = 0.0014).

Journal: Cell reports

Article Title: Granulins rescue inflammation, lysosome dysfunction, lipofuscin, and neuropathology in a mouse model of progranulin deficiency

doi: 10.1016/j.celrep.2024.114985

Figure Lengend Snippet: (A) Volcano plot of upregulated (yellow) and downregulated proteins (blue) in the thalamus of GFP- Grn −/− vs. GFP- Grn +/+ mice (fold change [FC] > 1.2, p < 0.05). (B) Bar graph of the most significantly enriched Gene Ontology (GO) terms describing the differentially expressed proteins in (A) (GFP- Grn −/− mice vs. GFP- Grn +/+ mice; FC = 1.2 and adjusted p value = 0.05). Displaying all significant changed modules ( p < 0.05). (C) Plot of PCs (PC1 vs. PC2) for GFP- Grn +/+ mice (blue), GFP- Grn −/− mice (gray), hPGRN- Grn −/− mice (purple), hGRN2- Grn −/− mice (green), and hGRN4-GFP- Grn −/− mice (yellow). Ellipses: 95% confidence interval. (D) Heatmap of top 140 proteins (rows) differentially expressed between GFP- Grn −/− and GFP- Grn +/+ and treatment groups (columns). Quantification of individual proteins shown (log 2 Z score transformed). Individual mouse numbers are below the column. (E) Bar plots comparing correction of elevated levels of LGALS3, CD68, GFAP, GPNMB, HEXB, LYZ2, MPEG1, SERPINA3N, and TPP1 in Grn −/− mice injected with GFP, hGRN2, hGRN4, or hPGRN. Mean (protein abundance) ± SD. One-way ANOVA with Tukey’s post hoc. n = 5–7 mice/group. * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001. (F) Correlation of hGRN2 (green) and hGRN4 (yellow) expression with galectin-3 (R = 0.78, p = 0.0011). (G) Correlation of hGRN2 (green) and hGRN4 (yellow) expression with P2RY12 (R = 0.77, p = 0.0014).

Article Snippet: Rabbit monoclonal anti-mouse CD68 , Cell Signaling Technology , Cat# 97778; RRID: AB_2928056.

Techniques: Transformation Assay, Injection, Quantitative Proteomics, Expressing

(A) Heatmap of differentially expressed (log 2 Z score-transformed) proteins associated with microglial activation and dysfunction (rows) in all treatment groups in GFP- Grn −/− compared to GFP- Grn +/+ (columns). (B) Abundance of CD45 (PTPRC) across all treatment groups. (C) Abundance of P2RY12 across all treatment groups. (D) Representative CD68 IHC of 12-month-old mouse coronal brain sections across all treatment groups. Scale bar: 2 mm. (E) Quantification of CD68 IHC signal of cortex, hippocampus, and thalamus. (F) Immunoblot of CD68 in cortical and thalamic brain tissue from all injection groups. (G) Quantification of immunoblot of cortical CD68 signal normalized to H3. (H) Quantification of immunoblot of thalamic CD68 signal normalized to H3. (I) Quantification of GPNMB levels in thalamus using ELISA. Data are presented as means ± SD. p values were calculated by one-way or two-way (E) ANOVA with Tukey’s post hoc analysis. * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.

Journal: Cell reports

Article Title: Granulins rescue inflammation, lysosome dysfunction, lipofuscin, and neuropathology in a mouse model of progranulin deficiency

doi: 10.1016/j.celrep.2024.114985

Figure Lengend Snippet: (A) Heatmap of differentially expressed (log 2 Z score-transformed) proteins associated with microglial activation and dysfunction (rows) in all treatment groups in GFP- Grn −/− compared to GFP- Grn +/+ (columns). (B) Abundance of CD45 (PTPRC) across all treatment groups. (C) Abundance of P2RY12 across all treatment groups. (D) Representative CD68 IHC of 12-month-old mouse coronal brain sections across all treatment groups. Scale bar: 2 mm. (E) Quantification of CD68 IHC signal of cortex, hippocampus, and thalamus. (F) Immunoblot of CD68 in cortical and thalamic brain tissue from all injection groups. (G) Quantification of immunoblot of cortical CD68 signal normalized to H3. (H) Quantification of immunoblot of thalamic CD68 signal normalized to H3. (I) Quantification of GPNMB levels in thalamus using ELISA. Data are presented as means ± SD. p values were calculated by one-way or two-way (E) ANOVA with Tukey’s post hoc analysis. * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.

Article Snippet: Rabbit monoclonal anti-mouse CD68 , Cell Signaling Technology , Cat# 97778; RRID: AB_2928056.

Techniques: Transformation Assay, Activation Assay, Western Blot, Injection, Enzyme-linked Immunosorbent Assay

Journal: Cell reports

Article Title: Granulins rescue inflammation, lysosome dysfunction, lipofuscin, and neuropathology in a mouse model of progranulin deficiency

doi: 10.1016/j.celrep.2024.114985

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Rabbit monoclonal anti-mouse CD68 , Cell Signaling Technology , Cat# 97778; RRID: AB_2928056.

Techniques: Recombinant, Plasmid Preparation, Virus, Staining, Blocking Assay, Hydrophilic Interaction Liquid Chromatography, Transfection, Magnetic Beads, Polymer, Avidin-Biotin Assay, Enzyme-linked Immunosorbent Assay, Software

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