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antibodies against egr1  (Proteintech)


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    Proteintech antibodies against egr1
    Antibodies Against Egr1, supplied by Proteintech, used in various techniques. Bioz Stars score: 96/100, based on 129 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/anti+egr1/EGR1+Antibody/pm41916145-168-10-15
    Average 96 stars, based on 129 article reviews
    antibodies against egr1 - by Bioz Stars, 2026-10
    96/100 stars

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    Related Articles

    Blocking Assay:

    Article Title: O-GlcNAc transferase influences the progression of degenerative cartilage disease in lumbar facet joint osteoarthritis through FoxO1 and EGR1
    Article Snippet: .. After transfer to membranes and blocking, the membranes were incubated overnight at 4 °C with primary antibodies, including anti-FoxO1 (rabbit, 18592-1-AP, Proteintech), anti-OGT (rabbit, 11576-2-AP, Proteintech), anti-RL2 (mouse, MA1-072, Invitrogen), anti-EGR1 (rabbit, 22008-1-AP), and anti-β-actin (rabbit, 20536-1-AP, Proteintech). .. Wash the membrane 3 times with TBST, then incubate the membrane with secondary antibody at room temperature in the dark for 1 h. Finally, develop with ECL (34580, Thermo, Massachusetts, USA), visualize the image using a gel imaging system (FL1500, Thermo Fisher Scientific, MA, USA), and analyze band intensity with ImageJ.

    Article Title: Chronic Exposure to Cigarette Smoke Induces Pyroptosis in Pulmonary Epithelial Cells via EGR1/USP44/TRAF6 Axis in COPD
    Article Snippet: .. After blocking for 1 hour, sections were incubated overnight at 4°C with primary antibodies: anti-EGR1 (55117-1-AP, Proteintech, China, 1:100), anti-USP44 (YT4841, Immunoway, China, 1:100), and anti-TRAF6 (YM8516, Immunoway, China, 1:100). ..

    Article Title: The EGR1/ZFP36 axis governs glycosphingolipid metabolic reprogramming in monocyte-derived macrophages in guillain-barré syndrome.
    Article Snippet: Equal amounts of protein (12 μg) were separated by SDS-PAGE and electrotransferred onto PVDF membranes (Millipore, USA). .. After blocking in 5% non-fat milk for 1 h at room temperature, the membranes were incubated overnight at 4 ◦C with the following primary antibodies: anti-EGR1 (1:1,000, 22008-1-AP, Proteintech), anti-ZFP36 (1:1,000, 12737-1-AP, Proteintech), anti-MBP (1:5000, 66003-1-IG, Proteintech), anti-S100B (1:10000, 15146-1-AP, Proteintech), anti-MPZ (1:1000, 10572-1-AP, Proteintech) and anti-GAPDH (1:5,000, 60004-1-Ig, Proteintech). .. After three washes with TBST, the membranes were incubated for 2 h at room temperature with HRP-conjugated goat anti-rabbit IgG secondary antibody (1:10,000, 111-035-003, Jackson ImmunoResearch).

    Article Title: O-GlcNAc transferase influences the progression of degenerative cartilage disease in lumbar facet joint osteoarthritis through FoxO1 and EGR1.
    Article Snippet: .. After transfer to membranes and blocking, the membranes were incubated overnight at 4 °C with primary antibodies, including anti-FoxO1 (rabbit, 18592-1-AP, Proteintech), anti-OGT (rabbit, 11576-2-AP, Proteintech), antiRL2 (mouse, MA1-072, Invitrogen), anti-EGR1 (rabbit, 22008-1-AP), and antiβ-actin (rabbit, 20536-1-AP, Proteintech). .. Wash the membrane 3 times with TBST, then incubate the membrane with secondary antibody at room temperature in the dark for 1 h. Finally, develop with ECL (34580, Thermo, Massachusetts, USA), visualize the image using a gel imaging system (FL1500, Thermo Fisher Scientific, MA, USA), and analyze band intensity with ImageJ.

    Article Title: IGF-1 regulates PEAR1 through Egr1 to Promote Skeletal Muscle Post-injury Regeneration.
    Article Snippet: This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability.. This version will undergo additional copyediting, typesetting and review before it is published in its final form.. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article.

    Incubation:

    Article Title: O-GlcNAc transferase influences the progression of degenerative cartilage disease in lumbar facet joint osteoarthritis through FoxO1 and EGR1
    Article Snippet: .. After transfer to membranes and blocking, the membranes were incubated overnight at 4 °C with primary antibodies, including anti-FoxO1 (rabbit, 18592-1-AP, Proteintech), anti-OGT (rabbit, 11576-2-AP, Proteintech), anti-RL2 (mouse, MA1-072, Invitrogen), anti-EGR1 (rabbit, 22008-1-AP), and anti-β-actin (rabbit, 20536-1-AP, Proteintech). .. Wash the membrane 3 times with TBST, then incubate the membrane with secondary antibody at room temperature in the dark for 1 h. Finally, develop with ECL (34580, Thermo, Massachusetts, USA), visualize the image using a gel imaging system (FL1500, Thermo Fisher Scientific, MA, USA), and analyze band intensity with ImageJ.

    Article Title: Chronic Exposure to Cigarette Smoke Induces Pyroptosis in Pulmonary Epithelial Cells via EGR1/USP44/TRAF6 Axis in COPD
    Article Snippet: .. After blocking for 1 hour, sections were incubated overnight at 4°C with primary antibodies: anti-EGR1 (55117-1-AP, Proteintech, China, 1:100), anti-USP44 (YT4841, Immunoway, China, 1:100), and anti-TRAF6 (YM8516, Immunoway, China, 1:100). ..

    Article Title: The EGR1/ZFP36 axis governs glycosphingolipid metabolic reprogramming in monocyte-derived macrophages in guillain-barré syndrome.
    Article Snippet: Equal amounts of protein (12 μg) were separated by SDS-PAGE and electrotransferred onto PVDF membranes (Millipore, USA). .. After blocking in 5% non-fat milk for 1 h at room temperature, the membranes were incubated overnight at 4 ◦C with the following primary antibodies: anti-EGR1 (1:1,000, 22008-1-AP, Proteintech), anti-ZFP36 (1:1,000, 12737-1-AP, Proteintech), anti-MBP (1:5000, 66003-1-IG, Proteintech), anti-S100B (1:10000, 15146-1-AP, Proteintech), anti-MPZ (1:1000, 10572-1-AP, Proteintech) and anti-GAPDH (1:5,000, 60004-1-Ig, Proteintech). .. After three washes with TBST, the membranes were incubated for 2 h at room temperature with HRP-conjugated goat anti-rabbit IgG secondary antibody (1:10,000, 111-035-003, Jackson ImmunoResearch).

    Article Title: O-GlcNAc transferase influences the progression of degenerative cartilage disease in lumbar facet joint osteoarthritis through FoxO1 and EGR1.
    Article Snippet: .. After transfer to membranes and blocking, the membranes were incubated overnight at 4 °C with primary antibodies, including anti-FoxO1 (rabbit, 18592-1-AP, Proteintech), anti-OGT (rabbit, 11576-2-AP, Proteintech), antiRL2 (mouse, MA1-072, Invitrogen), anti-EGR1 (rabbit, 22008-1-AP), and antiβ-actin (rabbit, 20536-1-AP, Proteintech). .. Wash the membrane 3 times with TBST, then incubate the membrane with secondary antibody at room temperature in the dark for 1 h. Finally, develop with ECL (34580, Thermo, Massachusetts, USA), visualize the image using a gel imaging system (FL1500, Thermo Fisher Scientific, MA, USA), and analyze band intensity with ImageJ.



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    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
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    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
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    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
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    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
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    antibodies against egr1  (Proteintech)
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    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
    Antibodies Against Egr1, supplied by Proteintech, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/anti+egr1/EGR1+Antibody/pm41916145-168-10-15
    Average 96 stars, based on 1 article reviews
    antibodies against egr1 - by Bioz Stars, 2026-10
    96/100 stars
    		  Buy from Supplier
    anti egr1  (Proteintech)
    96
    Proteintech anti egr1
    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
    Anti Egr1, supplied by Proteintech, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/anti+egr1/EGR1+Antibody/pm41916145-164-25-28
    Average 96 stars, based on 1 article reviews
    anti egr1 - by Bioz Stars, 2026-10
    96/100 stars
    		  Buy from Supplier
    anti egr1 antibody  (Cell Signaling Technology Inc)
    96
    Cell Signaling Technology Inc anti egr1 antibody
    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential <t>Egr1</t> expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).
    Anti Egr1 Antibody, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/anti+egr1/EGR1+Rabbit+mAb/pm41826328-281-9-11
    Average 96 stars, based on 1 article reviews
    anti egr1 antibody - by Bioz Stars, 2026-10
    96/100 stars
    		  Buy from Supplier

    Image Search Results


    Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential Egr1 expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).

    Journal: Frontiers in Immunology

    Article Title: Myeloid EGR1 contributes to Th17/Treg imbalance during acute liver allograft rejection in a rat model

    doi: 10.3389/fimmu.2026.1901189

    Figure Lengend Snippet: Single-cell transcriptomic analysis of GSE285476 reveals immune-cell remodeling and preferential Egr1 expression in myeloid cells during acute liver allograft rejection. (A, B) Violin plots showing the distributions of the number of detected genes (nFeature_RNA) and total transcript counts (nCount_RNA) across the six individual samples following and prior to quality-control filtering, respectively. (C) Identification of highly variable genes used for downstream dimensionality reduction; red dots indicate the 3,000 selected variable features. (D) Principal component analysis (PCA) showing the distribution of cells from the six individual liver graft samples. (E) Genes with the largest contributions to the first four principal components (PC1–PC4). (F) Elbow plot showing the standard deviations of the principal components used to guide dimensionality selection. (G) UMAP visualization of unsupervised clustering, identifying 23 transcriptionally distinct cell clusters. (H) Heatmap showing representative marker-gene expression used for annotation of the major cell populations. (I) Relative cellular composition of the syngeneic and rejection groups, showing marked remodeling of the graft cellular landscape during rejection. (J) UMAP visualization showing the distribution of the seven major annotated cell populations in the syngeneic and rejection groups. (K) UMAP visualization of the seven major annotated cell populations, including hepatocytes, cholangiocytes, endothelial cells, fibroblasts, hepatic stellate cells, myeloid cells, and lymphoid cells (left), together with the distribution of Egr1 expression across the graft cellular landscape (right). (L) Dot plot showing the expression of representative stress-responsive and immediate-early genes (Fos, Fosb, Jun, Junb, Jund, Atf3, Egr1, Dusp1, and Ier3) across the major cell populations. Dot size represents the percentage of cells expressing each gene, and color intensity represents average scaled expression. Stress-responsive genes were broadly distributed across multiple cell types, whereas Egr1 showed more prominent expression in myeloid than in lymphoid cells. (M) Comparison of dissociation stress scores between the syngeneic and rejection groups. No significant difference was observed between the two groups (P = 0.10).

    Article Snippet: After blocking, the membranes were incubated with primary antibodies against EGR1 (1:10,000, Novus Biologicals) and GAPDH (1:10,000, Sigma, USA), followed by peroxidase-conjugated secondary antibodies.

    Techniques: Single Cell, Analysis, Immune Cell, Expressing, QC/EQA/Proficiency, Selection, Marker, Gene Expression, Comparison

    Temporal expression profile of EGR1 in liver grafts post-transplantation. (A) Representative immunohistochemistry (IHC) images and quantification of EGR1-positive cells per field in liver tissues from the Sham, Syngraft, and Allograft groups at 6 h, 12 h, 24 h, 5 d, and 10 d post-operation. Quantification was performed by counting EGR1-positive cells per field. Scale bars = 50 μm. (B) Relative mRNA expression levels of Egr1 in liver grafts determined by RT-qPCR. (C) EGR1 protein concentrations in the respective groups. (D) Representative Western blot images (left) and densitometric quantification (right) of EGR1 protein expression in liver grafts at indicated time points. Data are presented as mean ± SD (n = 3 per group). *p < 0.05, **p < 0.01, ***p < 0.001; ns, no significance. Allo, allograft; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IHC, immunohistochemistry; RT-qPCR, reverse transcription quantitative polymerase chain reaction; Syn, syngraft.

    Journal: Frontiers in Immunology

    Article Title: Myeloid EGR1 contributes to Th17/Treg imbalance during acute liver allograft rejection in a rat model

    doi: 10.3389/fimmu.2026.1901189

    Figure Lengend Snippet: Temporal expression profile of EGR1 in liver grafts post-transplantation. (A) Representative immunohistochemistry (IHC) images and quantification of EGR1-positive cells per field in liver tissues from the Sham, Syngraft, and Allograft groups at 6 h, 12 h, 24 h, 5 d, and 10 d post-operation. Quantification was performed by counting EGR1-positive cells per field. Scale bars = 50 μm. (B) Relative mRNA expression levels of Egr1 in liver grafts determined by RT-qPCR. (C) EGR1 protein concentrations in the respective groups. (D) Representative Western blot images (left) and densitometric quantification (right) of EGR1 protein expression in liver grafts at indicated time points. Data are presented as mean ± SD (n = 3 per group). *p < 0.05, **p < 0.01, ***p < 0.001; ns, no significance. Allo, allograft; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IHC, immunohistochemistry; RT-qPCR, reverse transcription quantitative polymerase chain reaction; Syn, syngraft.

    Article Snippet: After blocking, the membranes were incubated with primary antibodies against EGR1 (1:10,000, Novus Biologicals) and GAPDH (1:10,000, Sigma, USA), followed by peroxidase-conjugated secondary antibodies.

    Techniques: Expressing, Transplantation Assay, Immunohistochemistry, Counting, Quantitative RT-PCR, Western Blot, Reverse Transcription, Real-time Polymerase Chain Reaction

    Virtual knockout analysis of Egr1 reveals immune-related regulatory perturbations in graft myeloid cells. (A) Proportion of significant and non-significant differentially regulated genes following virtual Egr1 knockout. Approximately 0.2% of the analyzed genes met the significance criterion. (B) Top five virtual-KO perturbed genes ranked according to the differential-regulation fold change (FC) relative to the expected network-perturbation level. S100a6, S100a4, MGC105649, Anxa2, and Lsp1 showed the strongest perturbation signals. (C) Differential-regulation plot showing the Z-score and −log10-adjusted P value for individual genes following virtual Egr1 knockout. Genes with prominent perturbation signals are labeled. (D, E) Correlation between S100a6 and Egr1 expression in myeloid cells from the day-7 syngeneic (D) and rejection (E) groups. S100a6 was weakly negatively correlated with Egr1 in both groups (syngeneic: R = −0.22, P < 2.2 × 10 - ¹ 6 ; rejection: R = −0.11, P < 2.2 × 10 - ¹ 6 ). Each point represents an individual myeloid cell, and the red line indicates the fitted trend. (F) Gene Ontology enrichment analysis of significant virtual-KO perturbed genes, showing representative Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) terms. (G) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of significant virtual-KO perturbed genes, highlighting pathways related to complement and coagulation cascades, cell adhesion, phagocytosis, hematopoietic cell lineage, NF-κB signaling, and efferocytosis. DR, differential regulation; FC, fold change relative to the expected differential-regulation distance; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

    Journal: Frontiers in Immunology

    Article Title: Myeloid EGR1 contributes to Th17/Treg imbalance during acute liver allograft rejection in a rat model

    doi: 10.3389/fimmu.2026.1901189

    Figure Lengend Snippet: Virtual knockout analysis of Egr1 reveals immune-related regulatory perturbations in graft myeloid cells. (A) Proportion of significant and non-significant differentially regulated genes following virtual Egr1 knockout. Approximately 0.2% of the analyzed genes met the significance criterion. (B) Top five virtual-KO perturbed genes ranked according to the differential-regulation fold change (FC) relative to the expected network-perturbation level. S100a6, S100a4, MGC105649, Anxa2, and Lsp1 showed the strongest perturbation signals. (C) Differential-regulation plot showing the Z-score and −log10-adjusted P value for individual genes following virtual Egr1 knockout. Genes with prominent perturbation signals are labeled. (D, E) Correlation between S100a6 and Egr1 expression in myeloid cells from the day-7 syngeneic (D) and rejection (E) groups. S100a6 was weakly negatively correlated with Egr1 in both groups (syngeneic: R = −0.22, P < 2.2 × 10 - ¹ 6 ; rejection: R = −0.11, P < 2.2 × 10 - ¹ 6 ). Each point represents an individual myeloid cell, and the red line indicates the fitted trend. (F) Gene Ontology enrichment analysis of significant virtual-KO perturbed genes, showing representative Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) terms. (G) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of significant virtual-KO perturbed genes, highlighting pathways related to complement and coagulation cascades, cell adhesion, phagocytosis, hematopoietic cell lineage, NF-κB signaling, and efferocytosis. DR, differential regulation; FC, fold change relative to the expected differential-regulation distance; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

    Article Snippet: After blocking, the membranes were incubated with primary antibodies against EGR1 (1:10,000, Novus Biologicals) and GAPDH (1:10,000, Sigma, USA), followed by peroxidase-conjugated secondary antibodies.

    Techniques: Knock-Out, Analysis, Labeling, Expressing, Coagulation

    EGR1-expressing myeloid cells and T cell-associated immune responses in liver allografts during acute rejection. (A) Representative immunohistochemistry (IHC) images and quantitative analysis of CD86-positive area in liver grafts from the Sham and Allograft groups at indicated time points after transplantation. (B) Representative multiplex immunofluorescence (mIF) staining showing CD86 (green) and EGR1 (red) expression in liver grafts. DAPI (blue) was used for nuclear counterstaining. (C) Representative mIF staining of CD4 (green) and IL-17A (red) in syngraft and allograft liver tissues. (D) Representative mIF staining of CD4 (green) and FOXP3 (red) in syngraft and allograft liver tissues. Scale bars = 50μm. EGR1, early growth response 1; IHC, immunohistochemistry; mIF, multiplex immunofluorescence; DAPI, 4’,6-diamidino-2-phenylindole; FOXP3, forkhead box P3.

    Journal: Frontiers in Immunology

    Article Title: Myeloid EGR1 contributes to Th17/Treg imbalance during acute liver allograft rejection in a rat model

    doi: 10.3389/fimmu.2026.1901189

    Figure Lengend Snippet: EGR1-expressing myeloid cells and T cell-associated immune responses in liver allografts during acute rejection. (A) Representative immunohistochemistry (IHC) images and quantitative analysis of CD86-positive area in liver grafts from the Sham and Allograft groups at indicated time points after transplantation. (B) Representative multiplex immunofluorescence (mIF) staining showing CD86 (green) and EGR1 (red) expression in liver grafts. DAPI (blue) was used for nuclear counterstaining. (C) Representative mIF staining of CD4 (green) and IL-17A (red) in syngraft and allograft liver tissues. (D) Representative mIF staining of CD4 (green) and FOXP3 (red) in syngraft and allograft liver tissues. Scale bars = 50μm. EGR1, early growth response 1; IHC, immunohistochemistry; mIF, multiplex immunofluorescence; DAPI, 4’,6-diamidino-2-phenylindole; FOXP3, forkhead box P3.

    Article Snippet: After blocking, the membranes were incubated with primary antibodies against EGR1 (1:10,000, Novus Biologicals) and GAPDH (1:10,000, Sigma, USA), followed by peroxidase-conjugated secondary antibodies.

    Techniques: Expressing, Immunohistochemistry, Analysis, Transplantation Assay, Multiplex Assay, Immunofluorescence, Staining

    EGR1-overexpressing BMDCs promote CD4 + T-cell proliferation and alter Th17/Treg differentiation in vitro . (A) Flow cytometry analysis and quantification of the surface maturation markers CD80, CD86, and MHC-II on bone marrow-derived dendritic cells (BMDCs) treated with PBS or LPS. (B) Western blot analysis (left) and quantification (right) verifying EGR1 overexpression (EGR1-OE) in BMDCs and its suppression by an EGR1 inhibitor (OE + Inh). (C) ELISA quantification of pro-inflammatory cytokines (TNF-α, IL-6, IL-12) and the anti-inflammatory cytokine (IL-10) in the BMDC culture supernatants. (D) CD4 + T cell proliferation assessed by EdU incorporation assay following co-culture with negative control (NC) or EGR1-overexpressing BMDCs. (E) Flow cytometry analysis of intracellular cytokines and transcription factors in co-cultured CD4 + T cells. Representative dot plots (left) and statistical quantification (right) showing the proportions of pro-inflammatory Th17 cells (CD4 + IL-17A + FOXP3 - ) and immunosuppressive Tregs (CD4 + FOXP3 + IL-17A - ) in the NC and EGR1 groups. The gating strategy is shown in . Data are presented as mean ± SEM of three independent experiments. * p < 0.05, *** p < 0.001. EGR1, early growth response 1; BMDC, bone marrow-derived dendritic cell; LPS, lipopolysaccharide; OE, overexpression; Inh, inhibitor; ELISA, enzyme-linked immunosorbent assay; EdU, 5-ethynyl-2’-deoxyuridine; MFI, mean fluorescence intensity; Treg, regulatory T cell; Th17, T helper 17 cell; FOXP3, forkhead box P3; IL-17, interleukin-17.

    Journal: Frontiers in Immunology

    Article Title: Myeloid EGR1 contributes to Th17/Treg imbalance during acute liver allograft rejection in a rat model

    doi: 10.3389/fimmu.2026.1901189

    Figure Lengend Snippet: EGR1-overexpressing BMDCs promote CD4 + T-cell proliferation and alter Th17/Treg differentiation in vitro . (A) Flow cytometry analysis and quantification of the surface maturation markers CD80, CD86, and MHC-II on bone marrow-derived dendritic cells (BMDCs) treated with PBS or LPS. (B) Western blot analysis (left) and quantification (right) verifying EGR1 overexpression (EGR1-OE) in BMDCs and its suppression by an EGR1 inhibitor (OE + Inh). (C) ELISA quantification of pro-inflammatory cytokines (TNF-α, IL-6, IL-12) and the anti-inflammatory cytokine (IL-10) in the BMDC culture supernatants. (D) CD4 + T cell proliferation assessed by EdU incorporation assay following co-culture with negative control (NC) or EGR1-overexpressing BMDCs. (E) Flow cytometry analysis of intracellular cytokines and transcription factors in co-cultured CD4 + T cells. Representative dot plots (left) and statistical quantification (right) showing the proportions of pro-inflammatory Th17 cells (CD4 + IL-17A + FOXP3 - ) and immunosuppressive Tregs (CD4 + FOXP3 + IL-17A - ) in the NC and EGR1 groups. The gating strategy is shown in . Data are presented as mean ± SEM of three independent experiments. * p < 0.05, *** p < 0.001. EGR1, early growth response 1; BMDC, bone marrow-derived dendritic cell; LPS, lipopolysaccharide; OE, overexpression; Inh, inhibitor; ELISA, enzyme-linked immunosorbent assay; EdU, 5-ethynyl-2’-deoxyuridine; MFI, mean fluorescence intensity; Treg, regulatory T cell; Th17, T helper 17 cell; FOXP3, forkhead box P3; IL-17, interleukin-17.

    Article Snippet: After blocking, the membranes were incubated with primary antibodies against EGR1 (1:10,000, Novus Biologicals) and GAPDH (1:10,000, Sigma, USA), followed by peroxidase-conjugated secondary antibodies.

    Techniques: In Vitro, Flow Cytometry, Analysis, Derivative Assay, Western Blot, Over Expression, Enzyme-linked Immunosorbent Assay, EdU Assay, Co-culture Assay, Negative Control, Cell Culture, Fluorescence