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staining include vtn antibody (Proteintech)

Name: staining include vtn antibody
Brand: Proteintech
Rating: 94 (3 reviews)

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Proteintech staining include vtn antibody

Figure 1. Wound-healing CAFs are correlated with immunosuppression and tumor growth rate. (A) Gene ontology biological process (GOBP) enrichment analysis was performed based on CAF gene expression in different CAF subtypes and the focused pathways and subtypes are marked in red. (B) Dot plot showing different types of fibroblast growth factor (FGF) expression in various subtypes of CAFs. (C) Interleukin-6 (IL-6) and C-X-C motif chemokine ligand 12 (CXCL12) expres- sion in each subtype of CAFs. (D) Several proteins that are only highly expressed on WH CAFs. (E) <t>Immunofluorescence</t> <t>staining</t> of vitronectin <t>(VTN)</t> (red) distribution in tumors, scale bar: 100 µm. (F) VTN immunofluorescence quantification results of selected tumors (n = 5 independent samples). (G) Comparison of tumor volume from high- and low-proportion groups on the last day (n = 5 independent samples). (H) Selected tumor volume curves from high- and low-proportion groups over 14 days (n = 5 independent samples). (I) Mean selected tumor volume curve compari- son from high- and low-proportion groups over 14 days (n = 5 independent samples). (J) Sirius red staining in high and low groups, scale bar: 1.25 mm (low magnification), 100 µm (high magnification). (K,M) Western blot analysis of collagen I and IL-6 protein in high- and low-proportion groups. (L) The immunofluorescence co-staining image of CD8 (green) and VTN (red), scale bar: 100 µm (1: Extracellular Matrix CAFs; 2: Matrix CAFs 1; 3: Development CAFs; 4: Antigen presentation CAFs 1; 5: Antigen presentation CAFs 2; 6: Wound healing CAFs; 7: Vascular CAFs; 8: Inflammatory CAFs; 9: Matrix CAFs 2). Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Staining Include Vtn Antibody, supplied by Proteintech, used in various techniques. Bioz Stars score: 94/100, based on 3 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer."

Article Title: Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer.

Journal: International journal of molecular sciences

doi: 10.3390/ijms26083525

Figure Legend Snippet: Figure 1. Wound-healing CAFs are correlated with immunosuppression and tumor growth rate. (A) Gene ontology biological process (GOBP) enrichment analysis was performed based on CAF gene expression in different CAF subtypes and the focused pathways and subtypes are marked in red. (B) Dot plot showing different types of fibroblast growth factor (FGF) expression in various subtypes of CAFs. (C) Interleukin-6 (IL-6) and C-X-C motif chemokine ligand 12 (CXCL12) expres- sion in each subtype of CAFs. (D) Several proteins that are only highly expressed on WH CAFs. (E) Immunofluorescence staining of vitronectin (VTN) (red) distribution in tumors, scale bar: 100 µm. (F) VTN immunofluorescence quantification results of selected tumors (n = 5 independent samples). (G) Comparison of tumor volume from high- and low-proportion groups on the last day (n = 5 independent samples). (H) Selected tumor volume curves from high- and low-proportion groups over 14 days (n = 5 independent samples). (I) Mean selected tumor volume curve compari- son from high- and low-proportion groups over 14 days (n = 5 independent samples). (J) Sirius red staining in high and low groups, scale bar: 1.25 mm (low magnification), 100 µm (high magnification). (K,M) Western blot analysis of collagen I and IL-6 protein in high- and low-proportion groups. (L) The immunofluorescence co-staining image of CD8 (green) and VTN (red), scale bar: 100 µm (1: Extracellular Matrix CAFs; 2: Matrix CAFs 1; 3: Development CAFs; 4: Antigen presentation CAFs 1; 5: Antigen presentation CAFs 2; 6: Wound healing CAFs; 7: Vascular CAFs; 8: Inflammatory CAFs; 9: Matrix CAFs 2). Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Techniques Used: Gene Expression, Expressing, Immunofluorescence, Staining, Comparison, Western Blot, Immunopeptidomics

Figure 3. Deep learning model to evaluate WH CAF level. (A) Representative SWE image of mice; each image is composed of a pseudo-color image and grayscale image. (B) Quantification results of VTN immunofluorescence staining. The solid red line represents the average (n = 105 independent samples). (C) Representative immunofluorescence staining of VTN (red) from high and low level, scale bar: 100 µm. (D) Mean stiffness value in high- and low-level groups (WH high-level group: n = 62, WH low-level group: n = 43). (E) The framework of our proposed deep learning model. (F) Performance of the deep learning model to predict the WH CAFs level. ROC: receiver operating characteristic curve; AUC, area under the curves. (G) Confusion matrices in two classification cate- gories. The confusion matrices showed the pairwise comparison. Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Figure Legend Snippet: Figure 3. Deep learning model to evaluate WH CAF level. (A) Representative SWE image of mice; each image is composed of a pseudo-color image and grayscale image. (B) Quantification results of VTN immunofluorescence staining. The solid red line represents the average (n = 105 independent samples). (C) Representative immunofluorescence staining of VTN (red) from high and low level, scale bar: 100 µm. (D) Mean stiffness value in high- and low-level groups (WH high-level group: n = 62, WH low-level group: n = 43). (E) The framework of our proposed deep learning model. (F) Performance of the deep learning model to predict the WH CAFs level. ROC: receiver operating characteristic curve; AUC, area under the curves. (G) Confusion matrices in two classification cate- gories. The confusion matrices showed the pairwise comparison. Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Techniques Used: Immunofluorescence, Staining, Comparison

Figure 4. FGFR inhibitor potentiates immune checkpoint inhibitors (ICIs) in WH CAF high-level tumors. (A) Schematic of the experimental protocol. SWE: shear wave elastography. (B) Representa- tive SWE images of tumors in high-level and low-level groups at the end of volume measurement. (C) Mean stiffness value of tumors in high-level and low-level groups (WH high-level group: n = 27, WH low-level group: n = 14). (D) Tumor volume curves in different groups during the 15-day monitoring period (Group: WH CAFs high level, WH CAFs high level with Anti-PD-1, WH CAFs high level with Anti-PD-1+Erdafitinib: n = 9; group: WH CAFs low level, WH CAFs low level with Anti-PD-1: n = 5; group: WH CAFs low level with Anti-PD-1+Erdafitinib: n = 4). (E) Comparison of immunohistochemical results of CD8 and CD3 in high- and low-level groups before and after Anti- PD-1+Erdafitinib treatment, scale: 100 µm. (F) Immunofluorescence staining and quantization results of FGFR2 and VTN proteins in WH CAFs high-level group before and after Anti-PD-1+Erdafitinib treatment, scale bar: 2000 µm (low magnification), 100 µm (high magnification). Data expressed as Mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Figure Legend Snippet: Figure 4. FGFR inhibitor potentiates immune checkpoint inhibitors (ICIs) in WH CAF high-level tumors. (A) Schematic of the experimental protocol. SWE: shear wave elastography. (B) Representa- tive SWE images of tumors in high-level and low-level groups at the end of volume measurement. (C) Mean stiffness value of tumors in high-level and low-level groups (WH high-level group: n = 27, WH low-level group: n = 14). (D) Tumor volume curves in different groups during the 15-day monitoring period (Group: WH CAFs high level, WH CAFs high level with Anti-PD-1, WH CAFs high level with Anti-PD-1+Erdafitinib: n = 9; group: WH CAFs low level, WH CAFs low level with Anti-PD-1: n = 5; group: WH CAFs low level with Anti-PD-1+Erdafitinib: n = 4). (E) Comparison of immunohistochemical results of CD8 and CD3 in high- and low-level groups before and after Anti- PD-1+Erdafitinib treatment, scale: 100 µm. (F) Immunofluorescence staining and quantization results of FGFR2 and VTN proteins in WH CAFs high-level group before and after Anti-PD-1+Erdafitinib treatment, scale bar: 2000 µm (low magnification), 100 µm (high magnification). Data expressed as Mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Techniques Used: Shear, Comparison, Immunohistochemical staining, Immunofluorescence, Staining

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Journal: International journal of molecular sciences

Article Title: Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer.

doi: 10.3390/ijms26083525

Figure Lengend Snippet: Figure 1. Wound-healing CAFs are correlated with immunosuppression and tumor growth rate. (A) Gene ontology biological process (GOBP) enrichment analysis was performed based on CAF gene expression in different CAF subtypes and the focused pathways and subtypes are marked in red. (B) Dot plot showing different types of fibroblast growth factor (FGF) expression in various subtypes of CAFs. (C) Interleukin-6 (IL-6) and C-X-C motif chemokine ligand 12 (CXCL12) expres- sion in each subtype of CAFs. (D) Several proteins that are only highly expressed on WH CAFs. (E) Immunofluorescence staining of vitronectin (VTN) (red) distribution in tumors, scale bar: 100 µm. (F) VTN immunofluorescence quantification results of selected tumors (n = 5 independent samples). (G) Comparison of tumor volume from high- and low-proportion groups on the last day (n = 5 independent samples). (H) Selected tumor volume curves from high- and low-proportion groups over 14 days (n = 5 independent samples). (I) Mean selected tumor volume curve compari- son from high- and low-proportion groups over 14 days (n = 5 independent samples). (J) Sirius red staining in high and low groups, scale bar: 1.25 mm (low magnification), 100 µm (high magnification). (K,M) Western blot analysis of collagen I and IL-6 protein in high- and low-proportion groups. (L) The immunofluorescence co-staining image of CD8 (green) and VTN (red), scale bar: 100 µm (1: Extracellular Matrix CAFs; 2: Matrix CAFs 1; 3: Development CAFs; 4: Antigen presentation CAFs 1; 5: Antigen presentation CAFs 2; 6: Wound healing CAFs; 7: Vascular CAFs; 8: Inflammatory CAFs; 9: Matrix CAFs 2). Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Article Snippet: The primary antibodies used for IF staining include VTN antibody (Proteintech, Wuhan, China, 66398-1-lg), FGFR2 antibody (Proteintech, Wuhan, China, 13042-1-AP), CD8a antibody (Proteintech, Wuhan, China, 29896-1-AP), and SHMT1 antibody (Proteintech, Wuhan, China, 67963-1-Ig).

Techniques: Gene Expression, Expressing, Immunofluorescence, Staining, Comparison, Western Blot, Immunopeptidomics

Journal: International journal of molecular sciences

Article Title: Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer.

doi: 10.3390/ijms26083525

Figure Lengend Snippet: Figure 3. Deep learning model to evaluate WH CAF level. (A) Representative SWE image of mice; each image is composed of a pseudo-color image and grayscale image. (B) Quantification results of VTN immunofluorescence staining. The solid red line represents the average (n = 105 independent samples). (C) Representative immunofluorescence staining of VTN (red) from high and low level, scale bar: 100 µm. (D) Mean stiffness value in high- and low-level groups (WH high-level group: n = 62, WH low-level group: n = 43). (E) The framework of our proposed deep learning model. (F) Performance of the deep learning model to predict the WH CAFs level. ROC: receiver operating characteristic curve; AUC, area under the curves. (G) Confusion matrices in two classification cate- gories. The confusion matrices showed the pairwise comparison. Data expressed as mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Techniques: Immunofluorescence, Staining, Comparison

Journal: International journal of molecular sciences

Article Title: Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer.

doi: 10.3390/ijms26083525

Figure Lengend Snippet: Figure 4. FGFR inhibitor potentiates immune checkpoint inhibitors (ICIs) in WH CAF high-level tumors. (A) Schematic of the experimental protocol. SWE: shear wave elastography. (B) Representa- tive SWE images of tumors in high-level and low-level groups at the end of volume measurement. (C) Mean stiffness value of tumors in high-level and low-level groups (WH high-level group: n = 27, WH low-level group: n = 14). (D) Tumor volume curves in different groups during the 15-day monitoring period (Group: WH CAFs high level, WH CAFs high level with Anti-PD-1, WH CAFs high level with Anti-PD-1+Erdafitinib: n = 9; group: WH CAFs low level, WH CAFs low level with Anti-PD-1: n = 5; group: WH CAFs low level with Anti-PD-1+Erdafitinib: n = 4). (E) Comparison of immunohistochemical results of CD8 and CD3 in high- and low-level groups before and after Anti- PD-1+Erdafitinib treatment, scale: 100 µm. (F) Immunofluorescence staining and quantization results of FGFR2 and VTN proteins in WH CAFs high-level group before and after Anti-PD-1+Erdafitinib treatment, scale bar: 2000 µm (low magnification), 100 µm (high magnification). Data expressed as Mean ± S.E.M; statistical comparisons were performed using Student’s t test.

Techniques: Shear, Comparison, Immunohistochemical staining, Immunofluorescence, Staining

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1) Product Images from "Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer."

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