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Novus Biologicals anti rat cd34

Tumor associated senescent <t>CD34</t> + CLDN5 + endothelial cells existed in HCC tumor tissue. ( A ) Schematic of the schedule for collection and processing of liver tissue for single-cell RNA sequencing. N, normal rat liver tissue; D4, liver tissue from D4 rat; D8, liver tissue from D8 rat; D12, liver tissue from D12 rat; D16T, tumor tissue from D16 rat; D16P, peri -tumor tissue from D16 rat. ( B-C ) UMAP plot showing the clustering results, single cells from livers exposed to DEN for different times and all cell types: Chalongiocytes, HPCs, Natural killer cells, T cells, B cells, Plasma cells, DCs, Neutrophils, MSCs, ECs, Monocytes and Hepatocytes. The different time points and cell types are indicated by different colors. ( D ) The process of categorizing and distinguishing cells based on their distinct marker gene expressions. ( E) UMAP plot showing the ECs clustering results. Different treatment times or ECs subclusters are indicated by different colors. ( F) The Ro/e method was used to estimate the tissue preference of each major ECs subcluster. Highlighting the Cluster 11. ( G ) Bar graphs showing the top 12 ontology terms of CD34 + CLDN5 + ECs which identified by KEGG and GO enrichment analysis. ( H ) mIHC of CD34, CLDN5 in liver specimens from the D16T and D16P. Scale bars, 50 μm. ( I-J ) mIHC of CD34, CLDN5, P16, P21 and γH2AX in tumor from the D16T. Scale bars, 20 μm. ( K) D16 rats' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells, and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( L ) Quantified data of senescent cell proportion in different non-parenchymal cells of D16 rats' tumor tissue. n = 3. Data are represented as mean. *** p < 0.001, **** p < 0.0001. ( M ) Liver cancer patients' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( N ) Quantified data of senescent cell proportion in different non-parenchymal cells of HCC patients' tumor tissue. n = 2. Data are represented as mean.

Anti Rat Cd34, supplied by Novus Biologicals, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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anti rat cd34 - by Bioz Stars, 2026-08

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1) Product Images from "CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma"

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

Journal: Journal of Advanced Research

doi: 10.1016/j.jare.2024.12.008

Tumor associated senescent CD34 + CLDN5 + endothelial cells existed in HCC tumor tissue. ( A ) Schematic of the schedule for collection and processing of liver tissue for single-cell RNA sequencing. N, normal rat liver tissue; D4, liver tissue from D4 rat; D8, liver tissue from D8 rat; D12, liver tissue from D12 rat; D16T, tumor tissue from D16 rat; D16P, peri -tumor tissue from D16 rat. ( B-C ) UMAP plot showing the clustering results, single cells from livers exposed to DEN for different times and all cell types: Chalongiocytes, HPCs, Natural killer cells, T cells, B cells, Plasma cells, DCs, Neutrophils, MSCs, ECs, Monocytes and Hepatocytes. The different time points and cell types are indicated by different colors. ( D ) The process of categorizing and distinguishing cells based on their distinct marker gene expressions. ( E) UMAP plot showing the ECs clustering results. Different treatment times or ECs subclusters are indicated by different colors. ( F) The Ro/e method was used to estimate the tissue preference of each major ECs subcluster. Highlighting the Cluster 11. ( G ) Bar graphs showing the top 12 ontology terms of CD34 + CLDN5 + ECs which identified by KEGG and GO enrichment analysis. ( H ) mIHC of CD34, CLDN5 in liver specimens from the D16T and D16P. Scale bars, 50 μm. ( I-J ) mIHC of CD34, CLDN5, P16, P21 and γH2AX in tumor from the D16T. Scale bars, 20 μm. ( K) D16 rats' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells, and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( L ) Quantified data of senescent cell proportion in different non-parenchymal cells of D16 rats' tumor tissue. n = 3. Data are represented as mean. *** p < 0.001, **** p < 0.0001. ( M ) Liver cancer patients' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( N ) Quantified data of senescent cell proportion in different non-parenchymal cells of HCC patients' tumor tissue. n = 2. Data are represented as mean.

Figure Legend Snippet: Tumor associated senescent CD34 + CLDN5 + endothelial cells existed in HCC tumor tissue. ( A ) Schematic of the schedule for collection and processing of liver tissue for single-cell RNA sequencing. N, normal rat liver tissue; D4, liver tissue from D4 rat; D8, liver tissue from D8 rat; D12, liver tissue from D12 rat; D16T, tumor tissue from D16 rat; D16P, peri -tumor tissue from D16 rat. ( B-C ) UMAP plot showing the clustering results, single cells from livers exposed to DEN for different times and all cell types: Chalongiocytes, HPCs, Natural killer cells, T cells, B cells, Plasma cells, DCs, Neutrophils, MSCs, ECs, Monocytes and Hepatocytes. The different time points and cell types are indicated by different colors. ( D ) The process of categorizing and distinguishing cells based on their distinct marker gene expressions. ( E) UMAP plot showing the ECs clustering results. Different treatment times or ECs subclusters are indicated by different colors. ( F) The Ro/e method was used to estimate the tissue preference of each major ECs subcluster. Highlighting the Cluster 11. ( G ) Bar graphs showing the top 12 ontology terms of CD34 + CLDN5 + ECs which identified by KEGG and GO enrichment analysis. ( H ) mIHC of CD34, CLDN5 in liver specimens from the D16T and D16P. Scale bars, 50 μm. ( I-J ) mIHC of CD34, CLDN5, P16, P21 and γH2AX in tumor from the D16T. Scale bars, 20 μm. ( K) D16 rats' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells, and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( L ) Quantified data of senescent cell proportion in different non-parenchymal cells of D16 rats' tumor tissue. n = 3. Data are represented as mean. *** p < 0.001, **** p < 0.0001. ( M ) Liver cancer patients' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( N ) Quantified data of senescent cell proportion in different non-parenchymal cells of HCC patients' tumor tissue. n = 2. Data are represented as mean.

Techniques Used: RNA Sequencing, Clinical Proteomics, Marker, Flow Cytometry

CD34 + CLDN5 + endothelial cells increased the cholangiocellular phenotype within HCC and were instrumental in the recruitment of mesenchymal stem cells into the tumor microenvironment. (A) Hierarchical clustering of cell abundance predicted per sample from TCGA of HCC cohort by ssGSEA. Shown are row z-score. (B) The Kaplan-Meier overall survival curves of patients in TCGA_LIHC by the gene signature expression of CD34 + CLDN5 + cells. p value was determined by Kaplan-Meier survival curves and log-rank test. (C) Schematic diagram for administration of CD34 + CLDN5 + ECs to DEN-induced HCC rats and the gross appearance of livers exposed to DEN for 16 weeks. PBS or CD34 + CLDN5 + ECs/CD34 − CLDN5 − ECs was administered by splenic injection. CD34 + CLDN5 + ECs, the rats were administered CD34 + CLDN5 + ECs; CD34 − CLDN5 − ECs, the rats were administered CD34 ,− CLDN5 ,− ECs. n = 6, respectively. (D) H&E of the different tumor from the rats liver. Scale bars, 50 μm. (E) mIHC of CK19, CD44, EPCAM in different tumors. Scale bars, 100 μm. (F) Schematic diagram illustrating the co-culture setup of RH-35 cells with the ECs and Quantified data of colony and sphere formation assay of the different RH35. Data are represented as mean ± SD. ns, not significant. FACS, fluorescence-activated cell sorting. (G) Chord diagrams showing cell–cell interactions among ECs and various cell clusters in TME according to L-R pairs analysis. Circling the interaction between ECs and MSCs (H) mIHC of CD34, CLDN5, CD146 in tumor from the D16T. Scale bars, 10 μm. (I) Schematic diagram illustrating the transwell migration assay between MSCs with the ECs for 48 h. Scale bars, 100 μm. (J) Bubble chart showing the L-R pairs enrichment analysis between the MSCs and different ECs. (K) The UMAP plot showing expression levels of Igf2 in ECs subtypes , with the lowest expression levels represented as gray dots and the highest expression levels as red dots. (L) The correlation (Pearson) between Igf2 and Cdkn2a in CD34 + CLDN5 + ECs. (M) mIHC of CD34, CLDN5 and IGF2 in tumor from the D16T. Scale bars, 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Figure Legend Snippet: CD34 + CLDN5 + endothelial cells increased the cholangiocellular phenotype within HCC and were instrumental in the recruitment of mesenchymal stem cells into the tumor microenvironment. (A) Hierarchical clustering of cell abundance predicted per sample from TCGA of HCC cohort by ssGSEA. Shown are row z-score. (B) The Kaplan-Meier overall survival curves of patients in TCGA_LIHC by the gene signature expression of CD34 + CLDN5 + cells. p value was determined by Kaplan-Meier survival curves and log-rank test. (C) Schematic diagram for administration of CD34 + CLDN5 + ECs to DEN-induced HCC rats and the gross appearance of livers exposed to DEN for 16 weeks. PBS or CD34 + CLDN5 + ECs/CD34 − CLDN5 − ECs was administered by splenic injection. CD34 + CLDN5 + ECs, the rats were administered CD34 + CLDN5 + ECs; CD34 − CLDN5 − ECs, the rats were administered CD34 ,− CLDN5 ,− ECs. n = 6, respectively. (D) H&E of the different tumor from the rats liver. Scale bars, 50 μm. (E) mIHC of CK19, CD44, EPCAM in different tumors. Scale bars, 100 μm. (F) Schematic diagram illustrating the co-culture setup of RH-35 cells with the ECs and Quantified data of colony and sphere formation assay of the different RH35. Data are represented as mean ± SD. ns, not significant. FACS, fluorescence-activated cell sorting. (G) Chord diagrams showing cell–cell interactions among ECs and various cell clusters in TME according to L-R pairs analysis. Circling the interaction between ECs and MSCs (H) mIHC of CD34, CLDN5, CD146 in tumor from the D16T. Scale bars, 10 μm. (I) Schematic diagram illustrating the transwell migration assay between MSCs with the ECs for 48 h. Scale bars, 100 μm. (J) Bubble chart showing the L-R pairs enrichment analysis between the MSCs and different ECs. (K) The UMAP plot showing expression levels of Igf2 in ECs subtypes , with the lowest expression levels represented as gray dots and the highest expression levels as red dots. (L) The correlation (Pearson) between Igf2 and Cdkn2a in CD34 + CLDN5 + ECs. (M) mIHC of CD34, CLDN5 and IGF2 in tumor from the D16T. Scale bars, 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Techniques Used: Expressing, Injection, Co-Culture Assay, Tube Formation Assay, Fluorescence, FACS, Transwell Migration Assay

CEBPβ bound with Igf2 -promoter-sequence in hepatic senescent endothelial cells to up regulate the IGF2 expression. (A) Heatmap of the AUC scores of expression regulation by transcription factors, as estimated using SCENIC, for each of the ECs from 16 clusters. Shown are the transcription factors having the highest difference in expression regulation estimates among different times ECs from livers of rats exposed to DEN. ( B) Shown the Top 30 transcription factors having the significant difference in expression regulation in CD34 + CLDN5 + ECs. ( C) The UMAP plot showing expression levels of Cebpb targets in ECs subtypes. ( D) The Schematic diagram of canonical CEBPβ-binding motif (from JASPAR Database) and three potential CEBPβ responsive elements (Primer A, Primer B, and Primer C) in the IGF2 promoter region. ( E) Enrichment of the fragments containing the CEBPβ binding sites within the IGF2 promoter in CD34 + CLDN5 + ECs by CUT&Tag-qPCR. Comparing to the background DNA fragment pulled down by IgG immunoprecipitation. Data are presented as mean ± SD. *** p < 0.001. ( F) mIHC of CD34, CLDN5 and CEBPβ in tumor from the D16T. Scale bars, 20 μm. ( G) Immunofluorescence staining of CEBPβ in CD34 + CLDN5 + ECs from D16 rats and normal 6 month-old normal rat livers. Scale bars, 100 μm. (H) Immunofluorescence staining of IGF2 for CD34 + CLDN5 + ECs from D16T treated with Cebpb -siRNA (100 nM) or scramble siRNA (as negative control) or not for 48 h. S1-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 1; S2-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 2; NC-ECs, negative control. Scale bars, 100 μm. ( I) Quantified data of immunofluorescence staining of IGF2 in CD34 + CLDN5 + ECs. Data are presented as mean ± SD. ns, no significant, *** p < 0.001.

Figure Legend Snippet: CEBPβ bound with Igf2 -promoter-sequence in hepatic senescent endothelial cells to up regulate the IGF2 expression. (A) Heatmap of the AUC scores of expression regulation by transcription factors, as estimated using SCENIC, for each of the ECs from 16 clusters. Shown are the transcription factors having the highest difference in expression regulation estimates among different times ECs from livers of rats exposed to DEN. ( B) Shown the Top 30 transcription factors having the significant difference in expression regulation in CD34 + CLDN5 + ECs. ( C) The UMAP plot showing expression levels of Cebpb targets in ECs subtypes. ( D) The Schematic diagram of canonical CEBPβ-binding motif (from JASPAR Database) and three potential CEBPβ responsive elements (Primer A, Primer B, and Primer C) in the IGF2 promoter region. ( E) Enrichment of the fragments containing the CEBPβ binding sites within the IGF2 promoter in CD34 + CLDN5 + ECs by CUT&Tag-qPCR. Comparing to the background DNA fragment pulled down by IgG immunoprecipitation. Data are presented as mean ± SD. *** p < 0.001. ( F) mIHC of CD34, CLDN5 and CEBPβ in tumor from the D16T. Scale bars, 20 μm. ( G) Immunofluorescence staining of CEBPβ in CD34 + CLDN5 + ECs from D16 rats and normal 6 month-old normal rat livers. Scale bars, 100 μm. (H) Immunofluorescence staining of IGF2 for CD34 + CLDN5 + ECs from D16T treated with Cebpb -siRNA (100 nM) or scramble siRNA (as negative control) or not for 48 h. S1-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 1; S2-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 2; NC-ECs, negative control. Scale bars, 100 μm. ( I) Quantified data of immunofluorescence staining of IGF2 in CD34 + CLDN5 + ECs. Data are presented as mean ± SD. ns, no significant, *** p < 0.001.

Techniques Used: Sequencing, Expressing, Binding Assay, Immunoprecipitation, Immunofluorescence, Staining, Negative Control

Tumor associated senescent endothelial cells are frequently observed in both intrahepatic cholangiocarcinoma and combined hepatocellular carcinoma-cholangiocarcinoma, and their presence is significantly correlated with a short survival duration in patients with liver cancer. (A-B) Schematic of processing of liver tissue for single-cell RNA sequencing, UMAP plot showing the clustering results, single cells from different liver tissue and relative abundance of the different cell types in different liver samples: Chalongiocytes, HPCs/CSCs, NK cells (Nature kill cells), T cells, B cells, Plasma cells, DCs, Neutrophils, Endothelial cells, Macro and Mono (Macrophages and Monocytes), Mast cells, Fibrolasts and Hepatocytes. The different samples and cell types are indicated by different colors. Highlight the CSCs in iCCA. Healthy, the liver tissue from the patients without HCC. HCC, the tumor from the patients with HCC. iCCA, the tumor from the patients with iCCA. (C) UMAP plot showing the ECs clustering results and relative abundance of the different ECs subtypes in HCC and iCCA. The different samples and ECs subtypes are indicated by different colors. Highlight the CD34 + CLDN5 + ECs in iCCA. (D) The UMAP plot showing expression levels of CEBPB , CD34 , CLDN5 , IGF2 , CDKN1A and TP53 in CD34 + CLDN5 + ECs. (E) The correlation analysis between IGF2 and CD34 + CLDN5 + ECs, IGF2 and MSCs, CD34 + CLDN5 + ECs and MSCs according to TCGA_LIHC. (F) The pie chart illustrating the proportion of CD34 + CLDN5 + ECs relative to the total ECs across various liver cancer. (G-J) Left four panels: the Kaplan-Meier overall survival curves of tumor tissues from 66 patients with liver cancer by co-expression of CD34, CLDN5, P16 and CEBPβ in mIHC. p value was determined by Kaplan-Meier survival curves and log-rank test. Right eight panels: CD34, CLDN5, p16 and CEBPβ co-expression was detected in tumor tissues from 66 patients with HCC by mIHC. Scale bars, 50 μm.

Figure Legend Snippet: Tumor associated senescent endothelial cells are frequently observed in both intrahepatic cholangiocarcinoma and combined hepatocellular carcinoma-cholangiocarcinoma, and their presence is significantly correlated with a short survival duration in patients with liver cancer. (A-B) Schematic of processing of liver tissue for single-cell RNA sequencing, UMAP plot showing the clustering results, single cells from different liver tissue and relative abundance of the different cell types in different liver samples: Chalongiocytes, HPCs/CSCs, NK cells (Nature kill cells), T cells, B cells, Plasma cells, DCs, Neutrophils, Endothelial cells, Macro and Mono (Macrophages and Monocytes), Mast cells, Fibrolasts and Hepatocytes. The different samples and cell types are indicated by different colors. Highlight the CSCs in iCCA. Healthy, the liver tissue from the patients without HCC. HCC, the tumor from the patients with HCC. iCCA, the tumor from the patients with iCCA. (C) UMAP plot showing the ECs clustering results and relative abundance of the different ECs subtypes in HCC and iCCA. The different samples and ECs subtypes are indicated by different colors. Highlight the CD34 + CLDN5 + ECs in iCCA. (D) The UMAP plot showing expression levels of CEBPB , CD34 , CLDN5 , IGF2 , CDKN1A and TP53 in CD34 + CLDN5 + ECs. (E) The correlation analysis between IGF2 and CD34 + CLDN5 + ECs, IGF2 and MSCs, CD34 + CLDN5 + ECs and MSCs according to TCGA_LIHC. (F) The pie chart illustrating the proportion of CD34 + CLDN5 + ECs relative to the total ECs across various liver cancer. (G-J) Left four panels: the Kaplan-Meier overall survival curves of tumor tissues from 66 patients with liver cancer by co-expression of CD34, CLDN5, P16 and CEBPβ in mIHC. p value was determined by Kaplan-Meier survival curves and log-rank test. Right eight panels: CD34, CLDN5, p16 and CEBPβ co-expression was detected in tumor tissues from 66 patients with HCC by mIHC. Scale bars, 50 μm.

Techniques Used: RNA Sequencing, Clinical Proteomics, Expressing

Tumor associated senescent endothelial cells recruited the mesenchymal stem cells into the TME, promoting the formation of cholangiocellular phenotype in HCC A schematic model of the mechanism by which CD34 + CLDN5 + ECs, with distinct senescent cellular features and high expression of IGF2, which was regulated by CEBPβ, recruited the MSCs into TME via IGF2R/MAPK. Then, MSCs in TME released the cytokines to induced evolution of the HCC cell into CSC-like characteristic tumor cell, promoted the increasing of cholangiocellular phenotype and malignant transformation of HCC.

Figure Legend Snippet: Tumor associated senescent endothelial cells recruited the mesenchymal stem cells into the TME, promoting the formation of cholangiocellular phenotype in HCC A schematic model of the mechanism by which CD34 + CLDN5 + ECs, with distinct senescent cellular features and high expression of IGF2, which was regulated by CEBPβ, recruited the MSCs into TME via IGF2R/MAPK. Then, MSCs in TME released the cytokines to induced evolution of the HCC cell into CSC-like characteristic tumor cell, promoted the increasing of cholangiocellular phenotype and malignant transformation of HCC.

Techniques Used: Expressing, Transformation Assay

Image Search Results

Immunohistochemical tissue staining in rats treated with UMUS on the 14th day from the moment of treatment start. A-Ki-67 in sub-dermal tissue marked with arrows; along collagen thick bunches, fibrocytes are gathered, stained with blue color; some of them are marked as blast forms (arrows); B - VEGF stained structures in both bones and neighboring sub-dermal tissue. C - CD31 in sub-dermal tissue (arrows); D and E - CD34 in bone and sub-dermal tissue, respectively; F-negative control. Calibrate at 100 mcm.

Journal: bioRxiv

Article Title: THE COMPARATIVE STUDY OF THE EFFECT OF LOW-INTENSITY BROADBAND AND LOW-INTENSITY PULSED ULTRASOUND ON THE AMPUTATIONAL MODEL OF WOUND

doi: 10.64898/2026.04.09.717366

Figure Lengend Snippet: Immunohistochemical tissue staining in rats treated with UMUS on the 14th day from the moment of treatment start. A-Ki-67 in sub-dermal tissue marked with arrows; along collagen thick bunches, fibrocytes are gathered, stained with blue color; some of them are marked as blast forms (arrows); B - VEGF stained structures in both bones and neighboring sub-dermal tissue. C - CD31 in sub-dermal tissue (arrows); D and E - CD34 in bone and sub-dermal tissue, respectively; F-negative control. Calibrate at 100 mcm.

Article Snippet: For the incubation the Abcam antibodies were used: CD31 (EPR17260-254 / ab213175); VEGF (ab100786); Ki67 [SP6] (ab16667), and for CD34 - AF4117 (R&D Systems).

Techniques: Immunohistochemical staining, Staining, Negative Control

Basic Characterization of the IMG-A1 Granulosa Cell Line. ( a , b ) Phase-contrast micrographs of the IMG-A1 cell culture. ( c ) Staining for senescence-associated β-galactosidase activity in passage 18 cells. ( d – f ) Immunocytochemical staining for the proliferation marker Ki67, showing a phase-contrast image ( d ), Ki67 immunofluorescence ( e ), and a merged image with DAPI nuclear counterstain ( f ). ( g , h ) Representative metaphase spreads showing a normal diploid karyotype (40 chromosomes) ( g ) and a near-tetraploid karyotype (78 chromosomes) from the IMG-A1 line ( h ). ( i – k ) Ploidy analysis by flow cytometry. Control blood cells ( i ) and primary granulosa cells ( j ) show characteristic diploid (2n) and tetraploid (4n, G2/M phase) peaks. The IMG-A1 culture ( k ) displays a predominantly near-tetraploid and near-octaploid cell population. ( l ) Flow cytometry analysis confirming the homogeneity of the IMG-A1 culture (passages 10 and 40) based on staining for the stromal marker CD29 and the absence of hematopoietic markers CD45, CD34, and the fibroblast marker CD90. Quadrant gates were set based on unstained controls. Scale bars = 100 µm.

Journal: Cells

Article Title: IMG-A1: A Novel Immortalized Granulosa Cell Line for Investigating FSH-Dependent Folliculogenesis and Ovarian Pathophysiology

doi: 10.3390/cells14241940

Figure Lengend Snippet: Basic Characterization of the IMG-A1 Granulosa Cell Line. ( a , b ) Phase-contrast micrographs of the IMG-A1 cell culture. ( c ) Staining for senescence-associated β-galactosidase activity in passage 18 cells. ( d – f ) Immunocytochemical staining for the proliferation marker Ki67, showing a phase-contrast image ( d ), Ki67 immunofluorescence ( e ), and a merged image with DAPI nuclear counterstain ( f ). ( g , h ) Representative metaphase spreads showing a normal diploid karyotype (40 chromosomes) ( g ) and a near-tetraploid karyotype (78 chromosomes) from the IMG-A1 line ( h ). ( i – k ) Ploidy analysis by flow cytometry. Control blood cells ( i ) and primary granulosa cells ( j ) show characteristic diploid (2n) and tetraploid (4n, G2/M phase) peaks. The IMG-A1 culture ( k ) displays a predominantly near-tetraploid and near-octaploid cell population. ( l ) Flow cytometry analysis confirming the homogeneity of the IMG-A1 culture (passages 10 and 40) based on staining for the stromal marker CD29 and the absence of hematopoietic markers CD45, CD34, and the fibroblast marker CD90. Quadrant gates were set based on unstained controls. Scale bars = 100 µm.

Article Snippet: PE CD34 Rat mAb [RAM34] , Elabscience Biotechnology Co., Ltd., Wuhan, China , E-AB-F1284D.

Techniques: Cell Culture, Staining, Activity Assay, Marker, Immunofluorescence, Flow Cytometry, Control

Journal: Journal of Advanced Research

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

doi: 10.1016/j.jare.2024.12.008

Figure Lengend Snippet: Tumor associated senescent CD34 + CLDN5 + endothelial cells existed in HCC tumor tissue. ( A ) Schematic of the schedule for collection and processing of liver tissue for single-cell RNA sequencing. N, normal rat liver tissue; D4, liver tissue from D4 rat; D8, liver tissue from D8 rat; D12, liver tissue from D12 rat; D16T, tumor tissue from D16 rat; D16P, peri -tumor tissue from D16 rat. ( B-C ) UMAP plot showing the clustering results, single cells from livers exposed to DEN for different times and all cell types: Chalongiocytes, HPCs, Natural killer cells, T cells, B cells, Plasma cells, DCs, Neutrophils, MSCs, ECs, Monocytes and Hepatocytes. The different time points and cell types are indicated by different colors. ( D ) The process of categorizing and distinguishing cells based on their distinct marker gene expressions. ( E) UMAP plot showing the ECs clustering results. Different treatment times or ECs subclusters are indicated by different colors. ( F) The Ro/e method was used to estimate the tissue preference of each major ECs subcluster. Highlighting the Cluster 11. ( G ) Bar graphs showing the top 12 ontology terms of CD34 + CLDN5 + ECs which identified by KEGG and GO enrichment analysis. ( H ) mIHC of CD34, CLDN5 in liver specimens from the D16T and D16P. Scale bars, 50 μm. ( I-J ) mIHC of CD34, CLDN5, P16, P21 and γH2AX in tumor from the D16T. Scale bars, 20 μm. ( K) D16 rats' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells, and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( L ) Quantified data of senescent cell proportion in different non-parenchymal cells of D16 rats' tumor tissue. n = 3. Data are represented as mean. *** p < 0.001, **** p < 0.0001. ( M ) Liver cancer patients' tumor tissue: CD34 + CLDN5 + cells proportion in non-parenchymal cells and the P16 + P21 + cells proportion in CD34 + CLDN5 + cells were measured by flow cytometry. ( N ) Quantified data of senescent cell proportion in different non-parenchymal cells of HCC patients' tumor tissue. n = 2. Data are represented as mean.

Article Snippet: Similarly, the primary hepatic ECs from D16 rat tumor tissue were stained by anti-rat CD45 (BD Pharmingen), anti-rat CD34 (Novus), anti-human/rat CLDN5 (Bioss), anti-human/rat P16 and anti-human/rat P21(SantaCruz) for detection by flow cytometry.

Techniques: RNA Sequencing, Clinical Proteomics, Marker, Flow Cytometry

Journal: Journal of Advanced Research

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

doi: 10.1016/j.jare.2024.12.008

Figure Lengend Snippet: CD34 + CLDN5 + endothelial cells increased the cholangiocellular phenotype within HCC and were instrumental in the recruitment of mesenchymal stem cells into the tumor microenvironment. (A) Hierarchical clustering of cell abundance predicted per sample from TCGA of HCC cohort by ssGSEA. Shown are row z-score. (B) The Kaplan-Meier overall survival curves of patients in TCGA_LIHC by the gene signature expression of CD34 + CLDN5 + cells. p value was determined by Kaplan-Meier survival curves and log-rank test. (C) Schematic diagram for administration of CD34 + CLDN5 + ECs to DEN-induced HCC rats and the gross appearance of livers exposed to DEN for 16 weeks. PBS or CD34 + CLDN5 + ECs/CD34 − CLDN5 − ECs was administered by splenic injection. CD34 + CLDN5 + ECs, the rats were administered CD34 + CLDN5 + ECs; CD34 − CLDN5 − ECs, the rats were administered CD34 ,− CLDN5 ,− ECs. n = 6, respectively. (D) H&E of the different tumor from the rats liver. Scale bars, 50 μm. (E) mIHC of CK19, CD44, EPCAM in different tumors. Scale bars, 100 μm. (F) Schematic diagram illustrating the co-culture setup of RH-35 cells with the ECs and Quantified data of colony and sphere formation assay of the different RH35. Data are represented as mean ± SD. ns, not significant. FACS, fluorescence-activated cell sorting. (G) Chord diagrams showing cell–cell interactions among ECs and various cell clusters in TME according to L-R pairs analysis. Circling the interaction between ECs and MSCs (H) mIHC of CD34, CLDN5, CD146 in tumor from the D16T. Scale bars, 10 μm. (I) Schematic diagram illustrating the transwell migration assay between MSCs with the ECs for 48 h. Scale bars, 100 μm. (J) Bubble chart showing the L-R pairs enrichment analysis between the MSCs and different ECs. (K) The UMAP plot showing expression levels of Igf2 in ECs subtypes , with the lowest expression levels represented as gray dots and the highest expression levels as red dots. (L) The correlation (Pearson) between Igf2 and Cdkn2a in CD34 + CLDN5 + ECs. (M) mIHC of CD34, CLDN5 and IGF2 in tumor from the D16T. Scale bars, 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Techniques: Expressing, Injection, Co-Culture Assay, Tube Formation Assay, Fluorescence, FACS, Transwell Migration Assay

Journal: Journal of Advanced Research

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

doi: 10.1016/j.jare.2024.12.008

Figure Lengend Snippet: CEBPβ bound with Igf2 -promoter-sequence in hepatic senescent endothelial cells to up regulate the IGF2 expression. (A) Heatmap of the AUC scores of expression regulation by transcription factors, as estimated using SCENIC, for each of the ECs from 16 clusters. Shown are the transcription factors having the highest difference in expression regulation estimates among different times ECs from livers of rats exposed to DEN. ( B) Shown the Top 30 transcription factors having the significant difference in expression regulation in CD34 + CLDN5 + ECs. ( C) The UMAP plot showing expression levels of Cebpb targets in ECs subtypes. ( D) The Schematic diagram of canonical CEBPβ-binding motif (from JASPAR Database) and three potential CEBPβ responsive elements (Primer A, Primer B, and Primer C) in the IGF2 promoter region. ( E) Enrichment of the fragments containing the CEBPβ binding sites within the IGF2 promoter in CD34 + CLDN5 + ECs by CUT&Tag-qPCR. Comparing to the background DNA fragment pulled down by IgG immunoprecipitation. Data are presented as mean ± SD. *** p < 0.001. ( F) mIHC of CD34, CLDN5 and CEBPβ in tumor from the D16T. Scale bars, 20 μm. ( G) Immunofluorescence staining of CEBPβ in CD34 + CLDN5 + ECs from D16 rats and normal 6 month-old normal rat livers. Scale bars, 100 μm. (H) Immunofluorescence staining of IGF2 for CD34 + CLDN5 + ECs from D16T treated with Cebpb -siRNA (100 nM) or scramble siRNA (as negative control) or not for 48 h. S1-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 1; S2-CEBPβ-ECs, CD34 + CLDN5 + ECs which treated with Cebpb -siRNA candidate 2; NC-ECs, negative control. Scale bars, 100 μm. ( I) Quantified data of immunofluorescence staining of IGF2 in CD34 + CLDN5 + ECs. Data are presented as mean ± SD. ns, no significant, *** p < 0.001.

Techniques: Sequencing, Expressing, Binding Assay, Immunoprecipitation, Immunofluorescence, Staining, Negative Control

Journal: Journal of Advanced Research

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

doi: 10.1016/j.jare.2024.12.008

Figure Lengend Snippet: Tumor associated senescent endothelial cells are frequently observed in both intrahepatic cholangiocarcinoma and combined hepatocellular carcinoma-cholangiocarcinoma, and their presence is significantly correlated with a short survival duration in patients with liver cancer. (A-B) Schematic of processing of liver tissue for single-cell RNA sequencing, UMAP plot showing the clustering results, single cells from different liver tissue and relative abundance of the different cell types in different liver samples: Chalongiocytes, HPCs/CSCs, NK cells (Nature kill cells), T cells, B cells, Plasma cells, DCs, Neutrophils, Endothelial cells, Macro and Mono (Macrophages and Monocytes), Mast cells, Fibrolasts and Hepatocytes. The different samples and cell types are indicated by different colors. Highlight the CSCs in iCCA. Healthy, the liver tissue from the patients without HCC. HCC, the tumor from the patients with HCC. iCCA, the tumor from the patients with iCCA. (C) UMAP plot showing the ECs clustering results and relative abundance of the different ECs subtypes in HCC and iCCA. The different samples and ECs subtypes are indicated by different colors. Highlight the CD34 + CLDN5 + ECs in iCCA. (D) The UMAP plot showing expression levels of CEBPB , CD34 , CLDN5 , IGF2 , CDKN1A and TP53 in CD34 + CLDN5 + ECs. (E) The correlation analysis between IGF2 and CD34 + CLDN5 + ECs, IGF2 and MSCs, CD34 + CLDN5 + ECs and MSCs according to TCGA_LIHC. (F) The pie chart illustrating the proportion of CD34 + CLDN5 + ECs relative to the total ECs across various liver cancer. (G-J) Left four panels: the Kaplan-Meier overall survival curves of tumor tissues from 66 patients with liver cancer by co-expression of CD34, CLDN5, P16 and CEBPβ in mIHC. p value was determined by Kaplan-Meier survival curves and log-rank test. Right eight panels: CD34, CLDN5, p16 and CEBPβ co-expression was detected in tumor tissues from 66 patients with HCC by mIHC. Scale bars, 50 μm.

Techniques: RNA Sequencing, Clinical Proteomics, Expressing

Journal: Journal of Advanced Research

Article Title: CD34 + CLDN5 + tumor associated senescent endothelial cells through IGF2-IGF2R signaling increased cholangiocellular phenotype in hepatocellular carcinoma

doi: 10.1016/j.jare.2024.12.008

Figure Lengend Snippet: Tumor associated senescent endothelial cells recruited the mesenchymal stem cells into the TME, promoting the formation of cholangiocellular phenotype in HCC A schematic model of the mechanism by which CD34 + CLDN5 + ECs, with distinct senescent cellular features and high expression of IGF2, which was regulated by CEBPβ, recruited the MSCs into TME via IGF2R/MAPK. Then, MSCs in TME released the cytokines to induced evolution of the HCC cell into CSC-like characteristic tumor cell, promoted the increasing of cholangiocellular phenotype and malignant transformation of HCC.

Techniques: Expressing, Transformation Assay

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