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Spatial distribution and expression of CAFs in ROC tissues. (a) Representative H&E and mIHC staining in TN, TAS, and ANS. Left: Entire section overview with distinct regions indicated by dashed lines (yellow: TN boundary, blue: TAS boundary, orange: ANS; scale bars: 2 mm). Right panels show zoom-in views of the indicated regions for H&E and mIHC (scale bars: 50 μm). mIHC images indicate the distribution of cell types marked by <t>αSMA</t> (red), FAP (orange), S100A4 (purple), PDPN (green), CD3 (cyan), PAX8 (yellow), and nuclear staining by DAPI (blue). (b–e) CAF subtypes marker expression quantified across distinct tissue compartments. (b) Representative mIHC staining and digital cell phenotype segmentation within TAS, TN, and ANS. Left columns represent raw immunofluorescence images, and right columns indicate digitally identified positive cells for each marker (scale bars: 100 μm). Quantitative comparison of CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) densities between TN versus TAS (c) and TAS versus ANS (d) as shown in representative image (b) ( n = 29 samples per group; statistical significance indicated by p -values). (e) PCA scatter plot demonstrating distinct clustering of samples based on CAF markers expression densities across regions (TN, TAS, ANS). (f) Digital spatial phenotyping of nearest-neighbor relationships between CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) and tumor cells (PAX8 + ). The upper row shows multiplex immunofluorescence images, and the lower row visualizes digitally identified spatial interactions, indicated by connecting lines to their nearest tumor cells (scale bars: 200 μm). (g) Boxplots show quantified average distances between CAF subtypes and nearest PAX8 + tumor cells, (h) and local densities within 300 μm of tumor cells as shown in representative image (f). Data are mean ± SEM; by one-way ANOVA. ANS, adjacent normal stroma; CAF, cancer-associated fibroblast; FAP, fibroblast activation protein; H&E, hematoxylin and eosin; mIHC, multiplex immunohistochemistry; PCA, principal component analysis; PDPN, podoplanin; ROC, Relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TAS, tumor-adjacent stroma; TN, tumor nests; αSMA, <t>α-smooth</t> <t>muscle</t> <t>actin.</t>

αsma, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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αsma - by Bioz Stars, 2026-10

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1) Product Images from "S100A4 characterize antigen-presenting cancer-associated fibroblasts and predicts surgical outcomes in relapsed ovarian cancer"

Article Title: S100A4 characterize antigen-presenting cancer-associated fibroblasts and predicts surgical outcomes in relapsed ovarian cancer

Journal: Therapeutic Advances in Medical Oncology

doi: 10.1177/17588359261436959

Figure Legend Snippet: Spatial distribution and expression of CAFs in ROC tissues. (a) Representative H&E and mIHC staining in TN, TAS, and ANS. Left: Entire section overview with distinct regions indicated by dashed lines (yellow: TN boundary, blue: TAS boundary, orange: ANS; scale bars: 2 mm). Right panels show zoom-in views of the indicated regions for H&E and mIHC (scale bars: 50 μm). mIHC images indicate the distribution of cell types marked by αSMA (red), FAP (orange), S100A4 (purple), PDPN (green), CD3 (cyan), PAX8 (yellow), and nuclear staining by DAPI (blue). (b–e) CAF subtypes marker expression quantified across distinct tissue compartments. (b) Representative mIHC staining and digital cell phenotype segmentation within TAS, TN, and ANS. Left columns represent raw immunofluorescence images, and right columns indicate digitally identified positive cells for each marker (scale bars: 100 μm). Quantitative comparison of CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) densities between TN versus TAS (c) and TAS versus ANS (d) as shown in representative image (b) ( n = 29 samples per group; statistical significance indicated by p -values). (e) PCA scatter plot demonstrating distinct clustering of samples based on CAF markers expression densities across regions (TN, TAS, ANS). (f) Digital spatial phenotyping of nearest-neighbor relationships between CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) and tumor cells (PAX8 + ). The upper row shows multiplex immunofluorescence images, and the lower row visualizes digitally identified spatial interactions, indicated by connecting lines to their nearest tumor cells (scale bars: 200 μm). (g) Boxplots show quantified average distances between CAF subtypes and nearest PAX8 + tumor cells, (h) and local densities within 300 μm of tumor cells as shown in representative image (f). Data are mean ± SEM; by one-way ANOVA. ANS, adjacent normal stroma; CAF, cancer-associated fibroblast; FAP, fibroblast activation protein; H&E, hematoxylin and eosin; mIHC, multiplex immunohistochemistry; PCA, principal component analysis; PDPN, podoplanin; ROC, Relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TAS, tumor-adjacent stroma; TN, tumor nests; αSMA, α-smooth muscle actin.

Techniques Used: Expressing, Staining, Marker, Immunofluorescence, Comparison, Multiplex Assay, Activation Assay, Immunohistochemistry, Binding Assay

scRNA-seq reveals transcriptional heterogeneity of CAF subtypes and highlights unique ECM remodeling and immune regulatory features of S100A4 + CAFs in ROC. (a–c) Single-cell transcriptome analysis of CAFs highlights distinct CAF subpopulations. UMAP visualization of stromal cells colored by sample origin (a) and unsupervised clustering identifying 11 distinct stromal clusters (0–10) (b). Dot plot illustrating key marker gene expression across stromal clusters; dot size indicates the proportion of cells expressing each gene, while color intensity reflects scaled expression (c). (d, e) Expression distribution and differential gene analysis of S100A4 + CAFs. UMAP plot showing gradient distribution of S100A4 expression levels and distribution of S100A4 + versus S100A4 – cells (d). Volcano plot showing significantly upregulated genes in S100A4 + CAFs compared to S100A4 – CAFs; significantly upregulated genes are labeled in red (e). (f, g) Functional enrichment analysis of S100A4 + CAFs-specific differentially expressed genes. GO enrichment analysis categorized into BP, CC, and MF (f). KEGG pathway enrichment analysis highlighting pathways related to antigen presentation as significantly enriched (g). (h, i) Spatial proximity analysis of CAF subtypes and CD3 + T cells by multiplex immunofluorescence. Representative images of multiplex immunofluorescence staining demonstrate spatial relationships between CD3 + T cells (cyan) and different CAF subpopulations (S100A4 + , purple; αSMA + , red; FAP + , orange; PDPN + , green) (h). Quantitative analysis showing significantly higher densities of S100A4 + CAFs around CD3 + T cells within 20 μm compared to other CAF subtypes (i). Scale bar: 50 μm. BP, biological processes; CAF, cancer-associated fibroblasts; CC, cellular components; ECM, extracellular matrix; FAP, fibroblast activation protein; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MC, mesothelial cells; MF, molecular functions; PDPN, podoplanin; ROC, Relapsed ovarian cancer; scRNA-seq, single-cell RNA sequencing; αSMA, α-smooth muscle actin.

Figure Legend Snippet: scRNA-seq reveals transcriptional heterogeneity of CAF subtypes and highlights unique ECM remodeling and immune regulatory features of S100A4 + CAFs in ROC. (a–c) Single-cell transcriptome analysis of CAFs highlights distinct CAF subpopulations. UMAP visualization of stromal cells colored by sample origin (a) and unsupervised clustering identifying 11 distinct stromal clusters (0–10) (b). Dot plot illustrating key marker gene expression across stromal clusters; dot size indicates the proportion of cells expressing each gene, while color intensity reflects scaled expression (c). (d, e) Expression distribution and differential gene analysis of S100A4 + CAFs. UMAP plot showing gradient distribution of S100A4 expression levels and distribution of S100A4 + versus S100A4 – cells (d). Volcano plot showing significantly upregulated genes in S100A4 + CAFs compared to S100A4 – CAFs; significantly upregulated genes are labeled in red (e). (f, g) Functional enrichment analysis of S100A4 + CAFs-specific differentially expressed genes. GO enrichment analysis categorized into BP, CC, and MF (f). KEGG pathway enrichment analysis highlighting pathways related to antigen presentation as significantly enriched (g). (h, i) Spatial proximity analysis of CAF subtypes and CD3 + T cells by multiplex immunofluorescence. Representative images of multiplex immunofluorescence staining demonstrate spatial relationships between CD3 + T cells (cyan) and different CAF subpopulations (S100A4 + , purple; αSMA + , red; FAP + , orange; PDPN + , green) (h). Quantitative analysis showing significantly higher densities of S100A4 + CAFs around CD3 + T cells within 20 μm compared to other CAF subtypes (i). Scale bar: 50 μm. BP, biological processes; CAF, cancer-associated fibroblasts; CC, cellular components; ECM, extracellular matrix; FAP, fibroblast activation protein; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MC, mesothelial cells; MF, molecular functions; PDPN, podoplanin; ROC, Relapsed ovarian cancer; scRNA-seq, single-cell RNA sequencing; αSMA, α-smooth muscle actin.

Techniques Used: Single Cell, Marker, Gene Expression, Expressing, Labeling, Functional Assay, Immunopeptidomics, Multiplex Assay, Immunofluorescence, Staining, Activation Assay, RNA Sequencing

Immune spatial interactions and prognostic significance of CD74 + S100A4 + antigen-presenting CAFs in ROC. (a, b) Spatial proximity analysis between CAF subpopulations and CD4 + T cells using mIHC and computational phenotyping. (a) Representative mIHC images showing spatial relationships between αSMA + , S100A4 + , CD74 + S100A4 + CAFs, and CD4 + T cells. Lines indicate nearest neighbor distances between cells. Scale bar, 50 µm. (b) Boxplot quantification of mean number of CD4 + T cells within 20 µm radius of each CAF subtype. CD74 + S100A4 + CAFs displayed significantly closer proximity to CD4 + T cells. ( p < 0.05) as shown in representative image (a). (c–e) Differences in CD74 + S100A4 + CAFs distribution and their spatial relationship with CD4 + T cells between patients achieving R0 versus Non-R0. (c) Representative images of mIHC staining illustrating differences in spatial cell arrangement. Scale bar, 200 µm. (d) Quantification of CD74 + S100A4 + CAFs densities (cells/mm²) and (e) mean count of CD4 + T cells within 20 µm of CD74 + S100A4 + CAFs between R0 and Non-R0 groups. (f, g) Prognostic significance of S100A4 + apCAFs based on multi-dataset transcriptomic analysis. (f) Forest plot showing HR of S100A4 + apCAFs-associated gene signature across 11 ovarian cancer datasets. Each horizontal black square represents the HR estimate from an individual dataset, and the horizontal line indicates the 95% CI. The overall HR for S100A4 + apCAFs is shown at the bottom, with the dashed vertical line indicating the reference value HR = 1. (g) In the TCGA ovarian cancer cohort, patients were stratified into a high-expression group (top 30%, n = 68, shown in blue) and a low-expression group (bottom 30%, n = 68, shown in red) based on the expression levels of the top 100 S100A4 + apCAFs signature genes. The Kaplan–Meier survival curves compare overall survival between these groups. The x -axis represents time since diagnosis (in months), and the y -axis indicates overall survival probability. CAF, cancer-associated fibroblasts; CI, confidence interval; HR, hazard ratio; mIHC, multiplex immunohistochemistry; ROC, relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TCGA, The Cancer Genome Atlas; αSMA, α-smooth muscle actin.

Figure Legend Snippet: Immune spatial interactions and prognostic significance of CD74 + S100A4 + antigen-presenting CAFs in ROC. (a, b) Spatial proximity analysis between CAF subpopulations and CD4 + T cells using mIHC and computational phenotyping. (a) Representative mIHC images showing spatial relationships between αSMA + , S100A4 + , CD74 + S100A4 + CAFs, and CD4 + T cells. Lines indicate nearest neighbor distances between cells. Scale bar, 50 µm. (b) Boxplot quantification of mean number of CD4 + T cells within 20 µm radius of each CAF subtype. CD74 + S100A4 + CAFs displayed significantly closer proximity to CD4 + T cells. ( p < 0.05) as shown in representative image (a). (c–e) Differences in CD74 + S100A4 + CAFs distribution and their spatial relationship with CD4 + T cells between patients achieving R0 versus Non-R0. (c) Representative images of mIHC staining illustrating differences in spatial cell arrangement. Scale bar, 200 µm. (d) Quantification of CD74 + S100A4 + CAFs densities (cells/mm²) and (e) mean count of CD4 + T cells within 20 µm of CD74 + S100A4 + CAFs between R0 and Non-R0 groups. (f, g) Prognostic significance of S100A4 + apCAFs based on multi-dataset transcriptomic analysis. (f) Forest plot showing HR of S100A4 + apCAFs-associated gene signature across 11 ovarian cancer datasets. Each horizontal black square represents the HR estimate from an individual dataset, and the horizontal line indicates the 95% CI. The overall HR for S100A4 + apCAFs is shown at the bottom, with the dashed vertical line indicating the reference value HR = 1. (g) In the TCGA ovarian cancer cohort, patients were stratified into a high-expression group (top 30%, n = 68, shown in blue) and a low-expression group (bottom 30%, n = 68, shown in red) based on the expression levels of the top 100 S100A4 + apCAFs signature genes. The Kaplan–Meier survival curves compare overall survival between these groups. The x -axis represents time since diagnosis (in months), and the y -axis indicates overall survival probability. CAF, cancer-associated fibroblasts; CI, confidence interval; HR, hazard ratio; mIHC, multiplex immunohistochemistry; ROC, relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TCGA, The Cancer Genome Atlas; αSMA, α-smooth muscle actin.

Techniques Used: Staining, Expressing, Biomarker Discovery, Multiplex Assay, Immunohistochemistry, Binding Assay

Incubation:

Article Title: IGFBP2 alteration contributes to prostate cancer progression by modulating prostate stroma activation.
Article Snippet: .. The membranes were blocked for 1 h at RT with 1% nonfat dry milk in PBS containing 0.1% Tween 20, and were subsequently incubated for 1 h at RT using anti-IGFBP2, anti-αSMA, anti-TGF-β, or anti-β-actin (Cell Signaling Technology, Beverly, MA) antibodies, diluted 1:1000 in blocking buffer. .. The membranes were washed and incubated with goat anti-rabbit IgG coupled to horseradish peroxidase (1:2000, Santa Cruz Biotechnologies).

Article Title: Extracellular vesicles derived from live or apoptotic mesenchymal stem cells: comparison of the effects of two extracellular vesicles on liver fibrosis.
Article Snippet: Subsequently, equal amounts of protein samples (30 μg) were loaded onto SDS-PAGE gels and transferred to nitrocellulose membranes (Bio-Rad Biotechnology, USA). .. The membranes were blocked in TBST buffer containing 2.5% skim milk for 30 min. Then, the membranes were incubated overnight at 4 °C with the following primary antibodies: anti-Caspase-3 (9662, Cell Signaling Technology, USA; diluted at 1:1000), anti-GM130 (ab52649, Abcam, UK; diluted at 1:1000), anti-Calnexin (10427-2-AP, Proteintech, USA; diluted at 1:1000), anti-CD81 (ab79559, Abcam, UK; diluted at 1:1000), anti-CD9 (ab236630, Abcam, UK; diluted at 1:1000), anti-Collagen I (ab270993, Abcam, UK; diluted at 1:1000), anti-αSMA (19245, Cell Signaling Technology, USA; diluted at 1:1000), anti-NOS2 (ab15323, Abcam, UK; diluted at 1:1000), antiARG-1 (ab124917, Abcam, UK; diluted at 1:1000), anti-STAT3 (10253-2-AP, Proteintech, USA; diluted at 1:1000), anti-STAT6 (9362, Cell Signaling Technology, USA; diluted at 1:1000), anti- NF-κB (8242, Cell Signaling Technology, USA; diluted at 1:1000), anti-AKT (4691, Cell Signaling Technology, USA; diluted at 1:1000), and anti-β-Actin (66009-1-Ig, Proteintech, USA; diluted at 1:5000). ..

Article Title: Akt inhibition enhances the antitumor efficacy of immune checkpoint blockades and radiotherapy in a syngeneic breast cancer model
Article Snippet: .. Slides were blocked with 3% BSA and incubated overnight at 4°C with anti-CD163 antibody (Abcam, UK; ab182422); anti-pAkt (Ser473) antibody (Cell Signaling Technology, USA; 4060s); anti-αSMA (Cell Signaling Technology, USA; 19245T); anti-E-cadherin (Cell Signaling Technology, USA; 3195T); and anti-Vimentin (Cell Signaling Technology, USA; 5741T). ..

Article Title: IGFBP2 alteration contributes to prostate cancer progression by modulating prostate stroma activation
Article Snippet: .. The membranes were blocked for 1 h at RT with 1% nonfat dry milk in PBS containing 0.1% Tween 20, and were subsequently incubated for 1 h at RT using anti-IGFBP2, anti-αSMA, anti-TGF-β, or anti-β-actin (Cell Signaling Technology, Beverly, MA) antibodies, diluted 1:1000 in blocking buffer. .. The membranes were washed and incubated with goat anti-rabbit IgG coupled to horseradish peroxidase (1:2000, Santa Cruz Biotechnologies).

Blocking Assay:

Article Title: Tenascin C + myofibroblasts exacerbate vascular neointimal hyperplasia by propagation of nerve-macrophage interactions in mice.
Article Snippet: After incubating Mu/Rb-HRP secondary antibody (AiFang biological, AFIHC001) for 1 h at RT, developed by staining with DAB kit (Servicebio, G1212) for 1min. .. The sections were blocked with blocking buffer (5% BSA and 0.2% Triton X-100 in PBS) for 1 h and stained with anti-TNC (1:200, Proteintech, 67710-1-Ig), anti-Ki67 (1:200, Servicebio, GB121141), anti-αSMA (1:500, Cell Signaling Technology, 48938S), anti-Fn1 (1:200, Proteintech, 66042-1-Ig), anti-Postn (1:200, Proteintech, 66491-1-Ig), antiCD68 (1:200, BIO-RAD, MCA1957), anti-CD11c (1:200, Cell Signaling Technology, 97585), anti-F4/80 (1:200, Proteintech, 29414-1-AP), antiPostn (1:200, Proteintech, 66491-1-Ig), anti-Synapsin1/2 (1:100, SYSY, 106 002), anti-TNF (1:200, Proteintech, 17590-1-AP), anti-NOS2 (1:200, Invitrogen, PA1-036), anti-ARG1(1:200, Cell Signaling Technology, 93668), anti-CCL2(1:200, Proteintech, 26161-1-AP) anti-CD45 (1:200, Abcam, ab10558) and anti-βIII-tubulin (1:1000, Abcam, ab78078) primary antibodies overnight at 4 °C. .. Afterwards, the sectionswere rinsed three times in PBS and stained with corresponding secondary antibodies (all from Invitrogen) for 1 h at RT.

Staining:

Image Search Results

(a-c) SM22α, eNOS, αSMA, and Fibronectin (FN) mRNA levels determined by qRT-PCR in human coronary artery endothelial cells (HCAEC) following treatment with TGFβ1 (10 ng/ml, 7d), hypoxia (5% O 2 , 4d) or TGFβ1 plus hypoxia (4d), in the absence or presence of ethanol (EtOH) at concentrations indicated (0-100 mM range). (d) SM22α, eNOS, and Cdh5 mRNA levels in HCAEC treated with TGFβ1 (10 mg/ml, 2d) +/- EtOH 25 mM or 100 mM. (e) Representative western blots showing CD31, Cdh5, SM22α, and SNAIL protein expression in HCAEC treated with TGFβ1, IL-1β, or Hypoxia +/- EtOH (25 mM or 100 mM). β-actin or GAPDH were used as loading controls. Data are mean±SEM, n=3. *p<0.05 vs control (no treatment), #p<0.05 vs TGFβ1 or hypoxia.

Journal: bioRxiv

Article Title: A Biphasic Effect of Alcohol on Endothelial Plasticity Through Regulation of Endothelial-to-Mesenchymal Transition

doi: 10.64898/2026.04.14.718463

Figure Lengend Snippet: (a-c) SM22α, eNOS, αSMA, and Fibronectin (FN) mRNA levels determined by qRT-PCR in human coronary artery endothelial cells (HCAEC) following treatment with TGFβ1 (10 ng/ml, 7d), hypoxia (5% O 2 , 4d) or TGFβ1 plus hypoxia (4d), in the absence or presence of ethanol (EtOH) at concentrations indicated (0-100 mM range). (d) SM22α, eNOS, and Cdh5 mRNA levels in HCAEC treated with TGFβ1 (10 mg/ml, 2d) +/- EtOH 25 mM or 100 mM. (e) Representative western blots showing CD31, Cdh5, SM22α, and SNAIL protein expression in HCAEC treated with TGFβ1, IL-1β, or Hypoxia +/- EtOH (25 mM or 100 mM). β-actin or GAPDH were used as loading controls. Data are mean±SEM, n=3. *p<0.05 vs control (no treatment), #p<0.05 vs TGFβ1 or hypoxia.

Article Snippet: Primary antibodies used include human CD31 (Cat. # AF806, R&D Systems), αSMA (MAB1420, R&D systems), SM22α (Cat. # 36090 CST), and CDH5 (Cat. # 2500, CST).

Techniques: Quantitative RT-PCR, Western Blot, Expressing, Control

(a) CD31 expression in HCAEC treated with TGFβ1 (10ng/ml), Hypoxia (5% O 2 ), or TGFβ1+ Hypoxia in the absence or presence of moderate dose ethanol (EtOH 25 mM). (b) αSMA expression in HCAEC treated with TGFβ +/- either moderate dose ethanol (25 mM EtOH) or high dose ethanol (100 mM EtOH). (c) SM22α and (d) αSMA expression in HUVEC treated with IL1β+TGFβ2 in the absence or presence of either 25 mM EtOH or 100 mM EtOH as indicated. Representative immunofluorescence images, from at least 3 experiments, shown.

Journal: bioRxiv

Article Title: A Biphasic Effect of Alcohol on Endothelial Plasticity Through Regulation of Endothelial-to-Mesenchymal Transition

doi: 10.64898/2026.04.14.718463

Figure Lengend Snippet: (a) CD31 expression in HCAEC treated with TGFβ1 (10ng/ml), Hypoxia (5% O 2 ), or TGFβ1+ Hypoxia in the absence or presence of moderate dose ethanol (EtOH 25 mM). (b) αSMA expression in HCAEC treated with TGFβ +/- either moderate dose ethanol (25 mM EtOH) or high dose ethanol (100 mM EtOH). (c) SM22α and (d) αSMA expression in HUVEC treated with IL1β+TGFβ2 in the absence or presence of either 25 mM EtOH or 100 mM EtOH as indicated. Representative immunofluorescence images, from at least 3 experiments, shown.

Article Snippet: Primary antibodies used include human CD31 (Cat. # AF806, R&D Systems), αSMA (MAB1420, R&D systems), SM22α (Cat. # 36090 CST), and CDH5 (Cat. # 2500, CST).

Techniques: Expressing, Immunofluorescence

(a) (b) Representative images of immunofluorescently stained carotid cross sections from sham-operated and ligated controls, moderate EtOH, and Binge EtOH experimental groups (males). Blue = Dapi nuclear stain, red = Tm-Cdh5+, and white=αSMA+. (c) Cells co-expressing Cdh5 and αSMA (i.e., myo-endothelial cells indicative of EndMT) were quantified using QuPath bioimage analysis software in carotid cross sections post-ligation from controls (grey bars), moderate EtOH (green bars), and binge EtOH (red bars) experimental groups. Bar graphs show cumulative data expressed as % Myoendothelial cells /full cross section, % Myoendothelial cells /neointima, or Number of myoendothelial cells/full cross section. Data are mean±SEM, n=25-30 sections from 5-6 mice). *p<0.05, ** P<0.001, **** p<0.0001.

Journal: bioRxiv

Article Title: A Biphasic Effect of Alcohol on Endothelial Plasticity Through Regulation of Endothelial-to-Mesenchymal Transition

doi: 10.64898/2026.04.14.718463

Figure Lengend Snippet: (a) (b) Representative images of immunofluorescently stained carotid cross sections from sham-operated and ligated controls, moderate EtOH, and Binge EtOH experimental groups (males). Blue = Dapi nuclear stain, red = Tm-Cdh5+, and white=αSMA+. (c) Cells co-expressing Cdh5 and αSMA (i.e., myo-endothelial cells indicative of EndMT) were quantified using QuPath bioimage analysis software in carotid cross sections post-ligation from controls (grey bars), moderate EtOH (green bars), and binge EtOH (red bars) experimental groups. Bar graphs show cumulative data expressed as % Myoendothelial cells /full cross section, % Myoendothelial cells /neointima, or Number of myoendothelial cells/full cross section. Data are mean±SEM, n=25-30 sections from 5-6 mice). *p<0.05, ** P<0.001, **** p<0.0001.

Article Snippet: Primary antibodies used include human CD31 (Cat. # AF806, R&D Systems), αSMA (MAB1420, R&D systems), SM22α (Cat. # 36090 CST), and CDH5 (Cat. # 2500, CST).

Techniques: Staining, Expressing, Software, Ligation

Journal: Therapeutic Advances in Medical Oncology

Article Title: S100A4 characterize antigen-presenting cancer-associated fibroblasts and predicts surgical outcomes in relapsed ovarian cancer

doi: 10.1177/17588359261436959

Figure Lengend Snippet: Spatial distribution and expression of CAFs in ROC tissues. (a) Representative H&E and mIHC staining in TN, TAS, and ANS. Left: Entire section overview with distinct regions indicated by dashed lines (yellow: TN boundary, blue: TAS boundary, orange: ANS; scale bars: 2 mm). Right panels show zoom-in views of the indicated regions for H&E and mIHC (scale bars: 50 μm). mIHC images indicate the distribution of cell types marked by αSMA (red), FAP (orange), S100A4 (purple), PDPN (green), CD3 (cyan), PAX8 (yellow), and nuclear staining by DAPI (blue). (b–e) CAF subtypes marker expression quantified across distinct tissue compartments. (b) Representative mIHC staining and digital cell phenotype segmentation within TAS, TN, and ANS. Left columns represent raw immunofluorescence images, and right columns indicate digitally identified positive cells for each marker (scale bars: 100 μm). Quantitative comparison of CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) densities between TN versus TAS (c) and TAS versus ANS (d) as shown in representative image (b) ( n = 29 samples per group; statistical significance indicated by p -values). (e) PCA scatter plot demonstrating distinct clustering of samples based on CAF markers expression densities across regions (TN, TAS, ANS). (f) Digital spatial phenotyping of nearest-neighbor relationships between CAF subtypes (αSMA + , FAP + , S100A4 + , PDPN + ) and tumor cells (PAX8 + ). The upper row shows multiplex immunofluorescence images, and the lower row visualizes digitally identified spatial interactions, indicated by connecting lines to their nearest tumor cells (scale bars: 200 μm). (g) Boxplots show quantified average distances between CAF subtypes and nearest PAX8 + tumor cells, (h) and local densities within 300 μm of tumor cells as shown in representative image (f). Data are mean ± SEM; by one-way ANOVA. ANS, adjacent normal stroma; CAF, cancer-associated fibroblast; FAP, fibroblast activation protein; H&E, hematoxylin and eosin; mIHC, multiplex immunohistochemistry; PCA, principal component analysis; PDPN, podoplanin; ROC, Relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TAS, tumor-adjacent stroma; TN, tumor nests; αSMA, α-smooth muscle actin.

Article Snippet: Sections underwent antigen retrieval in citrate or EDTA buffer, followed by endogenous peroxidase blocking with 3% H 2 O 2 , serum blocking, and overnight incubation at 4°C with primary antibodies: αSMA (#19245, Cell Signaling Technology, Danvers, MA, USA), FAP (#ab207178, Abcam, Cambridge, UK), S100A4 (#13018, Cell Signaling Technology, Danvers, MA, USA), PDPN (#26981, Cell Signaling Technology, Danvers, MA, USA), PAX8 (#1F8-3A8, Thermo Fisher Scientific, Waltham, MA, USA), and CD74 (#77274, Cell Signaling Technology, Danvers, MA, USA).

Techniques: Expressing, Staining, Marker, Immunofluorescence, Comparison, Multiplex Assay, Activation Assay, Immunohistochemistry, Binding Assay

Journal: Therapeutic Advances in Medical Oncology

Article Title: S100A4 characterize antigen-presenting cancer-associated fibroblasts and predicts surgical outcomes in relapsed ovarian cancer

doi: 10.1177/17588359261436959

Figure Lengend Snippet: scRNA-seq reveals transcriptional heterogeneity of CAF subtypes and highlights unique ECM remodeling and immune regulatory features of S100A4 + CAFs in ROC. (a–c) Single-cell transcriptome analysis of CAFs highlights distinct CAF subpopulations. UMAP visualization of stromal cells colored by sample origin (a) and unsupervised clustering identifying 11 distinct stromal clusters (0–10) (b). Dot plot illustrating key marker gene expression across stromal clusters; dot size indicates the proportion of cells expressing each gene, while color intensity reflects scaled expression (c). (d, e) Expression distribution and differential gene analysis of S100A4 + CAFs. UMAP plot showing gradient distribution of S100A4 expression levels and distribution of S100A4 + versus S100A4 – cells (d). Volcano plot showing significantly upregulated genes in S100A4 + CAFs compared to S100A4 – CAFs; significantly upregulated genes are labeled in red (e). (f, g) Functional enrichment analysis of S100A4 + CAFs-specific differentially expressed genes. GO enrichment analysis categorized into BP, CC, and MF (f). KEGG pathway enrichment analysis highlighting pathways related to antigen presentation as significantly enriched (g). (h, i) Spatial proximity analysis of CAF subtypes and CD3 + T cells by multiplex immunofluorescence. Representative images of multiplex immunofluorescence staining demonstrate spatial relationships between CD3 + T cells (cyan) and different CAF subpopulations (S100A4 + , purple; αSMA + , red; FAP + , orange; PDPN + , green) (h). Quantitative analysis showing significantly higher densities of S100A4 + CAFs around CD3 + T cells within 20 μm compared to other CAF subtypes (i). Scale bar: 50 μm. BP, biological processes; CAF, cancer-associated fibroblasts; CC, cellular components; ECM, extracellular matrix; FAP, fibroblast activation protein; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MC, mesothelial cells; MF, molecular functions; PDPN, podoplanin; ROC, Relapsed ovarian cancer; scRNA-seq, single-cell RNA sequencing; αSMA, α-smooth muscle actin.

Techniques: Single Cell, Marker, Gene Expression, Expressing, Labeling, Functional Assay, Immunopeptidomics, Multiplex Assay, Immunofluorescence, Staining, Activation Assay, RNA Sequencing

Journal: Therapeutic Advances in Medical Oncology

Article Title: S100A4 characterize antigen-presenting cancer-associated fibroblasts and predicts surgical outcomes in relapsed ovarian cancer

doi: 10.1177/17588359261436959

Figure Lengend Snippet: Immune spatial interactions and prognostic significance of CD74 + S100A4 + antigen-presenting CAFs in ROC. (a, b) Spatial proximity analysis between CAF subpopulations and CD4 + T cells using mIHC and computational phenotyping. (a) Representative mIHC images showing spatial relationships between αSMA + , S100A4 + , CD74 + S100A4 + CAFs, and CD4 + T cells. Lines indicate nearest neighbor distances between cells. Scale bar, 50 µm. (b) Boxplot quantification of mean number of CD4 + T cells within 20 µm radius of each CAF subtype. CD74 + S100A4 + CAFs displayed significantly closer proximity to CD4 + T cells. ( p < 0.05) as shown in representative image (a). (c–e) Differences in CD74 + S100A4 + CAFs distribution and their spatial relationship with CD4 + T cells between patients achieving R0 versus Non-R0. (c) Representative images of mIHC staining illustrating differences in spatial cell arrangement. Scale bar, 200 µm. (d) Quantification of CD74 + S100A4 + CAFs densities (cells/mm²) and (e) mean count of CD4 + T cells within 20 µm of CD74 + S100A4 + CAFs between R0 and Non-R0 groups. (f, g) Prognostic significance of S100A4 + apCAFs based on multi-dataset transcriptomic analysis. (f) Forest plot showing HR of S100A4 + apCAFs-associated gene signature across 11 ovarian cancer datasets. Each horizontal black square represents the HR estimate from an individual dataset, and the horizontal line indicates the 95% CI. The overall HR for S100A4 + apCAFs is shown at the bottom, with the dashed vertical line indicating the reference value HR = 1. (g) In the TCGA ovarian cancer cohort, patients were stratified into a high-expression group (top 30%, n = 68, shown in blue) and a low-expression group (bottom 30%, n = 68, shown in red) based on the expression levels of the top 100 S100A4 + apCAFs signature genes. The Kaplan–Meier survival curves compare overall survival between these groups. The x -axis represents time since diagnosis (in months), and the y -axis indicates overall survival probability. CAF, cancer-associated fibroblasts; CI, confidence interval; HR, hazard ratio; mIHC, multiplex immunohistochemistry; ROC, relapsed ovarian cancer; S100A4, S100 calcium-binding protein A4; TCGA, The Cancer Genome Atlas; αSMA, α-smooth muscle actin.

Techniques: Staining, Expressing, Biomarker Discovery, Multiplex Assay, Immunohistochemistry, Binding Assay

Validation of the spatial distribution and function of CAFs and TILs using clinical HCC samples. (A) Pathological images illustrating the boundary between tumor and non-tumor areas, stained for αSMA (red), CD8 (blue), and CD4 (yellow). (B) Classification of tissue into 6 regions based on distance from the tumor septa, with binary processing of αSMA, CD8, and CD4 signals using ImageJ. (C) Positive areas (%) for αSMA, CD8, and CD4 across areas [ⅰ]–[ⅵ]. Data are presented as mean ± SD. * p <0.05, ** p <0.01, and *** p <0.001 by ANOVA. (D) Kaplan–Meier analysis of overall survival (OS) and recurrence-free survival (RFS) in areas [ⅰ] and [ⅵ], stratified by high or low CD8 levels. (E) Comparison of αSMA-positive areas in area [ⅳ] and CD8-positive areas in areas [ii], [iii], and [ⅳ], analyzed in 2 groups. The rightmost plot compares αSMA-positive and CD8-positive areas within area [ⅰ]. The Student t test or the Welch test was applied based on the equality of variances. Mean ± SD. * p <0.05. (F) Kaplan–Meier analysis of OS and RFS based on the ratio of CD8-positive areas in areas [ⅰ]–[ⅵ], stratified into groups with high and low ratios. Abbreviation: CAF, cancer-associated fibroblast.

Journal: Hepatology Communications

Article Title: Spatial and functional polarization of cancer-associated fibroblasts with CXCR4-mediated immune modulation in hepatocellular carcinoma

doi: 10.1097/HC9.0000000000000930

Figure Lengend Snippet: Validation of the spatial distribution and function of CAFs and TILs using clinical HCC samples. (A) Pathological images illustrating the boundary between tumor and non-tumor areas, stained for αSMA (red), CD8 (blue), and CD4 (yellow). (B) Classification of tissue into 6 regions based on distance from the tumor septa, with binary processing of αSMA, CD8, and CD4 signals using ImageJ. (C) Positive areas (%) for αSMA, CD8, and CD4 across areas [ⅰ]–[ⅵ]. Data are presented as mean ± SD. * p <0.05, ** p <0.01, and *** p <0.001 by ANOVA. (D) Kaplan–Meier analysis of overall survival (OS) and recurrence-free survival (RFS) in areas [ⅰ] and [ⅵ], stratified by high or low CD8 levels. (E) Comparison of αSMA-positive areas in area [ⅳ] and CD8-positive areas in areas [ii], [iii], and [ⅳ], analyzed in 2 groups. The rightmost plot compares αSMA-positive and CD8-positive areas within area [ⅰ]. The Student t test or the Welch test was applied based on the equality of variances. Mean ± SD. * p <0.05. (F) Kaplan–Meier analysis of OS and RFS based on the ratio of CD8-positive areas in areas [ⅰ]–[ⅵ], stratified into groups with high and low ratios. Abbreviation: CAF, cancer-associated fibroblast.

Article Snippet: Sections were blocked with DAKO Serum-Free Protein Blocking and incubated overnight at 4 °C with primary antibodies against αSMA (D4K9N; Cell Signaling Technology), CD8 (C8/144B; Cell Signaling Technology), CD4 (EP204; Cell Signaling Technology), and CXCR4 (ab124824; Abcam).

Techniques: Biomarker Discovery, Staining, Comparison

Validation of CXCR4 localization and function in CAFs and TILs using clinical HCC samples. (A) Gradient map of CXCR4-positive areas in case 3. (B) Comparison of CXCR4-positive areas in area [ⅴ] between tumors ≥2 cm and <2 cm, in areas [ⅳ]+[ⅴ] between tumors ≥2 cm and <2 cm, and in areas [iii]+[ⅳ] between Vp-positive and Vp-negative cases, using the Wilcoxon rank-sum test. Mean ± SD. * p <0.05. (C) Comparison of αSMA-positive areas in area [ⅴ] between high and low CD8 ratio (area [ⅰ]/[ⅵ]) groups, and comparison of CXCR4-positive areas in area [ⅴ] between the same 2 groups, using the Wilcoxon rank-sum test. Mean ± SD. (D) CXCR4 localization visualized by immunohistochemistry, showing CXCR4 concentrated outside the septa with TILs clustered in the adjacent outer region. (E) Schematic presentation of polarization of CAF clusters and CXCR4-mediated immune modulation. Abbreviations: apCAF, antigen-presenting cancer-associated fibroblasts; CAFs, cancer-associated fibroblast; HCC, hepatocellular carcinoma; iCAF, inflammatory cancer-associated fibroblast; myCAF, myofibroblastic cancer-associated fibroblast; TILs, tumor-infiltrating lymphocytes; Vp, portal vein invasion.

Journal: Hepatology Communications

Article Title: Spatial and functional polarization of cancer-associated fibroblasts with CXCR4-mediated immune modulation in hepatocellular carcinoma

doi: 10.1097/HC9.0000000000000930

Figure Lengend Snippet: Validation of CXCR4 localization and function in CAFs and TILs using clinical HCC samples. (A) Gradient map of CXCR4-positive areas in case 3. (B) Comparison of CXCR4-positive areas in area [ⅴ] between tumors ≥2 cm and <2 cm, in areas [ⅳ]+[ⅴ] between tumors ≥2 cm and <2 cm, and in areas [iii]+[ⅳ] between Vp-positive and Vp-negative cases, using the Wilcoxon rank-sum test. Mean ± SD. * p <0.05. (C) Comparison of αSMA-positive areas in area [ⅴ] between high and low CD8 ratio (area [ⅰ]/[ⅵ]) groups, and comparison of CXCR4-positive areas in area [ⅴ] between the same 2 groups, using the Wilcoxon rank-sum test. Mean ± SD. (D) CXCR4 localization visualized by immunohistochemistry, showing CXCR4 concentrated outside the septa with TILs clustered in the adjacent outer region. (E) Schematic presentation of polarization of CAF clusters and CXCR4-mediated immune modulation. Abbreviations: apCAF, antigen-presenting cancer-associated fibroblasts; CAFs, cancer-associated fibroblast; HCC, hepatocellular carcinoma; iCAF, inflammatory cancer-associated fibroblast; myCAF, myofibroblastic cancer-associated fibroblast; TILs, tumor-infiltrating lymphocytes; Vp, portal vein invasion.

Techniques: Biomarker Discovery, Comparison, Immunohistochemistry

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