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Ultivue Inc multiplex immunofluorescence
Multiplex Immunofluorescence, supplied by Ultivue Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/multiplex+immunofluorescence/immunofluorescence+multiplex/pm42083302-52-73-81
Average 86 stars, based on 1 article reviews
multiplex immunofluorescence - by Bioz Stars, 2026-09
86/100 stars

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Related Articles

Staining:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..

Multiplex Assay:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..


Immunofluorescence:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..


Imaging:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..

Mass Cytometry:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..

Expressing:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..

Spatial Transcriptomics:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..

Gene Expression:

Article Title: Spatial Multiomics Reveal Insights Into ADC Efficacy.
Article Snippet: .. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense FIGURE 1 Integration of spatial multiomics using predictive modeling for antibody–drug conjugate (ADC) response prediction. (A) Hematoxylin and eosin (H&E) staining provides a histological context for tumor classification and guides digital pathology integration. (B) Multiplex immunofluorescence, including CODEX, imaging mass cytometry (IMC), Ultivue, enables ADC target expression analysis on a subcellular level (membranous vs. intracellular). (C) Spatial transcriptomics analysis captures gene expression heterogeneity, revealing resistant niches and regulatory pathways. (D) The tissue predictive model spatial features to identify novel ADC biomarkers based on the local expression patterns. (E) Spatially integrated models enable the prediction of patient responses. (F) Spatial models can further decode novel resistance mechanisms within the tumor microenvironment. ..



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<t>qmIF</t> analysis reveals predominant AR + BCL-2 - cells in untreated primary PCa and markedly increased (AR + or AR - ) BCL-2 + cells in CRPC. a In benign prostatic glands (HPCa14N), BCL-2 + cells are mainly in the basal cell layer whereas AR + cells in luminal layer. Cytokeratin (CK) staining was used to mark epithelial compartment. Note both AR and BCL-2 were also expressed in stromal cells (scale bar, 50 μm). Magnified images of individual stains from the boxed region in the whole-mount (WM) image (top) were presented below (scale bar, 20 μm for all 4 lower panels). b Primary PCa is characterized by significantly increased AR + BCL-2 - cells. Shown above are two WM images of HPCa31T (scale bars, 800 μm) and down below zoom-in images of individual or merged markers (scale bars, 80 μm). c , d Increased cellular heterogeneity and markedly expanded (AR + or AR - ) BCL-2 + cell population in CRPC. c WM low-magnification image showing AR/BCL-2 expression (scale bar, 300 μm). d Zoom-in images of the boxed area in c showing individual markers (scale bar, 50 μm). e Relationship between (CK + ) AR-expressing and/or BCL-2-expressing cells in benign tissue (top), primary tumor (middle) and CRPC (bottom). Regression line, and Pearson R and P values are indicated. f Box plots summarizing the relative % of 4 PCa cell subtypes in WM images analyzed in benign tissues, primary tumors and CRPC. Each dot in the box plots represents a CK + ROI. P values were determined by repeated measures two-way ANOVA with Bonferroni multiple comparison test
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Image Search Results


qmIF analysis reveals predominant AR + BCL-2 - cells in untreated primary PCa and markedly increased (AR + or AR - ) BCL-2 + cells in CRPC. a In benign prostatic glands (HPCa14N), BCL-2 + cells are mainly in the basal cell layer whereas AR + cells in luminal layer. Cytokeratin (CK) staining was used to mark epithelial compartment. Note both AR and BCL-2 were also expressed in stromal cells (scale bar, 50 μm). Magnified images of individual stains from the boxed region in the whole-mount (WM) image (top) were presented below (scale bar, 20 μm for all 4 lower panels). b Primary PCa is characterized by significantly increased AR + BCL-2 - cells. Shown above are two WM images of HPCa31T (scale bars, 800 μm) and down below zoom-in images of individual or merged markers (scale bars, 80 μm). c , d Increased cellular heterogeneity and markedly expanded (AR + or AR - ) BCL-2 + cell population in CRPC. c WM low-magnification image showing AR/BCL-2 expression (scale bar, 300 μm). d Zoom-in images of the boxed area in c showing individual markers (scale bar, 50 μm). e Relationship between (CK + ) AR-expressing and/or BCL-2-expressing cells in benign tissue (top), primary tumor (middle) and CRPC (bottom). Regression line, and Pearson R and P values are indicated. f Box plots summarizing the relative % of 4 PCa cell subtypes in WM images analyzed in benign tissues, primary tumors and CRPC. Each dot in the box plots represents a CK + ROI. P values were determined by repeated measures two-way ANOVA with Bonferroni multiple comparison test

Journal: Signal Transduction and Targeted Therapy

Article Title: Single-cell imaging analysis, therapeutic modeling and a Phase Ib trial validate BCL-2 as a target across heterogeneous castration-resistant prostate cancer

doi: 10.1038/s41392-026-02700-w

Figure Lengend Snippet: qmIF analysis reveals predominant AR + BCL-2 - cells in untreated primary PCa and markedly increased (AR + or AR - ) BCL-2 + cells in CRPC. a In benign prostatic glands (HPCa14N), BCL-2 + cells are mainly in the basal cell layer whereas AR + cells in luminal layer. Cytokeratin (CK) staining was used to mark epithelial compartment. Note both AR and BCL-2 were also expressed in stromal cells (scale bar, 50 μm). Magnified images of individual stains from the boxed region in the whole-mount (WM) image (top) were presented below (scale bar, 20 μm for all 4 lower panels). b Primary PCa is characterized by significantly increased AR + BCL-2 - cells. Shown above are two WM images of HPCa31T (scale bars, 800 μm) and down below zoom-in images of individual or merged markers (scale bars, 80 μm). c , d Increased cellular heterogeneity and markedly expanded (AR + or AR - ) BCL-2 + cell population in CRPC. c WM low-magnification image showing AR/BCL-2 expression (scale bar, 300 μm). d Zoom-in images of the boxed area in c showing individual markers (scale bar, 50 μm). e Relationship between (CK + ) AR-expressing and/or BCL-2-expressing cells in benign tissue (top), primary tumor (middle) and CRPC (bottom). Regression line, and Pearson R and P values are indicated. f Box plots summarizing the relative % of 4 PCa cell subtypes in WM images analyzed in benign tissues, primary tumors and CRPC. Each dot in the box plots represents a CK + ROI. P values were determined by repeated measures two-way ANOVA with Bonferroni multiple comparison test

Article Snippet: To confirm and extend these findings, we employed Vectra-based quantitative multiplex immunofluorescence (qmIF) to assess PCa cells expressing BCL-2 and/or AR in regular FFPE (formalin-fixed and paraffin-embedded) sections as well as TMA (tissue microarray) and whole-mount (WM) sections from benign prostate ( n = 123), treatment-naïve primary PCa ( n = 125) and treatment-failed CRPC ( n = 25) (Fig. ; supplementary Figs. – , supplementary Table ).

Techniques: Staining, Expressing, Comparison

Vectra and IMC analysis reveals castration-induced dynamic changes in AR +/- BCL-2 +/- cell types in 4 xenograft CRPC models. a – d tSNE plots of IMC-derived single-cell imaging data. Shown are the individual and merged tSNE plots from LNCaP-AD/AI ( a ), LAPC9-AD/AI ( b ), LAPC4-AD/AI ( c ), and VCaP-AD/AI ( d ) xenografts. Columns represent merged AD and AI cell clusters (first column), individual AD and AI cell populations and contours (second and third columns), and AR and BCL-2 expression overlaid on tSNE maps (fourth and fifth columns). Blue and red contours indicate AD and AI cell subpopulations, respectively. e LAPC4-AD tumors are populated mostly by AR + BCL-2 - PCa cells. Shown on top are qmIF WM images (scale bar, 800 μm) and at the bottom zoom-in images (scale bar, 80 μm for all panels). f LAPC4-AI (1° CRPC) tumors are populated by AR cyto BCL-2 + PCa cells. Shown on top are qmIF WM images (scale bar, 800 μm) and at the bottom zoom-in images illustrating cytoplasmic AR + LAPC4-AI cells with upregulated BCL-2 (scale bar, 80 μm for all panels). g IMC images of AR and BCL-2 in LAPC4-AD and LAPC4-AI tumors (scale bar, 200 μm) with representative zoom-in images of AR and BCL-2 shown below (scale bar, 100 μm for all panels)

Journal: Signal Transduction and Targeted Therapy

Article Title: Single-cell imaging analysis, therapeutic modeling and a Phase Ib trial validate BCL-2 as a target across heterogeneous castration-resistant prostate cancer

doi: 10.1038/s41392-026-02700-w

Figure Lengend Snippet: Vectra and IMC analysis reveals castration-induced dynamic changes in AR +/- BCL-2 +/- cell types in 4 xenograft CRPC models. a – d tSNE plots of IMC-derived single-cell imaging data. Shown are the individual and merged tSNE plots from LNCaP-AD/AI ( a ), LAPC9-AD/AI ( b ), LAPC4-AD/AI ( c ), and VCaP-AD/AI ( d ) xenografts. Columns represent merged AD and AI cell clusters (first column), individual AD and AI cell populations and contours (second and third columns), and AR and BCL-2 expression overlaid on tSNE maps (fourth and fifth columns). Blue and red contours indicate AD and AI cell subpopulations, respectively. e LAPC4-AD tumors are populated mostly by AR + BCL-2 - PCa cells. Shown on top are qmIF WM images (scale bar, 800 μm) and at the bottom zoom-in images (scale bar, 80 μm for all panels). f LAPC4-AI (1° CRPC) tumors are populated by AR cyto BCL-2 + PCa cells. Shown on top are qmIF WM images (scale bar, 800 μm) and at the bottom zoom-in images illustrating cytoplasmic AR + LAPC4-AI cells with upregulated BCL-2 (scale bar, 80 μm for all panels). g IMC images of AR and BCL-2 in LAPC4-AD and LAPC4-AI tumors (scale bar, 200 μm) with representative zoom-in images of AR and BCL-2 shown below (scale bar, 100 μm for all panels)

Article Snippet: To confirm and extend these findings, we employed Vectra-based quantitative multiplex immunofluorescence (qmIF) to assess PCa cells expressing BCL-2 and/or AR in regular FFPE (formalin-fixed and paraffin-embedded) sections as well as TMA (tissue microarray) and whole-mount (WM) sections from benign prostate ( n = 123), treatment-naïve primary PCa ( n = 125) and treatment-failed CRPC ( n = 25) (Fig. ; supplementary Figs. – , supplementary Table ).

Techniques: Derivative Assay, Single Cell, Imaging, Expressing