Background The study of biomarkers in the tumor microenvironment is required to uncover biomarkers important in cancer research. Multiplex Immunofluorescence (mIF) is a strong tool for identifying various cell types and studying direct co-localizations of biomarkers at the same time. We apply mIF tyramide signal amplification to create biomarker panels on a single slide of FFPE tumor tissues. These panels can be used in various cancer models to answer a wide range of scientific inquiries.
Methods Tyramide signal amplification system uses HRP-conjugated secondary antibodies to covalently link a fluorophore to the tissue. This immunolabeling system utilizes tyramide, which forms strong covalent bonds that can withstand rounds of antibody stripping, to transition to sequential staining. Fluorophores that are linked to tyramide form covalent bonds with antibody targets; these bonds are stronger than those between primary antibodies and their target antigens so only the fluorescent label remains after stripping steps. Thus, the tyramide based assay facilitates antibody panel design as multiple primary antibodies can be used irrespective of host species thus increasing the number of biomarkers that can be visualized simultaneously on a single slide.
Results We developed multiplex immunofluorescence panels for cancer studies; starting with generating multiplex immunofluorescence panels in human tissue samples and TMAs to investigate interactions between biomarkers associated with prostate cancer. High resolution whole-slide images were analyzed using Visiopharm software’s AI models to visualize markers co-expression, identify selected phenotypes and compare expression patterns of each biomarker across healthy and diseased samples. AI software derives quantitative data from the IF panels.
Conclusions This system creates a customizable framework that can be applied to comparable models for the construction and optimization of multiplex panels, followed by high throughput staining and automated image analysis to offer continuous statistical data for interpretation. Thus allowing researchers to understand their models better and identify potential treatments faster.
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