FAP6-19

Multiplex immunofluorescence IHC detects multiple protein biomarkers in one tissue section by sequential antibody staining, HRP-mediated tyramide fluorophore deposition, heat-mediated antibody stripping, nuclear counterstaining, multispectral imaging, spectral unmixing, and digital cell phenotyping; TSA deposits fluorophore near the antigen so the fluorescence signal remains after primary and secondary antibodies are removed, enabling repeated staining cycles, including with antibodies from the same host species. Classic FFPE tumor immune-profiling applications use panels such as CD3, CD8, CD68/CD163, FOXP3, PD-1, PD-L1, pancytokeratin, Ki67, and DAPI to identify tumor cells, immune-cell subsets, checkpoint-marker expression, co-expression phenotypes, cell density, and spatial relationships in the tumor microenvironment.

Multiplex immunohistochemistry (mIHC), also known as tyramide dignal amplification (TSA), is an enzymatic detection method that uses horseradish peroxidase (HRP) to perform high-density in-situ labeling of target proteins or nucleic acids.

Cancer immunology studies how the immune system recognizes, suppresses, edits, or fails to eliminate malignant cells through tumor antigen release, antigen presentation, T-cell priming, immune trafficking, tumor-cell killing, and feedback inhibition in the tumor microenvironment. The cancer-immunity cycle links tumor antigenicity, dendritic-cell priming, CD8+ T-cell infiltration, cytotoxic function, and immune-checkpoint regulation to tumor rejection or immune escape. Immune-checkpoint pathways such as PD-1/PD-L1 and CTLA-4 suppress antitumor T-cell activity and can be therapeutically blocked, but many tumors remain resistant because of poor antigen presentation, weak T-cell infiltration, suppressive myeloid cells, regulatory T cells, and tumor-intrinsic immune-exclusion programs. Unresolved questions include which immune-cell states predict response, how tumor-intrinsic pathways exclude immune cells, how myeloid suppression limits checkpoint blockade, and which combination strategies

Choosing the right model animal is a validity-driven decision in which the species, strain, sex, age, genetic background, disease-induction method, outcome measures, and welfare burden must match the scientific question rather than laboratory tradition or convenience. A model should be selected by judging face validity, construct validity, and predictive validity: whether it resembles the human phenotype, whether it reproduces relevant mechanisms, and whether results are likely to predict human biology or treatment response. Animal studies often fail to translate because of species differences, weak disease resemblance, poor experimental design, inadequate reporting, publication bias, and underuse of randomization, blinding, and sample-size justification. Unresolved questions include how to rank competing models objectively, how much human-disease complexity must be reproduced for a given objective, and when non-animal systems such as organoids, ex vivo tissue, or computational models