Immunohistochemistry is used in clinical practice, where tissue biopsies from every potential cancer patient are collected, fixed in formalin and embedded on paraffin.
While this enabled remote consultations and facilitated image archiving, it did not fundamentally alter the core process of pathology: the manual interpretation of tissue morphology by human experts.
Tissue cytometry emerged as a transformative extension of digital pathology, promising to bridge the gap between image-based analysis and quantitative, data-driven insights.
By employing computational algorithms and machine learning models, it can accurately segment nuclei, identify cell types, and quantify protein expression levels within the tissue context.
[10] Following this study, Zhou R et al. published a method to quantify prostate-specific acid phosphatase (PSAP) in histologic sections of prostate tumor with the peroxidase-antiperoxidase (PAP) complex technique using diaminobenzidine (DAB) as a substrate.
[12] By precisely delineating individual nuclei, researchers can extract valuable information about nuclear size, shape, and texture, which can be correlated with various pathological conditions.
One reason is that by using this technology the complex tissue architecture stays intact and therefore also spatial relationships between cellular phenotypes and/or multicellular structures can be analyzed.
[20] Tissue cytometry can also used to investigate MSCs interaction with glioblastoma: to characterize cell fusion, extracellular vesicle transfer and intercellular communications.
[21] Additionally, tissue cytometry is utilized to image the murine hippocampus and visualize M1/M2 microglia in mice with MSCs transplantation as a model for Alzheimer’s disease.
[23] Another finding illustrates loss of germinal centers in lymph nodes and spleens in acute COVID-19, which was shown by multi-color immunofluorescence cytometry.