Tissue image cytometry or tissue cytometry is a method of digital histopathology and combines classical digital pathology (glass slides scanning and virtual slide generation) and computational pathology (digital analysis) into one integrated approach with solutions for all kinds of diseases, tissue and cell types as well as molecular markers and corresponding staining methods to visualize these markers. Tissue cytometry uses virtual slides as they can be generated by multiple, commercially available slide scanners, as well as dedicated image analysis software – preferentially including machine and deep learning algorithms. Tissue cytometry enables cellular analysis within thick tissues, retaining morphological and contextual information, including spatial information on defined cellular subpopulations.
In this process, a tissue sample, either formalin-fixed paraffin-embedded (FFPE) or frozen tissue section, also referred to as "cryocut", is labelled with either immunohistochemistry(IHC) or immunofluorescent markers, scanned with high-throughput slide scanners and the data gathered from virtual slides is processed and analyzed using software that is able to identify individual cells in tissue context automatically and distinguish between nucleus and cytoplasm for each cell. Additional algorithms can identify cellular membranes, subcellular structures (like cytoskeletal fibers, vacuoles, nucleoli) and/or multicellular tissue structures (glands, glomeruli, epidermis, or tumor foci).Fluorescence Activated Cell Sorting (FACS) is a method of analysis that measures fluorescence signals on single cells, where the signal comes from antibody-mediated staining techniques and phenotypes detected by flow cytometry. The major limitation of flow cytometry is that it can only be applied – as the name suggest – to cells in solution. Although methods of "solubilizing" solid tissue exist, any such processing irrevocably destroys the tissue architecture and any spatial context. Hence, tissue cytometry complements the use of flow cytometry and fluorescence microscope in basic research, clinical practice, and clinical trials by providing FACS-like analyses on solid tissue sections (as well as adherent cell cultures) in situ. The advantage of tissue cytometry against flow cytometry is that tissue cytometry does not require the cells to be suspended in fluid, aiding in maintaining the integrity of the tissue structure, morphology, and contextual information, further assisting in precise and accurate contextual analysis that are not possible in flow cytometry.
History Immunohistochemistry is used in clinical practice, where tissue biopsies from every potential cancer patient are collected, fixed in formalin and embedded on paraffin. These tissue sections are serially cut in a microtome to provide thin sections, representing the diagnostic material for clinical diagnoses. Once stained initially with hematoxylin and eosin stain to detect cancer cells. Multiple marker staining is performed for proliferation, lineage, prognostic and oncogenic targets. Pathologists used optical microscope for the evaluation through the objective lenses and conclude the diagnosis by scoring the staining in percentage or as positive/negative. Visual evaluation provides a subjective diagnosis and plan of treatment. By converting glass slides into digital images, digital pathology changed how pathologists interacted with tissue specimens. However, the initial phase of digital pathology primarily focused on image viewing and sharing. 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. A more robust and automated system was designed to perform flow cytometry-like analyses on immunostained cells in a fixed tissue and termed tissue cytometry. The technique was introduced in the 1990s based on patents by Steiner and Ecker, describing a procedure for "Cytometric Analysis of Diverse Cell Populations in Tissue Sections or Cell Culture Visualized Through Fluorescence Dyes and/or Chromogens". Tissue cytometry emerged as a transformative extension of digital pathology, promising to bridge the gap between image-based analysis and quantitative, data-driven insights. At its core, tissue cytometry enables the automated and quantitative analysis of cellular and tissue features. 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. Additional patents were filed in the early 21st century by Hernani et al. to perform virtual flow cytometry on immunostained tissue. The latter's basics were derived from the procedure presented in 1982 by Gillete et al., describing the qualitative analysis of spectral mixtures by using factor analysis in conjunction with a spectral reference library. 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. The integration of AI and machine learning has been instrumental in the development of tissue cytometry. For instance, AI-driven algorithms can be trained to identify specific cell types, detect subtle morphological changes associated with disease, or quantify the density of immune cells within a tumor microenvironment. By precisely delineating individual nuclei, researchers can extract valuable information about nuclear size, shape, and texture, which can be correlated with various pathological conditions. Similarly, tissue segmentation algorithms enable the identification of different tissue compartments, such as tumor, stroma, and immune infiltrate, facilitating the analysis of spatial relationships between cellular components.
Tissue Cytometers A tissue cytometer has two main components: (I) a high-throughput scanner to acquire the high-quality virtual image of immunohistochemical and/or fluorescent marker labelled tissue sections, (II) software for image analysis and data interpretation. Modern tissue cytometers can analyze many thousands of cells within the tissue sample in "real time".
Applications of Tissue cytometry
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