Next-gen histopathology

Alexander Stickler-Barang & Felicitas Mungenast present a decision support system that is shaping the future of tissue studies

The in-depth, functional analysis of tissue sections, including quantification of multiple molecular markers and cell populations in spatial context of histology and subsequent correlation analysis and statistical evaluation, has been increasingly moving into focus in research and is currently in the translational phase towards routine diagnostics. The intensifying requirements of modern histopathology and precision medicine now depend upon more than what classical immunohistochemistry (IHC) can deliver. The information whether a specific marker is expressed, or where the expression takes place within a tissue or cell represents only a small fragment of contemporary scientific requirements of differential diagnostics – we now must focus as well upon the analysis of high numbers of biomarkers of cellular phenotypes, many of which may be defined by combinations of multiple markers (e.g. regulatory T-cells) including the spatial relationships among those cells and/or histological substructures.

The increasing importance of immunophenotyping

Immunophenotyping refers to the identification of specific cellular phenotypes by using a combination of markers which are present on the surface, in the cytoplasm or the nucleus of cell types and/or during specific phases of a cell’s life (proliferation, activation, senescence, apoptosis, etc.). Only a handful of cell types can be easily identified by a single marker, in most cases a combination of markers must be detected on the same cell (co-expression analysis) to accurately determine the specific cellular subtype. Functional markers may add additional methodological complexity and make the necessary staining protocol hard to align with the fluorescence microscope that most researchers will have available in their lab.

Immunophenotyping in tissue sections allows to determine biomarker expression for the diagnosis of various types of diseases, or to determine the immune status of a given tissue section in situ. Using tissue cytometry to decipher the anti-tumoral immune response directly at the tumour site is of special interest because it provides deep insight to the predictive and prognostic relevance of tumour infiltrating immune cell subsets as well as immune checkpoints and their use as potential targets for therapeutic intervention. With the increasing amount of cellular and molecular markers that need to be determined simultaneously in histological samples, imaging techniques and instruments that support this need have become essential tools of precision medicine. A recently published review(1) gives a comprehensive overview in respect to prognostic/predictive relevance of immune cells within different cancer types.

Tissue cytometry as the optimal tool

Novel multi-marker staining techniques (such as multiplexing) and the ever-increasing number of biomarkers and cellular sub-/phenotypes of interest lead to new challenges pertaining to automated scanning, high-dimensional data mining, data storage and contextual image analysis. Comprehensive technologies that combine all aspects of in-depth tissue analysis lead to a work load reduction for researchers and pathologists due to a high level of automation in addition to a standardised and optimised analysis workflow.

Tissue cytometry (or next-generation digital histopathology) represents an optimal method for such analysis since it combines the (i) scanning of HE, IHC, IF, as well as multiplexed tissue sections in various modes (brightfield, widefield fluorescence, confocal, multispectral) and (ii) the biomedical analysis and quantification of biomarkers, cellular phenotypes etc. accompanied with follow-up data mining. Cellular phenotype quantification in tissue sections very much resembles flow cytometry while retaining the analysis in the context of the native tissue microenvironment.

Comprehensive solution

The recent decision by the United States FDA to approve the first AI-based image analysis software for clinical diagnosis of prostate cancer samples clearly indicates that not only AI technology has reached a maturity level that allows clinical implementation but also that the market is ready to accept and regulatory bodies are willing to approve AI-based decision support systems for routine diagnostics.

TissueGnostics (TG) has been a trendsetter in automated scanning of tissue sections and their in-depth cytometrical analysis since it was founded in 2003. TG provides flexible and comprehensive tissue cytometers in various imaging/analysis configurations. These tools have been shown to serve as valuable decision support systems for histopathological analysis.(2)

TG’s most recently launched model is the TissueFaxs Chroma, which is ideal for high-content phenotyping and in-depth tissue microenvironment characterisation of IF processed tissue sections, TMAs and cells. This system enables high-throughput automated scanning by minimising multispectral overlap and maximising scanning speeds without compromising spectral specificity and data integrity through an optimised set of filters that eliminate channel bleed through.

The tissue cytometry platform includes a multitude of analysis tools comprising classical image analysis as well as AI-technologies in the form of custom-tailored apps. These powerful analysis tools are required to address complex biomedical questions, in which it is important to understand and focus on the biological question rather than the methodological answer.

Among almost 2,000 TG reference publications are many high-impact and breakthrough research applications, not limited to but including: single cell detection/analysis with phenotypic and functional characterisation of cellular sub-populations in spatial context; analysis of cell to cell contacts and proximity measurements to histological metastructures (blood vessels, tumour, stroma, etc.); and structural analysis and determination of subcellular marker localisation. Other applications include: tissue classification for phenotyping of morphological tissue entities; molecular single-cell profiling using antibody labelling, FISH, CISH, RNA-ISH, RNAScope; quantification of cellular pathogens, including intracellular parasites (e.g. leishmania) and viral load (e.g. SARS-CoV-2, influenza, HIV, Zika, etc.); and cellular in-depth characterisation of organoids, spheroids and embryoid bodies.

The TissueFaxs Chroma outputs data that perfectly matches the current trend of exploring multiple markers within tissue section/cells to better understand interactions as well as spatial relationships among and between different cell types and cellular subpopulations.


1. Mungenast F., Fernando A., Nica R., Boghiu B., Lungu B., Batra J., Ecker R.C. (2021) Next-Generation Digital Histopathology of the Tumuor Microenvironment. Genes 2021, 12, 538.

2. Schlederer M., Mueller KM., Haybaeck J., Heider S., Huttary N., Rosner M., et al. (2014) Reliable Quantification of Protein Expression and Cellular Localisation in Histological Sections. PLoS ONE 9(7): e100822.

Alexander Stickler-Barang & Felicitas Mungenast are with TissueGnostics

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