TissueGnostics
Next-Gen Digital Pathology

“While pathologists have evolved from using microscope oculars to computer screens, the process of recognizing relevant structures and extracting diagnostic and prognostic information from tissue samples is still largely manual.”— Dr. Rupert Ecker.

His words perfectly illustrate the need for digital pathology to take the next step forward and enable pathologists to perform computational analyses of diverse tissue samples.

At the vanguard of heralding this transformation is Dr. Ecker’s company, TissueGnostics, with its well-established reputation as a specialist in innovative and fully-integrated biomedical image analysis.



He further notes that evaluating the expression of a single marker using conventional microscopy is no longer adequate to understand critical cellular mechanisms that can drive impactful clinical outcomes and research breakthroughs.

“While the human brain is a powerful interpolator, accurately correlating molecular expression patterns across multiple markers that are often stained using different fluorophores is impossible,” states Dr. Ecker, co-owner and CEO.

Typically, expression studies involve researchers using fluorescence-activated cell sorting (FACS), also known as flow cytometry, to quantify antibody-mediated staining for cells in suspension. The fact that FACS cannot be applied to solid tissue sections limits its use to analyze how specific markers are expressed on a tissue level. TissueGnostics’ flagship— the TissueFAXS platform—brings the idea of flow cytometry to tissue sections by replacing the traditional approach of manual analysis and visual assessment/ estimation with the automated quantification of marker expressions on a single-cell level in a tissue environment.

To enable automated quantification of (bio-)marker expression in clinical settings, TissueGnostics has designed COLUBRIS—an AI-powered nextgeneration tissue cytometer—which leverages ML-based data mining to automatically identify molecular signatures and cellular correlations in high-plex assays for spatial biology. This brand-new boxed instrument will be immensely beneficial in clinical environments where pathologists follow a manual and repetitive analysis routine that often leads to errors. COLUBRIS essentially provides higher reliability as well as throughput by offering greater automation through AI, i.e., a Deep Learning/Machine Learning-based analytical framework.

The TissueFAXS platform is available in multiple configurations for brightfield, fluorescence, multispectral, confocal, and high-throughput scanning, along with whole slide imaging (WSI), contextual tissue cytometry, quantitative single-cell analysis, and computational pathology, also referred to as “next generation histopathology.”

They also support both major sample preparation techniques, formalin-fixed paraffinembedded (FFPE), as well as frozen tissue sections, offering a single, streamlined workflow to examine the slide, extract relevant information, and understand crucial tissue-related details.Typically, users place several slide(s) on one of the instruments and select a scanning profile comprising the marker and fluorochrome/chromophore combination, camera exposure times, and other technical parameters. Following this, TissueFAXS or COLUBRIS automatically previews, detects tissue regions, and focuses, scans, and analyzes samples to deliver rapid and accurate results.

From a software standpoint, TissueFAXS and COLUBRIS are backed by comprehensive image analysis/ tissue cytometry capabilities that use ML algorithms proven to offer structure recognition and single cellbased analysis in any tissue sample. In a tumor microenvironment, for example, TissueGnostics’ solutions generate precise data scattergrams and histograms along with information on changes in cell population and tumor grade.

Exemplifying the transformative applications of TissueGnostics’ solutions is a success story where its instruments assisted a team at the Medical University of Vienna (MUV) to effectively find a cure for a previously untreatable form of non-Hodgkin’s lymphoma. Initially, the academic institution analyzed tissue samples of a patient in the terminal stage of cancer progression to uncover novel therapies that could improve his life expectancy.

While the human brain is a powerful interpolator, accurately correlating molecular expression patterns across multiple markers that are often stained using different fluorophores is impossible

In pursuit of this, the research team at MUV started using TissueGnostics’ instruments, which allowed them to identify through in-situ studies that blocking the platelet-derived growth factor receptor (PDGFR) had remarkable effects even on a late disease stage and brought the patient into remission within two weeks of starting on an experimental therapy.

According to this project’s lead pathologist, TissueGnostics’ platforms and software capabilities allowed them to gain information that would have been overlooked if they were using traditional tools and visual estimation to assess changes on a tissue level in situ. This novel therapy’s success, thanks to its instruments, gained international attention with leading peer-reviewed publications and renowned media outlets in Central Europe and beyond, hailing it as a breakthrough in cancer treatment.



TissueGnostics regularly collaborates with academic partners and publicly funded research consortia, like the Marie Skłodowska Curie Innovative Training Networks and Doctoral Networks in the EU, to drive many similar successes. In Australia, TissueGnostics works with the Australian Research Council’s (ARC) Center for Cell and Tissue Engineering Technologies, run by Monash University in Melbourne and Queensland University of Technology (QUT) in Brisbane. Dr. Ecker understands that to adapt to evolving market needs, it is essential to collaborate with partners working at the leading edge.

Moving forward, TissueGnostics aims to continue expanding the analytical capabilities of its platforms by developing and incorporating novel technologies and training better and more comprehensive deep learning models that can help clients stay at the frontiers of the changing histopathology and biomedical research domain.