Dr. Florian Gruber and Christopher Kremslehner, MUV, summarize what is known about the cell senescence in skin and present new research done in the SKINMAGINE lab, focusing on aging in tissues using multimodal imaging.

The IF Artificial Skin App stratifies skin equivalents into dermis and epidermis, further dividing the epidermis into stratum corneum, suprabasal, and basal layers. It outputs area, mean staining intensity, nuclei counts, and % of marker-positive cells for each layer and sublayer. Read publication here 

AI-powered tissue classification applies machine learning to identify and categorize tissue types. It supports consistent, quantitative analysis of morphology, tumor microenvironments, and disease processes in research and diagnostics.

Pyknotic nuclei, key indicators of cell death, are critical in histopathology and disease research. Using advanced imaging and TissueGnostics’ IF Pyknotic Nuclei App, researchers can achieve automated, accurate detection and quantification for deeper diagnostic insights.

AI is transforming imaging cytometry by enabling automated, accurate nuclei segmentation and tissue detection. With solutions like StrataQuest, researchers gain higher precision, efficiency, and insights into cellular structures for biomedical research and diagnostics

Deep learning has become a powerful tool in biomedical imaging, enabling accurate cell segmentation, phenotype detection, and analysis of complex tissue structures. TissueGnostics offers AI-driven nuclei segmentation to improve efficiency and precision in image analysis.

Yan et al. identified COL8A1 as a potential biomarker of poor chemotherapy response and survival in PDAC. TissueFAXS and StrataQuest enabled imaging and segmentation of tumor structures.

Joint TissueGnostics & PreciPoint webinar on AI-driven image analysis tools: ML-based tissue classification, deep-learning nuclei segmentation, and spatial phenotyping in stained sections.