Determines the invasiveness of breast cancer by distinguishing between invasive and non-invasive tumor components using two serially stained slides: one with the myoepithelial cell nuclear marker p63 and the other with a cytokeratin (CK) tumor marker like PCK. VirtualDoubleStaining™ combines the two slides to identify invasive tumor structures while excluding non-invasive ones such as ductal carcinoma in situ (DCIS). This protocol can be combined with any biomarker analysis using VirtualTripleStaining (VTS) to focus on invasive tumor components.

Evaluates the homo- or heterogeneity of adipose tissue based on cell size and shape. The protocol includes three individual APPs that output the membrane-to-cell ratio, cell size, and cell shape, respectively.

Automatically identify invasive cancer areas, count tumor nuclei based on Ki-67 expression, and calculate the proliferation index.

Assesses tumors by determining Ki-67 positivity using two serial sections stained for Ki-67 and CK5. The protocol identifies tumor regions automatically on CK5 stained slides using the VirtualDoubleStaining™ technique and overlays these regions on the Ki-67 stained slide, facilitating automatic identification of tumor regions.

Assesses the number of Ki-67 positive cells in the epidermis, indicating increased epidermal hyperplasia associated with inflammatory skin diseases like atopic dermatitis and psoriasis. The protocol quantifies the number and area of Ki-67 positive cells within the epidermis and the entire tissue section, with manual assessment of the outlined epidermis region as needed.

Automatically detects and quantifies Ki-67 positive cells within tumor regions using Ki-67/MART1-IHC double staining, eliminating the need for manual outlining. Only an overall region of interest (ROI) needs to be defined. Provides the number of positive nuclei and the ratio to the total nuclei within the tumor region, ensuring accurate differentiation between melanomas and nevi.

Detects Ki-67 negative and positive nuclei, classifies the positive nuclei into 1+, 2+, and 3+ categories, and calculates the proliferation index for neuroendocrine tissue stained for Ki-67. This assessment correlates with tumor grade and clinical course, making the Ki-67 proliferative index an important prognostic variable for neuroendocrine neoplasms.

Detects nuclei within tumor regions using double Ki-67+p16 staining, providing proliferation rates (area and number) of nuclei. This protocol outlines tumor regions automatically based on the double staining, requiring only an overall region of interest (ROI) for analysis, and aids in assessing cervical dysplasia and differentiating cervical adenocarcinoma from endometrial adenocarcinoma.

Detects and classifies nuclei based on mPGES1 staining expression in the tumor microenvironment (TME) of melanoma. The “10132 – mPGES1, Melanoma, TME” protocol categorizes nuclei as negative, 1+, 2+, or 3+ according to the level of mPGES1 expression around each nucleus, aiding in the assessment of inflammation and hypoxia conditions within various human cancers.

Assesses tumors by determining Ki-67 positivity, detecting and classifying nuclei as positive or negative, and calculating an average proliferation index for the entire tumor region. Tumor regions must be manually identified and outlined within a region of interest (ROI). For an advanced workflow, tumor regions can be automatically identified from cytokeratin stained slides using the “20002 – PCK VDS, Tumor Detection” protocol, and the “10114 – Hot Spot” protocol generates a heatmap to identify a hot spot, reporting the proliferative index for both the entire tumor region and the hot spot.

Developed for the University of Edinburgh, Scotland, this application quantifies axon density across brain areas in neurons stained with mCherry and/or GFP. It measures fluorescence density as the fraction of pixels above a set threshold value in brain sections, providing a list of brain areas and their fluorescence density levels. The application can also be modified to detect pre-synaptic targets labeled via retrograde tracing methods.

Quantifies nuclear expression of MCM7, a DNA replication licensing factor up-regulated in various cancers, including prostate and bladder cancer. The approach used for calculating expression has been shown to be highly prognostic in non-muscle invasive bladder cancer, correlating well with manual scoring in large patient cohorts.