AI-Powered SPatial Data
imageDx unlocks advanced AI-based digital assays to quantify spatial relationships of cell phenotypes & tissue features.
Spatial biology is a powerful approach to patient stratification and therapy guidance across therapeutic areas. Reveal Biosciences applies the latest advances in AI to novel pattern discovery and spatial biomarker characterization, revealing new patient data insights.
Explore with Spatial Data Outputs
Cell-to-Cell Proximity Analysis
Gather deep insights into the spatial relationships between multiple cell phenotypes.
Example of IF-stained whole slide image with masks identifying two cell phenotypes (green and yellow). Distances (red line) from each Cell Type A (green) to the nearest Cell Type B (yellow) is quantified and visualized with a histogram.
Multiplex IHC/IF, Co-registered Serial Sections
Cell-to-Feature Proximity Analysis
Interrogate the distance and relationships between the biomarker expression and tissue features.
The distance from each positive cell to the tumor border is defined as negative for cells inside the tumor, and positive for cells outside the tumor. The distribution of distances to the tumor border for two different cell types is visualized using a violin plot.
Single Stain IHC/ IF, Multiplex IHC/IF, Co-registered Serial Sections
Dispersion and Neighborhood Analysis
Measure the extent of dispersion of each phenotype to enable quantitative comparison between samples and treatment groups.
Cell intensity can be calculated in a predefined neighborhood radius and visualized as a heatmap.
Single Stain IHC/ IF, Multiplex IHC/IF, Single Stain or Multiplex ISH/FISH, Co-registered Serial Sections
Tissue Microenvironment Classification
Classify samples into biologically meaningful subgroups based on immune cell density in the Tissue Microenvironment (TME).
Immune cell density (heatmap) outside and inside of the tumor (blue outline) are calculated to generate a tumor infiltration ratio score.
Single Stain IHC/ IF, Multiplex IHC/IF, Co-registered Serial Sections
Spatial AI Data for Co-registered Serial Sections
Align and stack stained serial sections to gain deeper spatial biology insights throughout a tissue sample.
INPUTS:
- Serial IHC/IF serial sections
- H&E + IHC serial sections
- H&E + IF/FISH serial sections
- IHC/IF + ISH/FISH serial sections
- Custom Co-Registration
OUTPUTS:
Co-register serial sections of IHC, H&E, IF, ISH, FISH, and specialty stained images to generate pseudo-multiplex data.
Interrogate the distance and relationships between biomarker expression and tumor borders in multiplex IF or co-registered, IHC-stained serial sections.
Generate immune cell infiltration ratio scores to classify samples into biologically meaningful subgroups based on immune cell density in the Tissue Microenvironment (TME). Enumerate various cell types neighboring a target cell or feature to measure multi-cell interactions.
Explore and quantify spatial relationships between RNA foci and tissue features such as tumor border, blood vessels, and more from co-registered ISH and H&E sections.
Evaluate the relationship between gene expression and protein expression using ISH sections co-stained or co-registered with IF, IHC, or special stain sections.
Generate custom spatial insights from co-registered sections specific to your unique study goals.
