Dual IHC

Dual IHC analysis can be applied to a variety of IHC applications. It allows for the identification of two different cell populations or co-localisation of two antigens to one cell on a single tissue section.

Analysis Approach
We develop a customised algorithm tailored to the specific markers in your study. This involves a 3-chromogen color deconvolution process within the Indica Labs HALO platform to separate the 2 IHC chromogens plus counterstain. Cell objects are formed by applying weighted optical density values for the individual chromogens. Each positive cell type is then identified using defined size, shape and subcellular compartment staining parameters. A classifier is developed and integrated into the algorithm to automatically segment the tissue region of interest (ROI) for analysis. The completed algorithm is applied to all tissue section images in the study in an automated and objective manner to generate detailed cell-by-cell data. Example analysis and data is shown below. For further analysis of dual IHC analysis please click here.

A. IHC stained whole tissue section (left) and classifier overlay (tumor – orange; stroma – blue; white space – white) B. IHC stained tissue section (left) and cell detection of beta-Catenin (brown) and Cyclin D1 (teal) within the viable tumor ROI. Histology performed by Propath UK.

Quantitative Readouts

  • Area of specific tissue regions of interest (ROI) including viable tumor and stroma content across the whole section
  • Quantification of cells positive for multiple chromogenic markers across a whole tissue section or within a defined ROI
  • Quantification of cells in which multiple chromogenic markers co-localise

Example Dual IHC Analysis Data

Benefits

  • Receive highly detailed data quantifying multiple single markers or colocalised markers on a cell-by-cell basis across a whole tissue section or within a defined ROI.
  • Our automated batch analysis service objectively analyses each section using the same algorithm to reduce variability and improve data quality and interpretation.