Abstract 1448: Cell DIVE™: A robust and standardized platform for multiplexed whole slide imaging and single cell analysis

Multiplexed tissue imaging provides unprecedented and novel insights into cell populations and spatial interactions. Cell DIVE™ multiplexed immunofluorescence (MxIF) platform (GEHC, Issaquah, WA) provides an iterative staining, imaging and dye inactivation workflow for over 60 biomarkers on a single fixed tissue section. It is also relatively unique in providing a robust calibration process (allowing multiple calibrated instruments to produce equivalent results) using a multi-function calibration plate. Automated software enables system calibrations and robust automated image processing (field flattening, autofluorescence (AF) removal, image registration). Calibration is particularly important when imaging whole tissue slide sections where 100s of fields of view (FOV) may need to be imaged (depending on sample dimensions) and stitched for down-stream visualization and single cell analysis. In the current study, we applied our calibrated whole slide imaging process to whole tissue tonsil sections, a commonly used control for immune and cancer biomarkers. Sections were stained with 30 antibodies conjugated with either Cy3 or Cy5 dyes, including DAPI, cytokeratin AE1, CD3, CD4, CD8, CD20, CD31, CD163, COX2, FOXP3, HIF1A, HLA1, K167, LAG3, MHCII, NAKATPASE, PCAD, PCK26, S6, S100 and VIMENTIN. Each antibody underwent a robust multi-step validation process to ensure staining sensitivity and specificity. After a two-step antigen retrieval process, background autofluorescence (AF) imaging and staining, samples were imaged in the DAPI, Cy3 and Cy 5 channels on a calibrated IN Cell 2500 with Cell DIVE image acquisition and image processing software. Over 850 fields of view (FOV) were imaged in each round, followed by AF removal, registered with baseline DAPI, and stitched with minimal “quilting” artefacts. Pseudo-colored virtual H&E were also generated for the entire stitched image using DAPI and AF to assist histology interpretation. Images were imported into Halo® image analysis software (www.indicalab.com) for single cell segmentation and phenotype quantification for all FOVs. Using a subset of markers, the germinal center, non-germinal center and squamous epithelium were segmented using a random forest classifier. The HighPlex FL module was used to segment and count the total number of cells in each region, and combinations of immune cell markers were used to determine phenotype. In summary we demonstrate a standardized staining, imaging and image processing wor