The growth in cancer immunotherapy agents requires a deeper understanding of the immune contexture of the tumor microenvironment (TME) through multi-marker phenotyping of specific cells in situ and analyze their biodistribution in the TME. There are many new methods to collect multi-marker images, with increasing ‘plex’ levels and speed every year. However, developments in analysis software with a single-package workflow for highplex imagery have not kept pace. We present here a comprehensive guided workflow designed specifically for highplex (40+ markers) image analysis, covering tissue segmentation, cell segmentation based on nuclear staining, cellular phenotyping, and spatial analyses.
Lung and colorectal tissue sections with a 40-marker IMC panel of structural, tumor, stroma, immune cell markers, and immunoregulatory proteins that are targets of immunotherapy, were imaged (Hyperion, Standard BioTools). Highplex image analysis was performed as a multi-step workflow in a single software package that includes: conversion of IMC images to pyramidal format; easy visualization methods for displaying different marker subsets; a paint-to-train algorithm for tissue segmentation (into tumor, stroma, necrosis, etc); deep-learning-based nuclear segmentation pre-trained specifically on IMC DNA channels; cellular phenotyping based on thresholds set based on visual assessment of positivity; spatial biodistribution metrics for cell populations; and a flexible set of outputs for further downstream analysis.
This demonstrates that a simple, guided workflow can be used to analyze highplex images of different tissue types with no programming knowledge and few changes between tissue types. Spatial biodistribution metrics and heatmaps were generated for each tissue type with a minimum of work required. Having a comprehensive guided workflow for the analysis of this complex data makes obtaining useful results from highplex images more accessible to biologists and immunologists by circumventing the requirement for expert programming for each specific application.