Pipeline for Assessing Tumor Immune Status Using Superplex Immunostaining and Spatial Immune Interaction Analysis

Abstract

The characteristics of the tumor microenvironment (TME) are closely linked to tumor progression and treatment response. The TME comprises various cell types, their spatial distribution, cell-cell interactions, and their organization into cellular niches or neighborhoods. To capture this complexity, several spatial profiling technologies have been developed. However, challenges such as low throughput, high costs, and complicated data analysis have limited their widespread use in immune research. In this study, we introduce the Cyclic-multiplex TSA (CmTSA) staining platform, a high-throughput superplex staining technology based on tyramide signal amplification (TSA) immunostaining combined with an efficient fluorophore recycling method. The CmTSA platform allows for the labeling of 30-60 antigens across multiple parallel formalin-fixed paraffin-embedded (FFPE) slides. Furthermore, the automated CmTSA workflow requires only standard histological equipment and conventional immunohistochemistry (IHC) primary antibodies (Abs), significantly reducing costs. While the superplex images produced contain extensive multidimensional information, extracting the spatial features of the TME from raw pixel data can be challenging. To address this, we present a computer vision-based analysis pipeline, which begins with deep learning-based algorithms to segment individual cells and identify cell types based on defined annotation rules. It then evaluates the spatial distribution tendencies of each cell type, the interaction intensity between paired cells, and the multicellular functional niches. This comprehensive approach enables researchers to visualize and quantify the types, states, and levels of immune activities within the TME effectively, advancing tumor immunology research and precision immune medicine.