An end-to-end workflow for multiplexed image processing and analysis.

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .