DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices

Summary

A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Graphical abstract

Highlights

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  DIMPLE is an R package to analyze and visualize multiplex imaging data

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  DIMPLE provides a scalable framework to quantify cellular interactions

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  Statistical modeling links tumor microenvironment to patient-level outcomes

The bigger picture

The tumor microenvironment (TME), the ecosystem of immune cells, extracellular matrix, blood vessels, and other cells that surrounds a tumor, is emerging as the next frontier in cancer research. It is firmly established that the presence and prevalence of specific immune cells within and around the tumor can predict patient outcomes, including their response to treatment and the progression of cancer. Multiplex imaging (MI) technologies such as PhenoImager, PhenoCycler, MIBI, and others provide a detailed view of the TME. These rapidly evolving technologies enable the discrimination of numerous cell types while preserving their spatial context. This allows for quantification of spatial cellular interactions or the tendency of cell types to co-locate. Evidence is mounting that these cellular interactions, beyond mere presence and prevalence, are associated with patient outcomes.

Abstract

The tumor microenvironment consists of multiple cell types that can interact in complex ways. Multiplex imaging technologies such as Vectra Polaris or PhenoCycler can be used to identify the precise spatial locations and phenotypes of cells in tissue samples of the tumor microenvironment. Masotti et al. present a software package, DIMPLE, that provides an end-to-end pipeline to quantify, visualize, and model spatial cellular interactions in multiplex imaging data.