Abstract
Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at “SIMPLI [https://github.com/ciccalab/SIMPLI]”.
Highlights
Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information
A method was considered compatible with a given imaging technology if this was reported in the original publication or other studies
SIMPLI performs the analysis of multiplexed imaging data in three steps (Methods, Fig. 1) integrating well-established and newly developed
Summary
We performed single-cell segmentation and quantified the main cell types identified in the original study[9] by applying expert-defined thresholds to the expression of markers representative of each population (CDX2, MUC1 or cytokeratin for tumour cells; CD34 or CD31 for endothelial cells; vimentin for stromal cells; CD11c for dendritic cells; CD38 for B cells; CD3 and CD4 for CD4+ T cells; CD3, CD4 and FOXP3 for Tregs; CD3 and CD8 for CD8+ T cells, CD68 for macrophages). Since the spatial randomness used as a baseline for the permutation test is an approximation of the highly organised structure of biological tissues, we sought further support this result through independent inspection of the spatial distributions of B cells in CLRs (Fig. 5g) and DII (Fig. 5h) in the histological images This result, not reported in the original study, showcases the discovery potential of the quantitative analysis of spatial relationships between cell populations implemented in SIMPLI. The SIMPLI graphical representations of the tissue composition as an overlay of cell boundaries colour-coded by cell populations greatly facilitate the visual inspection of their spatial interactions in their original tissue context
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