A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotypingat single-cell resolution.

Abstract

Multiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells 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 the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at:https://github.com/ciccalab/SIMPLI

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* To further clarify the extent of methodological advance and the advantages of SIMPLI compared to existing counterparts, we revised Introduction, Table 1, Fig.1, Supplementary Fig.1. We compared the analysis of normalised and raw images, p.11-12, Supplementary figure 2. We reassigned cells in the lamina propria varying the mask overlap, p.12, Supplementary figure 2. We added another segmentation method and compared the results with the one already implemented in SIMPLI, p.17, Supplementary figure 3. We compared the cell phenotypes from unsupervised clustering at various resolution as well as with those from thresholding, p.17-18, Supplementary figure 3. We repeated the spatial analysis between PD1+CD8+ T cells and PDL1+CD68+ macrophages after re-identifying the latter with the thresholding approach, p.21-22. We revised the heterotypic spatial analysis using more restrictive cut-offs and adding a permutation test to strengthen the results, p.27. We expanded and clarified the modularity in the choice of analysis methods in terms of: Cell segmentation (conventional vs deep learning), Phenotyping (unsupervised vs thresholding), Spatial analysis (homotypic vs heterotypic). These are now described in the text (p.7-9) as well as in the revised Figures 1 and S1. We added further recommendations on the parameter choice in the software documentation and in the Methods and discussed the method's limitations (p.31). Finally, we added the Data Availability and Code Availability sections (p. 42), deposited all new data in Zenodo (Access Codes provided in the text).

* https://github.com/ciccalab/SIMPLI