A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at 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

Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

“…( A ) IMC analysis workflow using SIMPLI. 8 For each region, images of the markers used ( Supplementary Table 5 ) were preprocessed to extract pixel intensities. Masks for tumor and stroma were derived and used for the pixel analysis.…”

“…IMC data analysis was done with SIMPLI. 8 Positive areas for combinations of markers were quantified and normalized over the tissue area or the area of selected immune populations. After segmentation, cell identities were assigned according to the highest overlap with marker-specific masks.…”

“…Comparison of T cells infiltrates between CRC groups. (A) IMC analysis workflow using SIMPLI 8. For each region, images of the markers used (Supplementary Table5) were preprocessed to extract pixel intensities.…”

Rarity: Discovering rare cell populations from single-cell imaging data

“…IMC data was generated for sixteen samples of non-cancerous colon mucosa were obtained from six individuals who underwent surgical resection of colorectal cancers (see Supplementary Information for full experimental details). The imaging data was originally published as part of (Bortolomeazzi et al 2022), but now we have additionally made available the processed single-cell data set (Märtens et al 2022). Gamma-delta T cells were expected to constitute less than 10% of T cells in human colon mucosa (Viney, MacDonald, and Spencer 1990), and their characteristic feature is that they are CD3-positive but both CD4-negative and CD8-negative.…”

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

“…SIMPLI's code, documentation and an example dataset are available at "SIMPLI [https:// github.com/ciccalab/SIMPLI]" 46 . The software code is protected by copyright.…”