cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

Abstract

SUMMARYHighly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

Cutaneous and systemic hyperinflammation drives maculopapular drug exanthema in severely ill COVID‐19 patients

“…For cell‐type annotation, we manually gated major cell types of interest using the cytomapper R package. 20 The following markers were used to define cell types: CD8 + T cells (CD3 + , CD8 + ), CD4 + T cells (CD3 + , CD8 − , CD4 + ), keratinocytes I (E‐cadherin + , Filaggrin − ), keratinocytes II (Filaggrin + ), Langerhans cells (LC; E‐cadherin + , Langerin + ), macrophages (CD163 + ), neutrophils (polymorphonuclear leukocytes (PMN); MPO + ), plasmacytoid dendritic cells (pDC; CD303 + ), vasculature (CD31 + ). Roughly, half of all cells were manually gated using the markers from above.…”

“…After single‐cell generation, all subsequent analyses were performed using R bioconductor. For cell‐type annotation, we manually gated major cell types of interest using the cytomapper R package 20 …”

“…For cell-type annotation, we manually gated major cell types of interest using the cytomapper R package. 20 The following markers were used to define cell types: CD8 + T cells (CD3 + , CD8 + ), CD4 + T cells (CD3 + , CD8 − , CD4 + ), keratinocytes After prediction of a cell-type for all unlabeled cells the classification results were inspected on images and additional rounds of cell labelling performed if needed. We excluded 1 HC sample, since it contained highly increased numbers of CD3 + T cells and was obtained from an excision of peritumoral tissue.…”

Complete response to neoadjuvant chemoradiotherapy in rectal cancer is associated with RAS/AKT mutations and high tumour mutational burden

“…com/ Boden mille rGroup/ imcto ols). Ilastik generated Probability probability maps and raw multi-channel files from each region of interest were analysed using CellProfiler and the Cytomapper R package (v3.12) [27,28].…”

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

“…For example, several computational approaches have been developed to process raw images and extract single-cell data either interactively (Ilastik 10 , CellProfiler4 11 , CODEX Toolkit 4 ) or via command line (imcyto 12 , ImcSegmentationPipeline 13 ). Distinct sets of tools can then perform cell phenotyping (CellProfiler Analyst 14 , Cytomapper 15 , Immunocluster 16 ) or analyse cell-cell spatial interactions (CytoMap 17 , ImaCytE 18 , SPIAT 19 , neighbouRhood 20 ). Similarly, a few tools enable direct pixel-based analysis through pixel classification 10 or quantification of pixel positive areas 11 .…”