@lassedochreden
@nilseling

1 Introduction

This vignette introduces the cytoviewer package for interactive multi-channel image visualization. Images as well as corresponding segmentation masks generated by imaging mass cytometry (IMC) and other highly multiplexed imaging techniques can be interactively visualized and explored.

The cytoviewer package builds on top of the cytomapper Bioconductor package (Eling et al. 2020) and extends the static visualization strategies provided by cytomapper via an interactive Shiny application. The cytoviewer package leverages the image handling, analysis and visualization strategies provided by the EBImage Bioconductor package and offers interactive image visualization strategies in a similar fashion as iSEE for single-cell data. In addition, building up on SingleCellExperiment, SpatialExperiment and cytomapper::CytoImageList classes, the cytoviewer package integrates into the Bioconductor framework for single-cell and image analysis.

Read the pre-print here.

1.1 Highly multiplexed imaging

Highly multiplexed imaging allows simultaneous spatially and single-cell resolved detection of dozens of biological molecules (e.g. proteins) in their native tissue context. As a result, these technologies allow an in-depth analysis of complex systems and diseases such as the tumor microenvironment (Jackson et al. 2020) and type 1 diabetes progression (Damond et al. 2019).

Imaging-based spatial proteomics methods (Moffitt, Lundberg, and Heyn 2022) can be broadly divided into fluorescent cyclic approaches such as tissue-based cyclic immunofluorescence (t-CyCIF) (Lin et al. 2018) and one-step mass-tag based approaches such as multiplexed ion beam imaging (MIBI) (Angelo et al. 2014) and IMC (Giesen et al. 2014).

Of note, the instructions below will focus on the visualization and exploration of IMC data as an example. However, data from other technologies such as t-CyCIF or MIBI, which produce pixel-level intensities and (optionally) segmentation masks, can be interactively visualized with cytoviewer as long as they have the appropriate input format (see Section Data input format).

1.1.1 Imaging mass cytometry

IMC, an advancement of CyTOF, combines antibodies tagged with isotopically pure rare earth metals with laser ablation and mass-spectrometry-based detection to produce high-dimensional images (Giesen et al. 2014). It captures the spatial expression of over 40 proteins in parallel at a sub-cellular resolution of 1 μm. Thus, IMC is able to detect cytoplasmic and nuclear localization of proteins.

1.2 Highly multiplexed image analysis

To fully leverage the information contained in IMC and multiplexed imaging data in general, computational tools are of key importance.

The main analysis steps, irrespective of the biological question, include 1) Visual inspection of images for quality control, 2) Image pre-processing and segmentation and 3) Single-cell and spatial analysis (Windhager, Bodenmiller, and Eling 2021).

A comprehensive end-to-end workflow for multiplexed image processing and analysis with detailed information for every analysis step can be found here.

Importantly, the cytoviewer package can support, simplify and improve any of these analysis steps with its easy-to-use interactive visualization interface in R.

Below we will showcase an example workflow that highlights the different functionality and potential application fields of cytoviewer.

1.3 Application overview

The cytoviewer interface is broadly divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads (Figure 2B).