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 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.

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.

1.3.1 Data input format

The cytoviewer package combines objects of SingleCellExperiment, SpatialExperiment and cytomapper::CytoImageList classes (from cytomapper) to visualize image- and cell-level information.

The cytoviewer function takes up to five arguments.

Firstly, image refers to a CytoImageList object containing one or multiple multi-channel images where each channel represents the pixel-intensities of one marker (proteins in IMC).

Secondly, mask refers to a CytoImageList object containing one or multiple segmentation masks. Segmentation masks are defined as one-channel images containing integer values, which represent the cell ids or background.

Thirdly, the object entry refers to a SingleCellExperiment or SpatialExperiment class object that contains cell-specific metadata in the colData slot.

Lastly, to match information between the CytoImageList objects and the SingleCellExperiment/SpatialExperiment object, two additional spots can be specified:

  • img_id: a single character indicating the colData (of the SingleCellExperiment/SpatialExperiment object) and elementMetadata (of the CytoImageList object) entry that contains the image identifiers. These image ids have to match between the SingleCellExperiment/ SpatialExperiment object and the CytoImageList objects.

  • cell_id: a single character indicating the colData entry that contains the cell identifiers. These should be integer values corresponding to pixel-values in the segmentation masks.

1.3.2 Data input variations

The functionality of cytoviewer depends on which input objects are user-provided. Below we describe the four use cases in respect to input objects and functionality.

1. Usage of cytoviewer with images, masks and object

The full functionality of cytoviewer can be leveraged when image, mask and object are provided, which is the main intended use case.

This allows image-level visualization (Composite and Channels), cell-level visualization, overlaying images with segmentation masks as well as metadata visualization.

2. Usage of cytoviewer with images only

If only the image object is specified, image-level visualization (Composite and Channels) is possible.

3. Usage of cytoviewer with images and masks

Image-level visualization (Composite and Channels), overlaying of images with segmentation masks and cell-level visualization is feasible when image and mask objects are provided.

4. Usage of cytoviewer with masks and object

If mask and object are specified, cell-level visualization as well as metadata visualization is possible.

2 Example workflow

2.1 Installation

The cytoviewer package can be installed from Bioconductor via:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("cytoviewer")

The development version of cytoviewer can be installed from Github via:

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")

remotes::install_github("BodenmillerGroup/cytoviewer")

To load the package in your R session, type the following:

library(cytoviewer)

2.2 Example dataset

For visualization purposes, we will use a toy dataset provided by the cytomapper package.

The dataset contains 3 images of \(100\mu{m}\) x \(100\mu{m}\) dimensions with 362 segmented cells and pixel-intensities for 5 proteins: H3, CD99, PIN, CD8a, and CDH. It is a small subset from a Type 1 Diabetes dataset (Damond et al. 2019).

Pixel-level intensities for all 5 markers (5 channels) are stored in the pancreasImages object.

The corresponding segmentation masks are stored in the pancreasMasks object.

All cell-specific metadata are stored in the colData slot of the corresponding SingleCellExperiment object: pancreasSCE.

For more detailed information on the dataset, please refer to the respective documentation (e.g. via ?pancreasImages or the vignette of the cytomapper package).

# Load example datasets 
library(cytomapper)
data("pancreasImages")
data("pancreasMasks")
data("pancreasSCE")

pancreasImages
## CytoImageList containing 3 image(s)
## names(3): E34_imc G01_imc J02_imc 
## Each image contains 5 channel(s)
## channelNames(5): H3 CD99 PIN CD8a CDH
pancreasMasks 
## CytoImageList containing 3 image(s)
## names(3): E34_mask G01_mask J02_mask 
## Each image contains 1 channel
pancreasSCE
## class: SingleCellExperiment 
## dim: 5 362 
## metadata(0):
## assays(2): counts exprs
## rownames(5): H3 CD99 PIN CD8a CDH
## rowData names(4): MetalTag Target clean_Target frame
## colnames(362): E34_824 E34_835 ... J02_4190 J02_4209
## colData names(9): ImageName Pos_X ... MaskName Pattern
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

2.3 Function call

Here as an example, we call cytoviewer with image, mask and object data to leverage all provided functionality.

This setting allows image-level visualization (Composite and Channels), cell-level visualization, overlaying images with segmentation masks as well as metadata visualization.

For further details, please refer to the ?cytoviewer manual or the Help page within the shiny application.

# Use cytoviewer with images, masks and object
app <- cytoviewer(image = pancreasImages, 
                  mask = pancreasMasks, 
                  object = pancreasSCE, 
                  img_id = "ImageNb", 
                  cell_id = "CellNb")

if (interactive()) {
  
  shiny::runApp(app, launch.browser = TRUE)

  }

2.4 Interface

The cytoviewer interface is divided into a Header, Sidebar and Body section (see Figure below).

The Header includes package version information, access to session information and the help page as well as a dropdown-menu for image downloads.

The Body features a Tabset-Panel layout allowing the user to switch between three image modes: Image-level (Composite and Channels) and Cell-level (Mask). Furthermore, the Composite and Mask tabs have zoom controls.

The Sidebar panel is subdivided into four sections: Sample selection, Image-level, Cell-level and General controls.