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Spectre Protocols - Spatial

Spectre enables the analysis of high-dimensional imaging data, including data generated using Fluidigm’s Hyperion Imaging Mass Cytometer (IMC). Our current workflows support a basic (using CellProfiler) or comprehensive (using Ilastik) cell and region segmentation approach, followed by cellular and spatial analysis using FlowJo or Spectre (in R). Please note: the original functions and workflows from SpectreMAP have now been directly incorporated in Spectre.

Getting started

Here we provide some helpful resources and protocols for getting started.

Overview of cell segmentation and spatial analysis
Image visualisation and TIFF export
Installing Spectre (R)
Here we provide background information on methods of analysing IMC data. Here we provide instructions for initial image visualisation and TIFF export using MCD Viewer or HistoCat++. From here, TIFF files can be exported for use in our segmentation approaches. Here we provide instructions for installing R, RStudio, and Spectre, along with introductory tutorials for getting started.

Cell segmentation

Here we provide protocols for performing initial cell segmentation.

Boundary-based multicut segmentation with Ilastik
Simple nuclear expansion segmentation with CellProfiler
Modified Bodenmiller lab segmentation approach

Some IMC images contain extremely dense collections of cells, where cytoplasmic (and sometimes) nuclear signal from one cell is difficult to distinguish from another. In this protocol we describe boundary-based segmentation using the 'multi-cut' workflow in Ilastik. Additional cell type and region masks can be included, which dramatically enhances analysis. This is the simplest form of cellular segmentation and analysis. The nuclear signal is identified, and the boundary is expanded outwards by a certain number of pixels. This then becomes the boundary of the cell mask. Although a very simplistic approach, only nuclear signal is required. In this approach, the user trains a classifier to identify and predict 'nuclear', 'cytoplasmic', or 'background' pixels. This can be used to create more comprehensive cell masks than can be achieved using simple pixel expansion methods. This is based on the Bodenmiller lab workflow described here. Additional cell type and region masks can be included, which dramatically enhances analysis.


Here we provide workflows for analysing HD imaging data following cell segmentation.

Spatial analysis workflow using Spectre
IMC data analysis using FlowJo
An analysis workflow in R using Spectre that facilitates simultaneous cellular and spatial analysis. A workflow for analysing IMC data using FlowJo, after initial conversion of TIFF files and masks into FCS files.

Other software and protocols

Here we provide links to other analysis software and protocols that are useful in the analysis of high-dimensional imaging data.

HistoCat (GUI, Matlab)
CytoMapper (R)
CytoMAP (GUI, Matlab)

HistoCat software from the Bodenmiller/Shapiro labs. CytoMapper software from the Bodenmiller lab. CytoMAP software from the Gerner lab.