Introduction

nf-core/mihcro is a bioinformatics pipeline designed to streamline the analysis of multiplex immunohistochemsitry (mIHC) samples.

The purpose of the pipeline is to convert tiled or stitched multi-channel microscopy images into clean single-cell data. It takes as input a samplesheet, and an optional list of markers present on the panel. It stitches images together if required, performs cell segmentation on the DAPI channel, quantifies outputs into single-cell format and then runs a few basic clustering analyses.

The segmentation model to be used can be selected (currently mesmer is default, and cellpose is also available). By default, the pipeline performs segmentation on the DAPI marker, but there is also the option to specify a membrane marker with which to segment whole cells. There are also optional preprocessing steps prior to segmentation for speed and accuracy, including DAPI background removal, Otsu thresholding, and downscaling to 1um/pixel scale.

The pipeline returns a processed TIFF file, a segmented image mask and summary, a cell x feature spreadsheet, as well as an HTML report on the cell x feature data, including segmentation summary statistics, marker intensity summary statistics, and UMAP reduction and clustering.

The pipeline accepts three main formats of input:

• Tiled microscopy images
• Pre-stitched microscopy images (ome.tiff files), from software such as QuPath or HALO (software versions v4.0+)
• Microscopy images output from older version of HALO, which are indica-formatted .tiff files.

Usage

Note

Given this pipeline is not yet an official nf-core pipeline, you will need to clone the repository prior to running!

First, prepare a samplesheet that follows the format below:

example_samplesheet.csv:

sample,tiffs,format
EXAMPLE_SAMPLE_NAME,/path/to/tiff/directory,tiles

Each row represents a directory which contains several .tiff tiles, or a single stitched .tiff or .ome.tiff.

The format column must be one of the following for each sample:

• tiles for tiled inputs
• stitched for pre-stitched, ome-tiff inputs
• fused for legacy HALO outputs (indica-format tiff files)

Next, you'll need to prepare a list of your markers, which will look something like this:

example_markers.csv:

marker_name
DAPI
PTPRC
HMWCK
CD3
CD11c

Each row represents a different channel in your images. Note that marker names cannot contain spaces!

Now, you can run the pipeline using the following basic parameters:

nextflow run mihcro \
   -profile <docker/singularity/.../institute> \
   --input example_samplesheet.csv \
   --markers example_markers.csv \
   --outdir <OUTDIR>

Note

At this stage of development, this pipeline only works with container -profile options (e.g. apptainer, docker, singluarity).

Warning

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation.

Pipeline output

The pipeline returns:

• A processed TIFF file in the OME-TIFF format
• A segmented image mask and summary image of the segmentation output, with DAPI in blue and segmentation borders in red.
• A cell x feature spreadsheet output by MCQuant
• An HTML report on the cell x feature data, including:
  • Segmentation summary statistics and basic QC
  • Marker intensity summary statistics
  • UMAP reduction, and clustering
  • Overviews of the marker signal in clusters
  • Spatial map of marker singal and cluster assignment

For more details about the output files and reports, and for examples of output from a full dataset, please refer to the output documentation.

Credits

This project was supported by Peter MacCallum Cancer Foundation - we gratefully acknowledge their financial support, which has been instrumental in the development and maintenance of this software.

We thank the following people for their extensive assistance in the development of this pipeline:

• Patrick Crock (principal developer)
• Song Li (pilot developer)
• Qing Siaw (developer/user training)
• Xuhan Zhang (data analyst)

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

The authors would also like to acknowledge the MCMICRO pipeline, after which this pipeline was first modelled:

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging.

Schapiro, D., Sokolov, A., Yapp, C. et al.

Nat Methods 19, 311–315 (2022). https://doi.org/10.1038/s41592-021-01308-y