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An end-to-end workflow for multiplexed image processing and analysis

Nature Protocols volume 18, pages 3565–3613 (2023)Cite this article

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Abstract

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell–cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5–6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/.

Key points

  • The protocol describes the analysis of data generated by highly multiplexed tissue imaging approaches, such as imaging mass cytometry. The presented workflow includes steps for imaging data visualization, data preprocessing, image segmentation, single-cell feature extraction, reading data into R, spillover correction, quality control, cell phenotyping and spatially resolved single-cell analysis.

  • The software packages used include napari, steinbock, DeepCell/Mesmer, Ilastik, CellProfiler, cytomapper and imcRtools.

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Fig. 1: Overview of the multiplexed tissue image analysis workflow.
Fig. 2: Data output after image processing and representation in R.
Fig. 3: Screenshot of napari and napari-imc.
Fig. 4: Typical multiplexed image processing workflows using steinbock.
Fig. 5: Overview of the pixel classification-based segmentation pipeline.
Fig. 6: Spillover correction for single-cell data and multi-channel images.
Fig. 7: Visual assessment of segmentation quality.
Fig. 8: Cell area distribution.
Fig. 9: Visualization of the image area covered by cells per image.
Fig. 10: Visualization of differences in marker distributions between patients.
Fig. 11: Low dimensional representation of single cells.
Fig. 12: Cluster parameter sweep for optimal parameter estimation.
Fig. 13: Visualization of cluster-specific expression patterns.
Fig. 14: Cell phenotypes displayed on UMAP embedding.
Fig. 15: Mean marker expression heat maps.
Fig. 16: Input, interpretability and output of spatial analysis approaches.
Fig. 17: Spatial tumor communities.
Fig. 18: Cellular neighborhood detection.
Fig. 19: Spatial context detection.
Fig. 20: Spatial patch detection.
Fig. 21: Cell–cell interaction testing.

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Code availability

The steinbock toolkit is available at https://github.com/BodenmillerGroup/steinbock. The pixel classification-based pipeline is available at https://github.com/BodenmillerGroup/ImcSegmentationPipeline. The cytomapper R/Bioconductor package is available at https://bioconductor.org/packages/cytomapper. The imcRtools R/Bioconductor package is available at https://bioconductor.org/packages/imcRtools. The napari-imc napari plugin is available at https://github.com/BodenmillerGroup/napari-imc. The readimc Python package is available at https://github.com/BodenmillerGroup/readimc. An extensive workflow on processing and analyzing multiplexed imaging data can be accessed at https://bodenmillergroup.github.io/IMCDataAnalysis/. The script reproducing the analysis presented in this protocol is available at https://github.com/BodenmillerGroup/IMCDataAnalysis/tree/main/publication.

Change history

  • 18 October 2023

    In the HTML version of the article initially published, there were some formatting errors in the presentation of the code which have now been corrected.

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Acknowledgements

We thank the Bodenmiller laboratory for helpful feedback and discussions, and the IMMUcan consortium for allowing us to use their data. Specifically, we would like to thank J.R. Fischer and T. Hoch for code contributions to the imcRtools package, as well as R. Casanova and N. Damond for testing the steinbock toolkit in Python code. We would further like to thank N. de Souza for critically commenting on the manuscript. Finally, we would like to express our appreciation of the larger bioimage analysis and open-source software communities for providing supportive feedback and packages critical to the development of the presented workflow. J.W. was funded by the CRUK IMAXT Grand Challenge and by two Chan Zuckerberg Initiative (CZI) napari Plugin Accelerator Grants (2021-239869(5022), 2021-239940(5022)). B.B. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 framework, ERC-2019-CoG: 866074 – Precision Motifs, a SNF Project Grant, a Promedica foundation grant and the CRUK IMAXT Grand Challenge. N.E. was funded by the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie Actions grant agreement no. 892225.

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Author notes

  1. Jonas Windhager

    Present address: SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden

  2. Vito Riccardo Tomaso Zanotelli

    Present address: Division of Metabolism and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland

Authors and Affiliations

  1. Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland

    Jonas Windhager, Vito Riccardo Tomaso Zanotelli, Daniel Schulz, Lasse Meyer, Michelle Daniel, Bernd Bodenmiller & Nils Eling

  2. Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland

    Jonas Windhager, Vito Riccardo Tomaso Zanotelli, Daniel Schulz, Lasse Meyer, Michelle Daniel, Bernd Bodenmiller & Nils Eling

  3. Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland

    Jonas Windhager & Lasse Meyer

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Contributions

J.W. developed readimc, napari-imc and the steinbock toolkit, and contributed to the pixel classification-based segmentation pipeline. V.R.T.Z. developed the pixel classification-based segmentation pipeline and contributed to the spillover correction and the interaction testing approaches. D.S. implemented the cellular neighborhood detection approach. L.M. wrote the spatial community and spatial context-related functions. M.D. generated the data and provided experimental information for the spillover correction approach. N.E. developed the cytomapper and imcRtools packages and contributed to the pixel classification-based segmentation pipeline. J.W., B.B. and N.E. conceived the study. J.W., B.B. and N.E. wrote the manuscript. All authors approve of the manuscript.

Corresponding authors

Correspondence to Bernd Bodenmiller or Nils Eling.

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Key references using this protocol

Hoch, T. et al. Sci. Immunol. 7, eabk1692 (2022): https://doi.org/10.1126/sciimmunol.abk1692

Jackson, H. W. et al. Nature 578, 615–620 (2020): https://doi.org/10.1038/s41586-019-1876-x

Damond, N. et al. Cell Metab. 29, 755–768.e5 (2019): https://doi.org/10.1016/j.cmet.2018.11.014

Ali, H. R. et al. Nat. Cancer 1, 163–175 (2020): https://doi.org/10.1038/s43018-020-0026-6

Schulz, D. et al. Cell Syst. 6, 531 (2018): https://doi.org/10.1016/j.cels.2017.12.001

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Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 18, 3565–3613 (2023). https://doi.org/10.1038/s41596-023-00881-0

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