Last updated: 2022-02-25

Checks: 1 1

Knit directory: MelanomaIMC/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

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The results in this page were generated with repository version 1b6c48a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


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    Ignored:    data/data_for_analysis/
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    Modified:   analysis/index.Rmd

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 1b6c48a toobiwankenobi 2022-02-25 index.html change
Rmd b4da5af toobiwankenobi 2022-02-23 adapt readme and add example images to Fig S9
html fe331cb toobiwankenobi 2022-02-22 re-run whole analysis
html 64e5fde toobiwankenobi 2022-02-16 change order and naming of supp fig files
html a4bcb73 toobiwankenobi 2022-02-09 clean repo
html 3da15db toobiwankenobi 2021-11-24 changes for revision
html 434eee4 toobiwankenobi 2021-09-23 Figure adaptions and new Supp Figure with gates
Rmd c4e2793 toobiwankenobi 2021-08-04 rearrange figure order to match pre-print
html c4e2793 toobiwankenobi 2021-08-04 rearrange figure order to match pre-print
html e9a4766 toobiwankenobi 2021-07-07 adapt
Rmd ee1595d toobiwankenobi 2021-02-12 clean repo and adapt files
html ee1595d toobiwankenobi 2021-02-12 clean repo and adapt files
html 2e443a5 toobiwankenobi 2021-02-09 remove files that are not needed
html d1c9a41 toobiwankenobi 2021-02-09 index file
html 3828d53 toobiwankenobi 2021-02-09 index file
html c19bae4 toobiwankenobi 2021-02-09 add index file
Rmd f075b06 toobiwankenobi 2020-07-28 Start workflowr project.

Multiplexed Imaging Mass Cytometry in Metastatic Melanoma Utilizing RNA and Protein Co-Detection Links Features of Response to Immunotherapy

Tobias Hoch\(^{1,2,3,5}\), Daniel Schulz\(^{1,2,5,*}\), Nils Eling\(^{1,2}\), Julia Martínez Gómez\(^{4}\), Mitch Levesque\(^{4}\), Bernd Bodenmiller\(^{1,2,*}\)

1 University of Zurich, Department of Quantitative Biomedicine, Zurich, 8057, Switzerland
2 ETH Zurich, Institute for Molecular Health Sciences, Zurich, 8093 Switzerland
3 Particles-Biology Interactions Lab, Empa, Swiss Federal Laboratories for Materials Science and Technology, St. Gallen, 9014, Switzerland
4 University Hospital Zurich, Department of Dermatology, Schlieren, 8952, Switzerland
5 These authors contributed equally
* Correspondence:

Abstract

Intratumoral immune cells are crucial for tumor control and anti-tumor responses during immunotherapy. Immune cell trafficking into tumors is mediated by chemokines, which are expressed and secreted upon various stimuli and interact with specific receptors. To broadly characterize chemokine expression and function in tumors, we have used multiplex mass cytometry-based imaging of protein markers and RNA transcripts to analyze the chemokine landscape and immune infiltration in metastatic melanoma samples. Tumors that lacked immune infiltration were devoid of most chemokines and exhibited particularly low levels of antigen presentation and inflammation. Infiltrated tumors were characterized by expression of multiple chemokines. CXCL9 and CXCL10 were often localized in patches associated with dysfunctional T cells expressing CXCL13 which was strongly associated with B cell patches and follicles. TCF7+ naïve-like T cells, which predict response to immunotherapy, were enriched in the vicinity of B cell patches and follicles. Our data highlight the strength of RNA and protein co-detection which was critical to deconvolve specialized immune microenvironments in inflamed tumors based on chemokine expression. Our findings further suggest that the formation of tertiary lymphoid structures is accompanied by naïve and naive- like T cell recruitment, which ultimately boosts anti-tumor activity.

How-To reproduce the pipeline

  1. Download the source code and the data required from the analysis here
  2. Install docker
  3. Pull the docker image with docker pull toobiwankenobi/melanoma_imc:latest
  4. Run the docker image with docker run -e PASSWORD=bioc -p 8787:8787 toobiwankenobi/melanoma_imc:latest/
  5. Make the source code and data accessible in order to use it in the RStudio interface
  6. Open the RStudio interface (http://localhost:8787/, user = rstudio, password = bioc) in your web browser
  7. Run Files!

Requirements

Certain analyses require extensive computational power (mainly memory) if processes run in parallel. If you don’t have these resources, the code might need to be adapted (i.e. parallelization needs to be reduced/removed). Raise an issue in this github repo to report any problems.

Raw Data

Raw Data (multi-channel .tiff files, single-cell masks, etc.) is available here