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. 2020 Jan 2;122(6):735–744. doi: 10.1038/s41416-019-0672-6

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — a A section of a PDE stained with DAPI as well as antibodies specific for immune cell markers (CD4, CD8, CD68 and FOXP3), PD-L1 and pan-cytokeratin followed by TSA-based fluorescent labelling is shown. This method allows for the characterisation of immune-cell subsets in the tumour microenvironment. b Measurement of inter-cell distances in PDEs pre- and post drug treatment. In this example, a melanoma PDE is analysed pre- and post-treatment with the anti-PD1 immunotherapy Nivolumab. To analyse inter-cell distances, PDEs are first stained with relevant cell markers such as tumour or immune-cell subsets followed by digital scanning of images (1). Cells are then identified and phenotyped using Inform software, and distances calculated using the R programming environment (2). The example shown in the histogram (3), indicates increased distance between CD8⁺ effector T cells and CD4⁺FOXP3⁺ regulatory T cells (Tregs) following Nivolumab treatment, confirming on-target effects of this anti-PD1 immunotherapy within the PDE. These images are the authors’ unpublished original data.