Identification and Characterisation of Infiltrating Immune Cells in Malignant Pleural Mesothelioma Using Spatial Transcriptomics

Our plain-English summary of Identification and Characterisation of Infiltrating Immune Cells in Malignant Pleural Mesothelioma Using Spatial Transcriptomics, by D. Shek, B. Gloss, J. Lai, et al (Methods and Protocols, 2023).

The study

• Authors: D. Shek, B. Gloss, J. Lai, L. Ma, H.E. Zhang, M.S. Carlino, H. Mahajan, A. Nagrial, B. Gao, S.A. Read, G. Ahlenstiel
• Journal: Methods and Protocols
• Published: 2023;6(2)
• Cited by: 10 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Increasing evidence strongly supports the key role of the tumour microenvironment in response to systemic therapy, particularly immune checkpoint inhibitors (ICIs). The tumour microenvironment is a complex tapestry of immune cells, some of which can suppress T-cell immunity to negatively impact ICI therapy. The immune component of the tumour microenvironment, although poorly understood, has the potential to reveal novel insights that can impact the efficacy and safety of ICI therapy. Successful identification and validation of these factors using cutting-edge spatial and single-cell technologies may enable the development of broad acting adjunct therapies as well as personalised cancer immunotherapies in the near future. In this paper we describe a protocol built upon Visium (10x Genomics) spatial transcriptomics to map and characterise the tumour-infiltrating immune microenvironment in malignant pleural mesothelioma. Using ImSig tumour-specific immune cell gene signatures and BayesSpace Bayesian statistical methodology, we were able to significantly improve immune cell identification and spatial resolution, respectively, improving our ability to analyse immune cell interactions within the tumour microenvironment.

In plain English

This article described a laboratory method (a protocol) tested in a small pilot study. It was not a study of asbestos exposure. The authors used a technology called Visium spatial transcriptomics, which measures gene activity at mapped spots across a tissue slice, to map immune cells in malignant pleural mesothelioma. They used stored biopsy tissue from four patients with metastatic disease recruited at two Sydney hospitals. The biopsies were taken before the patients were treated with ipilimumab and nivolumab. Single marker genes could not reliably identify immune cells. The authors therefore used published immune-cell gene sets (ImSig) and a statistical tool (BayesSpace) that increased resolution 6-fold. In two samples, myeloid cells (monocytes, macrophages and neutrophils) dominated the tumour, and lymphocytes dominated the nearby non-tumour tissue. In the other two samples the two areas were more similar. The authors did not run statistical tests on these immune-cell comparisons because there were only four samples. The authors concluded that they had successfully set up a framework to identify and characterise these immune cells. They noted that the biopsies came from different parts of the tumours.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Methods protocol tested in a pilot study of archived (formalin-fixed paraffin-embedded) tumour biopsy tissue using Visium spatial transcriptomics
• Who was studied: Stored biopsy tissue blocks from 4 patients with metastatic malignant pleural mesothelioma (coded A1, B1, C1, D1), recruited at Blacktown and Westmead Public Hospitals, Sydney, Australia, within the ICEMELT study (NCT04631731). All were treated with ipilimumab plus nivolumab, and the biopsies were taken before that therapy. Three biopsies came from the original tumour site and one (D1) from a metastatic site in the temporal lobe.
• Exposure: Asbestos exposure was not studied. Asbestos is mentioned only in the background on mesothelioma incidence.
• Main results:
  • Across the four samples, the method produced 9041 barcoded arrays, with an average of 3125 genes detected per spot.
  • Sequencing depth was not enough to identify immune cells from a single marker gene. The two T-cell markers CD3 and CD2 gave different T-cell maps.
  • The BayesSpace statistical method increased the resolution of each 55 µm spot 6-fold.
  • In sections A1 and B1, myeloid cells dominated the tumour tissue and lymphocytes dominated the adjacent tissue.
  • In sections C1 and D1, immune cell composition was more similar between tumour and adjacent tissue.
  • The share of immune cell spots with a proliferation signature was increased in tumour tissue. The interferon signature was generally higher in immune cells of adjacent non-tumour tissue.
  • In tumour tissue, T-cell and NK-cell spots generally showed more interactions with myeloid cell populations and fewer with other lymphocytes.
• The authors' conclusion: The authors concluded that in this pilot study they successfully implemented a Visium-based framework to identify and characterise tumour-infiltrating immune cells in archived tissue, using various R software packages.
• Limitations the authors state:
  • No statistics were performed on the immune cell composition data because of insufficient power (only 4 sections). Significant (p less than 0.05) differentially expressed genes were still reported and annotated.
  • Each 55 µm spot likely captured gene activity from several cells at once, diluting information on cell identity.
  • The enhanced BayesSpace images could not be exported to the Loupe browser and aligned with gene expression data, so differential gene expression could not be performed on them.
  • The biopsy tissue was collected retrospectively.
  • Biopsies came from different locations within the tumours and so had different immune cell profiles.
  • There is no open-source, well-documented software for analysing spatial transcriptomics data.

Topics

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Systemic Therapy, checkpoint inhibitors.

Related studies

• Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed paraffin-embedded tumor samples — N. Ozirmak Lermi, M. Molina Ayala, et al. (2025)
• Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples — N.O. Lermi, M.M. Ayala, et al. (2025)
• In-depth profiling of tumor tissue derived from malignant pleural mesothelioma patients identifies potential biomarkers predicting response to immune-checkpoint inhibitor therapy — D. Shek, B. Gao, et al. (2024)
• DGAT: A Dual-Graph Attention Network for Inferring Spatial Protein Landscapes from Transcriptomics — H. Wang, B. Cody, et al. (2025)
• Spatial Landscape of Malignant Pleural and Peritoneal Mesothelioma Tumor Immune Microenvironments — X. Ma, D. Lembersky, et al. (2024)
• Characterisation of mesothelioma-initiating cells and their susceptibility to anti-cancer agents — E.A. Pasdar, M. Smits, et al. (2015)

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