Skip to main content
Advertisement

Main menu

  • Home
  • Articles
    • Newest Articles
    • Current Issue
    • Methods & Resources
    • Author Interviews
    • Archive
    • Subjects
  • Collections
  • Submit
    • Submit a Manuscript
    • Author Guidelines
    • License, Copyright, Fee
    • FAQ
    • Why submit
  • About
    • About Us
    • Editors & Staff
    • Board Members
    • Licensing and Reuse
    • Reviewer Guidelines
    • Privacy Policy
    • Advertise
    • Contact Us
    • LSA LLC
  • Alerts
  • Other Publications
    • EMBO Press
    • The EMBO Journal
    • EMBO reports
    • EMBO Molecular Medicine
    • Molecular Systems Biology
    • Rockefeller University Press
    • Journal of Cell Biology
    • Journal of Experimental Medicine
    • Journal of General Physiology
    • Journal of Human Immunity
    • Cold Spring Harbor Laboratory Press
    • Genes & Development
    • Genome Research

User menu

  • My alerts

Search

  • Advanced search
Life Science Alliance
  • Other Publications
    • EMBO Press
    • The EMBO Journal
    • EMBO reports
    • EMBO Molecular Medicine
    • Molecular Systems Biology
    • Rockefeller University Press
    • Journal of Cell Biology
    • Journal of Experimental Medicine
    • Journal of General Physiology
    • Journal of Human Immunity
    • Cold Spring Harbor Laboratory Press
    • Genes & Development
    • Genome Research
  • My alerts
Life Science Alliance

Advanced Search

  • Home
  • Articles
    • Newest Articles
    • Current Issue
    • Methods & Resources
    • Author Interviews
    • Archive
    • Subjects
  • Collections
  • Submit
    • Submit a Manuscript
    • Author Guidelines
    • License, Copyright, Fee
    • FAQ
    • Why submit
  • About
    • About Us
    • Editors & Staff
    • Board Members
    • Licensing and Reuse
    • Reviewer Guidelines
    • Privacy Policy
    • Advertise
    • Contact Us
    • LSA LLC
  • Alerts
  • Follow LSA on Bluesky
  • Follow lsa Template on Twitter
Resource
Transparent Process
Open Access

Single-cell RNA sequencing of human breast tumour-infiltrating immune cells reveals a γδ T-cell subtype associated with good clinical outcome

Katerina Boufea, View ORCID ProfileVictor Gonzalez-Huici, View ORCID ProfileMarcus Lindberg, Nelly N Olova, View ORCID ProfileStefan Symeonides, Olga Oikonomidou, View ORCID ProfileNizar N Batada  Correspondence email
Katerina Boufea
1Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Victor Gonzalez-Huici
1Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Victor Gonzalez-Huici
Marcus Lindberg
1Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marcus Lindberg
Nelly N Olova
2MRC Human Genetics Unit, University of Edinburgh, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Symeonides
3Cancer Research UK Edinburgh Centre, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stefan Symeonides
Olga Oikonomidou
3Cancer Research UK Edinburgh Centre, University of Edinburgh, Western General Hospital, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nizar N Batada
1Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nizar N Batada
  • For correspondence: nizar.batada@gmail.com
Published 2 December 2020. DOI: 10.26508/lsa.202000680
  • Article
  • Figures & Data
  • Info
  • Metrics
  • Reviewer Comments
  • PDF
Loading

Article Figures & Data

Figures

  • Supplementary Materials
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1. Unsupervised analysis of single-cell RNA sequencing (scRNA-seq) data on γδ T cells from peripheral blood of healthy adult donors identifies multiple subtypes.

    (A) UMAP-based projection of the merged single cell gene expression data of blood derived γδ T cells from three healthy donors. Different clusters are named arbitrarily with c.γδ prefix (to indicate γδ cells from circulation). (B) Overlay of data source on UMAP-based projection of scRNA-seq data. Cells from donor HD4 and HD5 were pooled before performing scRNA-seq and labeled as HD4/5. The number of cells from each dataset is shown above the projection. (C) Labeling of cells positive for TCR delta genes. Overlay of cells that have genes mapping to the TRDC (left), TRDV2 (middle) and TRGV9 (right) gene segments. (D) Quantification of TRDV2 and TRGV9 in each cluster. y-axis shows the per cent of cells within each cluster (x-axis) of the merged data that is positive for TRDV2 (blue) and TRGV9 (red) gene segments. Numbers above the bars show the number of positive cells. (E) UMAP of PBMC cells coloured by the expression of a selected set of markers, LEF1, SOX4, CCR7, IFNG, CCR6, and RORC. Grey indicates zero expression and purple indicates high expression. (F) Scores of curated effector gene sets for IFNγ production, IL17A production, cytotoxicity, adaptive (i.e., antigen presentation on MHC class 1), and innate gene sets in each of the blood γδ T-cell cluster (x-axis). (G) Heat map showing genes (rows) enriched in each of the cluster (columns). Yellow represents enrichment and purple represents depletion. Only the top four genes per cluster are labeled.

  • Figure S1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S1. Evaluation of robustness of PBMC data clustering.

    (A) Bar plot showing the percentage of cells per post-Canonical Correlation Analysis cluster of the PBMC data in each cell-cycle phase predicted by cyclone1. (B) UMAP of post-Canonical Correlation Analysis clustered PBMC data after removal of cell-cycle and mitochondrial genes. ARI (title) indicates agreement to the original clustering shown in Fig 1. (C) Accuracy of clustering subsamples of the original PBMC data of different proportions of cells (x-axis). [1] Scialdone A, Natarajan KN, Saraiva LR, Proserpio V, Teichmann SA, Stegle O, Marioni JC, Buettner F (2015) Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 10.1016/j.ymeth.2015.06.021.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2. Validation of the blood γδ T-cell subtypes.

    (A) Feature plot showing CD16 and CD28, which are published markers of the δ2 subtype of blood γδ T cells (Ryan et al, 2016). Each dot is a cell. Grey indicates no expression and purple indicates high expression. (B) Scores for published gene signatures of CD16 (white) and CD28 (black) δ2 subtypes in the clusters found in our blood γδ T-cell scRNA-seq data (x-axis). P-values were computed using Wilcoxon signed-rank test. (C) Feature plot showing expression of GPR56 and CXCR6, markers that appear to be mutually exclusive in blood δ2 subtypes. (D) Flow cytometry based validation of novel markers, GPR56 (y-axis) and CXCR6 (x-axis), in peripheral blood γδ T cells TCRδ2 subtypes. Healthy donor identities (which include blood γδ T cells from two new donors, HD9 and HD10) are indicated in the title. Numbers in each quadrant indicate percentage of δ2 cells.

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3. Characterization of breast tumour-infiltrating γδ T cells uncovers a subtype that is associated with favourable outcome.

    (A) UMAP-based projection of the merged single-cell gene expression data of breast tumour-infiltrating immune cell datasets from two patients. Three clusters were double positive for CD3 and TRDC were classified as γδ T cells (CD3E+). Mph, Macrophage (CD14+), T-reg (FOXP3+), regulatory T cells; B, B cells (CD19+); CD8-T, CD8+ αβ T cells; CD4-T, CD4+ αβ T cells. (B) Overlay of donor identity on UMAP-based projection of scRNA-seq data. The number of cells from each donor is shown above the projection. BC1 is a triple-negative subtype and BC2 is a Her2+ subtype of breast cancer (BC). (C) Identification of cells positive for genes encoding TCR δ chain. Overlay of cells that have genes mapping to the TRDC (left) and TRDV2 (right) gene segments. (D) Quantification of enrichment of genes encoding TRDV2 and TRGV9 γ chain in the BC γδ T clusters. y-axis shows the per cent of cells positive for the indicated TRDV2 (blue) and TRGV9 (white) gene within each BC γδ T-cell cluster (x-axis). Numbers above bars show the number of positive cells. (E) UMAP of BC γδ T cells coloured by the expression of a selected set of markers for identification of IFNG and IL17A subtypes. Grey indicates no expression and purple for high expression. (F) Distribution of gene signature scores (y-axis) for IFNγ production, IL17A production, cytotoxicity, adaptive (i.e., antigen presentation on MHC class 1), and innate gene sets in each of the BC γδ T-cell cluster (x-axis). (G) Kaplan–Meir survival curve of the The Cancer Genome Atlas breast cancer data (Ciriello et al, 2015). Patients were partitioned into high and low group based on scores for gene signatures of each of the indicated BC γδ T-cell cluster. y-axis shows overall survival. (H) Heat map showing top differentially expressed genes (row labels) between the three BC γδ T-cell subtypes. Yellow represents high expression and purple represents low expression.

  • Figure S2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S2. Specificity of BC extracted γδT-subtype gene signatures.

    (A, B, C) Boxplots showing the distribution of scID scores of the BC1 cells within each cluster (x-axis) for the γδT.1 (A), γδT.2 (B), and γδT.3 (C) gene signatures. P-values indicate significance levels of Wilcoxon test between the mean scores of two clusters.

  • Figure S3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure S3. Predictors associated with better survival are lower in TCGA BRCA patients that score higher for expression of BC cluster 2 gene signature

    . (A) Boxplot of expression (y-axis) of CD3, CD4, and CD8 genes in the The Cancer Genome Atlas (TCGA) breast cancer data samples with high (red) and low (blue) expression of the BC γδ-T.2 subtype gene signature (x-axis). (B) Boxplot of mutational load (y-axis) of the TCGA breast cancer data samples with high and low expression of the BC γδ-T.2 subtype gene signature (x-axis). Wilcoxon rank test was used to compute statistical significance of different scores within each cluster. (C) Boxplot of cytolytic score (y-axis) of the TCGA breast cancer data samples with high and low expression of the BC γδ-T.2 subtype gene signature (x-axis). Wilcoxon rank test was used to compute statistical significance of different scores within each cluster.

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4. Comparison of γδ T cells from peripheral blood and breast tumour.

    (A) Comparison of expression of selected genes (rows) involved in activation, cytotoxicity, exhaustion, and naive T-cell state between blood γδ T-cell and breast tumour-infiltrating γδ T-cell subtypes. Grey represents low average expression and red represents high average expression of the genes in each subtype (columns). (B) Assessment of similarity of the γδ T-cell subtypes in blood and breast tumour. Boxplot of scID scores (y-axis) of the BC cluster-specific gene signatures in the blood clusters (x-axis). Scores above the dashed line indicates enrichment of the indicated gene signature. Mann–Whitney U test was used to compute statistical significance of different scores within each cluster. “***” indicates P < 0.001. (C) Feature plots showing expression of suggested cluster defining markers in the γδ T-cell subtypes in blood (top row) and BC (bottom row). Grey indicates low expression and purple indicates high expression. (D) Table summarizing the proposed refinement of subtype classification of γδ T cells supported by the scRNA-seq data from this study.

Supplementary Materials

  • Figures
  • Table S1 Antibodies used for sorting and validation.

  • Table S2 Cluster and donor identity of PBMC cells.

  • Table S3 Differentially expressed genes between PBMC clusters.

  • Table S4 Cluster and donor identity of BRCA cells.

  • Table S5 Differentially expressed genes between breast tumour-infiltrating γδ T subtypes.

  • Table S6 Differentially expressed genes in γδ T subtypes between all breast tumour-infiltrating immune cells.

  • Supplemental Data 1.

    [LSA-2020-00680_Supplemental_Data_1.zip]

PreviousNext
Back to top
Download PDF
Email Article

Thank you for your interest in spreading the word on Life Science Alliance.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Single-cell RNA sequencing of human breast tumour-infiltrating immune cells reveals a γδ T-cell subtype associated with good clinical outcome
(Your Name) has sent you a message from Life Science Alliance
(Your Name) thought you would like to see the Life Science Alliance web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
γδ T-cell single-cell RNA-seq
Katerina Boufea, Victor Gonzalez-Huici, Marcus Lindberg, Nelly N Olova, Stefan Symeonides, Olga Oikonomidou, Nizar N Batada
Life Science Alliance Dec 2020, 4 (1) e202000680; DOI: 10.26508/lsa.202000680

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
γδ T-cell single-cell RNA-seq
Katerina Boufea, Victor Gonzalez-Huici, Marcus Lindberg, Nelly N Olova, Stefan Symeonides, Olga Oikonomidou, Nizar N Batada
Life Science Alliance Dec 2020, 4 (1) e202000680; DOI: 10.26508/lsa.202000680
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
Issue Cover

In this Issue

Volume 4, No. 1
January 2021
  • Table of Contents
  • Cover (PDF)
  • About the Cover
  • Masthead (PDF)
Advertisement

Jump to section

  • Article
    • Abstract
    • Introduction
    • Results
    • Discussion
    • Materials and Methods
    • Data Availability
    • Acknowledgements
    • References
  • Figures & Data
  • Info
  • Metrics
  • Reviewer Comments
  • PDF

Subjects

  • Cancer
  • Genomics & Functional Genomics
  • Immunology

Related Articles

  • Boufea, K., Gonzalez-Huici, V., Lindberg, M., Olova, N. N., Symeonides, S., Oikonomidou, O., & Batada, N. N. (2023). Correction: Single-cell RNA sequencing of human breast tumour-infiltrating immune cells reveals a γδ T-cell subtype associated with good clinical outcome. Life Science Alliance, 6(2), e202201848. Accessed July 15, 2025. https://doi.org/10.26508/lsa.202201848.

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Brown preadipocyte heterogeneity
  • Leishmania infection alters extracellular vesicles
Show more Resource

Similar Articles

EMBO Press LogoRockefeller University Press LogoCold Spring Harbor Logo

Content

  • Home
  • Newest Articles
  • Current Issue
  • Archive
  • Subject Collections

For Authors

  • Submit a Manuscript
  • Author Guidelines
  • License, copyright, Fee

Other Services

  • Alerts
  • Bluesky
  • X/Twitter
  • RSS Feeds

More Information

  • Editors & Staff
  • Reviewer Guidelines
  • Feedback
  • Licensing and Reuse
  • Privacy Policy

ISSN: 2575-1077
© 2025 Life Science Alliance LLC

Life Science Alliance is registered as a trademark in the U.S. Patent and Trade Mark Office and in the European Union Intellectual Property Office.