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. 2025 Apr;45(4):465-470.
doi: 10.1002/cac2.12651. Epub 2025 Jan 15.

Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

Antonia Stubenvoll  1 , Maria Schmidt  2 , Johanna Moeller  3 , Max Alexander Lingner Chango  3 , Carolyn Schultz  1 , Olga Antoniadou  1 , Henry Loeffler-Wirth  2 , Stephan Bernhart  4 , Florian Große  5   6 , Beatrice Thier  7 , Annette Paschen  7 , Ulf Anderegg  1 , Jan C Simon  1 , Mirjana Ziemer  1 , Clara T Schoeder  3 , Hans Binder  2 , Manfred Kunz  1
Affiliations

Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

Antonia Stubenvoll et al. Cancer Commun (Lond). 2025 Apr.
No abstract available

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Conflict of interest statement

Manfred Kunz has received honoraria from the Speakers Bureau of Roche Pharma and travel support from Novartis Pharma GmbH and Bristol‐Myers Squibb GmbH. Jan Christoph Simon has received speaker's fees from Bristol‐Myers Squibb, Roche Pharma AG, Novartis and MSD Sharp & Dohme as well as financial support for congress attendance from Bristol‐Myers Squibb, MSD Sharp & Dohme and Novartis. Mirjana Ziemer has received speaker's fees from Bristol‐Myers Squibb, MSD Sharp & Dohme GmbH, Pfizer Pharma GmbH and Sanofi‐Aventis Deutschland GmbH and received financial support for congress participation from Bristol‐Myers Squibb and serves as a member of expert panels on cutaneous adverse reactions for Pfizer INC. Clara Tabea Schoeder has received research support from Navigo Protein GmbH, Halle (Saale), Germany.

Figures

FIGURE 1
FIGURE 1
Single‐cell RNA and single‐cell ATAC sequencing of primary melanoma and melanocytic nevus samples. Ten primary melanoma samples (MM) and three benign melanocytic nevus (Nev) samples were analyzed by 10x Genomics single‐cell RNA sequencing (scRNA‐seq) technology. Five MM samples and one Nev sample were analyzed by single‐cell ATAC (scATAC) sequencing. (A) Overview of study design. (B) Analysis of scRNA‐seq data from melanoma and nevus samples using the CellRanger software (10x Genomics), the R package Seurat (https://satijalab.org/seurat/) and principal component analysis (PCA). Specific cell type populations were identified by computing cell‐type‐specific positive markers using the FindAllMarkers function. Visualization was performed by Uniform Manifold Approximation and Projection (UMAP) of the integrated dataset, colored by cell type. (C) Zoom‐in of the UMAP of the melanoma cell cluster from scRNA‐seq data shown in (B). Gene expression data were analyzed by RNA velocity and latent time (LT) [4] analysis to show developmental trajectories of the melanoma cells. RNA velocity arrows point towards the right edge of the cluster, towards differentiated cells, and LT points in the opposite direction, towards de‐differentiated cells, due to different mathematical approaches. (D) Heatmap of the dynamics of gene expression patterns during LT development of melanoma cells. LT profiles of 50 consecutive genes (top to bottom) reveal the shift of maximum gene expression from earlier to later LT. Examples of individual genes are highlighted, reflecting different stages of de‐differentiation. Gene set enrichment is shown on the right. (E) scRNA‐seq data from melanoma cells and cytotoxic T cells were subjected to ligand‐receptor analysis using the LIANA software (https://saezlab.github.io/liana/). Circos plots of ligand‐receptor interactions between different melanoma cell differentiation subtypes (lower part, in different colors) and T cells (upper part, in blue). The indicated genes of melanoma subclusters in the lower parts encompass different melanoma subclusters. The indicated genes of T cells in the upper parts encompass all receptors as arrowheads of different melanoma cell ligands. The figure legend for melanoma subclusters in panel (C) also applies to the inner rings of the circus plots in (E). (F) Melanoma patient survival data from The Cancer Genome Atlas (TCGA). Survival curves of melanoma patients with high and low levels, respectively, of CD58 and CD2 expression using patient data from TCGA. “High‐high” indicates high CD58 and high CD2 expression; “low‐low” indicates low CD58 and low CD2 expression. Analysis was performed using cSurvival (https://tau.cmmt.ubc.ca/cSurvival/). Significance levels were determined by a Cox proportional hazards model and Log‐rank test. (G) Activation of autologous CD8+ tumor‐infiltrating lymphocytes (TILs) by melanoma cells in co‐culture in the presence or absence of antibodies blocking CD58 and/or CD59. Left: Quantification of IFN‐γ‐producing CD8+ T cells. Fold change is given as the mean±SEM from three independent experiments. The first column indicates IFN‐γ‐production in T cell monoculture. Right: Cytotoxicity of CD8+ TIL against autologous Ma‐Mel‐86c cells in the presence or absence of an anti‐CD58 blocking antibody. The percentage of killed melanoma cells is given as the mean±SEM from three independent experiments. Significantly different experimental groups are indicated: * P < 0.05, ** P < 0.01 by two‐tailed paired t‐test. (H) Recombinant proteins were generated for human CD2, CD58, and CD59. Bio‐layer interferometry (BLI) measurements for recombinant CD2 protein interaction with recombinant CD58, CD59, and a negative control protein (SARS‐CoV‐2 receptor binding domain) were performed. Binding between these interaction partners was measured with CD58, CD59, and negative control concentrations ranging from 3.33 µmol/L to 0.01 µmol/L, with baseline at c = 0 µmol/L. The dissociation constant (Kd) for CD58 with CD2 was calculated as 0.99 (R2 of concentration response curve). (I) Single‐cell Assay for Transposase Accessible Chromatin sequencing (scATAC‐seq) was performed on single‐cell nuclei from melanoma cells of different samples and analyzed by CellRanger software (10x Genomics). Peak information was converted to genomic ranges using the GenomicRanges package (https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html). A representative sample from 6 measured samples is shown, with signal tracks from the indicated genomic region. This figure refers to melanoma sample MM11 (immune hot). Signal tracks were generated using the RunChromVAR function in the Signac package (https://github.com/stuart‐lab/signac). Co‐accessible links were determined with the Cicero R package (https://github.com/cole‐trapnell‐lab/cicero‐release). (J) The highlighted peak upstream of CD2 in melanoma samples was analyzed for binding motives of transcription factors. The analysis was conducted using the RunChromVAR function from the Signac package (https://github.com/stuart‐lab/signac/issues/9), in combination with the JASPAR2020 motif databank, based on the BSgenome.Hsapiens.UCSC.hg38 genome (https://jaspar2020.genereg.net/tools/; https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/). Binding motifs at the indicated genomic position are shown for different transcription factors in two representative melanoma samples (MM06; immune hot and MM09; immune intermediate). Abbreviations: ATAC, Assay for Transposase Accessible Chromatin; Fb, fibroblasts; KC, keratinocytes; premit, premitotic; postmit, postmitotic; gl, glands; LE, lymphatic endothelial cells; VE, vascular endothelial cells; Tcyt, cytotoxic T cells; Tdp, double (CD4 and CD8) positive T cells; Teff_mem, effector memory T cells; Treg, regulatory T cells; NK, natural killer cells; cDC1/2, type 1 and type 2 conventional dendritic cells; pDC, plasmacytoid dendritic cells; LT, latent time; Pt., patient; Ma‐Mel‐86c, Mannheim melanoma cell line 86c; TCGA, The Cancer Genome Atlas; TIL, tumor‐infiltrating lymphocytes; BLI, Bio‐layer interferometry; SARS‐CoV‐2, severe acute respiratory syndrome coronavirus type 2.
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