An Update on Single-Cell RNA Sequencing in Illuminating Disease Mechanisms of Cutaneous T-Cell Lymphoma

Abstract

Cutaneous T-cell Lymphomas (CTCLs) are a heterogeneous group of non-Hodgkin lymphomas that currently have an incompletely understood pathophysiology and several challenges in both diagnosis and management. Single-cell RNA sequencing (scRNA-seq) is a powerful tool that enables the analysis of gene expression at the individual-cell level, revealing cellular heterogeneity and a complex tumor microenvironment. As single-cell RNA sequencing has become increasingly utilized, we aimed to provide an update on recent notable applications of single-cell RNA sequencing in CTCL and their findings. The included studies highlight the intricate network of interactions in the tumor microenvironment that contributes to tumorigenesis. While CTCL is notoriously heterogeneous, our results identify key markers that prove promising for diagnosis, prognostication, and therapeutic targets.

Keywords: CTCL; Sézary syndrome; biomarkers; cutaneous T-cell lymphoma; genomic; mycosis fungoides; scRNA-seq; single-cell RNA sequencing; transcriptomic; tumor microenvironment.

Figure 1

Single-cell RNA sequencing steps. (1) Lesional tissue or blood samples are collected from patients. (2) The collected sample is dissociated into a cell suspension. (3) Cells are sorted and isolated into individual cells. (4) RNA is extracted from individual cells, barcoded, and amplified as cDNA. (5) Cells are sequenced. (6) Each cell’s gene expression profile is quantified, generating a matrix of gene expression values across individual cells. (7) Computational analysis, such as t-SNE (t-distributed stochastic neighbor embedding), is performed to visualize clustering of cells based on similar expression patterns, allowing for identification of distinct cell types (each color in the t-SNE plot represents a different cell type identified through clustering). (8) Exploratory analyses can be performed, including differential gene expression analysis (A), trajectory analysis (B), cell–cell communication analysis (C), and spatial transcriptomic analysis (D). Created in BioRender. S, S. (2025) https://BioRender.com/kml8qb1. Accessed 20 July 2025.

Figure 2

Trends in PubMed Search Results for Single-cell RNA (scRNA-seq) sequencing overall and in Cutaneous T-cell Lymphoma (CTCL) Publications (2009–July 2025). Bar chart (gray) represents the number of PubMed search results for scRNA-seq studies per year (left y-axis, gray), while the line graph (blue) shows the number of search results for combined searches for CTCL and scRNA-seq per year (right y-axis, blue) from 2009 to 2025. (See supplemental methods for exact search terms).

Figure 3

Study Selection Flow Diagram.

Figure 4

Signaling pathways of CD4+ and CD8+ malignant T-cell clones. Schematic illustrating functional and transcriptional pathways enriched in malignant CD4+ and CD8+ T-cell clones. Malignant CD4+ clones (left, green) exhibit upregulation of genes associated with central memory (Tcm) differentiation (e.g., CCR7, SELL, LEF1, TCF7, PASK), tissue-resident memory (Trm) differentiation (CD69, CXCR4, NR4A1), increased migration and chemotaxis (LAIR2, TIAM1, RIPOR2), multi-drug resistance (TMEM243), and clonal proliferation (TNFRSF4, PIM2, PRDX, NPM1, CCND2, CCND3, DUSP4) are also upregulated. Malignant CD8+ clones (right, blue) similarly show markers of Trm differentiation (CD69, CXCR4, NR4A1), activation of cytotoxic effectors (NKG7, CTSW, GZMA, GZMM, NCR3, PRF1), and clonal proliferation (DDIT4, JAK2 signaling). Created in BioRender. S, S. (2025) https://BioRender.com/ezs4hew. Accessed 20 July 2025.

Figure 5

Cell–cell Interaction and Signaling Pathway of CD4+ T cells and B cells in the Tumor Microenvironment. Direct T and B cell interactions via co-stimulatory receptor-ligand pairs (CD70–CD27, CD40L–CD40, CD58–CD2, CD28–CD86) lead to T cell activation and support malignant T-cell proliferation. Concurrently, CXCL13–CXCR5 signaling promotes lymphoid structure formation. Upregulation of EBI3, GMDS, and LMO2 in B cells suggests germinal center-like differentiation. These coordinated events culminate in the clustering of tertiary lymphoid structures, which may further sustain malignant proliferation and immune modulation within the tumor niche. Created in BioRender. S, S. (2025) https://BioRender.com/6fky8v4. Accessed 20 July 2025.

Figure 6

Keratinocyte, Fibroblast, Monocyte, Macrophage, and Dendritic Cell Interactions in the Tumor Microenvironment. Keratinocytes (top left panel) upregulate MHCII, attract Th1/NK cells, and activate protein kinase B, FAK/PTK2, and MAPK pathways. Fibroblasts (bottom left panel) communicate with tumor cells via IL-6/JAK2/STAT3 and IL-6/HIF-1α pathways, inducing MHCII and chemokine expression. Among the myeloid cells (right panel), monocytes express CXCL8 and CLEC7A and engage in FAS–TNF signaling, contributing to stress response, immunosuppression, and apoptosis. M2 Macrophages and Tumor Associated Macrophages (TAMs), activating NF-kB and promoting increased leukocyte motility, IFN signaling, and reactive oxygen species production. Dendritic cells mediate cancer immune evasion via CD58-CD2 interactions and promote Th2-skewed immunity with upregulated LGAL29, TNFSF12, and downregulated IL-23A and IL-18. Created in BioRender. S, S. (2025) https://BioRender.com/prjxamo. Accessed 20 July 2025.