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. 2023 Feb 14;7(3):445-457.
doi: 10.1182/bloodadvances.2022008168.

Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations

Jingjing Ren  1 , Rihao Qu  2   3 , Nur-Taz Rahman  4 , Julia M Lewis  1 , Amber Loren Ong King  1 , Xiaofeng Liao  5 , Fatima N Mirza  1 , Kacie R Carlson  1 , Yaqing Huang  3 , Scott Gigante  6 , Benjamin Evans  7 , Barani Kumar Rajendran  5 , Suzanne Xu  1 , Guilin Wang  8 , Francine M Foss  9 , William Damsky  1   3 , Yuval Kluger  3 , Smita Krishnaswamy  10 , Michael Girardi  1
Affiliations

Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations

Jingjing Ren et al. Blood Adv. .

Abstract

The incidence of cutaneous T-cell lymphoma (CTCL) increases with age, and blood involvement portends a worse prognosis. To advance our understanding of the development of CTCL and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and T-cell receptor (TCR) sequencing of peripheral blood CD4+ T cells from patients with CTCL to reveal disease-unifying features. The malignant CD4+ T cells of CTCL showed highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. Potential of heat diffusion for affinity-based trajectory embedding transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.

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Conflict of interest statement

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Characterization of transcriptional heterogeneity between patients with CTCL by paired scRNAseq and scTCRseq analyses of CD4+T cells. CD4+ T cells purified from the peripheral blood of 11 patients with CTCL underwent paired single-cell mRNA and TCR VDJ sequencing. (A) Summary of study design including sample preparation, sequencing, multidimensional data analysis, and mechanistic studies. (B) TCR clonotype frequency in purified CD4+ T cells from patients with CTCL (P1-P11) and healthy controls (N1-N3). Dominant clonotype: most frequent complete TCRα and TCRβ CDR3 transcripts. Dominant clonotype–like: TCRα CDR3 or TCRβ CDR3 matching the top clonotype in the absence of any transcript for the other chain. Other clonotypes: all other TCRα and/or TCRβ CDR3 transcripts. Note that in P6, 2 complete TCRα and 2 complete TCRβ sequences were present in the dominant clonotype. (C) A UMAP resulting from the integration of the scRNAseq transcriptomes of samples derived from 11 patients with CTCL (112 840 single cells distributed by annotated, unsupervised clustering) highlights interpatient diversity (each patient with CTCL is distinctly colored), as well as commonality of normal CD4+ T cells among the different patients (blue). (D) Total CD4+ T cells from each patient are individually highlighted throughout the integrated UMAP: normal CD4+ T cells (blue), CTCL cells (highlighted in red according to the dominant CDR3 sequence of each patient). (E) T-cell subsets based on characteristic gene expression are highlighted throughout the integrated UMAP. Th2-like: GATA3 >1, CCR4 >1; Treg-like: FOXP3 >1, CTLA4 >1; Tfh-like: PDCD1 >1, CXCR5 >1; Th17-like: RORC >1, CCR6 >1. (The gene cutoff of 1 indicates that the normalized and scaled gene expression is >1.) UMAP, Uniform Manifold Approximation and Projection.
Figure 2.
Figure 2.
PHATE analysis reveals circulating putative precancerous intermediate CD4+T-cell populations in CTCL. (A) Representative UMAP plot of CD4+ T cells from a patient with CTCL resulted in 2 major separate annotated clusters: CTCL cells (red) and non-CTCL CD4+ T cells (blue+purple). (C) The non-CTCL CD4+ T cells were further divided into 2 clusters: population 1 (blue), and population 2 (purple). (B,D) The dominant TCR clonotype is highlighted in yellow over the same UMAP. A group of cells containing the dominant clonotype fell within the non-CTCL CD4+ T-cell population upon unsupervised clustering. A comparison of the mean frequency of the dominant (malignant) TCR clonotype vs all other TCR clonotypes in the non-CTCL CD4+ T-cell population 2 (E) and in the total non-CTCL CD4+ T-cell population (F) revealed that the dominant clonotype frequency was overrepresented in both populations (n = 10, paired t test). (G) PHATE mapping revealed the non-CTCL CD4+ T-cell population 2 to be an intermediate group of cells (purple) falling between normal CD4+ T cells (blue) and CTCL cells (red). (H) This intermediate group contained cells with the dominant clonotype, highlighted in yellow throughout the PHATE map. UMAP, Uniform Manifold Approximation and Projection.
Figure 3.
Figure 3.
Multidimensional characterization of the intermediate cell population in CTCL. (A) Three annotated groups of cells from each single-patient analysis are shown on the UMAP plot resulting from integration of the transcriptomes of 11 patients. DEGs among the 3 groups are presented in a heat map. (B) Bar plot displaying the composition of SSNVs (identified via CellSNP) of the intermediate and CTCL groups of a representative patient. SSNVs found in the normal CD4+ T-cell population were considered to be individual-specific normal variants (Normal SNV [Background]). Common SSNVs are those mutations found in both the intermediate and CTCL cell populations. (C) Representative inferred SCNV map (identified via InferCNV). (D) Landscape of inferred SCNVs among 10 patients with CTCL. (E) The composition of SCNVs (identified via InferCNV) of the intermediate and CTCL groups. (F) PHATE plot of a single patient with the SCNV level of each cell highlighted. (G) The significantly different level of SCNVs between the intermediate and CTCL cell groups (unpaired t test, ∼10 000 cells).
Figure 4.
Figure 4.
Transcriptional characteristics of common features among heterogeneous patients with CTCL. (A) Side-by-side view of the integrated UMAP of CD4+ T cells from 11 patients with CTCL and 3 healthy controls, along with a heat map displaying the DEGs when all CTCLs cells were compared with all normal CD4+ T cells combined from patients and healthy controls (heat map: minimum percentage >0.25%, minimum difference in percentages >0.2; adjusted P ≤ .05, log fold change threshold = 0.25). (B) Pathway analysis of all DEGs revealed 15 enriched (adjusted P ≤ .05) pathways in the CTCLs. (C) Plots demonstrate increased expression of the central memory T-cell activation genes SELL, CCR7, ITGB1, BRD2, TNFRSF25, REL, TSPAN2, TNFRSF4, and NR4A2 and the T-cell exhaustion genes TIGIT, TOX, CTLA4, LAG3, and PDCD1 in CTCLs vs normal CD4+ T cells, as well as increased CD82, CCR4, and KIR3DL2 gene expression in CTCLs. (D) The percentage of CTCLs that proliferate in response to TCR engagement was significantly increased after a period of rest in vitro. Each line represents 1 patient-derived sample (n = 3). (E) Representative flow cytometric analysis of the proliferative capacity of CTCL cells. Cells were cultured with no stimulation for 4 days (No Stim) or were stimulated immediately after isolation (No Rest + Stim) or after a 4-day rest (Rest+Stim). Simulation consisted of anti-CD3+anti-CD28 for 2 days followed by washing and a 2-day expansion in the absence of stimuli. UMAP, Uniform Manifold Approximation and Projection.
Figure 5.
Figure 5.
CTCL hallmark gene CD82 regulates proliferation and apoptosis via the JAK-STAT signaling pathway. (A) Genes CD82, CCR4, and KIR3DL2 displayed a similar monotonical expression pattern increasing from normal to intermediate to CTCL groups. (B) Mean fluorescence intensity (MFI) of CD82 protein expression in CD4+ CTCL cells compared with normal CD4+ T cells from CTCL patient–derived PBMCs (n = 9; 1-tailed t test). (C) Representative histograms comparing CD82 expression in purified CTCL cells that have undergone either CD82 knockout (CD82-KO, blue) or mock knockout (CD82-NC [negative control], red). (D) Bulk RNAseq was used to confirm CD82 expression in purified CTCL cells before and after CD82 knockout. (E) Comparison of the relative percentage of CTCL cell proliferation in CD82 knockout and mock knockout CTCLs (n = 4; 1-tailed paired t test). (F) Representative flow cytometric analysis of the proliferative capacity of CTCL cells (CD82-KO and CD82-NC) cultured for 2 days after anti-CD3+anti-CD28 stimulation for 2 days. (G) Comparison of the percentage of apoptotic cells present in CD82 knockout and mock knockout CTCLs in the proliferation experiment in panel E. (H) Comparison of the relative level of phosphorylation of JAK2, JAK3, STAT5, AKT1, and PI3K in activated and proliferated CTCL cells after CD82 knockout or mock knockout cells cultured for 2 days after anti-CD3+anti-CD28 stimulation for 2 days. (I) CTCL cells isolated from patient peripheral blood were incubated for 72 hours with a range of concentrations of various JAK inhibitors, from which the 50% inhibitory concentrations were calculated. Each dot represents a single patient’s response to a single drug.
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