To overview the cellular ecosystem of cHL, we compiled a census of 243,753 single cell transcriptomes from lymphoma-affected and unaffected lymph nodes. We sourced data from Aoki et al10 incorporating droplet encapsulation single cell transcriptomes (10X genomics) from reactive and cHL nodes (Fig. 1a). Additionally, we performed scRNAseq (10X genomics) with cell suspensions from lymph nodes from deceased organ donors, and two lymphoma lymph nodes, one with nodular sclerosis cHL (NSCHL), and one with nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), a biologically distinct subtype of Hodgkin lymphoma. The combined dataset comprises tissue from 13 non-lymphoma donors (8 deceased donors, 5 donors with reactive lymph node hyperplasia), one NLPHL, and 23 cHL lymph nodes (Fig. 1a, Table 4).
After quality control (Supplementary Methods, Supplementary Fig. 1a-b), we performed dataset integration and dimensionality reduction using single-cell variational inference13 and annotated cell-types on the basis of marker genes and external dataset validation (Fig. 1b, Supplementary Fig. 1c-e).
The cellular ecosystem of cHL encompassed subsets of CD4+ and CD8+ T-cells, including regulatory T-cells (Treg), T follicular helper cells (Tfh), and exhausted CD4+ (ThExh) and CD8+ T-cells (CD8 TExh) cells (Fig. 1b). ThExh matched the recently identified LAG3+ subset, and expressed checkpoint molecules and exhaustion markers including CD27, TNFRSF18, LAG3, and ICOS10,31 (Supplementary Fig. 1f). The B cell compartment split into two clusters of memory and naive subsets, in addition to plasmablasts, and germinal center (GC) B cells (Fig. 1b, Supplementary Fig. 1c-e).
The inclusion of non-lymphoma lymph nodes, including healthy, non-reactive tissue, allowed us to robustly test the differential abundance of cells between lymphoma and ‘non-lymphoma’, using a neighborhood partitioning approach (MiloR – Methods)18. Lymphoma was enriched for cytotoxic lymphocyte subsets including effector memory CD8+ T-cells (CD8 Tem) and NK cells, in addition to CD4+ T-cell subsets expressing checkpoint apparatus, including ThExh and Tfh cells (Fig. 1f, Supplementary Fig. 2b). Within the lymphoma samples, cHL was enriched for exhausted T-cell subsets, in contrast with the NLPHL sample which was dominated by cytotoxic subsets (NK cells, CD8 Tem, and CD8 Tcm) (Supplementary Fig. 2c).
We did not detect HRSC, likely due to the rarity, size, and fragility of these cells. To probe their transcriptional programs, which potentially orchestrate this immunoregulatory milieu, we leveraged a microarray dataset profiling microdissected HRSC and GCs (Methods)32. Differential expression between HRSC and GC identified an HRSC gene signature, including TNFRSF8 (CD30) (Fig. 1g). Scoring of transcription factor regulons demonstrated NF-κB activation (NFKB1 and its activatory heterodimer partner RELA) in HRSC (Fig. 1h), consistent with previous reports33. We next performed in silico identification of interactions between active transcription factors and potential targets within the HRSC geneset (Methods). This demonstrated an NF-κB-centric network coordinating the upregulation of the chemokines CCL5, CCL17, and CCL22 capable of the positioning and retention of ThExh via CCR5 and CCR4 ligation (Fig. 1i, Supplementary Fig. 1f).
Within the myeloid compartment, we identified macrophages coexpressing CD14, CD68, and resident macrophage markers FOLR2 and MRC1 (Fig. 1c-d). These cells were transcriptionally distinct from classical monocytes, which expressed a characteristic signature including S100A9, CD14, VCAN, and FCN134 (Fig. 1c-d). Amongst DCs, we identified cDC1 (key transcripts: CLEC9A, CADM1, IDO1) and cDC2 (key transcripts: CD1C, CLEC10A, FCER1A) (Fig. 1c-d). We also identified LAMP3+ DCs in both healthy and some lymphoma samples, expressing the chemokine receptor CCR7 and the chemokines CCL17 and CCL19, which we termed “activated DC” (aDC), consistent with the nomenclature of transcriptionally similar cells described in human thymus and spleen35,36, and in murine lung neoplasms37 (Fig. 1c-d, Supplementary Fig. 1d). We found a population of plasmacytoid DC (pDC) with a dominant contribution from lymphoma samples (Fig. 1e, Supplementary Fig. 2a), expressing IL3RA (CD123) and CLEC4C (Fig. 1c-d).
Within the stromal cell compartment, we identified fibroblasts (key transcripts: LUM, DCN), and endothelial cells (key transcripts: CLDN5, PECAM1, and PLVAP (fenestrated endothelium)) (Fig. 1b, Supplementary Fig. 1c).
We used this lymph node-wide account of the immune landscape of cHL as a reference to interrogate the cellular composition of cHL tissues, focusing on myeloid cells.
We first identified spatially distinct microenvironments using targeted spatial transcriptomic profiling (Nanostring GeoMx Cancer Transcriptome Atlas), defining 300 µm diameter regions of interest (ROI) in both PD-L1high and PD-L1low regions of 9 NSCHL and 1 Mixed Cell cHL (MCCHL) lymph nodes and follicular and interfollicular regions of one control reactive lymph node (Supplementary Fig. 3a-b). We represented these transcriptional profiles in a shared-nearest neighbor graph and identified 5 clusters (Fig. 2a-b). We then deconvoluted the cell composition of each cluster using our scRNAseq reference, followed by cell-type count estimation and differential cell-type abundance analysis (Fig. 2c, Methods). The clusters encompassed two ‘neoplastic’ PD-L1high HRSC-enriched clusters (clusters 3 & 4), two ‘non-neoplastic’ PD-L1low HRSC-depleted clusters (clusters 1 & 5), and one intermediate neighborhood (cluster 2).
Clusters 1 and 5 were PD-L1low and represented fibrotic regions and healthy follicular microenvironments respectively. Cluster 1 was enriched for stromal (fibroblast and endothelial) cells, classical monocytes and pDC, but was depleted of cDC (Fig. 2C). Differentially expressed genes in this cluster included genes encoding signaling mediators of fibrosis FGFR4, TGFB2, and PTCH1 (Supplementary Fig. 3c). In contrast cluster 5 was exclusively derived from control samples and enriched for GC B cells, Tfh, and plasmablasts (Fig. 2a-c).
The PD-L1high neoplastic clusters exhibited divergent leukocyte enrichment. Cluster 3 was enriched for ThExh, Th, and NK cells. In contrast, cluster 4 represented a myeloid-enriched niche, characterized classical monocyte, macrophage, and cDC2 infiltration (Fig. 2c). Marker genes for cluster 4 highlighted inflammatory signaling, with upregulation of chemokines associated with Th2 responses and CCR3-dependent eosinophil recruitment (CCL18, CCL13, CCL24, CCL26, CCL23) and granulocyte-attracting chemokines CXCL1, and CXCL6 (Supplementary Fig. 3c).
We next sought to identify transcriptional correlates of these microenvironments in publicly available gene expression data. We performed Weighted Correlation Network Analysis (WGCNA) using data from 130 diagnostic cHL lymph nodes22,38. This analysis yielded 6 modules of co-expressed genes (modules A-F) (Fig. 2d). We performed enrichment analysis using both scRNAseq references and Gene Ontology terms to annotate these modules and found that whilst module C corresponds to the cell cycle program (Supplementary Fig. 3d), the other modules strikingly mirror the tissue niche patterns identified in our spatially resolved (Nanostring) transcriptomics data (Fig. 2d), suggesting a highly conserved organization of pathological niches in cHL.
Module A enriched for stromal cell signatures similarly to PD-L1low nanostring cluster 1. In contrast, module B represented myeloid-skewed inflammation, enriching for monocyte, macrophage, and cDC2 signatures, aligning to nanostring cluster 4 (Fig. 2d).
We then interrogated the relationship between module gene expression and treatment outcomes. High expression of the fibrosis/stroma enriched module was associated with treatment success, whereas high expression of the myeloid enriched module was associated with early treatment failure. Expression of module F (B cell follicle) was not associated with differences in outcome (Fig. 2d).
Given variation in microenvironmental myeloid infiltration appears to be a conserved and prognostically important feature of cHL pathobiology, we sought to define MNP within the HRSC niche at single-cell resolution. Using marker gene co-expression patterns in the scRNAseq data (Supplementary Fig. 3e), we designed multiplexed immunofluorescence panels to identify MNPs in cHL tissue sections (n=54 tumors, representative images: Fig. 2e-g). We identified CADM1+/CD11c+ cDC1, CD1c+/CD11c+ cDC2, LAMP3+ aDC with distinctive dendritic morphology, CD123+ pDC, CD11c+ monocytes and macrophages (CD11c+ ONLY), and CD30+ HRSC (Fig. 2d). We then phenotyped segmented cells and performed neighborhood analyses, taking a 25 µm-radius neighborhood around each CD30+ HRSC and measuring the relative enrichment of MNP subsets in aggregated neighborhoods, compared to ‘non-neighborhood’ regions (Fig. 2f). This revealed enrichment of cDC2 and CD11c+ monocytes in the immediate vicinity of HRSC. In contrast, both pDC and aDC were excluded from the HRSC niche and occupy regions with a low density of CD11c+ cells (Fig. 2e-h, Fig. 3a-h).
We next asked which signals might coordinate the positioning of MNP subsets and T-cells found in close association with HRSC. To interrogate ligand-receptor interactions (LRI), we calculated the statistical enrichment of candidate LRI between MNPs and T-cells in our scRNAseq data, using the CellPhoneDB tool19. This analysis predicted paracrine CCL3 and CCL4 signaling by macrophages and classical monocytes to CCR5- and CCR1-expressing cDC2 and ThExh (Fig. 4a). Furthermore, classical monocyte-derived CXCL10 was predicted to signal to cDC1, ThExh, and Tfh via CXCR3. Nominated inhibitory interactions from ThExh included TIGIT signaling via NECTIN2 expressed by cDC2, classical monocytes, and macrophages (Fig. 4a). Analysis of putative LRI interactions inferred from Nanostring cluster 4 and WGCNA module B also highlighted CCR1- and CCL5- mediated signaling (Supplementary Fig. 4a). A proposed schematic illustrates potential interactions between HRSC, cDC2, classical monocytes, and TExh (Fig. 4b).
We were surprised that the HRSC-associated MNP network includes cDC2 and occasional cDC1, which play key roles in immunosurveillance. Although macrophage expression of PD-L1 is established, upregulation of inhibitory ligands and receptors by DC has not been described in cHL. To test this, we designed a second IF panel to examine expression of PD-L1, IDO1, and TIM-3 on CD11c+/CD68- cDC1 and cDC2, CD11c+/CD68+ monocytes, and CD11c-/CD68+ macrophages, and to assess their spatial relationships to CD30+ HRSC (Table 3).
HRSC exhibited extensive PD-L1 expression as expected. We also found enrichment of PD-L1+ CD11c+/CD68+ and CD11c+/CD68- MNPs in CD30+ HRSC neighborhoods (Fig. 5a-d). Similarly, we found variable co-expression of the co-inhibitory receptor TIM-3, and IDO1 - an immunomodulating tryptophan catabolizing enzyme - on CD11c+/CD68+ and CD11c+/CD68- MNPs in close proximity to HRSC (Fig. 5a-e, Supplementary Fig. 5).
The proportion of CD11c+/CD68- and CD11c+/CD68+ MNPs expressing inhibitory molecules increased with age at diagnosis (p <0.05), but there were no significant differences in the proportion of MNPs expressing inhibitory molecules according to sex, histological subtype, or EBV status (Supplementary Fig. 6). This finding suggests immunosuppressive signaling in pediatric and young-adult cHL may differ from cHL in older adults.