Distinct blood endothelial cells shape spatial niches in angioimmunoblastic T-cell lymphoma
Bibliographic record
Abstract
Abstract Background Angioimmunoblastic T cell lymphoma (AITL) is a subtype of peripheral T-cell lymphoma with a poor prognosis. Pathologically, AITL is characterized by the proliferation of diverse stromal cells (SCs), including blood endothelial cells (BECs) and follicular dendritic cells (FDCs). However, the tumor histology exhibits considerable heterogeneity, both between and within patients. It is hypothesized that these SCs form a supportive tumor microenvironment, and their histological heterogeneity may influence the clinical course. We previously reported that single-cell RNA sequencing (scRNA-seq) of SCs in lymph nodes (LNs) identified 30 distinct SC subclusters (Nat Cell Biol, 2022). However, the specific SC subclusters involved in AITL tumorigenesis remain undefined. Aims We aimed to characterize the SC heterogeneity in AITL microenvironments by employing scRNA-seq and spatial multi-omics at single-cell resolution. Methods Non-hematopoietic cells were isolated from 10 AITL and 5 normal LNs using magnetic- and fluorescence-activated cell sorting, followed by scRNA-seq. Based on the scRNA-seq data of SCs and immune cells, we designed a 289-gene panel for spatial transcriptomics (ST) using Xenium in situ, and a 50-protein panel for spatial proteomics (SP) using PhenoCycler fusion targeting immune cell and SC subclusters. These assays were applied to 27 formalin-fixed paraffin-embedded AITL samples. Whole exome sequencing (WES) was successfully performed on 26 of the 27 samples. We analyzed a bulk RNA-seq dataset of 97 AITL to validate the prognostic potential of gene expression patterns. Results First, we analyzed scRNA-seq data from 49,363 stroma cells and identified 30 SC subclusters consistent with our prior findings. Notably, transitional BECs between capillary BECs and activated high endothelial venules (C-aHEVs) expressed markers of both capillary BECs and HEVs, and were further subdivided into C-aHEV1 and C-aHEV2. Both tip cells, which contribute to angiogenesis, and C-aHEV2 were increased in AITL samples. Differentially expressed genes analysis revealed that both C-aHEV1 and C-aHEV2 highly expressed ACKR1. Notably, C-aHEV1 exhibited high expression of chemokines such as CXCL10 and CXCL12, whereas C-aHEV2 expressed genes encoding extracellular matrix proteins. Interaction analysis revealed that the CCL5-ACKR1 axis, which is known as a regulator of leukocyte migration, was activated between CD8-positive T cells and C-aHEVs. Furthermore, the COL15A1-integrin and SPARC-ENG axis, both of which have been reported to promote angiogenesis, were specifically enriched between C-aHEV2 and other HEVs. ST analysis of 2,020,192 cells identified 10 BEC subclusters, 5 non-endothelial SC subclusters, and 12 immune/tumor cell subclusters. Spatial proximity measurement revealed that C-aHEV1 was the BEC subcluster most closely associated with tumor cells. Spatial niche analysis revealed that C-aHEV1, together with FDCs, formed a distinct tumor-associated niche (CFT niche). On the other hand, C-aHEV2 was localized farther from the tumor than C-aHEV1, and formed a niche in conjunction with other HEV subclusters (HEV niche). The CFT niche-dense area and the HEV niche-dense area were often in close proximity, and the densities of CXCR4, encoding areceptor for CXCL12, and LAG3 and CXCR6, serving as T-cell exhaustion markers, were high in the CFT niche. SP analysis detected 6,172,072 cells and identified SCs containing 5 BEC components (artery, capillary, C-aHEV, HEV, and vein), tumor cells, and immune cell subtypes. C-aHEVs tended to be located closer to tumor cells than other BECs and formed a unified vascular niche in conjunction with HEV. WES analysis revealed the recurrent G17V RHOA mutations in 19 samples. Notably, samples harboring G17V RHOA mutations exhibited significantly higher densities of C-aHEV2 cells. Bulk RNA-seq analysis revealed that patients with high expression of C-aHEVs and other HEV-related signature exhibited poorer survival outcomes. Summary/ Conclusion By integrating scRNA-seq and spatial data, we identified C-aHEVs as key components in shaping the AITL microenvironment through the recruitment of immune cells. As the gene expression level, C-aHEVs can be subdivided into C-aHEV1 and C-aHEV2, which exhibited distinct properties. Further investigation is needed to validate the mechanisms by which these BEC clusters interact with tumor cells and contribute to tumorigenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".