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[Preprint]. 2023 Nov 10:rs.3.rs-3558517.
doi: 10.21203/rs.3.rs-3558517/v1.

Single cell atlas of kidney cancer endothelial cells reveals distinct expression profiles and phenotypes

Yuexin Xu  1 , Chris P Miller  1 , Jun Xue  2 , Ying Zheng  2 , Edus H Warren  1 , Scott S Tykodi  2 , Shreeram Akilesh  2
Affiliations

Single cell atlas of kidney cancer endothelial cells reveals distinct expression profiles and phenotypes

Yuexin Xu et al. Res Sq. .

Update in

Abstract

Background: Tumor endothelial cells (TECs) represent the primary interface between the tumor microenvironment and circulating immune cells, however their phenotypes are incompletely understood in highly vascularized clear cell renal cell carcinoma (ccRCC).

Methods: We purified tumor and matched normal endothelial cells (NECs) from ccRCC specimens and performed single-cell RNA-sequencing to create a reference-quality atlas available as a searchable web resource for gene expression patterns. We established paired primary TECs and NECs cultures for ex vivo functional testing.

Results: TECs from multiple donors shared a common phenotype with increased expression of pathways related to extracellular matrix regulation, cell-cell communication, and insulin-like growth factor signaling that was conserved in comparison to hepatocellular carcinoma associated TECs, suggesting convergent TEC phenotypes between unrelated tumors. Cultured TECs stably maintained a core program of differentially regulated genes, were inherently resistant to apoptosis after vascular endothelial growth factor removal and displayed increased adhesiveness to subsets of immune cells including regulatory T-cells.

Conclusions: Our studies delineate unique functional and phenotypic properties of TECs, which may provide insights into their interactions with available and emerging therapies. Functional phenotypes of cultured TECs suggest potential mechanisms of resistance to both antiangiogenic and immune-based therapies.

Keywords: Endothelial cells; Immune cell adhesion; Renal cell carcinoma; Single-cell RNA sequencing.

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Conflict of interest statement

Additional Declarations: No competing interests reported. Competing Interests The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1. Analysis of scRNA-seq transcriptome data for TECs and NECs from ccRCC patients.
(A) UMAP plot of scRNA-seq dataset from 4 subjects colored by clusters. The dashed line marks the endothelial cell cluster. UMAP plots marked by (B) tissue origin, (C) pseudotime trajectory, (D) general EC lineages and (E) angiogenic ECs. (F) Quantification of angiogenic NECs and TECs numbers. Each dot represents one patient. Statistics: *, P < 0.05, t-test. (G) Volcano plot of DEGs in TECs compared with NECs. Dashed lines, cutoffs: P < 1x10−5; FC = 1. (H) Upregulated reactome pathways in TECs with DEGs at log2FC ≥ 1 compared with NECs. (I) Upregulated reactome pathways in NECs with DEGs at log2FC ≥ 1 compared with TECs.
Figure 2
Figure 2. IGFBP3 and IGFBP5 expression in TECs and NECs.
(A) The top three DEGs in TEC, NEC and transitional EC clusters. (B) Single-cell expression of IGFBP3 and IGFBP5 in TECs and NECs. (C) RNA-ISH distribution of IGFBP3 and IGFBP5 in tumor and normal kidney tissue. Specific regions of expression are marked.
Figure 3
Figure 3. Comparison of EC clusters between previously published ccRCC scRNA-seq and this study.
(A) UMAP plot of integrated NEC and TEC populations from ccRCC in previously published studies and this study. The dashed line indicates the cells that are clustered in the common clusters. (B) Overlap between differentially expressed genes (compared to NAT, cutoffs at logFC = 0.25, minimum detection fraction = 0.1) in two previously published studies and this study. (C)Common reactome pathways that are derived from the overlapped DEGs in TECs (D)UMAP plot colored by tissue origin of previously published datasets and this dataset (E) IGFBP3 and IGFBP5 expression in TECs and NECs.
Figure 4
Figure 4. Comparison of EC clusters between the previously published HCC scRNA-seq data and RCC data from this study.
(A) UMAP plot of integrated TEC populations from HCC and RCC. (B) DEG (TECs compared to NECs, cutoffs at logFC = 0.25, minimum detection fraction = 0.1) overlap in the HCC studies and this RCC study. (C) Common reactome pathways that are derived from the overlapping DEGs (D) UMAP plot colored by tissue origin of ECs from HCC and RCC. (E) IGFBP3and IGFBP5 expression in HCC and RCC.
Figure 5
Figure 5. Phenotype and gene expression of in vitro cultured TECs and NECs
(A) Cell morphology of cultured TECs and NECs at passage 3 in a 10X brightfield microscope. (B) Venn diagram of DEGs (padj < 0.05) in TECs compared with NECs and the overlap between cultured and primary ECs (C, D) Preserved pathways in cultured TECs derived from overlapping differentially expressed genes that are upregulated (log2FC ≥ 1) or downregulated (log2FC ≤ −1). (E) Key marker gene expression compared to NECs in the primary, passage 2, and passage 3 cultures.
Figure 6
Figure 6. Co-culture of immune cells and in vitro established Ecs.
(A) The percent of live EC (DAPI- population) after 48 hours of VEGF withdrawal (B) The percent of culture confluency over time. (C) Adherent leukocytes (green) and ECs (red) after 24 hours of co-culture. (D) Adherent CD45+ leukocyte / endothelial cell ratio normalized to PBMC CD45+ / NECs determined by flow cytometry after 24 hours of each co-culture combination. (E) Averaged adherent CD45+ leukocytes from different origins / EC ratio normalized to averaged CD45+ / NECs. (F) Adherent leukocyte composition on TECs. The percent of total leukocytes of each leukocyte population on TECs were compared to the corresponding % of population on NECs. Fold changes are displayed. Statistics: t-test, ** P ≤ 0.01, *** P ≤ 0.001, **** P ≤ 0.0001
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