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. 2025 Feb;15(2):e70159.
doi: 10.1002/ctm2.70159.

Single-cell transcriptomics and metabolomic analysis reveal adenosine-derived metabolites over-representation in pseudohypoxic neuroendocrine tumours

Yuval Kahan Yossef  1   2 , Liav Sela Peremen  1   2 , Alona Telerman  2 , Gil Goldinger  3 , Sergey Malitsky  4 , Maxim Itkin  4 , Reut Halperin  1   2 , Naama Peshes Yaloz  2 , Amit Tirosh  1   2
Affiliations

Single-cell transcriptomics and metabolomic analysis reveal adenosine-derived metabolites over-representation in pseudohypoxic neuroendocrine tumours

Yuval Kahan Yossef et al. Clin Transl Med. 2025 Feb.
No abstract available

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

FIGURE 1
FIGURE 1
Metabolomic analysis of vPNET versus sPNET samples. Partial Least Squares Discriminant Analysis (PLS‐DA) plot demonstrates the clear separation between vPNET and sPNET samples (A), Variable Importance in Projection (VIP) plot highlights key metabolites influencing group differentiation, with adenosine monophosphate (AMP) identified as a major contributor (B), Volcano Plot displays significantly different metabolites, with those elevated in vPNET on the left and those in sPNET on the right. AMP is marked by a dashed circle, indicating its higher abundance in vPNET (C), and heatmap with an unsupervised hierarchical clustering, revealing unique metabolic signatures for vPNET (right) and sPNET (left), with AMP significantly higher in vPNET (yellow frame, D).
FIGURE 2
FIGURE 2
Single‐nucleus RNA sequencing (snRNA‐seq analysis) was used to identify cell populations in sPNET and vPNET samples. Uniform Manifold Approximation and Projection (UMAP) plot displays the various cell types identified across all samples (A), based on gene expression of cell‐specific biomarkers (B) and in each sample separately (C). Copy Number (CN) Variation analysis was conducted in vPNET and sPNET (upper and lower panel, respectively), to identify malignant and normal cells (D). Cells with CN alterations were identified as malignant, as shown in UMAP plots of the tumoral, normal and filtered‐out cells (E). CN alterations were summarized and compared between vPNET and sPNET samples (F).
FIGURE 3
FIGURE 3
Pathway analysis and multi‐omics analysis of tumoral cells. Volcano plot displays the differential gene expression analysis of malignant cells in vPNET versus sPNET (A). Single‐cell pathway analysis comparing vPNET (left) to sPNET samples (right) based on Hallmark database (B), and on metabolic pathways from various databases (C). Integrated snRNA‐seq and metabolomics analysis, provided as a multi‐omics heatmap, depicting pseudohypoxia‐related genes across all cells from both vPNET and sPNET samples (D), and pathway enrichment, analysing the top 50 most variably detected metabolites from the multi‐omics data revealed purine metabolism as the most significantly enriched pathway (E). Immunofluorescence staining demonstrating expression of adenosine receptors in vPNET: neuroendocrine cells, identified by synaptophysin (red), adenosine receptor 2B (green) and DAPI staining (blue) to indicate nuclei, co‐localized in the merged image (F).
FIGURE 4
FIGURE 4
Cell trajectory and pseudo‐time analysis. Uniform Manifold Approximation and Projection (UMAP) visualization shows selected pseudo‐time points in neuroendocrine (NE) cells subgroups (A), and a bar‐plot showing the development of the different NE cells along the pseudo‐time axis (B). UMAP of NE cells coloured according to their pseudo‐time in vPNET (left) and sPNET (right) samples (C). Panel compares the expression patterns of key genes (e.g. EPO, PDX1, SYP and VEGFA) along the pseudo‐time trajectory in vPNET (left) and sPNET (right) samples (D).
See this image and copyright information in PMC

References

    1. Lonser RR, Glenn GM, Walther M, et al. von Hippel−Lindau disease. Lancet. 2003;361(9374):2059‐2067. doi:10.1016/S0140-6736(03)13643-4 - DOI - PubMed
    1. Buckley M, Terwagne C, Ganner A, et al. Saturation genome editing maps the functional spectrum of pathogenic VHL alleles. Nat Genet. 2024;56(7):1446‐1455. doi:10.1038/s41588-024-01800-z - DOI - PMC - PubMed
    1. Jaakkola P, Mole DR, Tian YM, et al. Targeting of HIF‐alpha to the von Hippel−Lindau ubiquitylation complex by O2‐regulated prolyl hydroxylation. Science. 2001;292(5516):468‐472. doi:10.1126/science.1059796 - DOI - PubMed
    1. Maxwell PH, Wiesener MS, Chang GW, et al. The tumour suppressor protein VHL targets hypoxia‐inducible factors for oxygen‐dependent proteolysis. Nature. 1999;399(6733):271‐275. doi:10.1038/20459 - DOI - PubMed
    1. Wicks EE, Semenza GL. Hypoxia‐inducible factors: cancer progression and clinical translation. J Clin Invest. 2022;132(11):e159839. doi:10.1172/JCI159839 - DOI - PMC - PubMed

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