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Single Cell RNA Sequencing Analysis of WT and CD169-DTR Adipose Tissue Vascular Fraction

ABSTRACT: Using t-distributed stochastic neighbor embedding (t-SNE) analysis, we identified 23 transcriptionally distinct cell clusters which are present in the eWAT when the four experimental groups were combined (ND and HFD with AMTs or without).Our analysis identifies six main cell clusters of CD45- cells: two adipocyte stem cell fractions (ASC1 and ASC2), stromal cells, two fractions of vascular endothelial cells (VEC1 and VEC2) as well as a population showing a gene signature for of mesothelial cells, among the CD45+ cells, seventeen lymphoid and myeloid cell subpopulations are detectable in the eWAT ranging from monocytes, monocyte-derived macrophages, dendritic cells, eosinophils, mast cells, B cells, CD4+ Th2, CD4+ Th17, Tregs and CD8+ T cells, NK cells, NKT cells In obese mice, one week absence of ATM in the eWAT affected mostly the non-haematopoetic cell compartment, while the total number of cell clusters and percentage of haematopoetic CD45+ cells were maintained roughtly the same between the four experimental groups, whereas ablation of ATMs in lean mice did not perturb dramatically the cluster distribution of CD45- cells.

ORGANISM(S): Mus musculus

PROVIDER: GSE157572 | GEO | 2021/03/19

REPOSITORIES: GEO

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Project description:Checkpoint blockade therapy using antibodies targeting CTLA4, PD1 or PDL1 have changed the way cancer patients with advanced disease are being treated, as evident by their FDA approval in a wide variety of malignancies, and their ability to induce high response rates when compared to conventional therapies (e.g. chemotherapy). However, even in melanoma, despite the high response rate, most patients are refractory to therapy or acquire resistance in 10-12 months. To identify key immunological elements coupled with response or resistance to checkpoint immunotherapy, we performed an unbiased analysis on 16,291 CD45+ immune cells from 32 patients (48 samples) treated with checkpoint therapy (anti-PD1=37; anti-PD1/CTLA4=11), using single cell transcriptomics. Initial unsupervised clustering of all CD45+ cells identified 11 clusters. When examining the association of these clusters with clinical outcome, we found that two clusters (B-cells, p=0.005; memory T-cells, p=0.03) were significantly enriched in responder lesions while 4 clusters (monocytes/macrophages, p=0.003; dendritic cells, p=0.015; exhausted CD8+ T-cells, p=0.005; and exhausted/cell-cycle lymphocytes, 1.3x10-5) are enriched in non-responder lesions. Next, by leveraging our unbiased single cell approach, we identified not only clusters but also specific markers associated with either responder lesions (PLAC8, LTB, LY9, SELL, TCF7, IGKC and CCR7) or non-responder ones (CCL3, CD38, HAVCR2, ENTPD1 and WARS), many of which were not previously reported to be associated with clinical outcome to checkpoint therapy. Due to the importance of CD8+ T-cells in controlling tumors, the significant association of T-cell states with clinical outcome, and their high abundance in melanoma tumors, we next focused our analysis on CD8+ T-cells and identified 2 main clusters CD8_G (with increased expression of genes linked to memory) and CD8_B (enriched for genes linked to cell dysfunction), that were significantly enriched in responder (CD8_G, p=1.4x10-6) and non-responder (CD8_B, p=0.005) lesions, respectively. Since cells with both states coexist in each of the responder and non-responder lesions, we decided to calculate the ratio between the number of cells in these 2 clusters and observed a significant separation between responders (CD8_G/CD8_B>1) and non-responders (CD8_G/CD8_B<1) when looking at all samples, baseline or post samples separately. Similar results were detected when looking at the expression of a single transcription factor, TCF7, in CD8 T-cells in an independent cohort (n=43) using a simple immunofluorescence assay. Additionally, we validated the identity and function of some of the newly identified cell states as well as their epigenetic landscape, and found that cells expressing the inhibitory molecules CD39 and TIM3, on top of being enriched in non-responder lesions, are likely to play a crucial role in T-cell exhaustion in melanoma. Collectively, our study provides extensive unbiased data in human tumors for discovery of predictors and therapeutic targets to checkpoint immunotherapy.

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Single Cell RNA Sequencing Analysis of WT and CD169-DTR Adipose Tissue Vascular Fraction

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