Additional file 1 of Kynurenine in IDO1high cancer cell-derived extracellular vesicles promotes angiogenesis by inducing endothelial mitophagy in ovarian cancer
Creators: name: Ying, Xiang
name: Zheng, Xiaocui
name: Zhang, Xiaoqian
name: Yin, Yujia
name: Wang, Xipeng
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Physiology
subject: Pharmacology
subject: Chemical Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer

description: Additional file 1: Figure S1. Characterization and internalization of EVs. a Images of CD9, CD81 and CD63 protein expression in cell-derived EVs by Western blot. b Images of labeled EVs endocytosed by endothelial cells. Scale bar:10 μm. Figure S2. OC cell-derived EVs promoted angiogenesis by activating mitophagy of endothelial cells. a Representative images and quantitation of mitotracker, LC3 and DAPI in endothelial cells treated with different groups imaged by confocal microscopy. Scale bar:10 μm. (n = 5, Mean ± SD, one-way ANOVA, ***P ＜ 0.001). b, Representative images and quantitative analysis of tube formation in different groups(n=5). Scale bar: 500 μm. (n = 5, Mean ± SD, one-way ANOVA, ***P ＜ 0.001). Figure S3. Metabolic profiles characterization of EVs from OC and non-OC plasma. a Images of CD9 and CD81 expression in plasma-derived EVs from OC and non-OC patients by Western blot. b QC sample correlation analysis of positive and negative ion mode. c Principal component analysis (PCA) of metabolome samples in negative ion mode. d Partial Least Squares Discrimination Analysis (PLS-DA) of metabolome samples in positive and negative ion mode. e Pie chart depicting classification of metabolites in negative ion mode. f Clustering heatmap of differential metabolites in positive and negative ion mode. g KEGG pathway enrichment of differentially accumulated metabolites in negative ion mode between OC and non-OC plasma derived-EVs. For b–g all n = 6 plasma-derived EVs from patients with OC or non-OC were performed for analysis. Figure S4. Clustering of cellular landscape between OC and non-OC samples by analyzing scRNA-seq data. a T-Distributed Stochastic Neighbor Embedding（tSNE）revealing 8 clusters by integrating analysis of OC and non-OC samples. b Percentage of major cell clusters in OC samples versus non-OC ovarian samples. c Percentage of epithelial cell sub-clusters in OC samples versus non-OC ovarian samples. d Distribution of IDO1 in epithelial cell sub-clusters of OC samples versus non-OC ovarian samples. For a–d all n = 7 OC and n = 5 non-OC samples obtained from GSE184880 dataset were used for analysis. Figure S5. Identification of hub genes associated with tryptophan metabolism in OC. a Heatmap showing differentiate expression genes (DEGs) in OC samples from TCGA ovarian cancer dataset and non-OC samples from GETx dataset. b Univariate Cox regression analysis indicating tryptophan metabolism related genes involving in OC prognosis of OCGA_OV dataset. Figure S6. Construction and validation of IDO1 over-expression and down-expression in the OC cell line. a Validation for IDO1 over-expression and down-expression in A2780 by RT-PCR (n = 3, Mean ± SD, one-way ANOVA, **P ＜ 0.01, ***P ＜ 0.001). b Validation for IDO1 over-expression and down-expression in A2780 by Western blot(n = 3, Mean ± SD, one-way ANOVA, *P ＜ 0.05, *P ＜ 0.01,***P ＜ 0.001). Table S1. Primary antibodies for Western blot, IHC or CoIP. Table S2. Primer sequence.