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Single-cell RNA sequencing reveals a unique pericyte type associated with capillary dysfunction.
Background: Capillary dysfunction has been implicated in a series of life- threatening vascular diseases characterized by pericyte and endothelial cell (EC) degeneration. However, the molecular profiles that govern the heterogeneity of pericytes have not been fully elucidated. Methods: Single-cell RNA sequencing was conducted on oxygen-induced proliferative retinopathy (OIR) model. Bioinformatics analysis was conducted to identify specific pericytes involved in capillary dysfunction. qRT-PCRs and western blots were conducted to detect Col1a1 expression pattern during capillary dysfunction. Matrigel co-culture assays, PI staining, and JC-1 staining was conducted to determine the role of Col1a1 in pericyte biology. IB4 and NG2 staining was conducted to determine the role of Col1a1 in capillary dysfunction. Results: We constructed an atlas of > 76,000 single-cell transcriptomes from 4 mouse retinas, which could be annotated to 10 distinct retinal cell types. Using the sub-clustering analysis, we further characterized retinal pericytes into 3 different subpopulations. Notably, GO and KEGG pathway analysis demonstrated that pericyte sub-population 2 was identified to be vulnerable to retinal capillary dysfunction. Based on the single-cell sequencing results, Col1a1 was identified as a marker gene of pericyte sub-population 2 and a promising therapeutic target for capillary dysfunction. Col1a1 was abundantly expressed in pericytes and its expression was obviously upregulated in OIR retinas. Col1a1 silencing could retard the recruitment of pericytes toward endothelial cells and aggravated hypoxia-induced pericyte apoptosis in vitro. Col1a1 silencing could reduce the size of neovascular area and avascular area in OIR retinas and suppressed pericyte-myofibroblast transition and endothelial-mesenchymal transition. Moreover, Col1a1 expression was up-regulated in the aqueous humor of the patients with proliferative diabetic retinopathy (PDR) or retinopathy of prematurity (ROP) and up-regulated in the proliferative membranes of PDR patients. Conclusions: These findings enhance the understanding of the complexity and heterogeneity of retinal cells and have important implications for future treatment of capillary dysfunction.
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Deciphering the composition and key driver genes of breast invasive micropapillary carcinoma by multi-omics analysis.
In this study, we delved into the intrinsic cellular components and transcriptomic signatures characterizing breast-invasive micropapillary carcinoma (IMPC). Employing bulk RNA sequencing, we conducted differential gene expression and functional profiles across breast cancer tissues. Single-cell transcriptome sequencing was performed on mixed IMPC samples. Moreover, a multicenter retrospective cohort of IMPC patients validated the critical role of KRT80. Our findings illuminated heightened activity in redox reactions and metabolism-related functions within IMPC compared to other tissue types. The single-cell atlas of IMPC demonstrated substantial heterogeneity predominantly driven by two distinct cell subsets: epithelioid and interstitial cells. Pseudotime analysis unveiled unique cell trajectories, and we found positive correlation between KRT80 expression and clinicopathological characteristics in IMPC. High KRT80 expression was associated with shorter overall survival for IMPC patients. This investigation unmasked extensive heterogeneity within breast IMPC tumors, delineating lineage distinctions across diverse cell clusters. It unveils potential prospective therapeutic targets with clinical relevance.
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Hypothesis-generating network toxicology analysis of potential molecular convergence between a polystyrene oligomer proxy and papillary thyroid carcinoma.
BACKGROUND: Polystyrene microplastics (PS-MPs) are emerging environmental contaminants with endocrine-disrupting and pro-inflammatory effects, but whether PS-MPs-associated molecular targets converge with papillary thyroid carcinoma (PTC) alterations remains unclear. This hypothesis-generating study aimed to identify candidate molecular networks and cellular contexts shared between a simplified polystyrene computational proxy and PTC. METHODS: Putative targets from CTD and from TargetNet/SwissTargetPrediction using a five-styrene-unit oligomer proxy were intersected with thyroid cancer-related genes from OMIM and GeneCards. Protein functional-association and enrichment analyses were followed by differential expression analysis in GSE33630 (45 normal and 49 PTC samples). LASSO and SVM-RFE identified core genes; stability was examined by repeated nested resampling conditional on the seven-gene candidate panel. Individual-gene tumor-normal discrimination was assessed in GSE33630 and GSE60542. Protein patterns were evaluated qualitatively in the Human Protein Atlas and preliminarily in 15 matched PTC-adjacent tissue pairs. CIBERSORT-estimated immune fractions and two single-cell datasets were analyzed as exploratory context. RESULTS: Among 144 putative proxy-associated targets and 2,139 thyroid cancer-related genes, 75 were shared. Seven candidates overlapped with 1,306 PTC differentially expressed genes, and BCL2, BAX, and FN1 were selected by both feature-selection methods and remained stable across the tested GeneCards thresholds and CTD-only sensitivity analysis. BCL2 was downregulated, whereas BAX and FN1 were upregulated in PTC; individual-gene AUCs were 0.963, 0.941, and 0.965 in GSE33630 and 0.942, 0.907, and 0.955 in GSE60542. HPA images and clinical IHC supported the same protein-level directions. After multiple-testing correction, only the CIBERSORT-estimated neutrophil fraction differed between groups (higher in PTC; q=0.0021). Single-cell analysis localized the three genes across epithelial, stromal, and immune compartments. CONCLUSION: BCL2, BAX, and FN1 are candidate molecular nodes at the convergence of polystyrene-proxy annotations and PTC-associated alterations. The findings do not demonstrate patient PS-MPs exposure, intact-particle effects, or causality, and require validation in material-characterized exposure experiments and exposure-aware clinical studies.
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EBNA1BP2 identified as potential prognostic biomarker for multiple tumor types in pan-cancer analysis.
BACKGROUND: Epstein-Barr virus (EBV) infection has been closely linked to the development of various types of cancer. EB nuclear antigen 1 binding protein 2 (EBNA1BP2) is a crucial molecule for stable isolation of EBV in latent infection. However, the role of EBNA1BP2 in multiple tumor types is remains unclear. In this study, we comprehensively analyzed the functional characteristics of EBNA1BP2 and investigate its potential as a prognostic biomarker in pan-cancer. METHODS: We utilized data from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases and employed various bioinformatics analysis tools, including TIMER2.0, HPA, GEPIA2.0, PrognoScan, cBioPortal, CancerSEA, and BioGRID to explore the expression pattern, prognostic value, immune infiltration, and methylation level of EBNA1BP2 in pan-cancer. Additionally, we conducted enrichment analysis of genes associated with EBNA1BP2 to identify potential biological functions and pathways. RESULTS: Our analysis revealed that EBNA1BP2 expression was significantly higher in tumor tissues compared to tumor-adjacent tissues. We observed that lower expression of EBNA1BP2 in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), sarcoma (SARC), and uterine carcinosarcoma (UCS) was significantly associated with improved overall survival (OS) and disease-free survival (DFS). Furthermore, the promoter methylation level of EBNA1BP2 was downregulated in the majority of cancer types. At the single-cell level, EBNA1BP2 was found to be positively correlated with cell cycle and DNA repair processes, while negatively correlated with hypoxia. Additionally, EBNA1BP2 was associated with the infiltration of immune cells such as B cells, cancer-associated fibroblast cells, and CD8+ T cells. Gene enrichment analysis indicated that EBNA1BP2 was mainly involved in nucleoplasm and RNA binding pathways. CONCLUSION: Our findings suggest that EBNA1BP2 may serve as a potential prognostic biomarker for survival in pan-cancer. Further experimental studies are needed to validate these findings and explore the underlying mechanisms by which EBNA1BP2 contributes to tumorigenesis.
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Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer.
Lung adenocarcinoma (LUAD) and squamous carcinoma (LUSC) are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms. The heterogeneity can be attributed to genetic, transcriptional, and epigenetic parameters. Here, we established a multi-omics atlas, integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing. We investigated their differences in genetic amplification, cellular compositions, and expression modules. We revealed that LUAD and LUSC contained amplifications occurring selectively in subclusters of AT2 and basal cells, and had distinct cellular composition modules associated with poor survival of lung cancer. Malignant and stage-specific gene analyses further uncovered critical transcription factors and genes in tumor progression. Moreover, we identified subclusters with proliferating and differentiating properties in AT2 and basal cells. Overexpression assays of ten genes, including sub-cluster markers AQP5 and KPNA2, further indicated their functional roles, providing potential targets for early diagnosis and treatment in lung cancer.
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Single-Cell Transcriptome Reveals Aquaporin-Mediated Carbon Nanosol-Induced Growth Promotion of Plants.
Carbon nanosol (CNS) is a novel carbon nanomaterial with the potential for enhancing plant growth, yet the underlying mechanism remains unclear. Application of 10 mg L-1 CNS significantly promotes plant growth, increasing fresh mass by 59.51%. Track of fluorescent labeling CNS reveals that it is rapidly absorbed by roots and entered the vascular bundle and cortex within 2 h. A single-cell transcriptomic atlas of tobacco roots response to CNS treatment is generated, which comprises 7,897 cells representing 13 distinct cell types. CNS is found to affect gene expression in a cell type-specific manner, suggesting the heterogeneity of plant response to CNS. Further pseudo-time trajectory analysis reveals that most cell types undergo cell fate transitions toward a more mature state under CNS treatment. In addition, aquaporin proteins NIPs and TIPs are found to be activated and significantly upregulated in epidermal, cortical, and endodermal cells. Further genetic and physiological findings reveal that growth enhance effect of CNS for tip1;1 and tip2;1 mutants is significantly weakened compared to the wild type, indicating that aquaporins play an important role in CNS-mediated plant growth promotion. Overall, these results provide new insights into the mechanism by which CNS promotes plant growth at the single-cell level.
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Multi-species single-cell transcriptomic analysis of ocular compartment regulons.
The retina is a widely profiled tissue in multiple species by single-cell RNA sequencing studies. However, integrative research of the retina across species is lacking. Here, we construct the first single-cell atlas of the human and porcine ocular compartments and study inter-species differences in the retina. In addition to that, we identify putative adult stem cells present in the iris tissue. We also create a disease map of genes involved in eye disorders across compartments of the eye. Furthermore, we probe the regulons of different cell populations, which include transcription factors and receptor-ligand interactions and reveal unique directional signalling between ocular cell types. In addition, we study conservation of regulons across vertebrates and zebrafish to identify common core factors. Here, we show perturbation of KLF7 gene expression during retinal ganglion cells differentiation and conclude that it plays a significant role in the maturation of retinal ganglion cells.
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Deciphering the distinct transcriptomic and gene regulatory map in adult macaque basal ganglia cells.
BACKGROUND: The basal ganglia are a complex of interconnected subcortical structures located beneath the mammalian cerebral cortex. The degeneration of dopaminergic neurons in the basal ganglia is the primary pathological feature of Parkinson's disease. Due to a lack of integrated analysis of multiomics datasets across multiple basal ganglia brain regions, very little is known about the regulatory mechanisms of this area. FINDINGS: We utilized high-throughput transcriptomic and epigenomic analysis to profile over 270,000 single-nucleus cells to create a cellular atlas of the basal ganglia, characterizing the cellular composition of 4 regions of basal ganglia in adult macaque brain, including the striatum, substantia nigra (SN), globus pallidum, and amygdala. We found a distinct epigenetic regulation on gene expression of neuronal and nonneuronal cells across regions in basal ganglia. We identified a cluster of SN-specific astrocytes associated with neurodegenerative diseases and further explored the conserved and primate-specific transcriptomics in SN cell types across human, macaque, and mouse. Finally, we integrated our epigenetic landscape of basal ganglia cells with human disease heritability and identified a regulatory module consisting of candidate cis-regulatory elements that are specific to medium spiny neurons and associated with schizophrenia. CONCLUSIONS: In general, our macaque basal ganglia atlas provides valuable insights into the comprehensive transcriptome and epigenome of the most important and populous cell populations in the macaque basal ganglia. We have identified 49 cell types based on transcriptomic profiles and 47 cell types based on epigenomic profiles, some of which exhibit region specificity, and characterized the molecular relationships underlying these brain regions.
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Integrated proteomic and single-nucleus transcriptomic profiling identifies prognostic markers and therapeutic targets in glioblastoma.
Glioblastoma (GBM) is the most aggressive primary malignant brain tumor in adults and remains associated with marked heterogeneity and poor prognosis. To identify prognostic biomarkers and characterize their cellular contexts, we integrated public proteomic and single-nucleus transcriptomic datasets. Protein expression data from the TCPA GBM cohort were used to construct a prognostic model by univariate and multivariable Cox regression analyses, followed by Kaplan-Meier survival, time-dependent ROC, and nomogram analyses. Single-nucleus RNA-seq data from GSE138794 were used to define the cellular distribution of model proteins and to perform marker-centered functional analyses. A four-protein signature consisting of BCL2, CTNNB1, CHEK2, and NDRG1_pT346 was established. The derived risk score significantly stratified patients into high- and low-risk groups with distinct overall survival and progression-free survival outcomes, and remained independently associated with prognosis after adjustment for clinical covariates. In the single-cell atlas, inferCNV-guided refinement identified malignant glioma cells and further resolved them into Glioma_MES, Glioma_PN, and Glioma_CL states. The four prognostic markers showed distinct cellular preferences: BCL2 and NDRG1 were mainly enriched in oligodendroglial-related populations and selected glioma states, whereas CHEK2 and CTNNB1 were more highly expressed in myeloid-associated populations. Among the four markers, only CHEK2- and NDRG1-defined malignant glioma subgroups showed sufficient transcriptional divergence for downstream analysis. CHEK2-positive glioma cells were enriched for DNA replication, chromosome segregation, DNA damage response, and checkpoint-related programs, whereas NDRG1-positive glioma cells were characterized by hypoxia response, glycolysis, and metabolic adaptation. CellChat analysis further revealed distinct communication patterns for CHEK2- and NDRG1-associated malignant glioma cells.
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An immunometabolism-related signature for renal clear cell carcinoma diagnosis and therapeutic target.
Kidney renal clear cell carcinoma (KIRC) lacks sensitive early diagnostic markers and effective therapeutic guidance. Given the tight crosstalk between tumor metabolism and immunity, we investigated immunometabolism for biomarker discovery. Transcriptomes from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were integrated. Immunometabolism-related genes were screened by weighted gene co-expression network analysis and differential expression, followed by three machine learning algorithms (least absolute shrinkage and selection operator, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) to select features and build a diagnostic model. Performance was validated in external cohorts. Multi-omics correlation, immune infiltration, drug-sensitivity, and survival analyses were conducted. Functional assays were performed in vitro and in vivo. Six biomarkers-ABCB1, Acyl-CoA Dehydrogenase Short/Branched Chain (ACADSB), PLA2G6, AKR1C3, PANK1, and Lactate Dehydrogenase B (LDHB)-were identified. The model showed strong discrimination (AUC 0.976 in TCGA; 0.902 in GSE126964; and 0.916 in GSE36895). The genes correlated with immune checkpoints, cytokine signaling, T-cell infiltration, and clinical parameters. Drug analyses suggested cisplatin and sunitinib downregulated oncogenic targets. Silencing ABCB1 or AKR1C3, or overexpressing LDHB, suppressed KIRC cell proliferation and migration in vitro; LDHB overexpression combined with sorafenib significantly reduced tumor growth in vivo. We propose a robust immunometabolism-based diagnostic model and six experimentally supported biomarkers for KIRC, providing mechanistic insight into tumor-immune interactions and potential avenues for personalized therapy.