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Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer.
Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed. Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package "limma," and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package "clusterProfiler." Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves. Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations. Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.
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Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis.
Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.
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CYB561 is a potential therapeutic target for breast cancer and is associated with immune cell infiltration.
BACKGROUND: Breast cancer (BC), a common malignant tumor originating from the terminal ductal lobular unit of the breast, poses a substantial health risk to women. Previous studies have associated cytochrome b561 (CYB561) with a poor prognosis in BC; however, its underlying mechanism of this association remains unclear. METHODS: We investigated the expression of CYB561 mRNA in BC using databases such as The Cancer Genome Atlas, Gene Expression Omnibus, Tumor-Normal-Metastatic plot, and Kaplan-Meier plotter databases. The prognostic value of CYB561 protein in BC was assessed in relation to its expression levels in tumor tissue samples from 158 patients with BC. The effect of CYB561 on BC progression was confirmed using in vivo and in vitro experiments. The biological functions and related signaling pathways of CYB561 in BC were explored using gene microarray, Innovative Pathway, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The correlation between CYB561 and the BC tumor immune microenvironment was evaluated using the CIBERSORT algorithm and single-cell analysis and further validated through immunohistochemistry of serial sections. RESULTS: Our study demonstrated that upregulation of CYB561 expression predicted poor prognosis in patients with BC and that CYB561 knockdown inhibited the proliferation, migration, and invasive ability of BC cells in vitro. CYB561 knockdown inhibited BC tumor formation in vivo.CYB561 was observed to modulate downstream tropomyosin 1 expression. Furthermore, CYB561 expression was associated with macrophage M2 polarization in the BC immune microenvironment. CONCLUSIONS: Elevated CYB561 expression suggests a poor prognosis for patients with BC and is associated with macrophage M2 polarization in the BC microenvironment. Therefore, CYB561 could potentially serve as a therapeutic target for BC treatment.
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Cross-species single-cell and spatial transcriptomic mapping reveals EFNA1-EPHA4-mediated stem-like epithelial-macrophage crosstalk driving colorectal cancer progression.
Colorectal cancer (CRC) typically follows the "normal-adenoma-carcinoma" (NAC) progression, with approximately 70-90% of cases driven by an adenomatous polyposis coli (APC) mutation-dependent pathway. The Apc-mutant (Min) mouse, valuable for dissecting gene function and mechanisms in CRC, provides an important basis for cross-species analyses with human data. Here, we performed a cross-species analysis of single-cell and spatial transcriptomic data across multiple stages of colorectal tissues in both humans and Min mice, constructing a spatiotemporal atlas. Our study identified key microenvironmental regulatory networks involved in CRC progression and highlighted the central role of epithelial-macrophage interactions within the tumor microenvironment. We further validated the suitability of the Min mouse as a model for the intrinsic Consensus Molecular Subtypes 2(iCMS2) microsatellite-stable (MSS) subtype of CRC. Focusing on the crosstalk between tumor-associated macrophages (TAMs) and epithelial cells, we identified the EFNA1-EPHA4 axis as a critical regulator promoting the immunosuppressive polarization of TAMs and enhancing tumor cell stemness. In addition, inhibition of EFNA1 was found to slow tumor growth. This study not only provides a systematic framework for mapping CRC correspondence between humans and mice, but also uncovers key molecular mechanisms underlying CRC progression and proposes promising therapeutic targets.
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Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19.
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV. RESULTS: In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients. CONCLUSIONS: Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.
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Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.
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Comprehensive bioinformatics and experimental analysis of PPOX reveals its carcinogenic effect in clear cell renal cell carcinoma.
BACKGROUND: Dysregulated heme biosynthesis is a hallmark of metabolic reprogramming in cancer; however, the role of protoporphyrinogen oxidase (PPOX), the penultimate enzyme in the heme synthesis pathway, remains poorly characterized in clear cell renal cell carcinoma (ccRCC). This study aims to elucidate the regulatory mechanism of PPOX on the malignant phenotype of ccRCC to evaluate its potential as a precision biomarker. METHODS: To investigate the role of PPOX in ccRCC, we integrated multi-omics bioinformatic analysis with experimental validation. First, PPOX expression and subcellular localization were assessed across multiple databases, including Tumor IMmune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). These findings were subsequently validated via immunohistochemistry (IHC) on ccRCC tissues and confirmed by RT-qPCR/Western blot across a panel of cell models (HK2, 786-O, 769-P, and A498). Utilizing the TCGA-KIRC cohort, Kaplan-Meier curves and univariate/multivariate Cox proportional hazards models were employed to determine the independent prognostic value of PPOX. Second, Mendelian Randomization (MR) using the inverse-variance weighted (IVW) method was performed to explore potential causal links between PPOX and ccRCC. Third, single-cell RNA sequencing (scRNA-seq) analysis mapped PPOX distribution within the tumor microenvironment (TME), while the CIBERSORT algorithm explored associations between PPOX expression, immune cell infiltration, and immune checkpoints. Fourth, potential therapeutic agents targeting PPOX were identified via drug sensitivity analysis and molecular docking. Finally, in vitro functional assays in ccRCC cell lines demonstrated the impact of PPOX on malignant phenotypes and the Wnt/β-catenin signaling pathway. RESULTS: PPOX was significantly upregulated in ccRCC and served as an independent risk factor for poor patient prognosis. A nomogram was initially constructed to illustrate its potential prognostic utility. MR analysis supported a potential causal relationship between PPOX levels and ccRCC risk. ScRNA-seq results revealed significant PPOX enrichment in malignant cell clusters, and its expression correlated with various immune cells and checkpoints, suggesting predictive value for immunotherapy. Furthermore, PPOX expression was associated with increased sensitivity to therapeutic agents such as 5-fluorouracil and doxorubicin. Mechanistic studies confirmed that PPOX expression positively correlated with cell proliferation, migration, and invasion, and regulated the Wnt/β-catenin signaling pathway. CONCLUSIONS: This study demonstrates that PPOX is a critical regulatory factor driving the malignant progression of ccRCC. The discovery of the PPOX-Wnt/β-catenin axis not only explains the biological basis of PPOX as a prognostic biomarker but also provides a potential metabolic therapeutic target for patients with advanced ccRCC.
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Analyzing the cellular and molecular atlas of ovarian mesenchymal cells provides a strategy against female reproductive aging.
Ovarian mesenchymal cells (oMCs) constitute a distinct microenvironment that supports folliculogenesis under physiological conditions. Supplementation of exogenous non-ovarian mesenchymal-related cells has been reported to be an efficient approach to improve ovarian functions. However, the development and cellular and molecular characteristics of endogenous oMCs remain largely unexplored. In this study, we surveyed the single-cell transcriptomic landscape to dissect the cellular and molecular changes associated with the aging of oMCs in mice. Our results showed that the oMCs were composed of five ovarian differentiated MC (odMC) populations and one ovarian mesenchymal progenitor (oMP) cell population. These cells could differentiate into various odMCs via an oMP-derived route to construct the ovarian stroma structures. Comparative analysis revealed that ovarian aging was associated with decreased quantity of oMP cells and reduced quality of odMCs. Based on the findings of bioinformatics analysis, we designed different strategies involving supplementation with young oMCs to examine their effects on female fertility and health. Our functional investigations revealed that oMCs supplementation prior to ovarian senescence was the optimal method to improve female fertility and extend the reproductive lifespan of aged females in the long-term.
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Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin.
The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.