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Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases.
It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.
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A single-cell multi-omic atlas of the human pancreas reveals potential for cellular plasticity across development, health, and disease.
The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating single-cell RNA sequencing (scRNA-seq)/single-nucleus RNA sequencing (snRNA-seq), snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and define HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.
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Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence.
During vertebrate embryonic development, neural crest-derived ectomesenchyme within the maxillary prominences undergoes precisely coordinated proliferation and differentiation to give rise to diverse craniofacial structures, such as tooth and palate. However, the transcriptional regulatory networks underpinning such an intricate process have not been fully elucidated. Here, we perform single-cell RNA-Seq to comprehensively characterize the transcriptional dynamics during mouse maxillary development from embryonic day (E) 10.5-E14.5. Our single-cell transcriptome atlas of ∼28,000 cells uncovers mesenchymal cell populations representing distinct differentiating states and reveals their developmental trajectory, suggesting that the segregation of dental from the palatal mesenchyme occurs at E11.5. Moreover, we identify a series of key transcription factors (TFs) associated with mesenchymal fate transitions and deduce the gene regulatory networks directed by these TFs. Collectively, our study provides important resources and insights for achieving a systems-level understanding of craniofacial morphogenesis and abnormality.
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Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq.
BACKGROUND: PANoptosis, a cell death pathway involving pyroptosis, apoptosis, and necroptosis, is pivotal in the development of malignancy. However, in the field of breast cancer, the interaction between PANoptosis and tumor cells has not been thoroughly explored. METHODS: We downloaded breast cancer data and GSE176078 single-cell sequencing dataset from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain PANoptosis-associated genes. To construct prognostic models, COX and LASSO regression was used to identify PANoptosis-associated genes with prognostic value. Finally, immune infiltration analysis and differential analysis of biological functions were performed. RESULTS: Risk grouping was performed according to the prognostic model constructed by COX regression and LASSO regression. The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes. In addition, the lower the risk score, the higher the degree of microsatellite instability (MSI). Meanwhile, radixin (RDX), the gene with the highest hazard ratio (HR) value among PANoptosis prognosis-related genes, was explicitly expressed in artery Iendothelial cells (ECs) and was widely involved in signaling pathways such as immune response and cell proliferation, possessing rich biological functions. CONCLUSION: We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of breast cancer patients. Furthermore, this study has led to a deeper understanding of the role of PANoptosis in breast cancer and has the potential to provide new directions for immunotherapy of breast cancer.
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Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model.
BACKGROUND: Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment. METHODS: Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored. RESULTS: A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways. CONCLUSION: Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.
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Single-cell profiling of the subgingival bacteria reveals transcriptional heterogeneity and niche-specific programs.
Periodontitis is a chronic inflammatory disease driven by subgingival dysbiosis. Despite progress in characterizing microbial diversity, functional heterogeneity at the single-cell level remains poorly understood, mainly due to challenges from low microbial biomass and host contamination. Here, we present a single-cell RNA sequencing framework for profiling subgingival bacteria, enabling high-resolution analysis of these communities in health and periodontitis. Using 16 subgingival samples, we generated an atlas spanning 133,458 cells across 285 species, including 57 core active species grouped into eight functional clusters. Health-associated subpopulations specializing in adhesion and polysaccharide degradation decline in periodontitis, coinciding with hypoxia and elevated amino acid availability. The keystone pathogens T. denticola and P. gingivalis display species-specific tendencies in amino acid metabolism-related transcriptional profiles, while P. intermedia harbors a proteolytic subpopulation enriched in periodontitis. These insights deepen our understanding of periodontitis pathogenesis and inform precision diagnostics and therapeutic strategies.
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Analysis of Microarray and Single-Cell RNA-Seq Finds Gene Co-Expression and Tumor Environment Associated with Extracellular Matrix in Epithelial-Mesenchymal Transition in Prostate Cancer.
A complex and gradual process, the epithelial-mesenchymal transition (EMT) occurs both during embryonic development and tumor progression. Cells undergo a transition from an epithelial to a mesenchymal state throughout this process. More and more evidence points to EMT as a cause of increased metastatic spread of prostate cancer (PCa), along with stemness enhancement and therapy resistance. Here, we used bioinformatic methods to analyze gene expression microarray data, single-cell RNA sequencing, oncogenes, and tumor suppressor genes (TSGs) in order to reconstruct the network of differentially expressed genes (DEGs) involved in the epithelial-mesenchymal transition with PCa. No prior study has documented this sort of analysis. We next validated our results using data from the Cancer Genome Atlas (TCGA), which included microarray and single-cell RNA sequencing. Potentially useful in PCa diagnosis and treatment are extracellular matrix in epithelial-mesenchymal transition genes, including ITGBL1, DSC3, COL4A6, ANGPT1, ARMCX1, MICAL2, and EPHA5. In this study, we aimed to shed light on the molecular characteristics and pathways of DEGs in PCa, as well as to identify possible biomarkers that are important in the development and advancement of this cancer. These insights have important implications for understanding prostate cancer progression and for the development of therapeutic strategies targeting ECM-mediated pathways.
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Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis.
OBJECTIVE: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model. METHODS: Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes. Immune cell infiltration, immune escape and drug sensitivity disparities between high- and low-risk groups were assessed using CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) methods. High- and low-risk cell populations were discerned based on the expression of prognostic genes in BRCA scRNA-seq data. RESULTS: M0 and M1 macrophages significantly promote the metastasis of breast cancer (BRCA). The developed prognostic model demonstrates good predictive performance for patient survival at 1, 3 and 5 years, with AUC values of 0.654, 0.602 and 0.635, respectively. Compared to the low-risk group, the high-risk group exhibits increased immune cell infiltration and higher levels of immune evasion. scRNA-seq data reveal that high-risk cells have significantly higher risk scores and exhibit notable differences in signalling pathways and intercellular communication patterns. CONCLUSIONS: This study presents a novel risk score model based on exosome-related genes, validated by comprehensive analyses including differential expression, survival analysis and external dataset validation. The model's clinical significance is reinforced through its ability to stratify patients into high- and low-risk groups with distinct survival outcomes and immune landscape characteristics. The integration of RNA-seq and scRNA-seq data highlights the predictive accuracy of the model and underscores its potential for identifying novel therapeutic targets and improving patient prognosis.
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Single-cell mapping of human endometrium and decidua reveals epithelial and stromal contributions to fertility.
The human endometrium undergoes dynamic changes across the menstrual cycle to establish a receptive state for embryo implantation. Using bulk and single-cell RNA-Seq, we characterized gene expression dynamics in the cycling endometrium and the decidua from early pregnancy. We demonstrated that during the mid-secretory phase - the period encompassing the window of implantation - secretory glandular epithelial cells undergo notable transcriptional changes and alterations in cell-cell communication. Through comprehensive analyses, we identified the glandular epithelium receptivity module (GERM) signature, comprising 556 genes associated with endometrial receptivity. This GERM signature was consistently perturbed across datasets of endometrial samples from women with impaired fertility, validating its relevance as a marker of receptivity. In addition to epithelial changes, we observed shifts in stromal cell populations, notably involving decidual and senescent subsets, which also play key roles in modulating implantation. Together, these findings provide a high-resolution transcriptomic atlas of the receptive and early pregnant endometrium and shed light on key molecular pathways underlying successful implantation.
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Single-cell RNA-Seq reveals transcriptional heterogeneity in sepsis and down-regulation of SNHG5/miR-324-5p/CDK16 axis in T cells.
OBJECTIVE AND DESIGN: T lymphopenia is a common phenomenon in the sepsis patients, closely related with secondary infection and patients death, whereas its mechanism is still largely unelucidated. Hence, it is urgent to study the mechanism underlying sepsis induced T lymphopenia and lower morbidity and mortality of septic infection. MATERIAL AND METHODS: We performed Single-cell RNA-seq (scRNA-seq) and created an atlas of peripheral blood mononuclear cells (PBMCs) from sepsis patients and healthy volunteers (n = 3). To further illustrate mechanisms related to fundamental biological in sepsis, we investigated SNHG5 mediated T cell apoptosis. RESULTS: We collected blood samples of 3 sepsis patients and 3 healthy donors, separated PBMCs, and performed scRNA-seq to create a atlas of PBMCs. 7 cell clusters were identified and annotated, followed by differentially expressed genes and pathway analysis. Then, we systematically discussed the cellular heterogeneity, and generated gene expression patterns of 7 different cellular cluster in sepsis and healthy group. Further analysis DEG and further bioinformatics analysis of different cellular cluster indicated that SNHG5/miR-324-5p/CDK16 axis contributed to inflammatory T cell apoptosis in sepsis. And further inhibitory and functional experiments indicated that SNHG5/miR-324-5p/CDK16 axis contributed to inflammatory T cell apoptosis in sepsis. CONCLUSION: This study unveiled molecular mechanisms related to T lymphopenia in sepsis, such as cell fate decisions and modulation of SNHG5/miR-324-5p/CDK16 axis, making SNHG5 a promising therapeutic measurement for sepsis.