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Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain.
Histone modifications underpin the cell-type-specific gene regulatory networks that drive the remarkable cellular heterogeneity of the adult mammalian brain. Here, we profiled four histone modifications jointly with transcriptome in 2.5 million nuclei across multiple adult mouse brain regions. By integrating these data with existing maps of chromatin accessibility, DNA methylation, and 3D genome organization, we established a unified regulatory framework for over 100 brain cell subclasses. This integrative epigenomic atlas annotates 81% of the genome, defining distinct active, primed, and repressive states. Notably, active chromatin states marked by combinatorial histone modifications more precisely identify functional enhancers than chromatin accessibility alone, while Polycomb- and H3K9me3-mediated repression contributes prominently to cell-type-specific regulation. Finally, this multi-modal resource enables deep learning models to predict epigenomic features and gene expression from DNA sequences. This work provides a comprehensive annotation of the mouse brain regulatory genome and a framework for interpreting non-coding variation in complex tissues.
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Identification and validation of ZAP70 as a potential biomarker associated with T cell infiltration in lung adenocarcinoma.
BACKGROUND: Zeta-chain-associated protein kinase 70 kDa (ZAP70) is a tyrosine kinase essential for T cell activation and differentiation and it is predominantly expressed in T cells. However, its clinical relevance and relationship with the immune microenvironment in lung adenocarcinoma (LUAD) remain unclear. Therefore, this study aimed to investigate the expression profile of ZAP70 in LUAD, evaluate its prognostic significance, and explore its correlation with the tumor immune microenvironment. METHODS: Transcriptomic and clinical data for LUAD were obtained from The Cancer Genome Atlas (TCGA) and supplemented with Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC) data, and Human Protein Atlas (HPA) resources. The messenger RNA (mRNA) and protein levels of ZAP70 were further validated in seven paired LUAD and adjacent normal tissues. RESULTS: Pan-cancer analysis revealed heterogeneous ZAP70 expression, with significantly reduced mRNA and protein levels in lung cancer, including LUAD. In LUAD, low ZAP70 expression was associated with advanced T stage, stage IV disease, M1 metastasis, and inferior overall survival, first progression, and post-progression survival. The Tumor Immune Estimation Resource (TIMER) analysis showed that higher ZAP70 expression correlated with increased infiltration of multiple immune cell types. Single-cell transcriptomic profiling demonstrated that ZAP70 was predominantly expressed in T cells, with LUAD tissues displaying altered T cell subset composition and an increased proportion of ZAP70low T cells compared with normal lung. Functional enrichment indicated that ZAP70-related genes were involved in cell cycle and immune-related pathways. Consistently, quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot assays confirmed reduced ZAP70 expression in LUAD tissues, particularly in patients with more advanced disease. CONCLUSIONS: These findings suggest that ZAP70 may serve as a potential biomarker and therapeutic target in LUAD, warranting further mechanistic and prospective clinical studies.
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Ancient ubiquitous protein 1 (AUP1) is a prognostic biomarker connected with TP53 mutation and the inflamed microenvironments in glioma.
INTRODUCTION: Glioblastoma (GBM) is the most common and lethal brain tumor. The current treatment is surgical removal combined with radiotherapy and chemotherapy, Temozolomide (TMZ). However, tumors tend to develop TMZ resistance which leads to therapeutic failure. Ancient ubiquitous protein 1 (AUP1) is a protein associated with lipid metabolism, which is widely expressed on the surface of ER and Lipid droplets, involved in the degradation of misfolded proteins through autophagy. It has recently been described as a prognostic marker in renal tumors. Here, we aim to use sophisticated bioinformatics and experimental validation to characterize the AUP1's role in glioma. MATERIAL AND METHODS: We collected the mRNA, proteomics, and Whole-Exon-Sequencing from The Cancer Genome Atlas (TCGA) for bioinformatics analyses. The analyses included the expression difference, Kaplan-Meier-survival, COX-survival, and correlation to the clinical factors (tumor mutation burden, microsatellite instability, and driven mutant genes). Next, we validated the AUP1 protein expression using immunohistochemical staining on the 78 clinical cases and correlated them with P53 and KI67. Then, we applied GSEA analyses to identify the altered signalings and set functional experiments (including Western Blot, qPCR, BrdU, migration, cell-cycle, and RNAseq) on cell lines when supplemented with small interfering RNA targeting the AUP1 gene (siAUP1) for further validation. We integrated the single-cell sequencing and CIBERSORT analyses at the Chinese Glioma Genome Atlas (CGGA) and Glioma Longitudinal AnalySiS (GLASS) dataset to rationale the role of AUP1 in glioma. RESULTS: Firstly, the AUP1 is a prognostic marker, increased in the tumor component, and correlated with tumor grade in both transcriptomes and protein levels. Secondly, we found higher AUP1 associated with TP53 status, Tumor mutation burden, and increased proliferation. In the function validation, downregulated AUP1 expression merely impacted the U87MG cells' proliferation instead of altering the lipophagy activity. From the single-cell sequencing and CIBERSORT analyses at CGGA and GLASS data, we understood the AUP1 expression was affected by the tumor proliferation, stromal, and inflammation compositions, particularly the myeloid and T cells. In the longitudinal data, the AUP1 significantly dropped in the recurrent IDH wildtype astrocytoma, which might result from increased AUP1-cold components, including oligodendrocytes, endothelial cells, and pericytes. CONCLUSION: According to the literature, AUP1 regulates lipophagy by stabilizing the ubiquitination of lipid droplets. However, we found no direct link between AUP1 suppression and altered autophagy activity in the functional validation. Instead, we noticed AUP1 expression associated with tumor proliferation and inflammatory status, contributed by myeloid cells and T cells. In addition, the TP53 mutations seem to play an important role here and initiate inflamed microenvironments. At the same time, EGFR amplification and Chromosome 7 gain combined 10 loss are associated with increased tumor growth related to AUP1 levels. This study taught us that AUP1 is a poorer predictive biomarker associated with tumor proliferation and could report inflamed status, potentially impacting the clinical application.
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Identification of NK cell marker genes based on single-cell sequencing to establish a prognostic signature in breast cancer.
BACKGROUND: Tumor-infiltrating natural killer (NK) cells are pivotal in modulating tumor progression, either by promoting or inhibiting neoplastic development. Nevertheless, the implications of NK cells in breast carcinoma remain inadequately understood. This investigation aimed to delineate the impact of NK cells on both the prognosis and the immune infiltration landscape in breast cancer. METHODS: NK cell marker genes were identified using single-cell sequencing data from breast cancer available in the Gene Expression Omnibus (GEO) database. A prognostic model was constructed based on data from The Cancer Genome Atlas (TCGA) and subsequently validated with the GEO dataset. Disparities in immune cell infiltration between low-risk and high-risk cohorts, as stratified by the prognostic model, were examined. Additionally, genes differentially expressed between these cohorts were subjected to enrichment analysis. RESULTS: A total of 29 NK cell marker genes were identified through single-cell sequencing, and a prognostic model was subsequently developed using machine learning techniques based on the TCGA data. This model demonstrated robust predictive performance when applied to both TCGA and GEO datasets. Notably, a significant difference in immune infiltration was observed between the low-risk and high-risk groups. The findings were further validated through enrichment analysis. CONCLUSIONS: In summary, we constructed a prognostic signature characterized by strong predictive performance, which has elucidated the critical role of NK cells in the pathogenesis of breast cancer. Furthermore, this model offers a predictive index and identifies a novel therapeutic target for the advancement of immunotherapeutic strategies in the clinical management of breast cancer patients.
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Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy.
Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.
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Cross-species and cross-platform analysis reveals the application value of guinea pig retina in myopia research at single-cell resolution.
This study delineates the single-cell transcriptomic landscape of pigmented guinea pig retinas-an emerging model for myopia research-through integrated single-cell and bulk RNA sequencing, cross-species comparative analysis, and functional validation using form-deprivation myopia models with intravitreal brimonidine intervention. Our results identified six major retinal cell types (rod cells, cone cells, bipolar cells, Müller glial cells, astrocytes, and retinal pigment epithelial cells) subdivided into 16 distinct subclusters, revealing multilineage pseudo time trajectories and specialized intercellular signaling networks: rod cells via NEGR/CADM, cone cells via MPZ, bipolar cells via SEMA6/PTPRM, and astrocytes via ESAM. Cross-species alignment demonstrated closer phylogenetic alignment with human retinal architecture than murine counterparts in pseudo time trajectories and cellular crosstalk. In therapeutic models, brimonidine treatment decelerated axial elongation while inducing selective reductions in cone cells, bipolar cells, and pigment epithelium, paralleled by concordant gene expression shifts. By establishing the first single cell atlas of guinea pig retina and validating its similarities to human ocular biology, this work not only consolidates guinea pigs as a valuable model for myopia research but also infers cellular and molecular dynamics underlying antimyopic interventions, offering a cross-species framework for retinal disease mechanism exploration and therapeutic development.
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Spatial transcriptomics: new dimension of understanding biological complexity.
Cells and tissues are exquisitely organized in a complex but ordered manner to form organs and bodies so that individuals can function properly. The spatial organization and tissue architecture represent a keynote property underneath all living organisms. Molecular architecture and cellular composition within intact tissues play a vital role in a variety of biological processes, such as forming the complicated tissue functionality, precise regulation of cell transition in all living activities, consolidation of central nervous system, cellular responses to immunological and pathological cues. To explore these biological events at a large scale and fine resolution, a genome-wide understanding of spatial cellular changes is essential. However, previous bulk RNA sequencing and single-cell RNA sequencing technologies could not obtain the important spatial information of tissues and cells, despite their ability to detect high content transcriptional changes. These limitations have prompted the development of numerous spatially resolved technologies which provide a new dimension to interrogate the regional gene expression, cellular microenvironment, anatomical heterogeneity and cell-cell interactions. Since the advent of spatial transcriptomics, related works that use these technologies have increased rapidly, and new methods with higher throughput and resolution have grown quickly, all of which hold great promise to accelerate new discoveries in understanding the biological complexity. In this review, we briefly discussed the historical evolution of spatially resolved transcriptome. We broadly surveyed the representative methods. Furthermore, we summarized the general computational analysis pipeline for the spatial gene expression data. Finally, we proposed perspectives for technological development of spatial multi-omics.
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Functional Roles and Targets of COVID-19 in Blood Cells Determined Using Bioinformatics Analyses.
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global epidemic with a high mortality rate. In this study, our goal was to identify the function and associated targets of SARS-CoV-2 from circulating monocytes in the blood and peripheral blood mononuclear cell (PBMC) dataset of patients with COVID-19. METHODS: The Gene Expression Omnibus database (GSE164805 and GSE180594) was used to identify differentially expressed genes (DEGs). Gene ontology function analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed using the DAVID database. RESULTS: Gene ontology analysis of DEG revealed that GSE164805 and GSE180594 were involved in the regulation of cell migration, upregulation of cell proliferation, and in the activation of the mitogen-activated protein kinase signaling pathway. Kyoto Encyclopedia of Genes and Genomes analysis of GSE164805 revealed that the DEGs were enriched in peroxisome, melanogenesis, and actin regulation. Peroxisome genes were highly expressed in patients with mild and severe COVID-19. Bioinformatics analysis to compare GSE180594 and public data for the single-cell atlas of the peripheral immune response in patients with COVID-19 showed that interferon-associated genes were highly increased in acute COVID-19 PBMC and in CD14+ and CD16+ monocytes from patients with COVID-19. CONCLUSIONS: We comprehensively analyzed the blood cell gene expression profile data of patients with COVID-19 using bioinformatics methods to preliminary understand the functions and associated targets of DEGs in the blood cells of these patients. Thus, our data provide targets for potential therapies against COVID-19.
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Single-cell transcriptomic analysis revealing changes in retinal cell subpopulation levels and the pathways involved in diabetic retinopathy.
BACKGROUND: Diabetic retinopathy (DR) is a common microvascular complication of diabetes and one of the most common causes of visual impairment and blindness. However, it is not yet known how abnormal retinal cell subpopulations contribute to disease progression. METHODS: In this study, we used the Gene Expression Omnibus database to construct a single-cell atlas of DR and healthy samples to explore changes in the abundance of different cell subpopulations, and identify the molecular pathways potentially involved in DR. RESULTS: Our results showed that DR was associated with significantly reduced numbers of bipolar cells, Müller glia, retinal pigment epithelial cells, and cone photoreceptors, but was also associated with significantly greater numbers of pericytes, rod photoreceptors, anaplastic cells, and microglia. Our results suggest that subpopulations of Müller glia, microglia, endothelial cells, and bipolar cells in DR tissues may be involved in various oxidative stress- and inflammation-related pathways. CONCLUSIONS: In summary, we showed that Müller glia, endothelial cells, microglia, and bipolar cells in DR tissues are involved in oxidative stress- and inflammation-related pathways, which may contribute to the progression of the disease and ultimately lead to visual impairment and blindness. This study will provide a theoretical basis for further exploring the specific mechanism of DR.
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Single-cell transcriptome landscape of zebrafish liver reveals hepatocytes and immune cell interactions in understanding nonalcoholic fatty liver disease.
Nonalcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease in the world. Immunity is the major contributing factor in NAFLD; however, the interaction of immune cells and hepatocytes in disease progression has not been fully elucidated. As a popular species for studying NAFLD, zebrafish, whose liver is a complex immune system mediated by immune cells and non-immune cells in maintaining immune tolerance and homeostasis. Understanding the cellular composition and immune environment of zebrafish liver is of great significance for its application in NAFLD. Here, we established a liver atlas that consists of 10 cell types using single-cell RNA sequencing (scRNA-seq). By examining the heterogeneity of hepatocytes and analyzing the expression of NAFLD-associated genes in the specific cluster, we provide a potential target cell model to study NAFLD. Additionally, our analysis identified two subtypes of distinct resident macrophages with inflammatory and non-inflammatory functions and characterized the successive stepwise development of T cell subclusters in the liver. Importantly, we uncovered the possible regulation of macrophages and T cells on target cells of fatty liver by analyzing the cellular interaction between hepatocytes and immune cells. Our data provide valuable information for an in-depth study of immune cells targeting hepatocytes to regulate the immune balance in NAFLD.