Браузер не даёт сохранять данные (например, приватный режим): закладки пропадут после закрытия вкладки.
Поиск статей PubMed
Ключевые слова, автор, журнал, годы — результаты из PubMed, аннотации с переводом на русский, закладки в браузере.
Найдено: 4 939
Страница 287 из 494-
Mapping the immune microenvironment for mandibular alveolar bone homeostasis at single-cell resolution.
Alveolar bone is the thickened ridge of jaw bone that supports teeth. It is subject to constant occlusal force and pathogens invasion, and is therefore under active bone remodeling and immunomodulation. Alveolar bone holds a distinct niche from long bone considering their different developmental origin and postnatal remodeling pattern. However, a systematic explanation of alveolar bone at single-cell level is still lacking. Here, we construct a single-cell atlas of mouse mandibular alveolar bone through single-cell RNA sequencing (scRNA-seq). A more active immune microenvironment is identified in alveolar bone, with a higher proportion of mature immune cells than in long bone. Among all immune cell populations, the monocyte/macrophage subpopulation most actively interacts with mesenchymal stem cells (MSCs) subpopulation. Alveolar bone monocytes/macrophages express a higher level of Oncostatin M (Osm) compared to long bone, which promotes osteogenic differentiation and inhibits adipogenic differentiation of MSCs. In summary, our study reveals a unique immune microenvironment of alveolar bone, which may provide a more precise immune-modulatory target for therapeutic treatment of oral diseases.
-
Single-Cell Transcriptomics Identifies Heterogeneity of Mouse Mammary Gland Fibroblasts With Distinct Functions, Estrogen Responses, Differentiation Processes, and Crosstalks With Epithelium.
Fibroblasts have been shown to be one of the essential players for mammary gland organization. Here, we identify two major types of mouse mammary gland fibroblasts through single-cell RNA sequencing analysis: Dpp4 + fibroblasts and Dpp4 - fibroblasts. Each population exhibits unique functional characteristics as well as discrete localization in normal mouse mammary glands. Remarkably, estrogen, a crucial mediator of mammary gland organization, alters the gene expression profiles of fibroblasts in a population-specific manner, without distinct activation of estrogen receptor signaling. Further integrative analysis with the inclusion of five other publicly available datasets reveals a directional differentiation among the mammary gland fibroblast populations. Moreover, the combination with the mouse mammary epithelium atlas allows us to infer multiple potential interactions between epithelial cells and fibroblasts in mammary glands. This study provides a comprehensive view of mouse mammary gland fibroblasts at the single-cell level.
-
Single-cell transcriptome analysis identifies neutrophil-related prognostic signatures in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with neutrophils playing complex dual roles in tumor progression and immune regulation. The heterogeneous nature of the tumor immune microenvironment necessitates precise molecular characterization for improved prognostic stratification and therapeutic targeting. This study aimed to develop and validate a neutrophil-related prognostic signature for HCC by integrating single-cell and bulk transcriptomic data, in order to improve prognostic stratification and identify potential therapeutic targets. METHODS: We performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 52,841 high-quality cells from HCC patients and controls using GSE282701 dataset. Neutrophil-specific differentially expressed genes were identified through integrated bioinformatics approaches. A prognostic signature was constructed using least absolute shrinkage and selection operator (LASSO) regression and validated across The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=424) and GSE14520 (n=221) cohorts. Immune microenvironment characterization was performed using CIBERSORT deconvolution, while drug sensitivity predictions utilized the Genomics of Drug Sensitivity in Cancer (GDSC) database. RESULTS: Single-cell analysis identified 304 neutrophil-specific genes significantly associated with HCC pathogenesis. Cox regression and LASSO analysis selected 14 genes for the final prognostic model, effectively stratifying patients into high-risk and low-risk groups with significantly different overall survival across all cohorts (P<0.001). The model demonstrated robust predictive performance with area under the curve (AUC) values exceeding 0.70 in training cohorts and 0.60 in external validation. High-risk patients exhibited distinct immune infiltration patterns characterized by increased immunosuppressive cell populations and reduced anti-tumor immunity. Pathway enrichment analysis revealed significant activation of PI3K/AKT/mTOR signaling, DNA repair mechanisms, and inflammatory pathways in high-risk patients. Drug sensitivity analysis identified six therapeutic compounds with differential responses between risk groups, providing preliminary evidence for potential personalized treatment selection. CONCLUSIONS: This neutrophil-related prognostic signature provides novel insights into HCC immune microenvironment heterogeneity and represents a promising candidate tool for risk stratification and treatment guidance that warrants further prospective validation. The identified molecular markers may serve as potential therapeutic targets for precision medicine approaches in HCC management.
-
Characterizing microglial heterogeneity in autophagy impairment of Paraquat-induced Parkinson's disease-like neurodegeneration.
Parkinson's disease (PD) is a prevalent neurodegenerative condition influenced by environmental elements, notably Paraquat (PQ), which is one of the known risk factors. Impaired autophagy is a critical factor in the pathogenesis of PD, yet the cellular heterogeneity related to autophagy in PD has not been thoroughly investigated. Here, we established a PQ-induced PD-like neurodegeneration model and found that PQ impairs autophagy during experimental PD progression. Using single-cell RNA sequencing (scRNA-seq), we elucidated the autophagy-related transcriptomic landscapes in this model, identifying microglia as the central cell type associated with PQ-induced autophagy across all brain cell types. Additionally, microglial subtypes in the PQ-exposed model exhibited significant heterogeneity in gene expression characteristics, biological functions, and roles in autophagic regulation. PQ exposure induced potential genetic transformations between microglial subtypes, which may further disrupt their immune response and energy metabolism regulation functions. Subsequently, we validated the identity transformation of microglia revealed by scRNA-seq in both in vivo and in vitro PQ exposure models. Moreover, we identified a specific microglial subtype primarily responsible for the autophagy-related changes observed in the PQ-exposed model. The expression of the autophagic subtype marker gene Inpp5d may contribute to the regulation of PQ-induced autophagic impairment in BV2 cells. This study generates the first scRNA-seq atlas of autophagy in the context of PQ exposure, highlighting the heterogeneity of microglial subtypes and identifying an autophagy-specific microglial subtype as a central mechanism in the pathology of PQ-induced PD-like neurodegeneration.
-
Single-cell Sequencing Data Reveals Aggressive CD68-type Macrophages and Prognostic Models in Bladder Cancer.
BACKGROUND: The highly heterogeneous, complex pathological histology, and clinical phenotype in bladder cancer (BC) plague the prognostic management of BC to the present day. METHODS: This study was conducted using single-cell sequencing data from the gene expression omnibus (GEO) database (GSE135337). A descending, annotated analysis was performed to identify the cell types contributing to BC aggressiveness. BC cell sequencing data from The Cancer Genome Atlas (TCGA) database were then combined with univariate, least absolute shrinkage and selection operator (LASSO), multivariate COX regression analysis to identify biomarkers of BC prognosis to construct a BC. We identified biomarkers of BC prognosis to construct a prognostic risk guidance system for BC. The feedback of patients in different risk strata to immunotherapy was analyzed. Finally, the regulation of prognostic genes on cancer cell activity was verified in vitro by Western blot and cell counting kit-8 (CCK8) assays. RESULTS: Macrophages specifically expressing CD68 in BC were the cell type with the highest AUCell score, and CD68 was the biomarker of Tumor-associated macrophages (TAMs). CD68 macrophages were potentially the critical cell type in the aggressive BC subtype. Through univariate, LASSO, multivariate COX-based regression analysis. CTSS, GMFG, ANXA5, GSN, SLC2A3, and FTL were authenticated as prognostic biomarkers (p < 0.05) and composed the Risk Score. Patients in the low-risk group showed an excellent survival advantage (p < 0.01) and immunotherapy feedback. Additionally, inhibition of GSN expression decreased EMT activity to inhibit bladder cancer cell viability. CONCLUSION: In conclusion, this study provided feedback on the immune cell types associated with aggressiveness in BC. Importantly, a prognostic management system for BC was created based on the genes involved, providing more insight into the aggressive pathological phenotype as well as the prognosis of BC.
-
Single-cell atlas reveals a pro-metastatic RELB+ neutrophil-myeloid subset underlying lymph node metastasis in EGFR-wildtype LUAD.
BACKGROUND: Lymph node metastasis is a critical event in the progression of EGFR wildtype lung adenocarcinoma (LUAD), a subtype lacking effective targeted therapies and associated with poor prognosis. The specific myeloid cell subsets and molecular mechanisms driving LNM in this context remain poorly understood. METHODS: To elucidate the cellular drivers of LNM, we performed an integrated analysis of multi-cohort single-cell RNA sequencing data from EGFR-wildtype LUAD patients and bulk transcriptomic data from The Cancer Genome Atlas (TCGA). Pathological lymph node status from the bulk RNA-seq cohort was mapped to the single-cell transcriptomes using the UCell algorithm. Myeloid cell heterogeneity was analyzed via sub-clustering, and the transcription factor regulon activity was inferred using pySCENIC. The functional role of the key regulator RELB was validated using siRNA-mediated knockdown in neutrophil-tumor cell co-culture systems, assessed by qPCR, proliferation, clonogenic, migration, and invasion assays. A RELB signature score was constructed based on its target genes and validated in independent cohorts for prognostic, immunotherapeutic, and drug sensitivity assessment. RESULTS: Myeloid cells were significantly enriched in the LNM group. The process of sub-clustering identified a pro-metastatic subset of ELANE-positive neutrophils that was specifically expanded in LNM samples, linked to advanced N stage, higher clinical stage, and shorter overall survival. RELB was identified as a central regulator of ELANE + Neu through transcription factor analysis, showing significantly heightened regulon activity in LNM. Using a neutrophil-tumor cell co-culture system, we found that RELB knockdown in neutrophils attenuated inflammatory signaling in tumor cells and subsequently reduced their proliferative, clonogenic, migratory, and invasive capacities. A high score of the RELB signature was a strong predictor of poor prognosis and was associated with pro-tumorigenic pathways, and the creation of an immunosuppressive microenvironment. Additionally, RELB scores were associated with lower tumor mutational burden, poorer response to immunotherapy, different drug sensitivity patterns, and increased expression of several antibody-drug conjugate targets. CONCLUSION: The study highlights a pro-metastatic ELANE + neutrophil subpopulation, with RELB acting as its primary transcriptional regulator. The RELB signature may serve as a biomarker for prognosis and treatment response prediction, indicating a potential target for precision treatment in EGFR wildtype LUAD.
-
AtlasGrabber: a software facilitating the high throughput analysis of the human protein atlas online database.
BACKGROUND: The human protein atlas (HPA) is an online database containing large sets of protein expression data in normal and cancerous tissues in image form from immunohistochemically (IHC) stained tissue microarrays. In these, the tissue architecture is preserved and thus provides information on the spatial distribution and localization of protein expression at the cellular and extracellular levels. The database is freely available online through the HPA website but currently without support for large-scale screening and analysis of the images in the database. Features like spatial information are typically lacking in gene expression datasets from homogenized tissues or single-cell analysis. To enable high throughput analysis of the HPA database, we developed the AtlasGrabber software. It is available freely under an open-source license. Based on a predefined gene list, the software fetches the images from the database and displays them for the user. Several filters for specific antibodies or images enable the user to customize her/his image analysis. Up to four images can be displayed simultaneously, which allows for the comparison of protein expression between different tissues and between normal and cancerous tissues. An additional feature is the XML parser that allows the extraction of a list of available antibodies, images, and genes for specific tissues or cancer types from the HPA's database file. RESULTS: Compared to existing software designed for a similar purpose, ours provide more functionality and is easier to use. To demonstrate the software's usability, we identified six new markers of basal cells of the prostate. A comparison to prostate cancer showed that five of them are absent in prostate cancer. CONCLUSIONS: The HPA is a uniquely valuable database. By facilitating its usefulness with the AtlasGrabber, we enable researchers to exploit its full capacity. The loss of basal cell markers is diagnostic for prostate cancer and can help refine the histopathological diagnosis of prostate cancer. As proof of concept, with the AtlasGrabber we identified five new potential biomarkers specific for prostate basal cells which are lost in prostate cancer and thus can be used for prostate cancer diagnostics.
-
A Multi-Regional Single-nucleus Atlas of the Huntington's Disease Brain.
Huntington's disease (HD) is a neurodegenerative disorder caused by an abnormal expansion of the CAG trinucleotide repeat sequence in the HTT gene. Research on HD, a rare condition, relies heavily on donations of post-mortem brain tissue, which are extremely valuable yet difficult to obtain. While numerous studies have published single-nucleus RNA sequencing (snRNA-seq) data from the brain tissues of HD patients, such datasets remain scattered across various public repositories. A lack of standardized metadata and analytical workflows has hindered the integration and reutilization of these resources. To address this challenge, we constructed a curated, multi-brain-region, single-nucleus atlas of controls/HD patients by integrating 104 tissue samples from 39 HD patients and 34 controls, spanning six brain regions: the striatum (caudate and putamen), frontal cortex, cingulate cortex, nucleus accumbens, hippocampus, and cerebellum. This atlas comprises 330,892 cells that have been subjected to standardized quality control, batch effect correction and cell type annotation. Here, we present the first large-scale, multi-region single-nucleus reference atlas for HD, providing a comprehensive resource for the research community. It will serve as a critical data foundation for deepening the investigation of cell-type-specific molecular mechanisms underlying HD, facilitating cross-brain-region experimental validation, and enable the identification of novel therapeutic targets.
-
Expressional Subpopulation of Cancers Determined by G64, a Co-regulated Module.
Studies of cancer heterogeneity have received considerable attention recently, because the presence or absence of resistant sub-clones may determine whether or not certain therapeutic treatments are effective. Previously, we have reported G64, a co-regulated gene module composed of 64 different genes, can differentiate tumor intra- or inter-subpopulations in lung adenocarcinomas (LADCs). Here, we investigated whether the G64 module genes were also expressed distinctively in different subpopulations of other cancers. RNA sequencing-based transcriptome data derived from 22 cancers, except LADC, were downloaded from The Cancer Genome Atlas (TCGA). Interestingly, the 22 cancers also expressed the G64 genes in a correlated manner, as observed previously in an LADC study. Considering that gene expression levels were continuous among different tumor samples, tumor subpopulations were investigated using extreme expressional ranges of G64-i.e., tumor subpopulation with the lowest 15% of G64 expression, tumor subpopulation with the highest 15% of G64 expression, and tumor subpopulation with intermediate expression. In each of the 22 cancers, we examined whether patient survival was different among the three different subgroups and found that G64 could differentiate tumor subpopulations in six other cancers, including sarcoma, kidney, brain, liver, and esophageal cancers.
-
Dynamic dysregulation of retrotransposons in neurodegenerative diseases at the single-cell level.
Retrotransposable elements (RTEs) are common mobile genetic elements comprising ∼42% of the human genome. RTEs play critical roles in gene regulation and function, but how they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis. We identify cell type marker RTEs in neurons, astrocytes, oligodendrocytes, and oligodendrocyte precursor cells that are related to these diseases. The differential expression analysis reveals the landscape of dysregulated RTE expression, especially L1s, in excitatory neurons of multiple neurodegenerative diseases. Machine learning algorithms for predicting cell disease stage using a combination of RTE and gene expression features suggests dynamic regulation of RTEs in AD. Furthermore, we construct a single-cell atlas of retrotransposable elements in neurodegenerative disease (scARE) using these data sets and features. scARE has six feature analysis modules to explore RTE dynamics in a user-defined condition. To our knowledge, scARE represents the first systematic investigation of RTE dynamics at the single-cell level within the context of neurodegenerative diseases.