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: 4 928
Страница 208 из 493-
Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level.
BACKGROUND: Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain. RESULTS: Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not. The results showed increased infiltration of immune cells such as monocyte macrophages, T cells, and natural killer (NK) cells in patients who did not respond to estrogen therapy. Our findings indicate that distinct fibroblast subsets are implicated in the modulation of the Wnt, Hippo, and Hedgehog signaling pathways, as evidenced by functional enrichment analyses. This may have implications for the therapeutic efficacy in patients with IUA. Furthermore, we delineated the markers and transcriptional status of different macrophage subsets and identified two cell clusters, CXCL10high and CCL4L2high macrophage subsets, which are intimately associated with inflammation and fibrosis. The state of fibrosis and inflammatory response in human endometrial tissues with disparate treatment outcomes is revealed, and providing evidence to clarify the underlying determinants of sensitivity to estrogen therapy. CONCLUSIONS: We described the transcriptional status of different cell subtypes in the two groups of patients, providing new ideas for exploring the molecular mechanism of the difference in the effectiveness of estrogen therapy in patients, and providing theoretical basis for providing precise and individualized treatment plans for IUA patients.
-
Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis.
We provide a single-cell atlas of idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease, by profiling 312,928 cells from 32 IPF, 28 smoker and nonsmoker controls, and 18 chronic obstructive pulmonary disease (COPD) lungs. Among epithelial cells enriched in IPF, we identify a previously unidentified population of aberrant basaloid cells that coexpress basal epithelial, mesenchymal, senescence, and developmental markers and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells, we identify an ectopically expanded cell population transcriptomically identical to bronchial restricted vascular endothelial cells in IPF. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells, we identify IPF myofibroblasts and invasive fibroblasts with partially overlapping cells in control and COPD lungs. Last, we confirm previous findings of profibrotic macrophage populations in the IPF lung. Our comprehensive catalog reveals the complexity and diversity of aberrant cellular populations in IPF.
-
DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.
Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.
-
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region.
The hypothalamus controls essential social behaviors and homeostatic functions. However, the cellular architecture of hypothalamic nuclei-including the molecular identity, spatial organization, and function of distinct cell types-is poorly understood. Here, we developed an imaging-based in situ cell-type identification and mapping method and combined it with single-cell RNA-sequencing to create a molecularly annotated and spatially resolved cell atlas of the mouse hypothalamic preoptic region. We profiled ~1 million cells, identified ~70 neuronal populations characterized by distinct neuromodulatory signatures and spatial organizations, and defined specific neuronal populations activated during social behaviors in male and female mice, providing a high-resolution framework for mechanistic investigation of behavior circuits. The approach described opens a new avenue for the construction of cell atlases in diverse tissues and organisms.
-
A spatially resolved single-cell landscape of colorectal cancer liver metastasis reveals a stromal-tumor glycolytic signaling interaction.
BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer mortality, with liver metastasis being the principal determinant of poor prognosis, but the spatial mechanisms orchestrating metastatic niches remain elusive. METHOD: To dissect the molecular and spatial dynamics of CRC progression, we constructed an integrative atlas using 35 single-cell RNA-seq datasets and spatial transcriptomics from primary tumors, liver metastases, and matched normal tissues. Malignant epithelial subpopulations were stratified via inferCNV and CytoTRACE analyses. Stromal-tumor interactions were dissected using CellChat and NicheNet, with functional validation through in vitro co-culture and immunohistochemistry. RESULT: We identified a transcriptionally distinct epithelial subpopulation, termed high-malignancy CRC (High-M CRC), enriched in metastatic lesions and characterized by enhanced stemness, MYC-driven transcriptional activity, and glycolytic reprogramming. Stromal-tumor interaction analyses revealed that cancer-associated fibroblasts (CAFs), particularly matrix CAFs (mCAFs), promote malignant progression via the HGF-MET-MYC signaling axis. Spatial transcriptomic mapping confirmed the physical proximity and molecular co-localization of High-M CRC cells and mCAFs, along with enriched glycolysis and MYC expression at the cell-cell interface. In vitro functional validation demonstrated that CAF-derived HGF activates MET-MYC signaling in CRC cells, enhancing their invasion and proliferation-effects reversible by MET knockdown. CONCLUSION: We unveil a spatially organized metabolic niche driven by stromal-tumor HGF-MET-MYC signaling. These findings offer novel insights into the stromal-tumor interaction and suggest actionable targets for therapeutic intervention in CRC.
-
Integrative multi-omic profiling of adult mouse brain endothelial cells and potential implications in Alzheimer's disease.
The blood-brain barrier (BBB) is primarily manifested by a variety of physiological properties of brain endothelial cells (ECs), but the molecular foundation for these properties remains incompletely clear. Here, we generate a comprehensive molecular atlas of adult brain ECs using acutely purified mouse ECs and integrated multi-omics. Using RNA sequencing (RNA-seq) and proteomics, we identify the transcripts and proteins selectively enriched in brain ECs and demonstrate that they are partially correlated. Using single-cell RNA-seq, we dissect the molecular basis of functional heterogeneity of brain ECs. Using integrative epigenomics and transcriptomics, we determine that TCF/LEF, SOX, and ETS families are top-ranked transcription factors regulating the BBB. We then validate the identified brain-EC-enriched proteins and transcription factors in normal mouse and human brain tissue and assess their expression changes in mice with Alzheimer's disease. Overall, we present a valuable resource with broad implications for regulation of the BBB and treatment of neurological disorders.
-
Integrative multi-omics and Mendelian randomization analysis reveal SPP1+ tumor-associated macrophage-driven prognostic signature for hepatocellular carcinoma.
BACKGROUND: The SPP1+ tumor-associated macrophages (TAMs) have been implicated in tumor metastasis and immune evasion. However, the prognostic significance of SPP1+ TAMs in hepatocellular carcinoma (HCC) remains largely unexplored. This study aimed to identify SPP1+ TAMs-related genes and construct a model to predict overall survival (OS) in HCC patients. METHODS: Single-cell RNA sequencing (scRNA-seq) datasets from HCC patients were analyzed to identify SPP1+ TAMs. SPP1+ TAMs-related risk score (STRS) was developed using Mendelian randomization (MR) analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression. HCC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were stratified into high- and low-STRS groups based on STRS. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and functional enrichment analysis were performed to assess the prognostic value of STRS. RESULTS: SPP1+ TAMs exhibited strong associations with immunosuppressive functions. 16 SPP1+ TAMs-related genes were used to construct STRS. Patients in the high-STRS group had significantly worse OS than those in the low-STRS group (p < 0.001). ROC analysis demonstrated robust predictive power, with AUC values ranging from 0.685 to 0.748 for 1-year OS, 0.717 to 0.739 for 2-year OS, and 0.719 to 0.738 for 3-year OS. The STRS model also exhibited strong predictive capability for the distinction of drug resistance. CONCLUSION: This study identified SPP1+ TAMs-related genes as key prognostic indicators in HCC. The STRS model provides an effective tool for predicting patient survival and may facilitate personalized treatment strategies for HCC. These findings enhance the understanding of TAMs-driven immune modulation in HCC and highlight potential therapeutic targets for improving patient outcomes.
-
Massively parallel single-nucleus RNA-seq with DroNc-seq.
Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.
-
Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types.
BACKGROUND: In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors. METHODS: Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index. RESULTS: In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index. CONCLUSIONS: The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.
-
BDCD: a comprehensive Brain Disease Cell-cell communication Database.
Dysregulated cell-cell communication (CCC) is increasingly recognized as a driver of brain disease pathology, contributing to neuroinflammation, synaptic dysfunction, and neurodegeneration. Nevertheless, existing resources remain limited in brain specificity, regional coverage, and functional annotation. To address this gap, we develop the Brain Disease Cell-cell communication Database (BDCD), the first comprehensive resource focused on CCC networks across major brain diseases. BDCD integrates 38 manually curated datasets, comprising 8 519 425 single cells from single-cell RNA-seq studies and 140 744 spots from spatial transcriptomic maps, spanning 14 brain regions and 13 canonical cell types covering Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, and multiple sclerosis. BDCD reconstructs more than 495 000 ligand-receptor interaction events and links them to structural features, genetic associations, pathways, and therapeutic modulators, including 6100 single nucleotide polymorphisms from genome-wide association studies, 3350 drugs, and 72 477 allosteric modulators. This comprehensive atlas enables cross-disease comparison and supports dynamic hypothesis generation, providing a foundation for mechanistic insights and therapeutic discovery. BDCD is publicly available at https://bioinfo.uth.edu/bdcd/. Database URL: https://bioinfo.uth.edu/bdcd/.