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: 4 932
Страница 270 из 494-
SPP1+ neutrophil and exhausted CD8⁺ T cells infiltration predicting the early recurrence of hepatocellular carcinoma patients after R0 resection.
BACKGROUND: Early recurrence of liver cancer is significantly associated with poor prognosis. Further dissection of the cellular heterogeneity in the tumor microenvironment (TME) of patients at high risk of early recurrence after liver cancer surgery is highly necessary. METHODS: This study integrated clinical data, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq data to reveal and verify a TME characteristic associated with high risk of early recurrence after liver cancer surgery. RESULTS: Clinical analysis of 402 patients identified neutrophil-to-lymphocyte ratio ≥3, alpha-fetoprotein ≥400 ng/mL, and microvascular invasion as independent predictors of early recurrence. scRNA-seq of 28 hepatocellular carcinoma (HCC) samples revealed nine neutrophil subtypes, with SPP1⁺ neutrophils (enriched in TNFα/NF-κB, hypoxia, and TGF-β pathways) and exhausted CD8⁺ T cells (expressing PD-1 and CTLA-4) significantly infiltrating the TME of patients with early recurrence. These findings were corroborated in an independent validation cohort comprising scRNA-seq data from 17 HCC tissues and 7 matched peripheral blood samples. Spatial analysis demonstrated their co-localization at tumor margins, forming an immunosuppressive barrier. Ligand-receptor interaction analysis highlighted SPP1-CD44, TGFB1-TGFBR, and TNF-TNFRSF axes as key mediators of neutrophil-T cell crosstalk, inducing T cell dysfunction. Validation in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma cohort comprising 365 patients confirmed that high infiltration of SPP1⁺ neutrophils and exhausted CD8⁺ T cells was significantly correlated with shorter recurrence-free (P < 0.0001) and overall survival (P < 0.0001). CONCLUSIONS: This study shows that the infiltration of SPP1⁺ neutrophils and exhausted CD8⁺ T cells is associated with a high risk of early recurrence in HCC. SPP1⁺ neutrophils may serve as key mediator of immune suppression through spatial interactions with exhausted CD8⁺ T cells, suggesting that they could be potential therapeutic targets to mitigate early recurrence in HCC.
-
m6A related metabolic genes in breast cancer and their relationship with prognosis.
Breast cancer (BC) is the most prevalent malignancy among women, with incidence rates rising annually. N6-methyladenosine (m6A) modification has been recognized as a key regulator in the onset and progression of BC. Nevertheless, the role of m6A-associated metabolic genes (mMGs) in BC regulation remains insufficiently understood. In this study, we first analyzed and clustered single-cell transcriptomic (scRNA-seq) data from the peripheral blood of BC patients. Differentially expressed genes (DEGs) across various cell populations were intersected with mMGs to identify differentially expressed mMGs (DEmMGs). The AUCell algorithm was employed to score DEmMGs across cell populations, followed by subgroup clustering of high-scoring cell types. Additionally, DEGs from BC and control transcriptomic (RNA-seq) data in The Cancer Genome Atlas (TCGA) were intersected with DEmMGs. BC subtypes were identified based on the expression levels of overlapping genes, and differences in survival rates and immune microenvironment characteristics were examined across subtypes. A BC risk model was constructed using Lasso-Cox regression, and variations in prognosis, tumor mutational burden, immune cell infiltration, and drug sensitivity were explored. Finally, real-time quantitative PCR (qRT-PCR) and Western blot (WB) were used to validate the identified prognostic genes. NDUFAB1, VDAC1, TYMP, UGDH, ATP6AP1, and ALDH2 showed consistent and significant expression differences between the BC and control groups. This study's identification of key prognostic genes and the development of a risk model offer potential new targets for therapeutic intervention and clinical management of BC.
-
Protocol for generating and characterizing Matrigel-free prostate cancer patient-derived organoids.
Here, we present a protocol for the generation of prostate cancer patient-derived organoids (PCa PDOs) in Matrigel-free conditions. We describe steps for sample selection, pathological resection, and tissue dissociation. We then detail procedures for the generation, maintenance, and characterization of the PCa PDOs. Finally, we provide guidance for the processing of PDOs for single-cell RNA sequencing (scRNA-seq) analysis and using the prostate PDO single-cell atlas (PPScA) for both novice and advanced bioinformatic users. For complete details on the use and execution of this protocol, please refer to Dolgos et al.1.
-
Characterization of immature ovarian teratomas through single-cell transcriptome.
INTRODUCTION: Immature ovarian teratomas are a type of malignant germ cell tumor composed of complicated cell types and are characterized by pathological features of immature neuroectodermal tubules/rosettes. However, there is a lack of understanding of patient-derived immature ovarian teratomas (PDT) at the single cell level. Moreover, whether stem cell lines derived from immature teratomas (CDT) can be used as models for research on PDT remains to be elucidated. METHODS: Single-cell RNA sequencing (scRNA-seq) and subsequent bioinformatic analysis was performed on three patient-derived immature ovarian teratomas (PDT) samples to reveal the heterogeneity, evolution trajectory, and cell communication within the tumor microenvironment of PDT. Validations were conducted in additional seven samples through multiplex immunofluorescence. RESULT: A total of qualified 22,153 cells were obtained and divided into 28 clusters, which can match to the scRNA-seq annotation of CDT as well as human fetal Cell Atlas, but with higher heterogeneity and more prolific cell-cell crosstalk. Radial glia cells (tagged by SOX2) and immature neuron (tagged by DCX) exhibited mutually exclusive expression and differentiated along distinct evolutionary trajectory from cycling neural progenitors. Proportions of these neuroectodermal cell subtypes may play important roles in PDT through contributing to the internal heterogeneity of PDTs. Moreover, the immune cells in PDTs were infiltrated rather than teratoma-derived, with more abundant macrophage in immature neuron than those in radial glia cells, and the infiltrated macrophage subtypes (i.e., M1 and M2) were significantly correlated to clinical grade. Overall, suppressed evolution process and transcriptome regulation in neuroectodermal cells, reduced cell-cell crosstalk, higher M1/M2 proportion ratio, and enhanced T cell effects in tumor microenvironment are enriched in patients with favorable prognosis. DISCUSSION: This study provides a comprehensive profile of PDT at the single cell level, shedding light on the heterogeneity and evolution of neuroectodermal cells within PDTs and the role of immune cells within the tumor microenvironment. Also, our findings highlight the potential usage of CDTs as a model for research on PDT.
-
A multi-omic atlas in the African turquoise killifish reveals increased glucocorticoid signaling as a hallmark of brain aging.
Aging is the leading risk factor for cognitive impairment and neurodegeneration, yet molecular changes that unfold in the brain over time, and how they drive this vulnerability, remain unclear. The naturally short-lived African turquoise killifish (Nothobranchius furzeri) offers a powerful model to understand brain aging on an accelerated timescale and test the impact of potential interventions. Here, we present a multi-omic atlas of brain aging of female and male African turquoise killifish from 2 independent genetic strains of different captive lifespans, encompassing single-nuclei RNA-seq, single nuclei ATAC-seq, and bulk ATAC-seq to capture transcriptional and regulatory changes. Interestingly, our atlas indicates that aging leads to a significant expansion of microglia numbers, regardless of sex or strain, which we independently validate using in-situ hybridization. In addition, we identify robust and conserved gene regulation changes, that are consistent with activation of glucocorticoid signalling as a hallmark (and potential driver) of vertebrate brain aging. Furthermore, pharmacological inhibition of glucocorticoid receptor activity starting at middle-age led to significant rescue of key molecular and cellular aging phenotypes. Thus, our study provides a powerful resource and framework to leverage the African turquoise killifish and rapidly uncover actionable pathways driving brain aging.
-
Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing.
Labor is a complex physiological process requiring a well-orchestrated dialogue between the mother and fetus. However, the cellular contributions and communications that facilitate maternal-fetal cross-talk in labor have not been fully elucidated. Here, single-cell RNA sequencing (scRNA-seq) was applied to decipher maternal-fetal signaling in the human placenta during term labor. First, a single-cell atlas of the human placenta was established, demonstrating that maternal and fetal cell types underwent changes in transcriptomic activity during labor. Cell types most affected by labor were fetal stromal and maternal decidual cells in the chorioamniotic membranes (CAMs) and maternal and fetal myeloid cells in the placenta. Cell-cell interaction analyses showed that CAM and placental cell types participated in labor-driven maternal and fetal signaling, including the collagen, C-X-C motif ligand (CXCL), tumor necrosis factor (TNF), galectin, and interleukin-6 (IL-6) pathways. Integration of scRNA-seq data with publicly available bulk transcriptomic data showed that placenta-derived scRNA-seq signatures could be monitored in the maternal circulation throughout gestation and in labor. Moreover, comparative analysis revealed that placenta-derived signatures in term labor were mirrored by those in spontaneous preterm labor and birth. Furthermore, we demonstrated that early in gestation, labor-specific, placenta-derived signatures could be detected in the circulation of women destined to undergo spontaneous preterm birth, with either intact or prelabor ruptured membranes. Collectively, our findings provide insight into the maternal-fetal cross-talk of human parturition and suggest that placenta-derived single-cell signatures can aid in the development of noninvasive biomarkers for the prediction of preterm birth.
-
Unified Mouse and Human Kidney Single-Cell Expression Atlas Reveal Commonalities and Differences in Disease States.
SIGNIFICANCE STATEMENT: Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. The authors characterized 18 different mouse kidney disease models at both bulk and single-cell gene expression levels and compared single-cell gene expression data from diabetic kidney disease (DKD) mice and from patients with DKD. Although single cell-level gene expression changes were mostly model-specific, different disease models showed similar changes when compared at a pathway level. The authors also found that changes in fractions of cell types are major drivers of bulk gene expression differences. Although the authors found only a small overlap of single cell-level gene expression changes between the mouse DKD model and patients, they observed consistent pathway-level changes. BACKGROUND: Mouse models have been widely used to understand kidney disease pathomechanisms and play an important role in drug discovery. However, these models have not been systematically analyzed and compared. METHODS: We analyzed single-cell RNA sequencing data (36 samples) and bulk gene expression data (42 samples) from 18 commonly used mouse kidney disease models. We compared single-nucleus RNA sequencing data from a mouse diabetic kidney disease model with data from patients with diabetic kidney disease and healthy controls. RESULTS: We generated a uniformly processed mouse single-cell atlas containing information for nearly 300,000 cells, identifying all major kidney cell types and states. Our analysis revealed that changes in fractions of cell types are major drivers of differences in bulk gene expression. Although gene expression changes at the single-cell level were mostly model-specific, different disease models showed similar changes when compared at a pathway level. Tensor decomposition analysis highlighted the important changes in proximal tubule cells in disease states. Specifically, we identified important alterations in expression of metabolic and inflammation-associated pathways. The mouse diabetic kidney disease model and patients with diabetic kidney disease shared only a small number of conserved cell type-specific differentially expressed genes, but we observed pathway-level activation patterns conserved between mouse and human diabetic kidney disease samples. CONCLUSIONS: This study provides a comprehensive mouse kidney single-cell atlas and defines gene expression commonalities and differences in disease states in mice. The results highlight the key role of cell heterogeneity in driving changes in bulk gene expression and the limited overlap of single-cell gene expression changes between animal models and patients, but they also reveal consistent pathway-level changes.
-
SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.
BACKGROUND: Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. RESULTS: The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. CONCLUSIONS: SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.
-
Development of a relapse-related RiskScore model to predict the drug sensitivity and prognosis for patients with ovarian cancer.
BACKGROUND: Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC. METHODS: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with in vitro assays. RESULTS: The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC50 values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, KRT19 silencing markedly inhibited the invasion and migration of OC cells. CONCLUSION: This study established a relapse-related RiskScore model based on five prognostic genes (LDHA, NOP58, NMU, KRT19, and RPS23), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies.
-
[Single-cell transcriptome analysis of multigrade glioma heterogeneity and immune microenvironment revealed potential prognostic biomarkers].
Glioma, the most common intrinsic tumor of the central nervous system, is characterized by its high incidence and poor prognosis. The aim of this study was to identify differentially expressed genes (DEGs) between glioblastoma multiforme (GBM) and low-grade glioma (LGG) to explore prognostic factors of different grades of gliomas. Single-cell transcriptome sequencing data of gliomas were collected from the NCBI Gene Expression Omnibus (GEO), which included a total of 29 097 cell samples from three datasets. For the analysis of human gliomas of different grades, 21 071 cells were obtained by filtering, and 70 genes were screened from differentially expressed genes by gene ontology (GO) analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, from which the gene DLL3 was focused by reviewing the literature. The TCGA-based gene expression profiling interactive analysis (GEPIA) database was used to explore the survival curves of genes in LGG and GBM, and the gene expression profiling interactive analysis and tumor immune estimation resource (TIMER) database was used to study the expression of key genes in gliomas of different grades, predicting biomarkers that were closely related to immunotherapy. The cBioPortal database was used to explore the relationship between DLL3 expression and 25 immune checkpoints. Gene set enrichment analysis (GSEA) further identified pathways associated with central genes. Finally, the efficacy of biomarkers in prognosis and prediction was validated in the Chinese glioma genome atlas (CGGA). These results demonstrated that prognostic genes are associated with tumor proliferation and progression. Analysis of biological information and survival suggested that these genes might serve as a promising prognostic biomarker and as new targets for selecting therapeutic strategies.