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 217 из 493-
Cross-Tissue Transcriptomic Convergence Identifies a Leuko-Cyte-Shared, Myeloid-Enriched Inflammatory Program in Frailty and Osteoarthritis.
Frailty and osteoarthritis (OA) frequently coexist in older adults, yet the extent to which they share molecular programs across tissues remains unclear. We integrated bulk transcriptomics, peripheral immune and OA meniscal single-cell atlases, inferred cell-cell communication, and an in vitro myeloid-chondrocyte conditioned-medium model. Frailty-associated vastus lateralis and OA synovium shared a 16-gene program enriched for inflammatory and migratory processes, which was refined by protein-protein interaction analysis into a 15-gene hub module (Hub-15). Donor-aware pseudobulk analysis showed coordinated Hub-15 upregulation in CD8 T, natural killer, B, and monocyte/macrophage-like cells, with the highest scores in monocyte/macrophage populations. In the meniscal atlas, donor-level analysis of 7 normal and 6 OA donors identified a 20.5 percentage point lower proportion of inflammatory/stress cells and a 17.3 percentage point higher proportion of fibrochondrocyte-like cells in OA (both false discovery rate = 0.010), whereas state-specific Hub-15 differences were not significant after correction. Exploratory communication analysis nominated directionally consistent candidate myeloid-to-structural signaling axes, although none remained significant after multiple-testing correction. Conditioned medium from cytokine-primed THP-1 cells elicited inflammatory and matrix-catabolic responses in C28/I2 cells. These findings identify a cross-tissue, leukocyte-shared, and myeloid-enriched inflammatory program that converges across independent frailty and OA cohorts, providing a hypothesis-generating framework for future mechanistic testing.
-
Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for sustainable and regenerative agriculture.
The plant root system architecture (RSA) functions in anchorage, acquisition of water and mineral nutrients, and exhibits pronounced phenotypic plasticity in response to the spatiotemporal heterogeneity of the soil environment. Resolving the regulatory networks that underpin root development is therefore a prerequisite for improving stress tolerance and yield. Single-cell RNA sequencing (scRNA-seq) resolves transcriptional landscapes at cellular resolution, discriminating root zonation, lineage trajectories and cell-type-restricted responses to environmental signals. Coupling scRNA-seq to epigenomic, proteomic and metabolomic profiling of the same cell populations links chromatin state to transcript, protein and metabolite output and therefore exposes the regulatory layers that govern root development and plasticity. Machine-learning models trained on single-cell matrices assist cell-type annotation, gene regulatory network inference and prioritization of candidate loci for precision breeding, although their output remains contingent on reference datasets that are still sparse for crop species. This review examines what scRNA-seq, spatial transcriptomics and machine learning have so far established about root cellular heterogeneity and regulatory architecture. This delimits the technical constraints that presently bound their application to crop improvement including protoplasting bias, transcript dropout and incomplete state of crop reference atlases to support sustainable and regenerative agriculture.
-
Understanding the immune microenvironment of ovarian cancer.
Ovarian cancer remains the most lethal gynecological malignancy, yet immune checkpoint inhibitors (ICIs) demonstrate limited clinical efficacy due to profound CD8+- T cell exhaustion within a highly heterogeneous tumor immune microenvironment (TME). Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of this complex ecosystem by resolving rare immune subsets, reconstructing lineage trajectories, and mapping dynamic intercellular signaling networks. Emerging single-cell atlases reveal that terminally exhausted T cells evolve from plastic progenitor populations, notably GZMK+- subsets, while ascitic fluid functions as a distinct immunomodulatory reservoir that continuously shapes intratumoral immune dynamics. Furthermore, multi-compartment transcriptomic profiling uncovers myeloid-lymphoid crosstalk that actively sustains localized immunosuppression and drives therapeutic resistance. These high-resolution molecular insights are increasingly leveraged to accurately predict ICI responsiveness, rationally design novel combinatorial regimens, and strategically guide next-generation adoptive cellular therapies. This review summarizes the scRNA-seq-driven advances in characterizing the ovarian cancer TME, emphasizing how single-cell resolution dismantles immunosuppressive barriers to T cell reinvigoration and establishes a robust translational framework for precision immunotherapy.
-
Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding.
Glioblastoma is a highly aggressive primary brain tumor with glioblastoma stem cells (GSCs) enforcing the intra-tumoral hierarchy. Plasma cells (PCs) are critical effectors of the B-lineage immune system, but their roles in glioblastoma remain largely unexplored. Here, we leverage single-cell RNA and B cell receptor sequencing of tumor-infiltrating B-lineage cells and reveal that PCs are aberrantly enriched in the glioblastoma-infiltrating B-lineage population, experience low level of somatic hypermutation, and are associated with poor prognosis. PCs secrete immunoglobulin G (IgG), which stimulates GSC proliferation via the IgG-FcγRIIA-AKT-mTOR axis. Disruption of IgG-FcγRIIA paracrine communication inhibits GSC proliferation and self-renewal. Glioblastoma-infiltrating PCs are recruited to GSC niches via CCL2-CCR2 chemokine program. GSCs further derive pro-proliferative signals from broadly utilized monoclonal antibody-based immune checkpoint inhibitors via FcγRIIA signaling. Our data generate an atlas of B-lineage cells in glioblastoma with a framework for combinatorial targeting of both tumor cell-intrinsic and microenvironmental dependencies.
-
Embryo-scale reverse genetics at single-cell resolution.
The maturation of single-cell transcriptomic technologies has facilitated the generation of comprehensive cellular atlases from whole embryos1-4. A majority of these data, however, has been collected from wild-type embryos without an appreciation for the latent variation that is present in development. Here we present the 'zebrafish single-cell atlas of perturbed embryos': single-cell transcriptomic data from 1,812 individually resolved developing zebrafish embryos, encompassing 19 timepoints, 23 genetic perturbations and a total of 3.2 million cells. The high degree of replication in our study (eight or more embryos per condition) enables us to estimate the variance in cell type abundance organism-wide and to detect perturbation-dependent deviance in cell type composition relative to wild-type embryos. Our approach is sensitive to rare cell types, resolving developmental trajectories and genetic dependencies in the cranial ganglia neurons, a cell population that comprises less than 1% of the embryo. Additionally, time-series profiling of individual mutants identified a group of brachyury-independent cells with strikingly similar transcriptomes to notochord sheath cells, leading to new hypotheses about early origins of the skull. We anticipate that standardized collection of high-resolution, organism-scale single-cell data from large numbers of individual embryos will enable mapping of the genetic dependencies of zebrafish cell types, while also addressing longstanding challenges in developmental genetics, including the cellular and transcriptional plasticity underlying phenotypic diversity across individuals.
-
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets.
BACKGROUND: High throughput single-cell transcriptomic technology produces massive high-dimensional data, enabling high-resolution cell type definition and identification. To uncover the expressional patterns beneath the big data, a transcriptional landscape searching algorithm at a single-cell level is desirable. RESULTS: We explored the feasibility of using DenseFly algorithm for cell searching on scRNA-seq data. DenseFly is a locality sensitive hashing algorithm inspired by the fruit fly olfactory system. The experiments indicate that DenseFly outperforms the baseline methods FlyHash and SimHash in classification tasks, and the performance is robust to dropout events and batch effects. CONCLUSION: We developed a method for mapping cells across scRNA-seq datasets based on the DenseFly algorithm. It can be an efficient tool for cell atlas searching.
-
A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation.
The human brain undergoes protracted postnatal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell-type-specific gene expression dynamics. Here, using single-nucleus RNA sequencing on temporal lobe tissue, including samples of African ancestry, we build a joint pediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between pediatric and adult cell subtypes, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in pediatric tissue. The resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.
-
Integrated Multi-Omics Analysis Identifies SRI as a Critical Target Promoting Gastric Cancer Progression and Associated with Poor Prognosis.
Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0-C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion.
-
Spatial Transcriptomics: Constructing a Single-Cell Resolution Transcriptome-Wide Expression Atlas.
The method described here aims at the construction of a single-cell resolution gene expression atlas for an animal or tissue, combining in situ hybridization (ISH) and single-cell mRNA-sequencing (scRNAseq).A high resolution and medium-coverage gene expression atlas of an animal or tissue of interest can be obtained by performing a series of ISH experiments, followed by a process of image registration and gene expression averaging. Using the overlapping fraction of the genes, concomitantly obtained scRNAseq data can be fitted into the spatial context of the gene expression atlas, complementing the coverage by genes.
-
A Comprehensive Immune Cell Atlas of Cystic Kidney Disease Reveals the Involvement of Adaptive Immune Cells in Injury-Mediated Cyst Progression in Mice.
BACKGROUND: Inducible disruption of cilia-related genes in adult mice results in slowly progressive cystic disease, which can be greatly accelerated by renal injury. METHODS: To identify in an unbiased manner modifier cells that may be influencing the differential rate of cyst growth in injured versus non-injured cilia mutant kidneys at a time of similar cyst severity, we generated a single-cell atlas of cystic kidney disease. We conducted RNA-seq on 79,355 cells from control mice and adult-induced conditional Ift88 mice (hereafter referred to as cilia mutant mice) that were harvested approximately 7 months post-induction or 8 weeks post 30-minute unilateral ischemia reperfusion injury. RESULTS: Analyses of single-cell RNA-seq data of CD45+ immune cells revealed that adaptive immune cells differed more in cluster composition, cell proportion, and gene expression than cells of myeloid origin when comparing cystic models with one another and with non-cystic controls. Surprisingly, genetic deletion of adaptive immune cells significantly reduced injury-accelerated cystic disease but had no effect on cyst growth in non-injured cilia mutant mice, independent of the rate of cyst growth or underlying genetic mutation. Using NicheNet, we identified a list of candidate cell types and ligands that were enriched in injured cilia mutant mice compared with aged cilia mutant mice and non-cystic controls that may be responsible for the observed dependence on adaptive immune cells during injury-accelerated cystic disease. CONCLUSIONS: Collectively, these data highlight the diversity of immune cell involvement in cystic kidney disease.