Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 898
Page 94 of 490-
Embryo-scale Visual Cell Sorting reveals a conserved transcriptomic signature of nucleolar size linked to proteostasis.
Nucleoli, nuclear speckles and other compartments regulate transcription, RNA processing, and chromatin organization within the nucleus, yet the relationship of their morphology to developmental gene expression programs in vivo is poorly understood. Here, we develop a high-throughput Visual Cell Sorting (VCS) workflow for fixed cells and nuclei that combines antibody-based photoconversion; GPU-accelerated, real-time image analysis; and three-level single-cell combinatorial indexing RNA-seq (sci-RNA-seq3) to link nuclear compartment morphology to single-nucleus transcriptomes at embryo scale. We use VCS to analyze and sort over 1 million mouse embryo-derived nuclei by nucleolar, nuclear speckle, or nuclear size and construct a transcriptional atlas annotated with nuclear compartment phenotypes. Nuclear compartment size varies both between and within lineages and is shaped by proliferation and differentiation. In extracellular matrix protein-producing cell types such as fibroblasts, chondrocytes, and osteoblasts, nucleolar enlargement is uncoupled from cell cycle, and in erythroid cells exhibit a sharp nucleolar contraction preceding cell-cycle exit. We identify a 41-gene transcriptional signature whose expression tracks nucleolar size, enriched for ribosome biogenesis, mitochondrial metabolism, unfolded protein response, stress granule, and ubiquitin-proteasome pathway components. We used this nucleolar transcriptional signature to annotate mouse, zebrafish and human developmental atlases with nucleolar size information, revealing a conserved coupling between nucleolar activity and proteostasis programs. Our work establishes Visual Cell Sorting as a scalable platform for mapping image-based phenotypes to molecular programs; details the relationship between nuclear compartment phenotypes and development; and provides a transcriptional signature to estimate nucleolar size from existing single-cell datasets.
-
Prognostic and diagnostic evaluation of HSPG2 gene expression in oral squamous cell carcinoma: A case-control study.
OBJECTIVE: This study aimed to evaluate HSPG2 expression in oral squamous cell carcinoma (OSCC) and validate its clinical relevance using external datasets, including The Cancer Genome Atlas (TCGA) and the single-cell GSE103322 dataset. DESIGN: A matched case-control study (2022-2024) enrolled 25 OSCC patients and 25 age- and sex-matched healthy controls, also balanced for smoking and alcohol status. Biological samples (formalin-fixed paraffin-embedded tissue, blood plasma, and saliva) were collected. RNA was extracted, converted to cDNA, and HSPG2 expression quantified using qRT-PCR. External validation was performed using TCGA, and single-cell analysis utilized GSE103322. Receiver operating characteristic (ROC) curves assessed diagnostic performance. RESULTS: HSPG2 expression was significantly elevated in OSCC tissues (p = 0.002) and blood plasma (p = 0.02), but not in saliva (p = 0.58). Increased expression was observed across genders, all age groups, tumor stages, and grades, and was independent of HPV status. TCGA analysis confirmed higher HSPG2 expression in tumors versus normal tissues across demographic and clinical subgroups, with high expression associated with poorer survival, particularly among African American patients. Single-cell RNA-seq revealed stromal enrichment of HSPG2, predominantly in endothelial cells and fibroblasts. ROC analysis demonstrated good diagnostic performance in tissue (AUC = 0.86) and blood plasma (AUC = 0.84), but limited value in saliva (AUC = 0.57). CONCLUSION: HSPG2 is overexpressed in OSCC tissues and plasma, correlating with tumor stage, grade, and poor survival, especially in African Americans. As a stromal-associated gene promoting tumor progression, HSPG2 holds promise as a diagnostic and prognostic biomarker in OSCC.
-
Combining single-cell RNA-sequencing with a molecular atlas unveils new markers for Caenorhabditis elegans neuron classes.
Single-cell RNA-sequencing (scRNA-seq) of the Caenorhabditis elegans nervous system offers the unique opportunity to obtain a partial expression profile for each neuron within a known connectome. Building on recent scRNA-seq data and on a molecular atlas describing the expression pattern of ∼800 genes at the single cell resolution, we designed an iterative clustering analysis aiming to match each cell-cluster to the ∼100 anatomically defined neuron classes of C. elegans. This heuristic approach successfully assigned 97 of the 118 neuron classes to a cluster. Sixty two clusters were assigned to a single neuron class and 15 clusters grouped neuron classes sharing close molecular signatures. Pseudotime analysis revealed a maturation process occurring in some neurons (e.g. PDA) during the L2 stage. Based on the molecular profiles of all identified neurons, we predicted cell fate regulators and experimentally validated unc-86 for the normal differentiation of RMG neurons. Furthermore, we observed that different classes of genes functionally diversify sensory neurons, interneurons and motorneurons. Finally, we designed 15 new neuron class-specific promoters validated in vivo. Amongst them, 10 represent the only specific promoter reported to this day, expanding the list of neurons amenable to genetic manipulations.
-
Comparative Analysis of Single-Cell RNA Sequencing Platforms and Methods.
Single-cell RNA sequencing (scRNA-seq) offers great new opportunities for increasing our understanding of complex biological processes. In particular, development of an accurate Human Cell Atlas is largely dependent on the rapidly advancing technologies and molecular chemistries employed in scRNA-seq. These advances have already allowed an increase in throughput for scRNA-seq from 96 to 80,000 cells on a single instrument run by capturing cells within nanoliter droplets. Although this increase in throughput is critical for many experimental questions, a thorough comparison between microfluidic-based, plate-based, and droplet-based technologies or between multiple available platforms utilizing these technologies is largely lacking. Here, we report scRNA-seq data from SUM149PT cells treated with the histone deacetylase inhibitor trichostatin A versus untreated controls across several scRNA-seq platforms (Fluidigm C1, WaferGen iCell8, 10x Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The primary goal of this project was to demonstrate RNA sequencing methods for profiling the ultra-low amounts of RNA present in individual cells, and this report discusses the results of the study, as well as technical challenges and lessons learned and present general guidelines for best practices in sample preparation and analysis.
-
Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN.
Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, inter-species genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here, we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN has a unique ability to detect functionally related genes co-expressed across species, redefining differential expression for cross-species analysis. We apply SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets. We show that cell embeddings learnt in SATURN can be effectively used to transfer annotations across species and identify both homologous and species-specific cell types, even across evolutionarily remote species. Finally, we use SATURN to reannotate the five species Cell Atlas of Human Trabecular Meshwork and Aqueous Outflow Structures and find evidence of potentially divergent functions between glaucoma associated genes in humans and other species.
-
Optimized protocol for isolation of high-quality single cells from the female mouse reproductive tract tissues for single-cell RNA sequencing.
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for enumerating the gene expression dynamics at single-cell resolution. Various organs comprising distinct cellular composition and architecture require unique approaches for highly viable single-cell preparation and reliable sequencing results. Here, we describe an optimized protocol for isolating the female reproductive tract (FRT), dissecting different FRT regions, and preparing high-viability single cells from the uterine endocervix and ectocervix to generate a complete molecular cell atlas by scRNA-seq for studying normal physiology and disease. For complete details on the use and execution of this protocol, please refer to Chumduri et al. (2021).
-
A single-embryo, single-cell time-resolved model for mouse gastrulation.
Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.
-
Integrated single-cell and bulk transcriptomic profiling reveals cancer-associated fibroblast heterogeneity in glioblastoma and establishes a clinically actionable prognostic model and preliminary experimental validation.
Cancer-associated fibroblasts (CAFs) critically regulate tumor progression, angiogenesis, metastasis, and therapeutic resistance. This study investigated the characteristics of CAFs in glioblastoma (GBM) and developed a CAF-based risk signature to predict patient prognosis. The single-cell RNA sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) database, whereas the bulk RNA-seq datasets were retrieved from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. The Seurat R package processed scRNA-seq data to identify CAF clusters using established markers. Prognostic genes were screened through univariate Cox regression, with Lasso regression constructing the final risk model. A nomogram incorporating clinical parameters was subsequently developed. Immunohistochemical validation was performed using the Human Protein Atlas (HPA) for the signature genes. The qRT-PCR validation was conducted in U251 and HA cells. ScRNA-seq analysis revealed five CAF clusters in GBM, including three prognostically relevant subtypes. Three key genes were refined to construct a risk signature functionally enriched in the the IL6_JAK_STAT3 signaling, P53 pathway, and inflammatory response. The signature correlated strongly with stromal and immune scores. Multivariate analysis confirmed risk signature independent prognostic value (P < 0.0001), followed by age (P = 0.005). The CAF-derived nomogram demonstrated superior predictive accuracy for 1-/2-year survival compared to clinical factors alone. The signature genes were validated in the HPA database and experimental validation. This study proposes CAF-derived molecular signatures as potential predictors of glioblastoma prognosis worthy of clinical validation. Systematic characterization of CAF heterogeneity may offer insights for interpreting GBM immunotherapy responses, providing a foundation for future exploration of stroma-targeted therapeutic strategies.
-
Deciphering HPV-Associated Immune Evasion in Cervical Cancer Through Multi-Omics Profiling and Computational Screening.
BACKGROUND: Human papillomavirus (HPV) infection is a major contributor to cervical cancer (CC), yet the molecular mechanisms driving HPV-associated immune evasion remain largely undefined. METHODS: Bulk RNA-seq (The Cancer Genome Atlas [TCGA]-CESC) and single-cell RNA-seq datasets (GSE171894, GSE197461) were analyzed to elucidate transcriptional and immune landscape differences between HPV-positive and HPV-negative cervical tumors. Differentially expressed genes (DEGs) were identified using DESeq2. Functional enrichment analyses were conducted through gene set enrichment analysis (GSEA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies. Immune evasion feature genes were selected employing LASSO, Random Forest, and SVM-RFE techniques. Regulatory networks for transcription factors and miRNAs were constructed. Immune infiltration was evaluated using CIBERSORT and ssGSEA. Validation of key signature genes was performed in CC cell lines (HeLa, SiHa, C33A, and W12) via real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot. The functional roles of IFNGR1 were examined through siRNA-mediated knockdown, complemented by CCK-8, colony formation, Transwell migration/invasion, and flow cytometry apoptosis assays. RESULTS: A total of 6266 DEGs effectively differentiated HPV-positive from HPV-negative tumors. HPV-positive tumors exhibited enrichment in viral infection and immune response pathways, while HPV-negative tumors demonstrated activation of oncogenic signaling. Machine learning algorithms identified IFNGR1, TRADD, and PSMB9 as immune evasion feature genes associated with HPV. Regulatory network analysis emphasized IRF1/IRF2 and several miRNAs as critical modulators. Immune infiltration analysis indicated increased infiltration of Dendritic and Plasma cells in HPV-positive tumors, correlating with TRADD expression. Kaplan-Meier analysis further showed a trend toward worse overall survival among HPV-positive patients with high IFNGR1 expression (hazard ratio [HR] = 1.78, 95% confidence interval [CI]: 0.86-3.68; log-rank p = 0.113). Notably, IFNGR1 was significantly upregulated in HPV-positive CC cell lines at both mRNA and protein levels. IFNGR1 knockdown markedly inhibited proliferation, colony formation, migration, and invasion, while enhancing apoptosis in HeLa and SiHa cells, thereby confirming its essential role in the progression of HPV-associated CC. CONCLUSION: This study identified IFNGR1 as a key immune evasion-related gene in HPV-positive CC, elucidating its regulatory network and functional contributions, while positioning it as a potential therapeutic target for HPV-associated tumors.
-
BBKNN: fast batch alignment of single cell transcriptomes.
MOTIVATION: Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods. AVAILABILITY AND IMPLEMENTATION: BBKNN is available at https://github.com/Teichlab/bbknn, along with documentation and multiple example notebooks, and can be installed from pip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.