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: 4 924
Page 211 of 493-
ScType enables fast and accurate cell type identification from spatial transcriptomics data.
SUMMARY: The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation. AVAILABILITY AND IMPLEMENTATION: scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.
-
A lamprey neural cell type atlas illuminates the origins of the vertebrate brain.
The vertebrate brain emerged more than ~500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey-a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits-based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of cell types, tissue structures and gene expression programs of the ancestral vertebrate brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral brain was probably devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors in the jawed vertebrate lineage. Altogether, our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.
-
Generating and Using Transcriptomically Based Retinal Cell Atlases.
It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain.
-
Molecular signals associated with intraperitoneal chemotherapy resistance in gastric cancer with peritoneal metastasis through PIPS GC trial integrated translational research.
Purpose Recent studies have investigated intraperitoneal paclitaxel (IP PTX) combined with systemic chemotherapy for the treatment of gastric cancer peritoneal metastases (GCPM). This clinical trial integrated translational study was conducted to identify biomarkers that can predict patient responses to IP PTX plus systemic systemic S-1 plus oxaliplatin (SOX) chemotherapy. Materials and methods Patients from the PIPS-GC phase Ib/II clinical trial, enrolled at the Korea University Guro Hospital, were included in analyses. Whole exome and transcriptome sequencing were performed on formalin-fixed, paraffin-embedded gastric tumor, normal gastric, and peritoneal tumor tissues. A metastatic tumor-specific gene set was analyzed using The Cancer Genome Atlas (TCGA) gastric cancer data and publicly available single-cell RNA sequencing (scRNA-seq) datasets. Spatial transcriptomic analysis of primary and peritoneal tumors from a non-responder was performed to validate candidate biomarkers. Results Nine patients with gastric cancer were enrolled; six in the response group and three in the non-response group. Candidate genes for predicting IP PTX plus systemic SOX chemotherapy response were identified and compared with TCGA-GC and scRNA-seq datasets. Spatial transcriptomics revealed higher expression of thrombospondin type 1 domain-containing protein 4 (THSD4) in peritoneal tumors, which was associated with chemotherapy resistance via midkine and epithelial-mesenchymal transition pathways. Conclusion This PIPS-GC clinical trial identified THSD4 as a potential biomarker for predicting IP PTX plus systemic SOX response in gastric cancer peritoneal metastasis. Further research is required to elucidate the mechanism of action by which THSD4 affects GCPM and validate its clinical utility as a predictive biomarker for IP PTX response.
-
Cell type-specific analysis by single-cell profiling identifies a stable mammalian tRNA-mRNA interface and increased translation efficiency in neurons.
The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using available mouse and human single-cell RNA-sequencing atlases. After showing the robustness of ATAC-sequencing measurements for the analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in both mouse and human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons, which clustered separately from other cell types. Integration of these data sets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of tRNAAla (AGC) anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell type-specific ribosome profiling, suggesting that the reduction of tRNAAla (AGC) anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency resolved at the cell-type level with single-cell information, identifies a conserved landscape of translation elongation across mammalian cellular diversity and evolution.
-
DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts.
DIANA-miRPath is an online miRNA analysis platform harnessing predicted or experimentally supported miRNA interactions towards the exploration of combined miRNA effects. In its latest version (v4.0, http://www.microrna.gr/miRPathv4), DIANA-miRPath breaks new ground by introducing the capacity to tailor its target-based miRNA functional analysis engine to specific biological and/or experimental contexts. Via a redesigned modular interface with rich interaction, annotation and parameterization options, users can now perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (updated DIANA-microT-CDS and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx) and adult/fetal single-cell atlases are integrated and can be used to assess the expression of enriched term components across a wide range of states. A discrete module enabling enrichment analyses using CRISPR knock-out screen datasets enables the detection of selected miRNAs with potentially crucial roles within conditions under study. Notably, the option to upload custom interaction, term, expression and screen sets further expands the versatility of miRPath webserver.
-
Multi-omics identification of an antibody dependent cellular phagocytosis related signature for hepatocellular carcinoma.
BACKGROUND: The reduced phagocytic activity of macrophages in the tumor microenvironment (TME) and the presence of anti-phagocytic factors in cancer cells hinder the effectiveness of immune checkpoint inhibitors (ICIs) in clinical settings. Consequently, our objective was to create a risk assessment model using antibody-dependent cellular phagocytosis (ADCP) regulators to serve as prognostic indicators and immunotherapy markers for individuals with hepatocellular carcinoma (HCC). METHODS: This research incorporated The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) group, the GSE14520 group, the International Cancer Genome Consortium (ICGC) group, and the single-cell dataset GSE166635. Machine learning was used to discover ADCP genes closely linked to prognosis, helping to determine the molecular subtypes of ADCP in HCC samples. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression algorithms were used to develop the ADCP-related signature (ADCPRS). This signature was evaluated using both training and external validation datasets. Furthermore, a nomogram that integrates ADCPRS was created to function as a quantitative instrument for predicting outcomes in clinical settings. A comprehensive multi-omics investigation was performed, integrating genomic, single-cell transcriptomic, and bulk transcriptomic analyses, with the aim of gaining a more profound understanding of the prognostic signature. We assessed how different risk categories respond to immunotherapy and identified drugs tailored to target these specific groups for customized treatment. RESULTS: We identified two ADCP subgroups by analyzing 91 genes linked to HCC prognosis. The two ADCP subgroups showed distinct outcomes, pathway activation, immune checkpoint gene expression levels, and immune cell infiltration within the TME. We developed a unified ADCPRS that excels in forecasting clinicopathological characteristics, patient outcomes, and TME stromal activity in HCC. The nomogram incorporating ADCPRS offered a numerical tool for use in medical practice. Notably, the high-risk group presented inflamed TME with an improved response to ICI. In addition, the high-ADCPRS group presented a high sensitivity to the derivative compounds provided by Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM). CONCLUSIONS: Our research developed a signature linked to ADCP, which shows promise as a key tool for predicting outcomes, implementing prevention, and enabling personalized treatment in HCC.
-
Spatial single-cell atlas reveals regional variations in healthy and diseased human lung.
Integration of scRNA-seq data from millions of cells revealed a high diversity of cell types in the healthy and diseased human lung. In a large and complex organ, constantly exposed to external agents, it is crucial to understand the influence of lung tissue topography or external factors on gene expression variability within cell types. Here, we apply three spatial transcriptomics approaches, to: (i) localize the majority of lung cell types, including rare epithelial cells within the tissue topography, (ii) describe consistent anatomical and regional gene expression variability within and across cell types, and (iii) reveal distinct cellular neighborhoods in specific anatomical regions and examine gene expression variations in them. We thus provide a spatially resolved tissue reference atlas in three representative regions of the healthy human lung. We further demonstrate its utility by defining previously unknown imbalances of epithelial cell type compositions in chronic obstructive pulmonary disease lungs. Our topographic atlas enables a precise description of characteristic regional cellular responses upon experimental perturbations or during disease progression.
-
[Application progress of single-cell RNA sequencing technology in breast development and related diseases].
The spatio-temporal heterogeneity of breast cell subsets forms the fundamental biological basis for physiological development and pathological progression, including tumorigenesis; however, its complex regulatory mechanisms are not yet fully elucidated. With its high-resolution capabilities, single-cell RNA sequencing (scRNA-seq) technology offers a powerful tool for dissecting this cellular heterogeneity. This technology enables the construction of high-precision breast cell atlases, the accurate identification of distinct cell subsets, and the reconstruction of differentiation trajectories from stem/progenitor cells to functional epithelial cells. By resolving the transcriptional regulatory networks that govern cell fate determination, intercellular communication patterns, and dynamic microenvironmental interactions, scRNA-seq has unveiled the molecular foundations of breast development and provided new perspectives on the pathogenesis of related diseases such as breast cancer and macromastia. Furthermore, scRNA-seq demonstrates significant potential for discovering early molecular markers of disease, deciphering tumor heterogeneity, and elucidating mechanisms of therapeutic resistance. The continued application of scRNA-seq for dissecting breast cell heterogeneity, combined with its integration with multi-modal data such as spatial omics, promises to provide critical evidence and new insights for revealing the molecular mechanisms of breast development-related diseases and for formulating precision therapeutic strategies.
-
A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates.
We present a multiomic cell atlas of human lung development that combines single-cell RNA and ATAC sequencing, high-throughput spatial transcriptomics, and single-cell imaging. Coupling single-cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial, and erythrocyte/leukocyte compartments from 5-22 post-conception weeks. We identify previously uncharacterized cell states in all compartments. These include developmental-specific secretory progenitors and a subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). To illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signaling and transcription factor hierarchies which we rigorously test using organoid models.