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: 4 905
Page 121 of 491-
Reconstructing donor genotypes from scRNA-seq for downstream eQTL and HLA-TCR analysis.
Single-cell eQTL studies can reveal heterogeneous genetic effects on gene expression that vary with cell type, but their wider application has been constrained by the frequent absence of matched genotype data. Here, we present single-cell Transcriptome Allelic Prediction and Association Studies (scTAPAS), a bioinformatic framework for reconstructing donor genotypes directly from single-cell RNA-sequencing reads and downstream genetic analysis. Applying scTAPAS to 5' single-cell RNA-sequencing data from the COVID-19 Multi-omic Blood Atlas (COMBAT), we show that reference-based imputation of sparse RNA-derived genetic information generates genotype dosages sufficiently accurate for association testing. Despite testing only 18.0% of the variants available from conventional array-based genotyping and imputation, scTAPAS recovers 68.6% of cell type-eGene pairs identified using conventional array-based genotypes, with strong concordance in effect sizes. We further show that scTAPAS-derived variants can support imputation of classical HLA alleles and enable HLA-aware QTL analyses, including associations between HLA variation and TCR repertoire at single-cell resolution. Using matched single-cell TCR-sequencing data, we identify CD4+ T cell-specific associations between HLA class II alleles and T-cell receptor (TCR) Vα gene segment usage, highlighting the potential of analyses that complement TCR sequencing at single-cell resolution with HLA types. These results show that genetic information recovered from single-cell RNA sequencing data can support both regulatory and immunogenetic association analyses, expanding the discovery potential of single-cell cohorts for which conventional genotyping or HLA typing is unavailable.
-
Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features.
With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.
-
Integrative Analysis of Bulk Multiomics and Single-Cell Transcriptomics Elucidates Age-Related Molecular Landscape Alterations in Cervical Cancer.
BACKGROUND: Young and elderly patients with cervical cancer represent two distinct demographic cohorts that have attracted considerable attention in clinical practice. However, the age-related molecular characteristics of these groups remain unclear. METHODS: Data from 307 patients with cervical cancer, including clinical information and comprehensive bulk multiomics data, were obtained from The Cancer Genome Atlas (TCGA). Single-cell RNA-sequencing (scRNA-seq) data for 11 cervical cancer samples were retrieved from the Gene Expression Omnibus (GEO). The patients were stratified into three age groups: young (<30 years), middle (30-64 years), and old (≥65 years). Comparative analyses of survival outcomes, expression profiles, tumor immune microenvironment, genomic and epigenetic features, treatment responsiveness, and cellular composition were performed. Additionally, external validation was performed using bulk transcriptomic and genomic data from the GEO and MSK-IMPACT datasets. RESULTS: Young patients exhibited a transcriptional landscape characterized by pronounced aggressiveness and elevated expression of immunosuppressive molecules. Conversely, elderly patients exhibited an increased level of genomic instability and mutations accompanied by an elevated tumor mutation burden and neoantigen counts. Notably, the elderly cohort had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, whereas the young group had a higher radiosensitivity index and reduced imputed sensitivity scores for multiple chemotherapeutic agents. Additionally, scRNA-seq identified epithelial clusters 5 and 9 as subtypes associated with elderly and young patients, respectively, with cluster 5 displaying characteristics resembling cancer stem cells and cluster 9 exhibiting enhanced oncogenic activity and immunosuppressive capabilities. CONCLUSIONS: Significant molecular disparities were evident among the young, middle-age, and elderly patients. Thus, age-specific management strategies should be considered in cervical cancer treatment.
-
Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression.
Neuroendocrine prostate cancer (NEPC) typically implies severe lethality and limited treatment options. The precise identification of NEPC cells holds paramount significance for both research and clinical applications, yet valid NEPC biomarker remains to be defined. Methods: Leveraging 11 published NE-related gene sets, 11 single-cell RNA-sequencing (scRNA-seq) cohorts, 15 bulk transcriptomic cohorts, and 13 experimental models of prostate cancer (PCa), we employed multiple advanced algorithms to construct and validate a robust NEPC risk prediction model. Results: Through the compilation of a comprehensive scRNA-seq reference atlas (comprising a total of 210,879 single cells, including 66 tumor samples) from 9 multicenter datasets of PCa, we observed inconsistent and inefficient performance among the 11 published NE gene sets. Therefore, we developed an integrative analysis pipeline, identifying 762 high-quality NE markers. Subsequently, we derived the NE cell-intrinsic gene signature, and developed an R package named NEPAL, to predict NEPC risk scores. By applying to multiple independent validation datasets, NEPAL consistently and accurately assigned NE feature and delineated PCa progression. Intriguingly, NEPAL demonstrated predictive capabilities for prognosis and therapy responsiveness, as well as the identification of potential epigenetic drivers of NEPC. Conclusion: The present study furnishes a valuable tool for the identification of NEPC and the monitoring of PCa progression through transcriptomic profiles obtained from both bulk and single-cell sources.
-
Single-Cell RNA Sequencing of the Primary Visual Cortex in Mice With Optic Nerve Injury.
PURPOSE: Glial cells play a critical role in primary visual cortex (V1 region) damage caused by optic nerve injury, but the mechanisms driving progression of V1 region injury and glial cell heterogeneity remain poorly understood. This study aimed to investigate the damage changes in the V1 region of mice after optic nerve crush (ONC) by single-cell RNA sequencing (scRNA-seq). METHODS: Hematoxylin and eosin (H&E) and immunofluorescence staining were used to evaluate the changes of retinal thickness, astrocytes, and microglia in the V1 region after ONC in mice. Single cell suspensions in the V1 region of mice were prepared and analyzed by scRNA-seq with Seurat, cellchat, CytoTRACE in R software. The expression of PTGDS and CRYAB was measured by qPCR, Western blot, and immunofluorescence. RESULTS: After unilateral ONC, retinal thinning in both eyes and activation of astrocytes and microglia in contralateral V1 region were observed. Genes related to neuroinflammation and apoptosis in the bilateral V1 region were upregulated, and the related pathways included MAPK, TNF, and apoptosis signaling pathways. Notably, the V1 region contralateral to the ONC eye exhibited more pronounced differential gene expression, and the protein expression of neuroinflammation-related genes Ptgds and Cryab increased. We further investigated the heterogeneity and pseudotime trajectories of astrocytes and microglia, demonstrating the key branches that dominate neuroinflammation. CONCLUSIONS: This study generates an atlas of the V1 region of the mouse brain, highlighting the role of astrocytes and microglia in the damage changes in the V1 region after ONC, and suggesting Ptgds and Cryab as potential targets to reduce neuroinflammation.
-
A single-cell transcriptomic atlas characterizes cell types and their molecular features in yak ovarian cortex.
The ovary as one of the most dynamic organs produces steroids to orchestrate female secondary sexual characteristics, harbors ovarian reserve for oocytes, releases mature oocytes for fertilization, and maintains pregnancy. Yak (Bos grunniens) is the only bovid animal that can adapt to the harsh climatic conditions on the Qinghai-Tibetan Plateau (altitudes of over 3000 m above sea level). However, the cellular atlas is composed of oocytes and other somatic cells, and their individual molecular characteristics remain to be elucidated in the yak ovary. Here, single-cell RNA sequencing (scRNA-seq) was performed to delineate the molecular signature of various cell types in the yak ovarian cortex. A cellular atlas of yak ovarian cortex was constructed successfully on the basis of the differentially expressed genes (DEGs) from the distinct cell types and their functional enrichment analysis, comprising endothelial cells, nature kill cells, stromal cells, smooth muscle cells, oocytes, macrophages, epithelial cells, and granulosa cells. Meanwhile, the signature genes were determined based on their expression specificity in each cell type. A cell-to-cell communication network was built in light of the differentially overexpressed ligand and receptor genes from each cell type. Further, the oocytes were subdivided into four subtypes based on their individual DEGs and the functional enrichment of the DEGs. FST and TOP2A were identified as maker genes for oocytes by immunostaining in the yak ovarian cortex. The cellular atlas reveals the biological characteristics of the ovarian cortex at the cellular molecular level and provides insights into female reproductive biology via cellular communications in the yak.
-
An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.
Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.
-
Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing.
Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.
-
Dual and spatially resolved drought responses in the Arabidopsis leaf mesophyll revealed by single-cell transcriptomics.
Drought stress imposes severe challenges on agriculture by impacting crop performance. Understanding drought responses in plants at a cellular level is a crucial first step toward engineering improved drought resilience. However, the molecular responses to drought are complex as they depend on multiple factors, including the severity of drought, the profiled organ, its developmental stage or even the cell types therein. Thus, deciphering the transcriptional responses to drought is especially challenging. In this study, we investigated tissue-specific responses to mild drought (MD) in young Arabidopsis thaliana (Arabidopsis) leaves using single-cell RNA sequencing (scRNA-seq). To preserve transcriptional integrity during cell isolation, we inhibited RNA synthesis using the transcription inhibitor actinomycin D, and demonstrated the benefits of transcriptome fixation for studying mild stress responses at a single-cell level. We present a curated and validated single-cell atlas, comprising 50 797 high-quality cells from almost all known cell types present in the leaf. All cell type annotations were validated with a new library of reporter lines. The curated data are available to the broad community in an intuitive tool and a browsable single-cell atlas (http://www.single-cell.be/plant/leaf-drought). We show that the mesophyll contains two spatially separated cell populations with distinct responses to drought: one enriched in canonical abscisic acid-related drought-responsive genes, and another one enriched in genes involved in iron starvation responses. Our study thus reveals a dual adaptive mechanism of the leaf mesophyll in response to MD and provides a valuable resource for future research on stress responses.
-
Single-Cell Deconvolution Reveals Phenotype-Associated Cellular States in the Silk Glands of Bombyx mori and Its Wild Ancestor.
Silk production is a classic example of a domestication trait, yet the cell-type-specific driver of its enhancement in the silkworm Bombyx mori remains unresolved. To address this, we integrated extensive bulk RNA-seq data with a single-nucleus RNA-seq atlas of silk glands (SGs) from domestic B. mori and wild B. mandarina for deconvolution analysis. This identified phenotype-associated cell subpopulations (Scissor+ and Scissor- cells) that enrich in B. mori and B. mandarina, respectively. Transcriptomic characterization revealed that B. mori SG cells exhibit a pervasive "pro-synthesis" transcriptional state, with concerted upregulation of silk protein genes and metabolic pathways. Conversely, B. mandarina cells maintained a "protective-adaptive" state, enriched for stress response and xenobiotic metabolism genes. Pseudotime analysis further delineated the cell state transitions, pinpointing key dynamic gene expression linked to high silk yield. Our findings demonstrate that domestication reshaped the silk gland cellular landscape, promoting a systemic shift toward a synthesis-optimized cell state. This study offers a new framework at the cellular level to elucidate the evolution of complex traits under selection.