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CellScope: high-performance cell atlas workflow with tree-structured representation.
Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels-such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research.
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SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.
It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.
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Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.
BACKGROUND: Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking. METHODS: This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings were then validated through the receiver operating characteristic (ROC) curve and Kaplan-Meier analysis, and were experimentally confirmed in GC samples by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Integrated analysis of TCGA and GEO databases, coupled with LASSO regression and RF algorithms, allowed us to identify 18 hub genes encoding differentially expressed secreted proteins in GC. The results of RT-PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan-Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. These findings were further confirmed by RT-PCR and ELISA, affirming the clinical utility of these four biomarkers. Additionally, CIBERSORT analysis indicated a potential correlation between the four biomarkers and the infiltration of B memory cells and Treg cells. CONCLUSION: This study unveiled four promising biomarkers present in the serum of patients with GC, which could serve as powerful indicators of GC and provide valuable insights for further research into GC pathogenesis.
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Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury.
An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.
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Comprehensive snRNA-Seq Datasets of Human and Mouse Podocytopathy Integrated with GWAS of Microalbuminuria.
Podocyte (POD) injury is a hallmark of glomerular diseases and a central cause of albuminuria and nephrotic syndrome. Despite the rapid advancements in single-cell sequencing technologies in recent years, POD capture remains limited, resulting in a lack of comprehensive single-cell datasets related to Podocytopathies (PCPs). In this study, we collected kidney tissues from patients with five types of PCP and established two mouse models of POD injury for single-nucleus RNA sequencing (sn-RNA seq). We successfully generated single-cell resolution kidney atlases of human and mouse PCPs and, in combination with microalbuminuria-associated genome wide association study (GWAS) dataset, identified potential therapeutic targets linked to POD injury. These findings provide valuable resources and a solid foundation for future research on PCPs, particularly on the molecular mechanisms underlying POD injury.
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Ce -NeRV3D - a C. elegans Neuron RNA-seq Visualization tool in 3D.
Caenorhabditis elegans provides a tractable model for neural circuit studies, with complete connectomes and single-cell transcriptomic atlases available. Here, we present Ce -NeRV3D ( C. elegans Neuron RNA-seq Visualization tool in 3D), a Blender plug-in that allows visualizing the expression patterns of an unlimited number of genes within the worm's 3D nervous system. The tool enables users to explore the spatial distribution of gene co-expression with customizable color mapping, overlay single-cell RNA sequencing data onto the anatomical structure, and interactively examine transcriptomic datasets in the context of detailed neural anatomy.
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Transcriptional Dynamics of Hepatic Sinusoid-Associated Cells After Liver Injury.
BACKGROUND AND AIMS: Hepatic sinusoidal cells are known actors in the fibrogenic response to injury. Activated hepatic stellate cells (HSCs), liver sinusoidal endothelial cells, and Kupffer cells are responsible for sinusoidal capillarization and perisinusoidal matrix deposition, impairing vascular exchange and heightening the risk of advanced fibrosis. While the overall pathogenesis is well understood, functional relations between cellular transitions during fibrogenesis are only beginning to be resolved. At single-cell resolution, we here explored the heterogeneity of individual cell types and dissected their transitions and crosstalk during fibrogenesis. APPROACH AND RESULTS: We applied single-cell transcriptomics to map the heterogeneity of sinusoid-associated cells in healthy and injured livers and reconstructed the single-lineage HSC trajectory from pericyte to myofibroblast. Stratifying each sinusoidal cell population by activation state, we projected shifts in sinusoidal communication upon injury. Weighted gene correlation network analysis of the HSC trajectory led to the identification of core genes whose expression proved highly predictive of advanced fibrosis in patients with nonalcoholic steatohepatitis (NASH). Among the core members of the injury-repressed gene module, we identified plasmalemma vesicle-associated protein (PLVAP) as a protein amply expressed by mouse and human HSCs. PLVAP expression was suppressed in activated HSCs upon injury and may hence define hitherto unknown roles for HSCs in the regulation of microcirculatory exchange and its breakdown in chronic liver disease. CONCLUSIONS: Our study offers a single-cell resolved account of drug-induced injury of the mammalian liver and identifies key genes that may serve important roles in sinusoidal integrity and as markers of advanced fibrosis in human NASH.
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Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma.
BACKGROUND: As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue. METHODS: Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data. RESULTS: Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression. CONCLUSIONS: Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.
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Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis.
Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods-LDSC-SEG, SNPsea, and scDRS-that have successfully identified relevant cell types in other diseases. Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA sequencing data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. This revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two understudied loci, ENTR1 (SDCCAG3) and B3GNT2. Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.
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Loss of wbp11 causes multi-system developmental defects: a zebrafish model of VACTERL association.
INTRODUCTION: VACTERL association is a congenital disorder characterized by the non-random co-occurrence of vertebral, anal, cardiac, tracheo-esophageal, renal, and limb anomalies. WBP11 has been identified as a candidate causative gene; however, existing heterozygous Wbp11 knockout mice recapitulate only a subset of the patient phenotypes, most notably lacking the cardiac defects, which limits a comprehensive understanding of the pathogenic mechanisms. Whether WBP11 loss of function is sufficient to induce the full multi-system spectrum of VACTERL association in a vertebrate model remains unexplored. METHODS: We established a wbp11 knockdown zebrafish model by microinjecting a translation-blocking morpholino (wbp11-MO) at the optimized concentration of 0.25 mM. Knockdown efficiency was validated by Western blot. Bioinformatics analysis of public single cell and expression atlas datasets was combined with whole-mount in situ hybridization to characterize the spatiotemporal expression of wbp11. Phenotypic defects across cardiac, vascular, spinal, fin, and renal systems were assessed by stereomicroscopy, transgenic reporter lines (Tg(myl7:EGFP) and Tg(flila:EGFP)), and whole mount in situ hybridization. Underlying molecular changes were interrogated by transcriptome sequencing (RNA-seq) coupled with differential alternative splicing analysis (rMATS) and quantitative real-time PCR validation. Rescue experiments were performed by co-injection of capped wbp11 mRNA. RESULTS: wbp11 is evolutionarily conserved and maternally expressed, peaking at the gastrula stage with elevated signals in the head, skeletal muscle, and heart. wbp11-MO injection reduced Wbp11 protein levels, induced a 70% overall malformation rate, and caused a general delay in embryonic development. wbp11-deficient larvae exhibited multi-system developmental defects that closely mirror the core clinical features of VACTERL association: cardiac malformations with pericardial edema and impaired heart looping; vascular defects including underdeveloped subintestinal vessels and reduced cranial vasculature; spinal curvature with aberrant notochord structure; hypoplasia of the pectoral, pelvic, anal, and caudal fins; and transcriptional dysregulation of kidney developmental markers. Transcriptomic and alternative splicing analyses revealed significant dysregulation of genes associated with cardiac function, angiogenesis, skeletal patterning, fin development, and kidney development, accompanied by widespread splicing aberrations. DISCUSSION: This study establishes the first wbp11-knockdown zebrafish model that partially recapitulates the key organ phenotypes of human VACTERL association notably including the cardiac defects absent in the mouse model and thereby provides a powerful platform for dissecting the splicing dependent molecular mechanisms underlying this disorder and for exploring potential therapeutic interventions.