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Investigating prognostic features in high-grade serous ovarian cancer through gene regulatory network inference with single-cell transcriptomic profiles.
This study aimed to identify prognostic features in high-grade serous ovarian cancer (HGSOC) through the application of gene regulatory network (GRN) inference with single-cell RNA-sequencing (scRNA-seq) profiles. To achieve this goal, we developed a workflow comprising scRNA-seq analysis, metacell construction, GRN inference, and a binary classification task for prognosis prediction. We curated 118,173 cells from HGSOC patients in three conditions (Before-chemotherapy, After-chemotherapy, and control samples) from previous studies, and then constructed 1,211 metacells. GRN inference analysis revealed 312 regulons, each consisting of one transcription factor and its targeted features. For prognosis evaluation, we used bulk RNA-seq data covering 342 HGSOC patients from The Cancer Genome Atlas (TCGA) and defined a binary outcome of overall survival ≥ 2 years from initial diagnosis, with censored cases at last follow-up assigned to the appropriate class by observed time. We prioritized the features of the TCGA data based on regulon information and differentially expressed features extracted from the metacell data. Our results demonstrated that regulon-based prognostic features were more effective than differential expression-based features in both Before-chemotherapy and After-chemotherapy groups. Our framework can be generalized to other types of cancer when single-cell data for GRN inference and bulk RNA-seq data with clinical outcomes are available.
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Multi-omics mapping of PSC genetic risk to a high TWAS-active JAML+ lipid-associated macrophage program: bridging single-cell heterogeneity, spatial fibrotic niches, and machine learning selection.
BACKGROUND: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease with progressive peribiliary inflammation and fibrosis. Disease-modifying therapies are lacking, and the cell-type-specific mechanisms linking genetic susceptibility to pathogenic immune states remain incompletely understood. METHODS: We performed a transcriptome-wide association study (TWAS) using PSC GWAS summary statistics and GTEx v8 liver eQTL weights. TWAS-prioritized genes were mapped onto a PSC liver single-cell RNA-seq atlas (GSE247128) using integrated gene set scoring (irGSEA). Monocyte/macrophage subsets were re-clustered and lipid-associated macrophages (LAMs) were stratified by TWAS activity. Core genes distinguishing high-versus low-activity LAMs were identified using seven machine-learning feature selection algorithms. JAML was validated in bulk transcriptomic cohorts (GSE119600, GSE177044), evaluated by immune infiltration analysis, examined by cell-cell communication inference (CellChat), assessed by virtual knockout (scTenifoldKnk), and spatially localized using Visium FFPE spatial transcriptomics (PSC: GSE245620; control: GSE240429), and validated at the protein level using Western blot and ELISA in an in vitro macrophage model. RESULTS: TWAS activity was predominantly enriched in the monocyte/macrophage lineage. Within this lineage, LAMs (TREM2+ GPNMB+ APOC1+) showed the highest TWAS activity and occupied late pseudotime states. High-activity LAMs were enriched in fibrosis-related pathways (e.g., TGF-β, NOTCH, WNT/β-catenin) and innate immune pathways (e.g., TLR2/4-MAPK, NLRP3 inflammasome). Multi-algorithm feature selection identified JAML (AMICA1) as a core discriminator of high-versus low-activity LAMs. JAML was upregulated in PSC in bulk cohorts, associated with higher macrophage and lower activated CD8+ T-cell infiltration, showed enhanced cell-cell communication signatures, and spatially co-localized with LAM and fibrosis scores in PSC tissue sections. In vitro, JAML upregulation accompanied NF-κB p65 phosphorylation and PD-L1 expression in LPS/IL-6-stimulated macrophages, and JAML knockdown attenuated these responses. CONCLUSION: Integrating TWAS with single-cell and spatial transcriptomics highlights a genetically linked, highly active LAM state in PSC and nominates JAML + LAMs as a testable effector population potentially connecting genetic susceptibility to peribiliary fibrosis, with in vitro evidence supporting a JAML/NF-κB/PD-L1 inflammatory axis.
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Harnessing Machine Learning and Multiomics to Construct a Tumor-Specific T Cell Signature for Prognostic Assessment and Precision Medicine in Lung Adenocarcinoma.
BACKGROUND: T cells are pivotal in mediating antitumor immunity in lung adenocarcinoma (LUAD). In this study, we aimed to profile T cell-related gene (TRG) expression and develop a prognostic indicator to identify patients with LUAD who may derive greater benefit from immunotherapy. PATIENTS AND METHODS: Transcriptomic and clinical data of patients with LUAD were sourced from The Cancer Genome Atlas and Gene Expression Omnibus databases. The prognostic relevance of tumor-infiltrating T cells was assessed, and TRGs were further pinpointed through single-cell RNA-seq (scRNA-seq) analysis. Weighted gene coexpression network analysis identified LUAD-specific modules. A T cell-related gene prognostic indicator (TRGPI) was subsequently developed using a machine learning framework, with the RSF + Ridge model chosen on the basis of cross-cohort performance. We further employed spatial transcriptomics to evaluate the most impactful prognostic TRG, providing spatial context to its expression patterns. RESULTS: Increased T cell infiltration correlated with improved survival outcomes in LUAD. The TRGPI, derived from both scRNA-seq and bulk transcriptomic data, demonstrated robust prognostic and predictive capabilities across multiple cohorts. Patients with a low TRGPI exhibited enhanced overall survival, more active immune and antibacterial pathways, a higher tumor mutation burden, and more favorable predicted responses to immunotherapy. TPI1 was identified as the most impactful prognostic TRG, and spatial transcriptomics analysis and functional assays further established the oncogenic role of TPI1 in LUAD. CONCLUSIONS: This study developed a novel, robust TRGPI that accurately predicts patient prognosis and immunotherapy responses in LUAD, providing a valuable tool for precision medicine and personalized treatment strategies.
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Single-cell RNA sequencing of mouse lower respiratory tract epithelial cells: A meta-analysis.
The respiratory system is a vital component of our body, essential for both oxygen uptake and immune defense. Knowledge of cellular composition and function in different parts of the respiratory tract provides the basis for a better understanding of the pathological processes involved in various diseases such as chronic respiratory diseases and cancer. Single-cell RNA sequencing (scRNA-seq) is a proficient approach for the identification and transcriptional characterization of cellular phenotypes. Although the mouse is an essential tool for the study of lung development, regeneration, and disease, a scRNA-seq mouse atlas of the lung in which all epithelial cell types are included and annotated systematically is lacking. Here, we established a single-cell transcriptome landscape of the mouse lower respiratory tract by performing a meta-analysis of seven different studies in which mouse lungs and trachea were analyzed by droplet and/or plate-based scRNA-seq technologies. We provide information on the best markers for each epithelial cell type, propose surface markers for the isolation of viable cells, harmonized the annotation of cell types, and compare the mouse single-cell transcriptomes with human scRNA-seq data of the lung.
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Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy response in colon cancer.
OBJECTIVE: This study explored the interactions between ferroptosis and lipid metabolism in colon cancer, established a prognostic model to elucidate immune microenvironment heterogeneity, and evaluated the prospects of immunotherapy. METHODS: Transcriptome sequencing and single-cell transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed. Nonnegative matrix factorization clustering and weighted gene coexpression network analysis identified ferroptosis- and lipid metabolism-related genes. Machine learning algorithms including support vector machine, random forest, extreme gradient boosting, and least absolute shrinkage and selection operator regression were used to construct a prognostic model. Expression patterns of selected genes were validated via Human Protein Atlas and immunohistochemistry. RESULTS: We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. Gene set enrichment analysis (GSEA) revealed that the high-risk group was significantly enriched in hypoxia, tumor angiogenesis, epithelial-mesenchymal transition (EMT), and extracellular matrix (ECM) component synthesis and interactions, suggesting enhanced invasiveness and metastatic potential. Conversely, the low-risk group was enriched in biological processes related to oxidation, lipid metabolism, and ferroptosis. Moreover, the high-risk group exhibited more pronounced stromal infiltration and immunosuppressive activity within the tumor microenvironment, suggesting a greater tendency toward immune escape. In contrast, the low-risk group showed better responses to immunotherapy, a finding validated across multiple real-world immunotherapy datasets. Additionally, cell-cell communication analysis based on single-cell datasets revealed that M2 macrophages might be associated with T-cell exhaustion through SPP1-CD44 ligand-receptor interactions, thereby exerting immunosuppressive effects. Finally, immunohistochemistry (IHC) experiments confirmed the differential expression patterns of the SHH, WDR72, and EPOP genes between tumor and normal tissues, corroborating our findings at the mRNA level. CONCLUSION: In this study, we conducted a comprehensive analysis of ferroptosis-lipid metabolism interactions in colon cancer by integrating bulk transcriptomic and single-cell RNA sequencing data. The prognostic model constructed on the basis of lipid metabolism and ferroptosis-related genes has potential as an independent prognostic biomarker for colon cancer patients and may serve as a predictor of immunotherapy response, facilitating the optimization of personalized therapeutic strategies.
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Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data.
Type B adverse drug reactions (ADRs) are iatrogenic immune-mediated syndromes with mechanistic etiologies that remain incompletely understood. Some of the most severe ADRs, including delayed drug hypersensitivity reactions, are T-cell mediated, restricted by specific human leukocyte antigen risk alleles and sometimes by public or oligoclonal T-cell receptors (TCRs), central to the immunopathogenesis of tissue-damaging response. However, the specific cellular signatures of effector, regulatory, and accessory immune populations that mediate disease, define reaction phenotype, and determine severity have not been defined. Recent development of single-cell platforms bringing together advances in genomics and immunology provides the tools to simultaneously examine the full transcriptome, TCRs, and surface protein markers of highly heterogeneous immune cell populations at the site of the pathological response at a single-cell level. However, the requirement for advanced bioinformatics expertise and computational hardware and software has often limited the ability of investigators with the understanding of diseases and biological models to exploit these new approaches. Here we describe the features and use of a state-of-the-art, fully integrated application for analysis and visualization of multiomic single-cell data called Visual Genomics Analysis Studio (VGAS). This unique user-friendly, Windows-based graphical user interface is specifically designed to enable investigators to interrogate their own data. While VGAS also includes tools for sequence alignment and identification of associations with host or organism genetic polymorphisms, in this review we focus on its application for analysis of single-cell TCR-RNA-Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE)-seq, enabling holistic cellular characterization by unbiased transcriptome and select surface proteome. Critically, VGAS does not require user-directed coding or access to high-performance computers, instead incorporating performance-optimized hidden code to provide application-based fast and intuitive tools for data analyses and production of high-resolution publication-ready graphics on standard specification laptops. Specifically, it allows analyses of comprehensive single-cell TCR sequencing (scTCR-seq) data, detailing (i) functional pairings of α-β heterodimer TCRs, (ii) one-click histograms to display entropy and gene rearrangements, and (iii) Circos and Sankey plots to visualize clonality and dominance. For unbiased single-cell RNA sequencing (scRNA-seq) analyses, users extract cell transcriptome signatures according to global structure via principal component analysis, t-distributed stochastic neighborhood embedding, or uniform manifold approximation and projection plots, with overlay of scTCR-seq enabling identification and selection of the immunodominant TCR-expressing populations. Further integration with similar sequence-based detection of surface protein markers using oligo-labeled antibodies (CITE-seq) provides comparative understanding of surface protein expression, with differential gene or protein analyses visualized using volcano plot or heatmap functions. These data can be compared to reference cell atlases or suitable controls to reveal discrete disease-specific subsets, from epithelial to tissue-resident memory T-cells, and activation status, from senescence through exhaustion, with more finite transcript expression displayed as violin and box plots. Importantly, guided tutorial videos are available, as are regular application updates based on the latest advances in bioinformatics and user feedback.
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Multi-omics analysis of tumor necrosis factor superfamily 4 reveals its prognostic value with T cell exhaustion feature in cancer.
BACKGROUND: As a member of the tumor necrosis factor (TNF) family, TNF superfamily 4 (TNFSF4) plays a crucial role in various immune-related processes. However, its biological function in pan-cancer remains largely unexplored. METHODS: RNA-sequencing (RNA-seq) data and corresponding clinical variables were obtained from the Cancer Genome Atlas (TCGA). Immunotherapy cohorts were retrieved from Gene Expression Omnibus (GEO). Tumor Immune Estimation Resource was used to evaluate tumor-infiltrating immune cell levels. The Tumor Immune Single-cell Hub (TISCH) 2 database was used to examine TNFSF4 expression across various tumor cell subsets. Gene set enrichment analysis (GSEA) was performed to investigate TNFSF4-associated signaling pathways. RESULTS: Bioinformatic analyses of TNFSF4 across TCGA cancers revealed that TNFSF4 expression was highly increased in cancers compared to normal tissues. Further immunohistochemistry staining of multiple tumor samples validated this finding. Univariate Cox regression and survival analyses identified TNFSF4 as a risk factor in most cancers. Furthermore, TNFSF4 expression increased with tumor stage progression in several cancers. In the two immunotherapy cohorts from the GEO database, the proportion of partial response/complete response patients was higher in the TNFSF4 low-expression group. Single-cell RNA-seq data from the TISCH database indicated that TNFSF4 expression was predominantly observed in proliferative and exhausted T cells. Correlation analysis demonstrated a positive association between TNFSF4 and immunomodulatory genes. Ultimately, GSEA revealed that TNFSF4 was related to immune response and epithelial-mesenchymal transition pathways. CONCLUSIONS: This multi-omics analysis highlights the role of TNFSF4 in tumor progression and immune modulation. High TNFSF4 expression correlates with poor survival and may affect immunotherapy efficacy, suggesting its potential prognostic biomarker and therapeutic target.
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Assembly and Exploration of a Single Cell Atlas of the Drosophila Larval Ventral Cord. Identification of Rare Cell Types.
Single-cell RNA sequencing provides a new approach to an old problem: how to study cellular diversity in complex biological systems. This powerful tool has been instrumental in profiling different cell types and investigating, at the single-cell level, cell states, functions, and responses. However, mining these data requires new analytical and statistical methods for high-dimensional analyses that must be customized and adapted to specific goals. Here we present a custom multistage analysis pipeline which integrates modules contained in different R packages to ensure flexible, high-quality RNA-seq data analysis. We describe this workflow step by step, providing the codes, explaining the rationale for each function, and discussing the results and the limitations. We apply this pipeline to analyze different datasets of Drosophila larval ventral cords, identifying and describing rare cell types, such as astrocytes and neuroendocrine cells. This multistage analysis pipeline can be easily implemented by both novice and experienced scientists interested in neuronal and/or cellular diversity beyond the Drosophila model system. © 2021 US Government.
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Meta-atlas of Juvenile and Adult Enteric Neuron scRNA-seq for Dataset Comparisons and Consensus on Transcriptomic Definitions of Enteric Neuron Subtypes.
BACKGROUND: The enteric nervous system (ENS) is a complex network of interconnected ganglia within the gastrointestinal (GI) tract. Among its diverse functions, the ENS detects bowel luminal contents and coordinates the passing of stool. ENS defects predispose to GI motility disorders. Previously, distinct enteric neuron types were cataloged by dye-filling techniques, immunohistochemistry, retrograde labeling, and electrophysiology. Recent technical advances in single cell RNA-sequencing (scRNA-seq) have enabled transcriptional profiling of hundreds to millions of individual cells from the intestine. These data allow cell types to be resolved and compared to using their transcriptional profiles ("clusters") rather than relying on antibody labeling. As a result, greater diversity of enteric neuron types has been appreciated. Because each scRNA-seq study has relied on different methods for cell isolation and library generation, numbers of neuron clusters and cell types detected differs between analyses. Cell counts in each dataset are particularly important for characterization of rare cell types since small numbers of profiled cells may not sample rare cell types. Importantly, each dataset, depending on the isolation methods, may contain different proportions of cells that are not detected in other datasets. Aggregation of datasets can effectively increase the total number of cells being analyzed and can be helpful for confirming the presence of low-abundance neuron types that might be absent or observed infrequently in any single dataset. RESULTS: Here we briefly systematically review each Mus musculus single cell or single nucleus RNA-sequencing enteric nervous system dataset. We then reprocess and computationally integrate these select independent scRNA-seq enteric neuron datasets with the aim to identify new cell types, shared marker genes across juvenile to adult ages, dataset differences, and achieve some consensus on transcriptomic definitions of enteric neuronal subtypes. CONCLUSIONS: Data aggregation generates a consensus view of enteric neuron types and improves resolution of rare neuron classes. This meta-atlas offers a deeper understanding of enteric neuron diversity and may prove useful to investigators aiming to define alterations among enteric neurons in disease states. Future studies face the challenge of connecting these deep transcriptional profiles for enteric neurons with historical classification systems.
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A single-cell analysis of the Arabidopsis vegetative shoot apex.
The shoot apical meristem allows for reiterative formation of new aerial structures throughout the life cycle of a plant. We use single-cell RNA sequencing to define the cellular taxonomy of the Arabidopsis vegetative shoot apex at the transcriptome level. We find that the shoot apex is composed of highly heterogeneous cells, which can be partitioned into 7 broad populations with 23 transcriptionally distinct cell clusters. We delineate cell-cycle continuums and developmental trajectories of epidermal cells, vascular tissue, and leaf mesophyll cells and infer transcription factors and gene expression signatures associated with cell fate decisions. Integrative analysis of shoot and root apical cell populations further reveals common and distinct features of epidermal and vascular tissues. Our results, thus, offer a valuable resource for investigating the basic principles underlying cell division and differentiation in plants at single-cell resolution.