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A single-cell atlas of the human airways from patients with allergic rhinitis comorbid with asthma.
BACKGROUND: Single-cell RNA profiling has already been applied to normal airway samples, but no data set includes brushings collected from patients with allergic rhinitis combined with asthma (ARcoAS) at distinct and well-identified microanatomical regions in the airways. OBJECTIVE: To investigate cell population distributions and transcriptional changes along the upper and lower airways of patients with ARcoAS by using single-cell RNA profiling. METHODS: Nasal and bronchial brush cells from patients with ARcoAS and healthy volunteers were collected for single-cell RNA sequencing analysis. The cellular clusters, differential gene analysis, cellular pathway enrichment, mimetic temporal analysis, and cellular communication were analyzed between the nasal and bronchial airways. RESULTS: A similar alteration in the cell profiles of the upper and lower airways was observed under chronic inflammatory conditions. A total of 3613 (57%) shared up-regulated genes, and 5410 (66%) shared down-regulated genes were identified between the upper and lower airways of ARcoAS. Impaired structure of ciliated cells and epithelial barrier was observed in diseased nasal and bronchial airways. The intercellular communication analysis revealed a decreased number and strength of intercellular interactions between epithelial cells and immune cells in the ARcoAS group. We found that the GZMA-PARD3 pathway disrupts the airway epithelial barrier in vitro. CONCLUSION: Our findings suggest that compromised ciliated cells and aberrant proliferation of basal cells may drive eosinophilic inflammation in response to type 2 inflammation in patients with ARcoAS. Robust characterization of a single-cell cohort in allergic airways establishes a valuable resource for future investigations and provides potential targets for tailored treatment.
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Single-nucleus transcriptome profiling of wild boar and domestic pig intestines reveals the spatiotemporal dynamics of immunity and nutrient absorption.
Pigs are important agricultural animals and valuable biomedical models. The intestinal tract is a crucial digestive organ and the largest immune organ. However, the function of pig intestines at single-cell resolution remains poorly understood. Here, we created single-nucleus transcriptomic maps of the ileum and cecum for wild boars, Bama Xiang pigs, and Large White pigs, aged 30, 42, 150, and 730 d. Our atlas revealed 19 major cell types and 58 cellular subtypes, including several previously uncharacterized cellular subtypes, such as EBF1+ fibroblasts, TMEM163+ macrophages, and neuron subtypes expressing FCAMR. We discovered and confirmed that ileum neurons, rather than cecum neurons, can regulate inflammatory responses, highlighting interactions of neurons with dendritic cells (DCs) and lymphatic endothelial cells (LECs) through the NAMPT-INSR ligand-receptor pair in the ileum. Microbial-derived short-chain fatty acids, such as propionic acid and acetic acid, enhanced plasma cell differentiation and humoral immune responses by upregulating XBP1 and SDC1 expression, thereby endowing wild boars with a stronger immune response than domestic pigs. We identified and validated the enterocyte-enriched transcription factors FOXO1 and NR1H4 in wild boars, which contributed to the superior nutrient absorption of wild boars relative to domestic pigs. Furthermore, we comprehensively characterized the postnatal development of wild boar intestinal cells and revealed that plasma cells presented the most pronounced developmental changes. We identified highly conserved cell types and features between pig and human intestines. Overall, our work provides a foundation for improving pig feed conversion and health while also providing a reference for research on human intestinal diseases.
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Single-Cell Annotation and Localization via Integrating Spatial Transcriptomics Maps the Mouse Ocular Atlas and RAO Dynamics.
Retinal artery occlusion (RAO), a blinding emergency, demands clarification of cellular spatiotemporal dynamics for targeted therapies. Single-cell RNA-seq is powerful but lacks spatial context, whereas spatial transcriptomics struggles with cell segmentation errors and low transcript capture efficiency. To overcome these, we developed the ASCAL (Automated Single-Cell Annotation and Localization) pipeline, integrating complementary spatial transcriptomics techniques: single-nucleus-resolution SeekSpace for cell annotation reference and uniform-coverage Stereo-seq for localization of annotated cells. This strategy enables automated annotation and localization of large-scale scRNA-seq datasets with minimal spatial sections. Leveraging ASCAL, we constructed a mouse whole-eye single-cell spatial atlas precisely mapping cell types across the ciliary body and retina. Moreover, we revealed pronounced spatial immune activation specifically enriched in the ganglion cell layer (GCL) in the RAO model, further validated by immunofluorescence staining. We also uncovered a selective depletion of active Rods. Importantly, RNAscope assays independently validated both the peripheral localization of this active Rod subcluster and its pronounced loss following RAO. Overall, this study provides a comprehensive spatial cell atlas of the whole eye and offers novel spatial-resolution insights into the pathological mechanisms of RAO, serving as a valuable resource for deciphering the physiological and pathological landscapes of the eye at single-cell resolution.
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Single-Cell Atlas of Human Temporomandibular Joint Condyle Identifies MXD4 as a Regulator of Chondrogenesis.
INTRODUCTION AND AIMS: The temporomandibular joint condyle (TMJC) is essential for mandibular and temporomandibular joint (TMJ) development. However, its cellular and molecular dynamics during human embryogenesis remain poorly understood. METHODS: We constructed a single-cell transcriptomic atlas of the human TMJC spanning five embryonic stages from 12 to 25 gestational weeks (GW12-25). Single-cell RNA sequencing was performed to identify cell types, and Monocle 3 and SCENIC analyses were used to infer developmental trajectories and identify key transcriptional regulators. Candidate regulators were functionally validated using zebrafish models. RESULTS: Single-cell RNA sequencing identified 20 major cell types within the TMJC. Immune cells were sparse at GW19 but expanded markedly by GW22. Trajectory analysis revealed a differentiation pathway from mesenchymal stem cells to chondrocytes. SCENIC analysis highlighted multiple transcription factors regulating distinct cell populations, among which MXD4 was identified for the first time as a candidate transcriptional regulator of cartilage development. Functional studies showed that mxd4 knockout in zebrafish led to craniofacial developmental abnormalities. CONCLUSION: This single-cell atlas provides a comprehensive resource for understanding the cellular and molecular mechanisms underlying TMJC development. MXD4 is identified as a candidate regulator in condylar morphogenesis. CLINICAL RELEVANCE: Preliminary investigation of the potential regulatory role of MXD4 in condylar cartilage formation may provide a theoretical basis for diagnostic, therapeutic, and translational studies of craniofacial malformations associated with condylar hypoplasia.
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cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution.
Single-cell analysis has rapidly expanded to produce cell atlases encompassing all human tissues. However, computational methods to deconvolute bulk samples using single-cell reference data have failed to keep pace with the increasing data size. Here we present cellGeometry, which uses non-negative geometric deconvolution (NGD), an intuitive vector projection method featuring non-negative matrix regularisation. Using matrix operations, cellGeometry scales to massive datasets and is ultrafast. Benchmarked using simulations from single-cell/nucleus RNA-Seq datasets with >3 million cells, cellGeometry is more accurate than existing methods and more robust against noise simulating different sequencing chemistries. It identifies outlying residual genes which may unveil pathogenic changes in gene expression and the presence of cell types absent from the reference. cellGeometry's flexible architecture allows merging of single-cell reference signatures to expand the range of cell types being deconvoluted. Validated against real bulk RNA blood and tissue samples, cellGeometry produces more accurate and realistic results.
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Single-cell atlas of the mouse ovary reveals molecular drivers of aging and senescence during the estropausal transition.
Reproductive aging in mice leads to estropause, characterized by estrous cycle irregularity and eventual cessation, yet its underlying mechanism remains unclear. Here, we present a comprehensive single-cell atlas of mouse ovaries across precisely defined reproductive stages-from young (regular cycling) through the estropausal transition (regular vs. irregular cycling) to post-estropause (acyclic)-and of ovary-specific senescent cells defined by high senescence-associated β-galactosidase activity. We mapped transcriptomic dynamics of ovarian aging and characterized the molecular features of ovarian senescent cells. Our analyses revealed that during the estropausal transition, irregularly cycling ovaries exhibited accelerated aging and cellular senescence features compared with regularly cycling counterparts, including increased transcriptional noise, altered conserved aging pathways such as oxidative phosphorylation and proteostasis, hormone dysregulation in granulosa cells, and elevated expression of the senescence marker Cdkn1a and senescence-associated secretory phenotype factors. This atlas delineates the cellular and molecular hallmarks of mouse ovarian aging and ovary-specific senescent cells, providing a resource for understanding the mechanisms underlying the estropausal transition.
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Prognostic model construction and immune microenvironment analysis of pyroptosis-related genes in hepatocellular carcinoma based on single-cell RNA sequencing.
BACKGROUND: Hepatocellular carcinoma (HCC) prognosis continues to be challenging due to tumor heterogeneity and dynamic immunosuppressive microenvironments. Although pyroptosis plays a critical role in tumor-immune interactions, its prognostic significance in HCC at single-cell resolution has not been systematically investigated. METHODS: We analyzed a publicly available single-cell RNA sequencing (scRNA-seq) data from 10 HCC tumors and paired adjacent tissue samples (60,496 cells) to elucidate pyroptosis-related gene (PRG) profiles. Differential expression and functional pathway analyses revealed PRG expression dynamics across cell subtypes. A LASSO-Cox prognostic model was developed using data from the liver hepatocellular carcinoma (LIHC) cohort of The Cancer Genome Atlas (TCGA) (n=365); the model was externally validated with International Cancer Genome Consortium (ICGC) datasets (n=231). Biological validation comprised reverse transcription quantitative polymerase chain reaction (RT-PCR) in HCC cell lines and immunohistochemical analysis of clinical specimens. RESULTS: The scRNA-seq atlas identified 10 cellular clusters with enriched expression of 29 PRGs, primarily in natural killer cells, T lymphocytes, monocytes, and macrophages. The prognostic model developed in this study stratified patients into high-risk and low-risk categories based on eight significant genes, achieving area under the curve (AUC) values of 0.73, 0.65, and 0.69 for overall survival at one-year, two-year, and three-year intervals, respectively. Furthermore, external validation using data from the ICGC confirmed the prognostic model's discriminative ability. Notably, high-risk patients demonstrated enhanced sensitivity to immunotherapy, as indicated by decreased tumor immune dysfunction and exclusion (TIDE) scores and increased expression of the immune checkpoints PD-1 and CTLA4. CONCLUSIONS: This study established a scRNA-seq-derived prognostic model based on PRGs, which offers insights into HCC immune landscape remodeling. The risk score and nomogram integrate tumor stages and pyroptosis-associated signatures, providing a clinical tool for personalized prognosis and therapeutic targeting.
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scGT:integration algorithm for single-cell RNA-seq and ATAC-seq based on graph transformer.
MOTIVATION: Multi-omics analysis of individual cells offers remarkable opportunities for exploring the dynamics and relationships of gene regulatory states across large atlas data. However, the current integration algorithms have limited performance, largely due to ignoring the impact of correlation features within the dataset on the discrepancies between omics. RESULTS: In this study, we propose scGT, a model based on Graph Transformer for single-cell RNA-seq and ATAC-seq data, which leverages the robust graph structures strengthened by correlation features present in each raw dataset to harmonize representations of multi-omics data, enabling the integration of multi-omics and effective label transfer. We compare scGT with other state-of-the-art methods on paired and unpaired datasets. The results show that scGT accomplishes more accurate label transfer and is capable of integrating datasets with millions of cells. Meanwhile, scGT achieves better performance for preserving biological variation during integration. AVAILABILITY AND IMPLEMENTATION: The source code and data used in this article can be found at https://github.com/Jinsl-lab/scGT.
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ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics.
Cell-type annotation remains a major challenge in single-cell and spatial omics analysis. Most existing methods rely on single-cell RNA sequencing (scRNA-seq) references or predefined marker sets. However, the scarcity of high-quality scRNA-seq references and marker sets makes relying on a single approach prone to bias and limits usability. Furthermore, available methods for cell-type annotation in single-cell ATAC-sequencing (scATAC-seq) and spatial transcriptomics datasets perform poorly. Here, we present ScInfeR, a graph-based cell-type annotation method that combines information from both scRNA-seq references and marker sets. By integrating these two data sources, ScInfeR can accurately annotate broad range of cell-types. It employs a hierarchical framework inspired by message-passing layers in graph neural networks to accurately identify cell subtypes. ScInfeR is highly versatile, supporting cell annotation across scRNA-seq, scATAC-seq, and spatial omics datasets. For scATAC-seq, it effectively utilizes chromatin accessibility data, while for spatial transcriptomics, it incorporates spatial coordinate information. Additionally, ScInfeR supports weighted positive and negative markers, allowing users to define marker importance in cell-type classification. Our extensive benchmarking across multiple atlas-scale scRNA-seq, scATAC-seq, and spatial datasets, evaluating 10 existing tools in over 100 cell-type prediction tasks, demonstrated ScInfeR's superior performance. Noteworthy, it exhibits robustness against batch effects arising in these datasets. To facilitate seamless annotation, we developed ScInfeRDB, an interactive database containing manually curated scRNA-seq references and marker sets for 329 cell-types, covering 2497 gene markers in 28 tissue types from human and plant. ScInfeR is available as an R package, with both the tool and database publicly accessible at https://www.swainasish.in/scinfer.
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Single-cell RNA sequencing unravels mosquito biology and host-pathogen interactions.
Single-cell RNA sequencing (scRNA-seq) is fundamentally reshaping the research paradigm of mosquito biology and host-pathogen interactions via its high-resolution transcriptomic profiling capability. This review first outlines the technical workflow and data analysis strategies of scRNA-seq, then highlights key applications of scRNA-seq in constructing mosquito cell atlases, deciphering post-blood-meal transcriptional remodeling, sexual dimorphism, and receptor coexpression patterns. Furthermore, the role of scRNA-seq in revealing the cellular tropism of mosquito-borne pathogens, host immune responses, and the identification of potential intervention targets is discussed. Despite current challenges such as the lack of specific cell markers and insufficient functional validation, scRNA-seq has provided a novel perspective for understanding mosquito biology at the single-cell level and laid an important foundation for developing targeted vector control strategies. Looking forward, integration with spatial transcriptomics, multiomics approaches, and dynamic experimental validation will further advance this field toward mechanistic research and translational applications.