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huSA: a comprehensive database for multi-dimensional resolution of bulk, single cell and spatial transcription profiles in skin diseases.
BACKGROUND: Skin diseases are among the most prevalent conditions worldwide, posing significant threats to human health by causing physical discomfort, psychological distress, and reduced quality of life. With the rapid advancement of high-throughput technologies, a substantial number of transcriptomic datasets, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq, have been generated in the field of dermatology over the past decade. However, the lack of effective integration and standardized analysis pipelines limits the full utilization of these valuable resources in skin disease research. OBJECTIVES: To address this gap, we aimed to construct a comprehensive, integrative, and user-friendly atlas that enables systematic exploration of skin transcriptomic data across multiple diseases and modalities. METHODS: We developed the Human Skin Atlas (huSA) ('https://humanskinatlas.com/index.html'), a publicly accessible database that incorporates data from 17 skin diseases and 63 independent datasets, including 1 434 scRNA-seq, 63 spatial transcriptomics, and 1 502 bulk RNA-seq samples. The database provides standardized cell-type annotations, differential gene expression analysis, cell-cell interaction mapping, pathway and metabolic module enrichment, transcription factor regulatory inference, and differentiation state assessment for scRNA-seq data. Data from identical skin diseases were further integrated to enhance biological signal detection. For visualization, we embedded the 'cell × gene' and 'Cirrocumulus' platforms, offering interactive and customizable gene expression visualizations at both single-cell and spatial levels with user-defined parameters. RESULTS: The huSA enables both individual dataset analysis and cross-dataset integration, providing robust, consistent, and scalable insights into skin disease biology. Demonstration analyses confirmed that results derived from either single datasets or aggregated multi-dataset integrations exhibited high reliability and biological relevance. The platform successfully supports diverse research needs, including cell-type-specific expression profiling, regulatory network construction, and spatial transcriptomic exploration. CONCLUSIONS: The Human Skin Atlas (huSA) represents a state-of-the-art integrative resource for the skin research community. By offering multiscale analyses and interactive visualization tools, the huSA accelerates the discovery of molecular mechanisms underlying skin diseases and facilitates translational research efforts aimed at improving skin health.
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Integrative Single-Cell and Spatial Transcriptomics Identifies CRIP1+ Prehypertrophic Chondrocytes as Inflammatory Hubs and a Valuable Diagnostic Biomarker for Osteoarthritis.
Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage destruction, yet the heterogeneity of transitional chondrocyte states and their contributions to pathogenesis remain incompletely understood. In this study, we constructed a single-cell transcriptomic atlas of human knee cartilage from 16 OA samples and 2 non-OA normal donors. High-resolution sub-clustering of the prehypertrophic chondrocytes (preHTC) compartment revealed eight transcriptionally distinct subtypes, among which CRIP1+ preHTC emerged as the most significantly expanded population in OA, particularly in weight-bearing regions. Pseudotemporal trajectory inference and regulon analysis identified EGR3 as a candidate key regulator for CRIP1+ preHTC. High-dimensional weighted gene coexpression network analysis (hdWGCNA) demonstrated matrix-remodeling and proteostasis-stress transcriptional programs were highly active in CRIP1+ preHTC. Cell-cell communication analysis uncovered that CRIP1+ preHTC acts as a central signaling hub in OA, with markedly enhanced FN1 signaling pathway. Spatial transcriptomics confirmed that CRIP1+ preHTC co-localizes with prefibroblasts (preFC) in pathological niches and interacts with other cells in the OA microenvironment. Finally, a machine-learning-based 10-gene classifier (JUN, MCOLN3, RHOC, AKR7A2, HSPG2, AK4, B4GALT2, CLSTN1, LRRC41, and GADD45A) derived from CRIP1+ preHTC-associated hub genes accurately discriminated OA from normal tissues in two independent bulk RNA-seq cohorts. Collectively, these findings suggest that CRIP1+ preHTC may represent a pathogenic cell state in OA, provide a multi-layered molecular framework that links a specific chondrocyte transitional subset to cartilage degradation, and offer a potentially useful gene signature for OA diagnosis.
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Cell atlases and the developmental foundations of the phenotype.
It is widely acknowledged that development shapes phenotypes, yet the extent to which genes with similar expression patterns during development lead to equivalent organismal phenotypes when mutated remains unclear. Here, we propose addressing this issue, which we term the [Formula: see text]evelopment-to-[Formula: see text]henotype, or [Formula: see text]-[Formula: see text], rule, by leveraging single-cell gene expression atlases and phenotypic ontologies, using Caenorhabditis elegans as a model system. This framework quantifies the proportionality between developmental expression and phenotypic similarities, demonstrating that the relationship holds on average. Genes that strongly fulfill the rule exhibit broad "housekeeping" expression and are associated with systemic phenotypes, whereas weak similarities correspond to specific expression patterns and specialized phenotypes. Deviations from the [Formula: see text]-[Formula: see text] rule provide insights into developmental divergence and phenotypic degeneracy, highlighting genes with narrow functional roles but systemic phenotypic impact. Furthermore, genes that closely adhere to the rule exhibit the highest pleiotropic impact on organismal traits. Our analysis also identifies cell types, such as ASK neurons, as key mediators of phenotype-specific gene contributions, exemplified by their association with chemosensory behavior and chemotaxis. These findings validate the [Formula: see text]-[Formula: see text] rule and underscore the role of cells as critical mediators of the genotype-phenotype map, offering a unified framework to understand the developmental origins of phenotypic complexity.
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CoxFormer enables spatial omics inference with multimodal generative modeling.
Gene co-expression maps transcriptome-wide gene-gene relationships, yet high-quality estimates cover less than half the genome. Meanwhile, spatial omics either profiles restricted in situ panels or lacks cellular resolution. Extending co-expression transcriptome-wide could overcome these limitations by inferring unassayed gene expression at subcellular resolution. Here we show that CoxFormer integrates literature-derived gene knowledge with co-expression networks from bulk tissues and large-scale single-cell atlases to learn 512-dimensional representations for 32,016 human genes. These embeddings capture functional gene relationships and serve as a generative prior for spatial inference across platforms and modalities. Without requiring a matched single-cell RNA-sequencing reference, CoxFormer supports four applications beyond measured genes: histology-based expression imputation, gene activity prediction from chromatin accessibility, subcellular super-resolution inference, and pathological region detection. Together, CoxFormer extends gene embedding from gene- and cell-level tasks to whole-transcriptome spatial inference, providing a unified framework for biological analysis beyond the limited gene coverage of current spatial omics technologies.
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Mapping fibroblast plasticity across non-cancer lung diseases at single-cell resolution.
BACKGROUND: Fibroblast plasticity underlies a wide spectrum of pulmonary diseases, yet the molecular programs governing fibroblast state transitions in non-malignant contexts remain poorly understood. METHODS: We integrated 1,470,730 single-cell transcriptomes across three non-cancer lung diseases to construct a comprehensive fibroblast atlas. Integrated clustering and regulon analyses were used to delineate fibroblast subtypes, disease-associated transcriptional programs, and state transitions. ANTXR1, identified as a representative regulator, was validated by tissue immunohistochemistry and siRNA knockdown in primary lung fibroblasts under TGF-β stimulation. RESULTS: Compared with malignancy, fibroblasts in non-cancer lungs exhibited relatively modest transcriptional alterations but maintained distinct disease-associated phenotypes. We further identified a set of fibroblast-specific genes unique to non-malignant contexts, defining molecular features that distinguish fibrotic remodeling from tumor-associated stromal activation. Analysis of fibroblast subtype- and disease-specific regulons revealed 76 gene signatures that were significantly inversely correlated with lung function and distinguished chronic from acute fibrotic fibroblast states. Functional validation demonstrated that ANTXR1 knockdown markedly reduced COL1A1 expression, underscoring its role in fibroblast activation. CONCLUSIONS: This work establishes a comprehensive single-cell fibroblast atlas of non-cancer lung diseases, uncovering disease- and subtype-specific regulatory programs and highlighting potential therapeutic targets for fibrotic remodeling.
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Combining a glycolysis‑related prognostic model based on scRNA‑Seq with experimental verification identifies ZFP41 as a potential prognostic biomarker for HCC.
Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis, and its heterogeneity affects the response to clinical treatments. Glycolysis is highly associated with HCC therapy and prognosis. The present study aimed to identify a novel biomarker for HCC by exploring the heterogeneity of glycolysis in HCC. The intersection of both marker genes of glycolysis‑related cell clusters from single‑cell RNA sequencing analysis and mRNA data of liver HCC from The Cancer Genome Atlas were used to construct a prognostic model through Cox proportional hazard regression and the least absolute shrinkage and selection operator Cox regression. Data from the International Cancer Genome Consortium were used to validate the results of the analysis. Immune status analysis was then conducted. A significant gene in the prognostic model was identified as a potential biomarker and was verified through in vitro experiments. The results revealed that the glycolysis‑related prognostic model divided patients with HCC into high‑ and low‑risk groups. A nomogram combining the model and clinical features exhibited accurate predictive ability, with an area under the curve of 0.763 at 3 years. The high‑risk group exhibited a higher expression of checkpoint genes and lower tumor immune dysfunction and exclusion scores, suggesting that this group may be more likely to benefit from immunotherapy. The tumor tissues had a higher zinc finger protein (ZFP)41 mRNA and protein expression compared with the adjacent tissues. In vitro analyses revealed that ZFP41 played a crucial role in cell viability, proliferation, migration, invasion and glycolysis. On the whole, the present study demonstrates that the glycolysis‑related prognostic gene, ZFP41, is a potential prognostic biomarker and therapeutic target, and may play a crucial role in glycolysis and malignancy in HCC.
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The Effect of Cuproptosis-Related Proteins on Macrophage Polarization in Mesothelioma is Revealed by scRNA-seq.
High invasiveness mesothelioma is a malignant tumor of the peritoneum or pleura. The effect of cuproptosis on mesothelioma (MESO) is still unknown, though. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets were used to identify differential genes linked to cuproptosis in mesothelioma. Multigene features were then created to assess the course of the disease. Use single-cell data and in vitro validation to uncover crucial gene regulation mechanisms. In MESO, we found nine differentially expressed genes linked to cuproptosis. Using univariate Cox and LASSO regression techniques, a 3-gene feature (P < 0.05) was created, showing a good predictive potential for survival time. According to the risk score, patients in the low-risk subset had a considerably greater survival rate than those in the high-risk subset (P = 0). The similar survival pattern and prediction performance are also seen in the validation queue. The findings of the drug sensitivity research indicate that in high-risk patients, vinblastine, paclitaxel, gefitinib, and erlotinib are sensitive medications (P < 0.05). Classical monocytes were identified as core cells connected to cuproptosis by the CellChat results. SLC31A1 is implicated in the positive regulation of M2 macrophage polarization, according to cell subtype analysis and in vitro confirmation. Genes linked to cuproptosis have a major influence on tumor immunity and can predict how MESO will progress.
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Integrated analysis of scRNA-seq and TCR-seq on T cells in aneurysmal subarachnoid hemorrhage: A descriptive, hypothesis-generating case study.
This studthe transcriptomic profiles and T cell receptor (TCR) repertoires of T cells in paired cerebrospinal fluid (CSF) and peripheral blood (PB) from 2 patients with aneurysmal subarachnoid hemorrhage (SAH). This descriptive work primarily generates research hypotheses and does not explore causal biological mechanisms. Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) were performed on T cells isolated from CSF and PB samples collected on day 7 post-onset from 2 male patients. A series of descriptive analyses were conducted, including T cell subset classification, pseudotime trajectory reconstruction, pathway enrichment, CellChat-based cell-cell communication analysis, and TCR clonality assessment. Public scRNA-seq data of CSF samples from 8 healthy individuals were downloaded from the Gene Expression Omnibus database and used as external healthy controls. A total of 6058 T cells for scRNA-seq and 5008 T cells for TCR-seq were analyzed in this study. Five distinct T cell subsets were observed. In these 2 patients, CSF-derived T cells showed stronger signaling pathway activation and increased clonal expansion than PB-derived T cells. Compared with healthy controls from the public dataset, T cells from SAH patients presented enhanced signaling pathway activity and more extensive cell-cell interactions. All above observations are limited to these 2 individuals and cannot be generalized to all SAH patients without large independent cohort validation. This descriptive case-based study establishes a high-resolution single-cell atlas of T cells from 2 SAH patients. The observed differences between CSF and PB, as well as between SAH patients and healthy controls, provide directions for future mechanistic investigations.
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Machine learning integrated bulk and single cell transcriptomic analyses reveal oxidative stress associated mitochondrial polarization signatures in diabetic foot ulcers.
Diabetic foot ulcers (DFU) are a major complication of diabetes, and its pathogenesis remains incompletely elucidated. Converging evidence indicates that oxidative stress and dysregulated mitochondrial polarization participate in DFU progression, nominating these processes as therapeutically actionable targets. This study integrates bulk and single-cell transcriptomic data with machine learning to reconstruct cross-scale, cell type-resolved molecular atlases and regulatory networks. Macrophages and fibroblasts emerged as communication hubs, dominating pathway enrichment and ligand-receptor programs such as macrophage migration inhibitory factor signaling pathway (MIF), ANNEXIN signaling pathway, and COMPLEMENT signaling pathway. Peptidylprolyl isomerase F (PPIF), which encodes cyclophilin D (CypD) and apolipoprotein E (APOE) were further prioritized as putative drivers within macrophages and fibroblasts, and a five-gene classifier was derived with robust performance (internal/external AUC = 0.833/0.933). Within DFU lesions, under the control of non-coding RNA circuitry, SOX5 may shape the inflammatory microenvironment, APOE may participate in lipid-metabolic remodeling, and PPIF (CypD) likely links reactive oxygen species (ROS) accumulation to a p53-dependent mitochondrial death pathway (necroptosis/apoptosis). Orthogonal validation showed significantly increased CypD in diabetic foot ulcer skin (DFUS) and diabetic foot ulcer tendon (DFUT) relative to diabetic foot skin (DFS) and DFT (Diabetic foot tendon), with up-regulated p53 and Cytc and down-regulated ApoE in DFUS; in primary foot-skin fibroblasts, a high-glucose plus tert-butyl hydroperoxide (HG+TBHP) model reproduced elevated ROS, loss of mitochondrial Δψm (mitochondrial membrane potential), growth restriction, and apoptosis, supporting a ROS-CypD/mPTP (mitochondrial permeability transition pore)-Δψm depolarization-p53/Cytc apoptosis axis. The delineated PPIF-centered regulatory network includes upstream transcription factors CEBPB/REL/SPI1 and a downstream ceRNA axis comprising miR-128-3p/miR-23a-3p-long non-coding RNA OIP5-AS1. Additionally, the significant role of polarization-specific reprogramming in regulating macrophage function highlights therapeutic strategies focused on metabolic reprogramming and inhibition of the PPIF/mPTP pathway. Collectively, a cell type-resolved molecular map of DFU is provided, healing-relevant cell populations and regulatory circuits are prioritized, and a translational, testable intervention framework is proposed.
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Macrophage heterogeneity and oncogenic mechanisms in lung adenocarcinoma: insights from scRNA-seq analysis and predictive modeling.
BACKGROUND: Macrophages play a dual role in the tumor microenvironment(TME), capable of secreting pro-inflammatory factors to combat tumors while also promoting tumor growth through angiogenesis and immune suppression. This study aims to explore the characteristics of macrophages in lung adenocarcinoma (LUAD) and establish a prognostic model based on macrophage-related genes. METHOD: We performed scRNA-seq analysis to investigate macrophage heterogeneity and their potential pseudotime evolutionary processes. Specifically, we used scRNA-seq data processing, intercellular communication analysis, pseudotime trajectory analysis, and transcription factor regulatory analysis to reveal the complexity of macrophage subpopulations. Data from The Cancer Genome Atlas (TCGA) was used to assess the impact of various macrophage subtypes on LUAD prognosis. Univariate Cox regression was applied to select prognostic-related genes from macrophage markers. We constructed a prognostic model using Lasso regression and multivariate Cox regression, categorizing LUAD patients into high and low-risk groups based on the median risk score. The model's performance was validated across multiple external datasets. We also examined differences between high and low-risk groups in terms of pathway enrichment, mutation information, tumor microenvironment(TME), and immunotherapy efficacy. Finally, RT-PCR confirmed the expression of model genes in LUAD, and cellular experiments explored the carcinogenic mechanism of COL5A1. RESULTS: We found that signals such as SPP1 and MIF were more active in tumor tissues, indicating potential oncogenic roles of macrophages. Using macrophage marker genes, we developed a robust prognostic model for LUAD that effectively predicts prognosis and immunotherapy efficacy. A nomogram was constructed to predict LUAD prognosis based on the model's risk score and other clinical features. Differences between high and low-risk groups in terms of TME, enrichment analysis, mutational landscape, and immunotherapy efficacy were systematically analyzed. RT-PCR and cellular experiments supported the oncogenic role of COL5A1. CONCLUSION: Our study identified potential oncogenic mechanisms of macrophages and their impact on LUAD prognosis. We developed a prognostic model based on macrophage marker genes, demonstrating strong performance in predicting prognosis and immunotherapy efficacy. Finally, cellular experiments suggested COL5A1 as a potential therapeutic target for LUAD.