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Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.
Subcellular spatial transcriptomics can resolve tissue architecture at cellular scale, but sparse gene panels and limited detection sensitivity constrain downstream analysis. Existing enhancement methods often require tissue-matched single-cell RNA sequencing (scRNA-seq) references and dataset-specific retraining. Here we show that stPainter, a conditional generative model pretrained on a pan-cancer scRNA-seq atlas, can enhance spatial transcriptomics data without matched references or retraining. Using a latent diffusion architecture guided by Stochastic Differential Equations (SDE), stPainter reconstructs expanded expression profiles from sparse measurements and produces latent representations for clustering and cell-state analysis. When we apply stPainter upon 6 spatial transcriptomics datasets of different cancer types, we demonstrate that our model empowers downstream biological analyses, including fine-grained subpopulation clustering and pathway enrichment. Comparison with spatially resolved proteomics (CODEX) provided independent support for regional agreement between imputed cellular compositions and protein-level tissue organization. These results establish stPainter as a scalable approach for analyzing tumor microenvironments without auxiliary sequencing data.
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An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.
BACKGROUND: As the first organ to develop in utero, the human heart undergoes extensive molecular, structural and metabolic remodeling during development and must sustain its function throughout life. RESULTS: We generate an integrated multiomic atlas of human cardiac cells, combining newly generated and publicly available single-nucleus RNA sequencing datasets from 299 donors and single nucleus ATAC-seq datasets from 106 donors. Developmental and disease-associated processes drive far more extensive molecular remodeling than sex-associated or aging-dependent effects. Across nearly all cardiac cell types, developmental and disease-driven changes exhibit strong overlap at both the transcriptomic and epigenomic levels, revealing widespread reactivation of fetal-associated gene programs beyond cardiomyocytes. Both cardiac development and disease show convergent shifts in intercellular communication, including increased TGFβ signaling. Integration of gene expression and chromatin accessibility data reveals putative cell-type-specific transcriptional factors driving fetal reactivation in major cardiac diseases. Spatial transcriptomics data orthogonally identifies localization of this fetal reactivation signature within spatially distinct niches in ischemic and fibrotic zones of acute myocardial infarction. Finally, we construct a cell-type-resolved enhancer-to-gene linkage map that refines the association of dilated and hypertrophic cardiomyopathy genetic risk loci to downstream target genes. CONCLUSIONS: This study presents a comprehensive multimodal, cell-type-resolved atlas of the human heart, providing a foundation for understanding human cardiac gene regulation across the human lifespan and in cardiac diseases.
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Deciphering the single-cell transcriptomic atlas of donkey (Equus asinus) gonad development in early gestation.
In donkey reproduction, the extended reproductive cycle is a pivotal factor contributing to the low reproductive rate. Gonadal development represents a highly complex biological process that involves sex determination and subsequent progression toward ovarian maturation. Currently, the mechanisms underlying ovarian development in donkeys remain poorly understood. To address this gap, we constructed single-cell transcriptome maps of the gonads of donkey embryos during pregnancy at four and eight weeks, thereby identifying the major cell types involved. During development, the morphology of the ovary undergoes significant changes, gradually transitioning from an initial long cylindrical shape to an oval form. Notably, differences exist in the cell type composition between the two ovarian tissues. Additionally, we observed variations in germ-cell-related genes, including DDX4, DAZL, POU5F1, and NANOG, which regulate the dynamic differentiation of germ cells across the two tissues. Cell communication interaction analysis uncovered variations in the signaling networks of TGF-β, AMH, BMP, and GDF associated with embryonic development in the two tissues. Furthermore, we examined functional distinctions in the epithelial cells of the two tissues and assessed the expression levels of genes linked to granulosa cells, revealing that granulosa cells likely originated from ovarian epithelial cells during early gestation. Collectively, this study elucidated the molecular characteristics of ovarian tissue transcription during early gestation, thereby facilitating further investigation into genetic markers and gene regulatory networks associated with ovarian development. This may provide valuable insights for research on reproductive disorders and genetic breeding.
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Spatial transcriptomic map of the mouse urinary bladder.
We present a spatial transcriptomic map of the mouse urinary bladder at near single-cell resolution using Visium HD technology with 8 × 8 μm bins. After quality filtering, we analyzed 66,471 bins and 9,364 genes. Cluster annotation was supported by canonical marker genes and reference datasets, including Tabula Muris and Mouse Cell Atlas. Thirteen distinct clusters corresponding to major bladder compartments—the urothelium, lamina propria, and smooth muscle—were identified. Gene expression patterns revealed gradual transitions across tissue layers rather than sharp boundaries. Within the urothelium, basal, intermediate, and umbrella cells were distinguished, with umbrella cells showing potentially polarized mRNA distribution. The lamina propria primarily consisted of fibroblasts and a small number of immune cells. Four smooth muscle clusters were detected, contrasting with the single cluster typically identified by non-spatial single-cell RNA sequencing. This spatially resolved transcriptome map highlights the advantages of spatial transcriptomics in complementing traditional histology and scRNA-seq. Our map provides a valuable resource for advancing understanding of mouse urinary bladder biology and establishes a foundation for future studies of bladder pathology.
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ScRNA-seq reveals dynamic macrophage heterogeneity in chronic liver disease progression and prognostic biomarkers KLF2/SPP1 in HCC.
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH)-induced chronic liver diseases (CLDs) were worldwide prevalence and incidence. The stage-resolved cellular and molecular programs remained incompletely defined. This study aimed to resolve stage-specific immune and transcriptional features across CLDs processes and to identify prognostic biomarkers. METHODS: We integrated single-cell RNA sequencing datasets from healthy liver, MASH, cirrhosis and HCC to construct a stage-resolved cellular atlas. We performed cell-state scoring, diffusion pseudotime, gene regulatory network inference, and cell-cell interaction to decipher various macrophages and T cells transcriptional profiles. We established a method of gene sets enrichment score to detect prognostic markers and employed RNA fluorescence in situ hybridization (FISH) to validate macrophage subtype abundances and spatial interactions. RESULTS: The integrated atlas revealed the heterogeneity cell-subtype composition and transcriptional features across CLD stages. In MASH, CXCL3+ macrophage and CXCL10+ macrophage were enriched and characterized by ETS2- and IRF1-driven inflammatory programs that might potentially contribute to the transition from MASH to HCC. SPP1+ macrophage was exclusive to HCC and might contribute to cytotoxic T-cell (Tc) dysfunction but do not directly demonstrate functional suppression or exhaustion.Subsequently, we sought to validate the robustness of these signature genes. We integrated clinical datasets from the TCGA-LIHC to validate signature genes in HCC derived from the scRNA-seq results and identify prognostic biomarker. Survival-linked analyses uncovered SPP1 and KLF2 as prognostic biomarkers. FISH confirmed stage-specific shifts in macrophage abundances and close spatial interactions between SPP1+ macrophages and Tc in HCC specimens. CONCLUSION: We provided a stage-resolved framework to delineated macrophage heterogeneity during CLDs progression and identified SPP1 and KLF2 as candidate prognostic biomarkers and potential therapeutic targets in HCC.
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Single-Cell Pan-Cancer Atlas Reveals GPR171 as a Candidate Marker of CD8+ T-Cell Dysfunction.
CD8+ T-cell exhaustion is a key mechanism of tumor immune evasion and a major limitation of current cancer immunotherapy. However, the molecular factors sustaining dysfunctional CD8+ T-cell states across cancers are not fully understood. Here, we identify GPR171 as a common feature of exhausted CD8+ T cells across multiple solid tumors based on integrated pan-cancer single-cell transcriptomic analyses. GPR171 is enriched in exhausted CD8+ T cells and is closely associated with immunosuppressive and exhaustion-related gene programs. It also shows a strong association with key immune regulatory genes such as CTLA4 and NR4A2. Functional analyses suggest that reduced GPR171 activity is associated with decreased expression of exhaustion-related genes and a shift toward cytotoxic and immune-activating programs. In parallel, a CREM-centered regulatory network emerges in exhausted CD8+ T cells and may act in concert with GPR171-associated programs to reinforce dysfunctional states. Overall, our results identify GPR171 as a candidate marker of CD8+ T-cell dysfunction across cancers and provide a systematic pan-cancer single-cell characterization of its association with immunosuppressive T-cell states, supporting its potential as a therapeutic target for restoring antitumor immunity.
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Multi-stage single-cell atlas of a chordate non-embryonic development unveils dedifferentiating bud founder cells.
Colonial tunicates are the only chordates capable of forming fully functional bodies from somatic tissues through non-embryonic development, or budding. In Botryllus schlosseri, budding produces new individuals (zooids) from a portion of the peribranchial epithelium of a preexisting zooid. While the anatomy of this process has been described in detail, the cellular and molecular mechanisms underpinning bud initiation remain elusive. Here, we present a multi-stage single-cell transcriptomic atlas of a non-embryonic development in a chordate, capturing initiation and development of zooids. We identify an epithelial founder cell population that gains developmental potential through possible dedifferentiation, challenging the long-standing hypothesis that a long-lived, circulating pluripotent stem cell drives new zooid formation. Although some circulating cells exhibit stem-like expression profiles, these populations are either hematopoietic or germline restricted lineages. This atlas provides a foundational resource for understanding the cellular origin and mechanisms underlying agametic development and possibly whole-body regeneration in chordates.
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The Fibro-Immune Landscape Across Organs: A Single-Cell Comparative Study of Human Fibrotic Diseases.
Fibrosis is a hallmark of the tumor microenvironment in many solid cancers, driving tumor progression, immune evasion, and treatment resistance; however, the molecular and cellular mechanisms underlying fibrogenesis-particularly stromal-immune crosstalk across organs-remain incompletely understood, compounded by organ-specific heterogeneity and a lack of reliable immune-related biomarkers. To address this, we performed an integrative single-cell RNA sequencing (scRNA-seq) analysis of fibrotic tissues from four major organs-liver, lung, heart, and kidney-alongside non-fibrotic controls, applying unsupervised clustering, trajectory inference, cell-cell communication modeling, and gene set variation analysis (GSVA) to map the fibro-immune landscape. Our analysis revealed both conserved and organ-specific features: fibroblasts were the dominant extracellular matrix (ECM)-producing cells in liver and lung, whereas endothelial-derived stromal populations prevailed in heart and kidney. Immune profiling uncovered distinct infiltration patterns-macrophages displayed organ-specific polarization states; T cells were enriched for tissue-resident subsets in lung and mucosal-associated invariant T (MAIT) cells in liver; and B cells exhibited marked heterogeneity, including a pathogenic interferon-responsive subset prominent in pulmonary fibrosis. GSVA further identified divergent signaling programs across organs and lineages, including TGF-β/TNF-α in the heart, NOTCH/mTOR in the kidney, glycolysis/ROS in the lung, and KRAS/interferon pathways in the liver. Cell-cell communication analysis highlighted robust crosstalk between macrophages, T/B cells, and stromal cells mediated by collagen, laminin, and CXCL signaling axes. Together, this cross-organ atlas delineates a highly heterogeneous fibro-immune ecosystem in human fibrotic diseases, revealing shared mechanisms alongside organ-specific regulatory networks, with immediate translational implications for precision anti-fibrotic therapy, immunomodulatory drug repurposing, and the development of context-specific biomarkers for clinical stratification and therapeutic monitoring.
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MHA, an interactive website for scRNA-seq data of male genitourinary development and disease.
BACKGROUND: The development of single-cell sequencing technology has expanded the understanding of cell heterogeneity and disease progression in the male genitourinary system. However, complex processing and unprofessional analytical annotations limit the daily use and widely sharing of published datasets. OBJECTIVES: Single-cell sequencing data of male-specific tissues and organs. MATERIALS AND METHODS: The data were downloaded from published studies and were processed based on the Seurat R package, including quality control, cell clustering, reduction and graph generation, and cell type annotation were differentiated by referring to the related paper or recognized cell markers. Input and visual results output through the Shiny package, which was loaded into the remote server. RESULTS: The current version of the Male Health Atlas database includes two species (human and mouse), five male-specific tissues and organs (testis, epididymis, vas deferens, corpus cavernosum, and prostate), and eight major cell types, with a total of 57 samples and 258,428 single-cell profiles. The results were divided into two main parts: Cell Clustering and Gene Display. In Cell Clustering section, visitors are free to change cell dimensionality reduction (t-distributed stochastic neighbor embedding, or uniform manifold approximation and projection), color palette, and annotation (cell type or sample type). The Gene Display section includes a reduced dimension scatter plot, violin plot, and bubble plot. Visitors can easily view the expression characteristics of single or multiple genes, and compare the expression differences between different cell types or groups. DISCUSSION AND CONCLUSION: Male Health Atlas is the first single-cell database website in the field of andrology and male reproduction, providing researchers with single-cell sequencing resources and an accessible tool. Male Health Atlas is freely available at http://malehealthatlas.cn/.
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Single-Cell and Bulk Transcriptomic Integration Reveals a Stemness-Related Astrocyte Subpopulation for Prognostic Risk Stratification in Glioblastoma.
INTRODUCTION: Glioblastoma (GBM) is an aggressive brain tumor with pronounced heterogeneity. Stemness-related cell subpopulations are crucial for progression and therapy resistance, but their prognostic role remains unclear. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify a stemness-high astrocyte subpopulation. Key genes were selected to construct a prognostic risk model using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which was validated in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts. Tumor microenvironment and pathway analyses were performed. in vitro functional assays were conducted on GBM cell lines. RESULTS: A four-gene signature (ALDOA, FABP5, TIMP1, and MT1M) was established. The risk model robustly stratified patients into high- and low-risk groups with distinct overall survival in both cohorts. Importantly, multivariate Cox regression confirmed the RiskScore as an independent prognostic factor beyond age and isocitrate dehydrogenase (IDH) mutation status. Functional validation revealed that ALDOA knockdown significantly suppressed GBM cell proliferation and migration in vitro. Further analyses showed that high-risk tumors were characterized by elevated immune/stromal scores, immunosuppressive cell infiltration, and activation of stemness-related pathways, including EGFR/MAPK, NF-αB, and VEGF-mediated angiogenesis. DISCUSSION: This integrated analysis identified a stemness-associated astrocyte subpopulation in GBM. The four-gene signature provides an independent prognostic tool and reflects immune microenvironment remodeling, offering insights into risk stratification and potential targeted therapy. CONCLUSION: We developed a stemness-associated four-gene signature that enables risk stratification in GBM and reveals an immunosuppressive microenvironment in high-risk tumors, providing new directions for prognosis and targeted therapy.