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The single-cell atlas of short-chain fatty acid receptors in human and mice hearts.
INTRODUCTION: The gut microbiota metabolite, short-chain fatty acids (SCFAs), can protect against multiple cardiovascular diseases, while the molecular targets and underlying mechanisms need to be elucidated. One of the primary mechanisms of SCFA benefits was the direct activation of a group of G-protein-coupled receptors (GPCRs), termed free fatty acid receptors (FFARs), the FFAR2 (GPR43), and FFAR3 (GPR41). At present, the distribution of FFAR2/3 in cardiac cells has not been entirely clarified. METHODS: Using 18 public single-cell RNA-seq and single-nuclear RNA-seq data of human and mouse hearts, we illustrate the entire atlas of FFAR2/3 distribution in different regions and cell types in normal and infarcted hearts. RESULTS AND DISCUSSION: We present the atlas of FFAR2/3 in the whole human body, normal and infarcted hearts at single-cell resolution. We also illustrated the entire atlas of FFAR2/3 in normal/ischemic hearts of newborn and adult mice by combining public and newly built sc/snRNA-seq datasets. These findings provide valuable information on the possible effect of SCFAs via FFAR2/3 in the heart and valuable references for future studies.
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A harmonized single-cell RNA-seq atlas of human localized and metastatic prostate cancers and benign tissues.
Single-cell RNA sequencing (scRNA-seq) effectively captures the differences in transcriptomic landscape of cell types and cell states between benign and cancer tissues. Pooling publicly available datasets distributed across independent studies enables increased sample representation and cross-study comparisons. Here we present a harmonized scRNA-seq atlas of the human prostate constructed by integrating 17 available studies, comprising 163 samples from 106 donors. The dataset contains benign tissue, primary tumors, and metastatic disease profiles. Raw sequencing FASTQ data files were uniformly reprocessed to minimize technical variability. Study metadata were curated and standardized using a unified schema capturing donor identity, tissue site, disease context, and histologic grade. Post quality control, the integrated dataset contains 754,000 high-quality cells. Harmonized cell type annotations were generated using a pseudobulk correlation framework informed by multiple reference resources. The workflow identified 17 distinct cell types representing epithelial, mesenchymal, and immune compartments of the prostate. The processed expression matrices, standardized metadata, and analysis workflows are publicly available to support reproducible analysis and enable exploration of heterogeneity across prostate disease states.
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A single-cell transcriptomic study of milk cells from dairy cows with divergent lactation performance.
High-lactation cows exhibit advantages in milk yield and quality compared to low-lactation cows; however, the underlying mechanisms remain unclear. Based on the demand for high-quality milk sources in the food industry, this study used single-cell sequencing technology (scRNA-seq) on the 10 × Chromium platform to analyze the milk cells of 10 Holstein cows (5 in the high-lactation group and 5 in the low-lactation group). The seven cell types included two types of epithelial cells (epithelial and secretory epithelial cells) and five types of immune cells (neutrophils, T cells, macrophages, B cells, and dendritic cells). Further sub-clustering analysis identified three epithelial cell types and nine T-cell subsets, and their differentiation paths were depicted through pseudo temporal analysis. Inter-group comparisons revealed differential genes and signaling pathways that affect lactation performance, such as lactation-related pathways (prolactin, protein export, thermogenesis) and immune-related pathways (Toll-like receptor, cytokine-receptor interaction, and NF-κB). In addition, this study elucidated the complex signaling relationships between epithelial and immune cells, especially the impact of CyPA, ICAM, and SELL signaling pathways on lactation. Moreover, additional analyses of macrophage and neutrophil subpopulations further revealed their interactions with epithelial cells, providing complementary insights into immune regulation during lactation.This study enriches the knowledge of cow lactation biology and provides a reference for the food industry to screen high-quality milk sources and optimize dairy processing technology.
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A single-cell atlas of diabetes-associated remodeling in the human corneal limbus.
PURPOSE: Diabetes mellitus (DM) disrupts epithelial integrity, sensory function, basement membrane composition, wound healing, and stem cell function. Corneal epithelial homeostasis and wound healing involve the complex coordination of various sub-populations of limbal cells. However, the transcriptomic changes at the single-cell level in these cell populations associated with diabetes have never been addressed. This study aims to generate a high-resolution single-cell transcriptomic atlas of human DM corneal limbal tissue to advance our conceptual understanding of human diabetes-associated changes in cell types and composition, transcriptional states, cellular trajectories, and intercellular communication. METHODS: Single-cell sequencing (scRNA-seq) was performed on isolated corneolimbal epithelial tissues from a total of six age-matched human donors, including three non-diabetic (N) and three DM donors using 10X Genomics. Selected transcriptomic findings were assessed at the protein level by immunofluorescence staining of corneal sections. RESULTS: Thirteen distinct cell clusters were identified based on the top expressed gene markers in both N and DM samples. Analyses of differentially expressed genes (DEGs), biological pathways, pseudotime trajectories, and subclustering identified DM-associated transcriptional changes, cell-cycle differences, and altered inferred intercellular signaling patterns. Pathway analysis highlighted extracellular matrix organization, adhesion, inflammation, and ligand-receptor interactions that may be associated with diabetes-impaired corneal homeostasis. Immunofluorescence showed corresponding differences in protein staining patterns for selected markers, including decreased IFITM3, MMP-1, periostin, LGI4, and eNOS, and increased ADAMTS-4. CONCLUSIONS: We provide the first high-resolution single-cell transcriptome atlas of DM-associated alterations in the human corneolimbal niche and identify molecular pathways and cellular populations that may contribute to compromised corneal homeostasis and regenerative capacity in DM.
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Single-Cell Transcriptome Analysis Reveals IRF1-Driven Epithelial States and Glycosaminoglycan-Glycolysis Coupling in Cisplatin-Resistant HGSOC.
OBJECTIVES: Cisplatin resistance is the principal cause of relapse in high-grade serous ovarian cancer (HGSOC), but bulk-expression signatures cannot localize resistant malignant states or the tumor-microenvironment (TME) interactions that sustain them. This study aimed to define cisplatin-resistant epithelial cell states and their regulatory and metabolic circuits by integrating multi-cohort single-cell transcriptomes with pharmacogenomic and clinical data. METHODS: We assembled 159,419 cells from 32 HGSOC tumors across six public single-cell RNA-sequencing cohorts and performed harmonized integration, clustering, and lineage annotation. Cisplatin response was mapped to single cells by coupling scRNA-seq data to Genomics of Drug Sensitivity in Cancer predicted cisplatin response score values using Scissor, with AUCell-based validation. We then applied receptor-ligand-based cell-cell communication analysis (CellChat), transcription-factor (TF) activity inference (SCENIC and NetAct with TRRUST), pseudotime trajectory reconstruction (Monocle3), and pathway-level metabolic scoring. Associations with drug sensitivity and patient outcome were evaluated in ovarian cancer cell lines and The Cancer Genome Atlas (TCGA) HGSOC cohort. RESULTS: Among 34 epithelial subclusters, 14 were significantly enriched for a cisplatin-resistant phenotype and collectively accounted for most predicted resistant cells. These states were transcriptionally characterized by stress, interferon, and apoptotic programs and formed dense communication hubs with endothelial cells, fibroblasts, myeloid cells, and T/NK cells via extracellular-matrix and adhesion pathways (for example, LAMA3-CD44, COL6A1/2-CD44, NECTIN3-NECTIN2, and CD99-CD99 interactions). TF-activity modeling converged on an IRF1-centered regulatory program that increased along an epithelial trajectory and coordinated inflammatory and apoptotic gene expression. Metabolically, resistance-enriched epithelial states showed selective up-regulation of glycosaminoglycan biosynthesis, particularly keratan sulfate, coupled to heightened glycolysis; glycolytic activity correlated with predicted cisplatin response score in cell lines and an IRF1/STAT1 readout (GBP3) stratified survival in TCGA HGSOC. CONCLUSION: Cisplatin resistance in HGSOC is encoded in discrete IRF1-driven epithelial states that are supported by specific TME communication networks and a glycosaminoglycan-glycolysis metabolic axis. This integrative single-cell informatics framework yields testable biomarkers and therapeutic targets for overcoming platinum resistance in ovarian cancer.
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From heterogeneity to mechanism: a systems-level analysis pinpoints the S100A8/A9 axis in colorectal cancer.
BACKGROUND: The functional roles of the S100 protein family in colorectal cancer (CRC) are enigmatic, marked by widespread dysregulation but limited prognostic utility. This heterogeneity obscures their potential as therapeutic targets. METHODS: We employed a multi-omics strategy to deconstruct this complexity, integrating bulk transcriptomics (TCGA), large-scale single-cell RNA-seq atlases, and machine learning-based prioritization. Cell-type-specific expression patterns were contextualized using the spatial annotation of cell origins available within the Pan-GI Cell Atlas. Key findings were validated experimentally via qRT-PCR in a 30-patient cohort and functionally assessed using in vitro assays. RESULTS: While 17 S100 genes were differentially expressed in bulk tumor tissue, they lacked collective prognostic power. Single-cell analysis resolved this paradox, revealing that the expression of key alarmins S100A8 and S100A9 is not tumor-intrinsic but is highly concentrated in tumor-infiltrating myeloid cells. This myeloid-specific signature was associated with high-grade disease and progressively increased from polyps to tumors. We experimentally validated that S100A8 and S100A9 mRNA levels are significantly elevated in CRC tissues versus adjacent normal controls (S100A8, p = 0.004; S100A9, p = 0.012). Functionally, recombinant S100A8 and S100A9 acted as drivers of inflammation, directly inducing a pro-inflammatory cascade including NF-κB, IL-6, and IL-8 in CRC cells. CONCLUSIONS: Our findings clarify the S100 family's role in CRC, identifying the myeloid-derived S100A8 and S100A9 axis as a key amplifier of the pro-tumorigenic inflammatory microenvironment. This axis represents a critical nexus between innate immunity and carcinogenesis, offering a promising target for novel anti-inflammatory or immunomodulatory therapies in CRC.
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Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model.
BACKGROUND: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. METHODS: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. RESULTS: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. CONCLUSIONS: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).
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OVsignGenes: A Gene Expression-Based Neural Network Model Estimated Molecular Subtype of High-Grade Serous Ovarian Carcinoma.
BACKGROUND/OBJECTIVES: High-grade serous carcinomas (HGSCs) are highly heterogeneous tumors, both among patients and within a single tumor. Differences in molecular mechanisms significantly describe this heterogeneity. Four molecular subtypes have been previously described by the Cancer Genome Atlas Consortium: differentiated, immunoreactive, mesenchymal, and proliferative. These subtypes may have varying degrees of progression, relapse-free survival, and overall survival, as well as response to therapy. The precise determination of these subtypes is certainly necessary both for diagnosis and future development of targeted therapies within personalized medicine. METHODS: In this study, we analyzed gene expression data based on bulk RNA-seq, scRNA-seq, and spatial transcriptomic data from six cohorts (totaling 535 samples, including 60 single-cell samples). Differential expression analysis was performed using the edgeR package. The KEGG database and GSVA package were used for pathways enrichment analysis. As a predictive model, a deep neural network was created using the keras and tensorflow libraries. RESULTS: We identified 357 differentially expressed genes among the four subtypes: 96 differentiated, 33 immunoreactive, 91 mesenchymal, and 137 proliferative. Based on these, we created OVsignGenes, a neural network model resistant to the effects of platform (test dataset AUC = 0.969). We then ran data from five more cohorts through our model, including scRNA-seq and spatial transcriptomics. CONCLUSIONS: Because the differentiated subtype is located at the intersection of the other three subtypes based on PCA and does not have a unique profile of differentially expressed genes or enriched pathways, it can be considered an initiating subtype of tumor that will develop into one of the three other subtypes.
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Metabolic Heterogeneity of Tumor Cells and its Impact on Colon Cancer Metastasis: Insights from Single-Cell and Bulk Transcriptome Analyses.
Background: Metabolic reprogramming plays a crucial role in the development of colorectal cancer (CRC), influencing tumor heterogeneity, the tumor microenvironment, and metastasis. While the interaction between metabolism and CRC is critical for developing personalized treatments, gaps remain in understanding how tumor cell metabolism affects prognosis. Our study introduces novel insights by integrating single-cell and bulk transcriptome analyses to explore the metabolic landscape within CRC cells and its mechanisms influencing disease progression. This approach allows us to uncover metabolic heterogeneity and identify specific metabolic genes impacting metastasis, which have not been thoroughly examined in previous studies. Methods: We sourced microarray and single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) and bulk sequencing data for CRC from The Cancer Genome Atlas (TCGA). We employed Gene Set Variation Analysis (GSVA) to assess metabolic pathway activity, consensus clustering to identify CRC-specific transcriptome subtypes in bulkseq, and rigorous quality controls, including the exclusion of cells with high mitochondrial gene expression in scRNA seq. Advanced analyses such as AUCcell, infercnvCNV, Non-negative Matrix Factorization (NMF), and CytoTRACE were utilized to dissect the cellular landscape and evaluate pathway activities and tumor cell stemness. The hdWGCNA algorithm helped identify prognosis-related hub genes, integrating these findings using a random forest machine learning model. Results: Kaplan-Meier survival curves identified 21 significant metabolic pathways linked to prognosis, with consensus clustering defining three CRC subtypes (C3, C2, C1) based on metabolic activity, which correlated with distinct clinical outcomes. The metabolic activity of the 13 cell subpopulations, particularly the epithelial cell subpopulation with active metabolic levels, was evaluated using AUCcell in scRNA seq. To further analyze tumor cells using infercnv, NMF disaggregated these cells into 10 cellular subpopulations. Among these, the C2 subpopulation exhibited higher stemness and tended to have a poorer prognosis compared to C6 and C0. Conversely, the C8, C3, and C1 subpopulations demonstrated a higher level of the five metabolic pathways, and the C3 and C8 subpopulations tended to have a more favorable prognosis. hdWGCNA identified 20 modules, from which we selected modules primarily expressed in high metabolic tumor subgroups and highly correlated with clinical information, including blue and cyan. By applying variable downscaling of RF to a total of 50 hub genes, seven gene signatures were obtained. Furthermore, molecules that were validated to be protective in GEO were screened alongside related molecules, resulting in the identification of prognostically relevant molecules such as UQCRFS1 and GRSF1. Additionally, the expression of GRSF1 was examined in colon cancer cell lines using qPCR and phenotypically verified by in vitro experiments. Conclusion: Our findings emphasize that high activity in specific metabolic pathways, including pyruvate metabolism and the tricarboxylic acid cycle, correlates with improved colon cancer outcomes, presenting new avenues for metabolic-based therapies. The identification of hub genes like GRSF1 and UQCRFS1 and their link to favorable metabolic profiles offers novel insights into tumor neovascularization and metastasis, with significant clinical implications for targeting metabolic pathways in CRC therapy.
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A molecular, spatial and regulatory atlas of the Hydra vulgaris nervous system.
Hydra vulgaris, a cnidarian with a simple nerve net, is an emerging model for developmental, regenerative and functional neuroscience. Its genetic tractability and capacity for whole-system imaging make it well suited for studying neuron replacement, regeneration and neural circuit function. Here, we present the most comprehensive molecular and spatial characterization of the H. vulgaris nervous system to date. Using single-cell RNA sequencing, we identified eight neuron types, each defined by distinct neuropeptide expression, and further resolved these into 15 transcriptionally distinct subtypes with unique spatial distributions and morphologies. These data enabled a detailed examination of the molecular components of neuronal communication and excitability. To investigate the gene regulatory networks underlying neuronal differentiation, we applied trajectory inference, identified key transcription factors, and performed ATAC-seq on sorted neurons to map chromatin accessibility. All datasets are available through an interactive, user-friendly web portal to support broad use by the research community. Together, these resources provide a foundation for uncovering molecular mechanisms that govern nervous system development, homeostasis, regeneration and function in H. vulgaris.