bims-sicarn Biomed News
on scRNA-seq
Issue of 2025–04–27
forty-six papers selected by
Anna Zawada, International Centre for Translational Eye Research
  1. Comprehensive evaluation of methods for identifying tissues or cell types of origin of the plasma cell-free transcriptome.
  2. How to view the female reproductive tract through single-cell looking glasses.
  3. A signature based on efferocytosis-related genes for the evaluation of prognosis and the tumour microenvironment in gastric cancer.
  4. Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.
  5. Elucidating Cellular Senescence-related Genes in Benign Prostatic Hyperplasia through Mendelian Randomization and Single-cell RNA Sequencing.
  6. Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer.
  7. Single-cell RNA sequencing reveals key signaling pathways and biological functions in giant cell tumors of bone.
  8. Benchmarking foundation cell models for post-perturbation RNA-seq prediction.
  9. Single-cell transcriptomic analysis identified resistant MDSCs and a stress-tolerant gene co-expression network as common MDSC features across multiple disease settings.
  10. Single-cell technology: the key to an improved understanding of the human endometrium in health and disease.
  11. Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.
  12. High-resolution single-cell RNA-seq data and heterogeneity analysis of human ESCs and ffEPSCs.
  13. Single-Cell and Spatial Transcriptomic Analysis of Maize Embryo Development: a Sample Preparation Protocol.
  14. Single-Cell and Spatial Transcriptomic Analysis of Maize Embryo Development.
  15. Placenta at single-cell resolution in early and late preeclampsia: insights and clinical implications.
  16. Uterine fibroids at single-cell resolution: unveiling cellular heterogeneity to improve understanding of pathogenesis and guide future therapies.
  17. Investigation of the role of GEM in systemic lupus erythematosus through multi-omics joint analysis.
  18. Transfer learning of multicellular organization via single-cell and spatial transcriptomics.
  19. Epithelial and macrophage cell interaction in cervical cancer through single-cell RNA-sequencing and spatial analysis.
  20. Single-cell and spatial transcriptomic insights into glioma cellular heterogeneity and metabolic adaptations.
  21. Mapping Cell Identity from scRNA-seq: A primer on computational methods.
  22. Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs.
  23. Single-cell analysis of the early Drosophila salivary gland reveals that morphogenetic control involves both the induction and exclusion of gene expression programs.
  24. Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.
  25. Unraveling the Therapeutic Potential of Scutellarin for Clear Cell Renal Cell Carcinoma: A Comprehensive Molecular Analysis.
  26. Unveiling the impact of ferroptosis on diabetes-associated cognitive decline through comprehensive single-cell RNA sequencing and experimental studies.
  27. scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.
  28. Nonlinear embedding and integration of omics data: a fast and tuning-free approach.
  29. Cancer-associated fibroblasts gene signature: a novel approach to survival prediction and immunotherapy guidance in colon cancer.
  30. Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis.
  31. A Multi-omics approach to identify and validate shared genetic architecture in rheumatoid arthritis, multiple sclerosis, and type 1 diabetes: integrating GWAS, GEO, MSigDB, and scRNA-seq data.
  32. Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.
  33. Integrated Analysis of PSMB8 Expression and Its Potential Roles in Hepatocellular Carcinoma.
  34. Pan-cancer analysis identified CD248 as a potential target for multiple tumor types.
  35. Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system.
  36. Single-cell RNA sequencing reveals a reprogramming of hepatic immune cells and a protective role for B cells in MASH-driven HCC.
  37. In-depth single-cell transcriptomic exploration of the regenerative dynamics in stony coral.
  38. Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition.
  39. Asherman syndrome at single-cell resolution.
  40. The spatial and single-cell landscape of skin: Charting the multiscale regulation of skin immune function.
  41. Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.
  42. Decoding the role of lipid metabolism in NSCLC: From macrophage subtype identification to prognostic model development.
  43. Transcriptomics, single-cell sequencing and spatial sequencing-based studies of cerebral ischemia.
  44. Prenatally derived macrophages support choroidal health and decline in age-related macular degeneration.
  45. Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics.
  46. Association Between T2DM, TyG Index, Multiple Sclerosis, and Rheumatoid Arthritis: Insights into Potential Mechanisms.
  1. PeerJ. 2025 ;13 e19241
    Comprehensive evaluation of methods for identifying tissues or cell types of origin of the plasma cell-free transcriptome.
    Tingyu Yang, Yulong Qin, Shuo Yan, Sijia Guo, Jinghua Sun, Jiayi Huang, Jiayi Li, Qing Zhou, Xin Jin, Wen-Jing Wang.
      Plasma cell-free RNA (cfRNA) is derived from cells in various tissues and organs throughout the body and reflects the physiological and pathological conditions. Identifying the origins of cfRNA is essential for comprehending its variations. Only a few tools are designed for cfRNA deconvolution, and most studies have relied on traditional bulk RNA methods. In this study, we employed human tissue and cell transcriptomic data as reference sets and evaluated the performance of seven deconvolution methods on cfRNA. We compared the analysis results of cell types and tissues of origin of plasma cfRNA and chose to use single-cell RNA sequencing (scRNA-seq) data as reference to conduct further evaluation of deconvolution methods. Subsequently, we assessed the accuracy and robustness of the methods by utilizing simulated cfRNA data generated from scRNA-seq. We also evaluated the methods' accuracy on real plasma cfRNA data by analyzing the correlation between the predicted cell proportions and the corresponding clinical indicators. Moreover, we compared the methods' effectiveness in revealing the impacts of diseases on cells and evaluated the performance of cancer classification models based on the cell origin data they provided. In summary, our study provides valuable insights into cfRNA origin analysis, enhancing its potential in biomedical research.
    Keywords:  Cancer classification; Cell origins; Plasma cfRNA; Tissue origins
    DOI:  https://doi.org/10.7717/peerj.19241
  2. Am J Obstet Gynecol. 2025 Apr;pii: S0002-9378(24)00895-0. [Epub ahead of print]232(4S): S21-S43
    How to view the female reproductive tract through single-cell looking glasses.
    Jaime Llera-Oyola, Raúl Pérez-Moraga, Marcos Parras, Beatriz Rosón.
      Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.
    Keywords:  biomedical studies; computational methods; decision making guidelines; molecular biomarkers; precision medicine; prognostic signatures; single-cell transcriptomics; therapeutic targets
    DOI:  https://doi.org/10.1016/j.ajog.2024.08.040
  3. Sci Rep. 2025 Apr 24. 15(1): 14226
    A signature based on efferocytosis-related genes for the evaluation of prognosis and the tumour microenvironment in gastric cancer.
    Lei Wang, Jingjing Ge, Zehua Wang, Wenjia Wang, Qing Hong, Yihua Fang, Jiayao Xu, Huiqiong Han, Yanru Qin.
      Gastric cancer (GC) is a highly malignant tumor of the digestive system. The process of efferocytosis has been confirmed to be closely associated with tumor progression and microenvironment remodeling. Nevertheless, the mechanism of efferocytosis in GC remains unclear. This study integrates single-cell RNA sequencing (scRNA-seq) datasets with the TCGA transcriptome data for GC, focusing on the expression and distribution of efferocytosis-related genes (ERGs) at the single-cell level in GC. The prognostic features of ERGs are determined by Cox and LASSO analysis. And we analyzed and evaluated the differences between the two groups of patients in terms of long-term prognosis, immune infiltration, expression of immune checkpoints, and response to chemotherapeutic drugs. Seven cell types were identified from 10 GC samples. ERGs were mainly concentrated in macrophages, dividing macrophages into 5 cell subtypes. LASSO combined with Cox ultimately confirmed 4 independent prognostic genes, and a prognostic nomogram was constructed based on gene risk scores and clinical features, which was validated in an independent dataset. Further studies revealed that ERGs were closely related to the patient's immune cell infiltration (especially M2 macrophages), immunotherapy response, and drug sensitivity. We developed an ERG-based predictive model that could serve as a valuable tool for prognosis assessment and decision support in the context of immunotherapy and chemotherapy.
    Keywords:  Efferocytosis; Gastric cancer; Immune infiltration; Prognostic signature; Single-cell RNA analysis
    DOI:  https://doi.org/10.1038/s41598-025-99133-2
  4. PLoS Comput Biol. 2025 Apr;21(4): e1012962
    Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.
    Alexander Morin, Ching Pan Chu, Paul Pavlidis.
      The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.
    DOI:  https://doi.org/10.1371/journal.pcbi.1012962
  5. J Gerontol A Biol Sci Med Sci. 2025 Apr 23. pii: glaf073. [Epub ahead of print]
    Elucidating Cellular Senescence-related Genes in Benign Prostatic Hyperplasia through Mendelian Randomization and Single-cell RNA Sequencing.
    Yichuan Wang, Liang Zhou, Jiahao Liu, Qing Zhou, Wei Xiong, Long Wang.
       BACKGROUND: Benign prostatic hyperplasia (BPH) is a widely observed disorder in older men, with substantial evidence indicating that cellular senescence serves a pivotal function in its progression. This investigation seeks to pinpoint cellular senescence-related genes causally connected with BPH and to examine their expression and regulatory networks across distinct prostate cells.
    METHODS: Using exposure data from the eQTLGen database and outcome data from both FinnGen Consortium and UKB database, Mendelian randomization (MR) was utilized to determine cell senescence genes that are causally linked to BPH. These associations were further validated through co-localization analysis. Expression patterns of these genes in different prostate cells were assessed via single-cell RNA sequencing (scRNA-seq), and changes along pseudotime were tracked. Regulatory networks were evaluated using SCENIC to identify key transcription factors involved.
    RESULTS: Six cell senescence genes causally linked to BPH were identified through MR. ATM, ATRAID, MAP2K1, and TP53 were identified as protective factors, whereas ITPR1 and SENP7 were associated with increased risk. Co-localization analysis suggested that ATM and TP53 are likely to share the same variant implicated in BPH. MAP2K1 expression demonstrated a steady decline along inferred pseudotime across fibroblasts, macrophages, T cells, and epithelial cells, while the remaining five genes exhibited an opposite trend. ATF3, EGR1, and FOS were pinpointed as the core transcription factors regulating these genes.
    CONCLUSIONS: These observations emphasize consistent expression patterns among different prostate cell types and suggest a highly interconnected regulatory network that underpins BPH pathology, thereby providing fresh perspectives on the molecular mechanisms underlying the disease.
    Keywords:  Benign prostatic hyperplasia; Mendelian randomization; SCENIC; Senescence; Single-cell analysis
    DOI:  https://doi.org/10.1093/gerona/glaf073
  6. Front Mol Biosci. 2025 ;12 1580622
    Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer.
    Qinqing Wu, Heng Cao, Jiangdong Jin, Dongxu Ma, Yixiao Niu, Yanping Yu, Xiang Wang, Yiqin Xia.
       Background: Breast cancer (BRCA) is a significant threat to women's health worldwide, and its progression is closely associated with the tumor microenvironment and gene regulation. Lactylation modification, as a key epigenetic mechanism in cancer biology, has not yet been fully elucidated in the context of BRCA. This study examines the regulatory mechanisms of lactylation-related genes (LRGs), specifically PRDX1, and their prognostic significance in BRCA.
    Methods: We integrated data from multiple databases, including Genome-Wide Association Study (GWAS) summary statistics, single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs associated with BRCA and comprehensively analysed the expression patterns of PRDX1, cell-cell communication networks, and spatial heterogeneity. Furthermore, we constructed and validated a prognostic model based on the gene expression profile of PRDX1-positive monocytes, evaluating it through Cox regression and LASSO regression analyses.
    Results: PRDX1 was identified as a key LRG significantly associated with BRCA risk (p_SMR = 0.0026). Single-cell RNA sequencing analysis revealed a significant upregulation of PRDX1 expression in monocytes, with enhanced cell-cell communication between PRDX1-positive monocytes and fibroblasts. Spatial transcriptomics analysis uncovered heterogeneous expression of PRDX1 in the tumor nest regions, highlighting the spatial interaction between PRDX1-positive monocytes and fibroblasts. The prognostic model constructed based on the gene expression profile of PRDX1-positive monocytes demonstrated high accuracy in predicting patient survival in both the training and validation cohorts. High-risk patients exhibited immune-suppressive microenvironment characteristics, including reduced immune cell infiltration and upregulation of immune checkpoint gene expression.
    Conclusion: This study reveals the key role of PRDX1 in BRCA progression, mainly through the regulation of the tumor microenvironment and immune escape mechanisms. The survival prediction model based on PRDX1 shows robust prognostic potential, and future research should focus on integrating PRDX1 with other biomarkers to enhance the precision of personalised medicine.
    Keywords:  PRDX1; breast cancer; lactylation; prognostic model; spatial transcriptomics
    DOI:  https://doi.org/10.3389/fmolb.2025.1580622
  7. Discov Oncol. 2025 Apr 19. 16(1): 573
    Single-cell RNA sequencing reveals key signaling pathways and biological functions in giant cell tumors of bone.
    Zuozhuan Lin, Zuoxi Chen.
       BACKGROUND: Giant cell tumor of bone (GCTB) represents a group of tumors that characterized by their heterogeneity and the presence of multiple cell types. It is essential to understand their molecular mechanisms for better designing therapeutic approaches.
    METHODS: We applied single cell RNA-sequencing technology to investigate differentiation process during GCTBs formation. DAVID was used to perform a pathway enrichment and Gene Ontology (GO) analyses on signaling pathways and biological functions. Cell-cell interactions and signaling networks are depicted with network diagrams and usages of heatmaps.
    RESULTS: The analysis identified numerous significant pathways, including Pathways in Parkinson's disease, Prion diseases and COVID-19. ViBe identified a wide range of biological process, cellular component and molecular function associated with tumorigenesis. Furthermore, we validate that the SPP1 signaling pathway is essential for cell-cell crosstalk, specifically functioning as a positive feedback loop between cancer-associated fibroblasts and macrophages.
    CONCLUSION: Our study revealed comprehensive molecular and intercellular interaction networks in GCTB, which may advance the understanding of the pathogenic mechanisms in GCTB and contribute essential information for its treatment. Our findings make confution in the complex functional dynamics in the TME.
    Keywords:  Cell–cell interactions; Gene ontology analysis; Giant cell tumor; SPP1 signaling pathway; Signaling pathways; Single-cell RNA sequencing
    DOI:  https://doi.org/10.1007/s12672-025-02353-1
  8. BMC Genomics. 2025 Apr 23. 26(1): 393
    Benchmarking foundation cell models for post-perturbation RNA-seq prediction.
    Gerold Csendes, Gema Sanz, Kristóf Z Szalay, Bence Szalai.
      Accurately predicting cellular responses to perturbations is essential for understanding cell behaviour in both healthy and diseased states. While perturbation data is ideal for building such predictive models, its availability is considerably lower than baseline (non-perturbed) cellular data. To address this limitation, several foundation cell models have been developed using large-scale single-cell gene expression data. These models are fine-tuned after pre-training for specific tasks, such as predicting post-perturbation gene expression profiles, and are considered state-of-the-art for these problems. However, proper benchmarking of these models remains an unsolved challenge. In this study, we benchmarked two recently published foundation models, scGPT and scFoundation, against baseline models. Surprisingly, we found that even the simplest baseline model-taking the mean of training examples-outperformed scGPT and scFoundation. Furthermore, basic machine learning models that incorporate biologically meaningful features outperformed scGPT by a large margin. Additionally, we identified that the current Perturb-Seq benchmark datasets exhibit low perturbation-specific variance, making them suboptimal for evaluating such models. Our results highlight important limitations in current benchmarking approaches and provide insights into more effectively evaluating post-perturbation gene expression prediction models.
    Keywords:  Benchmark; Foundaton model; Perturbation; RNA-seq
    DOI:  https://doi.org/10.1186/s12864-025-11600-2
  9. Front Immunol. 2025 ;16 1565211
    Single-cell transcriptomic analysis identified resistant MDSCs and a stress-tolerant gene co-expression network as common MDSC features across multiple disease settings.
    Tianmeng Chen, Julia Hughes, Alyssa Gregory, Julia Conroy, Patricia Loughran, Jinming Song, Wei Chen, Timothy Billiar.
       Background: Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immunosuppressive myeloid cells. The identification of a molecular signature common to MDSC regardless of tissue source would aid in the classification of cells as MDSCs.
    Methods: Single-cell RNA sequencing (scRNA-seq) was performed on GM-CSF+ IL-6-induced human MDSCs to characterize the extent of heterogeneity within monocytic MDSCs (M-MDSCs). Cytokine-treated PBMCs were also cultured in the absence of serum to include an additional element of cell stress. Independent published bulk and single-cell transcriptomic datasets were used for validation.
    Findings: A cluster of cells with preserved MDSC features was induced by the combination of inflammatory signals and cell stress in the form of serum starvation (resistant MDSCs, rMDSCs). A gene co-expression module (the yellow module) was identified specific to rMDSCs. The genes upregulated in MDSCs can be further classified into stress-tolerant vs. -sensitive features. This yellow module mostly contained stress-tolerant genes and showed excellent separation for distinguishing M-MDSCs from control cells across a range of in vitro and in vivo conditions (ROC AUC = 0.954), a feature not found in the stress-sensitive genes. Importantly, rMDSCs were identified in scRNA-seq datasets of immune cells from multiple human cancer types. Tumor C1Q macrophages, which have been associated with immunosuppression, highly expressed the yellow module gene signature.
    Interpretation: These results demonstrate the importance of the combined roles of inflammation and cellular stress in shaping the features of M-MDSCs and highlight cellular resilience represented by rMDSCs and the role of stress-tolerant features in defining common MDSC features.
    Keywords:  MDSCs; cellular stress; myeloid derived suppressor cells; scRNA-seq; stress tolerant
    DOI:  https://doi.org/10.3389/fimmu.2025.1565211
  10. Am J Obstet Gynecol. 2025 Apr;pii: S0002-9378(24)00897-4. [Epub ahead of print]232(4S): S43-S53
    Single-cell technology: the key to an improved understanding of the human endometrium in health and disease.
    Javier Gonzalez-Fernandez, Sofía Zaragozano, Ana Monteagudo-Sánchez, Carlos Simon, Felipe Vilella.
      Cyclic exposure of the endometrium to ovarian sex steroids during the menstrual cycle induces a transition between proliferative and receptive states involving a different variety of cell types (ie, epithelial, stromal, endothelial, and immune cells) in preparation for embryo implantation during the narrow window of implantation. The study of the female reproductive system cells across these different phases contributes to our understanding of the healthy endometrium at the cellular level, supporting comparisons with pathological conditions, such as endometriosis, endometrial cancer, or Asherman's syndrome. Single-cell RNA sequencing technology represents a powerful tool that can discern the gene expression profiles of each cell within a tissue sample and has recently revealed the complex collaborations taking place between diverse cell types during the distinct endometrial phases. This review aims to summarize those studies that have employed single-cell RNA sequencing to deepen our understanding of the endometrium at single-cell resolution during the menstrual cycle. We discuss the transitions taken by distinct cell populations across the proliferative and secretory phases and the general importance of these transitions to successful embryo implantation. Furthermore, we analyze the use of single-cell RNA sequencing technology to study in vitro models of healthy endometrium and endometrial carcinoma. We believe that future studies using single-cell RNA sequencing will be essential to understanding the behavior of the endometrium as a whole and identifying potential avenues for the improved management of endometrial diseases.
    Keywords:  endometrial cancer; endometrial models; healthy endometrium, menstrual cycle; personalized medicine, single-cell analysis; transcriptomics
    DOI:  https://doi.org/10.1016/j.ajog.2024.08.042
  11. Front Immunol. 2025 ;16 1541939
    Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.
    Chenhui Feng, Zhimiao Wei, Xiaohui Li.
       Background: The bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper understanding of their role in the pathogenesis of KD.
    Method: We analyzed the public single-cell RNA sequencing (scRNA-seq) dataset GSE1687323 to characterize the immune cell-type landscape in KD. Gene sets related to BAM and FAM were collected from the Gene Set Enrichment Analysis (GSEA) database and previous literature. We analyzed the cellular heterogeneity of BAM and FAM at the single-cell level using R packages. Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. The cellular expression patterns of signature genes were further validated using our own scRNA-seq dataset. Finally, quantitative real-time PCR (qRT-PCR) was performed to validate the expression levels of signature genes in KD, and Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate their diagnostic potential.
    Results: Enhanced BAM and FAM were detected in monocytes and natural killer (NK) cells from KD in the public scRNA-seq dataset. Our scRNA-seq data confirmed the signature genes identified by machine learning algorithms: Vimentin (VIM) and chloride intracellular channel 1 (CLIC1) were upregulated in monocytes, while integrin subunit beta 2 (ITGB2) was elevated in NK cells of KD. qRT-PCR results also validated the bioinformatic analysis. Moreover, these genes demonstrated significant diagnostic potential. In the training dataset (GSE68004), the area under the curve (AUC) values and 95% CI were as follows: VIM: 0.914 (0.863-0.966), ITGB2: 0.958 (0.925-0.991), and CLIC1: 0.985 (0.969-1). The validation dataset (GSE73461) yielded similarly robust results, with AUC values and 95% CI: VIM: 0.872 (0.811-0.934), ITGB2: 0.861 (0.795-0.928), and CLIC1: 0.893 (0.837-0.948).
    Conclusion: This study successfully identified and validated VIM and CLIC1 in monocytes, as well as ITGB2 in NK cells, as novel metabolism-related genes in KD. These findings suggest that BAM and FAM may play crucial roles in KD pathogenesis. Furthermore, these signature genes hold promising potential as diagnostic biomarkers for KD.
    Keywords:  Kawasaki disease; bile acid metabolism; fatty acid metabolism; machine learning; single-cell RNA sequencing
    DOI:  https://doi.org/10.3389/fimmu.2025.1541939
  12. Sci Data. 2025 Apr 22. 12(1): 669
    High-resolution single-cell RNA-seq data and heterogeneity analysis of human ESCs and ffEPSCs.
    Lihang Zhu, Ran Zheng, Shanshan Wen, Wei Jiang.
      This study presents a comprehensive transcriptomic analysis of feeder-free extended pluripotent stem cells (ffEPSCs) and their parental human embryonic stem cells (ESCs), providing new insights into understanding human early development and cellular heterogeneity of pluripotency. Leveraging Smart-seq2-based single-cell RNA sequencing (scRNA-seq), we have compared gene expression profiles between ESCs and ffEPSCs and uncovered distinct subpopulations within both groups. Through pseudotime analysis, we have mapped the transition process from ESCs to ffEPSCs, revealing critical molecular pathways involved in the shift from a primed pluripotency to an extended pluripotent state. Additionally, we have employed repeat sequence analysis based on the latest T2T database and identified the stage-specific repeat elements contributing to regulating pluripotency and developmental transitions. This dataset deepens our understanding on early pluripotency and highlights the role of repeat sequences in early embryonic development. Our findings thus offer valuable resources for researchers in stem cell biology, pluripotency, early embryonic development, and potential cell therapy and regenerative medical applications.
    DOI:  https://doi.org/10.1038/s41597-025-05024-6
  13. Cold Spring Harb Protoc. 2025 Apr 23.
    Single-Cell and Spatial Transcriptomic Analysis of Maize Embryo Development: a Sample Preparation Protocol.
    Hao Wu, Michael J Scanlon.
      Maize is an important crop that contributes to the modern economy in various ways, including use for human consumption, as animal feed, and in industrial products. Research on maize is crucial for understanding plant development, which in turn provides valuable insight into improvement of maize crops to meet the food demands of a growing population. Maize embryogenesis, which is the primordial stage of the corn life cycle, determines the fundamental body plan and developmental programs that organize the tissue patterning and subsequent growth and reproduction of the corn plant. Investigating maize embryogenesis at high cellular resolution can enhance our understanding of the homology, ontogeny, and developmental genetic mechanisms of embryonic organ morphogenesis. However, until recently, no published studies have used methods for analyzing maize embryo development at single-cell resolution. This protocol describes single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic analyses, which are powerful, combinatorial tools that can be used to study maize embryogenesis at the single-cell level within a spatial context. These tools have the power to reveal transcriptomic relationships between tissues/organs, and to provide insight into the gene regulatory networks operating during embryogenesis. In this protocol, we describe a detailed procedure to prepare maize embryo samples for construction of scRNA-seq and Visium spatial transcriptomic libraries that are suitable for massively parallel sequencing. Our protocol borrows from prior published studies and manufacturer's instructions and is optimized for studies of the maize embryo.
    DOI:  https://doi.org/10.1101/pdb.prot108645
  14. Cold Spring Harb Protoc. 2025 Apr 23.
    Single-Cell and Spatial Transcriptomic Analysis of Maize Embryo Development.
    Hao Wu, Michael J Scanlon.
      Plant embryogenesis encompasses the biological processes wherein the zygote (fertilized egg) undergoes cell division, cell expansion, and cell differentiation to develop histological tissue layers, meristems, and various organs comprising the primordial body plan of the organism. Studies of embryogenesis in the agronomically important maize crop advance our understanding of the fundamental mechanism of plant development, which, upon translation, may advance agronomic improvement, optimization of conditions for somatic embryogenesis, and plant synthetic biology. Maize embryo development is coordinated temporally and spatially and is regulated by interactive genetic networks. Single-cell RNA sequencing (RNA-seq) and spatial transcriptomics are powerful tools to examine gene expression patterns and regulatory networks at single-cell resolution and in a spatial context, respectively. Single-cell technology enables profiling of three-dimensional samples with high cellular resolution, but it can be difficult to identify specific cell clusters due to a lack of known markers in most plant species. In contrast, spatial transcriptomics provide transcriptomic profiling of discrete regions within a sectioned, two-dimensional sample, although single-cell resolution is typically not obtained and fewer transcripts per cell are detected than in single-cell RNA-seq. In this review, we describe the combined use of these two transcriptomic strategies to study maize embryogenesis with synergistic results.
    DOI:  https://doi.org/10.1101/pdb.top108468
  15. Am J Obstet Gynecol. 2025 Apr;pii: S0002-9378(25)00074-2. [Epub ahead of print]232(4S): S176-S189
    Placenta at single-cell resolution in early and late preeclampsia: insights and clinical implications.
    Ido Solt, Sarah M Cohen, Inbal Admati, Ofer Beharier, Omri Dominsky, Simcha Yagel.
      Preeclampsia, one of the great obstetrical syndromes, manifests through diverse maternal and fetal complications and remains a leading contributor to adverse perinatal outcomes. In this review, we describe our work on single-cell and single-nuclei RNA sequencing to elucidate the molecular mechanisms that underlie early- and late-onset preeclampsia. Analysis of 46 cell types, encompassing approximately 90,000 cells from placental tissues collected after delivery, demonstrated cellular dysregulation in early-onset preeclampsia, whereas late-onset preeclampsia showed comparatively subtle changes. These findings were observed in all cell lines, including all types of trophoblast, lymphoid, myeloid, stromal, and endothelial cells. Key findings in early-onset preeclampsia included disrupted syncytiotrophoblast and extravillous trophoblast angiogenic signaling, characterized by an up-regulation of FLT1 and down-regulation of PGF, consistent with an angiogenic imbalance. The stromal and vascular compartments exhibited stress-induced transcriptomic shifts. Both endothelial cells and pericytes showed evidence of stress, including up-regulation of heat shock proteins and markers of apoptosis. In addition, the inflammation- and stress-responsive states were more abundant in early-onset preeclampsia than in matched controls. Inflammatory pathways were markedly up-regulated in both the maternal and fetal immune cells; for example, we observed a marked increase in pro-inflammatory cytokines, including secreted phosphoprotein 1 and C-X-C motif chemokine ligand 2 and 3. Conversely, late-onset preeclampsia retained adaptive placental features with localized dysregulation of extracellular matrix remodeling and angiogenic markers, underscoring its possible maternal cardiovascular etiology. Single-cell and single-nuclei RNA sequencing investigations of placental tissues support the proposed classification of preeclampsia into a placental dysfunction type, primarily presenting early in pregnancy, and a maternal cardiovascular maladaptation type, primarily presenting later in pregnancy, each with distinct biomarkers, risk factors, and therapeutic targets. The early-onset preeclampsia findings advocate for interventions that target angiogenic pathways, such as RNA-based therapies that target specific cells of the placenta, to modulate soluble fms-like tyrosine kinase-1 levels. In contrast, late-onset preeclampsia management may benefit from maternal cardiovascular optimization, including individualized antihypertensive and metabolic treatments. These results underscore the heterogeneity of preeclampsia, emphasizing the need for individualized diagnostic and therapeutic strategies. This molecular atlas of preeclampsia advances our understanding of the complex interplay among elements of the maternal-placental-fetal array, thereby bridging clinical phenotypes and cellular mechanisms. Future research should focus on integrating these insights into longitudinal studies to develop precision medicine approaches for preeclampsia to enhance outcomes for mothers and neonates.
    Keywords:  PlGF; early-onset preeclampsia; endothelial cells; immune cells; late-onset preeclampsia; maternal cardiovascular adaptation; placenta; preeclampsia; sFlt-1; single nuclei RNA sequencing; single-cell RNA sequencing; stromal cells; trophoblast
    DOI:  https://doi.org/10.1016/j.ajog.2025.01.041
  16. Am J Obstet Gynecol. 2025 Apr;pii: S0002-9378(24)00878-0. [Epub ahead of print]232(4S): S124-S134
    Uterine fibroids at single-cell resolution: unveiling cellular heterogeneity to improve understanding of pathogenesis and guide future therapies.
    Jyoti Goad, Aleksandar Rajkovic.
      Uterine leiomyomas or fibroids are benign tumors of the myometrium that affect approximately 70% of reproductive-age women. Fibroids continue to be the leading cause of hysterectomy, resulting in substantial healthcare costs. Genetic complexity and lack of cellular and molecular understanding of fibroids have posed considerable challenges to developing noninvasive treatment options. Over the years, research efforts have intensified to unravel the genetic and cellular diversities within fibroids to deepen our understanding of their origins and progression. Studies using immunostaining and flow cytometry have revealed cellular heterogeneity within these tumors. A correlation has been observed between genetic mutations in fibroids and their size, which is influenced by cellular composition, proliferation rates, and extracellular matrix accumulation. Fibroids with mediator complex subunit 12 (MED12) mutation are composed of smooth muscle cells and fibroblasts equally. In contrast, the fibroids with high-mobility group AT-hook 2 (HMGA2) translocation are 90% composed of smooth muscle cells. More recently, single-cell RNA sequencing in the myometrium and MED12 mutation carrying fibroids has identified further heterogeneity in smooth muscle cells and fibroblast cells, identifying 3 different smooth muscle cell populations and fibroblast cell populations. Although both myometrium and fibroids have similar cellular composition, these cells differs in their transcriptomic profile and have specialized roles, underscoring the complex cellular landscape contributing to fibroid pathogenesis. Furthermore, not all smooth muscle cells in MED12-mutant fibroid carry the MED12 mutation, suggesting that MED12-mutant fibroids might not be monoclonal in nature. This review describes the intricacies of fibroid biology revealed by single-cell RNA sequencing. These advances have identified new cellular targets for potential therapies, provided insights into treatment resistance, and laid the groundwork for more personalized approaches to fibroid management. As we continue to unravel the cellular and molecular complexity of fibroids, we anticipate that this knowledge will translate into more effective and less invasive treatments, ultimately improving outcomes for the millions of women affected by this condition.
    Keywords:  HMGA2; MED12; cellular heterogeneity; collagen; fibroblast population; fibroid; pathway; single-cell RNA sequencing; smooth muscle cell populations
    DOI:  https://doi.org/10.1016/j.ajog.2024.08.037
  17. Front Immunol. 2025 ;16 1569605
    Investigation of the role of GEM in systemic lupus erythematosus through multi-omics joint analysis.
    Ruofei Chen, Xiao Zhang, Yifang Shang, Huaixuan Zhang, Xiaolei Li, Hanren Dai, Zongwen Shuai.
       Background: Systemic lupus erythematosus (SLE) is a persistent autoimmune disorder marked by dysregulation of the immune system, resulting in extensive tissue inflammation and subsequent damage. Fibroblasts are essential contributors to the pathogenesis of SLE, particularly in driving the progression of tissue fibrosis and inflammation. Recent research has proposed that the GEM gene may regulate fibroblast activity in SLE. However, the precise molecular mechanisms through which GEM modulates fibroblast functions in the context of SLE are yet to be fully elucidated. Gaining insight into these mechanisms is crucial for uncovering potential therapeutic targets aimed at addressing fibrosis and inflammation associated with SLE.
    Methods: Single-cell RNA sequencing was integrated with cell-based assays, such as quantitative reverse transcription PCR (qRT-PCR) and functional cellular experiments, to investigate the underlying mechanisms. The regulatory mechanisms of GEM in fibroblasts were analyzed through functional cell assays.
    Results: Differential gene expression in fibroblast subpopulations was identified through single-cell RNA sequencing, with GEM emerging as a key gene implicated in these alterations. Trajectory analysis indicated that GEM expression correlated with fibroblast proliferation and migration. Subsequent experiments confirmed that GEM regulates fibroblast viability and influences SLE disease progression through modulation of cell proliferation, migration, and apoptosis.
    Conclusions: GEM is highly differentially expressed in fibroblast subpopulations within SLE, and its altered expression impacts fibroblast proliferation and migration. GEM may regulate fibroblast activity and apoptosis, potentially contributing to the progression of SLE.
    Keywords:  GEM; SLE; fibroblast; immunology; therapeutic target
    DOI:  https://doi.org/10.3389/fimmu.2025.1569605
  18. PLoS Comput Biol. 2025 Apr 21. 21(4): e1012991
    Transfer learning of multicellular organization via single-cell and spatial transcriptomics.
    Yecheng Tan, Ai Wang, Zezhou Wang, Wei Lin, Yan Yan, Qing Nie, Jifan Shi.
      Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) measures spatial locations of individual or group of cells, with more restrictions on gene information. Here we show a transfer learning method named iSORT to decipher spatial organization of cells by integrating scRNA-seq and ST data. iSORT trains a neural network that maps gene expressions to spatial locations. iSORT can find spatial patterns at single-cell scale, identify spatial-organizing genes (SOGs) that drive the patterning, and infer pseudo-growth trajectories using a concept of SpaRNA velocity. Benchmarking on a range of biological systems, such as human cortex, mouse embryo, mouse brain, Drosophila embryo, and human developmental heart, demonstrates iSORT's accuracy and practicality in reconstructing multicellular organization. We further conducted scRNA-seq and ST sequencing from normal and atherosclerotic arteries, and the functional enrichment analysis shows that SOGs found by iSORT are strongly associated with vascular structural anomalies.
    DOI:  https://doi.org/10.1371/journal.pcbi.1012991
  19. Front Immunol. 2025 ;16 1537785
    Epithelial and macrophage cell interaction in cervical cancer through single-cell RNA-sequencing and spatial analysis.
    Zhichao Wang, Long Cheng, Guanghui Li, Huiyan Cheng.
       Background: Cervical cancer (CC) is a major global health issue, ranking sixth in cancer-related mortality. The tumor microenvironment (TME) plays a crucial role in tumor growth. This study explored the cellular composition and immunological landscape of CC using various genomic data sources.
    Methods: Data from the Cancer Genome Atlas and Gene Expression Omnibus were analyzed, including single-cell RNA sequencing, spatial transcriptome analysis, and survival data. Gene set variation analysis (GSVA) identified pathways in CD8+ cells, macrophages, and epithelial cells. Immunohistochemistry assessed marker expression in CC and normal tissues. Tumor immune dysfunction and exclusion (TIDE) scores differentiated high- and low-macrophage groups. Cell-cell communication analyses highlighted interactions between macrophages and epithelial cells.
    Results: Macrophage markers correlated with overall survival (OS) and disease-free survival (DFS). Epithelial cell subgroups 1 and 4, along with CD8+ T cells, were associated with OS. TIDE scores varied between groups. Specific ligand-receptor interactions were found between macrophages and epithelial cell subgroup 1. Triptolide was effective in epithelial cell subgroup 1, while memantine was more effective in macrophages.
    Conclusion: Epithelial-macrophage interactions in the TME are crucial for CC progression and treatment, offering a potential immunotherapeutic strategy.
    Keywords:  cell-cell communication; cervical cancer (CC); immunotherapy; macrophages; tumor microenvironment (TME)
    DOI:  https://doi.org/10.3389/fimmu.2025.1537785
  20. Front Immunol. 2025 ;16 1561388
    Single-cell and spatial transcriptomic insights into glioma cellular heterogeneity and metabolic adaptations.
    Yixin Fu, Yong Yi, Yongxiang Shao, Jingcheng Jiang, Qingshan Deng.
      Glioblastoma, one of the most aggressive and heterogeneous malignant tumors, presents significant challenges for clinical management due to its cellular and metabolic complexity. This review integrates recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to elucidate glioblastoma's cellular heterogeneity and metabolic reprogramming. Diverse cellular subpopulations, including malignant proliferative cells, stem-like cells, mesenchymal-like cells, and immune-related cells, contribute to tumor progression, treatment resistance, and microenvironmental interactions. Spatial transcriptomics has further revealed distinct spatial distributions of these subpopulations, highlighting differences in metabolic activities between the tumor core and periphery. Key metabolic adaptations, such as enhanced glycolysis, fatty acid oxidation, and glutamine metabolism, play critical roles in supporting tumor growth, immune evasion, and therapeutic resistance. Targeting these metabolic pathways, especially in combination with immunotherapy, represents a promising avenue for glioblastoma treatment. This review emphasizes the importance of integrating single-cell and spatial multi-omics technologies to decode glioblastoma's metabolic landscape and explore novel therapeutic strategies. By addressing current challenges, such as metabolic redundancy and spatiotemporal dynamics, this work provides insights into advancing precision medicine for glioblastoma.
    Keywords:  glioma; metabolic reprogramming; single-cell; tumor microenvironment; tumor-associated macrophages
    DOI:  https://doi.org/10.3389/fimmu.2025.1561388
  21. Comput Struct Biotechnol J. 2025 ;27 1559-1569
    Mapping Cell Identity from scRNA-seq: A primer on computational methods.
    Daniele Traversa, Matteo Chiara.
      Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.
    Keywords:  Cell identity; Cell type annotation; RNAseq; ScRNAseq; Transcriptomics
    DOI:  https://doi.org/10.1016/j.csbj.2025.03.051
  22. Front Immunol. 2025 ;16 1541252
    Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs.
    Hang Deng, Xu Wang, Zi-Ang Jiang, Jian Xu, Yu Zhang, Yao Zhou, Jun Gong, Xiang-Yu Lu, Yi-Fu Hou, Hao Zhang.
       Background: Hepatocellular carcinoma (HCC) is the leading cause of tumor-related mortality worldwide. There is an urgent need for predictive biomarkers to guide treatment decisions. This study aimed to identify robust prognostic genes for HCC and to establish a theoretical foundation for clinical interventions.
    Methods: The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. Immune infiltration and drug sensitivity analyses were conducted to assess the impact of these genes and to explore potential chemotherapeutic agents for HCC. Additionally, single-cell analysis was employed to identify key cellular players and their interactions within the tumor microenvironment. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized to validate the roles of these prognostic genes in HCC.
    Results: A total of eight prognostic genes were identified (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4). The risk model, constructed based on these genes, was effective in predicting survival outcomes for HCC patients. CEP55 exhibited the strongest positive correlation with activated CD4 T cells. The top 10 drugs showed increased sensitivity in the low-risk group. B cells were identified as key cellular components with the highest interaction numbers and strengths with macrophages in both HCC and control groups. Prognostic genes were more highly expressed in the initial state of B cell differentiation. RT-qPCR confirmed significant upregulation of MCM10, KIF18A, CDC45, and PLK4 in HCC tissues (p< 0.05).
    Conclusion: This study successfully identified eight prognostic genes (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4), which provided new directions for exploring the potential pathogenesis and clinical treatment research of HCC.
    Keywords:  combination algorithms; drug sensitivity; hepatocellular carcinoma; prognostic genes; single-cell sequencing analysis
    DOI:  https://doi.org/10.3389/fimmu.2025.1541252
  23. PLoS Biol. 2025 Apr 21. 23(4): e3003133
    Single-cell analysis of the early Drosophila salivary gland reveals that morphogenetic control involves both the induction and exclusion of gene expression programs.
    Annabel May, Katja Röper.
      How tissue shape and therefore function is encoded by the genome remains in many cases unresolved. The tubes of the salivary glands in the Drosophila embryo start from simple epithelial placodes, specified through the homeotic factors Scr/Hth/Exd. Previous work indicated that early morphogenetic changes are prepatterned by transcriptional changes, but an exhaustive transcriptional blueprint driving physical changes was lacking. We performed single-cell-RNAseq-analysis of FACS-isolated early placodal cells, making up less than 0.4% of cells within the embryo. Differential expression analysis in comparison to epidermal cells analyzed in parallel generated a repertoire of genes highly upregulated within placodal cells prior to morphogenetic changes. Furthermore, clustering and pseudotime analysis of single-cell-sequencing data identified dynamic expression changes along the morphogenetic timeline. Our dataset provides a comprehensive resource for future studies of a simple but highly conserved morphogenetic process of tube morphogenesis. Unexpectedly, we identified a subset of genes that, although initially expressed in the very early placode, then became selectively excluded from the placode but not the surrounding epidermis, including hth, grainyhead and tollo/toll-8. We show that maintaining tollo expression severely compromised the tube morphogenesis. We propose tollo is switched off to not interfere with key Tolls/LRRs that are expressed and function in the tube morphogenesis.
    DOI:  https://doi.org/10.1371/journal.pbio.3003133
  24. Sci Rep. 2025 Apr 24. 15(1): 14251
    Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.
    Jianghao Xiong, Jiali Chen, Jianjun Zhou, Chao Li, Jifeng Wu.
      Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right heart failure and death. Despite advancements in diagnosis and treatment, it remains incurable, and its mechanisms are poorly understood. This study aimed to integrate multi-omics data analysis and machine learning techniques to uncover the molecular characteristics and subtypes of PAH, providing insights into precise diagnosis and therapeutic strategies. We employed consensus clustering to classify PAH patients into subgroups based on multi-omics data. Differential expression and enrichment analyses were conducted to identify key genes and pathways. Machine learning models were developed to predict PAH subtypes and assess their diagnostic performance. PAH patients were divided into two subgroups: C1 and C2. The C2 subgroup showed significantly upregulated hypoxia-related genes, indicating distinct pathogenic mechanisms. Key genes associated with hypoxia, immune regulation, and inflammation were identified, alongside enriched pathways such as TNF, IL-17, and HIF-1 in the C2 subgroup. Machine learning models achieved high accuracy (AUC > 0.85) in distinguishing hypoxia-associated subtypes, supporting their utility for precise diagnosis. Potential therapeutic targets were identified in the TNF and HIF-1 pathways. This study provides novel insights into PAH's molecular subtypes and their distinct mechanisms, offering diagnostic tools and potential therapeutic targets for personalized treatment. Validation in larger cohorts and experimental studies is essential to confirm the identified biomarkers and pathways.
    Keywords:  Hypoxia; Immune regulation and inflammation; Machine learning; Pulmonary arterial hypertension
    DOI:  https://doi.org/10.1038/s41598-025-99025-5
  25. Curr Pharm Des. 2025 Apr 22.
    Unraveling the Therapeutic Potential of Scutellarin for Clear Cell Renal Cell Carcinoma: A Comprehensive Molecular Analysis.
    Yangyang Bai, Yilin Guo, Ruiting Chen, Jijian Sun, Ranlu Liu.
       BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma, is a significant global health issue. Despite advancements in surgery and systemic therapies, drug resistance remains a challenge, and more effective treatments are needed. Scutellarin, a natural flavonoid with anticancer properties, is a promising therapeutic option for ccRCC.
    METHODS: This present study identified the potential target genes of scutellarin by searching four databases and utilized the TCGA-KIRC and GSE53757 datasets to identify ccRCC features genes. Protein-protein interaction networks and molecular complex detection analyses determined the hub genes through which scutellarin acts on ccRCC. Differential expression, receiver operating characteristic analysis, survival, and immune cell infiltration analyses were conducted successively on these hub genes in tumor and normal tissues to verify their clinical significance. The intracellular mechanism of the hub genes was explored using a single-cell dataset (GSE222703) to elucidate the intracellular pathway through which scutellarin exerts its anti-ccRCC effects. At last, molecular docking and molecular dynamics simulations were performed to confirm the stability of the receptor protein of the hub gene binding to scutellarin.
    RESULTS: 158 scutellarin targets were collected and identified through database searches. Analyzing the TCGA-KIRC and GSE53757 data separately identified finally 132 ccRCC feature genes through differential expression analysis and WGCNA. Protein-protein interaction network and molecular complex detection analyses revealed 26 hub genes potentially involved in hinge pathways of scutellarin in ccRCC. Differential expression analysis revealed significant differences in the expression of these hub genes between tumor and normal tissues. Receiver operating characteristic analysis demonstrated the fine diagnostic efficacy of these hub genes. Survival analysis indicated that the hub genes TYMS and CDCA2 were associated with a better prognosis, whereas the remaining hub genes had a poorer prognosis. Enrichment analysis revealed that hub genes mainly involved oxidative stress and cell cycle regulation. Single-cell RNA sequencing analysis suggested that most hub genes exert their effects on T helper cells. Molecular docking results showed stable docking of hub genes with scutellari, except for SPAG5 and ASPM. Molecular dynamics simulations of the most stable docking sites, KIF20A, TYMS, and KIF18B, indicated stable complex formation compared with that of the internal reference protein GAPDH.
    CONCLUSION: This integrated study provides a comprehensive analysis of the molecular targets and pathways affected by scutellarin in ccRCC. The identified hub genes and their related pathways present exciting prospects for therapeutic intervention and highlight the potential of scutellarin as a novel treatment for ccRCC. Additional research is necessary to investigate the precise molecular mechanisms and therapeutic advantages of scutellarin in preclinical and clinical contexts.
    Keywords:  Clear cell renal cell carcinoma; molecular docking; molecular dynamics simulation.; network pharmacology; scutellarin; single-cell RNA sequencing
    DOI:  https://doi.org/10.2174/0113816128340451241224055536
  26. FEBS J. 2025 Apr 20.
    Unveiling the impact of ferroptosis on diabetes-associated cognitive decline through comprehensive single-cell RNA sequencing and experimental studies.
    Yiping Zhang, Xiaolan Hu, Shoulin Chen, Fuzhou Hua, Zhenguo Zeng.
      Diabetes-associated cognitive decline (DACD) is defined as an impairment of cognitive functions, including memory, attention and executive functions, attributed to chronic hyperglycemia and metabolic dysregulation associated with type 2 diabetes mellitus (T2DM). Ferroptosis is a regulated form of cell death that is dependent on iron and is primarily characterized by the excessive accumulation of lipid peroxides within cellular membranes, and also plays a critical role by exacerbating neuronal loss and synaptic dysfunction. The present study aims to use single-cell RNA sequencing (scRNA-seq) technology to investigate the role of ferroptosis in microglia and oligodendrocytes in DACD, thereby elucidating the pathogenesis of DACD. scRNA-seq and bulk RNA-seq datasets were analyzed for differential gene expression in hippocampus samples of T2DM and control mice, with an emphasis on oligodendrocytes and microglia cell types. We further constructed a T2DM model in mice and conducted behavioral analyses to evaluate cognitive functions. Additionally, we explored the role of ferroptosis in the progression of DACD disease by knocking down transferrin receptor 1 (Tfr1) using small interfering RNA and utilizing the ferroptosis inhibitor ferrostatin-1. The study identified significant alterations in the expression of ferroptosis-related genes Fth1, Slc40a1, Slc3a2, Trf, Tfrc and Sat1 in T2DM mice, suggesting the possible involvement of ferroptosis in DACD. Knocking down Tfr1 and inhibiting ferroptosis could significantly alleviate inflammation and oxidative stress damage in oligodendrocytes. This research provides new perspectives into the pathophysiology of DACD, emphasizing the critical role of ferroptosis and offering a potential therapeutic target to mitigate neurological damage and cognitive impairment associated with T2DM.
    Keywords:  Tfr1; diabetes‐associated cognitive decline; ferroptosis; oligodendrocyte; scRNA‐seq
    DOI:  https://doi.org/10.1111/febs.70101
  27. IET Syst Biol. 2025 Apr 22. e12107
    scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.
    Yanru Gao, Hongyu Duan, Fanhao Meng, Conghui Zhang, Xiyue Li, Feng Li.
      Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often face the challenges of high dimensionality, large number of samples, high sparsity and sample imbalance. The traditional methods of cell type recognition have been challenged. The authors propose a deep residual generation model based on semi-supervised learning (scRSSL) to address these challenges. ScRSSL creatively introduces residual networks into semi-supervised generative models. The authors take advantage of its semi-supervised learning to solve the problem of sample imbalance. During the training of the model, the authors use a residual neural network to accomplish the inference of cell types so that local features of single-cell data can be extracted. Because of the semi-supervised learning approach, it can automatically and accurately predict individual cell types in datasets, even with only a small number of cell labels. Experimentally, the authors' method has proven to have better performance compared to other methods.
    Keywords:  bioinformatics; deep generative model; deep learning; semi‐supervised learning; single cell
    DOI:  https://doi.org/10.1049/syb2.12107
  28. Brief Bioinform. 2025 Mar 04. pii: bbaf184. [Epub ahead of print]26(2):
    Nonlinear embedding and integration of omics data: a fast and tuning-free approach.
    Shengjie Liu, Tianwei Yu.
      The rapid progress of single-cell technology has facilitated cost-effective acquisition of diverse omics data, allowing biologists to unravel the complexities of cell populations, disease states, and more. Additionally, single-cell multi-omics technologies have opened new avenues for studying biological interactions. However, the high dimensionality and sparsity of omics data present significant analytical challenges. Dimension reduction (DR) techniques are hence essential for analyzing such complex data, yet many existing methods have inherent limitations. Linear methods like principal component analysis (PCA) struggle to capture intricate associations within data. In response, nonlinear techniques have emerged, but they may face scalability issues, be restricted to single-omics data, or prioritize visualization over generating informative embeddings. Here, we introduce dissimilarity based on conditional ordered list (DCOL) correlation, a novel measure for quantifying nonlinear relationships between variables. Based on this measure, we propose DCOL-PCA and DCOL-Canonical Correlation Analysis for dimension reduction and integration of single- and multi-omics data. In simulations, our methods outperformed nine DR methods and four joint dimension reduction methods, demonstrating stable performance across various settings. We also validated these methods on real datasets, with our method demonstrating its ability to detect intricate signals within and between omics data and generate lower dimensional embeddings that preserve the essential information and latent structures.
    Keywords:  DCOL-correlation; multi-omics integration; multimodal data; nonlinear dimensionality reduction; single-cell analysis
    DOI:  https://doi.org/10.1093/bib/bbaf184
  29. Front Immunol. 2025 ;16 1532306
    Cancer-associated fibroblasts gene signature: a novel approach to survival prediction and immunotherapy guidance in colon cancer.
    Wenbing Zhang, Chi Yang, Ye Lu, Chenling Tang, Mengyu Zhao, Zhaohui Wang, Jidong Gao, Shuangjiu Hu, Zhihua Chen.
       Background: Fibroblasts can regulate tumour development by secreting various factors. For COAD survival prediction and CAFs-based treatment recommendations, it is critical to comprehend the heterogeneity of CAFs and find biomarkers.
    Methods: We identified fibroblast-associated specific marker genes in colon adenocarcinoma by single-cell sequencing analysis. A fibroblasts-related gene signature was developed, and colon adenocarcinoma patients were classified into high-risk and low-risk cohorts based on the median risk score. Additionally, the impact of these risk categories on the tumor microenvironment was evaluated. The ability of CAFGs signature to assess prognosis and guide treatment was validated using external cohorts. Ultimately, we verified MAN1B1 expression and function through in vitro assays.
    Results: Relying on the bulk RNA-seq and scRNA-seq data study, we created a predictive profile with 11 CAFGs. The profile effectively differentiated survival differences among cohorts of colon adenocarcinoma patients. The nomogram further effectively predicted the prognosis of COAD patients, with low-risk patients having a better prognosis. A higher immune infiltration rate and lower IC50 values of anticancer drugs were significant in the high-risk group. In cellular experiments, Following MAN1B1 knockdown, in cell assays, the colony formation, migration, and invasion ability of HCT116 and HT29 cell lines decreased.
    Conclusion: Our CAFG signature provides important insights into the role of CAF cells in influencing COAD prognosis. It may also serve as a guide for selecting immunotherapy options and predicting chemotherapy responses in COAD patients.
    Keywords:  MAN1B1; cancer-associated fibroblasts; colon adenocarcinoma; signature; tumor immune microenvironment
    DOI:  https://doi.org/10.3389/fimmu.2025.1532306
  30. Cancer Cell Int. 2025 Apr 22. 25(1): 159
    Identifying PSIP1 as a critical R-loop regulator in osteosarcoma via machine-learning and multi-omics analysis.
    Jiangbo Nie, Shijiang Wang, Yanxin Zhong, Feng Yang, Jiaming Liu, Zhili Liu.
      Dysregulation of R-loops has been implicated in tumor development, progression, and the regulation of tumor immune microenvironment (TME). However, their roles in osteosarcoma (OS) remain underexplored. In this study, we firstly constructed a novel R-loop Gene Prognostic Score Model (RGPSM) based on the RNA-sequencing (RNA-seq) datasets and evaluated the relationships between the RGPSM scores and the TME. Additionally, we identified key R-loop-related genes involved in OS progression using single-cell RNA sequencing (scRNA-seq) dataset, and validated these findings through experiments. We found that patients with high-RGPSM scores exhibited poorer prognosis, lower Huvos grades and a more suppressive TME. Moreover, the proportion of malignant cells was significantly higher in the high-RGPSM group. And integrated analysis of RNA-seq and scRNA-seq datasets revealed that PC4 and SRSF1 Interacting Protein 1 (PSIP1) was highly expressed in osteoblastic and proliferative OS cells. Notably, high expression of PSIP1 was associated with poor prognosis of OS patients. Subsequent experiments demonstrated that knockdown of PSIP1 inhibited OS progression both in vivo and in vitro, leading increased R-loop accumulation and DNA damage. Conversely, overexpression of PSIP1 facilitated R-loop resolution and reduced DNA damage induced by cisplatin. In conclusion, we developed a novel RGPSM that effectively predicted the outcomes of OS patients across diverse cohorts and identified PSIP1 as a critical modulator of OS progression by regulating R-loop accumulation and DNA damage.
    Keywords:  Machine-learning; Multi-omics analysis; Osteosarcoma; PSIP1; R-loop
    DOI:  https://doi.org/10.1186/s12935-025-03775-1
  31. Funct Integr Genomics. 2025 Apr 21. 25(1): 91
    A Multi-omics approach to identify and validate shared genetic architecture in rheumatoid arthritis, multiple sclerosis, and type 1 diabetes: integrating GWAS, GEO, MSigDB, and scRNA-seq data.
    Tailin Wang, Qian He, Kei Hang Katie Chan.
      The notable comorbidity among autoimmune diseases underscores their shared genetic underpinnings, particularly evident in rheumatoid arthritis (RA), type 1 diabetes (T1D), and multiple sclerosis (MS). However, the exact components and mechanisms of this shared genetic structure remain poorly understood. Here we show that ROMO1 is a key shared genetic component among RA, MS, and T1D. Using differential gene expression (DGE) and LASSO regression analyses of bulk RNA-seq data from whole blood tissues, we identified ROMO1 as a potential shared genetic factor. A multi-sample analysis with external Gene Expression Omnibus (GEO) data revealed ROMO1's consistent association with immune cell patterns across tissues in all three diseases. Single-gene Gene Set Enrichment Analysis (GSEA) suggested ROMO1's involvement in the reactive oxygen species (ROS) pathway, which was further substantiated by conjoint analysis with 256 ROS pathway-related genes(ROSGs) from Molecular Signatures Database (MSigDB). Single-gene Receiver Operating Characteristic (ROC) analysis highlighted ROMO1's potential as a disease biomarker. Single-cell RNA sequencing (scRNA-seq) analysis showed significantly altered ROMO1 expression in monocytes and other immune cells compared to healthy control (HC). Immune infiltration analysis revealed ROMO1's significant association with monocytes across all three diseases. Furthermore, two-sample Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) data demonstrated that ROMO1 could regulate epitopes on monocytes, potentially lowering autoimmune disease risk. Our findings clarify the importance of ROMO1 in the shared genetic architecture of RA, MS, and T1D, and its underlying mechanism in disease development.
    DOI:  https://doi.org/10.1007/s10142-025-01598-x
  32. Interdiscip Sci. 2025 Apr 24.
    Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.
    Hongjing Ai, Rongfang Nie, Xiaosheng Wang.
      Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms the single-cell gene expression matrix into a pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to pancreatic ductal adenocarcinoma (PDAC) scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggested a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.
    Keywords:  Autoencoder; Cancer-associated fibroblast; Pancreatic ductal adenocarcinoma; Pathway enrichment; Single-cell clustering
    DOI:  https://doi.org/10.1007/s12539-025-00705-7
  33. Dig Dis Sci. 2025 Apr 22.
    Integrated Analysis of PSMB8 Expression and Its Potential Roles in Hepatocellular Carcinoma.
    Ruijiao Lu, Xieyidai Abuduhailili, Yuxia Li, Senyu Wang, Xigang Xia, Yangchun Feng.
       BACKGROUND: Hepatocellular carcinoma (HCC) represents a highly aggressive malignancy with significant global health implications. The proteasome subunit beta type-8 (PSMB8) gene, known for its association with hepatitis B virus susceptibility, has emerged as a potential regulator of tumor progression. However, its functional role and clinical significance in HCC remain poorly characterized.
    METHODS: We conducted a comprehensive multi-omics analysis to elucidate the role of PSMB8 in HCC. PSMB8 expression profiles were derived from The Cancer Genome Atlas and validated using the GSE76427 dataset. Prognostic significance was assessed through Kaplan-Meier survival analysis. Then, we systematically evaluated the relationships between PSMB8 expression and clinicopathological features, somatic mutations, immune cell infiltration, immune regulatory genes, and immune checkpoint responses. Single-cell RNA sequencing data from the Tumor Immune Single-cell Hub database were analyzed to determine cell type-specific PSMB8 expression. Tissue-level validation was performed using multiplex immunofluorescence staining on HCC tissue microarrays.
    RESULTS: PSMB8 demonstrated significant overexpression in HCC tissues and exhibited strong prognostic value. Single-cell analysis revealed predominant PSMB8 expression in T and B cell populations. Notably, PSMB8 expression showed significant positive correlations with immune checkpoint molecules PD-L1/CD274 and CD27. Functional enrichment analysis implicated PSMB8 in multiple oncogenic pathways, particularly proteasome-related processes.
    CONCLUSION: Our findings position PSMB8 as a promising prognostic biomarker and potential therapeutic target in HCC. The observed associations with immune checkpoint molecules and proteasomal pathways suggest its potential role in modulating tumor immunity and protein homeostasis, warranting further investigation into its mechanistic contributions to HCC progression.
    Keywords:  Hepatocellular carcinoma; Immunofluorescence; PSMB8; Prognosis; Tumor microenvironment
    DOI:  https://doi.org/10.1007/s10620-025-09040-9
  34. Front Pharmacol. 2025 ;16 1554632
    Pan-cancer analysis identified CD248 as a potential target for multiple tumor types.
    Li Guo, Yan Liao, Xuyang Zhang, Rongjuan Guo, Zheng Wang, Deqin Yang.
       Objective: Tumors remain a major cause of death worldwide due to late-stage presentation and late diagnosis. Cell therapies have revolutionized the landscape in the precision treatment of tumors. However, there are still many challenges that limit the therapeutic efficacy. Additionally, cancer treatment also entails a major financial burden throughout the entire phase, making it preferable to find a specific biomarker for the early prognosis of the tumor.
    Methods: In this study, the role of CD248 in pan-cancer was analyzed through diverse tumor-associated databases, such as the Human Protein Atlas Database, the GEPIA2 Database, the cBioPortal Database, the TIMER Database, the STRING tool, and so on. In addition, CD248 mRNA and protein levels were assessed in a series of head and neck squamous cell carcinoma (HNSC) cell lines using qRT-PCR and Western blot. Furthermore, siCD248 was used to detect the effect of CD248 on the invasion, migration, and proliferation of HNSC cells by transwell assay, scratch wound healing assay, and EdU assay, respectively.
    Results: CD248 expression was significantly increased and correlated with advanced stage and poor prognosis in various tumors. Genetic alterations of CD248 were also associated with a poor prognosis of patients. Single-cell sequencing revealed that CD248 was mainly expressed on fibroblasts within the stroma, and its expression was positively correlated with the infiltration of immune cells in tumors. In addition, CD248 interacted with 11 common tumor biomarkers. Experiment results indicated that CD248 mRNA and protein expression were upregulated in HNSC cell lines, and inhibition of CD248 suppresses the invasion, migration, and proliferation of HNSC cells.
    Conclusion: High CD248 expression played a crucial role in pan-cancer, including immune cell infiltration, tumor progression and metastasis, and patient prognosis. CD248 plays a crucial role in tumor cells' functions, including invasion, migration, and proliferation. All these findings indicated that CD248 may be a novel oncoprotein and a potential therapeutic target for pan-cancer.
    Keywords:  CD248; immune cell; overall survival; pan-cancer; single-cell sequencing; targeted therapy
    DOI:  https://doi.org/10.3389/fphar.2025.1554632
  35. Sci Rep. 2025 Apr 22. 15(1): 13901
    Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system.
    Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang.
      Neuroendocrine prostate cancer (NEPC), a subtype of prostate cancer (PCa) with poor prognosis and high heterogeneity, currently lacks accurate markers. This study aims to identify a robust NEPC classifier and provide new perspectives for resolving intra- tumoral heterogeneity. Multi-omics analysis included 19 bulk transcriptomics, 14 single-cell transcriptomics, 1 spatial transcriptomics, 16 published NE signatures and 10 cellular experiments combined with multiple machine learning algorithms to construct a novel NEPC classifier and classification. A comprehensive single-cell atlas of prostate cancer was created from 70 samples, comprising 196,309 cells, among which 9% were identified as NE cells. Within this framework and in combination with bulk transcriptomics, a total of 100 high-quality NE-specific feature genes were identified and differentiated into NEPup sig and NEPdown sig. The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. Hierarchical clustering based on NEP100 revealed four distinct NEPC subtypes, designated VR_O, Prol_N, Prol_P, and EMT_Y, each of which presented unique biological characteristics. This allows us to select different targeted therapeutic strategies for different subtypes of phenotypic pathways. Notably, NEP100 expression correlated positively with neuroendocrine differentiation and disease progression, while the VR-NE phenotype dominated by VR_O cells indicated a propensity for treatment resistance. Furthermore, AMIGO2, a component of the NEP100 signature, was associated with chemotherapy resistance and a poor prognosis, indicating that it is a pivotal target for future therapeutic strategies. This study used multi-omics analysis combined with machine learning to construct a novel NEPC classifier and classification system. NEP100 provides a clinically actionable framework for NEPC diagnosis and subtyping.
    Keywords:  Computational biology and bioinformatics; Multi-omics; Neuroendocrine prostate cancer (NEPC); Tumor biomarkers; Tumor heterogeneity
    DOI:  https://doi.org/10.1038/s41598-025-96683-3
  36. Hepatol Commun. 2025 May 01. pii: e0668. [Epub ahead of print]9(5):
    Single-cell RNA sequencing reveals a reprogramming of hepatic immune cells and a protective role for B cells in MASH-driven HCC.
    Haiguang Wang, Adam Herman, Fanta Barrow, Amal Abdel-Ghani, Micah Draxler, Gavin Fredrickson, Preethy Parthiban, Davis M Seelig, Sayeed Ikramuddin, Xavier S Revelo.
       BACKGROUND: HCC, the most common form of liver cancer, is one of the leading causes of cancer-related deaths worldwide. Although the immune system plays a crucial role in liver cancer pathogenesis, the immune landscape within metabolic dysfunction-associated steatohepatitis-driven HCC remains poorly understood.
    METHODS: In this study, we used the high-fat, high-carbohydrate diet fed major urinary protein-urokinase-type plasminogen activator mouse model of metabolic dysfunction-associated steatohepatitis-driven HCC. We performed single-cell RNA sequencing on intrahepatic immune cells to characterize their heterogeneity and gene expression profiles. Additionally, we examined the role of B cells in antitumor immunity by depleting B cells in μMT mice and analyzing the effects on liver cancer progression.
    RESULTS: Our analysis revealed significant shifts in intrahepatic immune cell populations, including B cells, T cells, and macrophages that undergo transcriptional reprogramming, suggesting altered roles in tumor immunity. Notably, an expanded subset of activated B cells in HCC mice showed an antitumor B cell gene expression signature associated with increased survival of patients with liver cancer. Consistently, B cell-deficient mice showed exacerbated liver cancer progression, a substantial reduction in intrahepatic lymphocytes, and impaired CD8+ T cell activation, suggesting that intrahepatic B cells may promote antitumor immunity by enhancing T cell responses.
    CONCLUSIONS: Our findings reveal a complex immune reprogramming within the metabolic dysfunction-associated steatohepatitis-driven HCC microenvironment and underscore a protective role for B cells in liver cancer. These results highlight B cells as potential targets for immunomodulatory therapies in HCC.
    Keywords:  B cells; T cells; immune landscape; liver cancer; macrophages
    DOI:  https://doi.org/10.1097/HC9.0000000000000668
  37. Commun Biol. 2025 Apr 23. 8(1): 652
    In-depth single-cell transcriptomic exploration of the regenerative dynamics in stony coral.
    Tingyu Han, Yuanchao Li, Hongwei Zhao, Junyuan Chen, Chunpeng He, Zuhong Lu.
      Coral reef ecosystems face escalating threats from anthropogenic global climate challenges, leading to frequent bleaching events. A key issue in coral transplantation is the inability of fragments to rapidly grow to sizes that can resist environmental pressures. The observation of accelerated growth during the early stages of coral regeneration provides new insights for addressing this challenge. To investigate the underlying molecular mechanisms, we study the fast-growing stony coral Acropora muricata. Using single-cell RNA sequencing, bulk RNA sequencing, and high-resolution micro-computed tomography, we identify a critical regeneration phase around 2-4 weeks post-injury. Single-cell transcriptome analysis reveals 11 function-specific cell clusters. Pseudotime analysis indicates epidermal cell differentiation into calicoblasts. Bulk RNA-seq results highlight a temporal limitation in coral's rapid regeneration. Through integrated multi-omics analysis, this study emphasizes the importance of a comprehensive understanding of coral regeneration, providing insights beyond fundamental knowledge and offering potential protective strategies to promote coral growth.
    DOI:  https://doi.org/10.1038/s42003-025-08089-6
  38. Sci Rep. 2025 Apr 21. 15(1): 13687
    Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition.
    Yaojun Wang, Qiang Li.
      This study explored the relationship between acute kidney injury (AKI) and chronic kidney disease (CKD), focusing on autophagy-related genes and their immune infiltration during the transition from AKI to CKD. We performed weighted correlation network analysis (WGCNA) using two microarray datasets (GSE139061 and GSE66494) in the GEO database and identified autophagy signatures by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA enrichment analysis. Machine learning algorithms such as LASSO, random forest, and XGBoost were used to construct the diagnostic model, and the diagnostic performance of GSE30718 (AKI) and GSE37171 (CKD) was used as validation cohorts to evaluate its diagnostic performance. The study identified 14 autophagy candidate genes, among which ATP6V1C1 and COPA were identified as key biomarkers that were able to effectively distinguish between AKI and CKD. Immune cell infiltration and GSEA analysis revealed immune dysregulation in AKI, and these genes were associated with inflammation and immune pathways. Single-cell analysis showed that ATP6V1C1 and COPA were specifically expressed in AKI and CKD, which may be related to renal fibrosis. In addition, drug prediction and molecular docking analysis proposed SZ(+)-(S)-202-791 and PDE4 inhibitor 16 as potential therapeutic agents. In summary, this study provides new insights into the relationship between AKI and CKD and lays a foundation for the development of new treatment strategies.
    Keywords:  AKI; CKD; Autophagy; Biomarkers; Machine learning
    DOI:  https://doi.org/10.1038/s41598-025-97269-9
  39. Am J Obstet Gynecol. 2025 Apr;pii: S0002-9378(24)01201-8. [Epub ahead of print]232(4S): S148-S159
    Asherman syndrome at single-cell resolution.
    María Pardo-Figuerez, Carlos Simon, Xavier Santamaria.
      This comprehensive review aimed to provide insights into the Asherman syndrome's historical background, clinical manifestations, classifications, obstetrical challenges, and current treatment approaches. The syndrome is characterized by intrauterine adhesions and fibrotic changes within the uterine tract as well as symptoms including menstrual irregularities, pelvic pain and infertility. The primary causes of Asherman syndrome are often associated with iatrogenic complications and congenital uterine defects. The syndrome results in certain obstetrical challenges, including recurrent pregnancy loss, placenta abnormalities, preterm birth, and intrauterine growth retardation, emphasizing the need for effective management. Hysteroscopic adhesiolysis represents the current gold standard treatment, but challenges persist because of adhesion recurrence and obstetrical complications. In this sense, emerging therapies were explored, including paracrine-acting factors, tissue-engineered scaffolds, and cell-based therapies. Autologous CD133+ bone marrow-derived stem cell therapy shows promise, with clinical trials demonstrating improved endometrial conditions and positive obstetrical outcomes. The review concludes by highlighting the potential of single-cell RNA sequencing to unravel the molecular mechanisms behind Asherman syndrome. This advanced technology offers insights into the gene expression profiles of individual cells, fostering a deeper understanding of Asherman syndrome pathogenesis and the development of innovative therapeutic strategies.
    Keywords:  Asherman syndrome; bone marrow; intrauterine adhesions; organoids; regenerative medicine; single-cell RNA sequencing; stem cell therapy
    DOI:  https://doi.org/10.1016/j.ajog.2024.12.023
  40. Semin Immunol. 2025 Apr 22. pii: S1044-5323(25)00030-2. [Epub ahead of print]78 101958
    The spatial and single-cell landscape of skin: Charting the multiscale regulation of skin immune function.
    Abiha Kazmi, Raman Gill, Paula Restrepo, Andrew L Ji.
      Immune regulation is a key function of the skin, a barrier tissue that exhibits spatial compartmentalization of innate and adaptive immune cells. Recent advances in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have facilitated systems-based investigations into the molecular and cellular features of skin immunity at single-cell resolution, identifying cell types that maintain homeostasis in a coordinated manner, and those that exhibit dysfunctional cell-cell interactions in disease. Here, we review how technological innovation is uncovering the multiple scales of heterogeneity in the immune landscape of the skin. The microanatomic scale encompasses the skin's diverse cellular components and multicellular spatial organization, which govern the functional cell interactions and behaviors necessary to protect the host. On the macroanatomic scale, understanding heterogeneity in cutaneous tissue architecture across anatomical sites promises to unearth additional functional immune variation and resulting disease consequences. We focus on how single-cell and spatial dissection of the immune system in experimental models and in humans has led to a deeper understanding of how each cell type in the skin contributes to overall immune function in a context-dependent manner. Finally, we highlight translational opportunities for adopting these technologies, and insights gleaned from them, into the clinic.
    Keywords:  Cutaneous immunology; Single-cell RNA sequencing; Spatial transcriptomics
    DOI:  https://doi.org/10.1016/j.smim.2025.101958
  41. Sci Rep. 2025 Apr 19. 15(1): 13575
    Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.
    Jinfeng Zhao, Longpeng Li, Yaxin Wang, Jiayu Huo, Jirui Wang, Huiwen Xue, Yue Cai.
      As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of breast cancer patients. Lactylation-associated subtypes were obtained by unsupervised consensus clustering analysis. Lactylation-related gene signature (LRS) was constructed by 15 machine learning algorithms, and the relationship between LRS and tumor microenvironment (TME) as well as drug sensitivity was analyzed. In addition, the expression of genes in the LRS in different cells was explored by single-cell analysis and spatial transcriptome. The expression levels of genes in LRS in clinical tissues were verified by RT-PCR. Finally, the potential small-molecule compounds were analyzed by CMap, and the molecular docking model of proteins and small-molecule compounds was constructed. LRS was composed of 6 key genes (SHCBP1, SIM2, VGF, GABRQ, SUSD3, and CLIC6). BC patients in the high LRS group had a poorer prognosis and had a TME that promoted tumor progression. Single-cell analysis and spatial transcriptome revealed differential expression of the key genes in different cells. The results of PCR showed that SHCBP1, SIM2, VGF, GABRQ, and SUSD3 were up-regulated in the cancer tissues, whereas CLIC6 was down-regulated in the cancer tissues. Arachidonyltrifluoromethane, AH-6809, W-13, and clofibrate can be used as potential target drugs for SHCBP1, VGF, GABRQ, and SUSD3, respectively. The gene signature we constructed can well predict the prognosis as well as the treatment response of BC patients. In addition, our predicted small-molecule complexes provide an important reference for personalized treatment of breast cancer patients.
    Keywords:  Breast cancer; Gene signature; Lactation; Tumor microenvironment
    DOI:  https://doi.org/10.1038/s41598-025-98255-x
  42. FASEB J. 2025 Apr 30. 39(8): e70467
    Decoding the role of lipid metabolism in NSCLC: From macrophage subtype identification to prognostic model development.
    Aoxiao He, Zhihao Huang, Xianglai Chen, Kai Qi, Shan Zhang, Fan Li, Hongcheng Lu, Jiakun Wang, Jinhua Peng, Chao Song.
      Lipid metabolism plays a pivotal role in shaping the tumor microenvironment, particularly by influencing macrophage function. This study aimed to identify lipid-associated macrophage (LAM) marker genes involved in the onset and progression of non-small cell lung cancer (NSCLC) through integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) analyses. Mutation and RNA-seq data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to explore the relationship between lipid metabolism pathways and NSCLC progression. scRNA-seq analysis revealed macrophage subtypes closely associated with lipid metabolism, with three key marker genes-S100A10, HLA-DMB, and CTSL-identified as predictive factors for patient prognosis. A prognostic risk scoring model was constructed and validated using survival analysis and ROC curves, demonstrating high accuracy in stratifying NSCLC patients by risk. Further in vivo experiments using subcutaneous tumor xenografts and lung metastasis models showed that S100A10 and CTSL promoted tumor growth and metastasis, while HLA-DMB inhibited these processes. Immune infiltration analysis highlighted the immunological relevance of the identified marker genes, providing insights into their functional roles. This study underscores the critical influence of LAMs in NSCLC progression and highlights a robust prognostic model that offers potential therapeutic targets for improving patient outcomes.
    Keywords:  CTSL; HLA‐DMB; S100A10; lipid metabolism; macrophages; non‐small cell lung cancer; prognostic model
    DOI:  https://doi.org/10.1096/fj.202500124
  43. Eur J Med Res. 2025 Apr 24. 30(1): 326
    Transcriptomics, single-cell sequencing and spatial sequencing-based studies of cerebral ischemia.
    Jiaming Liu, Tao Qing, Mei He, Liu Xu, Zhuxiang Wu, Meiting Huang, Zheyu Liu, Ye Zhang, Zisheng Li, Wenhui Yang, Junbo Liu, Jie Li.
      With high disability and mortality rate as well as highly complex pathogenesis, cerebral ischemia is highly morbid, prone to recurrence. To comprehensively understand the pathophysiological process of cerebral ischemia and to find new therapeutic strategies, a new approach to cerebral ischemia transcriptomics has emerged in recent years. By integrating data from multiple levels of transcriptomics, such as transcriptomics, single-cell transcriptomics, and spatial transcriptomics, this new approach can provide powerful help in revealing the molecular mechanisms of cerebral ischemia occurrence and development. Key findings highlight the critical roles of inflammation, blood-brain barrier dysfunction, and mitochondrial dysregulation in cerebral ischemia, offering potential biomarkers and therapeutic targets for early diagnosis and personalized treatment. A review of the research progress of cerebral ischemic injury mechanism under the analysis of the comprehensive transcriptomics research method was presented in this article, aiming to study the potential mechanism to provide new, innovative therapeutic strategies for this disease.
    Keywords:  Cerebral ischemia; Multi-omics study; Single-cell transcriptomics; Spatial transcriptomics; Transcriptomics
    DOI:  https://doi.org/10.1186/s40001-025-02596-2
  44. J Exp Med. 2025 Jul 07. pii: e20242007. [Epub ahead of print]222(7):
    Prenatally derived macrophages support choroidal health and decline in age-related macular degeneration.
    Seth D Fortmann, Blake F Frey, Robert F Rosencrans, Yvonne Adu-Rutledge, Edgar Ready V, Kameron V Kilchrist, Robert S Welner, Michael E Boulton, Daniel R Saban, Maria B Grant.
      Hallmark findings in age-related macular degeneration (AMD) include the accumulation of extracellular lipid and vasodegeneration of the choriocapillaris. Choroidal inflammation has long been associated with AMD, but little is known about the immune landscape of the human choroid. Using 3D multiplex immunofluorescence, single-cell RNA sequencing, and flow cytometry, we unravel the cellular composition and spatial organization of the human choroid and the immune cells within it. We identify two populations of choroidal macrophages with distinct FOLR2 expression that account for the majority of myeloid cells. FOLR2+ macrophages predominate in the nondiseased eye, express lipid-handling machinery, uptake lipoprotein particles, and contain high amounts of lipid. In AMD, FOLR2+ macrophages are decreased in number and exhibit dysfunctional lipoprotein metabolism. In mice, FOLR2+ macrophages are negative for the postnatal fate-reporter Ms4a3, and their depletion causes an accelerated AMD-like phenotype. Our results show that prenatally derived resident macrophages decline in AMD and are implicated in multiple hallmark functions known to be compromised in the disease.
    DOI:  https://doi.org/10.1084/jem.20242007
  45. BMC Cancer. 2025 Apr 21. 25(1): 745
    Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics.
    Chunlan Liu, Tianjian Ding, Rong Zou, Aili Zhang, Zhengzhuo Zhi, Sili Wang.
       BACKGROUND: As part of the innate immune system, NK cells contribute to optimizing cancer immunotherapy strategies and are becoming a focal point in cancer research. However, limited research has been conducted to further investigate changes in NK cell subsets and their critical genes following ibrutinib treatment in CLL patients.
    METHODS: Peripheral blood samples from patients clinically and pathologically diagnosed with monoclonal B-cell lymphocytosis (MBL), newly diagnosed with CLL (ND-CLL), postibrutinib-treated patients who achieved a complete response (CR) or partial response (PR), and those with Richter's syndrome (RS) were collected. Single-cell transcriptome sequencing was performed, followed by pseudotemporal analysis and functional enrichment to characterize the NK cell subsets. Mendelian randomization analysis and colocalization analysis were employed to identify key genes. Multiple algorithms were used for immune infiltration analysis, and drug sensitivity analysis was conducted to pinpoint potential therapeutic agents.
    RESULTS: Three distinct NK cell subsets were identified: CD56bright_NK cells, CD56dim_NK cells, and a highly cytotoxic CLL_NK subset. The core genes of the CLL_NK subset were elucidated through Mendelian randomization and colocalization analyses. A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL.
    CONCLUSION: The CLL_NK subset plays a crucial role in the development and progression of CLL. The CNI, derived from its key genes, holds promise as a predictor of immune therapeutic responses, highlighting the significance of CLL_NK subset dynamics and their genetic underpinnings in CLL management.
    Keywords:  Chronic lymphocytic leukemia; Immunotherapy; Mendelian randomization; NK cells; Single-cell transcriptomics
    DOI:  https://doi.org/10.1186/s12885-025-14166-0
  46. Mol Neurobiol. 2025 Apr 25.
    Association Between T2DM, TyG Index, Multiple Sclerosis, and Rheumatoid Arthritis: Insights into Potential Mechanisms.
    Yi Peng, Chenxi Liu, Runxin Gan, Wei Liu.
      Abundant research indicates that type 2 diabetes mellitus (T2DM) and insulin resistance (IR) have a certain association with autoimmune-related diseases (ARDs). However, the conclusions remain elusive. Therefore, this study aimed to explore whether there are causal associations between T2DM and IR indicator, triglyceride-glucose (TyG) index with ARDs, and evaluate the impact of immune cells. Comprehensive Mendelian randomization (MR) analysis combined with Bayesian colocalization was employed to investigate the relationship between T2DM, TyG index, ARDs, and specific-marker immune cells by extracting summary-level data from various genome-wide association studies (GWASs). Further investigations utilizing single-cell RNA sequencing (scRNA-seq) analysis were performed to explore the potential molecular mechanisms underlying the MR analysis results. Causal associations of T2DM with multiple sclerosis (MS) and rheumatoid arthritis (RA) were detected. Additionally, the TyG index was genetically predicted to be associated with MS. Furthermore, immune cells were found to be related to T2DM and TyG index, of which CD3 on naive CD8 + T cell mediate the effect on the association between TyG index and multiple sclerosis (MS). Additionally, genetic variation of TCF7L2 and TNFSF13B were assessed to play a crucial role in the aforementioned associations supported by colocalization analysis. This study identified T2DM and IR related to increased risk of MS and RA. The analysis of relevant immune cells and shared genetic loci provides a novel direction for exploring comorbidity mechanisms.
    Keywords:  Autoimmune-related diseases; Comorbidity; Insulin resistance; Mendelian randomization; T2DM
    DOI:  https://doi.org/10.1007/s12035-025-04961-y
This page is being maintained by Thomas Krichel. It was last updated on 2025‒04‒27 at 6:10.