bims-sicarn Biomed News
on scRNA-seq
Issue of 2025–05–11
fifty-nine papers selected by
Anna Zawada, International Centre for Translational Eye Research
  1. Single-cell transcriptomics of human skin reveals age-associated specificity of distinct cell populations.
  2. Single-cell RNA sequencing (scRNA-seq) and its insights into cellular heterogeneity in atherosclerosis.
  3. scDGG: Dynamic gene graphs for enhancing clustering analysis of single-cell RNA sequencing data via spatiotemporal representations.
  4. Isoform-resolution single-cell RNA sequencing reveals the transcriptional panorama of adult Baoshan pig testis cells.
  5. Allometry of cell types in planarians by single-cell transcriptomics.
  6. scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.
  7. Single-cell RNA Sequencing Identifies Prognostic Biomarkers in Extramedullary Multiple Myeloma.
  8. Characterising Shared and Specific Cell-Cell Communication in Cardiomyopathy Subtypes From Single-Cell Transcriptomics Data.
  9. Investigating the molecular mechanisms and clinical potential of APO+ endothelial cells associated with PANoptosis in the tumor microenvironment of hepatocellular carcinoma using single-cell sequencing data.
  10. The immunosuppressive role of VSIG4 in colorectal cancer and its interaction with the tumor microenvironment.
  11. Single-Cell Transcriptomic Analysis of the Potential Mechanisms of Follicular Development in Stra8-Deficient Mice.
  12. Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.
  13. Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.
  14. Unveiling the role of TCF19 in intervertebral disc degeneration with single-cell and bulk RNA sequencing.
  15. Temporal transcriptomic changes in the THY-Tau22 mouse model of tauopathy display cell type- and sex-specific differences.
  16. Single-cell RNA sequencing in the study of human retinal organoids.
  17. Investigating the mechanisms by which low NAT1 expression in tumor cells contributes to chemo-resistance in colorectal cancer.
  18. Comprehensive analysis of plasma cell heterogeneity and immune interactions in multiple myeloma.
  19. Single-Cell RNA Sequencing Outperforms Single-Nucleus RNA Sequencing in Analyzing Pancreatic Cell Diversity and Gene Expression in Goats.
  20. Single-cell transcriptomics and metabolomics reveal the potential role of ASRGL1 in metabolic reprogramming and invasion of nasopharyngeal carcinoma cells.
  21. Multiomics Approach Distinguishes SPTBN4 as a Key Molecule in Diagnosis, Prognosis, and Immune Suppression of Testicular Seminomas.
  22. Single-cell RNA Sequencing Analysis Reveals the Regulatory Functions of Copines Family Genes in Testicular Cancer Progression.
  23. Diversity and functional specialization of oyster immune cells uncovered by integrative single-cell level investigations.
  24. Deconvoluting single-cell transcriptomics reveals cellular programs regulated by cell-cell communication in colorectal cancer.
  25. Biomarkers Related to Interferon-γ Pathway in Myocardial Ischemia-Reperfusion Injury and the Potential Molecular Mechanisms.
  26. Identification of Multi-Landscape and Cell Interactions in the Tumor Microenvironment Through High-Coverage Single-Cell Sequencing.
  27. Oncostatin-M: a novel leukocyte-derived factor facilitating the ovulatory process in the human ovary.
  28. Integration of bulk RNA-seq and scRNA-seq reveals transcriptomic signatures associated with deep vein thrombosis.
  29. Single-cell transcriptomics reveal the effects of chemoimmunotherapy on hypopharyngeal cancer and its tumor microenvironment.
  30. TREM2+ macrophages accumulate in childhood IgA nephropathy and soluble TREM2 represents a reliable non-invasive biomarker.
  31. Identification of programmed cell death-related genes and construction of a prognostic model in oral squamous cell carcinoma using single-cell and transcriptome analysis.
  32. Single-cell and bulk transcriptome sequencing identifies circadian rhythm disruption and cluster-specific clinical insights in colorectal tumorigenesis.
  33. Two types of regenerative cell populations appear in acute liver injury.
  34. Genetic association between gene expression profiles in oligodendrocyte precursor cells and psychiatric disorders.
  35. METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery.
  36. The cancer stem cells characteristics analysis of LGR5 + cells that influence lung cancer risk by using single cell RNA-seq analysis.
  37. A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response.
  38. TGF-β1 induces autophagy and mediates the effect on macrophages differentiation in primary liver cancer.
  39. Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.
  40. Integrated multiomics and Mendelian randomization identify CHIT1 as a novel sepsis biomarker and therapeutic target.
  41. Exome and Transcriptome Analysis Links Calcium Channel Pathway Aberrations to Botox Resistance in Hailey-Hailey Disease.
  42. Single-Cell RNA Sequencing and Spatial Transcriptomics Reveal Unique Subpopulations of Infiltrating Macrophages and Dendritic Cells Following AKI.
  43. Single-Cell RNA Sequencing of Baseline Immune Profiles After Third Vaccination Associated with Subsequent SARS-CoV-2 Infection in Naïve Individuals.
  44. Identification of novel biomarkers and drug targets for frailty-related skeletal muscle aging: a multi-omics study.
  45. Research on Peripheral Nerve Aging and Degeneration: Cellular Changes and Mechanism Exploration From the Perspective of Single-Cell Sequencing.
  46. Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.
  47. Decoding the Resistin-CAP1 Pathway in Intermediate Monocytes Mediating Liver Allograft Rejection.
  48. IRF5 Mediates Artery Inflammation in Salt-Sensitive Hypertension by Regulating STAT1 and STAT2 Phosphorylation to Increase ESM1 Transcription: Insights from Bioinformatics and Mechanistic Analysis.
  49. Comparative analysis of gene regulation in single cells using Compass.
  50. Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data.
  51. Decoding immune cell interactions during cardiac allograft vasculopathy: insights derived from bioinformatic strategies.
  52. Single-Cell RNA Sequencing Reveals the Immune Landscape of Granulomatous Mastitis.
  53. Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer.
  54. Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.
  55. Development and validation of a basement membrane-associated immune prognostic model for hepatocellular carcinoma.
  56. Identifying Aberrant 1CM-Related Pathways by Multi-Omics Analysis and Validating Tumor Inhibitory Effect of One-Carbon Donor Betaine in Gastric Cancer.
  57. Spatial and Temporal Single-Cell Profiling of RNA Compartmentalization in Neurons with Nanotweezers.
  58. Identification of the distinct immune microenvironment features associated with progression following high dose melphalan and autologous stem cell transplant in multiple myeloma.
  59. Inferred developmental origins of brain tumors from single-cell RNA-sequencing data.
  1. Arch Dermatol Res. 2025 May 05. 317(1): 748
    Single-cell transcriptomics of human skin reveals age-associated specificity of distinct cell populations.
    Lingzhu Liao, Suzheng Zheng, Jiaxin Meng, Yunfeng Hu, Shi Wu.
       BACKGROUND: The skin, as the body's largest organ, undergoes significant changes with aging, impacting its structural integrity, repair capacity, and immune function. Previous studies have highlighted the heterogeneity of skin cells, particularly fibroblasts, and their role in skin homeostasis. However, the molecular and functional dynamics of these cell populations during aging, especially in sun-protected areas, remain underexplored. This study aims to elucidate the age-related changes in skin cell populations, focusing on dermal fibroblasts, using single-cell RNA sequencing (scRNA-seq) to provide insights into the mechanisms of intrinsic skin aging.
    METHODS: We utilized single-cell RNA sequencing (scRNA-seq) to analyze skin samples from healthy human donors. A total of over 5,000 cells were included in the study, representing a variety of skin cell types, including fibroblasts, keratinocytes, and immune cells. To further investigate the biological functions and differences between cell types, we performed differential gene expression analysis, Gene Ontology (GO) enrichment, and KEGG pathway analysis. Additionally, pseudotime analysis was conducted to examine cellular differentiation trajectories, while CellChat analysis was used to assess intercellular communication in the skin. These methodologies allowed us to identify key cell populations, their functional properties, and the impact of aging on skin cell interactions.
    RESULTS: Study revealed significant diversity and functional specialization among skin cells, defining several major cell subgroups. These subgroups exhibited specific spatial localization in different regions of the skin and demonstrated distinct functional characteristics, such as secretion, mesenchymal activity, and immune regulation. Importantly, we found that with aging, there is a general reduction in the "activation" (i.e., activity and functionality) of skin cells. Aging not only affects individual cell types, such as fibroblasts, but also leads to a marked decrease in interactions between different skin cell types, including cell communication at the dermal-epidermal junction. These findings highlight the complexity of skin aging as a process involving multiple cell types and their interactions.
    CONCLUSION: Our work provides new insights into the functional specialization and aging process of skin cells. We identified a key age-related change in human skin: the partial loss of cell identity and function. These findings contribute to the understanding of skin aging mechanisms and associated phenotypes, offering potential directions for future anti-aging therapeutic strategies.
    Keywords:  Cell heterogeneity; Fibroblasts; Single-cell RNA sequencing; Skin aging
    DOI:  https://doi.org/10.1007/s00403-025-04222-x
  2. Vascul Pharmacol. 2025 May 07. pii: S1537-1891(25)00038-2. [Epub ahead of print] 107499
    Single-cell RNA sequencing (scRNA-seq) and its insights into cellular heterogeneity in atherosclerosis.
    Baixue Yu, Miron Sopic, Judith C Sluimer.
      Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity in human biology, providing novel insights into disease mechanisms. In cardiovascular disease (CVD), scRNA-seq enables precise mapping of complex cell populations, uncovering unique cell types and states that influence disease progression and suggest new therapeutic targets. In atherosclerosis (AS), scRNA-seq has redefined plaque pathology by identifying distinct cell types, including endothelial cells (ECs), smooth muscle cells (SMCs), fibroblasts, macrophages, T cells, and B cells, each with specific roles in plaque stability, inflammation, and disease progression. In our review, we summarized these major cellular populations and their cellular heterogeneity in non-diseased and atherosclerotic aorta, as identified by scRNA-seq in mice and human tissues. We discussed conserved and species-specific subpopulations, their defining markers, and their functional implications in plaque progression. In addition, we integrated findings from scRNA-seq with experimental studies to highlight key molecular targets with therapeutic potential. In the future, these insights offer a refined cellular and molecular framework of atherosclerosis and may help the development of targeted interventions to promote plaque stabilization and reduce cardiovascular risk.
    Keywords:  Atherosclerosis; Cellular heterogeneity; Single-cell RNA sequencing (scRNA-seq)
    DOI:  https://doi.org/10.1016/j.vph.2025.107499
  3. Comput Biol Chem. 2025 Apr 30. pii: S1476-9271(25)00143-4. [Epub ahead of print]118 108483
    scDGG: Dynamic gene graphs for enhancing clustering analysis of single-cell RNA sequencing data via spatiotemporal representations.
    Junnan Li, Wei Zhang, Qi Dai.
      For high-throughput single-cell RNA sequencing (scRNA-seq) data, spatial features have emerged as a powerful representations for downstream analysis. These spatial features contain but not limited to gene graphs and cell graphs. Specifically, gene graphs have been inferred to capture functional interactions between transcriptional factors and marker genes, which are associated with abnormal expression patterns and molecular heterogeneity. Furthermore, incorporation of spatial features is useful to enhance the accuracy of single-cell clustering. However, static gene graphs encode limited cellular information in conveying dynamic regulatory mechanisms that govern cell fates as well as disease progression. To alleviate this drawback, this work extracts and employs dynamic gene graphs, which contribute to a more comprehensive observation of regulatory mechanisms. This study proposes an multi-view graph learning architecture named scDGG to compress dynamic gene graphs from various signaling pathways, with each graph representing a specific biological context. Experimental results about benchmark scRNA-seq datasets have demonstrated the effectiveness and advantages of the scDGG method over SOTA single-cell clustering approaches that take deep learning architecture. It seems dynamic gene graphs could be regarded as high-quality graph representations that outperform static spatial features in single-cell clustering.
    Keywords:  Clustering; Dynamic gene graph; Multi-view clustering; ScRNA-seq data
    DOI:  https://doi.org/10.1016/j.compbiolchem.2025.108483
  4. BMC Genomics. 2025 May 08. 26(1): 459
    Isoform-resolution single-cell RNA sequencing reveals the transcriptional panorama of adult Baoshan pig testis cells.
    Wan Lin, Xia Zhang, Zhipeng Liu, Hailong Huo, Yongcheng Chang, Jiading Zhao, Shaorong Gong, Guiying Zhao, Jinlong Huo.
       BACKGROUND: As the primary organ of the male reproductive system, the testis facilitates spermatogenesis and androgen secretion. Due to the complexity of spermatogenesis, elucidating cellular heterogeneity and gene expression dynamics within the porcine testis is critical for advancing reproductive biology. Nevertheless, the cellular composition and regulatory mechanisms of porcine testes remain insufficiently characterized. In this study, we applied integrated long-read (Nanopore) and short-read (Illumina) scRNA-seq to Baoshan pig testes, establishing a comprehensive transcriptional profile to delineate cellular heterogeneity and molecular regulation.
    RESULTS: Through systematic analysis of testicular architecture and the temporal progression of spermatogenesis, we characterized 11,520 single cells and 23,402 genes, delineating germ cell developmental stages: proliferative-phase spermatogonia (SPG), early-stage spermatocytes (Early SPC) and late-stage spermatocytes (Late SPC) during meiosis, and spermiogenic-phase round spermatids (RS) followed by elongating/elongated spermatids (ES), culminating in mature spermatozoa (Sperm). We further identified nine distinct testicular cell types, with germ cells spanning all developmental stages and somatic components comprising Sertoli cells, macrophages, and peritubular myoid cells as microenvironmental constituents, revealing the cellular heterogeneity of testicular tissue and dynamic characteristics of spermatogenesis. We obtained the dynamic expression changes of 16 vital marker genes during spermatogenesis and performed immunofluorescence validation on 7 marker genes. Gene ontology analysis revealed that germ cells at various stages were involved in specific biological processes, while cell communication networks highlighted eight pivotal signaling pathways, including MIF, NRG, WNT, VEGF, BMP, CCL, PARs, and ENHO pathways. Long-read sequencing further captured the full integrity and diversity of RNA transcripts, identifying 60% of the novel annotated isoforms and revealing that FSM isoforms exhibited longer transcript lengths, longer coding sequences, longer open reading frames, and a great number of exons, suggesting the complexity of isoforms within the testicular microenvironment.
    CONCLUSIONS: Our results provide insight into the cellular heterogeneity, intercellular communication, and gene expression/transcript diversity in porcine testes, and offer a valuable resource for understanding the molecular mechanisms of porcine spermatogenesis.
    Keywords:  Cellular heterogeneity; Long-read sequencing; Porcine testis; Single-cell RNA sequencing; Spermatogenesis
    DOI:  https://doi.org/10.1186/s12864-025-11636-4
  5. Sci Adv. 2025 May 09. 11(19): eadm7042
    Allometry of cell types in planarians by single-cell transcriptomics.
    Elena Emili, Alberto Pérez-Posada, Virginia Vanni, David Salamanca-Díaz, Dianalí Ródriguez-Fernández, Maria D Christodoulou, Jordi Solana.
      Allometry explores the relationship between an organism's body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet the study of cell-type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell-type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for these studies. We used scRNA-seq to examine cell-type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the regulated gene modules of distinct cell types in response to body size. This research sheds light on the molecular and cellular aspects of cell-type allometry in planarians and underscores the utility of scRNA-seq in these investigations.
    DOI:  https://doi.org/10.1126/sciadv.adm7042
  6. PLoS Comput Biol. 2025 May;21(5): e1013027
    scSDNE: A semi-supervised method for inferring cell-cell interactions based on graph embedding.
    Chenchen Jia, Haiyun Wang, Jianping Zhao, Junfeng Xia, Chunhou Zheng.
      As a fundamental characteristic of multicellular organisms, cell-cell communication is achieved through ligand-receptor (L-R) interactions, enabling the exchange of information and revealing the diversity of biological processes and cellular functions. To gain a comprehensive understanding of these complex interaction mechanisms, we constructed a manually curated L-R interaction database and developed a semi-supervised graph embedding model called scSDNE for inferring cell-cell interactions mediated by L-R interactions. scSDNE model utilizes the power of deep learning to map genes from interacting cells into a shared latent space, allowing for a nuanced representation of their relationships. Leveraging the prior information provided by database, scSDNE can infer significant L-R pairs involved in intercellular communication. Experiments on real single-cell RNA sequencing (scRNA-seq) datasets demonstrate that our method detects interactions with a high degree of reliability compared with other methods. More importantly, the model integrates gene regulation information within cells to enhance the accuracy and biological interpretability of the inferences. Our method provides a more comprehensive view of cell-cell interactions, offering new insights into complex intercellular communication.
    DOI:  https://doi.org/10.1371/journal.pcbi.1013027
  7. Curr Med Chem. 2025 May 05.
    Single-cell RNA Sequencing Identifies Prognostic Biomarkers in Extramedullary Multiple Myeloma.
    Menghan Yang, Fan Yu, Hui Qin.
       BACKGROUND: Multiple myeloma (MM) is the second most common hematologic malignancy, accounting for approximately 10% of all hematological cases, with higher morbidity and mortality.
    OBJECTIVE: This study aimed to investigate the clonal evolutionary characteristics to identify novel prognostic biomarkers associated with extramedullary progression in MM.
    METHODS: We downloaded transcriptomic profiles and single-cell microarray (scRNA-seq) data from public databases. Then, we used the LASSO method to develop a prognostic signature and validated its efficacy using external MM cohorts. We evaluated the differences in the immune microenvironment and drug sensitivity (IC50) between the different risk score groups. scRNA-seq analysis identified key cell types through AUCell scores, cell communication, and differentiation trajectory analyses.
    RESULTS: In total, 126 DEGs were identified as crucial genes associated with extramedullary and intramedullary MM. After LASSO analysis, seven signature genes were selected to develop a risk score model, and high-risk patients showed worse outcomes. Subsequently, the nomogram incorporating age, albumin, b2m, LDH, and RiskScore predicted 1-, 3-, and 5-year outcomes with high AUCs. Immune analyses showed that 25 immune cell types, 35 immune checkpoints, 27 chemokines, 20 MHC molecules, and 14 receptor- related genes differed significantly between the two risk groups. We also identified 116 drugs (roscovitine and JNK inhibitor VIII) with significantly different IC50 values between the two risk groups. CD4+ T cells exhibited the highest signature gene activity. CellChat analysis demonstrated enhanced communication between CD4+, NK, and CD8+ T cells.
    CONCLUSION: Our study has proposed a risk score model based on seven identified signature genes for MM prognosis and revealed CD4+ T cells to be a major immune cell type associated with MM progression, contributing to personalized treatment decision-making and precise risk stratification of MM.
    Keywords:  Multiple myeloma; extramedullary.; prognosis; single-cell profile
    DOI:  https://doi.org/10.2174/0109298673352012250414100227
  8. J Cell Mol Med. 2025 May;29(9): e70554
    Characterising Shared and Specific Cell-Cell Communication in Cardiomyopathy Subtypes From Single-Cell Transcriptomics Data.
    Wenqi Tao, Miao Gong, Zunping Ke.
      Cardiomyopathy encompasses a diverse range of conditions characterised by extensive molecular heterogeneity, particularly the variations in cell-cell communication events such as ligand-receptor interactions and downstream signalling. Understanding the common and unique features of these intercellular interactions is crucial for advancing targeted treatments. We analysed single-cell RNA sequencing datasets from the ventricular regions of patients with arrhythmogenic cardiomyopathy (ACM), dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and healthy donors (HD), as well as ischemic cardiomyopathy (ICM). Our analyses focused on cell type-specific disease preferences, differential gene expression, pathway enrichment and particularly cell-cell communication. We observed that inflammatory, autoimmune, angiogenesis, lymphangiogenesis and fibrotic extracellular matrix deposition are consistently enriched across all four disease subtypes, highlighting their universal significance in disease progression through intercellular interactions. Additionally, we identified subtype-specific pathways that reflect distinct intercellular communication patterns unique to each disease subtype: arrhythmia-associated pathways in ACM, chronic inflammation-related pathways in DCM, ECM remodelling pathways in HCM and ischaemic injury and recovery pathways in ICM.
    Keywords:  cell–cell communications; diease specific intercellular interactions; molecular pathways; single nucleus RNA sequencing
    DOI:  https://doi.org/10.1111/jcmm.70554
  9. Transl Oncol. 2025 May 02. pii: S1936-5233(25)00133-0. [Epub ahead of print]57 102402
    Investigating the molecular mechanisms and clinical potential of APO+ endothelial cells associated with PANoptosis in the tumor microenvironment of hepatocellular carcinoma using single-cell sequencing data.
    Zhaorui Cheng, Xiangyu Yang, Yi Ren, Huimin Wang, Qi Zhang, Sailing Lin, Wenhao Wu, Xiaolu Yang, Jiahan Zheng, Xinzhu Liu, Xin Tao, Xiaoyong Chen, Yuxin Qian, Xiushen Li.
       INTRODUCTION: PANoptosis is a newly identified form of programmed cell death that integrates elements of pyroptosis, apoptosis, and necroptosis. It plays a pivotal role in shaping the tumor immune microenvironment. Despite its significance, the specific functions and mechanisms of PANoptosis within the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) remain unclear. This study aims to investigate these mechanisms using single-cell RNA sequencing data.
    METHODS: Single-cell RNA sequencing data from HCC patients were obtained from the GEO database. The AUCell algorithm was used to quantify PANoptosis activity across various cell types in the TME. Cell populations with high PANoptosis scores were further analyzed using CytoTRACE and scMetabolism to assess their differentiation states and metabolic profiles. Associations between these high-score cell subsets and patient prognosis, tumor stage, and response to immunotherapy were examined. Cell-cell communication analysis was performed to explore how PANoptosis-related APO+ endothelial cells (ECs) may influence HCC progression. Immunofluorescence staining was used to assess the spatial distribution of APO+ ECs in tumor and adjacent tissues. Finally, a CCK8 assay was conducted to evaluate the effect of APOH+ HUVECs on HCC cell proliferation.
    RESULTS: A total of 16 HCC patient samples with single-cell RNA sequencing data were included in the study. By calculating the PANoptosis scores of different cell types, we found that ECs, macrophages, hepatocytes, and fibroblasts exhibited higher PANoptosis scores. The PANoptosis scores, differentiation trajectories, intercellular communication, and metabolic characteristics of these four cell subpopulations with high PANoptosis scores were visualized. Among all subpopulations, APO+ ECs demonstrated the most significant clinical relevance, showing a positive correlation with better clinical staging, prognosis, and response to immunotherapy in HCC patients. Cellular communication analysis further revealed that APO+ ECs might regulate the expression of HLA molecules, thereby influencing T cell proliferation and differentiation, potentially contributing to improved prognosis in HCC patients. Immunofluorescence staining results indicated that APO+ ECs were primarily located in the adjacent tissues of HCC patients, with lower expression in tumor tissues. The results of cellular experiments showed that APOH+ HUVECs significantly inhibited the proliferation of HCC cells.
    CONCLUSIONS: This study systematically mapped the cellular landscape of the TME in HCC patients and explored the differences in differentiation trajectories, metabolic pathways, and other aspects of subpopulations with high PANoptosis scores. Additionally, the study elucidated the potential molecular mechanisms through which APO+ ECs inhibit HCC cell proliferation and improve prognosis and immunotherapeutic efficacy in HCC patients. This research provides new insights for clinical prognosis evaluation and immunotherapy strategies in HCC.
    Keywords:  Endothelial cells; Hepatocellular carcinoma; Immunotherapy; PANoptosis; Tumor microenvironment (TME)
    DOI:  https://doi.org/10.1016/j.tranon.2025.102402
  10. Discov Oncol. 2025 May 03. 16(1): 664
    The immunosuppressive role of VSIG4 in colorectal cancer and its interaction with the tumor microenvironment.
    H G Zhang, W R Yao, Z Y Zhou, L Liu.
       BACKGROUND: The tumor microenvironment in colorectal cancer (CRC) significantly influences disease progression and immune responses, particularly the role of macrophages in regulating immune evasion requires further investigation.
    METHODS: This study integrated data from the TCGA-COAD dataset with the GEO database, along with single-cell RNA sequencing data, to systematically analyze key genes in colorectal cancer. R software was utilized for data normalization and differential analysis, with criteria set at ∣log2FoldChange ∣ > 1 and adjusted p-value < 0.05 for gene selection. The Seurat package was employed for clustering single-cell data, while the "Monocle2" algorithm was used to perform pseudo-time analysis on the differentiation trajectory of macrophages. Additionally, non-negative matrix factorization (NMF) was applied for subtype classification of CRC patients, and various machine learning algorithms (such as LASSO and random forest models) were utilized to identify key pathogenic genes. Finally, PCR was employed to validate the expression of these key genes, and immune analysis software was used to assess their impact on immune cells, alongside pathway enrichment analysis.
    RESULTS: Through the integration of multi-omics data, we identified significant differential expression of VSIG4, CYBBC3AR1, and FCGR1A in CRC patients. LASSO and random forest models selected these three genes as critical pathogenic factors for CRC, with AUC values exceeding 0.8 across multiple machine learning models, demonstrating their high diagnostic efficacy. PCR validation further supported the differential expression of VSIG4 and other genes in CRC. Single-cell transcriptomic analysis revealed that VSIG4 was highly enriched in specific macrophage subpopulations and significantly influenced the tumor microenvironment by regulating CD8 + T cell immune exhaustion. Pseudo-time analysis indicated that the differentiation trajectory of macrophages during tumor progression was closely associated with VSIG4 expression. Additionally, cell communication analysis. highlighted the important role of VSIG4 in the interactions between macrophages and endothelial cells. Pathway enrichment analysis demonstrated that VSIG4 expression was closely linked to the regulation of the JAK-STAT pathway and metabolic pathways such as the TCA cycle.
    CONCLUSION: This study provides the first evidence that VSIG4, CYBBC3AR1, and FCGR1A play critical roles in the immune microenvironment of colorectal cancer, particularly emphasizing the immunoregulatory function of VSIG4 in macrophage activity and CD8 + T cell immune exhaustion. PCR validation further confirmed the differential expression of these genes. These findings offer new insights into the molecular mechanisms of CRC and provide a potential theoretical basis for targeting VSIG4 in immunotherapy.
    Keywords:  Colorectal Cancer (CRC); Evasion; Immune; Macrophages; Tumor Microenvironment; VSIG4
    DOI:  https://doi.org/10.1007/s12672-025-02411-8
  11. Int J Mol Sci. 2025 Apr 15. pii: 3734. [Epub ahead of print]26(8):
    Single-Cell Transcriptomic Analysis of the Potential Mechanisms of Follicular Development in Stra8-Deficient Mice.
    Han Wang, Qingchun Liu, Shunfeng Cheng, Lan Li, Wei Shen, Wei Ge.
      Follicle development is a critical process in mammalian reproduction, with significant implications for ovarian reserve and fertility. Stra8 is a known key factor regulating the initiation of meiosis; however, oocyte-like cells still appear in Stra8-deficient mice. Nevertheless, the underlying mechanism remains unclear and requires further investigation. Therefore, we used single-cell RNA sequencing to construct a comprehensive transcriptional atlas of ovarian cells from both wild-type and Stra8-deficient mice at embryonic stages E14.5 and E16.5. With stringent quality control, we obtained a total of 14,755 single cells of six major cell types. A further fine-scale analysis of the germ cell clusters revealed notable heterogeneity between wild-type and Stra8-deficient mice. Compared to the wild-type mice, the deficiency in Stra8 led to the downregulation of meiosis-related genes (e.g., Pigp, Tex12, and Sycp3), and the upregulation of apoptosis-related genes (e.g., Fos, Jun, and Actb), thereby hindering the meiotic process. Notably, we observed that, following Stra8 deficiency, the expression levels of Sub1 and Stk31 remained elevated at this stage. Furthermore, an RNA interference analysis confirmed the potential role of these genes as regulatory factors in the formation of primordial follicle-like cells. Additionally, Stra8 deficiency disrupted the signaling between germ cells and pregranulosa cells that is mediated by Mdk-Sdc1, leading to the abnormal expression of the PI3K/AKT signaling pathway. Together, these results shed light on the molecular processes governing germ cell differentiation and folliculogenesis, emphasizing the complex role of Stra8 in ovarian function.
    Keywords:  Stra8; oogenesis; single-cell RNA sequencing
    DOI:  https://doi.org/10.3390/ijms26083734
  12. Int J Mol Sci. 2025 Apr 13. pii: 3682. [Epub ahead of print]26(8):
    Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.
    Lingyan Xiang, Jiajun Yang, Jie Rao, Aolong Ma, Chen Liu, Yuqi Zhang, Aoling Huang, Ting Xie, Haochen Xue, Zhengzhuo Chen, Jingping Yuan, Honglin Yan.
      Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biomarkers and prognostic models. Programmed cell death (PCD) pathways serve as a critical factor in tumor development and treatment response. However, the relationship between the diverse patterns of PCD and NAC in BRCA remains unclear. We integrated machine learning and multiple bioinformatics tools to explore the association between 19 PCD patterns and the prognosis of NAC within a cohort of 921 BRCA patients treated with NAC from seven multicenter cohorts. A prognostic risk model based on PCD-related genes (PRGs) was constructed and evaluated using a combination of 117 machine learning algorithms. Immune infiltration analysis, mutation analysis, pharmacological analysis, and single-cell RNA sequencing (scRNA-seq) were conducted to explore the genomic profile and clinical significance of these model genes in BRCA. Immunohistochemistry (IHC) was employed to validate the expression of select model genes (UGCG, BTG22, TNFRSF21, and MYB) in BRCA tissues. We constructed a PRGs prognostic risk model by using a signature comprising 20 PCD-related DEGs to forecast the clinical outcomes of NAC in BRCA patients. The prognostic model demonstrated excellent predictive accuracy, with a high concordance index (C-index) of 0.772, and was validated across multiple independent datasets. Our results demonstrated a strong association between the developed model and the survival prognosis, clinical pathological features, immune infiltration, tumor microenvironment (TME), gene mutations, and drug sensitivity of NAC for BRCA patients. Moreover, IHC studies further demonstrated that the expression of certain model genes in BRCA tissues was significantly associated with the efficacy of NAC and emerged as an autonomous predictor of outcomes influencing the outcome of patients. We are the first to integrate machine learning and bulk and scRNA-seq to decode various cell death mechanisms for the prognosis of NAC in BRCA. The developed unique prognostic model, based on PRGs, provides a novel and comprehensive strategy for predicting the NAC outcomes of BRCA patients. This model not only aids in understanding the mechanisms underlying NAC efficacy but also offers insights into personalized treatment strategies, potentially improving patient outcomes.
    Keywords:  breast cancer; bulk and single-cell RNA sequencing; machine learning; neoadjuvant chemotherapy; programmed cell death
    DOI:  https://doi.org/10.3390/ijms26083682
  13. Front Immunol. 2025 ;16 1572034
    Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.
    Han Yan, Xinyu Ji, Bohan Li.
       Objective: This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to construct a prognostic signature (ICDRS) and explore its clinical and biological implications, including its association with immune cell infiltration, tumor microenvironment (TME), and drug sensitivity.
    Methods: Publicly available datasets from TCGA and GEO, including scRNA-seq (GSE222315, 9 samples) and bulk RNA-seq (TCGA-BLCA, 403 samples; GSE13507, 160 samples), were analyzed. Single-cell data were processed using Seurat, and ICD scores were calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) identified ICD-related modules, and machine learning algorithms (Lasso, Ridge, CoxBoost) were employed to construct the ICDRS. Survival analysis, immune infiltration, pathway enrichment, and drug sensitivity were evaluated to validate the model.
    Results: The ICDRS, based on eight key genes (IL32, AHNAK, ANXA5, FN1, GSN, CNN3, FXYD3, CTSS), effectively stratified BLCA patients into high- and low-risk groups with significant differences in overall survival (OS, P < 0.001). High ICDRS scores were associated with immune-suppressive TME, including increased infiltration of T cells CD4 memory resting (P = 0.02) and macrophages M0/M1/M2 (P = 0.01). Pathway enrichment revealed correlations with cholesterol homeostasis, epithelial-mesenchymal transition (EMT), and KRAS signaling. Drug sensitivity analysis showed high-risk groups were resistant to Cisplatin (P = 0.003), Mitomycin C (P = 0.01), and Paclitaxel (P = 0.004), with IC50 values significantly higher than low-risk groups.
    Conclusion: The ICDRS serves as a robust prognostic biomarker for BLCA, offering insights into tumor immune evasion mechanisms and potential therapeutic targets. Its integration with clinical features enhances personalized treatment strategies, highlighting the importance of ICD in BLCA immunotherapy and precision medicine. The model's predictive accuracy and biological relevance were validated across multiple datasets, underscoring its potential for clinical application.
    Keywords:  bladder cancer; immunogenic cell death; immunotherapy; machine learning; multi-omics integration; prognosis signature
    DOI:  https://doi.org/10.3389/fimmu.2025.1572034
  14. Sci Rep. 2025 May 08. 15(1): 16043
    Unveiling the role of TCF19 in intervertebral disc degeneration with single-cell and bulk RNA sequencing.
    Xianwei He, Liwei Wu, Hao Zhou.
      Intervertebral disc degeneration (IDD) is a prevalent cause of low back pain, significantly impacting health worldwide. While IDD is associated with aging, its precise molecular mechanisms remain inadequately understood, limiting the development of targeted therapies. Nucleus pulposus cells (NPCs) are crucial to maintaining disc integrity and are central to understanding IDD progression. This study used single-cell and bulk RNA sequencing to dissect the cellular landscape and gene expression profiles in IDD. By analyzing these data, we identified distinct NPC subtypes and their roles in the degenerative disc microenvironment. Pseudotime and cellular communication network analyses further elucidated the temporal progression and signaling interactions of NPCs during disc degeneration. Four critical genes-TCF19, GDF15, RNMT, and C12orf45-were identified as significantly upregulated in IDD. TCF19 emerged as a key gene in the transitional states of NPCs, suggesting its pivotal role in IDD progression. In vivo experiments using a rat model indicated that Tcf19 knockdown significantly mitigated disc degeneration, reducing both abnormal collagen deposition and inflammation markers. This study unveils the complex molecular dynamics within IDD, providing new insights into distinct NPC subtypes and key genetic players. TCF19, in particular, holds promise as a therapeutic target for IDD. Our findings lay the groundwork for developing targeted treatment strategies, potentially improving the management and outcomes for individuals suffering from disc degeneration.
    Keywords:  Bulk sequencing; Cell communication; Intervertebral disc degeneration (IDD); Nucleus pulposus cells (NPCs); Single-Cell transcriptomics; TCF19
    DOI:  https://doi.org/10.1038/s41598-025-01180-2
  15. Acta Neuropathol Commun. 2025 May 07. 13(1): 93
    Temporal transcriptomic changes in the THY-Tau22 mouse model of tauopathy display cell type- and sex-specific differences.
    Muhammad Ali, Pierre Garcia, Laetitia P Lunkes, Alessia Sciortino, Melanie H Thomas, Tony Heurtaux, Kamil Grzyb, Rashi Halder, Alexander Skupin, Luc Buée, David Blum, Manuel Buttini, Enrico Glaab.
       BACKGROUND: Tauopathies, including Alzheimer's disease (AD) and frontotemporal dementia (FTD), display sex-specific differences in prevalence and progression, but the underlying molecular mechanisms remain unclear. Single-cell transcriptomic analysis of animal models can reveal how AD pathology affects different cell types across sex and age.
    OBJECTIVE: To understand sex-specific and sex-dimorphic transcriptomic changes in different cell types and their age-dependence in the THY-Tau22 mouse model of AD-linked tauopathy.
    METHODS: We applied single-cell RNA sequencing (scRNA-seq) to cortical tissue from male and female THY-Tau22 and wild-type mice at 17 months of age, when they had prominent tau inclusion pathology, and compared the results with corresponding data previously obtained at 7 months of age. Using differential statistical analysis for individual genes, pathways, and gene regulatory networks, we identified sex-specific, sex-dimorphic, and sex-neutral changes, and looked at how they evolved over age. To validate the most robust findings across distinct mouse models and species, the results were compared with cortical scRNA-seq data from the transgenic hAPP-based Tg2576 mouse model and human AD.
    RESULTS: We identified several significant sex-specific and sex-dimorphic differentially expressed genes in neurons, microglia, astrocytes and oligodendrocytes, including both cross-sectional changes and alterations from 7 months to 17 months of age. Key pathways affected in a sex-dependent manner across age included neurotransmitter signaling, RNA processing and splicing, stress response pathways, and protein degradation pathways. In addition, network analysis revealed the AD-associated genes Clu, Mbp, Fos and Junb as relevant regulatory hubs. Analysis of age-dependent changes highlighted genes and pathways associated with inflammatory response (Malat1, Cx3cr1), protein homeostasis (Cst3), and myelin maintenance (Plp1, Cldn11, Mal) that showed consistent sex-dependent changes as the THY-Tau22 mice aged. Multiple genes with established implications in AD, including the long non-coding RNA gene Malat1, displayed concordant sex-specific changes in mouse models and human AD.
    CONCLUSIONS: This study provides a comprehensive single-cell transcriptomic characterization of sex-linked and age-dependent changes in the THY-Tau22 tauopathy model, revealing new insights into the interplay between age-dependent AD-like pathologies and sex. The identified sex-specific changes and their conservation across models and human AD highlight molecular targets for further preclinical investigation of sex-specific therapeutic strategies in AD.
    Keywords:  Age differences; Alzheimer's disease; Sex differences; Single-cell RNA sequencing; THY-Tau22 mouse model; Tauopathy; Transcriptomics
    DOI:  https://doi.org/10.1186/s40478-025-02013-z
  16. Exp Eye Res. 2025 May 02. pii: S0014-4835(25)00188-5. [Epub ahead of print]256 110417
    Single-cell RNA sequencing in the study of human retinal organoids.
    Yi Chen, Hanyue Xu, Lirong Xiao, Ming Zhang, Naihong Yan.
      Single-cell RNA sequencing (scRNA-seq) has transformed the study of retinal development and diseases by enabling a detailed analysis of cellular diversity within retinal organoids (ROs). ROs generated from pluripotent stem cells mimic the essential characteristics of the human retina and provide a valuable in vitro model for investigating retinal development, cell interactions, and disease mechanisms. This review summarizes the application of scRNA-seq on RO research, emphasizing its capacity to identify distinct cell populations, uncover developmental trajectories, and reveal the molecular signatures of retinal diseases. scRNA-seq provides new insights into retinal neurogenesis, cellular diversity, and the pathophysiology of retinal degenerative diseases. This technology has enabled the identification of novel biomarkers and potential therapeutic targets. Integrating scRNA-seq with other technologies, such as spatial transcriptomics and CRISPR-based screening, can further deepen our understanding of retinal biology and improve treatment strategies.
    Keywords:  Culture protocols; Retina transplantation; Retinal organoids; Single-cell RNA sequencing; Stem cells
    DOI:  https://doi.org/10.1016/j.exer.2025.110417
  17. Clin Epigenetics. 2025 May 06. 17(1): 77
    Investigating the mechanisms by which low NAT1 expression in tumor cells contributes to chemo-resistance in colorectal cancer.
    Zheng Yuan, Kai Fang, Xinsheng Miao, Yan Zhang, Menghui Gu, Wei Xu, Hao Li, Dawei Zhu, Jiahui Zhou, Jian Sun, Xinhua Gu.
       BACKGROUND: In the therapeutic landscape of colorectal cancer (CRC), chemo-resistance poses a significant and prevalent obstacle that complicates treatment efficacy and patient outcomes. Over time, cancer cells can develop mechanisms to resist the toxic effects of chemo-therapy drugs, leading to reduced sensitivity or complete insensitivity to these agents. The enzyme Arylamine N-acetyltransferase 1 (NAT1) has emerged as a promising target in strategies aimed at overcoming this challenge. NAT1 is involved in the metabolism of various xenobiotics, including some chemotherapeutic agents. Understanding the complex interactions between NAT1 and chemotherapeutic agents, as well as the molecular mechanisms underlying chemo-resistance, is crucial for the development of novel therapeutic approaches.
    OBJECTIVE: This study aimed to assess the role of NAT1 in mediating chemo-resistance in CRC, with the goal of identifying novel strategies to overcome this clinical challenge.
    METHODS: We conducted a comprehensive analysis using various bioinformatics tools and in vitro experiments to evaluate the effect of NAT1 expression on chemo-resistance in CRC. Furthermore, we employed a multi-omics approach, including metabolomics and next-generation sequencing, to uncover the mechanisms by which NAT1 influences chemo-resistance. Additionally, we utilized single-cell RNA sequencing (scRNA-seq), the Cellchat assay, and western blot to explore the intercellular communication between tumor and endothelial cells in the context of anti-PD-1 therapy and NAT1's impact.
    RESULTS: Our study reveals that decreased NAT1 expression in CRC tumor tissues, relative to adjacent normal tissues, is significantly associated with a poorer patient prognosis. Experimental data indicate that silencing NAT1 in CaCO2 and HCT116 cell lines results in heightened resistance to five chemotherapeutic agents: vinblastine, docetaxel, gemcitabine, vincristine, and daporinad. Additionally, NAT1 silencing increases the proportion of LGR5+ cells, which are known to be chemo-resistant. Our research further revealed that exposure to these five drugs induces a decrease in NAT1 expression within CRC cells. Mechanistic insights show that NAT1 knockdown triggers a metabolic reprogramming in CRC cells, shifting from oxidative phosphorylation and the tricarboxylic acid cycle to a preference for glycolysis. Furthermore, silencing of NAT1 in CRC cells leads to an up-regulation of VEGFA expression. Notably, the application of anti-PD-1 therapy was demonstrated to significantly disrupt the VEGFA-VEGFR axis signaling, an interaction critical between CRC cells and endothelial cells. This discovery underscores the potential of targeting the VEGFA pathway as a therapeutic approach to mitigate the adverse effects associated with NAT1 down-regulation in CRC.
    CONCLUSION: Our study underscores the multifaceted role of NAT1 in modulating chemo-sensitivity, cellular metabolism, and angiogenesis in CRC. These findings position NAT1 as a compelling candidate for a biomarker and a potential therapeutic target, offering new avenues for CRC management.
    Keywords:  Chemo-resistance; Colorectal cancer; LGR5; NAT1; VEGFA-VEGFR axis
    DOI:  https://doi.org/10.1186/s13148-025-01882-4
  18. Front Immunol. 2025 ;16 1549742
    Comprehensive analysis of plasma cell heterogeneity and immune interactions in multiple myeloma.
    Shuang Qu, Zhihai Zheng, Xiaoling Guo, Jiaqi Mei, Sicong Jiang, Biyun Chen.
      This study focused on the role of plasma cells in multiple myeloma (MM) and the associated potential mechanisms. Transcriptomic data of MM and various gene sets from several public databases were downloaded for subsequent analyses. Through single-cell sequencing, 10 major cell types were identified and annotated. The differential gene expression and pathway enrichment between different plasma cell subtypes as well as cell communication analysis, transcriptional regulation analysis, and enrichment analysis in conjunction with the malignant subpopulation were performed. Next, the samples were clustered into two groups by applying non-negative matrix factorization (NMF). Additional analysis revealed notable disparities in survival between the two clusters, correlation with genes involved in classical metabolic pathways and pathway dysregulation, thus confirming the stability and validity of the clustering. Subsequently, Weighted Gene Co-expression Network Analysis was performed and hub genes from the modules most strongly associated with the clustering groups were extracted. We then constructed a prognostic prediction model using Least Absolute Shrinkage and Selection Operator and multiCox regression analysis. The predictive accuracy of the model was evaluated and robustness were confirmed in a separate validation cohort. The gene and pathway dysregulation for the two risk groups was analyzed. Ultimately, an investigation was conducted into the association between the risk model and various immunological features, in terms of antitumor immunotherapy, the tumor microenvironment, and immune checkpoints. This study provides an in-depth investigation into the potential mechanisms underlying MM development and offers new directions to improve therapeutic approaches and enhance patient outcomes.
    Keywords:  multiple myeloma; plasma cells; single-cell sequencing; tumor microenvironment; weighted gene coexpression network analysis
    DOI:  https://doi.org/10.3389/fimmu.2025.1549742
  19. Int J Mol Sci. 2025 Apr 21. pii: 3916. [Epub ahead of print]26(8):
    Single-Cell RNA Sequencing Outperforms Single-Nucleus RNA Sequencing in Analyzing Pancreatic Cell Diversity and Gene Expression in Goats.
    Jie Cheng, Tianxi Zhang, Yan Cheng, Kefyalew Gebeyew, Zhiliang Tan, Zhixiong He.
      The objective of this study was to determine whether single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) was more effective for studying the goat pancreas. Pancreas tissues from three healthy 10-day-old female Xiangdong black goats were processed into single-cell and single-nucleus suspensions. These suspensions were then used to compare cellular composition and gene expression levels following library construction and sequencing. Both scRNA-seq and snRNA-seq were eligible for primary analysis but produced different cell identification profiles in pancreatic tissue. Both methods successfully annotated pancreatic acinar cells, ductal cells, alpha cells, beta cells, and endothelial cells. However, pancreatic stellate cells, immune cells, and delta cells were uniquely annotated by scRNA-seq, while pancreatic stem cells were uniquely identified by snRNA-seq. Furthermore, the genes related to digestive enzymes showed a higher expression in scRNA-seq than in snRNA-seq. In the present study, scRNA-seq detected a great diversity of pancreatic cell types and was more effective in profiling key genes than snRNA-seq, demonstrating that scRNA-seq was better suited for studying the goat pancreas. However, the choice between scRNA-seq and snRNA-seq should consider the sample compatibility, technical differences, and experimental objectives.
    Keywords:  cell type; goats; pancreas; sequencing
    DOI:  https://doi.org/10.3390/ijms26083916
  20. Int J Biochem Cell Biol. 2025 May 02. pii: S1357-2725(25)00055-X. [Epub ahead of print]185 106788
    Single-cell transcriptomics and metabolomics reveal the potential role of ASRGL1 in metabolic reprogramming and invasion of nasopharyngeal carcinoma cells.
    YingYing Pei, Chunlin Li, Bin Zhang, Qi Zheng, Shengnan Chen, Ji Li.
      Nasopharyngeal carcinoma (NPC) is an aggressive and highly metastatic malignancy, presenting significant challenges for early diagnosis and treatment. Asparaginase-like protein 1 (ASRGL1) is an important enzyme involved in amino acid metabolism, and previous studies have linked it to the progression of various tumors. However, the specific role of ASRGL1 in NPC remains unclear. This study analyzed multiple publicly available datasets related to NPC. We conducted single-cell RNA sequencing (scRNA-seq) analysis on the GSE150430 dataset to identify different cell subpopulations and examine ASRGL1 expression and its functional implications. The expression of ASRGL1 and its correlation with EMT were validated using transcriptomic data. The expression of ASRGL1 in C666-1 cells was interfered with by siRNA, cell proliferation and invasion were detected by CCK8, EdU, plate cloning, Transwell and scratch method, and EMT was evaluated by detecting the expression of E-cadherin and N-cadherin. Amino acid metabolomics and GC-MS headspace metabolomics were used to analyze the effects of ASRGL1 knockdown on the metabolic pattern of NPC cells. This study found that ASRGL1 was mainly expressed in fibroblasts, epithelial cells and myeloid cells in nasopharyngeal carcinoma (NPC). The ASRGL1-cell gene was significantly enriched in the epithelial-mesenchymal transition pathway. Knocking down ASRGL1 can further inhibit the proliferation, invasion and EMT of C666-1 cells. At the same time, the utilization of various amino acids was significantly reduced, and further GC-MS metabolomics analysis showed that the cell metabolism was unbalanced. This study elucidates the expression characteristics and potential functional roles of asparaginase-like protein 1 (ASRGL1) in nasopharyngeal carcinoma (NPC), providing new insights into its potential as a diagnostic marker and therapeutic target.
    Keywords:  Asparaginase-like protein 1; Nasopharyngeal carcinoma; Single-Cell Transcriptomics and Metabolomics; Therapeutic target
    DOI:  https://doi.org/10.1016/j.biocel.2025.106788
  21. Int J Genomics. 2025 ;2025 3530098
    Multiomics Approach Distinguishes SPTBN4 as a Key Molecule in Diagnosis, Prognosis, and Immune Suppression of Testicular Seminomas.
    Jianfeng Xiang, Yanjie Xiang, Qintao Ge, Yunhong Zhou, Hailiang Zhang, Wenhao Xu, Shifang Zhou, Liang Chen.
      Background: Testicular seminomas, a common germ cell tumor, poses clinical challenges due to its molecular heterogeneity and limited biomarkers for precise diagnosis and prognosis. Leveraging multiomics approaches enables the comprehensive dissection of tumor complexity and facilitates the identification of key molecules influencing disease progression and therapeutic response. Methods: Single-cell RNA transcriptomic sequencing (scRNA-seq) was utilized to explore the cellular and transcriptional heterogeneity of testicular seminomas. High-dimensional weighted gene coexpression network analysis (hdWGCNA) identified gene modules linked to tumor progression. Public datasets were integrated for gene expression and survival analyses, and drug sensitivity patterns were assessed using the GDSC database. Results: scRNA-seq analysis revealed heterogeneous epithelial populations, with Epi1 cells exhibiting SLC5A5 and SPTBN4 as risk factors for advanced progression of seminomas. hdWGCNA identified nine gene modules, with the M6 module significantly enriched in Epi1 cells, implicating pathways such as negative regulation of ERAD and selective mRNA degradation. SPTBN4 was markedly upregulated in seminoma compared to nonseminomatous tumors and normal tissues, and its high expression was associated with poorer clinical outcomes and immunosuppressive microenvironments. Immune pathway analyses highlighted reduced antigen presentation and increased neutrophil extracellular trap (NET) formation in the SPTBN4-high group, suggesting diminished immunotherapeutic efficacy. Conversely, the SPTBN4-high group exhibited increased sensitivity to multiple chemotherapeutic agents, including thapsigargin and sorafenib, indicating its potential as a predictive marker for chemotherapy. Conclusion: In conclusion, this multiomics study identifies SPTBN4 as a central biomarker in testicular seminomas, encompassing diagnostic, prognostic, and therapeutic dimensions. The integration of single-cell transcriptomics, hdWGCNA, and drug sensitivity analyses underscores the molecular complexity of seminomas and highlights the translational potential of SPTBN4 in guiding personalized treatment strategies. These findings provide a foundation for leveraging multiomics approaches to advance the clinical management of testicular seminomas and other heterogeneous malignancies.
    Keywords:  SPTBN4; immune suppression; multiomics analysis; prognostic biomarker; single-cell RNA sequencing; testicular seminomas; tumor microenvironment
    DOI:  https://doi.org/10.1155/ijog/3530098
  22. Endocr Metab Immune Disord Drug Targets. 2025 May 06.
    Single-cell RNA Sequencing Analysis Reveals the Regulatory Functions of Copines Family Genes in Testicular Cancer Progression.
    Nan Li, Kai Yu, Delun Huang, Xuehong Zhu, Zhong Lin.
      The aim of this study is to investigate the expression patterns and regulatory functions of Copines family genes in different cellular subpopulations in testicular cancer based on single-cell data and to analyze the regulatory mechanism of Copines family genes in cancer.
    BACKGROUND: Testicular cancer is a frequently diagnosed male tumor. Emerging evidence suggests that Copines family genes are implicated in a variety of cancer phenotypes and cancer progression. Analyzing the expression pattern of Copines family genes in testicular cancer may help improve the treatment efficacy of the cancer.
    OBJECTIVE: This study sought to characterize the expression profiles of Copines family genes in the cellular subpopulations of testicular cancer and to identify key signaling pathways through which they regulate cancer progression.
    METHODS: Based on single-cell transcriptomic data of testicular cancer, we classified testicular cancer cell subpopulations and analyzed the expressions of Copines family genes in each subpopulation. Cell subpopulations were grouped according to the expression levels of Copines family genes, and differentially expressed Copines family genes between the groups were screened by differential expression analysis. Functional enrichment analysis on the differentially expressed genes (DEGs) was performed with a clusterprofiler package. Functional pathways enriched by the Copines family genes were calculated by AUCell enrichment score. Copy number variation (CNV) analysis was performed using inferCNV to analyze gene mutation patterns across cellular subpopulations, and pseudotime analysis was conducted using Monocle to infer cellular differentiation pathways of cellular subpopulations.
    RESULTS: Single-cell clustering identified four major cell subpopulations, namely, NK/T cells, tumor cells, B cells, and macrophages. Notably, the control samples had a relatively small proportion of tumor cells. Further clustering of the tumor cells identified six cell subpopulations, among which multiple Copines genes, especially CPNE1 and CPNE3, showed a high expression. The testicular cancer samples were grouped by the expression patterns of Copines genes, and the DEGs between groups included GNLY, MGP1, GFD2, CCL21, SPARCL13 as well as some other genes involved in the malignant progression of cancer. Pseudotime analysis showed that the upregulated genes were enriched in cell migration and PI3K-Akt pathway, while the downregulated genes were related to immunity. This indicated that the Copines genes regulated the cellular heterogeneity and malignant transformation in testicular cancer.
    CONCLUSION: This study revealed the potential molecular mechanism through which Copines family genes drove the progression of testicular cancer through regulating PI3K-Akt signaling pathway and cell cycle, providing a new target for the development of precision treatment targeting Copines family genes and prognostic assessment of the cancer.
    Keywords:  Copines; PI3K-AKT; Testicular cancer; cell cycle regulation; scRNA-seq; testicular cancer progression.
    DOI:  https://doi.org/10.2174/0118715303375462250430055914
  23. Elife. 2025 May 09. pii: RP102622. [Epub ahead of print]13
    Diversity and functional specialization of oyster immune cells uncovered by integrative single-cell level investigations.
    Sebastien De La Forest Divonne, Juliette Pouzadoux, Oceane Romatif, Caroline Montagnani, Guillaume Mitta, Delphine Destoumieux-Garzón, Benjamin Gourbal, Guillaume M Charriere, Emmanuel Vignal.
      Mollusks are a major component of animal biodiversity and play a critical role in ecosystems and global food security. The Pacific oyster, Crassostrea (Magallana) gigas, is the most farmed bivalve mollusk in the world and is becoming a model species for invertebrate biology. Despite the extensive research on hemocytes, the immune cells of bivalves, their characterization remains elusive. Here, we were able to extensively characterize the diverse hemocytes and identified at least seven functionally distinct cell types and three hematopoietic lineages. A combination of single-cell RNA sequencing, quantitative cytology, cell sorting, functional assays, and pseudo-time analyses was used to deliver a comprehensive view of the distinct hemocyte types. This integrative analysis enabled us to reconcile molecular and cellular data and identify distinct cell types performing specialized immune functions, such as phagocytosis, reactive oxygen species production, copper accumulation, and expression of antimicrobial peptides. This study emphasized the need for more in depth studies of cellular immunity in mollusks and non-model invertebrates and set the ground for further comparative immunology studies at the cellular level.
    Keywords:  Crassostrea gigas; Magallana gigas; hematopoiesis; hemocyte; immunology; inflammation; innate immunity
    DOI:  https://doi.org/10.7554/eLife.102622
  24. bioRxiv. 2025 Apr 15. pii: 2025.04.09.648030. [Epub ahead of print]
    Deconvoluting single-cell transcriptomics reveals cellular programs regulated by cell-cell communication in colorectal cancer.
    Han Zhang, Binfeng Lu, Gregory F Cooper, Anwaar Saeed, John W Paisley, Xinghua Lu, Lujia Chen.
      Cells within a tissue microenvironment communicate through intricate cell-cell communication (CCC) networks. In this meta-analysis of eight single-cell cohorts encompassing 153 patients and 279 samples, we advance the understanding of CCC networks in colorectal cancers through a novel analytical framework. Employing hierarchical language modeling, we identify gene expression modules (GEMs) that mirror single-cell signaling states, crucial for deciphering the complexity of intercellular interactions. By applying causal discovery methods, we systematically uncover GEMs likely regulated by ligand-receptor signaling and cross-cell-type communication. This analysis reveals cross-cell-type CCC programs, marked by highly correlated GEMs across various cell types, shedding light on the intricate CCC networks within the tumor microenvironment. Spatial transcriptomics further validate these findings by demonstrating the co-localization of GEMs within CCC programs in distinct spatial domains, emphasizing the spatial dynamics of tumor intercellular communication. Our interactive website ( http://44.192.10.166:3838/ ) and analytical framework equip researchers with powerful tools to explore these complex mechanisms, potentially uncovering novel drug targets and refining strategies for precision immunotherapies. This comprehensive study not only presents a detailed catalog of CCC networks driven by ligand-receptor interactions in colorectal cancer but also highlights the significance of integrating multi-sample and patient data to unravel the molecular underpinnings of cancer communication pathways.
    DOI:  https://doi.org/10.1101/2025.04.09.648030
  25. Cardiovasc Toxicol. 2025 May 09.
    Biomarkers Related to Interferon-γ Pathway in Myocardial Ischemia-Reperfusion Injury and the Potential Molecular Mechanisms.
    Fang Jia, Wei Jiang, Yan Zhang, Lisha Zhang, Tuo Han, Danmeng Liu, Jiahong Xue, Fuxue Deng.
      Although reperfusion therapy can reduce the mortality of myocardial infarction, it results in myocardial ischemia-reperfusion injury (MIRI). The molecular mechanism by which the interferon-γ pathway affects MIRI is unclear, so we addressed this problem by mining transcriptome and single-cell sequencing data. The GSE160516 and GSE83472 datasets, single cell RNA sequencing (scRNA-seq) data of GSE227088 dataset and 182 interferon-γ pathway related genes (IRGs) were retrieved and incorporated into this study. The differentially expressed genes (DEGs) between MIRI and control samples were searched, the candidate genes were obtained by intersecting DEGs with IRGs. The protein-protein interaction (PPI) analysis was utilized for selecting key genes from candidate genes. Moreover, key genes with significant expression and consistent trend in GSE160516 and GSE83472 datasets were selected as biomarkers. The biological functions and regulatory mechanism of biomarkers were investigated by enrichment analysis and predicting the upstream molecules targeting them. Ulteriorly, cell clusters were identified via unsupervised cluster analysis and merged into different cell types by cell annotation. Cell types in which biomarkers observably and differentially expressed were selected as crucial cell types. Finally, cell communication and pseudo-time analysis were implemented based on crucial cell types. Totally 34 candidate genes were searched by overlapping 1,930 DEGs with 182 IRGs. Nine key genes were singled out from candidate genes, of which Myd88 and Trp53 were significantly upregulated in the MIRI samples of GSE160516 and GSE83472 datasets, so they were identified as biomarkers. Besides, they participated in pathways such as ribosome, spliceosome and cell cycle. Myd88 might be simultaneously regulated by mmu-miR-361-3p and mmu-miR-421-3p, and Trp53 could be regulated by Abl1 and Tead2. Totally 25 cell clusters were merged into six cell types, of which three crucial cell types (cardiomyocyte, fibroblast, and macrophage) could interact with each other through receptor-ligand. Pseudo-time analysis revealed states 1, 2, and 5 of macrophages might be associated with MIRI. Two biomarkers (Myd88 and Trp53) related to IRGs in MIRI were mined, providing a reference for elucidating the mechanism of interferon-γ pathway on MIRI.
    Keywords:  Interferon-γ pathway related genes; Myd88 and Trp53; Myocardial ischemia–reperfusion injury; Single cell
    DOI:  https://doi.org/10.1007/s12012-025-09999-x
  26. Small Methods. 2025 May 05. e2500241
    Identification of Multi-Landscape and Cell Interactions in the Tumor Microenvironment Through High-Coverage Single-Cell Sequencing.
    Wenlong Zhong, Ligang Wang, Chengjunyu Zhang, Tonglei Guo, Lihua Zhao, Daqin Wu, Fei Xie, Xiao Wang, Xiuxin Li, Fangxiao Wang, Minghui Li, Weiyue Gu, Tianxin Lin, Xu Chen.
      Single-cell RNA sequencing (scRNA-seq) is a widely used method for classifying cell types and states and revealing disease mechanisms. However, most contemporary scRNA-seq platforms fail to explore the multilandscape of RNA. Here, a microfluidic chip is designed that combines oligo-dT primers and Random Bridging Co-labeling (RBCL) RNA sequencing to develop an innovative Chigene scRNA-seq technology that can identify gene expression, mutations, and RNA splicing landscapes at the single-cell level. The Chigene scRNA-seq platform demonstrated exceptional performance, with minimal doublet rates of 0.94% (Chigene V1) and 1.93% (Chigene V2). Both versions exhibit high sensitivity, with Chigene V2 achieving nearly 100% RNA coverage and detecting over 1800 genes per cell on average. Targeted capture of single-cell gene mutations enhances mutation detection sensitivity. Moreover, this Chigene V2 platform is validated in clinical samples for its ability to detect mutations, gene fusions, and alternative splicing. The reliability of the platform is further corroborated via known functional gene mutation (CDKN1A) and fusion (FGFR3-TACC). To validate this method's potential for discovering novel gene mutations in clinical samples, the investigation revealed an intriguing cell subpopulation carrying an ARHGAP5 mutation in urothelial carcinoma. These cells exhibited high-frequency mRNA splicing and exhibited specific crosstalk with T cells, distinguishing them from the subpopulation with the ARHGAP5 wild-type phenotype. Overall, this method provides a robust scRNA-seq platform suitable for comprehensive analyses of clinical specimens at different genetic information levels, thereby offering significant potential in the discovery of novel genes and interactions at the single-cell level.
    Keywords:  high‐coverage; multi‐landscape; random bridging co‐labeling; single‐cell sequencing; urologic tumors
    DOI:  https://doi.org/10.1002/smtd.202500241
  27. Hum Reprod. 2025 May 06. pii: deaf076. [Epub ahead of print]
    Oncostatin-M: a novel leukocyte-derived factor facilitating the ovulatory process in the human ovary.
    Yohan Choi, Mats Brännström, James W Akin, Ashley Teate, Lane K Christenson, Thomas E Curry, Misung Jo.
       STUDY QUESTION: Does leukocyte-derived Oncostatin-M (OSM) regulate the ovulatory process in human dominant follicles?
    SUMMARY ANSWER: Leukocyte-derived OSM activates key signaling pathways in human preovulatory granulosa cells and modulates steroidogenesis, prostaglandin synthesis, and tissue remodeling in human ovulatory follicles.
    WHAT IS KNOWN ALREADY: Leukocytes are essential regulators of ovulation. Our recent single-cell RNA sequencing (scRNA-seq) has identified diverse leukocyte subpopulations in follicular aspirates obtained from IVF patients and revealed the expression of OSM in leukocytes and its receptors (OSMR, LIFR, IL6ST) in follicular cells. However, the function of leukocyte-derived OSM in human ovulatory follicle remains unclear.
    STUDY DESIGN, SIZE, DURATION: This study analyzed dominant follicles from naturally cycling women (n = 19) across the periovulatory period and follicular aspirates from IVF patients (n = 12). Primary human granulosa/lutein cells (hGLCs) treated with hCG and/or recombinant human OSM (rhOSM) were used to assess its functional effects.
    PARTICIPANTS/MATERIALS, SETTING, METHODS: Our recent scRNA-seq dataset was used to identify cell types expressing OSM and its receptors in human follicular aspirates. The expression of OSM and its receptors was assessed in dominant follicles by quantitative PCR (qPCR) and immunohistochemistry. hGLCs were treated with hCG and/or rhOSM, and functional analyses included qPCR, western blotting, RNA sequencing, and hormone assays for progesterone (P4), estradiol (E2), and prostaglandin E2 (PGE2) production.
    MAIN RESULTS AND THE ROLE OF CHANCE: Bioinformatics analysis of scRNA-seq revealed that OSM is exclusively expressed in leukocytes, whereas its receptors are predominantly expressed in granulosa cells. Immunohistochemistry and qPCR analyses exhibited increased OSM expression in leukocytes and receptor expression in granulosa cells, respectively, after ovulatory hCG administration (P < 0.05). Western blotting demonstrated that rhOSM treatment activated STAT3, ERK1/2, AKT, and p38MAPK pathways in hGLCs. RNA sequencing and following qPCR revealed rhOSM-induced significant transcriptional changes in genes involved in steroidogenesis, prostaglandin synthesis/transport, inflammation, and tissue remodeling (FDR < 0.05). Functionally, rhOSM increased P4 and PGE2 secretion (P < 0.05) while decreasing E2 production (P < 0.05), suggesting a role in ovulation and luteinization.
    LARGE SCALE DATA: RNA sequencing datasets are available in the Gene Expression Omnibus under accession number GSE277343.
    LIMITATIONS, REASONS FOR CAUTION: This study was conducted using in vitro hGLC cultures, which may not fully recapitulate in vivo ovulatory dynamics. Additionally, the findings are specific to human samples and require validation in other mammalian species.
    WIDER IMPLICATIONS OF THE FINDINGS: These results suggest that leukocyte-derived OSM is a key cytokine regulating ovulatory events, providing novel insights into the immunoendocrine crosstalk within the human follicle. This study enhances our understanding of cytokine-mediated follicular maturation and may have implications for improving ovulation-related fertility treatments.
    STUDY FUNDING/COMPETING INTERESTS: This study was supported by grants P01HD71875 (to M.J. and T.E.C.), R01HD096077 (to M.J.), R03HD095098 (to Y.C.), and R01HD115554 (to Y.C.). The authors declare no competing interests.
    Keywords:  OSM; Oncostatin-M; granulosa cell; human; inflammation; leukocyte; ovulation
    DOI:  https://doi.org/10.1093/humrep/deaf076
  28. Front Genet. 2025 ;16 1551879
    Integration of bulk RNA-seq and scRNA-seq reveals transcriptomic signatures associated with deep vein thrombosis.
    Bao-Ze Pan, Ming-Jun Jiang, Li-Ming Deng, Jie Chen, Xian-Peng Dai, Zi-Xuan Wu, Zhi-He Deng, Dong-Yang Luo, Yang-Yi-Jing Wang, Dan Ning, Guo-Zuo Xiong, Guo-Shan Bi.
       Background: Deep vein thrombosis (DVT) is a prevalent peripheral vascular disease. The intricate and multifaceted nature of the associated mechanisms hinders a comprehensive understanding of disease-relevant targets. This study aimed to identify and examine the most distinctive genes linked to DVT.
    Methods: In this study, the bulk RNA sequencing (bulk RNA-seq) analysis was conducted on whole blood samples from 11 DVT patients and six control groups. Topology analysis was performed using seven protein-protein interaction (PPI) network algorithms. The combination of weighted correlation network analysis (WGCNA) and clinical prediction models was employed to validate hub DEGs. Furthermore, single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood samples from 3 DVT patients and three control groups to probe the cellular localization of target genes. Based on the same methodology as the internal test set, 12 DVT patients and six control groups were collected to construct an external test set and validated using machine learning (ML) algorithms and immunofluorescence (IF). Concurrently, the examination of the pathways in disparate cell populations was conducted on the basis of the CellChat pathway.
    Results: A total of 193 DEGs were identified in the internal test set. Additionally, a total of eight highly characteristic genes (including TLR1, TLR7, TLR8, CXCR4, DDX58, TNFSF10, FCGR1A and CD36) were identified by the PPI network algorithm. In accordance with the WGCNA model, the aforementioned genes were all situated within the blue core module, exhibiting a correlation coefficient of 0.84. The model demonstrated notable disparities in TLR8 (P = 0.018, AUC = 0.847), CXCR4 (P = 0.00088, AUC = 1.000), TNFSF10 (P = 0.00075, AUC = 0.958), and FCGR1A (P = 0.00022, AUC = 0.986). Furthermore, scRNA-seq demonstrated that B cells, T cells and monocytes play an active role in DVT. In the external validation set, CXCR4 was validated as a potential target by the ML algorithm and IF. In the context of the CellChat pathway, it indicated that MIF - (CD74 + CXCR4) plays a potential role.
    Conclusion: The findings of this study indicate that CXCR4 may serve as a potential genetic marker for DVT, with MIF - (CD74 + CXCR4) potentially implicated in the regulatory mechanisms underlying DVT.
    Keywords:  ScRNA-seq; WGCNA; bulk RNA-seq; deep vein thrombosis; machine learning; venous thromboembolism
    DOI:  https://doi.org/10.3389/fgene.2025.1551879
  29. Oral Oncol. 2025 May 03. pii: S1368-8375(25)00176-9. [Epub ahead of print]165 107347
    Single-cell transcriptomics reveal the effects of chemoimmunotherapy on hypopharyngeal cancer and its tumor microenvironment.
    Xiaoli Zhu, Haibo Xu, Yulan Zhao, Wenwen Diao, Fangxu Yan, Yingying Zhu, Tiantian Zhao, Xingming Chen.
      Induction chemoimmunotherapy (ICIT) has emerged as a potential treatment option for resectable hypopharyngeal cancer (HPC), while its effectiveness remains limited to a significant portion of HPC cases, and a major challenge behind lies in the lack of reliable molecular markers to identify treatment-resistant patients. In this study, we analyzed biopsy samples of HPC patients collected before and after ICIT, classifying them based on treatment response. By investigating the tumor microenvironment (TME) at the single-cell level, we demonstrated that the heterogeneity within the TME is closely linked to different treatment outcomes. Specially, a strong treatment response correlated with a subpopulation of cancer-associated fibroblasts (CAFs). Additionally, we identified that the S100A2 gene is highly expressed in tumor cells and appears to influence ICIT efficacy. Using bulk RNA sequencing, we estimated cell composition and validated these observations at the protein level. Our research provides a novel approach for identifying genes and cell populations that predict treatment responses in HPC, potentially enabling the timely identification of treatment-resistant patients. This could increase the likelihood of preserving laryngeal function and optimizing treatment strategies.
    Keywords:  Cancer-associated fibroblasts; Hypopharyngeal carcinoma; Induction chemoimmunotherapy; S100A2; Single-cell RNA sequencing; Tumor microenvironment
    DOI:  https://doi.org/10.1016/j.oraloncology.2025.107347
  30. Exp Physiol. 2025 May 05.
    TREM2+ macrophages accumulate in childhood IgA nephropathy and soluble TREM2 represents a reliable non-invasive biomarker.
    Ling Yu, Xuan Gang, Jingjing Wang, Guoping Huang, Qiuyu Li, Weizhong Gu, Haidong Fu, Jianhua Mao.
      IgA nephropathy (IgAN) is a common type of primary glomerulonephritis in children. The pathogenesis of childhood IgAN remains unclear, and there is a lack of effective non-invasive biomarkers for this disease. Single-cell RNA sequencing was performed in children with IgAN to delineate cellular and molecular compositions, and subcluster analysis for macrophages was conducted. Blood samples were collected from 38 children with IgAN to measure soluble TREM2 (sTREM2) and soluble CD163 (sCD163) levels and analyse their clinical significance. Single-cell RNA sequencing identified distinct cell clusters in both parenchymal and stromal compartments. Mesangial components were classified into vascular smooth muscle cells/pericytes, mesangial cells, fibroblasts and activated myofibroblasts. Patients with IgAN had a marked increase in myofibroblasts and immune cells in comparison to the control group. Remarkable infiltration of macrophages was observed in the kidneys of IgAN patients, and a subgroup of marcophages with high TREM2 expression was enriched. Children with IgAN exhibited significantly higher plasma sTREM2 levels than healthy individuals, and the sTREM2 level was correlated with sCD163 abundance. Importantly, an increased sTREM2 level was positively associated with the severity of proteinuria. Moreover, the elevation of sTREM2 was correlated with a more advanced pathological grading. In summary, we unveiled a remarkable remodelling of the stromal cellular landscape in childhood IgAN, and TREM2+ macrophages were found to accumulate. We identified that the plasma sTREM2 level was associated with clinical and pathological severity and therefore constituted a potential non-invasive biomarker for children with IgAN.
    Keywords:  TREM2; biomarker; childhood IgA nephropathy; macrophage; soluble TREM2
    DOI:  https://doi.org/10.1113/EP092716
  31. Discov Oncol. 2025 May 09. 16(1): 713
    Identification of programmed cell death-related genes and construction of a prognostic model in oral squamous cell carcinoma using single-cell and transcriptome analysis.
    Yongheng Li, Yang Yu, Shaonan Hu, Simin Li.
       BACKGROUND: Oral squamous cell carcinoma (OSCC) is characterized by poor prognosis and high mortality. Understanding programmed cell death-related genes could provide valuable insights into disease progression and treatment strategies.
    METHODS: RNA-sequencing data from 341 OSCC tumor tissues and 31 healthy samples were analyzed from TCGA database, with validation using 76 samples from GSE41613. Single-cell RNA sequencing data was obtained from GSE172577 (6 OSCC samples). Differentially expressed genes (DEGs) were identified and intersected with 1,254 programmed cell death-related genes. A protein-protein interaction network was constructed, and key modules were identified. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed to build a prognostic model. Model performance was evaluated using Kaplan-Meier analysis, ROC curves, and nomogram validation.
    RESULTS: The study identified 200 candidate genes from the intersection of DEGs and programmed cell death-related genes, which were further refined to 57 hub genes through PPI network analysis. A prognostic signature consisting of five genes (MET, GSDMB, KIT, PRKAG3, and CDKN2A) was established and validated. The model demonstrated good predictive performance in both training and validation cohorts (AUC > 0.6 for 1-, 2-, and 3-year survival). Single-cell analysis revealed that prognostic genes were predominantly expressed in stromal and epithelial cells. Cell communication analysis indicated strong interactions between stromal and epithelial cells.
    CONCLUSIONS: This study developed and validated a novel five-gene prognostic signature for OSCC based on programmed cell death-related genes. The model shows promising clinical application potential for risk stratification and personalized treatment of OSCC patients.
    Keywords:  Oral squamous cell carcinoma; Prognostic model; Programmed cell death; Single-cell RNA sequencing; Tumor microenvironment
    DOI:  https://doi.org/10.1007/s12672-025-02520-4
  32. Discov Oncol. 2025 May 08. 16(1): 693
    Single-cell and bulk transcriptome sequencing identifies circadian rhythm disruption and cluster-specific clinical insights in colorectal tumorigenesis.
    Chen Liu, Jingyang Liu, Jing Shao, Xiaoman Zhao, Lin Xie, Mengyao Shang, Ying Li, Weiming Li.
       BACKGROUND: Colorectal cancer (CRC) is one of the most common malignant tumors in the digestive system worldwide, with its mortality ranking second among all cancers. Studies have indicated that disruptions in circadian rhythm (CR) are associated with the occurrence of various cancers; however, the relationship between CR and CRC requires further evidence, and research on the application of CR in CRC is still limited.
    METHODS: In this study, we employed both bulk and single-cell RNA sequencing to explore the dysregulation of CR in patients with CRC. By constructing a CR subtype classifier, we conducted an in-depth analysis of the prognostic significance, the status of the tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy between different CR clusters. Furthermore, we developed a CR scoring system (CRS) using machine learning to predict overall survival and identified several genes as potential targets affecting CRC prognosis.
    RESULTS: Our findings revealed significant alterations in CR genes and status between CRC and normal tissues using bulk and single-cell transcriptome sequencing. Patients with CRC could be categorized into two distinct CR clusters (CR cluster 1 and 2). The prognosis of CR cluster 2, with higher epithelial-mesenchymal transition (EMT) and angiogenesis scores, was significantly worser than that of CR cluster 1. These clusters exhibited distinct levels of tumor-infiltrating lymphocytes. CR cluster 2 with a notably higher proportion of patients with microsatellite-instability-high (MSI-H), potentially benefit from ICB therapy. The proportion of patients belonging to consensus molecular subtype 4 (CMS4) in CR cluster 2 was also notably higher than in CR cluster 1. Additionally, the CRS combined with tumor stage demonstrated superior overall survival prediction efficacy compared to traditional tumor stage. We revealed a potential link between model genes (LSAMP, MS4A2, NAV3, RAB3B, SIX4) and the disruption of CR and patient prognosis.
    CONCLUSION: This study not only provide new insights into the assessment of CR status in CRC patients but also develop a prognosis model based on CR-related genes, offering a new tool for personalized risk assessment in CRC.
    Keywords:  Circadian rhythm; Colorectal cancer; Immune checkpoint blockade therapy; Prognostic model; Single-cell RNA sequencing
    DOI:  https://doi.org/10.1007/s12672-025-02521-3
  33. Stem Cell Reports. 2025 Apr 28. pii: S2213-6711(25)00107-9. [Epub ahead of print] 102503
    Two types of regenerative cell populations appear in acute liver injury.
    Tomomi Aoyagi, Takeshi Goya, Koji Imoto, Yuki Azuma, Tomonobu Hioki, Motoyuki Kohjima, Masatake Tanaka, Yoshinao Oda, Yoshihiro Ogawa.
      The liver has a robust regenerative capacity. However, the mechanisms underlying this process remain unclear. Numerous studies on liver regeneration have been previously conducted using partial hepatectomy models, which may not fully represent acute liver injury with inflammation and necrosis. This is commonly observed in the majority of clinical cases. In this study, we conducted a single-cell RNA sequencing (RNA-seq) analysis of liver regeneration in acetaminophen-treated mice using publicly available data. We found that two distinct populations of regenerative cells simultaneously appeared within the same regenerative process. The two populations significantly differed in terms of cell morphology, differentiation, localization, proliferation rate, and signal response. Moreover, one of the populations was induced by contact with necrotic tissue and demonstrated a higher proliferative capacity with a dedifferentiated feature. These findings provide new insights into liver regeneration and therapeutic strategies for liver failure.
    Keywords:  acetaminophen; acute liver injury; damage-induced liver injury; dedifferentiation; hepatocytes; liver regeneration
    DOI:  https://doi.org/10.1016/j.stemcr.2025.102503
  34. Front Psychiatry. 2025 ;16 1566155
    Genetic association between gene expression profiles in oligodendrocyte precursor cells and psychiatric disorders.
    Reon Kondo, Hiroki Kimura, Masashi Ikeda.
       Background: Although neuronal dysfunction has been the focus of many studies on psychiatric disorders, accumulating evidence suggests that white matter abnormalities and oligodendrocyte lineage cells, including oligodendrocyte precursor cells (OPCs), play an important role. Beyond their established contribution to myelination, synaptic genes in OPCs form connections to neurons and influence neuronal circuits and plasticity, thereby potentially contributing to psychiatric pathology.
    Methods: We analyzed publicly available single-nucleus RNA sequencing (snRNA-seq) data from white matter cells of healthy donors with SCZ genome-wide association study (GWAS) summary statistics. We assessed cell-type-specific enrichment of SCZ-associated genetic variants and performed weighted gene co-expression network analysis (WGCNA) to identify disease-related gene modules in implicated cell types.
    Results: OPCs exhibited significant enrichment of SCZ-associated genetic risk variants and showed pronounced specificity in gene expression patterns. Through WGCNA, we identified a distinct co-expression module in OPCs that was enriched for synaptic genes associated with SCZ.
    Conclusion: The present results highlight the previously underappreciated role of OPCs in psychiatric disorders, suggesting that OPC-involved synaptic interactions may contribute to the pathophysiology of SCZ. This work underscores the importance of considering OPCs as active players in neural network dysfunction, with potential implications for future therapeutic strategies.
    Keywords:  autism spectrum disorder; oligodendrocyte precursor cells; schizophrenia; single-cell analysis; weighted gene co-expression network analysis
    DOI:  https://doi.org/10.3389/fpsyt.2025.1566155
  35. Front Immunol. 2025 ;16 1582203
    METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery.
    Xie Weng, Yangyue Huang, Zhuoya Fu, Xingli Liu, Fuli Xie, Jiale Wang, Qiaohua Zhu, Dayong Zheng.
       Background: Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, partly due to an incomplete understanding of the metabolic and immune dysregulation driving its progression. Here, we uncover a novel role of METTL1 in driving nucleotide metabolism reprogramming, which significantly modulates the tumor immune microenvironment.
    Methods: Utilizing an integrated multi-omics approach, we analyzed nucleotide metabolism-related genes derived from TCGA, GEO, and ICGC datasets. Non-negative matrix factorization (NMF) clustering stratified HCC patients into distinct subgroups with varied clinical features. Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes that were subsequently used to construct robust prognostic models via multiple machine learning algorithms. These computational findings were validated through in vitro experiments, immune infiltration assessments, and single-cell RNA sequencing analysis.
    Results: Our analyses demonstrate that METTL1 is markedly upregulated in HCC, driving a reprogramming of nucleotide metabolism that modulates the expression of key immune checkpoints, including PD-L1 and CTLA-4. This regulation is associated with an immunosuppressive tumor microenvironment, reduced infiltration of activated T cells, and poorer clinical outcomes. Moreover, the prognostic model integrating METTL1 expression and immune checkpoint profiles shows strong predictive performance across independent cohorts, highlighting its potential clinical utility.
    Conclusion: This study highlights the innovative role of METTL1-driven nucleotide metabolism reprogramming in reshaping the immune microenvironment of HCC. The findings provide novel insights into HCC pathogenesis and pave the way for developing personalized therapeutic strategies based on targeting METTL1 and its associated metabolic pathways.
    Keywords:  METTL1; immune cell correlation; non-negative matrix factorization clustering; nucleotide metabolism; pancreatic hepatocellular carcinoma
    DOI:  https://doi.org/10.3389/fimmu.2025.1582203
  36. Sci Rep. 2025 May 08. 15(1): 16085
    The cancer stem cells characteristics analysis of LGR5 + cells that influence lung cancer risk by using single cell RNA-seq analysis.
    Ge Wen, Shaoqing Niu, Shiqi Mei, Senming Wang.
      Lung adenocarcinoma (LUAD) is the most popular lung cancer type with highly mortality. We performed a single cell RNA-seq analysis to explore characteristic of cancer stem cells in LUAD. We downloaded the single cell RNA-seq data (GSE149655) from the GEO database, the scRNA-seq analysis was performed by using the "Seurat" and "harmony" R package. The FindMarkers function and "ClusterProlifer" package was used for differentially expressed genes (DEGs) and function enrichment analysis. The protein-protein interaction and transcriptional regulatory network were performed by STRING and ChIPBase database. Immunohistochemistry tests to be used to observe differences in the expression of specific genes in LUAD and paracancerous tissue samples. BEAS-2B and A549 cells was used for vitro assay and the qRT-PCR, western blotting, wound healing, trans-well assays, EdU tests, and flow cytometry were performed. A total of 9 cell clusters were obtained after scRNA-seq analysis, in which the cancer stem cells had higher proportion in LUAD samples. Subsequently, function enrichment analysis revealed that the amino sugar and nucleotide sugar metabolism and DNA replication pathways were activated in cancer stem cells (CSCs), which were further sub-divided into 3 subtypes, the LGR5 + stem cell is a major contributor to cancer progression, its hub genes, such as HLA-DPB1, CD74, CTSH and HLA-DRB5 mediated the unique transcriptional state. In addition, the marker genes of three CSCs were also overexpressed in LUAD cells and the CXCL3 played an important role in mediating cell proliferation, apoptosis, migration and invasion of tumor. We performed a scRNA-seq analysis and identified the LGR5 + stem cell as a major contributor in LUAD progression, our findings are expected to provide new insights into the pathogenesis of LUAD.
    Keywords:  Drug resistance; LGR5 + stem cells; Lung adenocarcinoma; Single-cell RNA sequencing; Tumor heterogeneity
    DOI:  https://doi.org/10.1038/s41598-025-00585-3
  37. PeerJ. 2025 ;13 e19369
    A prognostic glycolysis-related gene signature in osteosarcoma: implications for metabolic programming, immune microenvironment, and drug response.
    Naiqiang Zhu, Jingyi Hou, Yu Zhang, Ning Yang, KaiKai Ding, Chengbing Chang, Yanqi Liu, Haipeng Gu, Bin Chen, Xu Wei, Liguo Zhu.
       Background/Aims: Osteosarcoma (OS), a malignant tumor originating in the bone or cartilage, primarily affects children and adolescents. Notably, glycolysis is the main target for metabolic programming to ensuring the energy supply for cancer. This study aimed to establish a glycolysis-related gene (GRG) risk signature in OS to comprehensively assessing the pathogenic, prognosis, and their application in predicting drug response.
    Methods: mRNA expression profiles were acquired from the Gene Expression Omnibus (GEO, GSE16091, GSE39058, and GSE21257). Using the non-negative matrix factorization (NMF) algorithm, patients with OS were stratified into distinct subgroups based on 288 GRGs identified through univariable Cox analysis. Univariate Cox regression analysis of differentially expressed genes (DEGs) between the molecular clusters was conducted to establish a risk signature comprising GRGs in OS. The prognostic efficacy of this risk signature was assessed via Kaplan-Meier curve analysis and Cox regression, evaluating its independence as a prognostic indicator. Additionally, the predictive potential of the risk model for drug response was evaluated using the "OncoPredict" package. Furthermore, the distribution of immune cell types in single-cell RNA sequencing (scRNA-seq) data was examined in correlation with the four identified GRGs risk signatures, followed by validation of expression levels in vitro using RT-PCR.
    Results: Patients diagnosed with OS were categorized into two distinct molecular subgroups, exhibiting notable variations in prognosis and tumor microenvironment. Univaria te Cox regression analysis was employed to identify four GRGs, namely chondroitin sulfate glucuronyltransferase (CHPF), Ras-related GTP-binding protein D (RRAGD), nucleoprotein TPR (TPR), and versican core protein (VCAN), which constitute a prognostic signature for patients with OS. This signature demonstrated robust prognostic value, as corroborated by Kaplan-Meier, univariate, and multivariate Cox regression analyses. Significant differences in tumor microenvironment immune infiltration (such as B cells, monocytes) were observed between molecular subgroups. Moreover, a significant disparity in drug sensitivity to AZD8055, paclitaxel, and PD0325901 was noted between the high-risk and low-risk cohorts, and the established four-gene risk signature served as dependable prognostic indicators in the validation cohort, confirmed at the cellular level through external dataset validation and reverse transcription quantitative PCR (RT-qPCR) experiments.
    Conclusion: A risk signature based on GRGs was established for OS, exhibiting robust predictive efficacy for prognostic assessment, and offering significant clinical utility for the prognosis of OS.
    Keywords:  Drug response; Glycolysis; Osteosarcoma; Prognosis; Risk signature
    DOI:  https://doi.org/10.7717/peerj.19369
  38. Int Immunopharmacol. 2025 May 07. pii: S1567-5769(25)00789-1. [Epub ahead of print]157 114799
    TGF-β1 induces autophagy and mediates the effect on macrophages differentiation in primary liver cancer.
    Chao Liu, Mingjie Li, Lichao Liu, Qian Xu, Linlin Zheng, Cailing Wu, Jinghua Ren, Tao Zhang, Haihong Wang, Zhenyu Lin.
       BACKGROUND: Tumor-associated macrophages (TAMs) are closely associated with tumor development and patient outcomes due to their plasticity and polarization capacity. Several distinct TAMs have been proposed, but a complete understanding of heterogeneity and differentiation spectrum of macrophage in human primary liver cancer remains elusive.
    METHODS: Deep single-cell RNA sequencing (scRNA-seq) data from 19 primary liver cancer patients were used to profile the transcriptomes of TAMs in liver cancer. Ingenuity pathway analysis (IPA) and in vitro experiments were used to explore possible mechanisms responsible for related signaling pathways altered at the transcriptional level. Finally, we analyzed the relationship between the abundance of the TAMs and the survival outcomes of the 428 patients in the Cancer Genome Atlas (TCGA).
    RESULTS: Transcriptional profiles allowed us to identify four distinct TAMs cell subsets based on molecular and functional properties and to reconstruct their developmental trajectory. Specifically, TAM_c4 was preferentially enriched and potentially expanded in the advanced-stage patients or those receiving immune checkpoint blockade therapy (ICT). Gene pathway analysis revealed aberrant TGFB1 activation in TAM_c4, which was experimentally confirmed to drive TAM phenotypic transitions via autophagy signaling. High abundance of TAM_c4 is found to be related to a short survival time and low abundance of CD8+ T cells in primary liver cancers.
    CONCLUSIONS: This integrated transcriptome compendium and experimental validation offer both mechanistic insights and a resource for understanding TAM heterogeneity in primary liver cancers.
    Keywords:  Phenotypic transition; Primary liver cancer; Single-cell RNA sequencing; TGFB1; Tumor-associated macrophages
    DOI:  https://doi.org/10.1016/j.intimp.2025.114799
  39. J Autoimmun. 2025 May 02. pii: S0896-8411(25)00064-2. [Epub ahead of print]154 103419
    Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.
    Maxwell McDermott, Wenyi Li, Yin-Hu Wang, Allen Y Chen, Rodrigo Lacruz, Bettina Nadorp, Stefan Feske.
      Sjogren's Disease (SjD) is an autoimmune disorder characterized by salivary and lacrimal gland dysfunction and immune cell infiltration leading to gland inflammation and destruction. Although SjD is a common disease, its pathogenesis is not fully understood. In this study, we conducted a single-cell transcriptome analysis of peripheral blood mononuclear cells (PBMC) from patients with SjD and symptomatic non-SjD controls to identify cell types and functional changes involved in SjD pathogenesis. All PBMCs populations showed marked differences in gene expression between SjD patients and controls, particularly an increase in interferon (IFN) signaling gene signatures. T and B cells of SjD patients displayed a depletion of ribosomal gene expression and pathways linked to protein translation. SjD patients had increased frequencies of naive B cells, which featured a unique gene expression profile (GEP) distinct from controls and had hallmarks of B cell hyperactivation. Non-negative matrix factorization (NMF) also identified several non-overlapping GEPs in CD4+ and CD8+ T cells with differential usage in SjD patients and controls. Of these, only the Th1 activation GEP was enriched in T cells of SjD patients whereas the other two GEPs were depleted in T cells, emphasizing the important role of Th1 cells in SjD. Our study provides evidence for aberrant and unique gene expression patterns in both B and T lymphocytes of SjD patients that point to their altered activation states and may provide new insights into the pathogenesis of SjD.
    Keywords:  Autoimmunity; CD4(+) T cells; Machine learning; Single-cell RNA sequencing; Sjogren's disease; Th1
    DOI:  https://doi.org/10.1016/j.jaut.2025.103419
  40. Sci Rep. 2025 May 05. 15(1): 15715
    Integrated multiomics and Mendelian randomization identify CHIT1 as a novel sepsis biomarker and therapeutic target.
    Guorui Li, Yunlong Mao, Jiaxiang Liao, Yuquan Zhou.
      Sepsis is characterized by severe organ failure due to an impaired response to infection. The underlying pathophysiology of sepsis is characterized by concurrent unbalanced hyperinflammatory and immunoparalysis. This study aimed to identify new key biomarkers that could predict outcomes in sepsis patients and explore theirunderlying molecular mechanisms. Bulk transcriptome data (GSE65682, GSE28750, GSE57065, GSE95233) and scRNA-seq data (GSE167363) of sepsis were obtained from the GEO database. Data for MR analysis were sourced from the eQTLGen Consortium and IEU OpenGWAS project. Prognostic biomarkers and potential drug targets for sepsis were identified through univariate Cox regression and MR analysis. The expression of these biomarkers was further validated using scRNA-seq data to investigate the underlying molecular mechanisms. Significantly higher expression of CHIT1 was found at sepsis non-survivor and associated with 28-day mortality of sepsis. scRNA-seq data of septic samples found that CHIT1 mainly expressed in neutrophils, which was also higher in sepsis non-survivors. The CHIT1 + neutrophils expressed higher inflammation related genes of S100A8, S100A9, S100A11, S100A12, IL1R2, IFNGR2, TLR2 and CXCL8 and reduced expression of HLA related genes of HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DRB1 and HLA-DRB5. Moreover, cell-chat analysis also showed that CHIT1 + neutrophils could interact with other immune cell types, including NK cells, erythroid cells, monocytes/macrophages, and DC by the way of ICAM1-(ITGAM + ITGB2) pathway. We identified CHIT1 as new biomarker and potential drug target for sepsis, which may intensify hyperinflammation and immune suppression of neutrophils. Developing immunotherapeutic strategies aimed at targeting CHIT1 would help to enhance sepsis outcomes.
    Keywords:  CHIT1; Immunity; MR analysis; Neutrophils; Sepsis; scRNA-seq
    DOI:  https://doi.org/10.1038/s41598-025-99619-z
  41. Br J Dermatol. 2025 May 03. pii: ljaf112. [Epub ahead of print]
    Exome and Transcriptome Analysis Links Calcium Channel Pathway Aberrations to Botox Resistance in Hailey-Hailey Disease.
    Yongyong Yuan, Hailun Wang, Meng Pan, Jie Zheng, Li Zhang, Han Cao, Xiaoying Chen, Yan Yang.
       BACKGROUND: The effectiveness of botulinum toxin A (BoNTA) in the treatment of Hailey-Hailey Disease (HHD) has shown heterogeneity in recent studies. However, there is currently no research investigating the underlying mechanism behind the variability in patient response.
    OBJECTIVES: To identify potential biomarkers and elucidate the underlying mechanisms of the heterogeneity in treatment efficacy of BoNTA for HHD.
    METHODS: Twelve HHD patients were administered standardized injections of BoNTA, with the primary endpoint being ≥ 75% improvement in Improvement Global Assessment (IGA) from baseline to month 6. A comprehensive multi-omics approach, including Whole-Exome Sequencing (WES), bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and Immunohistochemistry (IHC) were utilized to investigate potential mechanisms underlying the heterogeneity of therapeutic efficacy. Additionally, an in vitro experiment was conducted to further validate cellular responses to BoNTA, providing further insights into the biological mechanisms involved.
    RESULTS: Ten of 12 patients (83%) achieved the primary endpoint with BoNTA treatment, while two patients (17%) showed no response at month 6. WES analysis did not find a significant association between the type of ATP2C1 genetic mutation in HHD patients and their response to BoNTA treatment. Both transcriptomic analysis and IHC of baseline skin lesions revealed an overactivated store-operated calcium entry (SOCE) pathway involving genes such as ITPKC and ORAI1 in keratinocytes, accompanied by activation of the NLRP1/IL-18/IL-1β inflammatory cascade in BoNTA resistance patients. We confirmed ATP2C1 loss triggered inflammatory responses in HaCat cells in vitro. BoNTA demonstrated potential anti-inflammatory efficacy as a calcium antagonist, while the upregulation of ORAI1/SOCE contributed to a diminished response to BoNTA.
    CONCLUSIONS: BoNTA treatment in HHD exhibits inter-individual variability. Although ATP2C1 genetic mutation type has no direct association with patients' response, combined transcriptomic analysis and IHC indicate the upregulation of ORAI1/SOCE pathway may potentially contribute to treatment resistance and serve as biomarkers for predicting patient responsiveness.
    DOI:  https://doi.org/10.1093/bjd/ljaf112
  42. Am J Physiol Renal Physiol. 2025 May 07.
    Single-Cell RNA Sequencing and Spatial Transcriptomics Reveal Unique Subpopulations of Infiltrating Macrophages and Dendritic Cells Following AKI.
    Arin L Melkonian, Matthew David Cheung, Elise N Erman, Kyle H Moore, Jeremie M P Lever, Yanlin Jiang, Zhengqin Yang, Brittany N Lasseigne, Anupam Agarwal, James F George.
      Kidney infiltrating macrophages (KIMs) and kidney dendritic cells (KDCs) are strongly associated with inflammation and fibrosis in acute kidney injury (AKI) and chronic kidney disease (CKD). Contrary to kidney resident macrophages (KRMs), which are self-renewing and present in the kidney prior to injury, KIMs are bone-marrow derived F4/80int, CD11bhigh macrophages that infiltrate the kidney during AKI. Here, we combined single-cell RNA sequencing (scRNAseq), spatial transcriptomics, and cellular indexing of transcriptomes and epitopes (CITE)-sequencing to elucidate temporal, spatial, and transcriptional characteristics of unique subpopulations of KIMs and KDCs in ischemia-induced AKI. scRNAseq revealed three KIM, two KDC, and one proliferative macrophage subpopulation. All 6 clusters were localized in unique, spatially constrained microenvironments and their locations were dynamically regulated following bilateral ischemic reperfusion injury. We showed that a specific Arginase 1-specific KIM cluster infiltrates the kidney cortex at day 1 post-ischemia. We also identified a macrophage subpopulation that expresses genes specific to cell proliferation that resides in the cortex in uninjured states and in the medulla at day 6 during the reparative phase of AKI. Gene ontology analysis revealed functional characteristics that distinguish each KIM and KDC population. By day 28 post-ischemia, the transcriptional profiles of KIMs upregulate C1q, Cd81, and Cd74, markers normally limited to KRMs in quiescence and early AKI. Because KIMs and KDCs are profoundly involved in AKI, it is paramount we understand their dynamics-temporally and spatially- and identify their key genes and surface protein markers to develop macrophage-specific therapeutics aimed towards targeting kidney disease.
    Keywords:  Acute kidney injury; kidney infiltrating macrophages; kidney resident macrophages; single-cell RNA sequencing; spatial transcriptomics; transcriptional reprogramming
    DOI:  https://doi.org/10.1152/ajprenal.00059.2025
  43. Int J Mol Sci. 2025 Apr 08. pii: 3494. [Epub ahead of print]26(8):
    Single-Cell RNA Sequencing of Baseline Immune Profiles After Third Vaccination Associated with Subsequent SARS-CoV-2 Infection in Naïve Individuals.
    Hyunhye Kang, Junseong Park, Hyunjoo Bae, Yeun-Jun Chung, Eun-Jee Oh.
      Even though vaccines protected many from infection, not all were protected, and vaccinated individuals displayed a wide range of clinical outcomes, from complete protection against infection to multiple breakthrough infections. This study aimed to identify baseline differences following identical ChAdOx1/ChAdOx1/BNT162b2 in infection-free and breakthrough-infected individuals to find molecular signatures linked to enhanced SARS-CoV-2 protection. Samples from a previous longitudinal study were analyzed, classifying subjects as 'Protected' or 'Infected' based on infection status over two years. SARS-CoV-2-specific immunological assays and single-cell RNA sequencing evaluated baseline differences. Although humoral response measurements showed no significant difference, enhanced cellular responses via enzyme-linked immunospot assays were observed in the Protected group. Differentially expressed genes and pathway analysis of T/NK subsets showed the Infected group had reduced inflammation and interferon responses. The Infected group also displayed downregulated interaction with CD4+ T cells. B subset analysis revealed more memory B cells in the Infected group, accompanied by downregulation of immune regulatory genes and upregulation of the small ubiquitin-related modifier pathway. Our findings revealed differential molecular signatures in the baseline immune subsets of vaccinated individuals with prolonged protection and breakthrough infection. Reduced immune regulation and altered cell interactions may contribute to breakthrough infection, providing insights for future vaccine development and targeted protective strategies.
    Keywords:  NK; SARS-CoV-2; innate immunity; interferon; memory B cell; single-cell RNA sequencing; vaccine
    DOI:  https://doi.org/10.3390/ijms26083494
  44. QJM. 2025 May 09. pii: hcaf108. [Epub ahead of print]
    Identification of novel biomarkers and drug targets for frailty-related skeletal muscle aging: a multi-omics study.
    Qijun Wang, Xuan Zhao, Wei Wang, Xiaolong Chen, Shibao Lu.
       BACKGROUND: Skeletal muscle aging is the major cause and hallmark of frailty, which poses a significant challenge to the healthcare system.
    AIM: This study aimed to identify the potential biomarkers for the early detection and therapeutic intervention of this age-related condition.
    METHODS: A transcriptomics-based methodology using machine learning algorithms was performed to select the biomarker genes. A predictive machine learning model for (pre-)frailty based on the transcriptomic profile of the biomarker genes was constructed and validated. The cell-type specific changes of the biomarkers during muscle aging were investigated in a single-cell RNA sequencing dataset of human skeletal muscle. Summary data-based Mendelian randomization (SMR) and Bayesian colocalization analyses were performed to identify biomarker genes with therapeutic effects on frailty-related skeletal muscle aging, and drug candidates were explored in the DSigDB database.
    RESULTS: We identified 24 biomarker genes, most of which were discovered for the first time. The optimal predictive model showed excellent performance in the external test set. Differential expression of the biomarkers in the single-cell dataset indicated a critical role of endothelial cells modulated by the marker genes MGP and ID1 in muscle degeneration. The SMR and colocalization analyses showed causal relationships between 2 marker genes (MGP and WAC) and frailty-related muscle aging. Potential therapeutics for MGP modulation were identified in the DSigDB database.
    CONCLUSIONS: This multi-omics study identified biomarkers associated with frailty-related muscle aging and provided new insights into the etiology and therapeutic targets for this age-related condition.
    Keywords:  Biomarker; Drug target; Frailty; Machine learning; Multi-omics; Skeletal muscle aging
    DOI:  https://doi.org/10.1093/qjmed/hcaf108
  45. Eur J Neurosci. 2025 May;61(9): e70129
    Research on Peripheral Nerve Aging and Degeneration: Cellular Changes and Mechanism Exploration From the Perspective of Single-Cell Sequencing.
    Yuyang Zhuo, Yingcai Xu, Xinzhe Qu, Qupeng Li, Maji Sun, Xiao Gao, Feng Yuan, Menghan Cao, Bin Pan.
      As age increases, there are structural and functional alterations in the peripheral nervous system (PNS), significantly affecting movement, sensation and autonomic function. Understanding the characteristics and mechanisms of PNS aging is crucial for preventing and treating related diseases. This study employed single-cell sequencing technology to analyse the dorsal root ganglia (DRG) and sciatic nerve (SN) of aging rats, in comparison with adult rats. The research investigated the mechanisms underlying PNS aging and degeneration, revealing the transcriptional profiles of various cell types. Significant differences were observed in the proportion of Schwann cells between the DRG and SN of adult and aged rats. The Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) revealed that pathways related to neurodegeneration were upregulated in Schwann cells. Additionally, lipid metabolism pathways were upregulated in the SN of aged rats, suggesting that certain lipid signalling molecules may influence cell proliferation. Through further re-clustering of myelinating Schwann cells, six distinct subtypes were identified. The anti-aging protein protocadherin 9 (PCDH9) was preliminarily screened and found to be significantly downregulated with age. In vitro experiments confirmed that PCDH9 expression is associated with Schwann cell proliferation and differentiation. By using gene expression analysis and cell type across several age groups, this study offers important insights into the mechanisms of PNS aging and degeneration.
    Keywords:  Schwann cells; aging; peripheral nervous system (PNS); protocadherin9 (PCDH9); single‐cell sequencing
    DOI:  https://doi.org/10.1111/ejn.70129
  46. Mol Med. 2025 May 08. 31(1): 179
    Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.
    Hongwei Wang, Yu-Nan Zhu, Sifan Zhang, Kexin Liu, Rong Huang, Zhigao Li, Lan Mei, Yingpu Li.
       BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive.
    METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters.
    RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy.
    CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.
    Keywords:  ANGPTL4; Breast cancer; CFD; Extracellular matrix; Immunotherapy
    DOI:  https://doi.org/10.1186/s10020-025-01237-y
  47. J Hepatol. 2025 May 07. pii: S0168-8278(25)00296-X. [Epub ahead of print]
    Decoding the Resistin-CAP1 Pathway in Intermediate Monocytes Mediating Liver Allograft Rejection.
    Peijun Yang, Xudan Wang, Weikang Wu, Juzheng Yuan, Xinrui Wang, Rui Ding, Weiwei Cao, Cong Li, Yinjie Wang, Zihan Xi, Kefeng Dou, Xiao Li, Kaishan Tao.
       BACKGROUND & AIMS: Lymphocytes are widely recognized as the primary mediators of cellular rejection post-liver transplantation. However, conventional immunosuppressive regimens that target lymphocytes, such as calcineurin phosphatase inhibitors, corticosteroids, or lymphocyte-depleting antibodies, can only partially mitigate rejection while inducing severe adverse effects. This necessitates the search for novel immunotherapeutic targets.
    METHODS: We harnessed the power of single-cell RNA sequencing and spatial transcriptome in 24 rat transplanted liver and peripheral blood single nucleated cells (PBMCs) samples to derive gene expression signatures recapitulating 13 cell phenotypes. We used flow cytometry, multifactor assays and multiple recombinant assays to validate in vitro and in vivo the role of the target protein Resistin on human T-cell function, as well as the Resistin-CAP1 interaction. Gold nanoparticles were used to package Retn siRNA sequences to validate the role of Retn knockdown on acute rejection after liver transplantation.
    RESULTS: By distinguishing between donor and recipient cells, we delineate the dynamic landscape of immune cells during allograft rejection and their spatial distributions across donors and recipients. Our findings underscore the pivotal role of recipient derived intermediate monocytes in cellular rejection. Using CellChat ligand-receptor analysis, we identify the Resistin-CAP1 pathway as a key mechanism by which intermediate monocytes participate in T cell-mediated rejection reactions. We confirm that Resistin knockdown significantly alleviates acute rejection after rat liver transplantation, markedly extending the survival of recipients using innovative nanogold technology.
    CONCLUSION: These findings offer insights into the dynamic changes in the alloimmune microenvironment, pinpointing intermediate monocytes as potential diagnostic and immunotherapeutic targets during allograft rejection. This study holds significant importance in advancing non-invasive diagnostic technologies and immunotherapeutic strategies for allogeneic rejection.
    IMPACT AND IMPLICATIONS: This study pioneers the application of spatial transcriptomics in liver transplantation, providing a comprehensive analysis of immune cell spatial distribution, complemented by Souporcell-based chimerism assessment. We demonstrate that intermediate monocytes play a pivotal role in T cell-mediated acute rejection via the Resistin-CAP1 signaling axis. Targeting this pathway using nanogold-siRNA technology effectively mitigates rejection and enhances graft survival. These findings contribute novel insights into the mechanisms of transplant rejection and present promising avenues for the development of targeted therapeutic and diagnostic strategies in liver transplantation.
    Keywords:  Allograft rejection; Intermediate monocytes; Liver transplantation; Resistin-CAP1 pathway; Single-cell sequencing
    DOI:  https://doi.org/10.1016/j.jhep.2025.04.037
  48. Int J Mol Sci. 2025 Apr 15. pii: 3722. [Epub ahead of print]26(8):
    IRF5 Mediates Artery Inflammation in Salt-Sensitive Hypertension by Regulating STAT1 and STAT2 Phosphorylation to Increase ESM1 Transcription: Insights from Bioinformatics and Mechanistic Analysis.
    Qiaoyu Shao, Hao Wang, Shicheng Li, Mengying Zeng, Shuyang Zhang, Xiaowei Yan.
      Salt-sensitive hypertension (SSH) is closely associated with arterial inflammation, yet its molecular mechanisms remain unclear. In this study, we utilized deoxycorticosterone acetate (DOCA)-salt-induced hypertensive mice, which exhibited elevated blood pressure and significant arterial inflammation. Single-cell RNA sequencing (scRNA-seq) identified interferon regulatory factor 5 (IRF5) and its downstream targets, signal transducer and activator of transcription (STAT), as key regulators of these inflammatory changes. In vivo, IRF5 levels were significantly elevated in the DOCA group, while STAT1 and STAT2 protein levels were comparable to those in the normal salt group. However, nuclear levels of phosphorylated STAT1 (pSTAT1) and phosphorylated STAT2 (pSTAT2) were markedly higher in the DOCA group. Furthermore, scRNA-seq analysis showed increased IRF5 expression in endothelial cells (ECs) in both human and mouse aorta samples. In vitro, IRF5 knockdown in artery ECs led to a reduction in nuclear pSTAT1 and pSTAT2 expression. These results suggest that IRF5 promotes STAT1 and STAT2 phosphorylation, enabling their nuclear translocation. Additionally, RNA sequencing indicated a positive correlation between endothelial cell-specific molecule 1 (ESM1) and STAT1/STAT2. Using the UCSC and JASPAR databases, we identified multiple binding sites for the STAT1::STAT2 dimer on the ESM1 promoter. Luciferase reporter assays revealed enhanced ESM1 transcription following pSTAT1::pSTAT2 binding, and pinpoint potential binding sites. Chromatin Immunoprecipitation Quantitative PCR (ChIP-qPCR) further confirmed the specific binding sites between the pSTAT1::pSTAT2 dimer and the ESM1 promoter. These findings highlight the critical role of the IRF5-pSTAT1::pSTAT2-ESM1 pathway in the pathogenesis of SSH and suggest potential therapeutic targets.
    Keywords:  ESM1; IRF5; STAT1; STAT2; artery inflammation; salt-sensitive hypertension
    DOI:  https://doi.org/10.3390/ijms26083722
  49. Cell Rep Methods. 2025 Apr 30. pii: S2667-2375(25)00071-2. [Epub ahead of print] 101035
    Comparative analysis of gene regulation in single cells using Compass.
    Changxin Wan, Yilong Qu, Zhiyou Ye, Tianbei Zhang, Huifang Ma, Ming Chen, Wenpin Hou, Zhicheng Ji.
      Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples.
    Keywords:  CP: Systems biology; cis-regulatory elements; gene regulation; single-cell ATAC-seq; single-cell multi-omics
    DOI:  https://doi.org/10.1016/j.crmeth.2025.101035
  50. STAR Protoc. 2025 May 07. pii: S2666-1667(25)00217-5. [Epub ahead of print]6(2): 103811
    Protocol for the imputation of stimulus-induced single-cell gene expression trajectories from time-series scRNA-seq data.
    Katherine M Sheu, Alexander Hoffmann.
      Single-cell RNA sequencing (scRNA-seq) measures cell-to-cell heterogeneous mRNA abundance but destroys the cell and precludes tracking of heterogeneous gene expression trajectories. Here, we present an approach to impute single-cell gene expression trajectories (scGETs) from time-series scRNA-seq measurements. We describe four main computational steps: dimensionality reduction, calculation of transition probability matrices, spline interpolation, and deconvolution to scGETs. Imputing scGETs can aid in studying heterogeneous stimulus responses over time, such as cancer cell responses to drugs or immune cell responses to pathogens. For complete details on the use and execution of this protocol, please refer to Sheu et al.1.
    Keywords:  RNA-seq; bioinformatics; gene expression; molecular biology
    DOI:  https://doi.org/10.1016/j.xpro.2025.103811
  51. Front Cardiovasc Med. 2025 ;12 1568528
    Decoding immune cell interactions during cardiac allograft vasculopathy: insights derived from bioinformatic strategies.
    Edward B Thorp, Aparnaa Ananthakrishnan, Connor W Lantz.
      Chronic allograft vasculopathy (CAV) is a major cause of late graft failure in heart transplant recipients, characterized by progressive intimal thickening and diffuse narrowing of the coronary arteries. Unlike atherosclerosis, CAV exhibits a distinct cellular composition and lesion distribution, yet its pathogenesis remains incompletely understood. A major challenge in CAV research has been the limited application of advanced "-omics" technologies, which have revolutionized the study of other vascular diseases. Recent advancements in single-cell and spatial transcriptomics, proteomics, and metabolomics have begun to uncover the complex immune-endothelial-stromal interactions driving CAV progression. Notably, single-cell RNA sequencing has identified previously unrecognized immune cell populations and signaling pathways implicated in endothelial injury and vascular remodeling after heart transplantation. Despite these breakthroughs, studies applying these technologies to CAV remain sparse, limiting the translation of these insights into clinical practice. This review aims to bridge this gap by summarizing recent findings from single-cell and multi-omic approaches, highlighting key discoveries, and discussing their implications for understanding CAV pathogenesis.
    Keywords:  bioinformatics & computational biology; cardiac allograft vasculopathy (CAV); immunity; macrophage - cell; transplantation (heart)
    DOI:  https://doi.org/10.3389/fcvm.2025.1568528
  52. Inflammation. 2025 May 08.
    Single-Cell RNA Sequencing Reveals the Immune Landscape of Granulomatous Mastitis.
    Yao Zhou, Xianguang Deng, Hui Ruan, Xing Xue, Zixuan Hu, Jie Gong, Shiting Wu, Lifang Liu.
      Granulomatous mastitis (GM) is a form of non-lactational breast inflammation that is closely associated with autoimmune processes, however its underlying pathogenesis remains elusive. In this study, we employed single-cell RNA sequencing (scRNA-seq) to conduct a comparative analysis of GM lesion tissues versus normal breast tissues, thereby unveiling the immune profile of GM tissues. Our investigation centered on T and NK cells, macrophages, epithelial cells, and endothelial cells. Notably, we observed a substantial infiltration of immune cells in GM tissues, accompanied by immune disorders, an elevation in Th1 cell counts, enrichment of the toll-like receptor (TLR) pathway, and upregulation of various factors including interferon-γ (IFN-γ), C-C motif chemokine ligand 3 (CCL3), CCL4, chemokine (C-X-C motif) ligand (CXCL) 13, CD69, signal transducer and activator of transcription 1 (STAT1), and heat shock protein family A member 1A (HSPA1A). Furthermore, the macrophage subpopulations in GM tissues exhibited a transition to a pro-inflammatory phenotype, enriched for pathways such as interferon-γ (IFN-γ), IFN-α, interleukin-6/janus kinase/signal transducer and activator of transcription 3 (IL-6/JAK/STAT3), and tumor necrosis factor-α/nuclear factor-κB (TNF-α/NF-κB). Mammary luminal cells demonstrated an impaired estrogenic profile yet displayed upregulation of prolactin downstream signaling pathways, namely the JAK/STAT and mitogen-activated protein kinase (MAPK) pathways. Additionally, vascular endothelial cells were found to recruit immune cells and exhibited a prominent angiogenic profile in GM tissues. Cellular interaction analysis unveiled an intricate network of interactions between mesenchymal and immune cells. This study provides a comprehensive immune landscape of granulomatous mastitis and offers some potential therapeutic targets for the disease.
    Keywords:  Granulomatous mastitis; Immunity; Macrophages; Single-cell RNA sequencing; T cells; Tissue
    DOI:  https://doi.org/10.1007/s10753-025-02310-8
  53. J Transl Med. 2025 May 08. 23(1): 519
    Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer.
    Can Zhang, Si Li, Jiyu Guo, Tao Pan, Ya Zhang, Yueying Gao, Jiwei Pan, Meng Liu, Qingyi Yang, Jinyang Yu, Juan Xu, Yongsheng Li, Xia Li.
       BACKGROUND: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.
    METHODS: We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.
    RESULTS: We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.
    CONCLUSION: Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.
    Keywords:  Cellular States; Clinical prognosis; Drug response; Immunotherapy; Tumor microenvironment
    DOI:  https://doi.org/10.1186/s12967-025-06521-3
  54. Front Immunol. 2025 ;16 1570378
    Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.
    Yuexi Yu, Huiru Liu, Kaipeng Liu, Meiqi Zhao, Yiyan Zhang, Runci Jiang, Fengmei Wang.
       Background: Accumulating evidence indicates that elevated polyamine levels are closely linked to tumor initiation and progression. However, the precise role of polyamine metabolism in hepatocellular carcinoma (HCC) remains poorly understood.
    Methods: We conducted differential expression analysis on bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify 65 polyamine metabolism-related genes. By employing unsupervised consensus clustering, AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network analysis (WGCNA), we identified polyamine metabolism-related genes at both the bulk RNA-seq and single-cell RNA-seq (scRNA-seq) levels. Utilizing 101 machine learning algorithms, we constructed a polyamine metabolism-related signature (PMRS) and validated its predictive power across training, testing, and external validation cohorts. Additionally, we developed a prognostic nomogram model by integrating PMRS with clinical variables. To explore immune treatment sensitivity, we assessed tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, mutation frequency, and immune checkpoint genes expression. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.
    Results: Using 101 machine learning algorithms, we established a polyamine metabolism-related signature comprising 9 genes, which exhibited strong prognostic value for HCC patients. Further analysis revealed significant differences in clinical features, biological functions, mutation profiles, and immune cell infiltration between high-risk and low-risk groups. Notably, TIDE analysis and immune phenotype scoring (IPS) demonstrated distinct immune treatment sensitivities between the two risk groups. RT-qPCR validation confirmed that these 9 genes were highly expressed in normal cells but significantly downregulated in tumor cells.
    Conclusions: Our study developed a polyamine metabolism-based prognostic risk signature for HCC, which may provide valuable insights for personalized treatment strategies in HCC patients.
    Keywords:  hepatocellular carcinoma; immune therapy; machine learning; multi-omics analysis; polyamine metabolism; single-cell RNA sequencing
    DOI:  https://doi.org/10.3389/fimmu.2025.1570378
  55. Transl Gastroenterol Hepatol. 2025 ;10 28
    Development and validation of a basement membrane-associated immune prognostic model for hepatocellular carcinoma.
    Qiji Ma, Yun Wang, Jie Xing, Tielin Wang, Gang Wang.
       Background: Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, has a poor prognosis primarily due to its invasive and metastatic characteristics. Cancer invasion through basement membrane (BM) is the pivotal initial step in tumor dissemination and metastasis. This study aimed to identify gene signatures associated with the BM to enhance the overall prognosis of HCC.
    Methods: In this study, we performed multiple bioinformatics analyses based on the RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consistent cluster analysis was conducted on 370 HCC patients, categorizing them into two distinct groups based on the expression profiles of 222 BM-related genes. Differentially expressed genes (DEGs) between these clusters were identified, followed by functional enrichment analysis. To explore the differences between the groups, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms were applied, along with the analysis of immune checkpoint molecules and human leukocyte antigen (HLA) expression levels. This helped in understanding the relationship between the HCC immune microenvironment and BM-related genes. A prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, with its performance subsequently estimated and validated. Additionally, hub biomarkers genes were identified using machine learning techniques, followed by an analysis of their functions and relationships with clinical characteristics. Finally, single-cell clustering analysis was employed to further investigate the expression distribution of these genes within the HCC immune microenvironment.
    Results: Following consistent cluster analysis, two groups were identified: the BM high group and the BM low group. Among the 6,221 DEGs between the two groups, 5,863 were upregulated and 358 were downregulated, with enrichment functions primarily associated with extracellular matrix (ECM) organization, cell adhesion, immune response, and metabolism. The expression levels of BM-related genes were found to regulate the HCC immune microenvironment. Using univariate Cox regression analysis, 60 prognostic BM-related genes were identified, leading to the establishment of an 11-gene prognostic model named BMscore to predict the overall survival (OS) of HCC patients. The high BMscore group indicated worse prognosis, and the model's predictive performance was validated using the GEO dataset. P3H1 and ADAMTS5 were identified as hub biomarkers, playing roles in cell proliferation and ECM metabolism, with their expression distributions mapped respectively.
    Conclusions: A prognostic model based on BM-related genes was successfully developed and shows promise for evaluating prognoses and offering personalized treatment recommendations.
    Keywords:  Hepatocellular carcinoma (HCC); basement membrane (BM); machine learning; prognostic model
    DOI:  https://doi.org/10.21037/tgh-24-89
  56. Int J Mol Sci. 2025 Apr 18. pii: 3841. [Epub ahead of print]26(8):
    Identifying Aberrant 1CM-Related Pathways by Multi-Omics Analysis and Validating Tumor Inhibitory Effect of One-Carbon Donor Betaine in Gastric Cancer.
    Jie Li, Huan Liu, Panpan Yang, Feng Zhu, Fei Shen, Geyu Liang.
      Metabolic reprogramming, a well-established hallmark of gastric carcinogenesis, has been implicated in driving tumor progression. Nevertheless, the precise mechanisms through which these metabolic alterations orchestrate gastric cancer (GC) pathogenesis remain incompletely elucidated. We conducted metabolomic analyses of plasma samples obtained from 334 patients with GC and healthy individuals to identify differential metabolites and metabolic pathways. Transcriptome sequencing was conducted on six pairs of tissues, and a joint analysis of the transcriptome and metabolome was performed. Single-cell sequencing data were acquired and co-analyzed with metabolomics to investigate metabolic abnormalities at the single-cell level. Finally, four representative metabolites selected using Random Forest analysis were subjected to cellular experiments to elucidate the mechanisms through which these metabolites exert their effects. Metabolomic analyses revealed that serine and glycine metabolism, glycolysis, and glutamate metabolism were significantly altered in GC, suggesting that one-carbon metabolism (1CM)-related pathways are aberrantly activated. A combined analysis of the transcriptome, single-cell transcriptome, and metabolomics indicated that pathways related to oxidative phosphorylation, nucleotide metabolism, and amino acid metabolism in epithelial cells were altered in GC. Cellular experiments demonstrated that the one-carbon donor metabolite betaine could inhibit the activity, invasion, and migration of GC cells while activating the phosphorylation of AMPKα. In conclusion, the 1CM-related pathway and the metabolite betaine play significant roles in GC, and the mechanisms through which the one-carbon donor betaine influences GC warrant further investigation.
    Keywords:  betaine; gastric cancer; multi-omics; one-carbon metabolism; transcriptomics
    DOI:  https://doi.org/10.3390/ijms26083841
  57. ACS Nano. 2025 May 06.
    Spatial and Temporal Single-Cell Profiling of RNA Compartmentalization in Neurons with Nanotweezers.
    Annie Sahota, Binoy Paulose Nadappuram, Zoe Kwan, Flavie Lesept, Jack H Howden, Suzanne Claxton, Josef T Kittler, Michael J Devine, Joshua B Edel, Aleksandar P Ivanov.
      Emerging techniques for mapping mRNAs within the subcellular compartments of live cells hold great promise for advancing our understanding of the spatial distribution of transcripts and enabling the study of single-cell dynamics in health and disease. This is particularly critical for polarized cells, such as neurons, where mRNA compartmentalization is essential for regulating gene expression, and defects in these localization mechanisms are linked to numerous neurological disorders. However, many subcellular analysis techniques require a compromise between subcellular precision, live-cell measurements, and nondestructive access to single cells in their native microenvironment. To overcome these challenges, we employ a single-cell technology that we have recently developed, the nanotweezer, which features a nanoscale footprint (∼100 nm), avoids cytoplasmic fluid aspiration, and enables rapid RNA isolation from living cells with minimal invasiveness. Using this tool, we investigate single-cell mRNA compartmentalization in the soma and dendrites of hippocampal neurons at different stages of neuronal development. By combining precise targeting with sequential sampling, we track changes in mRNA abundance at dendritic spine regions of the same neuron, both before and after stimulation. This minimally invasive approach enables time-resolved, subcellular gene expression profiling of the same single cell. This could provide critical insights into polarized cells and advance our understanding of biological processes and complex diseases.
    Keywords:  RNA; nanobiopsy; nanotweezer; neuron; single-cell; synaptic plasticity
    DOI:  https://doi.org/10.1021/acsnano.5c02056
  58. Cancer Immunol Res. 2025 May 08.
    Identification of the distinct immune microenvironment features associated with progression following high dose melphalan and autologous stem cell transplant in multiple myeloma.
    Parvathi Sudha, Travis S Johnson, Habib Hamidi, Ke Yang, Enze Liu, Brent Smith, Vivek Chopra, Michael Nixon, Faiza Zafar, Sherif S Farag, Gareth J Morgan, Ola Landgren, Kelvin Lee, Attaya Suvannasankha, Magdalena Czader, Rafat Abonour, Mohammad Abu Zaid, Brian A Walker.
      A key treatment for patients with multiple myeloma is high-dose melphalan followed by autologous stem cell transplant (ASCT). It can provide a deep response with long-term remission. However, some patients progress quickly, and it is not clear why that is. Here, we performed single-cell RNA and T-cell receptor (TCR) sequencing of the immune microenvironment of 40 patients before and after ASCT to determine if differences in the immune composition could define those who would progress. Clear differences in cell populations were identified in progressors, including increased T-cell infiltration, decreased TCR diversity, and decreased frequency of monocytes and CD56bright NK cells. We identified cell interactions that predicted progression including increased frequency of CD8+ exhausted T cells and stromal cells and decreased frequency of CD56bright NK cells and plasmacytoid dendritic cells. We propose and validate a model of progression that can also be determined by flow cytometry. Together these data highlight the importance of the immune microenvironment in understanding responses to ASCT.
    DOI:  https://doi.org/10.1158/2326-6066.CIR-25-0019
  59. Neurooncol Adv. 2025 Jan-Dec;7(1):7(1): vdaf016
    Inferred developmental origins of brain tumors from single-cell RNA-sequencing data.
    Su Wang, Rachel Naomi Curry, Malcolm F McDonald, Hyun Yong Koh, Anders W Erickson, Claudia L Kleinman, Michael D Taylor, Ganesh Rao, Benjamin Deneen, Arif O Harmanci, Akdes Serin Harmanci.
       Background: The reactivation of neurodevelopmental programs in cancer highlights parallel biological processes that occur in both normal development and brain tumors. Achieving a deeper understanding of how dysregulated developmental factors play a role in the progression of brain tumors is therefore crucial for identifying potential targets for therapeutic interventions. Single-cell RNA-sequencing (scRNA-Seq) provides an opportunity to understand how developmental programs are dysregulated and reinitiated in brain tumors at single-cell resolution. The aim of this study is to identify the developmental origins of brain tumors using scRNA-Seq data.
    Methods: Here, we introduce COORS (Cell Of ORigin like CellS), a computational tool trained on developmental human brain single-cell datasets that annotates "developmental-like" cell states in brain tumors. COORS leverages cell type-specific multilayer perceptron models and incorporates a developmental cell type tree that reflects hierarchical relationships and models cell type probabilities.
    Results: Applying COORS to various brain cancer datasets, including medulloblastoma (MB), glioma, and diffuse midline glioma (DMG), we identified developmental-like cells that represent putative cells of origin in these tumors. Our method provides both cell of origin classification and cell age regression, offering insights into the developmental cell types of tumor subgroups. COORS identified outer radial glia developmental cells within IDHWT glioma cells whereas oligodendrocyte precursor cells (OPCs) and neuronal-like cells in IDHMut. Interestingly, IDHMut subgroup cells that map to OPC show bimodal distributions that are both early and late weeks in development. Furthermore, COORS offers a valuable resource by providing novel markers linked to developmental states within MB, glioma, and DMG tumor subgroups.
    Conclusions: Our work adds to our cumulative understanding of brain tumor heterogeneity and helps pave the way for tailored treatment strategies.
    Keywords:  artificial neural network model; brain tumor; cell of origin; scRNA-Seq
    DOI:  https://doi.org/10.1093/noajnl/vdaf016
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