bims-sicarn 2025-05-04 papers

bims-sicarn

Biomed News

on scRNA-seq

Issue of 2025–05–04
58 papers selected by
Anna Zawada, International Centre for Translational Eye Research

Integration of single-cell and bulk RNA sequencing identifies and validates T cell-related prognostic model in hepatocellular carcinoma.
Single-cell sequencing combined with bulk RNA seq reveals the roles of natural killer cell in prognosis and immunotherapy of hepatocellular carcinoma.
Unveiling the immune microenvironment of complex tissues and tumors in transcriptomics through a deconvolution approach.
Development of a hypoxia-responsive macrophage prognostic model using single-cell and bulk RNA sequencing in pancreatic cancer.
Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach.
Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma.
Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of tumor-associated neutrophils of head and neck squamous cell carcinoma.
Mendelian randomization combined with single-cell sequencing data analysis of chemokines and chemokine receptors and key genes and molecular mechanisms associated with epilepsy.
Integrated spatial and single cell transcriptomics identifies PRKDC as a dual prognostic biomarker and therapeutic target in hepatocellular carcinoma.
Discovering governing equations of biological systems through representation learning and sparse model discovery.
Comprehensive analysis based on IFN-γ and SASP related genes, bulk RNA and single-cell sequencing to evaluate the prognosis and immune landscape of stomach adenocarcinoma.
Discovery of Genomic Targets and Therapeutic Candidates for Liver Cancer Using Single-Cell RNA Sequencing and Molecular Docking.
Identification and validation of biomarkers in Alzheimer's disease based on machine learning algorithms and single-cell sequencing analysis.
Comprehensive single-cell transcriptome analysis of autologous platelet-rich plasma therapy on human thin endometrium.
Oxidative Phosphorylation Pathway in Ankylosing Spondylitis: Multi-Omics Analysis and Machine Learning.
Target Screening and Single Cell Analysis of Diabetic Retinopathy and Hepatocarcinoma.
The Application of scRNA-Seq in Heart Development and Regeneration.
Integrated Analysis of Single-Cell and Transcriptome Data Reveals the Role and Regulatory Mechanisms of Neuroinflammation in Parkinson's Disease.
High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samples.
scRNA-Seq-Based Transcriptome Profiling and Relevant Bioinformatics Approaches to Uncover Novel Insights in Studying Human Spermatogenesis.
scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.
Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning.
Comprehensive Characterization of the Oxidative Stress Profiles in Neonatal Necrotizing Enterocolitis.
Identification of druggable genetic targets for prostate cancer risk based on mendelian randomization and single-cell RNA sequencing.
Computational Approaches in Spatial Transcriptomics for the Study of Mammalian Spermatogenesis.
Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset.
Ferroptosis and low-grade Glioma: The breakthrough potential of NUAK2.
Exploring Immune-Related Ferroptosis Genes in Thyroid Cancer: A Comprehensive Analysis.
Prognosis, immunological features and potential mechanisms of HKR1 in prostate cancer via single-cell and bulk RNA-sequencing.
Calbindin 2 as a Novel Biomarker and Therapeutic Target for Abdominal Aortic Aneurysm: Integrative Analysis of Human Proteomes and Genetics.
Multi-omics analysis unveils a four-gene prognostic signature in esophageal squamous carcinoma and the therapeutic potential of PKP1.
scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.
Charting the Single-Cell and Spatial Landscape of IDH-Wildtype Glioblastoma with GBmap.
Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies.
Functional Specificity of Astrocyte Subtypes in Alzheimer's Disease: Decoding Disease Mechanisms Through Network-based Analysis of Integrated Single-Nuclei Multi-Omic Data.
Characterization of T cells in the progression of dry eye disease using single-cell RNA sequencing in mice.
Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke.
Integrative Analysis of Lactylation-Associated Features in Abdominal Aortic Aneurysm and Its Immune Microenvironment Utilizing scRNA-seq and Bulk RNA Sequencing.
Integrated bulk RNA and single-cell analysis with experimental validation reveal oxidative stress-related diagnostic biomarkers for osteoporosis.
Bioinformatic analysis and experimental verification reveal expansion of monocyte subsets with an interferon signature in systemic lupus erythematosus patients.
Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium.
Identification of PANoptosis related biomarkers to predict hepatic ischemia‒reperfusion injury after liver transplantation.
Identification of KIFC3 as a Colorectal Cancer Biomarker and Its Regulatory Mechanism in the Immune Microenvironment Based on Integrated Analysis of Multi-Omics Databases.
Single-cell sequencing reveals a regulatory role of WT1 in mast cell proliferation in endometrial polyps.
Dissecting the Heterogeneity of Senescent Cells Through CITE-seq Integration.
Molecular landscape of atherosclerotic plaque progression: insights from proteomics, single-cell transcriptomics and genomics.
Single-nucleus RNA sequencing and spatial transcriptomics reveal an immunosuppressive tumor microenvironment related to metastatic dissemination during pancreatic cancer liver metastasis.
Novel biomarkers related to mitochondrial permeability transition driven-necrosis in hypertrophic cardiomyopathy.
Identification and Validation of Prognostic Genes Related to Histone Lactylation Modification in Glioblastoma: An Integrated Analysis of Transcriptome and Single-cell RNA Sequencing.
Single-cell epigenetics and multiomics analysis in kidney research.
Cross-Species Multi-Omics Analysis Reveals Myeloid-Driven Endothelial Oxidative Stress in Ischemic Stroke.
A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion.
In silico-based analysis and in vitro experiments identify SIGMAR1 as a potential marker of putative lung cancer stem cells.
TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.
Interpretable Machine Learning reveals the Role of PANoptosis in the Diagnosis and Subtyping of NAFLD.
Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer.
Adhesion-Related Pathways and Functional Polarization of Astrocytes in Traumatic Brain Injury: Insights from Single-cell RNA Sequencing.
Molecular mechanisms after optic nerve injury: Neurorepair strategies from a transcriptomic perspective.

PLoS One. 2025 ;20(5): e0322706

Integration of single-cell and bulk RNA sequencing identifies and validates T cell-related prognostic model in hepatocellular carcinoma.

Yuzhi Zhang, Haiyan Zhang, Lixin Liu.

Hepatocellular carcinoma (HCC) is a lethal malignancy, and predicting patient prognosis remains a significant challenge in clinical treatment. T cells play a crucial role in the tumor microenvironment, influencing tumorigenesis and progression. In this study, we constructed a T cell-related prognostic model for HCC. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, we identified 6,281 T cells from 10 HCC patients and subsequently identified 855 T cell-related genes. Comprehensive analyses were conducted on T cells and their associated genes, including enrichment analysis, cell-cell communication, trajectory analysis, and transcription factor analysis. By integrating scRNA-seq and bulk RNA-seq data with prognostic information from The Cancer Genome Atlas (TCGA), we identified T cell-related prognostic genes and constructed a model using LASSO regression. The model, incorporating PTTG1, LMNB1, SLC38A1, and BATF, was externally validated using the International Cancer Genome Consortium (ICGC) database. It effectively stratified patients into high- and low-risk groups based on risk scores, revealing significant differences in immune cell infiltration between these groups. Differential expression levels of PTTG1 and BATF between HCC and adjacent non-tumor tissues were further validated by immunohistochemistry (IHC) in 25 patient tissue samples. Moreover, a Cox regression analysis was performed to integrate risk scores with clinical features, resulting in a nomogram capable of predicting patient survival probabilities. This study introduces a novel prognostic risk model for HCC patients, aimed at stratifying patients by risk, enhancing personalized treatment strategies, and offering new insights into the role of T cell-related genes in HCC progression.

DOI: https://doi.org/10.1371/journal.pone.0322706
Sci Rep. 2025 May 01. 15(1): 15314

Single-cell sequencing combined with bulk RNA seq reveals the roles of natural killer cell in prognosis and immunotherapy of hepatocellular carcinoma.

Jiahao Wu, Fan Yang, Guanqun Huang.

  Hepatocellular carcinoma (HCC) is a type of highly heterogeneous tumor characterized by a high mortality rate and poor prognosis. Natural Killer cells (NK cells) are important immune cells that play an important role in anti-tumor activities, antiviral responses, and immune regulation. The relationship between NK cells and HCC remains unclear. It would be valuable to identify a NK-related prognostic signature for HCC. WGCNA and single-cell sequencing RNA were performed to identify NK cell related genes. Gene Enrichment Analysis were used to identify the potential signal pathway. After combing genes from WGCNA and scRNA, Unicox, LASSO + StepCox and Multicox analysis were used to filter prognostic-related gene and construct a prognostic model. Then we performed Proposed time analysis to identify the developmental trajectories of NK cells. Finally, ssGSEA and estimate methods were used to evaluate the immune microenvironment and sensitivity drugs. Using the scRNA-seq data, we identified 1396 genes with high NK cell scores. Based on the results of scRNA-seq, 250 NK-related genes were identified from WGCNA. We identified 223 intersecting genes between the scRNA-seq and WGCNA. After integrating clinical data with the bulk RNA-seq data of these intersecting genes, we constructed a prognostic model to accurately predict the prognosis of HCC patients. Eventually, we found that high-risk HCC patients exhibited worse survival outcomes and lower sensitivity to immunotherapy. We constructed a risk model based on NK cell-related genes that can predict the prognosis of HCC patients accurately. This model can also predict the immunotherapy response of HCC effectively.

Keywords:  Hepatocellular carcinoma; Immunotherapy; Natural killer cell; ScRNA; WCGNA

DOI:  https://doi.org/10.1038/s41598-025-99638-w
BMC Cancer. 2025 May 01. 25(Suppl 1): 733

Unveiling the immune microenvironment of complex tissues and tumors in transcriptomics through a deconvolution approach.

Shu-Hwa Chen, Bo-Yi Yu, Wen-Yu Kuo, Ya-Bo Lin, Sheng-Yao Su, Wei-Hsuan Chuang, I-Hsuan Lu, Chung-Yen Lin.

  Accurately resolving the composition of tumor-infiltrating leukocytes is pivotal for advancing cancer immunotherapy strategies. Despite the success of some clinical trials, applying these strategies remains limited due to the challenges in deciphering the immune microenvironment. In this study, we developed a streamlined, two-step workflow to address the complexity of bioinformatics processes involved in analyzing immune cell composition from transcriptomics data. Our dockerized toolkit, DOCexpress_fastqc, integrates the hisat2-stringtie pipeline with customized scripts within Galaxy/Docker environments, facilitating RNA sequencing (RNA-seq) gene expression profiling. The output from DOCexpress_fastqc is seamlessly formatted with mySORT, a web application that employs a deconvolution algorithm to determine the immune content across 21 cell subclasses. We validated mySORT using synthetic pseudo-bulk data derived from single-cell RNA sequencing (scRNA-seq) datasets. Our predictions exhibit strong concordance with the ground-truth immune cell composition, achieving Pearson's correlation coefficients of 0.871 in melanoma patients and 0.775 in head and neck cancer patients. Additionally, mySORT outperforms existing methods like CIBERSORT in accuracy and provides a wide range of data visualization features, such as hierarchical clustering and cell complexity plots. The toolkit and web application are freely available for the research community, providing enhanced resolution for conventional bulk RNA sequencing data and facilitating the analysis of immune microenvironment responses in immunotherapy. The mySORT demo website and Docker image are free at https://mysort.iis.sinica.edu.tw and https://hub.docker.com/r/lsbnb/mysort_2022 .

Keywords:  Alpha diversity; Beta diversity; Cancer; Deconvolution; Immune microenvironment; Immunotherapy; Precision medicine

DOI:  https://doi.org/10.1186/s12885-025-14089-w
PLoS One. 2025 ;20(5): e0322618

Development of a hypoxia-responsive macrophage prognostic model using single-cell and bulk RNA sequencing in pancreatic cancer.

Heming Ge, Gerrit Wolters-Eisfeld, Thilo Hackert, Yuqiang Li, Cenap Güngör.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a low survival rate and limited responsiveness to current therapies. The role of hypoxia in the tumor microenvironment is critical, influencing tumor progression and therapy resistance. The aim of this study was to implement the complex dynamics of the hypoxic tumor microenvironment in PDAC in a hypoxia-related prognosis model.
METHODS: We utilized single-cell RNA sequencing (scRNA-seq) data and integrated it with TCGA-PAAD database to identify hypoxia-responsive macrophage subsets and related genes. Kaplan-Meier survival analysis, Cox regression, and Lasso regression methods were employed to construct and validate a hypoxia-related prognostic model. The model's effectiveness was evaluated through its predictive capabilities regarding chemotherapy sensitivity and overall survival.
RESULTS: Our research integrated data from scRNA-seq and the TCGA-PAAD database to construct a hypoxia-related prognostic model that encompassed 13 critical genes. This hypoxia model independently predicted chemotherapy response and poor outcomes, outperforming traditional clinicopathologic features. Additionally, a pan-cancer analysis affirmed the relevance of our hypoxia-related genes across multiple malignancies, particularly highlighting KRTCAP2 as a pivotal biomarker associated with worse prognosis and reduced immune infiltration.
CONCLUSION: Our findings underscored the prognostic potential of hypoxia-related model and offered a novel avenue for therapeutic targeting, aiming to ameliorate outcomes in pancreatic cancer.

DOI: https://doi.org/10.1371/journal.pone.0322618
J Transl Med. 2025 May 02. 23(1): 504

Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach.

Yanggang Hong, Deqi Wang, Zeyu Liu, Yuxin Chen, Yi Wang, Jiajun Li.

   BACKGROUND: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored.
METHODS: This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein-protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins.
RESULTS: Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis.
CONCLUSIONS: Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

Keywords:  Hepatocellular carcinoma; Machine learning; Molecular docking; Network toxicology; PFAS; Single-cell sequencing

DOI:  https://doi.org/10.1186/s12967-025-06517-z
World J Surg Oncol. 2025 Apr 30. 23(1): 175

Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma.

Jiafen Xie, Xiaoming Wang.

   BACKGROUND: Protein aggrephagy, a selected autophagy process response for degrading protein aggregates, plays a critical role in various cancers. However, its regulatory mechanisms and clinical implications in hepatocellular carcinoma (HCC) remain largely unexplored.
METHODS: We integrated bulk RNA-seq data from TCGA and single-cell RNA sequencing (scRNA-seq) data from GEO databases to systematically analyze aggrephagy-related genes (AGGRGs) in HCC. Prognostic aggrephagy-related genes (AGGRGs) were identified through univariate Cox and LASSO regression analyses, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups based on the median risk score. Comparative analyses were performed to assess clinical outcomes, pathway enrichment, and drug sensitivity. Independent risk factors were incorporated a nomogram using univariate and multivariate Cox regression. At the single-cell level, the AGG scores were calculated using AUCell algorithm, and cell interactions and pseudotime trajectory analyses were conducted. Finally, protein levels of key AGGRG was assessed via tissue microarray.
RESULTS: Eight AGGRGs (PFKP, TPX2, UBE2S, GOT2, ST6GALNAC4, ADAM15, G6PD, and KPNA2) were identified as prognostic markers for HCC. The high-risk group exhibited significantly worse survival outcomes, heightened drug resistance, and enrichment in cell cycle, mTORC1 signaling, and reactive oxygen species pathways. Single-cell transcriptomic analysis revealed 11 distinct cell types within the HCC tumor microenvironment (TME), including hepatocytes, T cells, NK cells, macrophages, monocytes, dendritic cells, plasma B cells, mature B cells, mast cells, endothelial cells, and fibroblasts. Hepatocytes exhibited the highest AGGRG scores and were associated with metabolic reprograming, proliferation, and immune evasion. Further subclustering of malignant hepatocytes using inferCNV revealed eight functionally heterogeneous subpopulations with extensive intercellular crosstalk. Trajectory analysis showed G6PD- and CCNB1-expressing subpopulations in early-to-intermediate differentiation states, whereas C3 and ARGs marked terminal differentiation. Notably, G6PD was predominantly expressed in early and mid-stages, while KPNA2, PFKP, and TPX2 were upregulated in advanced tumor states. Immunohistochemical (IHC) validation confirmed significant overexpression of G6PD in HCC tissues compared to adjacent normal tissues.
CONCLUSION: These findings provide a molecular framework for targeting aggrephagy pathways in HCC treatment strategies.

Keywords:  Aggrephagy; Hepatocellular carcinoma; Immunohistochemical analysis; Prognostic genes; Single-cell RNA-sequencing

DOI:  https://doi.org/10.1186/s12957-025-03816-z
BMC Cancer. 2025 May 01. 25(1): 821

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of tumor-associated neutrophils of head and neck squamous cell carcinoma.

Haiyang Cui, Zhikai Li, Yukun Liu, Yuchuan Yang, Linkuan Huangfu, Jinglin Kong, Xiaocong Sun, Shibo Gao, Daoke Yang, Yingjuan Zheng.

   BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a lethal malignancy with a high recurrence and distant metastasis rate, posing significant challenges to patient prognosis. Recent studies suggest that tumor-associated neutrophils (TANs) can modulate immune cell infiltration and influence tumor initiation and progression. However, the potential clinical significance of TANs in HNSCC remains insufficiently explored.
METHODS: TANs-specific marker genes were identified via single-cell sequencing data from HNSCC. Based on data from The Cancer Genome Atlas (TCGA), a prognostic risk model was constructed using TANs cell marker genes, and the model was validated with data from the Gene Expression Omnibus (GEO) database. The associations between the TANs signature and clinical characteristics, functional pathways, immune cell infiltration, immune checkpoint expression, and responses to immunotherapy and chemotherapy, were then investigated. Cell counting kit-8(CCK-8), Transwell, and wound healing assays were conducted to assess the functional role of TANs marker molecules.
RESULTS: TANs characteristic genes were identified from single-cell sequencing data from HNSCC patients. On the basis of these characteristic genes, a tumor-associated neutrophils-associated signature (NRS) was developed and validated across internal and external cross-platform cohorts through comprehensive procedures. The NRS demonstrated robust and reliable performance in predicting overall survival. Additionally, patients with a low NRS showed enhanced immune cell infiltration, active lipid metabolism, and increased sensitivity to immunotherapy. In contrast, patients with a high NRS exhibited poor prognostic outcomes, advanced clinical stages, and significant associations with HNSCC metastasis and progression. Furthermore, we identified a TANs-associated biomarker, OLR1, and validated that OLR1 promotes HNSCC proliferation, invasion, and migration through CCK-8, Transwell invasion, and wound healing assays.
CONCLUSION: This study has developed a promising TANs-based tool that may aid in personalized treatment and prognostic management for patients with HNSCC.

Keywords:   OLR1 ; Head and neck squamous cell carcinoma; Single-cell RNA sequencing; Tumor-associated neutrophils

DOI:  https://doi.org/10.1186/s12885-025-14179-9
Neuroreport. 2025 Apr 29.

Mendelian randomization combined with single-cell sequencing data analysis of chemokines and chemokine receptors and key genes and molecular mechanisms associated with epilepsy.

Lin-Ming Zhang, Tao Zeng, Bing-Ran Zhang, Qiu-Juan Zhang, Shu-Ji Gao, Yan-Lin Zhu, Ming-Wei Liu.

  To explore the functions and potential regulatory mechanisms of chemokine and chemokine receptor (CCR)-related genes in epilepsy. CCRs were identified as candidate genes and their causal relationship with epilepsy was rigorously evaluated via Mendelian randomization analysis. Subsequently, single-cell RNA sequencing (scRNA-seq) data were analyzed to identify and classify cell clusters into distinct types based on cellular annotation. Differential expression analysis was conducted to pinpoint key genes by overlapping the candidate gene set with differentially expressed genes (DEGs). Furthermore, potential therapeutic drugs for epilepsy were predicted, offering novel avenues for disease management and treatment. In total, 6395 DEGs were identified across the six cell clusters. After their intersection,CCRL2, XCL2, CXCR5, CXCL1, and CX3CR1 were pinpointed as key genes. Microglia, T cells, B cells, and macrophages have been emerged as critical cells. Furthermore, CXCL1 was regulated by hsa-miR-570-3p and hsa-miR-532-5p. Notably, CXCR5, CXCL1, and CX3CR1 were associated with 27 drug compounds. This comprehensive study leveraged scRNA-seq and transcriptomic data to elucidate the roles of CCR-related genes in epilepsy. Notably, CCRL2, XCL2, CXCR5, CXCL1, and CX3CR1 were identified as key genes implicated in epilepsy, whereas microglia, T cells, B cells, and macrophages were recognized as critical contributors to the development of epilepsy. Regulating the expression of CCRL2, XCL2, CXCR5, CXCL1, and CX3CR1, along with the activity of these immune cells may offer therapeutic potential for the alleviation of epilepsy.

Keywords:  Mendelian randomization analysis; chemokine and chemokine receptors; epilepsy; single-cell RNA sequencing data

DOI:  https://doi.org/10.1097/WNR.0000000000002168
Sci Rep. 2025 Apr 28. 15(1): 14834

Integrated spatial and single cell transcriptomics identifies PRKDC as a dual prognostic biomarker and therapeutic target in hepatocellular carcinoma.

Wenpeng Pang, Yunyong Wang, Xiaohang Lu, Minpeng Li, Fuli Long, Songlin Chen, Yuan Yu, Mingfen Li, Hongsheng Lin.

  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with a pressing need for effective biomarkers and therapeutic targets. Despite the clinical use of alpha-fetoprotein (AFP) as a diagnostic biomarker, its limitations in sensitivity and specificity necessitate the identification of novel markers. In this study, we investigated the role of Protein Kinase, DNA-Activated, Catalytic Subunit (PRKDC) in HCC prognosis and its potential as a therapeutic target. Utilizing spatial transcriptomics and single-cell RNA sequencing (scRNA-seq), we dissected the cellular composition of PRKDC in HCC tissue samples, revealing its high expression in malignant cell subpopulations and its association with the tumor immune microenvironment. Through clinical signature analysis, we observed widespread PRKDC expression in HCC tissues, particularly in immune cells, highlighting its link to immune cell infiltration. Further analyses confirmed high PRKDC expression in malignant cells and its inhibitory effect on immune cell infiltration. Copy number variation (CNV) analysis revealed significant genomic instability, with PRKDC exhibiting both amplifications and deletions across chromosomal regions, underscoring its role in tumorigenesis. Functional overexpression of PRKDC in HCC cell lines enhanced cell proliferation, migration, and altered cell cycle dynamics, with a notable increase in the G2/S phase. Taken together, we first to integrate spatial transcriptomics and single-cell transcriptomics and bulk RNA-seq to reveal that PRKDC is a reliable prognostic biomarker and a potential therapeutic target. High PRKDC expression is associated with shorter survival times and an abnormal tumor microenvironment, highlighting its impact on immune cell infiltration and HCC prognosis. Targeting PRKDC could selectively inhibit its expression in tumor cells, providing new strategies for HCC treatment.

Keywords:  Hepatocellular carcinoma; Multi-omics analysis; PRKDC; Therapeutic target; Tumor microenvironment

DOI:  https://doi.org/10.1038/s41598-025-98866-4
NAR Genom Bioinform. 2025 Jun;7(2): lqaf048

Discovering governing equations of biological systems through representation learning and sparse model discovery.

Mehrshad Sadria, Vasu Swaroop.

Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional representation and uncover the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological data, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERA's ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.

DOI: https://doi.org/10.1093/nargab/lqaf048
Genes Genomics. 2025 Apr 28.

Comprehensive analysis based on IFN-γ and SASP related genes, bulk RNA and single-cell sequencing to evaluate the prognosis and immune landscape of stomach adenocarcinoma.

Jie Yang, Junwei Han.

   BACKGROUND: Stomach adenocarcinoma (STAD) represents the predominant subtype of gastric cancer, known for its drug resistance, unfavorable prognosis, and low cure rates. IFN-γ serves as a cytokine generated by immune cells, instrumental in tumor immune clearance and essential to the tumor microenvironment. The aging-associated secretory phenotype (SASP) can modify the local tissue environment, facilitating gastric cancer progression and chemotherapy resistance.
OBJECTIVE: This study intends to identify STAD subtypes based on IFN-γ and SASP-related genes and to develop a risk prognostic model for predicting patient survival, tumor immune microenvironment, and responses to drug treatment.
METHODS: The genomic and clinical datasets originate from the Cancer Genome Atlas (TCGA) database, while the genes associated with IFN-γ and SASP come from pertinent scholarly articles. We discovered the prognostic genes linked to IFN-γ and SASP in STAD using Cox regression analysis. Next, we applied non-negative matrix factorization (NMF) to categorize LIHC into distinct molecular subtypes, identifying differentially expressed genes across these subtypes. Following this, we developed a predictive model using Cox and LASSO regression analyses to stratify patients into specific risk categories, validating the model to assess the prognostic significance of the identified signatures. Lastly, we integrated single-cell data to elucidate the immune landscape of STAD and identified potential drugs along with their sensitivity profiles.
RESULTS: We identified 17 prognostic genes related to IFN-γ and SASP, successfully classifying patients into two distinct molecular subtypes. These subtypes exhibited notable differences in immune profiles and prognostic outcomes. We pinpointed three differentially expressed genes to establish risk characteristics and created a prognostic model capable of accurately predicting patient outcomes. Our findings revealed a strong association between STAD and the extracellular matrix, low-risk group exhibited favorable prognosis, and may derive greater benefits from immunotherapy.
CONCLUSION: We developed a risk model using IFN-γ and SASP-associated genes to predict the prognosis of STAD patients more accurately. Additionally, we assessed the immune landscape of STAD by integrating bulk RNA and single-cell sequencing analyses. This approach may yield valuable insights for clinical decision-making and immunotherapy strategies in STAD.

Keywords:  Gastric cancer; Interferon-γ; Prognostic signature; Senescence-associated secretory phenotypes

DOI:  https://doi.org/10.1007/s13258-025-01646-7
Biology (Basel). 2025 Apr 17. pii: 431. [Epub ahead of print]14(4):

Discovery of Genomic Targets and Therapeutic Candidates for Liver Cancer Using Single-Cell RNA Sequencing and Molecular Docking.

Biplab Biswas, Masahiro Sugimoto, Md Aminul Hoque.

  Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein-protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies.

Keywords:  drug suggestion; genomic; liver cancer; scRNA-seq; survival analysis

DOI:  https://doi.org/10.3390/biology14040431
Comput Biol Chem. 2025 Apr 23. pii: S1476-9271(25)00135-5. [Epub ahead of print]118 108475

Identification and validation of biomarkers in Alzheimer's disease based on machine learning algorithms and single-cell sequencing analysis.

Yun Fan, XiaoLong Wang, Yun Ling, QiuYi Wang, XiBin Zhou, Kai Li, ChunXiang Zhou.

   OBJECTIVE: Alzheimer's disease (AD) is a complicated neurodegenerative disease with unknown pathogenesis. Identifying possible diagnostic markers of AD is essential to elucidate its mechanisms and facilitate diagnosis.
METHODS: A total of 295 samples (153 AD and 142 normal) were analyzed from two datasets (GSE122063 and GSE132903) in the Gene Express Omnibus (GEO) database. Differentially expressed genes (DEGs) between groups were identified and dimensionality reduction was applied to identify feature genes (key genes) using three algorithms of machine learning including least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and Random forest (RF). In addition, we obtained sample data from single-cell RNA datasets GSE157827, GSE167490, and GSE174367 to classify cells into different types and examined changes in gene expression and their correlation with AD progression. Immunofluorescence assay was used to verify the expression of key genes in animal experiments.
RESULTS: To identify diagnostic genes associated with AD, we analyzed two datasets and identified 379 DEGs which might be related to the onset of AD, and 115 of them were up-regulated and 264 down-regulated. Three algorithms of machine learning were adopted to reduce the dimensions of these DEGs and finally six core DEGs CD86, SCG3, VGF, PRKCG, SPP1, and TPI1 of AD were identified. Diagnostic analyses showed that SCG3 was substantially down-regulated in the AD group, and its AUC was higher in both the training and validation sets (0.845, 0.927, and 0.917, respectively). Transcriptome sequencing results further revealed that SCG3 expression was down-regulated in multiple cell types in the AD group and SCG3 expression in the hippocampus was found significantly reduced in the AD group.
CONCLUSIONS: This study systematically identified and validated the potential of SCG3 as an early diagnostic biomarker for AD through several technical strategies. The findings provided new biomarkers for early detection of AD and laid a foundation for future clinical applications.

Keywords:  Alzheimer's disease; Biomarkers; Machine learning; Single-cell analysis; Transcriptome sequencing

DOI:  https://doi.org/10.1016/j.compbiolchem.2025.108475
Sci Rep. 2025 Apr 26. 15(1): 14637

Comprehensive single-cell transcriptome analysis of autologous platelet-rich plasma therapy on human thin endometrium.

Jie Zeng, Jingjing Quan, Haiying Liu, Wenyan Geng, Fuman Qiu, Jianqiao Liu.

  Therapeutics for thin endometrium (TE) have emerged, with autologous platelet-rich plasma (PRP) therapy gaining significant attention. In the present study, ten eligible TE patients were recruited for PRP infusion. Endometrial tissue biopsies collected before and after PRP therapy (paired samples) were subjected to single-cell RNA sequencing (scRNA-seq). Additionally, haematoxylin and eosin (HE) and immunohistochemistry (IHC) were employed to validate changes in protein markers. The results demonstrated PRP therapy increased the average endometrial thickness in these patients. Cellular trajectory reconstruction analysis using gene counts and expression (CytoTRACE) scores indicated that high-stemness cells were more enriched in proliferating stromal cells (pStr) or stromal cells (Str) in post-PRP samples, while greater stemness was observed in glandular epithelial cells (GE) and luminal epithelial cells (LE). Gene set variation analysis (GSVA) revealed significant differences in mesenchymal‒epithelial transition (MET)-related gene signature scores between paired samples. Furthermore, an increased number of macrophages, particularly M1-type macrophages, was detected in post-PRP samples. As the first study to investigate the effects of PRP therapy via transcriptomic analysis, our findings suggest PRP therapy may enhance high-stemness, stimulate MET, and boost macrophage function. These insights contribute to a better understanding of the mechanisms underlying PRP therapy and its potential in treating TE patients.

Keywords:  Macrophage polarization; Mesenchymal‒epithelial transition; Platelet-rich plasma; Single-cell RNA sequencing; Stem cell; Thin endometrium

DOI:  https://doi.org/10.1038/s41598-025-99468-w
Int J Rheum Dis. 2025 May;28(5): e70175

Oxidative Phosphorylation Pathway in Ankylosing Spondylitis: Multi-Omics Analysis and Machine Learning.

Yuling Chen, Yuan Xu, Shuangyan Cao, Qing Lv, Yuanchun Ye, Jieruo Gu.

   INTRODUCTION: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the axial skeleton, characterized by immune microenvironment dysregulation and elevated cytokines like TNF-α and IL-17. Mitochondrial oxidative phosphorylation (OXPHOS), crucial for immune cell function and survival, is implicated in AS pathogenesis. This study explores OXPHOS-related mechanisms in AS, identifies key genes using machine learning, and highlights potential therapeutic targets for precision medicine.
MATERIALS AND METHODS: Peripheral blood mononuclear cells (PBMCs) bulk transcriptomic and single-cell RNA sequencing (scRNA-seq) data from AS patients were analyzed to investigate the role of the OXPHOS pathway in AS. Weighted gene co-expression network analysis (WGCNA) was performed to identify key gene modules associated with OXPHOS. Machine learning techniques, including support vector machine with recursive feature elimination (SVM-RFE), random forest, and least absolute shrinkage and selection operator (LASSO), were applied to identify significant AS-related genes. Real-time PCR (RT-PCR) was used to quantify gene expression, examine their patterns in specific cell subtypes, and explore their functional implications.
RESULTS: Pathway enrichment analysis identified OXPHOS as a significantly enriched pathway distinguishing AS patients from healthy controls, with high normalized enrichment scores and significant group separation in principal component analysis. ScRNA-seq revealed significantly higher OXPHOS scores in AS patients, especially in dendritic cells (DCs) and monocytes, highlighting cell type-specific dysregulation. WGCNA identified two key gene modules (MEyellow and MEtan) that are closely associated with OXPHOS. Three hub genes-LAMTOR2, APBB1IP, and DGKQ-were screened using machine learning methods and validated by RT-PCR and scRNA-seq. Among them, LAMTOR2 was significantly more highly expressed in patients with AS, and functional analyses showed that it plays a role in promoting TH17 cell differentiation, which highlights its potential as a therapeutic target for ankylosing spondylitis.
CONCLUSION: This multi-omics study provides valuable insights into the complex interplay between OXPHOS and AS. The identified genes, particularly LAMTOR2, serve as potential therapeutic targets, contributing to our understanding of AS mechanisms and paving the way for precision medicine in AS treatment.

Keywords:  ScRNA‐seq; ankylosing spondylitis; machine learning; oxidative phosphorylation

DOI:  https://doi.org/10.1111/1756-185X.70175
J Cell Mol Med. 2025 May;29(9): e70521

Target Screening and Single Cell Analysis of Diabetic Retinopathy and Hepatocarcinoma.

Yinan Shao, Bingfen Duan, Haotian Li, Xiaonan Li, Shijing Peng, Haowen Zheng, Zhipeng You.

  The association between liver cancer and diabetes has been a longstanding focus in medical research. Current evidence suggests that diabetes is an independent risk factor for the development of liver cancer. Diabetic retinopathy (DR), a prevalent neurovascular complication of diabetes, has yet to be fully characterised concerning liver cancer. Therefore, this study seeks to identify shared genes and pathways between liver cancer and DR to uncover potential therapeutic targets. Immune infiltration and cell communication in liver cancer were analysed using the GEO single-cell dataset GSM7494113. Single-cell RNA sequencing data from rat retinas were obtained from the GEO datasets GSE209872 and GSE160306. Ferritin phagocytosis-related genes were retrieved from the GeneCards database. The SeuratR package was employed for single-cell clustering analysis, while the CellChat package assessed differences in intercellular communication. Genes shared between DR and liver cancer were identified, and the DGIDB database was consulted to predict potential drug-gene interactions targeting membrane proteins involved in ferritin phagocytosis. Key ferritin phagocytosis (FRHG) genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). After annotating the single-cell data through dimensionality reduction and clustering, the expression of genes associated with membrane protein-related ferritinophagy was notably elevated in both HCC and DR samples. Based on the expression of ferritinophagy-related genes, the ferritin deposition score in Müller cells from the DR group was significantly higher than that in the control group. Cell communication analysis revealed that central hub genes associated with ferritinophagy, such as PSAP and MK, along with other signalling pathways, were significantly upregulated in the high Müller group compared to the low Müller group. In contrast, VEGF expression was enhanced in the low Müller group. Importantly, the machine learning model constructed using these key hub genes demonstrated high diagnostic efficacy for both HCC and DR. Finally, by simulating a hyperosmotic diabetic microenvironment, we confirmed in vitro that high glucose conditions significantly stimulate the expression of the shared key hub genes in both HCC and DR. The present study identified the connection between ferritinophagy-related subgroups of cells and key hub genes in both HCC and DR, providing new insights into DR-associated biomarkers and the shared pathological regulatory pathways with HCC. These findings further suggest potential therapeutic targets for both diseases.

Keywords:  Müller cells; diabetic retinopathy; ferritin phagocytosis; liver cancer

DOI:  https://doi.org/10.1111/jcmm.70521
Genesis. 2025 Apr;63(2): e70013

The Application of scRNA-Seq in Heart Development and Regeneration.

Junying Gao, Lindong Yu, Haoran Qi, Jun Qi, Zhaodi Zheng.

  Single-cell RNA sequencing (scRNA-seq) is a rapidly developing and useful technique for elucidating biological mechanisms and characterizing individual cells. Tens of millions of patients worldwide suffer from heart injuries and other types of heart disease. Neonatal mammalian hearts and certain adult vertebrate species, such as zebrafish, can fully regenerate after myocardial injury. However, the adult mammalian heart is unable to regenerate the damaged myocardium. scRNA-seq provides many new insights into pathological and normal hearts and facilitates our understanding of cellular responses to cardiac injury and repair at different stages, which may provide critical clues for effective therapies for adult heart regeneration. In this review, we summarize the application of scRNA-seq in heart development and regeneration and describe how important molecular mechanisms can be harnessed to promote heart regeneration.

Keywords:  adult heart regeneration; embryonic heart development; neonatal heart regeneration; scRNA‐seq

DOI:  https://doi.org/10.1002/dvg.70013
Inflammation. 2025 Apr 26.

Integrated Analysis of Single-Cell and Transcriptome Data Reveals the Role and Regulatory Mechanisms of Neuroinflammation in Parkinson's Disease.

Yao Geng, Rui-Yu Wang, Man-Yu Dong, Yi-Lun Qian, Xi-Hui Wang, Wen-Wen Xia, Ying Shen, Ke-Zhong Zhang.

  There is increasing interest in developing therapeutic interventions aimed at preventing neuroinflammation in Parkinson's disease (PD). However, the specific characteristics of inflammation across different cell types and the underlying mechanisms of PD-related inflammation remain inadequately understood. In this study, we conducted an analysis of single-cell RNA sequencing (scRNA-seq) and microarray data derived from human PD midbrain tissue, specifically focusing on the substantia nigra compacta (SNc). These datasets were sourced from the (GEO) database. We utilized GSVA, GSEA, as well as KEGG and GO analyses to explore transcriptional variations associated with PD. Furthermore, trajectory and SCENIC analyses were conducted to uncover the mechanisms underlying PD progression. Subsequent animal and cellular experiments validated the role of the regulon in regulating neuroinflammation. Results: Our analysis revealed that microglia displayed the highest levels of inflammatory activity, characterized by an increased abundance of microglia in the proinflammatory activated state within the midbrain and SNc of PD patients. This finding was further validated in a PD mouse model induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). The transcription factor STAT3 demonstrated significant upregulation and was implicated in promoting the inflammatory response and activating microglia within the PD context. In the 1-methyl-4-phenylpyridine (MPP +)-induced BV2 cell model, inhibition of STAT3 led to reduced levels of inflammation, hindered STAT3 phosphorylation, and decreased the production of inflammatory factors. Furthermore, the downregulation of P-STAT3 alleviated the harmful effects on SH-SY5Y cells that were cocultured in the conditioned medium. Conclusions: Our study underscored the pivotal role of the transcription factor STAT3 as a central regulator of proinflammatory activation in microglia within PD. These findings offer fresh insights into PD pathogenesis and suggest potential avenues for the development of novel therapeutic strategies.

Keywords:  Microglia; Neuroinflammation; Parkinson’s disease; Single cell; Transcription factor regulons

DOI:  https://doi.org/10.1007/s10753-025-02306-4
bioRxiv. 2025 Apr 08. pii: 2025.04.02.646189. [Epub ahead of print]

High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samples.

Li Yao, Sagar R Shah, Abdullah Ozer, Junke Zhang, Xiuqi Pan, Tianyu Xia, Vrushali D Fangal, Alden King-Yung Leung, Meihan Wei, John T Lis, Haiyuan Yu.

Biological systems exhibit remarkable heterogeneity, characterized by intricate interplay among diverse cell types. Resolving the regulatory processes of specific cell types is crucial for delineating developmental mechanisms and disease etiologies. While single-cell sequencing methods such as scRNA-seq and scATAC-seq have revolutionized our understanding of individual cellular functions, adapting bulk genome-wide assays to achieve single-cell resolution of other genomic features remains a significant technical challenge. Here, we introduce Deep-learning-based DEconvolution of Tissue profiles with Accurate Interpretation of Locus-specific Signals (DeepDETAILS), a novel quasi-supervised framework to reconstruct cell-type-specific genomic signals with base-pair precision. DeepDETAILS' core innovation lies in its ability to perform cross-modality deconvolution using scATAC-seq reference libraries for other bulk datasets, benefiting from the affordability and availability of scATAC-seq data. DeepDETAILS enables high-resolution mapping of genomic signals across diverse cell types, with great versatility for various omics datasets, including nascent transcript sequencing (such as PRO-cap and PRO-seq) and ChIP-seq for chromatin modifications. Our results demonstrate that DeepDETAILS significantly outperformed traditional statistical deconvolution methods. Using DeepDETAILS, we developed a comprehensive compendium of high-resolution nascent transcription and histone modification signals across 39 diverse human tissues and 86 distinct cell types. Furthermore, we applied our compendium to fine-map risk variants associated with Primary Sclerosing Cholangitis (PSC), a progressive cholestatic liver disorder, and revealed a potential etiology of the disease. Our tool and compendium provide invaluable insights into cellular complexity, opening new avenues for studying biological processes in various contexts.

DOI: https://doi.org/10.1101/2025.04.02.646189
Adv Exp Med Biol. 2025 ;1469 173-205

scRNA-Seq-Based Transcriptome Profiling and Relevant Bioinformatics Approaches to Uncover Novel Insights in Studying Human Spermatogenesis.

Xiaolong Wu, Hanchao Liu, Lin Hua, Xintao Gao, Longfei Hu, Lingling Wang, Tiao Bu, Fei Sun, C Yan Cheng.

  This study employs single-cell RNA sequencing (scRNA-seq) to investigate human spermatogenesis across developmental stages and under pathological conditions, including non-obstructive azoospermia (NOA). The analysis highlights the critical role of Sertoli cells in supporting germ cell development by providing structural support, paracrine factors, and essential nutrients. Pathological conditions, such as NOA and Klinefelter syndrome, reveal significant disruptions in Sertoli cell function, including impaired cell signaling, mitochondrial activity, and transcriptional regulation. These changes are closely linked to defects in germ cell progression and spermatogenic failure. Comparative profiling also identifies stage-specific transcriptional changes in both Sertoli and Leydig cells, uncovering their dynamic interactions with germ cells. This work provides new insights into the cellular and molecular mechanisms of spermatogenesis and identifies potential biomarkers and therapeutic targets, particularly emphasizing the pivotal contributions of Sertoli cells in maintaining testicular homeostasis and fertility.

Keywords:  Gene profiling; Human spermatogenesis; Infertility; Single-Cell RNA-Seq

DOI:  https://doi.org/10.1007/978-3-031-82990-1_9
Nat Commun. 2025 Apr 26. 16(1): 3941

scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.

Chang Su, Dongsoo Lee, Peng Jin, Jingfei Zhang.

Mapping enhancers and target genes in disease-related cell types provides critical insights into the functional mechanisms of genome-wide association studies (GWAS) variants. Single-cell multimodal data, which measure gene expression and chromatin accessibility in the same cells, enable the cell-type-specific inference of enhancer-gene pairs. However, this task is challenged by high data sparsity, sequencing depth variation, and the computational burden of analyzing a large number of pairs. We introduce scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p-values. In blood and brain data, scMultiMap shows appropriate type I error control, high statistical power, and computational efficiency (1% of existing methods). When applied to Alzheimer's disease (AD) data, scMultiMap gives the highest heritability enrichment in microglia and reveals insights into the regulatory mechanisms of AD GWAS variants.

DOI: https://doi.org/10.1038/s41467-025-59306-z
Biomedicines. 2025 Mar 30. pii: 826. [Epub ahead of print]13(4):

Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning.

Qiong Li, Hongde Liu.

  Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.

Keywords:  breast cancer; cellular senescence; machine learning; prognostic signature; single-cell RNA sequencing; telomeres; tumor microenvironment

DOI:  https://doi.org/10.3390/biomedicines13040826
Int J Med Sci. 2025 ;22(9): 2139-2154

Comprehensive Characterization of the Oxidative Stress Profiles in Neonatal Necrotizing Enterocolitis.

Xiaofeng Xiong, Luyao Wu, Xin Liu, Jing Wang, Jun Xiao, Ke Chen, Didi Zhuansun, Xinyao Meng, Jiexiong Feng, Xuyong Chen.

  Objective: This study aims to portray the characteristics of oxidative stress (OS) in cases of Necrotizing enterocolitis (NEC), identify the hub genes and associated mechanisms involved, and explore potential drugs for NEC. Methods: We performed a comprehensive analysis integrating bulk-RNA sequencing and single-cell RNA sequencing datasets, coupled with various techniques including differential analysis, gene set enrichment analysis, and immune infiltration analysis. We aimed to systematically elucidate the variations in functions related to OS among distinct cell populations within both NEC and non-NEC tissues. Additionally, we depicted the longitudinal changes in immune cells, with a particular focus on macrophages, throughout the progression of NEC. NEC mice model was established and RT-qPCR was performed to validate the expression of the hub genes. Results: In total, 465 OS related genes were found, and 53 of them were significantly differentially expressed. These genes were mainly involved in several signaling pathways, such as TNF signaling pathway, IL-17 signaling pathway, FOXO signaling pathway, inflammatory bowel disease. The top 10 hub genes were MMP2, IL1A, MMP3, HGF, HP, IL10, PPARGC1A, TLR4, MMP9 and HMOX1. Ten kinds of drug were discovered as the potential treatment for NEC. Four specific macrophages subtypes and relative function were identified in NEC. RT-qPCR and immunofluorescence staining confirmed the expression of the hub genes in NEC model. Conclusions: This investigation yielded innovative insights into the immune environment and therapeutic methodologies directed at oxidative stress in the pathogenesis of NEC.

Keywords:  immune infiltration; macrophages; neonatal necrotizing enterocolitis; oxidative stress; single cell RNA-sequencing.

DOI:  https://doi.org/10.7150/ijms.109008
Int Urol Nephrol. 2025 Apr 30.

Identification of druggable genetic targets for prostate cancer risk based on mendelian randomization and single-cell RNA sequencing.

Liantai Song, Xinyang He, Yibing Duan, Yifan Chi, Reng Li, Cancan Li, Yutian Liu, Mengxin Yang, Jiameng Wei, Yujia Zhao, Qian Xu.

   PURPOSE: This study aimed to identify genetic targets linked to prostate cancer risk using advanced genetic analysis techniques.
OBJECTIVE: The goal was to conduct a comprehensive analysis using Mendelian Randomization (MR), colocalization, and single-cell RNA sequencing to identify druggable genes as potential therapeutic targets or diagnostic markers.
METHODS: The study involved selecting 2608 druggable genes by intersecting expression Quantitative Trait Loci (eQTLs) with druggable genome databases. MR analysis using prostate cancer GWAS data identified genes with causal associations to prostate cancer risk. Colocalization analysis confirmed shared genetic variants influencing both the exposure and outcome. Single-cell RNA sequencing assessed gene expression in prostate tumor cell types, while a phenome-wide association study (PheWAS) evaluated potential side effects.
RESULTS: MR analysis identified 58 genes associated with prostate cancer risk, with 12 validated by colocalization analysis. Five genes (BAK1, ATP1B2, PEMT, TPM3, ZDHHC7) demonstrated strong colocalization, indicating potential as drug targets. Single-cell RNA sequencing revealed their enrichment in prostate tumor T cells and macrophages. PheWAS suggested minimal side effects for most, except BAK1, which was linked to increased platelet counts.
CONCLUSION: This study identified several genetic targets associated with prostate cancer risk, highlighting the potential for targeted therapy. By integrating Mendelian randomization analysis, colocalization analysis, and single-cell RNA sequencing, the accuracy of target validation was improved, which may provide new directions for targeted therapy in prostate cancer.

Keywords:  Colocalization; Druggable genome; Mendelian randomization; Phenome-wide association studies; Prostate cancer

DOI:  https://doi.org/10.1007/s11255-025-04525-y
Adv Exp Med Biol. 2025 ;1469 163-172

Computational Approaches in Spatial Transcriptomics for the Study of Mammalian Spermatogenesis.

Deina Bossa, Melanie Evans, Shreya Rajachandran, Xin Zhang, Qiqi Cao, Haiqi Chen.

  Spermatogenesis is a complex and dynamic cellular differentiation process critical to male fertility. Although the full continuum of gene expression patterns from spermatogonial stem cells (SSCs) to spermatozoa in steady state was characterized using single-cell RNA sequencing technologies, the transcriptional dynamics of spermatogenesis within its native tissue context was largely unexplored. The recent development of spatial transcriptomics (ST) technologies has transformed male fertility research from a single-cell level to a two-dimensional spatial coordinate system and facilitated the study of spermatogenesis in the native environment of both the rodent and human testes. The spatial gene expression information generated by these ST technologies requires new computational approaches to extract novel biological insights. These requirements include, but are not limited to, spatial mapping of testicular cell types, identifying spatially variable genes, and understanding the molecular cross-talk between testicular cell types. Here, we review computational approaches that have been used to dissect mammalian spermatogenesis in the context of ST. We also highlight new computational approaches that can be leveraged to reveal novel insights into male fertility.

Keywords:  Cell-cell communication; Spatial Transcriptomics; Spatially variable genes; Spermatogenesis; Testis

DOI:  https://doi.org/10.1007/978-3-031-82990-1_8
BMC Genomics. 2025 Apr 29. 26(1): 416

Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset.

Daigo Okada, Jianshen Zhu, Kan Shota, Yuuki Nishimura, Kazuya Haraguchi.

   BACKGROUND: Understanding cellular diversity throughout the body is essential for elucidating the complex functions of biological systems. Recently, large-scale single-cell omics datasets, known as omics atlases, have become available. These atlases encompass data from diverse tissues and cell-types, providing insights into the landscape of cell-type-specific gene expression. However, the isolated effect of the tissue environment has not been thoroughly investigated. Evaluating this isolated effect is challenging due to statistical confounding with cell-type effects, which arises from the highly limited subset of tissue-cell-type combinations that are biologically realized compared to the vast number of theoretical possibilities.
RESULTS: This study introduces a novel data analysis framework, named the Combinatorial Sub-dataset Extraction for Confounding Reduction (COSER), which addresses statistical confounding by using graph theory to enumerate appropriate sub-datasets. COSER enables the assessment of isolated effects of discrete variables in single cells. Applying COSER to the Tabula Muris Senis single-cell transcriptome atlas, we characterized the isolated impact of tissue environments. Our findings demonstrate that some genes are markedly affected by the tissue environment, particularly in modulating intercellular diversity in immune responses and their age-related changes.
CONCLUSION: COSER provides a robust, general-purpose framework for evaluating the isolated effects of discrete variables from large-scale data mining. This approach reveals critical insights into the interplay between tissue environments and gene expression.

Keywords:  Effect of tissue environment; Graph theory; Maximal biclique enumeration; Single cell RNA-seq

DOI:  https://doi.org/10.1186/s12864-025-11614-w
Free Radic Biol Med. 2025 Apr 24. pii: S0891-5849(25)00245-X. [Epub ahead of print]234 203-219

Ferroptosis and low-grade Glioma: The breakthrough potential of NUAK2.

Kan Wang, Yifei Xiao, Jiangbin Ren, Chengcheng Zuo, Luyan Mu, Qingla Li, Yuwen Song.

   BACKGROUND: Low-grade glioma (LGG) is a primary brain tumor with high cellular heterogeneity and recurrence, leading to poor prognosis. Standard treatments (surgery, radiotherapy, and chemotherapy) have limited efficacy. Ferroptosis, an iron-dependent form of regulated cell death, is a potential therapeutic target, while dysregulated ferroptosis-related genes (FRGs) may drive tumor progression and therapy resistance.
METHODS: This study integrated multi-omics data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) to identify FRGs associated with LGG prognosis. Single-cell RNA sequencing (scRNA-seq) and pseudotime trajectory analysis were performed to investigate their functional roles. Key findings were validated through in vitro and in vivo experiments.
RESULTS: We identified 345 FRGs associated with LGG prognosis, which are involved in oxidative stress response, cell proliferation, and immune regulation. High-risk patients exhibited an immunosuppressive tumor microenvironment with elevated levels of M2 macrophages and Treg cells but reduced CD8+ T cell infiltration. Pseudotime trajectory analysis highlighted the dynamic roles of macrophages and astrocytes in immune evasion and microenvironment remodeling. Notably, the NUAK2 gene emerged as a key driver of tumor progression and immune suppression. In vitro and in vivo experiments confirmed that targeting NUAK2 significantly reduced tumor cell viability and growth, underscoring its critical regulatory role in LGG.
CONCLUSIONS: Our study provides comprehensive insights into the role of FRGs in LGG prognosis and tumor microenvironment regulation, with a particular focus on the NUAK2 gene. As a potential therapeutic target, NUAK2's critical role in tumor progression and immune evasion offers a new direction for LGG treatment. Future research should focus on validating NUAK2's role in larger cohorts and exploring its clinical application as a biomarker and therapeutic target.

Keywords:  Ferroptosis; Immunotherapy; Low-grade glioma; NUAK2; Prognosis; Tumor microenvironment

DOI:  https://doi.org/10.1016/j.freeradbiomed.2025.04.035
Biomedicines. 2025 Apr 08. pii: 903. [Epub ahead of print]13(4):

Exploring Immune-Related Ferroptosis Genes in Thyroid Cancer: A Comprehensive Analysis.

Zixuan Ru, Siwei Li, Minnan Wang, Yanan Ni, Hong Qiao.

  Background: The increasing incidence and poor outcomes of recurrent thyroid cancer highlight the need for innovative therapies. Ferroptosis, a regulated cell death process linked to the tumour microenvironment (TME), offers a promising antitumour strategy. This study explored immune-related ferroptosis genes (IRFGs) in thyroid cancer to uncover novel therapeutic targets. Methods: CIBERSORTx and WGCNA were applied to data from TCGA-THCA to identify hub genes. A prognostic model composed of IRFGs was constructed using LASSO Cox regression. Pearson correlation was employed to analyse the relationships between IRFGs and immune features. Single-cell RNA sequencing (scRNA-seq) revealed gene expression in cell subsets, and qRT-PCR was used for validation. Results: Twelve IRFGs were identified through WGCNA, leading to the classification of thyroid cancer samples into three distinct subtypes. There were significant differences in patient outcomes among these subtypes. A prognostic risk score model was developed based on six key IRFGs (ACSL5, HSD17B11, CCL5, NCF2, PSME1, and ACTB), which were found to be closely associated with immune cell infiltration and immune responses within the TME. The prognostic risk score was identified as a risk factor for thyroid cancer outcomes (HR = 14.737, 95% CI = 1.95-111.65; p = 0.009). ScRNA-seq revealed the predominant expression of these genes in myeloid cells, with differential expression validated using qRT-PCR in thyroid tumour and normal tissues. Conclusions: This study integrates bulk and single-cell RNA sequencing data to identify IRFGs and construct a robust prognostic model, offering new therapeutic targets and improving prognostic evaluation for thyroid cancer patients.

Keywords:  TCGA; bioinformatics analysis; biomarker; immune-related ferroptosis; prognostic model; qRT–PCR validation; thyroid cancer; tumour microenvironment

DOI:  https://doi.org/10.3390/biomedicines13040903
BMC Cancer. 2025 May 01. 25(1): 822

Prognosis, immunological features and potential mechanisms of HKR1 in prostate cancer via single-cell and bulk RNA-sequencing.

Jing Zhai, Shiwei Liu, Tiantian Wang, Yi Wang, Yu Peng.

   BACKGROUND: Given the limitations of conventional therapies in prostate cancer (PCa) management, identifying novel biomarkers capable of predicting tumor prognosis and immunotherapy response is critically important. This article revealed the prognosis, immunological characteristics, and potential mechanisms of HKR1 in PCa via bulk and single-cell RNA sequencing (scRNA-Seq).
METHODS: Bulk and scRNA-Seq analyses of HKR1 in PCa were collected from online databases. Differential expression and Cox regression analyses were carried out to evaluate its expression and prognosis values in PCa, respectively. Correlation analyses were performed to evaluate associations between HKR1 expression and enriched pathways, immune cell infiltration, and other relevant biological processes.
RESULTS: HKR1 showed higher expression in PCa than in normal tissues, as verified by qPCR in both PCa cell lines and tissue samples (p < 0.05). ScRNA-seq analysis demonstrated HKR1 expression in malignant cells, epithelial cells, and immune cell populations. Moreover, PCa sufferers with higher HKR1 expressions were linked with poorer prognoses, and Cox regression analysis suggested it was an independent indicator in PCa (p < 0.05). Further, we shed light on the fact that the toll-like receptor, the TGF-beta, and the p53 pathways were significantly related to HKR1 expression in PCa. HKR1 was also found to be markedly linked to immunity in PCa (p < 0.05). Notably, we characterized two novel lncRNA-RBP-HKR1 regulatory axes that potentially modulate HKR1 transcriptional dynamics in prostate carcinogenesis.
CONCLUSIONS: HKR1 played an undeniable role in the prognosis and immunological potential of PCa, providing evidence for the molecular mechanisms of HKR1 in PCa.

Keywords:  HKR1; Immunotherapy; Prognosis; Prostate cancer; ScRNA-sequencing

DOI:  https://doi.org/10.1186/s12885-025-14230-9
J Am Heart Assoc. 2025 May 02. e039195

Calbindin 2 as a Novel Biomarker and Therapeutic Target for Abdominal Aortic Aneurysm: Integrative Analysis of Human Proteomes and Genetics.

Yulin Bao, Jiayi Chen, Xudong Han, Ye He, Tongtong Yang, Xinying Shi, Jiawen Chen, Lingfeng Gu, Sibo Wang, Liping Xie, Hao Wang, Liansheng Wang.

   BACKGROUND: Abdominal aortic aneurysm (AAA) is a clinical life-threatening issue. No pharmacological treatments are currently approved for the prevention and treatment of AAA. Therefore, identifying novel biomarkers and therapeutic targets is crucial for improving AAA management and outcomes.
METHODS: To identify plasma proteins with potential causal effects on AAA, we integrated genetic evidence from proteome-wide Mendelian randomization, genetic correlation, and colocalization analysis. The role of identified proteins in AAA was further explored through the phenome-wide association study and mediation analysis. Multiomics data analysis, including bulk RNA sequencing, single-cell/single-nucleus RNA sequencing, and spatial transcriptomics, was employed to characterize the expression patterns of these proteins. Experimental validation was performed using an AAA model in apolipoprotein E-deficient mice infused with angiotensin II. Druggability analysis was conducted to identify drug candidates, which were tested in preclinical mouse models.
RESULTS: CALB2 (calbindin 2) was identified as having a causal effect on AAA and may influence the progression of AAA through the regulation of lipid metabolism. Multiomics analysis revealed that CALB2 is predominantly expressed in the mesothelial cells of adipose tissues. Inhibition of CALB2 in an AAA mouse model alleviated AAA progression. Druggability analysis identified lenalidomide and genistein as potential therapeutic candidates, and experiments confirmed their efficacy in preventing AAA development.
CONCLUSIONS: This study identifies CALB2 as being associated with an increased risk of AAA and suggests that i might be a novel biomarker and therapeutic molecule for AAA management. Lenalidomide and genistein hold promising potential as treatments for patients with AAA.

Keywords:  Mendelian randomization; abdominal aortic aneurysm; calbindin 2; plasma protein; proteogenomic

DOI:  https://doi.org/10.1161/JAHA.124.039195
BMC Cancer. 2025 Apr 25. 25(1): 777

Multi-omics analysis unveils a four-gene prognostic signature in esophageal squamous carcinoma and the therapeutic potential of PKP1.

Xiuzhi Zhang, Zhi Wang, Yutong Zhao, Hua Ye, Tiandong Li, Han Wang, Guiying Sun, Feifei Liang, Liping Dai, Peng Wang, Xiaoli Liu.

   BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, characterized by high heterogeneity and poor outcomes. Effective classification for patient stratification and identifying reliable markers for prognosis prediction and treatment choice are crucial.
METHODS: Integration of single-cell RNA-sequencing (RNA-seq) and bulk RNA-seq analyses were used to characterize ESCC. Non-negative matrix factorization (NMF) clustering was performed to stratify the ESCC patients into different subtypes and the clinical and pathological features of the ESCC subtypes were compared. Cox regression analysis and LASSO regression analysis were used to select key genes and construct a risk model for ESCC. The associations of the key genes with anti-cancer drug sensitivities in ESCC cell lines were investigated. RT-qRCR experiments, proteomics analysis, and multiplex immunohistochemistry (mIHC) experiments were used to validate the results. Furthermore, one identified gene was selected to investigate its correlation with EGFR expression and the gene effect scores of various potential gene targets across pan-cancer.
RESULTS: The study identified the dysregulated distributions of epithelial cells and fibroblasts as characteristic of ESCC. ESCC patients could be classified into four distinct subtypes with unique cell type features and prognoses. With the gene makers of the cell type features, a four-gene prognostic signature for ESCC was constructed. The CCND1-PKP1-JUP-ANKRD12 model could effectively discriminate the survival status of ESCC patients, independent of various pathological and clinical features. The risk score for the samples was correlated with the expression levels of immunoregulatory genes. The prognostic effects of CCND1, PKP1, and JUP were confirmed at the protein level. The phosphorylation levels of PKP1, JUP, and ANKRD12 were found to be dysregulated in ESCC tumors. Their expression dysregulation and heterogeneity were demonstrated in ESCC cell lines. All four genes were significantly correlated with at least one of the anti-cancer drug sensitivities in ESCC cell lines. PKP1 expression was significantly correlated with EGFR expression and gene effect scores in multiple cancers.
CONCLUSIONS: We conclude that the CCND1-PKP1-JUP-ANKRD12 signature may serve as a novel indicator for ESCC prognosis and diagnosis. PKP1 expression might provide new clues for gene therapy efficacy in multiple cancers.

Keywords:  Esophageal squamous cell carcinoma; Gene effect; Heterogeneity; JUP; PKP1; scRNA-seq

DOI:  https://doi.org/10.1186/s12885-025-14150-8
PLoS Comput Biol. 2025 Apr;21(4): e1012924

scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.

Xiaopeng Wei, Jingli Wu, Gaoshi Li, Jiafei Liu, Xi Wu, Chang He.

It is a significant step for single cell analysis to identify cell types through clustering single-cell RNA sequencing (scRNA-seq) data. However, great challenges still remain due to the inherent high-dimensionality, noise, and sparsity of scRNA-seq data. In this study, scPEDSSC, a deep sparse subspace clustering method based on proximity enhancement, is put forward. The self-expression matrix (SEM), learned from the deep auto-encoder with two part generalized gamma (TPGG) distribution, are adopted to generate the similarity matrix along with its second power. Compared with eight state-of-the-art single-cell clustering methods on twelve real biological datasets, the proposed method scPEDSSC can achieve superior performance in most datasets, which has been verified through a number of experiments.

DOI: https://doi.org/10.1371/journal.pcbi.1012924
Neuro Oncol. 2025 May 02. pii: noaf113. [Epub ahead of print]

Charting the Single-Cell and Spatial Landscape of IDH-Wildtype Glioblastoma with GBmap.

Cristian Ruiz-Moreno, Sergio Marco Salas, Erik Samuelsson, Mariia Minaeva, Ignacio Ibarra, Marco Grillo, Sebastian Brandner, Ananya Roy, Karin Forsberg-Nilsson, Mariette E G Kranendonk, Fabian J Theis, Mats Nilsson, Hendrik G Stunnenberg.

   BACKGROUND: Glioblastoma (GB), particularly IDH-wildtype, is the most aggressive brain malignancy with a dismal prognosis. Despite advances in molecular profiling, the complexity of its tumor microenvironment and spatial organization remains poorly understood. This study aimed to create a comprehensive single-cell and spatial atlas of GB to unravel its cellular heterogeneity, spatial architecture, and clinical relevance.
METHODS: We integrated single-cell RNA sequencing data from 26 datasets, encompassing over 1.1 million cells from 240 patients, to construct GBmap, a harmonized single-cell atlas. High-resolution spatial transcriptomics was employed to map the spatial organization of GB tissues. We developed the Tumor Structure Score (TSS) to quantify tumor organization and correlated it with patient outcomes.
RESULTS: We showcase the applications of GBmap for reference mapping, transfer learning, and biological discoveries. GBmap revealed extensive cellular heterogeneity, identifying rare populations such as tumor-associated neutrophils and homeostatic microglia. Spatial analysis uncovered seven distinct tumor niches, with hypoxia-dependent niches strongly associated with poor prognosis. The TSS demonstrated that highly organized tumors, characterized by well-defined vasculature and hypoxic niches, correlated with worse survival outcomes.
CONCLUSIONS: This study provides a comprehensive resource for understanding glioblastoma heterogeneity and spatial organization. GBmap and the TSS provide an integrative view of tumor architecture in GB, highlighting hypoxia-driven niches that may represent avenues for further investigation. Our resource can facilitate exploratory analyses and hypothesis generation to better understand disease progression.

Keywords:  Glioblastoma; Hypoxia; Single-cell atlas; Spatial transcriptomics; Tumor organization

DOI:  https://doi.org/10.1093/neuonc/noaf113
J Transl Med. 2025 May 02. 23(1): 503

Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies.

Zipei Song, Yuheng Wang, Miaolin Zhu, Pengpeng Zhang, Zhihua Li, Xin Geng, Xincen Cao, Jianan Zheng, Jianwei Tang, Liang Chen.

   BACKGROUND: Lung adenocarcinoma (LUAD) presents a considerable danger to human health and has evolved into a major public health concern. Ribosome biogenesis (RiboSis) is a critical process for synthesizing ribosomes, closely associated with cancer initiation, progression, and treatment resistance, potentially serving as a target for future cancer therapies.
METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) technology, a single-cell atlas of LUAD was delineated, focusing on the analysis of T cell subpopulations. Cells were scored based on the expression patterns of 331 genes associated with RiboSis across different cell types, and monocle2 was employed to analyze the developmental trajectory of CD4+ T cells. Employing various machine learning algorithms, a ribosome biogenesis-related signature (RBS) was constructed and compared to 140 published LUAD prognostic models. The relationship between RBS risk scores and various factors in LUAD patients, including prognosis, the tumor immune microenvironment (TIME), responsiveness to immunotherapy, and sensitivity to pharmacological treatments was specifically analyzed. Immunohistochemistry was utilized to validate the expression levels of immune markers in the high- and low- RBS groups, and in vitro experiments were performed to validate the functional role of the pivotal gene KIF23 in the progression of LUAD.
RESULTS: Using single-cell analysis, two distinct T cell subtypes were identified: CD8+ interferon (IFN) response T cells and CD4+ stress response T cells. It was observed that CD4+ naive-like T cells exhibit high expression of RiboSis-related genes, with a gradual decrease in RiboSis activity as CD4+ T cells develop. Compared to other prognostic models, RBS demonstrated superior performance in prognosis prediction. The low-RBS group exhibited a tumor microenvironment (TME) more favorable for efficient immune monitoring and reaction, higher responsiveness to immunotherapy, and a better prognosis. Immunohistochemistry confirmed higher expression levels of immune markers in the low-RBS group, while in vitro experiments validated the promoting role of KIF23 in LUAD cell proliferation, migration and invasion.
CONCLUSION: This study delves into the relationship between RiboSis and LUAD cell subpopulations, identifying a potent prognostic biomarker for LUAD. This biomarker aids in assessing immunotherapy efficacy in LUAD patients, ultimately enhancing their prognosis and guiding clinical decision-making.

Keywords:  Diagnostic biomarkers; Immunotherapy response; Lung adenocarcinoma; Machine learning; Ribosome biogenesis; Single-cell RNA sequencing

DOI:  https://doi.org/10.1186/s12967-025-06489-0
Mol Neurobiol. 2025 Apr 29.

Functional Specificity of Astrocyte Subtypes in Alzheimer's Disease: Decoding Disease Mechanisms Through Network-based Analysis of Integrated Single-Nuclei Multi-Omic Data.

Atılay İlgün, Tunahan Çakır.

  Alzheimer's disease (AD) is the most common cause of dementia. Recent studies have revealed incontrovertible roles of astrocytes in the pathology of AD. Considering the conflicting behaviours of astrocytes in AD brain, they have been proposed to have subtypes. In this study, astrocytes from two publicly available single-nuclei transcriptome datasets were integrated to provide in-depth characterization of astrocyte subtypes in AD. Differentially expressed genes within each astrocyte subtype were analyzed by mapping them onto a human protein-protein interaction network to discover subnetworks with biologically relevant genes. Integrating single-nuclei datasets and using network-based analysis approach led to higher sensitivity in capturing AD-related genes compared to traditional approaches. One of the identified subtypes was highly representative of neurotoxic reactive astrocytes in AD. The results show that A1 reactive astrocytes could have an enhancing role for the amyloid beta and neurofibrillary tangle accumulation through MAPK10, MAPT, and TMED10, which were all found to be differentially expressed in this subtype during AD in our analysis. Moreover, single-nuclei ATAC-Seq data from the same tissue was re-analyzed to evaluate astrocyte subtypes at multi-omic level. It was found that astrocyte subtypes underwent epigenetic reprogramming during AD. Potential transcription factors were also identified for the regulation of the genes that exhibited alterations in both promoter accessibility and gene expression in AD. Comparative analysis of single-nuclei RNA-Seq and ATAC-Seq datasets showed that PTN gene, which was reported to be important for AD pathology, is likely regulated by ATF3 transcription factor in subtype-specific manner in astrocytes.

Keywords:  Alzheimer’s disease; Astrocytes; Multi-omics; Protein–protein interactions; Single-nuclei sequencing; Transcriptome

DOI:  https://doi.org/10.1007/s12035-025-04965-8
Eur J Med Res. 2025 Apr 28. 30(1): 338

Characterization of T cells in the progression of dry eye disease using single-cell RNA sequencing in mice.

Zhizhi Zhang, Liwei Zhang, Baihua Chen.

   BACKGROUND: Increasing evidence indicated that T cells have significant effects in dry eye disease (DED). However, the regulatory role of T cells in DED remains unclear.
METHODS: In this study, we examined immune responses throughout the progression in murine DED model. Using cytometry by time-of-flight (CyTOF) and single-cell RNA sequencing (scRNA-seq), we observed dynamic alterations in the proportions of immune cell landscape. Pseudotime trajectory and cell-cell communication analyses further illustrated T-cell differentiation and interaction networks.
RESULTS: CD4+ and CD8+ T cells exhibited an initial decline on Day 3 (D3) and followed by a recovery on Day 7 (D7). Single-cell transcriptomics provided insights into 15 distinct subsets of T cells with heterogeneous functional states. Pseudotime trajectory analysis demonstrated coordinated differentiation patterns of CD4+ and CD8+ T cells, indicating their collaborative involvement in the inflammatory process.
CONCLUSIONS: Our results clarify the dynamics of the adaptive immune response in DED and indicate that targeting T cells may serve as a promising immune-modulatory approach in the treatment of DED model.

Keywords:  Dry eye disease (DED); Immune modulation; Immune response; ScRNA-seq; T cell; Therapy

DOI:  https://doi.org/10.1186/s40001-025-02607-2
Open Med (Wars). 2025 ;20(1): 20251178

Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke.

Jinzhong Yao, Huan Deng, Peng Wang, Bo Li, Zaisheng Qin.

   Objectives: This study aims to elucidate the dynamic changes in lactate-related genes (LRGs) in microglia following ischemic stroke (IS) and their associations with immune cells.
Methods: We performed differential expression analysis on bulk-sequencing (GSE30655 and GSE35338) and scRNA-seq data (GSE174574) to identify differentially expressed genes. These genes were intersected with lactate genes from MSigDB to identify post-stroke LRGs. We used t-SNE to visualize LRG distribution across cell types and selected microglia for cell-cell communication, pseudo time, and functional enrichment analyses. These findings were integrated with the GSE225948 scRNA-seq dataset to examine LRG trends in the chronic phase of IS. Finally, CIBERSORT was used to explore immune cell infiltration changes and LRG-immune cell associations post-IS.
Results: Nine LRGs were identified, including Spp1, Per2, Col4a1, Sfxn4, C1qbp, Myc, Apln, Cdo1, and Cav1, with Spp1, C1qbp, and Myc highly expressed in microglia. C1qbp and Myc are crucial in the acute phase, while Spp1 impacts both acute and chronic phases of IS. Microglia subcluster analysis revealed four subclusters (MG0-MG3). Immune cell infiltration analysis showed significant associations between these genes and immune cells.
Conclusion: In summary, Spp1, C1qbp, and Myc are LRGs that are predominantly expressed in microglia and play regulatory roles in various stages of IS.

Keywords:  ischemic stroke; lactate metabolism; microglia; single-cell RNA sequencing

DOI:  https://doi.org/10.1515/med-2025-1178
Circ J. 2025 Apr 29.

Integrative Analysis of Lactylation-Associated Features in Abdominal Aortic Aneurysm and Its Immune Microenvironment Utilizing scRNA-seq and Bulk RNA Sequencing.

Jiexin Li, Zhaoqi Huang, Jiaqi He, Ying Yang, Yangxin Chen.

   BACKGROUND: Abdominal aortic aneurysm (AAA) is a vascular disease strongly associated with immune dysregulation and metabolic disturbances. Although lactate metabolism and its associated process, lactylation, have been implicated in various diseases, their specific role in AAA pathogenesis remains poorly understood.
METHODS AND RESULTS: In this study, we used a multi-faceted approach, integrating single-cell and bulk RNA data analyses, with the objective of elucidating the interrelationship between lactylation and immune response in AAA patients. The result revealed significant heterogeneity in lactylation levels across different immune cell types. Cells with higher lactylation activity exhibited markedly elevated immune response scores. Differential expression and correlation analyses identified 65 lactylation-associated genes, which were further evaluated in the bulk RNA sequencing data to assess their relationship with the immune microenvironment in patients with AAA. Using 113 combinations of machine-learning algorithms, we identified 8 lactylation-related hub genes. The immune infiltration analysis demonstrated that these genes were linked to a multitude of immune cells. The animal experiments corroborated that Tnfsf8, Hist1 h2ag, Cd79b, Cd69, and Bank1 were upregulated in the AAA group, while Rpl36a and Rps29 were downregulated in the AAA group.
CONCLUSIONS: This study highlighted a potentially critical link between lactylation and immune dysregulation in AAA, thereby advancing our comprehension of the function of lactylation in AAA.

Keywords:  Abdominal aortic aneurysm; Immune infiltration; Lactylation

DOI:  https://doi.org/10.1253/circj.CJ-24-0892
PLoS One. 2025 ;20(4): e0322326

Integrated bulk RNA and single-cell analysis with experimental validation reveal oxidative stress-related diagnostic biomarkers for osteoporosis.

Cheng Zhong, Liping Zhong.

Osteoporosis (OP) is a systemic skeletal disorder characterized by reduced bone mass and deterioration of bone microarchitecture, which increases fracture risk and impairs physical function. This study explores the role of CHRM2 in osteogenic differentiation and evaluates its potential as a biomarker for OP. Single-cell RNA sequencing revealed distinct differences in cell type distributions between OP patients and healthy controls, notably an increase in M1 macrophages and regulatory T cells in OP patients. Functional enrichment analysis underscored the involvement of regulatory T cells in OP pathogenesis. Furthermore, CHRM2 was identified as a key gene associated with oxidative stress. In vitro experiments demonstrated that CHRM2 knockdown enhanced osteogenic differentiation while suppressing cell proliferation, likely via interactions with COL4A2. These findings suggest that CHRM2 plays a negative regulatory role in osteogenic differentiation and may serve as both a diagnostic biomarker and a potential therapeutic target for early-stage OP.

DOI: https://doi.org/10.1371/journal.pone.0322326
Arthritis Res Ther. 2025 Apr 25. 27(1): 96

Bioinformatic analysis and experimental verification reveal expansion of monocyte subsets with an interferon signature in systemic lupus erythematosus patients.

Jimin Zhang, Wuwei Zhuang, Yan Li, Chaoqiong Deng, Jingxiu Xuan, Yuechi Sun, Yan He.

   BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by chronic inflammation and multi-organ damage. A central factor in SLE pathogenesis is the excessive production of type I interferon (IFN-I), which drives immune dysregulation. Monocytes, key components of the immune system, significantly contribute to IFN-I production. However, their specific roles in SLE remain incompletely understood.
METHODS: This study utilized bioinformatics and statistical analyses, including robust rank aggregation (RRA), DESeq2, and limma, to analyze transcriptome data from peripheral blood mononuclear cells (PBMCs) and monocytes of SLE patients and healthy controls. Single-cell RNA sequencing (scRNA-seq) data were processed using the Seurat R package to identify and characterize monocyte subsets with a strong IFN-driven gene signature. Flow cytometry was employed to validate key findings, using markers such as CD14, SIGLEC1, and IRF7 to confirm monocyte subset composition.
RESULTS: Our research has found that monocytes in SLE undergo IFN-driven transcriptional reprogramming, with the upregulation of key interferon signature genes (ISGs), forming the SLE-Related Monocyte Signature (SLERRAsignature). Moreover, the composition of mononuclear phagocyte subsets in SLE patients changes, with an increase trend in the proportion of the CD14Mono8 subset in the flare group. The differentially expressed genes (DEGs) in 13 mononuclear phagocyte subsets of SLE are mainly ISGs, and the expression of ISGs is higher in severe patients. We identified SIGLEC1+IRF7+ monocytes among these subsets and for the first time discovered this group of cells in the peripheral blood of healthy individuals. In SLE, the enrichment score of the gene set representing SIGLEC1+IRF7+ monocytes is positively correlated with the severity of SLE. Finally, flow cytometry confirmed that the frequency of CD14+SIGLEC1+IRF7+ monocytes in PBMCs was higher in SLE compared with healthy controls.
CONCLUSIONS: Our study found that the expansion of IFN-I-producing monocyte subsets, particularly the CD14+SIGLEC1+IRF7+ subset, plays a crucial role in SLE pathogenesis. This subset may serve as a potential biomarker and therapeutic target for managing SLE.

Keywords:  CD14+SIGLEC1+IRF7+ monocytes; Interferon-stimulated genes; Single-cell RNA sequencing; Systemic lupus erythematosus; Type I interferon

DOI:  https://doi.org/10.1186/s13075-025-03560-5
J Int Med Res. 2025 Apr;53(4): 3000605251333646

Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium.

Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen.

  BackgroundKnee osteoarthritis is a debilitating disease with a complex pathogenesis. Synovitis, which refers to inflammation of the synovial membrane surrounding the joint, is believed to play an important role in the development and progression of knee osteoarthritis. To better understand the molecular mechanisms underlying knee osteoarthritis, we conducted a comprehensive analysis of gene expression in knee osteoarthritis synovium using machine learning.MethodsDifferentially expressed genes between knee osteoarthritis and control synovial tissues were analyzed using the GSE55235 dataset. We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine-recursive feature elimination, to screen for key genes. Then, we validated the key genes using an external dataset (GSE51588) and an in vitro knee osteoarthritis animal model. CIBERSORT was used to compare immune cell infiltration levels between knee osteoarthritis and control synovial tissues and determine their relationship with the key genes. Finally, we performed a Connectivity Map analysis to screen for potential small-molecule compounds. Moreover, we conducted single-cell RNA sequencing analysis using knee joint tissues to annotate different subtypes of cells.ResultsA total of 930 differentially expressed genes were identified. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination identified FOSL2 and RHoBTB1 as key genes. The expression levels of both genes were further validated in the GSE51588 dataset as well as verified through an in vitro experiment involving a knee osteoarthritis mouse model. Multiple significant correlation pairs were found between the immune cell infiltration levels. We unveiled the genetic basis of knee osteoarthritis using genome-wide association study and specific signaling pathways through gene set enrichment analysis. The GeneCards database was used to obtain 3032 pathogenic genes associated with knee osteoarthritis, and we found that RHoBTB1 expression was significantly negatively correlated and FOSL2 expression was significantly positively correlated with interleukin-1β expression. We predicted several small-molecule compounds based on Connectivity Map analysis. Finally, single-cell RNA sequencing analysis revealed the expression levels of the two key genes in chondrocytes and tissue stem cells.ConclusionFOSL2 and RHoBTB1 may play key roles in the pathogenesis of knee osteoarthritis, exhibiting correlations with immune cell infiltration levels. These findings indicate that these genes have potential as therapeutic targets. However, further research and validation are necessary to confirm their exact roles and therapeutic potential in knee osteoarthritis.

Keywords:  Knee osteoarthritis; immunological regulators; machine learning; regulatory pathways; synovium

DOI:  https://doi.org/10.1177/03000605251333646
Sci Rep. 2025 May 02. 15(1): 15437

Identification of PANoptosis related biomarkers to predict hepatic ischemia‒reperfusion injury after liver transplantation.

Zhihong Chen, Junwei Zhang, Lei Zhang, Yaoge Liu, Ting Zhang, Xinting Sang, Yiyao Xu, Xin Lu.

  Hepatic ischemia-reperfusion injury (HIRI) is a major complication following liver transplantation. Bioinformatic analysis was performed to elucidate the PANoptosis-related molecular mechanisms underlying HIRI. Comprehensive analysis of bulk and single-cell RNA sequencing data from human liver tissue before and after HIRI was performed. Differential expression analysis, weighted gene coexpression analysis, and protein interaction network analysis were used to identify candidate biomarkers. Multiple machine learning methods were utilized to screen for core biomarkers and construct a diagnostic predictive model. Functional and interaction analyses of the genes were also performed. Cellular clustering and annotation, pseudotemporal trajectory, and intercellular communication analyses of HIRI were conducted. Six PANoptosis-associated genes (CEBPB, HSPA1A, HSPA1B, IRF1, SERPINE1, and TNFAIP3) were identified as HIRI-related biomarkers. These biomarkers are regulated by NF-κB and miRNA-155. A nomogram for HIRI prediction based on these biomarkers was constructed and validated. In addition, the heterogeneity and dynamic changes in macrophage subpopulations during HIRI were revealed, highlighting the roles of Kupffer cells and monocyte-derived macrophages in modulating the hepatic microenvironment. The MIF and VISFATIN signaling pathways play important roles in the interaction between macrophages and other cells. These findings enhance our understanding of the mechanisms of PANoptosis in HIRI and provide a new basis and potential targets for prevention and treatment strategies for HIRI.

Keywords:  Bioinformatics analysis; Hepatic ischemia-reperfusion injury; Machine learning; Mononuclear phagocyte; PANoptosis

DOI:  https://doi.org/10.1038/s41598-025-99264-6
Biomedicines. 2025 Apr 02. pii: 859. [Epub ahead of print]13(4):

Identification of KIFC3 as a Colorectal Cancer Biomarker and Its Regulatory Mechanism in the Immune Microenvironment Based on Integrated Analysis of Multi-Omics Databases.

Fen Wang, Xinxin Zeng, Jielun Wen, Kexin Xian, Feng Jin, Sunfang Jiang, Liyue Sun.

  Background: Studies suggest that kinesin family (KIF) members can promote the occurrence of colorectal cancer (CRC). However, the mechanism of action has not yet been elucidated. The aim of this study was to identify CRC biomarkers associated with KIF members and to investigate their biological mechanisms in the treatment of colorectal cancer by analyzing multi-omics data. Methods: CRC-related datasets and KIF member-related genes (KIFRGs) were used. First, differentially expressed genes (DEGs) and differentially expressed methylation genes (DEMGs) in the TCGA-CRC were identified separately using different expression analyses (CRC vs. control). The intersecting genes were selected by overlapping the DEGs, DEMGs, and KIFRGs. Candidate genes were identified using survival analysis (p < 0.05). Subsequently, based on the candidate genes, biomarkers were selected by gene expression validation and survival analysis. Subsequently, functional enrichment, immune cell infiltration, and drug sensitivity analyses were performed. Single-cell analysis was utilized to perform cell annotation, and then function enrichment and pseudo-temporal analyses were performed. Results: The 12 intersecting genes were identified by overlapping 12,479 DEGs, 11,319 DEMGs, and 43 KIFRGs. The survival analysis showed that Kinesin Family Member C2 (KIFC2) and Kinesin Family Member C3 (KIFC3) had significant differences in survival (p < 0.05). Moreover, KIFC3 passed the gene expression validation and survival analysis validation (p < 0.05); thus, KIFC3 was deemed a biomarker. Subsequently, the pathways involved in KIFC3 were detected, such as the Ecm receptor intersection and chemokine signaling pathway. In addition, we found that KIFC3 was significantly positively correlated with natural killer (NK) cells (r = 0.455, p < 0.05) and NK T cells (r = 0.411, p < 0.05). Moreover, in the drug sensitivity of the CRC, the potential therapeutic benefits of AZD.2281, nilotinib, PD.173074, and shikonin were detected. Furthermore, using single-cell analysis, 16 cell clusters were annotated, and epithelial cells and M2-like macrophages were enriched in "rheumatoid arthritis". Additionally, we observed that most M1-like macrophages were present in the early stages of differentiation, whereas M2-like macrophages were predominant in the later stages of differentiation. Conclusions: This study identifies KIFC3 as a CRC biomarker through multi-omics analysis, highlighting its unique expression, survival association, immune correlations, and drug sensitivity for potential diagnostic and therapeutic applications.

Keywords:  KIFC3; ScRNA-seq; colorectal cancer; kinesin family member

DOI:  https://doi.org/10.3390/biomedicines13040859
FASEB J. 2025 May 15. 39(9): e70512

Single-cell sequencing reveals a regulatory role of WT1 in mast cell proliferation in endometrial polyps.

Yu Lin, Yiwen Qi, Yutong Yao, Huan Tong, Lan Chen, Weizhou Song, Guojing Li, Hong Xu, Xiaoying He.

  Endometrial polyps are the predominant structural anomalies of the endometrial mucosa observed in unexplained infertility cases, potentially compromising endometrial receptivity and suggesting shared etiological characteristics. However, their comprehensive cell atlas and immune landscape remain inadequately defined. In this study, we employed single-cell RNA sequencing and bulk RNA-seq to systematically analyze ectopic endometrial polyps (EPs) alongside adjacent eutopic endometrial tissues (EUs). This enabled us to delineate alterations in cell composition and transcriptional dynamics across diverse cell types associated with endometrial polyps. Notably, we observed an increase and activation of mast cells, with significant transcriptional profile variations. Through transcription regulatory network analysis, WT1 was identified as a pivotal transcriptional regulator mediating mast cell proliferation in EPs, concomitant with the dysregulation of WT1 target genes involved in cell growth. These findings provide novel insights into the cellular heterogeneity and molecular mechanisms of endometrial polyps at single-cell resolution, presenting potential therapeutic targets for clinical intervention.

Keywords:  WT1; endometrial polyp; mast cells; single‐cell RNA sequencing

DOI:  https://doi.org/10.1096/fj.202500116R
Methods Mol Biol. 2025 ;2908 99-109

Dissecting the Heterogeneity of Senescent Cells Through CITE-seq Integration.

Kotb Abdelmohsen, Krystyna Mazan-Mamczarz, Dimitrios Tsitsipatis, Martina Rossi, Rachel B Munk, Myriam Gorospe.

  Cellular senescence, a state of persistent growth arrest following cell damage, is associated with aging and age-related diseases. Understanding cell heterogeneity within senescent populations is crucial for developing therapies to mitigate senescence-associated pathologies. The protocol described here outlines an integrated approach to exploit the presence of cell surface proteins on subsets of senescent cells to study their heterogeneity at the single-cell level. After identifying senescence-associated surface proteins by mass spectrometry (MS) and then performing cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) single-cell analysis, we were able to identify unique transcriptomic programs associated with specific surface protein markers expressed in some senescent cells but not in others. We illustrate the utility of this approach by investigating the complex heterogeneity of senescent cell populations. However, this methodology can be applied to other biological scenarios where cells with unique transcriptomic profiles can be studied individually, thanks to the presence of specific cell surface proteins that distinguish them from other cells within the same population.

Keywords:  CITE-seq; Cell cycle; Multiomics; Proteome; Senescence; Single-cell transcriptome; Surface proteins; Transcriptome

DOI:  https://doi.org/10.1007/978-1-0716-4434-8_7
BMC Med. 2025 May 01. 23(1): 254

Molecular landscape of atherosclerotic plaque progression: insights from proteomics, single-cell transcriptomics and genomics.

Chaonan Wang, Yuyao Feng, Xiaoyan Liu, Haidan Sun, Zhengguang Guo, Jiang Shao, Kang Li, Junye Chen, Keqiang Shu, Deqiang Kong, Jiaxian Wang, Yiran Li, Xiangling Lei, Chen Li, Bao Liu, Wei Sun, Zhichao Lai.

   BACKGROUNDS: Atherosclerosis is a major contributor to cardiovascular diseases worldwide. Despite advancements in understanding its pathology, significant gaps remain in the molecular characterization of atherosclerotic plaques. This study addresses this gap by extensively profiling the proteomic landscape of carotid atherosclerotic plaques, classified under the American Heart Association (AHA) types IV to VI, to identify potential biomarkers and therapeutic targets.
METHODS: The study employed an integrated approach using data-independent acquisition (DIA) proteomics, single-cell RNA sequencing, and Mendelian randomization (MR). A total of 87 human carotid plaques were analyzed to identify and quantify protein expression. These proteins were then mapped to specific regions within the plaques, such as the fibrous cap and lipid core, and further validated in independent samples and single-cell datasets. Furthermore, Mendelian randomization techniques were employed to assess causal relationships between identified proteins levels and ischemic stroke.
RESULTS: The proteomic analysis of the 87 carotid plaques revealed 6143 proteins, highlighting diverse expression profiles across different plaque stages. Notably, proteins like CD44 and GAL-1 were predominantly expressed in the fibrous cap, suggesting a role in plaque stability, while TREM2, SMAD3, and IL-6R showed higher expression in the lipid core, indicating involvement in inflammatory processes. These findings were further corroborated by single-cell RNA sequencing, revealing cell-specific expression patterns that align with the observed proteomic data. Additionally, MR analysis indicated the causal role of IL6R, CD44, and SMAD3 in ischemic stroke.
CONCLUSIONS: This study provides valuable insights into the progression of atherosclerotic plaques, identifying key proteins that could serve as potential biomarkers and therapeutic targets. It enhances our molecular understanding of atherosclerosis and opens up new avenues for treatment. Additionally, our study demonstrates the accuracy and robustness of proteomics in prioritizing genes associated with plaque-related traits.

Keywords:  Atherosclerosis; Carotid artery stenosis; Mendelian randomization; Proteomics; Therapeutic targets

DOI:  https://doi.org/10.1186/s12916-025-04058-2
Theranostics. 2025 ;15(11): 5337-5357

Single-nucleus RNA sequencing and spatial transcriptomics reveal an immunosuppressive tumor microenvironment related to metastatic dissemination during pancreatic cancer liver metastasis.

Hongsen Liu, Mengting Chen, Bo Hong, Yufei Xiao, Qianming Chen, Yun Qian.

  Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by early liver metastasis and high mortality. The tumor microenvironment plays a pivotal role in tumor progression; however, the immune microenvironment's involvement in PDAC liver metastasis remains poorly understood. Methods: This study investigates cellular heterogeneity in primary tumor (PT) and liver metastasis (LM) tissues of PDAC using single-nucleus RNA sequencing and spatial transcriptomics. Intra-tumor heterogeneity and cell interactions were elucidated through deconvolution, intercellular signalling, pseudotime analysis, and immune infiltration profiling. The spatial distribution of immune cells was assessed by multiplexed immunofluorescence staining, and prognostic models were developed and validated through immunohistochemistry (IHC). Analyzing the regulatory role of CITED4 in the invasion and metastasis of pancreatic cancer cells through transwell assay and scratch wound healing assay. Results: A total of 62,326 cells were sequenced, with metastatic dissemination cells showing significant upregulation of epithelial-mesenchymal transition (EMT)-related genes during liver metastasis. Spatial transcriptomics revealed the enrichment of metastatic dissemination cells and FOXP3-related Treg cells at the tumor front in PT tissues. In comparison to LM tissues, the tumor front in PT tissues fosters an immunosuppressive microenvironment through the accumulation of Treg cells. Interaction analysis identified the SPP1 pathway as a key promoter of this immunosuppressive environment. Furthermore, prognostic models highlighted CITED4 as critical biomarkers in PDAC. Elevated CITED4 expression is correlated with liver metastasis and poor prognosis in patients with PDAC. siRNA-mediated knockdown of CITED4 suppresses the invasion and metastasis of pancreatic cancer cells. Conclusions: In summary, this study revealed that Treg cell alterations, mediated by metastatic dissemination cells within the immune microenvironment, significantly contribute to PDAC liver metastasis, and that CITED4 enhances the metastatic potential of metastatic dissemination cells.

Keywords:  CITED4; Pancreatic d.uctal adenocarcinoma; Single-nucleus RNA sequencing; Spatial transcriptomics; Treg cells; metastatic dissemination

DOI:  https://doi.org/10.7150/thno.108925
J Cardiol. 2025 Apr 28. pii: S0914-5087(25)00106-6. [Epub ahead of print]

Novel biomarkers related to mitochondrial permeability transition driven-necrosis in hypertrophic cardiomyopathy.

Wenqiang Qi, Linghua Fu, Qinghua Wu, Pingping Yang.

   BACKGROUND: The role of mitochondrial permeability transition driven necrosis-related genes (MPTDN-RGs) in hypertrophic cardiomyopathy (HCM) is unclear. This investigation combined transcriptomics and Mendelian randomization (MR) analysis to explore the association of MPTDN-RGs with HCM.
METHODS: GSE36961 (training set), GSE141910 (validation set), and GSE174691 (single-cell dataset) were retrieved from Gene Expression Omnibus (GEO) database. This study is based on scRNA-seq and transcriptome sequencing (mRNA Sequencing, mRNA-seq) data combined with MR, and use MPTDN-RGs to identify genes of HCM.
RESULTS: Based on 51 interaction genes overlapped by 250 module genes and 154 differentially expressed genes, the top 10 genes within protein-protein interaction (PPI) core network were regarded as candidate genes. ITGB2 and STAT3 were screened out as genes by multiple analysis methods. MR results revealed that ITGB2 was a risk factor, while STAT3 was a protective factor for HCM. Gene set enrichment analysis (GSEA) indicated that ITGB2 and STAT3 were involved in complement and coagulation cascade. Moreover, ITGB2 had the strongest positive and significant correlations with myeloid-derived suppressor cells and chemokine receptor. Single cell analysis showed that STAT3 was highly expressed in endothelial cells, while ITGB2 was significantly greater in dendritic cells. During the process of differentiation, the expression of ITGB2 and STAT3 were decreased, and dendritic cells gradually differentiated and matured to play a role in immune function.
CONCLUSION: To our knowledge, this is the first study to identify the novel genes related to MPTDN in HCM by combining transcriptomics and MR analysis. Two key genes play a critical role in HCM. ITGB2 and STAT3 deserve further investigation as potential therapeutic targets for HCM.

Keywords:  Hypertrophic cardiomyopathy; ITGB2; Immunity; Mitochondrial permeability transition driven necrosis; STAT3

DOI:  https://doi.org/10.1016/j.jjcc.2025.04.008
J Cancer. 2025 ;16(7): 2145-2166

Identification and Validation of Prognostic Genes Related to Histone Lactylation Modification in Glioblastoma: An Integrated Analysis of Transcriptome and Single-cell RNA Sequencing.

Wenfeng He, Ruihong Chen, Guangliang Chen, Lihan Zhang, Yuhang Qian, Jie Zhou, Jianhua Peng, Vincent Kam Wai Wong, Yong Jiang.

  Background: The impact of histone lactylation modification (HLM) on glioblastoma (GBM) progression is not well understood. This study aimed to identify HLM-associated prognostic genes in GBM and explore their mechanisms of action. Methods: The presence and role of lactylation in glioma clinical tissue samples and its correlation with GBM progression were analysed through immunohistochemical staining and Western blotting. Sequencing data for GBM were obtained from publicly available databases. An initial correlation analysis was performed between global HLM levels and GBM. Differentially expressed HLM-related genes (HLMRGs) in GBM were identified by intersecting differentially expressed genes (DEGs) from the TCGA-GBM dataset, key module genes derived from weighted gene coexpression network analysis (WGCNA), and previously reported HLMRGs. Prognostic genes were subsequently identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses, which formed the basis for constructing a risk prediction model. Associations between HLMRGs and GBM were further evaluated via single-cell RNA sequencing (scRNA-seq) datasets. Complementary analyses, including functional enrichment, immune infiltration, somatic mutation, and nomogram-based survival prediction, were conducted alongside in vitro experiments. Additionally, drug sensitivity and Chinese medicine prediction analyses were performed to identify potential therapeutic agents for GBM. Results: We detected a significant increase in global lactylation levels in GBM, which correlated with patient survival. We identified 227 differentially expressed HLMRGs from the intersection of 3,343 differentially expressed genes and 948 key module genes, indicating strong prognostic potential. Notably, genes such as SNCAIP, TMEM100, NLRP11, HOXC11, and HOXD10 were highly expressed in GBM. Functional analysis suggested that HLMRGs are involved primarily in pathways related to cytokine‒cytokine receptor interactions, cell cycle regulation, and cellular interactions, including microglial differentiation states. Further connections were established between HLMRGs and infiltrating immune cells, particularly type 1 T helper (Th1) cells, as well as mutations in genes such as PTEN. The potential therapeutic agents identified included ATRA and Can Sha. Conclusion: The HLM-related gene risk prediction model shows substantial promise for improving patient management in GBM, providing crucial insights for clinical prognostic evaluations and immunotherapeutic approaches in GBM.

Keywords:  Glioblastoma; Histone lactylation modification; Prognostic genes; Single-cell RNA sequencing

DOI:  https://doi.org/10.7150/jca.110646
Clin Exp Nephrol. 2025 Apr 25.

Single-cell epigenetics and multiomics analysis in kidney research.

Seishi Aihara, Yoshiharu Muto.

  The rapid evolution of single-cell sequencing technologies has significantly advanced our knowledge of cellular heterogeneity and the underlying molecular basis in healthy and diseased kidneys. While single-cell transcriptomic analysis excels in characterizing cell states in the heterogeneous population, the complex regulatory mechanisms governing the gene expressions are difficult to decipher using transcriptomic data alone. Single-cell sequencing technology has recently extended to include epigenome and other modalities, allowing single-cell multiomics analysis. Especially, the integrative analysis of epigenome and transcriptome dissects the cell-specific, gene-regulatory mechanisms driving cellular heterogeneity. An increasing number of single-cell multimodal atlases are being generated in nephrology research, offering novel insights into cellular diversity and the underpinning epigenetic regulation. This ongoing paradigm shift in kidney research accelerates the identification of new biomarkers and potential therapeutic targets, promoting clinical translation. In this era of transformative nephrology research, the basic knowledge of single-cell sequencing analysis and multiomics approach is valuable not only for basic science researchers but for all nephrologists. This review overview single-cell analysis, with a focus on emerging epigenomic and multiomics approaches and their application to kidney research.

Keywords:  Epigenetics; Kidney; Multiomics; Single-cell analysis

DOI:  https://doi.org/10.1007/s10157-025-02679-8
Front Biosci (Landmark Ed). 2025 Apr 16. 30(4): 37429

Cross-Species Multi-Omics Analysis Reveals Myeloid-Driven Endothelial Oxidative Stress in Ischemic Stroke.

Ziqi Cheng, Hua Zhu, Shi Feng, Yonggang Zhang, Xiaoxing Xiong.

   BACKGROUND: Ischemic stroke is a leading cause of mortality and disability worldwide, yet the interplay between peripheral and central immune responses is still only partially understood. Emerging evidence suggests that myeloid cells, when activated in the periphery, infiltrate the ischemic brain and contribute to the disruption of the blood-brain barrier (BBB) through both inflammatory and metabolic mechanisms.
METHODS: In this study, we integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and flow cytometry data from human and mouse models of ischemic stroke. Mouse stroke models were induced by transient middle cerebral artery occlusion (tMCAO), and brain tissues were later collected at specified time points for analysis. We examined time-dependent transcriptional changes in the peripheral blood, delineated cell-type-specific responses by single-cell profiling, and validated myeloid infiltration into the ischemic brain. We also investigated endothelial metabolic reprogramming and oxidative stress by combining scMetabolism analyses (a computational R package for inferring metabolic pathway activity at the single-cell level) with in vitro oxygen-glucose deprivation/reperfusion (OGD/R) experiments.
RESULTS: Cross-species bulk RNA-seq revealed a modest early immune shift at 3 h post-stroke, escalating significantly by 24 h, with robust myeloid-centric gene signatures conserved in humans and mice. Single-cell analyses confirmed a pronounced expansion of neutrophils, monocytes, and megakaryocytes in peripheral blood, coupled with a decrease in T and B lymphocytes. Spatial transcriptomics and flow cytometry demonstrated substantial infiltration of CD11b+ myeloid cells into the infarct core, which showed extensive interaction with endothelial cells. Endothelial scRNA-seq data showed reductions in the oxidative phosphorylation, glutathione, and nicotinate metabolic pathways, together with elevated pentose phosphate pathway activity, suggestive of oxidative stress and compromised antioxidant capacity. Functional scoring further indicated diminished endothelial inflammation/repair potential, while in vitro OGD/R experiments revealed morphological disruption, CD31 downregulation, and increased 4-hydroxynonenal (4-HNE), underscoring the importance of endothelial oxidative damage in BBB breakdown.
CONCLUSIONS: These multi-omics findings highlight the existence of a coordinated peripheral-central immune axis in ischemic stroke, wherein myeloid cell recruitment and endothelial metabolic vulnerability jointly exacerbate inflammation and oxidative stress. The targeting of endothelial oxidative injury and myeloid-endothelial crosstalk may represent a promising strategy to mitigate secondary brain injury in ischemic stroke.

Keywords:  blood-brain barrier; cross-species analysis; endothelial cells; ischemic stroke; metabolic reprogramming; myeloid cells; oxidative stress; single-cell RNA sequencing

DOI:  https://doi.org/10.31083/FBL37429
Res Sq. 2025 Apr 25. pii: rs.3.rs-6314842. [Epub ahead of print]

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion.

Mustafa Khasraw, Kelly Hotchkiss, Kenan Zhang, Anna Corcoran, Elizabeth Owens, Pamela Noldner, Chelsea Railton, Kyra Van Batavia, Ying Zhou, Jodie Jepson, Kirit Singh, Roger McLendon, Kristen Batich, Anoop Patel, Katayoun Ayasoufi, Michael Brown, Evan Calabrese, Jichun Xie, Jose Conejo-Garcia, Beth Shaz, John Hickey.

Tumor-infiltrating lymphocyte (TIL) therapy, recently approved by the FDA for melanoma, is an emerging modality for cell-based immunotherapy. However, its application in immunologically "cold" tumors such as glioblastoma remains limited due to sparse T cell infiltration, antigenic heterogeneity, and a suppressive tumor microenvironment. To identify genomic and spatial determinants of TIL expandability, we performed integrated, multimodal profiling of high-grade gliomas using spectral flow cytometry, TCR sequencing, single-cell RNA-seq, Xenium in situ transcriptomics, and CODEX spatial proteomics. Comparative analysis of TIL-generating (TIL⁺) versus non-generating (TIL⁻) tumors revealed that IL7R expression, structured perivascular immune clustering, and tumor-intrinsic metabolic programs such as ACSS3 were associated with successful TIL expansion. In contrast, TIL⁻tumors were enriched for neuronal lineage signatures, immunosuppressive transcripts including TOX and FERMT1 , and tumor-connected macrophages. This study defines spatial and molecular correlates of TIL manufacturing success and establishes a genomics-enabled selection platform for adoptive T cell therapy. The profiling approach is now being prospectively implemented in the GIANT clinical trial (NCT06816927), supporting its translational relevance and scalability across glioblastoma and other immune-excluded cancers.

DOI: https://doi.org/10.21203/rs.3.rs-6314842/v1
Discov Oncol. 2025 Apr 26. 16(1): 620

In silico-based analysis and in vitro experiments identify SIGMAR1 as a potential marker of putative lung cancer stem cells.

Tetsuhiro Horie, Ayane Kuwano, Takuya Sakamoto, Yuka Nakamura, Kayoko Yamaguchi, Ikuhiro Tanida, Satoshi Osawa, Kazuo Yasumoto, Yasuhito Ishigaki.

BACKGROUND: Lung cancer is the leading cause of cancer-related mortality worldwide; however, despite the development and clinical application of various drugs, the prognosis remains poor. One reason for this is the high rate of recurrence and metastasis. The cancer stem cell (CSC) theory has been proposed to explain their root cause, and removal of CSCs is necessary to cure cancer completely; however, detailed profiles of lung CSCs have not been clarified. Here, we used single-cell RNA sequencing (scRNA-seq) data to identify novel markers for lung CSCs and validated their expression and function in vitro.
METHODS: A549-derived tumorspheres were used as a model for lung CSCs. To identify genes upregulated in CSC-like cells, we reanalyzed two publicly available scRNA-seq datasets from human lung cancer tissues. Additionally, trajectory analysis was performed to examine changes in candidate gene expression during CSC differentiation. The role of these candidate genes in CSC regulation was further investigated through functional assays.
RESULTS: Tumorspheres exhibited increased expression of well-established CSC markers. scRNA-seq analysis suggested that SIGMAR1 expression was significantly upregulated in CSC-like cells and decreased with differentiation. Furthermore, siRNA-mediated SIGMAR1 knockdown suppressed tumorsphere self-renewal capacity and reduced CSC marker expression.
CONCLUSIONS: We propose that SIGMAR1 serves as a potential functional marker of CSCs and plays a crucial role in regulating self-renewal capacity. Targeting SIGMAR1 may provide a novel therapeutic strategy for preventing metastasis and recurrence-major clinical challenges in lung cancer treatment. Future studies should investigate the underlying mechanisms by which SIGMAR1 modulates CSC properties.

DOI: https://doi.org/10.1007/s12672-025-02394-6
Comput Methods Programs Biomed. 2025 Apr 24. pii: S0169-2607(25)00226-3. [Epub ahead of print]267 108809

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

Qing Zhang, Xiaoxiao Wu, Xiang Li, Wei Ma, Tongquan Wu, Liuyue Li, Fan Hu, Yicheng Xie, Xinglong Wu.

   BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, conventional supervised or semi-supervised methodologies are contingent on expert labels and incur substantial labeling costs, In contrast self-supervised pre-training strategies leverage unlabeled data during the pre-training phase and utilise a limited amount of labeled data in the fine-tuning phase, thereby greatly reducing labor costs. Furthermore, the fine-tuning does not need to learn the feature representations from scratch, enhancing the efficiency and transferability of the model.
METHODS: The proposed methodology is outlined below. The deep learning framework, TransAnno-Net, is based on transfer learning and a Transformer architecture. It has been designed for efficient and accurate cell type annotations in large-scale scRNA-seq datasets of mouse lung organs. Specifically, TransAnno-Net is pre-trained on the scRNA-seq lung data of approximately 100,000 cells to acquire gene-gene similarities via self-supervised learning. It is then migrated to a relatively small number of datasets to fine-tune specific cell type annotation tasks. To address the issue of imbalance in cell types commonly observed in scRNA-seq data, we applied a random oversampling technique is applied to the fine-tuned dataset. This is done to mitigate the impact of distributional imbalance on the annotation outcomes.
RESULTS: The experimental findings demonstrate that TransAnno-Net exhibits superior performance with an AUC of 0.979, 0.901, and 0.982, respectively, on three mouse lung datasets, outperforming eight state-of-the-art (SOTA) methods. In addition, TransAnno-Net demonstrates robust performance on cross-organ, cross-platform datasets, and is competitive with the fully supervised learning-based method.
CONCLUSION: The TransAnno-Net method is a highly effective cross-platform and cross-data set single-cell type annotation method for mouse lung tissues and supports cross-organ cell type annotation. This approach is expected to enhance the efficiency of research on the biological mechanisms of complex biological systems and diseases.

Keywords:  cell type annotation; deep learning; mouse lung tissue; self-supervised pretraining; single-cell RNA sequencing

DOI:  https://doi.org/10.1016/j.cmpb.2025.108809
Immunobiology. 2025 Apr 26. pii: S0171-2985(25)00043-9. [Epub ahead of print]230(3): 152909

Interpretable Machine Learning reveals the Role of PANoptosis in the Diagnosis and Subtyping of NAFLD.

Feng Tian, Yuqi Zhao, Xinyang He, Yu Zhang, Minxuan Hu, Yiwei Liang, Ziyou Tian, Yaxian Gao, Yongwei Wang.

  Non-alcoholic fatty liver disease (NAFLD) is a global health challenge characterized by complex pathogenesis and limited therapeutic options. Emerging evidence highlights PANoptosis-a coordinated interplay of pyroptosis, apoptosis, and necroptosis-as a critical driver of metabolic and immune dysregulation in NAFLD. Here, we integrated multiple datasets and interpretable machine learning to unravel the role of PANoptosis in NAFLD diagnosis, subtyping, and immune microenvironment remodeling. By intersecting differentially expressed genes and PANoptosis-related genes, we identified 9 hub genes (e.g., TRADD, CASP6, TNFRSF1A and TNFAIP3) and constructed a robust diagnostic model (AUC = 0.976) validated via SHAP analysis and nomogram. Unsupervised consensus clustering stratified NAFLD patients into two PANoptosis-driven subtypes (C1/C2 and CA/CB), revealing distinct immune cell infiltration patterns and pathway activation. Single-cell sequencing further localized hub genes to immune cells and revealed their cell communication, implicating their roles in the progression of NAFLD. Molecular docking studies identified fostamatinib and minocycline as potential therapeutic candidates, while pan-cancer analysis revealed that TNFRSF1A overexpression is associated with poor prognosis across multiple cancer types. This study enhances the understanding of PANoptosis as a crucial diagnostic and therapeutic target in NAFLD, providing novel insights into immune-mediated pathogenesis and paving the way for treatment strategies.

Keywords:  Diagnostic model; Molecular docking; Non-alcoholic fatty liver disease; PANoptosis; Single-cell sequencing; Subtypes

DOI:  https://doi.org/10.1016/j.imbio.2025.152909
Sci Rep. 2025 Apr 25. 15(1): 14536

Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer.

Danfeng Zhao, Zhenjie Zang, Haodong Li, Ruiyu Li, Guanbo Wang, Keqin Zhang, Tongxiang Diao, Qiang Fu.

  The improvement of the prediction of prostate cancer (PCa) is a major challenge in disease management. This study analysed a total of 147,856 cells and identified 15 distinct cell types using single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data from TCGA and GEO databases. Of these cells, 31,256 exhibited a high telomere-related gene score and were predominantly composed of myeloid dendritic cells (mDCs). Simultaneously, pseudo-temporal analysis indicated that mDCs are in the later stages of the differentiation trajectory, suggesting the significant role of mDCs as telomere-active cells in the development of PCa. Analysis of cell-cell communication revealed significant differences, particularly an increase in communication between mDCs and CTLs, alongside a decrease in communication between mDCs and B cells. These variations may represent critical nodes influencing the development of PCa. Additionally, two hub genes were utilized to create risk models, with ROC curves confirming their predictive efficacy for 3-, 5-, and 10-year survival rates in patients. Functional analysis of these genes was conducted, and NPY siRNA transfection notably inhibited proliferation in LNCaP and DU145 cells. Furthermore, the models demonstrated that high-risk patients had poorer overall survival, greater immune infiltration, and reduced sensitivity to chemotherapeutic drugs.

Keywords:  Prostate cancer; Risk mode; Single-cell RNA-sequencing; Telomere-related gene; Tumour immune environment

DOI:  https://doi.org/10.1038/s41598-025-98663-z
Neuromolecular Med. 2025 Apr 27. 27(1): 30

Adhesion-Related Pathways and Functional Polarization of Astrocytes in Traumatic Brain Injury: Insights from Single-cell RNA Sequencing.

Xiaoyan Liu, Ji Xia, Wenjing Shao, Xiaoming Li, Danfeng Yuan, Jingru Xie, Liang Zhang, Yuqian Tang, Hui Zhao, Pengfei Wu.

  Traumatic brain injury (TBI) induces profound functional heterogeneity in astrocytes, yet the regulatory mechanisms underlying this diversity remain poorly understood. In this study, we analyzed single-cell RNA sequencing data from the cortex and hippocampus of TBI mouse models to characterize astrocyte subtypes and their functional dynamics. We identified two major reactive subtypes: A1 astrocytes, enriched in inflammatory response, synaptic regulation, and neurodegenerative disease-related pathways; and A2 astrocytes, enriched in lipid metabolism, extracellular matrix (ECM) remodeling, and phagosome formation pathways. These functional differences were consistently observed across datasets with varying injury severities. Notably, adhesion-related pathways-including gap junctions, adherens junctions, and calcium-dependent adhesion-showed significant subtype-specific expression patterns and temporal shifts. Pseudotime trajectory analysis further suggested a potential transition between A1 and A2 states, accompanied by dynamic regulation of adhesion-related genes. Our findings highlight the complex and context-dependent roles of astrocytes in TBI and propose cell adhesion as a key modulator of astrocyte functional polarization.

Keywords:  Adhesion; Astrocyte; Heterogeneity; Single-cell RNA sequencing; TBI

DOI:  https://doi.org/10.1007/s12017-025-08858-w
Neural Regen Res. 2025 Apr 29.

Molecular mechanisms after optic nerve injury: Neurorepair strategies from a transcriptomic perspective.

Xiaxue Chen, Muyang Wei, Guangyu Li.

   ABSTRACT: Retinal ganglion cells, a crucial component of the central nervous system, are often affected by irreversible visual impairment due to various conditions, including trauma, tumors, ischemia, and glaucoma. Studies have shown that the optic nerve crush model and glaucoma model are commonly used to study retinal ganglion cell injury. While these models differ in their mechanisms, both ultimately result in retinal ganglion cell injury. With advancements in high-throughput technologies, techniques such as microarray analysis, RNA sequencing, and single-cell RNA sequencing have been widely applied to characterize the transcriptomic profiles of retinal ganglion cell injury, revealing underlying molecular mechanisms. This review focuses on optic nerve crush and glaucoma models, elucidating the mechanisms of optic nerve injury and neuron degeneration induced by glaucoma through single-cell transcriptomics, transcriptome analysis, and chip analysis. Research using the optic nerve crush model has shown that different retinal ganglion cell subtypes exhibit varying survival and regenerative capacities following injury. Single-cell RNA sequencing has identified multiple genes associated with retinal ganglion cell protection and regeneration, such as Gal, Ucn, and Anxa2. In glaucoma models, high-throughput sequencing has revealed transcriptomic changes in retinal ganglion cells under elevated intraocular pressure, identifying genes related to immune response, oxidative stress, and apoptosis. These genes are significantly upregulated early after optic nerve injury and may play key roles in neuroprotection and axon regeneration. Additionally, CRISPR-Cas9 screening and ATAC-seq analysis have identified key transcription factors that regulate retinal ganglion cell survival and axon regeneration, offering new potential targets for neurorepair strategies in glaucoma. In summary, single-cell transcriptomic technologies provide unprecedented insights into the molecular mechanisms underlying optic nerve injury, aiding in the identification of novel therapeutic targets. Future researchers should integrate advanced single-cell sequencing with multi-omics approaches to investigate cell-specific responses in retinal ganglion cell injury and regeneration. Furthermore, computational models and systems biology methods could help predict molecular pathways interactions, providing valuable guidance for clinical research on optic nerve regeneration and repair.

Keywords:  RNA sequencing; glaucoma; microarray; neurodegeneration; optic nerve crush; optic nerve regeneration; retinal ganglion cell; single-cell RNA sequencing; transcriptome

DOI:  https://doi.org/10.4103/NRR.NRR-D-24-00794