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Single-cell spatial transcriptomics of tertiary lymphoid organ-like structures in human atherosclerotic plaques.
Tertiary lymphoid organs have been identified in the arterial adventitia in both mouse models of atherosclerosis and patients with atherosclerosis, yet their role in the disease remains insufficiently explored. Here we present a spatially resolved single-cell transcriptome atlas of human atherosclerotic plaques, identifying 14 distinct cell types and providing evidence of plaque tertiary lymphoid organs (PTLOs). The development of PTLOs was associated with the expression of lymphangiogenic chemokine genes and the adhesion molecule gene in fibroblast-like smooth muscle cells. PTLOs harbor abundant B cells with expanded and diversified B cell receptors, suggesting substantial immune involvement. We also observed that B cells may be exchanged between PTLOs and perivascular adipose tissues. The presence of PTLO-like structures correlates with cerebrovascular events, which may be mediated by PTLO-derived IgG antibodies enhancing macrophage functional activity. Our findings suggest the existence and characteristics of PTLOs in human atherosclerosis, elucidating their cellular functions and clinical implications and offering avenues for understanding, diagnosing and treating this condition.
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Single cell approaches to address adipose tissue stromal cell heterogeneity.
A central function of adipose tissue is in the management of systemic energy homeostasis that is achieved through the co-ordinated regulation of energy storage and mobilization, adipokine release, and immune functions. With the dramatic increase in the prevalence of obesity and obesity-related metabolic disease over the past 30 years, there has been extensive interest in targeting adipose tissue for therapeutic benefit. However, in order for this goal to be achieved it is essential to establish a comprehensive atlas of adipose tissue cellular composition and define mechanisms of intercellular communication that mediate pathologic and therapeutic responses. While traditional methods, such as fluorescence-activated cell sorting (FACS) and genetic lineage tracing, have greatly advanced the field, these approaches are inherently limited by the choice of markers and the ability to comprehensively identify and characterize dynamic interactions among stromal cells within the tissue microenvironment. Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool for deconvolving cellular heterogeneity and holds promise for understanding the development and plasticity of adipose tissue under normal and pathological conditions. scRNAseq has recently been used to characterize adipose stem cell (ASC) populations and has provided new insights into subpopulations of macrophages that arise during anabolic and catabolic remodeling in white adipose tissue. The current review summarizes recent findings that use this technology to explore adipose tissue heterogeneity and plasticity.
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A senescence-based machine learning model prognosticates and personalizes therapy in cervical cancer.
BACKGROUND: Cervical cancer remains a major global health burden. This study aimed to characterize the role of cellular senescence in the tumor microenvironment (TME) and its clinical implications, filling the critical gap in understanding senescence landscapes in cervical cancer. METHODS: We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and multi-omics analyses to profile the senescence landscape in cervical cancer. A 17-gene senescence-related signature (SRS) was constructed from 266 key genes via Boruta feature selection, and a combined random survival forest (RSF) + gradient boosting machine (GBM) machine learning model was developed for prognostic stratification and validated in The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and GSE44001 cohorts. We further analyzed immune profiles, genomic alterations, and drug sensitivity across SRS-defined risk groups, and performed transcriptome RNA-seq, in vitro assays, and in vivo xenograft models to investigate the anti-tumor efficacy of LY3177833 alone or with paclitaxel in SiHa and HeLa cells. RESULTS: Single-cell analysis identified fibroblasts and cancer cells as the primary senescent populations linked to stromal remodeling. The SRS model stratified patients into high- and low-risk groups with distinct survival outcomes; high-risk patients showed metabolic reprogramming, enhanced pro-tumorigenic interactions, increased co-mutations, reduced immune checkpoint expression, and poorer therapy responses. Transcriptome analyses confirmed that the LY3177833-paclitaxel combination inhibited proliferation-related pathways (MITOTIC_SPINDLE and G2M_CHECKPOINT) in both cell lines, with in vitro assays showing LY3177833 induced senescence and synergized with paclitaxel via apoptosis, and in vivo models validating suppressed tumor growth. CONCLUSIONS: This study defines a senescence-rich microenvironment linked to aggressive cervical cancer biology. The SRS serves as a robust prognostic and predictive biomarker, providing a framework for personalized risk assessment. Furthermore, the LY3177833-paclitaxel combination exhibits synergistic anti-tumor effects, offering a promising therapeutic strategy.
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Lactylation-driven gene signatures define breast cancer prognosis: a predictive model and insights into immune microenvironment dynamics.
BACKGROUND: Breast cancer is a leading cause of cancer-related mortality worldwide, with poor prognosis largely due to its invasive and metastatic nature. Tumor lactylation plays a crucial role in cancer progression by influencing immune modulation and metabolic reprogramming. This study aimed to identify lactylation-related gene signatures associated with breast cancer prognosis and develop a predictive survival model. METHODS: Bioinformatics analyses were performed using RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consensus clustering analysis was applied to classify breast cancer samples into distinct groups based on lactylation-related gene expression. Differentially expressed genes (DEGs) between clusters were identified and subjected to functional enrichment analysis. To assess immune-related differences between groups, the ESTIMATE and CIBERSORT algorithms were used, along with an analysis of human leukocyte antigen (HLA) and immune checkpoint molecule expression levels, to explore the relationship between lactylation and the breast cancer immune microenvironment. A prognostic model was constructed using univariate Cox and Lasso regression analyses, followed by validation. Machine learning techniques identified key biomarkers, which were further analyzed for clinical relevance. Additionally, single-cell clustering was performed to investigate the expression patterns of these genes within the breast cancer microenvironment. RESULTS: Consensus clustering identified two distinct groups: high and low lactylation. Differentially expressed genes were enriched in pathways related to cytokine-cytokine receptor interaction, immune response, cell activation, and adhesion. Lactylation-related genes were found to influence immune cell infiltration in the breast cancer microenvironment. Thirty-seven prognostic lactylation-related genes were identified through univariate Cox regression and used to develop a predictive model. The high-risk group was associated with poorer survival, and the model's performance was validated in the GEO cohort. Specific hub genes involved in immune modulation and malignant cell proliferation were also identified. CONCLUSION: We successfully developed a lactylation-based prognostic model that can assess breast cancer prognosis and potentially guide personalized treatment strategies.
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A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.
Single-cell RNA sequencing has provided unprecedented insights into the cellular heterogeneity of acute and chronic pulmonary injury. Lack of data-driven analytical frameworks remains a significant barrier to reproducible and biologically robust analyses. To address this challenge, we developed a modular, interactive analysis platform for single-cell RNA sequencing data processing and visualization. As a case study, we applied the framework to an immune-enriched murine lung dataset containing samples from different batches and across four conditions: control, inflammation and remodeling, fibrosis, and fibrotic lung with ozone exposure. Manual annotations performed on individual samples were used as ground-truth labels to evaluate automated cell-type annotations generated using the Tabula Muris Senis and LungMAP reference atlases. These annotations were also utilized to benchmark the performance of data preprocessing and integration combinations on batch-effect removal and the preservation of biological identity. Within the LIANA framework, we evaluated eight inference algorithms and 18 references for cell-cell communication analysis. In our results, we outlined the framework of the analysis pipeline and interactive platform. Through manual annotation, we identified 36 distinct cell populations, highlighting the limitations of automated reference-based annotations in resolving cell states missing from underlying references - particularly injury-induced monocyte-derived macrophages and eosinophils. In our dataset, Log-normalization outperformed SCTransform in both preserving cell-type separation and batch mixing, while FastMNN provided the best balance between technical batch correction and the retention of cell subtype identities. Cell-cell communication analysis revealed that permutation-based algorithms used by CellChat and CellPhoneDB improved prediction confidence, while MouseConsensus and OmniPath resources increased interaction coverage. These findings demonstrate that preprocessing, integration, and cell annotation strategies should be selected in a context-dependent, data-driven manner rather than relying on default workflows. The annotated murine lung dataset expands the existing atlas, and the modular interactive platform offers a practical framework to improve accessibility, reproducibility, and analytical precision in pulmonary research.
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ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-CoV-2 COVID-19.
BACKGROUND: Pandemic COVID-19 by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) infection is facilitated by the ACE2 receptor and protease TMPRSS2. Modestly sized case series have described clinical factors associated with COVID-19, while ACE2 and TMPRSS2 expression analyses have been described in some cell types. Patients with cancer may have worse outcomes to COVID-19. METHODS: We performed an integrated study of ACE2 and TMPRSS2 gene expression across and within organ systems, by normal versus tumor, across several existing databases (The Cancer Genome Atlas, Census of Immune Single Cell Expression Atlas, The Human Cell Landscape, and more). We correlated gene expression with clinical factors (including but not limited to age, gender, race, body mass index, and smoking history), HLA genotype, immune gene expression patterns, cell subsets, and single-cell sequencing as well as commensal microbiome. RESULTS: Matched normal tissues generally display higher ACE2 and TMPRSS2 expression compared with cancer, with normal and tumor from digestive organs expressing the highest levels. No clinical factors were consistently identified to be significantly associated with gene expression levels though outlier organ systems were observed for some factors. Similarly, no HLA genotypes were consistently associated with gene expression levels. Strong correlations were observed between ACE2 expression levels and multiple immune gene signatures including interferon-stimulated genes and the T cell-inflamed phenotype as well as inverse associations with angiogenesis and transforming growth factor-β signatures. ACE2 positively correlated with macrophage subsets across tumor types. TMPRSS2 was less associated with immune gene expression but was strongly associated with epithelial cell abundance. Single-cell sequencing analysis across nine independent studies demonstrated little to no ACE2 or TMPRSS2 expression in lymphocytes or macrophages. ACE2 and TMPRSS2 gene expression associated with commensal microbiota in matched normal tissues particularly from colorectal cancers, with distinct bacterial populations showing strong associations. CONCLUSIONS: We performed a large-scale integration of ACE2 and TMPRSS2 gene expression across clinical, genetic, and microbiome domains. We identify novel associations with the microbiota and confirm host immunity associations with gene expression. We suggest caution in interpretation regarding genetic associations with ACE2 expression suggested from smaller case series.
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Development and validation of a basement membrane-associated immune prognostic model for hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, has a poor prognosis primarily due to its invasive and metastatic characteristics. Cancer invasion through basement membrane (BM) is the pivotal initial step in tumor dissemination and metastasis. This study aimed to identify gene signatures associated with the BM to enhance the overall prognosis of HCC. METHODS: In this study, we performed multiple bioinformatics analyses based on the RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consistent cluster analysis was conducted on 370 HCC patients, categorizing them into two distinct groups based on the expression profiles of 222 BM-related genes. Differentially expressed genes (DEGs) between these clusters were identified, followed by functional enrichment analysis. To explore the differences between the groups, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms were applied, along with the analysis of immune checkpoint molecules and human leukocyte antigen (HLA) expression levels. This helped in understanding the relationship between the HCC immune microenvironment and BM-related genes. A prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, with its performance subsequently estimated and validated. Additionally, hub biomarkers genes were identified using machine learning techniques, followed by an analysis of their functions and relationships with clinical characteristics. Finally, single-cell clustering analysis was employed to further investigate the expression distribution of these genes within the HCC immune microenvironment. RESULTS: Following consistent cluster analysis, two groups were identified: the BM high group and the BM low group. Among the 6,221 DEGs between the two groups, 5,863 were upregulated and 358 were downregulated, with enrichment functions primarily associated with extracellular matrix (ECM) organization, cell adhesion, immune response, and metabolism. The expression levels of BM-related genes were found to regulate the HCC immune microenvironment. Using univariate Cox regression analysis, 60 prognostic BM-related genes were identified, leading to the establishment of an 11-gene prognostic model named BMscore to predict the overall survival (OS) of HCC patients. The high BMscore group indicated worse prognosis, and the model's predictive performance was validated using the GEO dataset. P3H1 and ADAMTS5 were identified as hub biomarkers, playing roles in cell proliferation and ECM metabolism, with their expression distributions mapped respectively. CONCLUSIONS: A prognostic model based on BM-related genes was successfully developed and shows promise for evaluating prognoses and offering personalized treatment recommendations.
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Single-Cell Gene Expression Analysis in Patients with Medullary Sponge Kidney and a Retrospective Study.
OBJECTIVE: To establish better diagnosis thinking and provide advanced understanding of MSK, the CT imaging features, clinical characteristics, and the expression of suspected genes in the kidney spatiotemporal immune zonation and fetal renal development were investigated. METHODS: 17 patients with MSK hospitalized in our hospital were selected as our research subjects. Human Phenotype Ontology, MalaCards: The Human Disease Database, GeneCards: The Human Gene Database, Human Protein Atlas, and Single Cell Expression Atlas were used to analyze this disease. RESULTS: In our 17 patients, the incidence of MSK tended to be the same in male and female, and the onset age of MSK was probably 31-50 years old. The top one related disease of MSK was nephrocalcinosis and the most frequent phenotype related to MSK was nephrolithiasis. In addition, the expression of HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 has been implicated in both human kidney immune zonation and fetal kidney development. CONCLUSIONS: HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 could be independent indicators for the diagnosis and preventive intervention of MSK patients, and abnormal kidney development due to mutations in key genes was the underlying cause of MSK.
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A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types.
The paraventricular hypothalamus (PVH) controls behavioral and physiologic processes, including appetite, social behavior, autonomic outflow, and pituitary hormone secretion. However, molecular markers for centrally projecting PVH neuron populations remain largely undefined, and a complete census of PVH cell types has not been established. Therefore, we performed extensive single-cell/nucleus RNA sequencing to catalog PVH neuron subtypes and multiplexed error-robust fluorescence in situ hybridization (MERFISH) to map them spatially. Our spatial transcriptomic atlas resolves 26 Sim1+ and 29 GABAergic neuron populations from the PVH and surrounding areas. Additionally, projection-based profiling identified neurons that project to the parabrachial region (PB) and spinal cord, helping to determine PVH populations that regulate satiety and sympathetic nervous system activity, respectively. Notably, activation of PB-projecting PVH neurons expressing Brs3 reduces food intake, and silencing them causes obesity. Together, this atlas contributes high-resolution PVH spatial and circuit-based gene expression profiles, representing a valuable resource for the field of homeostasis.
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Integrated Pan-Cancer Analysis and Experimental Verification of the Roles of Retinoid-Binding Proteins in Breast Cancer.
BACKGROUND: Retinoid-binding proteins (RBPs) regulate retinoid metabolism and signaling, but their roles across human cancers remain incompletely defined. METHODS: We conducted a comprehensive analysis using bioinformatics tools and experimental validations, examining RBP expression profiles across cancer types based on data from The Cancer Genome Atlas (TCGA). We employed survival analysis using the Kaplan-Meier method and utilized single-cell RNA sequencing (scRNA-seq) to investigate the roles of RBP4 and RBP7 in the tumor microenvironment. RESULTS: Our analysis revealed significant downregulation of RBPs in multiple cancers, with RBP4 and RBP7 showing notable expression variations linked to tumor stages and grades. Cox analysis identified RBP4 as a protective gene in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and mesothelioma (MESO), while RBP7 exhibited protective effects in breast cancer (BRCA) and uveal melanoma (UVM). CONCLUSIONS: This pan-cancer and single-cell integrative analysis highlights the complex roles of RBPs in cancer progression and their potential as prognostic biomarkers, particularly RBP4 and RBP7 in breast cancer. These findings warrant further investigation into the functional mechanisms of RBPs, which may provide valuable strategies for therapeutic interventions.