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Localized PD-1 CAR T therapy reprograms neuroinflammation.
B cell-depleting therapies are effective in multiple sclerosis (MS), yet some patients relapse, underscoring the need for more precise interventions. To identify new therapeutic targets, we generated a single-cell RNA sequencing (scRNA-seq) atlas of cerebrospinal fluid (CSF), brain, and blood from non-inflammatory controls and patients with MS or other neuroinflammatory diseases. We found disease-associated enrichment of class-switched immunoglobulin G+ (IgG+) B cells and plasma cells in MS CSF. Unbiased analysis identified a rare disease-enriched subset of activated, T cell receptor (TCR)-restricted, PD-1+ T follicular helper-like cells with B cell-recruiting features. To target this population, we developed PD-1-directed chimeric antigen receptor (CAR) T cells that selectively depleted pathogenic PD-1+ CD4 T cells and locally released IL-10. This strategy attenuated central nervous system (CNS) inflammation, reprogrammed the local immune milieu, and improved clinical outcomes across murine neuroinflammation models. These findings define a CNS-localized adaptive immune circuit in MS and nominate programmable PD-1 CAR T cells as a strategy to disrupt it.
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Single-cell CAR T atlas reveals type 2 function in 8-year leukaemia remission.
Despite a high response rate in chimeric antigen receptor (CAR) T cell therapy for acute lymphocytic leukaemia (ALL)1-3, approximately 50% of patients relapse within the first year4-6, representing an urgent question to address in the next stage of cellular immunotherapy. Here, to investigate the molecular determinants of ultralong CAR T cell persistence, we obtained a single-cell multi-omics atlas from 695,819 pre-infusion CAR T cells at the basal level or after CAR-specific stimulation from 82 paediatric patients with ALL enrolled in the first two CAR T ALL clinical trials and 6 healthy donors. We identified that elevated type 2 functionality in CAR T infusion products is significantly associated with patients maintaining a median B cell aplasia duration of 8.4 years. Analysis of ligand-receptor interactions revealed that type 2 cells regulate a dysfunctional subset to maintain whole-population homeostasis, and the addition of IL-4 during antigen-specific activation alleviates CAR T cell dysfunction while enhancing fitness at both transcriptomic and epigenomic levels. Serial proteomic profiling of sera after treatment revealed a higher level of circulating type 2 cytokines in 5-year or 8-year relapse-free responders. In a leukaemic mouse model, type 2high CAR T cell products demonstrated superior expansion and antitumour activity, particularly after leukaemia rechallenge. Restoring antitumour efficacy in type 2low CAR T cells was attainable by enhancing their type 2 functionality, either through incorporating IL-4 into the manufacturing process or by priming manufactured CAR T products with IL-4 before infusion. Our findings provide insights into the mediators of durable CAR T therapy response and suggest potential therapeutic strategies to sustain long-term remission by boosting type 2 functionality in CAR T cells.
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A longitudinal molecular and cellular lung atlas of lethal SARS-CoV-2 infection in K18-hACE2 transgenic mice.
BACKGROUND: The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to approximately 500 million cases and 6 million deaths worldwide. Previous investigations into the pathophysiology of SARS-CoV-2 primarily focused on peripheral blood mononuclear cells from patients, lacking detailed mechanistic insights into the virus's impact on inflamed tissue. Existing animal models, such as hamster and ferret, do not faithfully replicate the severe SARS-CoV-2 infection seen in patients, underscoring the need for more relevant animal system-based research. METHODS: In this study, we employed single-cell RNA sequencing (scRNA-seq) with lung tissues from K18-hACE2 transgenic (TG) mice during SARS-CoV-2 infection. This approach allowed for a comprehensive examination of the molecular and cellular responses to the virus in lung tissue. FINDINGS: Upon SARS-CoV-2 infection, K18-hACE2 TG mice exhibited severe lung pathologies, including acute pneumonia, alveolar collapse, and immune cell infiltration. Through scRNA-seq, we identified 36 different types of cells dynamically orchestrating SARS-CoV-2-induced pathologies. Notably, SPP1+ macrophages in the myeloid compartment emerged as key drivers of severe lung inflammation and fibrosis in K18-hACE2 TG mice. Dynamic receptor-ligand interactions, involving various cell types such as immunological and bronchial cells, defined an enhanced TGFβ signaling pathway linked to delayed tissue regeneration, severe lung injury, and fibrotic processes. INTERPRETATION: Our study provides a comprehensive understanding of SARS-CoV-2 pathogenesis in lung tissue, surpassing previous limitations in investigating inflamed tissues. The identified SPP1+ macrophages and the dysregulated TGFβ signaling pathway offer potential targets for therapeutic intervention. Insights from this research may contribute to the development of innovative diagnostics and therapies for COVID-19. FUNDING: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020M3A9I2109027, 2021R1A2C2004501).
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AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.
The complexity of Alzheimer's disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms. However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA sequencing dataset comprising over 6 million nuclei from the prefrontal cortex of >1,000 individual brains, covering a variety of disease phenotypes. Here, leveraging this dataset, we developed a computational framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype-associated cells (PACs) from 584 donors with AD-related phenotypes. PASCode ensembles multiple statistical methods into a graph neural model for robust scoring. Comparing PACs within 27 brain cell subclasses, we prioritized cell subpopulations and their expressed genes for various AD phenotypes. For instance, we identified microglia subpopulations implicated in AD pathology; reactive astrocyte subtypes with altered neuroprotective and neurotoxic gene expression that likely confer cognitive resilience; and enhanced excitatory/inhibitory imbalance and mitochondrial dysfunction in cognitively impaired AD donors. We also identified many PACs for multiple phenotypes, including the astrocytes between AD and depression showing specific gene expression patterns such as inflammation and endoplasmic reticulum stress pathways. These prioritized subpopulations, genes and pathways potentially offer valuable insights for precision diagnostic and therapeutic development. We also validated our findings in external population-scale datasets including AD and major depressive disorder, compiled an AD-phenotypic single-cell atlas and delivered the framework as an open-source tool with pre-trained models and a web application for community use.
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Single-Cell Transcriptomic Profiling Reveals Cellular Heterogeneity and Identifies Novel Therapeutic Targets in Osteosarcoma.
BACKGROUND: Osteosarcoma is the most prevalent primary malignant bone tumor predominantly affecting children and adolescents, yet prognosis for metastatic disease remains dismal. Understanding the cellular complexity within the tumor microenvironment is essential for developing targeted therapeutic strategies. METHODS: We performed comprehensive single-cell RNA sequencing analysis on an osteosarcoma tissue sample (GSM4952363) using the Seurat pipeline (v4.3.0). Following rigorous quality control (200-6000 genes per cell, < 15% mitochondrial reads), cells were filtered for downstream analysis. Dimensionality reduction (PCA and UMAP) and unsupervised clustering (Louvain algorithm, resolution = 0.8) identified seven distinct cellular clusters. Differential expression analysis (Wilcoxon rank-sum test, |log₂FC| > 0.25, adjusted p < 0.05) identified cluster-specific markers, while Gene Ontology and KEGG pathway enrichment analyses (clusterProfiler, adjusted p < 0.05) revealed functional programs. Four candidate genes (F11, ACRP2, LEPR, and POSTN) were selected for validation by quantitative real-time PCR (mRNA level) and ELISA (protein level) in MG-63 osteosarcoma cells compared to hFOB 1.19 normal osteoblasts. RESULTS: Single-cell transcriptomic profiling identified seven distinct cellular clusters within the osteosarcoma microenvironment, including macrophages (Cluster 0, 28.0%), osteoblasts (Cluster 1, 24.2%), fibroblasts (Cluster 2, Fibro_COMP, 14.7%), proliferating cells (Cluster 3, 12.3%), osteoclasts (Cluster 4, 11.6%), monocytes (Cluster 5, 6.3%), and T cells (Cluster 6, 2.9%). Functional enrichment analysis highlighted activation of PI3K-Akt signaling, focal adhesion, and extracellular matrix organization as core pathways. qRT-PCR validation (mRNA level) demonstrated that F11 was significantly downregulated (0.31 ± 0.04 vs. 1.00 ± 0.07, 69% reduction, p < 0.001), whereas ACRP2 (2.87 ± 0.33-fold), LEPR (4.52 ± 0.48-fold), and POSTN (6.23 ± 0.57-fold) were significantly upregulated (all p < 0.001). ELISA validation (protein level) confirmed consistent trends: F11 protein decreased by 65% (0.35 ± 0.05 vs. 1.00 ± 0.08, p < 0.001), whereas ACRP2 (2.64 ± 0.29-fold), LEPR (4.18 ± 0.44-fold), and POSTN (5.89 ± 0.53-fold) protein levels were elevated (all p < 0.001). Among the four candidates, POSTN exhibited the most pronounced changes at both mRNA and protein levels, suggesting its involvement in osteosarcoma matrix remodeling. CONCLUSIONS: This study provides a single-cell transcriptomic atlas of the osteosarcoma microenvironment, revealing substantial cellular heterogeneity. The differentially expressed genes F11, ACRP2, LEPR, and POSTN represent candidate biomarkers that warrant further investigation for their potential roles in osteosarcoma biology and as putative therapeutic targets.
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Pan-cancer analysis of GJB5 as a novel prognostic and immunological biomarker.
Gap junction protein B5 (GJB5, also known as Connexin 31.1) has recently been reported to be downregulated in several cancer types, where it functions primarily as a tumor suppressor in cancers such as melanoma and non-small cell lung cancer (NSCLC). However, there no reports describing its prognostic and immunological roles in pan-cancer. This study evaluated the association of GJB5 in various cancer types by a comprehensive pan-cancer analysis. The differential GJB5 expression in tumor and adjacent tissues acquired from The Cancer Genome Atlas (TCGA) databases was compared. Furthermore, univariate Cox regression and Kaplan-Meier survival analyses were performed to assess the influence of GJB5 on the disease-specific survival (DSS), disease-free interval (DFI), clinical stage, progression-free interval (PFI), and overall survival (OS) in various cancers. Moreover, the levels of GJB5 and its activity in the tumor microenvironment were assessed via the Tumor Immune Single-cell Hub (TISCH). In addition, the biological importance of GJB5 levels in various cancers was further assessed via Gene Set Enrichment Analysis. Tumor-Immune System Interactions Database (TISIDB) and Tumor Immune Estimation Resource Database 2.0 (TIMER2.0) tools indicated that GJB5 affected the tumor's immune infiltration potential. This research also evaluated the association of GJB5 with immune features: immune modulatory genes, tumor mutational burden (TMB), and microsatellite instability (MSI). The data indicated that enhanced GJB5 level was linked to worse DFI, OS, PFI, and DSS in some cancers. Additionally, GJB5 level was positively related to immune modulatory genes, TMB, immune cell infiltration, immunological checkpoints, and MSI in malignancies. Furthermore, our study demonstrated that GJB5 was upregulated in colorectal cancer tissues compared to normal tissues. We also assessed GJB5 expression across various pancreatic cell lines. Notably, GJB5 was highly expressed in pancreatic cancer cells relative to normal pancreatic epithelial cells. Additionally, GJB5 knockdown in pancreatic cancer cells resulted in a significant reduction in cell proliferation. In summary, the findings indicated the potential of GJB5 as a prospective prognostic indicator and immunological biomarker.
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Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response.
The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein-protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.
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Identification of RNMT as an immunotherapeutic and prognostic biomarker: From pan-cancer analysis to lung squamous cell carcinoma validation.
BACKGROUND: Dysregulation of RNA guanine-7 methyltransferase (RNMT) plays a crucial role in the tumor progression and immune responses. However, the detailed role of RNMT in pan-cancer is still unknown. METHODS: Bulk transcriptomic data of pan-cancer were obtained from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases. Single-cell transcriptomic and proteomics data of lung squamous cell carcinoma (LUSC) were analyzed in the Tumor Immune Single-cell Hub 2 (TISCH2) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, respectively. The correlation between RNMT expression and cancer prognosis was analyzed by Cox proportional hazards regression and Kaplan-Meier analyses. The correlation of RNMT expression with common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) was analyzed. Additionally, the correlation between RNMT expression and immune infiltration level was evaluated. A total of 1287 machine learning combinations were used to construct prognostic models for LUSC. qRT-PCR and Western blot were used to validate the bioinformatics findings of RNMT upregulation in LUSC. RESULTS: RNMT was widely expressed across different cancers, with significant correlation to prognosis in cancers such as kidney chromophobe (KICH) (p = 0.0033, HR = 7.12), liver hepatocellular carcinoma (LIHC) (p = 0.01, HR = 1.41), and others. Notably, RNMT participates in the regulation of the tumor microenvironment. RNMT expression positively correlated with immune cell expression (Spearman's rank correlation, p < 0.05). Moreover, RNMT expression was strongly associated with immunoregulators, TMB, MSI, MMR, and DNMT in most cancer types. Notably, RNMT expression displayed excellent prognostic and immunological performance in LUSC. The expression of RNMT was mainly enriched in B cells of LUSC tissues. qRT-PCR and Western blot verified the high expression of RNMT in LUSC. CONCLUSION: RNMT expression widely correlated with prognosis and immune infiltration in various tumors, especially LUSC. The RNMT detection may provide a new idea for future tumor immune studies and treatment strategies.
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Construction of a Mitochondria-Related Gene Diagnostic Model Based on Integrated Multiomics Data and Functional Validation of ANK2 as a Key Regulator in Colorectal Cancer.
Colorectal cancer (CRC) is one of the most common malignancies of the digestive tract globally, characterized by high incidence, difficulty in early diagnosis, and poor prognosis. Traditional screening methods have limitations in sensitivity and specificity, thus necessitating the development of novel, efficient molecular diagnostic approaches. Recent studies have highlighted the crucial role of mitochondrial dysfunction in the initiation and progression of various cancers, suggesting that mitochondria-related genes (MRGs) could serve as promising diagnostic targets for CRC. In this study, we integrated transcriptomic data from 1174 samples across The Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) public datasets (GSE21510, GSE44076, and GSE9348) and combined it with MRG data from the MitoCarta3.0 database for a systematic analysis of differentially expressed genes (DEGs). Using LASSO regression and SVM-RFE, two machine learning algorithms, we identified eight key MRGs (ABCG2, ANK2, MACC1, PMAIP1, SLC22A5, SLC25A34, ACAT1, and PDK4) and constructed an early diagnostic model for CRC. Receiver operating characteristic (ROC) curve analysis confirmed the diagnostic efficacy of the model. Gene interaction networks were constructed using GeneMANIA, demonstrating the potential synergistic roles of these genes in regulating cellular metabolism, drug efflux, and immune modulation. CIBERSORT immune cell infiltration analysis revealed significant correlations between these genes and various immune cell subtypes, including T cells, macrophages, and dendritic cells. Further integration of single-cell RNA sequencing data (GSE245552) identified the specific expression patterns of the diagnostic model genes across different cell types. Additionally, we conducted an in-depth investigation of the ANK2 gene. Immunohistochemistry (HPA database), qRT-PCR, and western blotting confirmed the significantly low expression of ANK2 in CRC tissues and cell lines. Moreover, TUNEL and angiogenesis assays showed that overexpression of ANK2 significantly promoted cell apoptosis and inhibited angiogenesis, suggesting that ANK2 may function as a key tumor suppressor in CRC. In conclusion, this study proposes and validates a CRC diagnostic model based on differentially expressed mitochondrial genes. We systematically explored the molecular mechanisms and immune microenvironment correlations of the model and confirmed the biological effects through single-cell and molecular biology experiments. Notably, we highlight the potential regulatory role of ANK2 in the progression of CRC. This research provides theoretical support and new directions for early screening, diagnostic biomarker identification, and targeted therapy strategies for CRC.
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Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target. METHODS: This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using in vivo and in vitro experiments such as RT-qPCR, Western Blot, immunohistochemistry, and subcutaneous tumor formation in mice, we further validated the functions of key nucleotide metabolism genes in cell lines and animals. RESULTS: Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration. CONCLUSION: The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.