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Single-cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells.
BACKGROUND: Traditional laboratory model organisms represent a small fraction of the diversity of multicellular life, and findings in any given experimental model often do not translate to other species. Immunology research in non-traditional model organisms can be advantageous or even necessary, such as when studying host-pathogen interactions. However, such research presents multiple challenges, many stemming from an incomplete understanding of potentially species-specific immune cell types, frequencies, and phenotypes. Identifying and characterizing immune cells in such organisms is frequently limited by the availability of species-reactive immunophenotyping reagents for flow cytometry, and insufficient prior knowledge of cell type-defining markers. RESULTS: Here, we demonstrate the utility of single-cell RNA sequencing (scRNA-Seq) to characterize immune cells for which traditional experimental tools are limited. Specifically, we used scRNA-Seq to comprehensively define the cellular diversity of equine peripheral blood mononuclear cells (PBMC) from healthy horses across different breeds, ages, and sexes. We identified 30 cell type clusters partitioned into five major populations: monocytes/dendritic cells, B cells, CD3+PRF1+ lymphocytes, CD3+PRF1- lymphocytes, and basophils. Comparative analyses revealed many cell populations analogous to human PBMC, including transcriptionally heterogeneous monocytes and distinct dendritic cell subsets (cDC1, cDC2, plasmacytoid DC). Remarkably, we found that a majority of the equine peripheral B cell compartment is comprised of T-bet+ B cells, an immune cell subpopulation typically associated with chronic infection and inflammation in human and mouse. CONCLUSIONS: Taken together, our results demonstrate the potential of scRNA-Seq for cellular analyses in non-traditional model organisms and form the basis for an immune cell atlas of horse peripheral blood.
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Longitudinal analysis of invariant natural killer T cell activation reveals a cMAF-associated transcriptional state of NKT10 cells.
Innate T cells, including CD1d-restricted invariant natural killer T (iNKT) cells, are characterized by their rapid activation in response to non-peptide antigens, such as lipids. While the transcriptional profiles of naive, effector, and memory adaptive T cells have been well studied, less is known about the transcriptional regulation of different iNKT cell activation states. Here, using single-cell RNA-sequencing, we performed longitudinal profiling of activated murine iNKT cells, generating a transcriptomic atlas of iNKT cell activation states. We found that transcriptional signatures of activation are highly conserved among heterogeneous iNKT cell populations, including NKT1, NKT2, and NKT17 subsets, and human iNKT cells. Strikingly, we found that regulatory iNKT cells, such as adipose iNKT cells, undergo blunted activation and display constitutive enrichment of memory-like cMAF+ and KLRG1+ populations. Moreover, we identify a conserved cMAF-associated transcriptional network among NKT10 cells, providing novel insights into the biology of regulatory and antigen-experienced iNKT cells.
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ITGB5-mediated biomechanical regulation in pancreatic ductal adenocarcinoma stroma impacts tumor progression and prognosis.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by stromal remodelling andstiffening, a process involving extracellular matrix (ECM) depositionand cross-linking, which forms a dense physical barrier that limits the penetration of chemotherapeutic agents and restricts immune cell-mediated cytotoxicity. In addition, the tumor stroma modulates nutrients availability and fosters cancer cell invasion and metastasis. Therefore, therapeutic strategies aimed at modulating the tumor stroma hold promise for overcoming some of the major challenges in pancreatic cancer treatment. However, most clinical trials of stromal-targeted therapies have not demonstrated meaningful improvements in overall survival, underscoring an urgent need to identify novel stromal targets and approaches that modulate the tumor microenviroment in ways that enhance therapeutic response and improve patient outcomes. METHODS: Transcriptomic datasets of PDAC sampleswere obtained from The Cancer Genome Atlas (TCGA). By integrating immune infiltration algorithms with weighted gene co-expression network analysis (WGCNA), this study aims to identify hub genes associated with cancer-associated fibroblasts (CAFs) infiltration in PDAC, explore the prognostic relevance of CAF infiltration, and construct a gene‑expression-based prognostic mode. Furthermore, we will employ a multi-modal approach incorporating magnetic resonance elastography, atomic force microscopy, single-cell RNA sequencing (scRNA-seq), histological assays, cell-based functional experiments, and animal models to identify candidate core genes involved in CAF infiltration and stromal stiffening in pancreatic cancer, and to assess the potential therapeutic relevance of targeting these genes. RESULTS: In PDAC, increased stromal stiffness and poor prognosis are both strongly linked to activation of CAFs. WGCNA identified six hub genes-ANTXR2, ANXA1, BCAT1, PLAU, TGFβ1, and ITGB5-which were used to construct a predictive model for estimating CAF abundance and forecasting PDAC prognosis. Single-cell RNA sequencing analysis showed that ITGB5 is highly expressed in CAFs. Both in vitro and in vivo experiments demonstrated that targeting ITGB5 in CAFs reduces tumor stiffness, thereby inhibiting the malignant progression of PDAC. These findings suggest a potential therapeutic benefit, although impact on clinical prognosis remains to be established. CONCLUSION: The integration of magnetic resonance elastography, atomic force microscopy, and single-cell sequencing allows detailed research characterization of the biomechanical properties of PDAC. ITGB5 has been identified as an important regulator of the biomechanical features of PDAC. Targeting ITGB5 in CAFs may reduce the excessive stiffness of PDAC tissue and mitigate gemcitabine-associated stromal fibrosis, suggesting a potential strategy to improve treatment response. The effect on patient prognosis, however, requires confirmation in clinical studies. TRIAL REGISTRATION: This study is a prospective single-center clinical trial, which has been registered in the U.S. Clinical Trials Database ( https://clinicaltrials.gov/study/NCT06526442 ) with the trial registration number NCT06526442. The registration date was October 9th, 2020.
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The developing mouse coronal suture at single-cell resolution.
Sutures separate the flat bones of the skull and enable coordinated growth of the brain and overlying cranium. The coronal suture is most commonly fused in monogenic craniosynostosis, yet the unique aspects of its development remain incompletely understood. To uncover the cellular diversity within the murine embryonic coronal suture, we generated single-cell transcriptomes and performed extensive expression validation. We find distinct pre-osteoblast signatures between the bone fronts and periosteum, a ligament-like population above the suture that persists into adulthood, and a chondrogenic-like population in the dura mater underlying the suture. Lineage tracing reveals an embryonic Six2+ osteoprogenitor population that contributes to the postnatal suture mesenchyme, with these progenitors being preferentially affected in a Twist1+/-; Tcf12+/- mouse model of Saethre-Chotzen Syndrome. This single-cell atlas provides a resource for understanding the development of the coronal suture and the mechanisms for its loss in craniosynostosis.
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Downregulation of PHB1 due to Loss of VHL Protein Expression Promotes the Malignancy Progression of Kidney Renal Clear Cell Carcinoma.
BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma, accounting for 75-80% of cases. Although Prohibitin 1 (PHB1) has been implicated in tumorigenesis, its role in KIRC remains unclear. This study aimed to investigate the expression, function, and underlying mechanisms of PHB1 in KIRC. METHODS: PHB1 expression and clinical relevance were analyzed using the Cancer Genome Atlas database (TCGA), Gene Expression Omnibus database (GEO), and TIMER3.0 datasets. Single-cell RNA sequencing (scRNA-seq) data from 19 KIRC patients and normal kidney samples were used to assess cell-type-specific expression. Functional experiments, including CCK-8, EdU incorporation, colony formation, and Transwell assays, were performed to evaluate the effects of PHB1 on cell proliferation, migration, and invasion. A xenograft model was used to evaluate the effects of PHB1 in vivo. Western blotting and correlation analyses were conducted to assess the potential regulatory interplay between VHL, SKP2, PHB1, and p53. RESULTS: PHB1 was significantly downregulated in KIRC tissues. Single-cell analysis showed reduced PHB1 expression in tumor-derived epithelial cells, fibroblasts, endothelial cells, and immune cells. Lower PHB1 levels were associated with poorer overall survival. PHB1 overexpression inhibited proliferation and migration, whereas knockdown promoted these effects. In vivo, PHB1 overexpression reduced tumor growth. Moreover, VHL knockout altered the protein levels of SKP2 and PHB1. Cellular manipulation of PHB1 moderately altered endogenous p53 protein expression. Based on TCGA data analysis, PHB1 expression showed a positive correlation with VHL and a negative correlation with SKP2. CONCLUSIONS: Collectively, PHB1 acts as a potential tumor suppressor in KIRC. Its expression pattern is associated with the VHL-SKP2 signaling axis, and the tumor-suppressive effects of PHB1 may be partially associated with the modulation of p53 expression. These findings suggest that PHB1 has the potential to serve as a reliable biomarker and therapeutic candidate for KIRC treatment.
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Leveraging multiple labeled datasets for the automated annotation of single-cell RNA and ATAC data.
The creation of single-cell atlases is essential for understanding cellular diversity and heterogeneity. However, assembling these atlases is challenging due to batch effects and the need for accurate and consistent cell annotation. Current methods for single-cell RNA and ATAC sequencing (scRNA-Seq and scATAC-Seq), while effective for integration, are not optimized for cell annotation. Additionally, many annotation tools rely on external databases or reference scRNA-Seq datasets, which may limit their adaptability to specific study needs, especially for rare cell-types or scATAC-Seq data. Here, we introduce JIND-Multi, a new framework designed to transfer cell-type labels across multiple annotated datasets. Notably, JIND-Multi can be applied to both scRNA-Seq and scATAC-Seq data, requiring in each case annotated data of the same type, contrary to most methods for scATAC-Seq data that require (paired) annotated scRNA-Seq data. In both cases, JIND-Multi significantly reduces the proportion of unclassified cells while maintaining the accuracy and performance of the original JIND model, and compares favorable to state-of-the-art methods. These results prove its versatility and effectiveness across different single-cell sequencing technologies. JIND-Multi represents an improvement in cell annotation, reducing unassigned cells and offering a reliable solution for both scRNA-Seq and scATAC-Seq data. Its ability to handle multiple labeled datasets enhances the precision of annotations, making it a valuable tool for the single-cell research community. JIND-Multi is publicly available at: https://github.com/ML4BM-Lab/JIND-Multi.git.
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Distinct subnetworks of the thalamic reticular nucleus.
The thalamic reticular nucleus (TRN), the major source of thalamic inhibition, regulates thalamocortical interactions that are critical for sensory processing, attention and cognition1-5. TRN dysfunction has been linked to sensory abnormality, attention deficit and sleep disturbance across multiple neurodevelopmental disorders6-9. However, little is known about the organizational principles that underlie its divergent functions. Here we performed an integrative study linking single-cell molecular and electrophysiological features of the mouse TRN to connectivity and systems-level function. We found that cellular heterogeneity in the TRN is characterized by a transcriptomic gradient of two negatively correlated gene-expression profiles, each containing hundreds of genes. Neurons in the extremes of this transcriptomic gradient express mutually exclusive markers, exhibit core or shell-like anatomical structure and have distinct electrophysiological properties. The two TRN subpopulations make differential connections with the functionally distinct first-order and higher-order thalamic nuclei to form molecularly defined TRN-thalamus subnetworks. Selective perturbation of the two subnetworks in vivo revealed their differential role in regulating sleep. In sum, our study provides a comprehensive atlas of TRN neurons at single-cell resolution and links molecularly defined subnetworks to the functional organization of thalamocortical circuits.
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The role of acrylamide exposure in prostate cancer progression: An integrated analysis based on network toxicology and experimental validation.
OBJECTIVE: Acrylamide (AA), a widespread environmental and food-borne pollutant, has been classified as a probable human carcinogen. However, its specific role and underlying mechanisms in the progression of prostate cancer (PCa) remain poorly elucidated. This study aims to comprehensively investigate the effect of AA on PCa progression and its molecular mechanisms. METHODS: Differentially expressed AA-related genes (DEARGs) were identified by intersecting data from the Comparative Toxicogenomics Database (CTD) and The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed to reveal biological functions of DEARGs. A prognostic signature was constructed using Lasso regression analysis. The expression and cellular localization of the key genes were analyzed using single-cell RNA sequencing (scRNA-seq) data. Additionally, the effects of glycidamide (GA) on PCa cells malignant phenotypes and tumor growth in vivo were experimentally validated. RESULTS: We identified a total of 165 DEARGs significantly associated with PCa. Functional analysis revealed they mainly enriched in PI3K-Akt and cAMP signaling pathway. The prognostic model constructed from the six DEARGs (CYP11A1, TUBB3, ALB, ESPL1, FOXN4, ISYNA1) demonstrated satisfactory predictive accuracy for biochemical recurrence-free survival (BCRFS) in PCa patients. scRNA-seq analysis delineated the specific expression of six target genes within the tumor microenvironment. It was found that expression of CYP11A1 was downregulated, while expressions of TUBB3, ALB, ESPL1, FOXN4 and ISYNA1 were upregulated after treatment with GA. In vitro and in vivo experiments confirmed that GA significantly enhanced proliferation, migration, invasion of PCa cells and tumor growth. RNA‑sequencing and functional rescue assays further revealed that the MAPK pathway may be a key mechanism by which GA promoted proliferation, invasion, and metastasis of PCa cells. CONCLUSION: This study successfully identified and validated an AA-related prognostic model to effectively predict BCRFS of PCa. The oncogenic effects of GA are primarily mediated by the activation of specific MAPK signaling pathways, elucidating a key mechanistic basis for its toxicity in PCa.
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Identification of 5-Gene Prognostic Markers and Functional Verification of RRBP1 in 5-Fluorouracil Resistance of Colorectal Cancer by Multi-Omics Analysis and Experimental Verification.
Acquired resistance to 5-fluorouracil (5-FU) is a primary clinical challenge in colorectal cancer (CRC) treatment. Our study aimed to identify key factors predictive of 5-FU resistance and to elucidate their functional mechanisms by combining multi-omics analysis with experimental verification. The prognostic model was constructed based on the gene expression omnibus (GEO, GSE196900, GSE166555) and the cancer genome atlas (TCGA)-Colon Adenocarcinoma (COAD) datasets combined with regression analysis. Kaplan-Meier (K-M), receiver operating characteristic (ROC) curve, and nomogram were used to evaluate the predictive performance of the prognostic model. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were used for functional enrichment analysis. Single-cell RNA sequencing (scRNA-seq, GSE166555) and qRT-PCR analysis were used to analyze the tumor microenvironment and gene expression. In cell experiments, CCK-8 assay measured IC50 value. Glycolysis metabolism was evaluated by detecting glucose consumption, lactic acid production, extracellular acidification rate (ECAR), and oxygen consumption rate (OCR); cell stemness was evaluated by sphere formation assay. A 5-gene prognostic model was successfully constructed, which could effectively distinguish the high-/low-risk groups of CRC patients and was significantly correlated with overall survival. Ribosome binding protein 1 (RRBP1) is highly expressed in cancer tissues of non-responders to chemotherapy. It is also highly expressed in tumor epithelial cells, and its high expression is closely related to aneuploidy characteristics, up-regulation of oncogenes, and activation of pro-survival pathways. In vitro experiments confirmed that knockdown of RRBP1 significantly enhanced the sensitivity of CRC cells to 5-FU and inhibited cell proliferation. Mechanistically, RRBP1 knockdown effectively reversed the enhanced glycolysis activity and stem cell-like properties of 5-FU-resistant cells. This study established RRBP1 as a key CRC prognostic factor and 5-FU resistance driver, operating through the regulation of cell glycolysis and stemness. RRBP1 emerges as a new biomarker and therapeutic target for predicting the efficacy of 5-FU.
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Pan-cancer single-cell landscape of tumor-infiltrating T cells.
T cells play a central role in cancer immunotherapy, but we lack systematic comparison of the heterogeneity and dynamics of tumor-infiltrating T cells across cancer types. We built a single-cell RNA-sequencing pan-cancer atlas of T cells for 316 donors across 21 cancer types and revealed distinct T cell composition patterns. We found multiple state-transition paths in the exhaustion of CD8+ T cells and the preference of those paths among different tumor types. Certain T cell populations showed specific correlation with patient properties such as mutation burden, shedding light on the possible determinants of the tumor microenvironment. T cell compositions within tumors alone could classify cancer patients into groups with clinical trait specificity, providing new insights into T cell immunity and precision immunotherapy targeting T cells.