Explore the vast range of titles available.

The gut microbiota influences host immunity and metabolism, and changes in its composition and function have been implicated in several non‐communicable diseases. Here, comparing germ‐free (GF) and specific pathogen‐free (SPF) mice using spatial transcriptomics, single‐cell RNA sequencing, and targeted bile acid metabolomics across multiple organs, we systematically assessed how the gut microbiota's absence affected organ morphology, immune homeostasis, bile acid, and lipid metabolism. Through integrated analysis, we detect marked aberration in B, myeloid, and T/natural killer cells, altered mucosal zonation and nutrient uptake, and significant shifts in bile acid profiles in feces, liver, and circulation, with the alternate synthesis pathway predominant in GF mice and pronounced changes in bile acid enterohepatic circulation. Particularly, autophagy‐driven lipid droplet breakdown in ileum epithelium and the liver's zinc finger and BTB domain‐containing protein (ZBTB20)‐Lipoprotein lipase (LPL) (ZBTB20‐LPL) axis are key to plasma lipid homeostasis in GF mice. Our results unveil the complexity of microbiota–host interactions in the crosstalk between commensal gut bacteria and the host. We present a multi‐organ single‐cell, spatial transcriptomics, and BA omics atlas of specific pathogen‐free (SPF) and germ‐free (GF) mice. We found plasma cell aggregation displays significant tissue heterogeneity depending on the gut microbiota. GF mice exhibit impaired follicular and marginal zone B cell maturation, linked to microbiota‐mediated modulation of Cr2 gene expression. The microbiota regulates the development and survival of neutrophils in the bone marrow, influences the development and differentiation of T cells in the thymus, and modulates intraepithelial γδ T cell composition and lipid absorption in the small intestine. The absence of microbiota in GF mice alters the intestinal mucosa zonation and triggers coordinated dynamics in intestinal lipid absorption, transport, chylomicron synthesis, lipid droplet formation, lipolysis, and fatty acid oxidation in the small intestine enterocytes. The liver microbiota‐dependent zinc finger and BTB domain‐containing protein (ZBTB20)‐Lipoprotein lipase (LPL) axis plays a role in plasma lipid homeostasis. Highlights Single‐cell, spatial transcriptomics, and bile acidomics atlases in germ‐free mice. Marked aberration and tissue heterogeneity in B, myeloid, and T/NK cells in germ‐free mice. Microbiota shapes mucosal zonation and modulates lipid dynamics of the small intestine. Germ‐free mice show liver bile acid synthesis and ileal reabsorption anomalies.

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models. Highlights Tumor heterogeneity and drug resistance reduce therapeutic efficacy in AML treatment. ScRNA-seq improves the accuracy and quality of HSCT, resulting in a durable antitumor response. ScRNA-seq introduces neoantigens, biomarkers and cell subtypes that may contribute to reversing resistance to standard therapies in AML. Combination approaches using scRNA-seq and AI may provide effective AML management in the future.

Background Epitranscriptomics, the study of RNA modifications such as N 6 -methyladenosine (m 6 A), provides a novel layer of gene expression regulation with implications for numerous biological processes, including cellular adaptation to hypoxia. Hypoxia-inducible factor-1 (HIF-1), a master regulator of the cellular response to low oxygen, plays a critical role in adaptive and pathological processes, including cancer, ischemic heart disease, and metabolic disorders. Recent discoveries accent the dynamic interplay between m 6 A modifications and HIF-1 signaling, revealing a complex bidirectional regulatory network. While the roles of other RNA modifications in HIF-1 regulation remain largely unexplored, emerging evidence suggests their potential significance. Main body This review examines the reciprocal regulation between HIF-1 and epitranscriptomic machinery, including m 6 A writers, readers, and erasers. HIF-1 modulates the expression of key m 6 A components, while its own mRNA is regulated by m 6 A modifications, positioning HIF-1 as both a regulator and a target in this system. This interaction enhances our understanding of cellular hypoxic responses and opens avenues for clinical applications in treating conditions like cancer and ischemic heart disease. Promising progress has been made in developing selective inhibitors targeting the m 6 A-HIF-1 regulatory axis. However, challenges such as off-target effects and the complexity of RNA modification dynamics remain significant barriers to clinical translation. Conclusion The intricate interplay between m 6 A and HIF-1 highlights the critical role of epitranscriptomics in hypoxia-driven processes. Further research into these regulatory networks could drive therapeutic innovation in cancer, ischemic heart disease, and other hypoxia-related conditions. Overcoming challenges in specificity and off-target effects will be essential for realizing the potential of these emerging therapies.

Background Esophageal Squamous Cell Carcinoma (ESCC) remains a predominant health concern in the world, characterized by high prevalence and mortality rates. Advances in single-cell transcriptomics have revolutionized cancer research by enabling a precise dissection of cellular and molecular diversity within tumors. Objective This study aims to elucidate the cellular dynamics and molecular mechanisms in ESCC, focusing on the transcriptional influence of STAT3 (Signal Transducer and Activator of Transcription 3) and its interaction with LHPP, thereby uncovering potential therapeutic targets. Methods Single-cell RNA sequencing was employed to analyze 44,206 cells from tumor and adjacent normal tissues of ESCC patients, identifying distinct cell types and their transcriptional shifts. We conducted differential gene expression analysis to assess changes within the tumor microenvironment (TME). Validation of key regulatory interactions was performed using qPCR in a cohort of 21 ESCC patients and further substantiated through experimental assays in ESCC cell lines. Results The study revealed critical alterations in cell composition and gene expression across identified cell populations, with a notable shift towards pro-tumorigenic states. A significant regulatory influence of STAT3 on LHPP was discovered, establishing a novel aspect of ESCC pathogenesis. Elevated levels of STAT3 and suppressed LHPP expression were validated in clinical samples. Functional assays confirmed that STAT3 directly represses LHPP at the promoter level, and disruption of this interaction by promoter mutations diminished STAT3's repressive effect. Conclusion This investigation underscores the central role of STAT3 as a regulator in ESCC, directly impacting LHPP expression and suggesting a regulatory loop crucial for tumor behavior. The insights gained from our comprehensive cellular and molecular analysis offer a deeper understanding of the dynamics within the ESCC microenvironment. These findings pave the way for targeted therapeutic interventions focusing on the STAT3-LHPP axis, providing a strategic approach to improve ESCC management and prognosis.

Aims Atrial fibrillation (AF) has high mortality and morbidity rates. However, the intracellular molecular complexity of the atrial tissue of patients with AF has not been adequately assessed. Methods and results We investigated the cellular heterogeneity of human atrial tissue and changes in differentially expressed genes between cells using single-cell RNA sequencing, fluorescence in situ hybridization, intercellular communication, and cell trajectory analysis. Using genome-wide association studies (GWAS) and proteomics, we discovered cell types enriched for AF susceptibility genes. We discovered eight different cell types, which were further subdivided into 23 subpopulations. In AF, the communication strength between smooth muscle cells (SMCs) and fibroblast (FB) 3 cells increased and the relevant signaling pathways were quite similar. Subpopulations of endothelial cells (ECs) are mainly involved in fibrosis through TXNDC5 and POSTN . AF susceptibility genes revealed by GWAS were especially enriched in neuronal and epicardial cells, FB3, and lymphoid (Lys) cells, whereas proteomic sequencing differential proteins were concentrated in FB3 cells and SMCs. Conclusions This study provides a cellular landscape based on the atrial tissue of patients with AF and highlights intercellular changes and differentially expressed genes that occur during the disease process. A thorough description of the cellular populations involved in AF will facilitate the identification of new cell-based interventional targets with direct functional significance for the treatment of human disease.

Background Cell subsets differentially modulate host immune responses to Mycobacterium tuberculosis (MTB) infection. However, the nature and functions of these subsets against osteoarticular tuberculosis (OTB) are unclear. Here, we aimed to understand the phenotypes and functions of immune cell subsets in patients with OTB using single-cell RNA sequencing (scRNA-Seq). Methods Pathological and healthy adjacent tissues were isolated from patients with OTB and subjected to scRNA-Seq. Unsupervised clustering of cells was performed based on gene expression profiles, and uniform manifold approximation and projection was used for clustering visualization. Results Thirteen cell subsets were identified in OTB tissues. scRNA-seq datasets of patients and healthy controls (HCs) showed that infection changed the frequency of immune cell subsets in OTB tissues. Myeloid cell examination revealed nine subsets. The frequency of macrophage-RGS1 high subsets decreased in OTB tissues; this increased MTB susceptibility in an SLC7A11/ferroptosis-dependent manner. Immunohistochemistry assays and flow cytometry for patients with OTB and osteoarticular bacterial infection (OBI) and HCs verified that the frequency of macrophage-RGS1 high subset decreased in OTB tissues and blood samples, thereby distinguishing patients with OTB from HCs and patients with OBI. Conclusion The macrophage-RGS1 high subset levels were decreased in patients with OTB, and would be up-regulated after effective treatment. Therefore, the clinical significance of this study is to discover that macrophage-RGS1 high subset may serve as a potential biomarker for OTB diagnosis and treatment efficacy monitoring.

Background Thoracic aortic dissection (TAD) is an irreversible cardiovascular disorder with high mortality and morbidity. However, the molecular mechanisms remain elusive. Thus, identifying an effective therapeutic target to prevent TAD is especially critical. The purpose of this study is to elucidate the potential mechanism of inflammation and vascular smooth muscle cell (VSMCs) phenotypic switch in β-aminopropionitrile fumarate (BAPN)-induced TAD. Methods A mouse model of TAD induced by BAPN and IL-1β -stimulated HVSMCs in vivo and in vitro models, respectively. ACE2 Knockdown mice treated with BAPN or without, and the TAD mouse model was treated with or without AAV-ACE2. Transthoracic ultrasound was conducted for assessment the maximum internal diameter of the thoracic aorta arch. RNA sequencing analysis was performed to recapitulate transcriptome profile changes. Western blot were used to detect the expression of MMP2, MMP9, ACE2, SIRT3, OPN, SM22α and other inflammatory markers. The circulating levels of ACE2 was measured by ELISA assay. Histological changes of thoracic aorta tissues were assessed by H&E, EVG and IHC analysis. Results We found that circulating levels of and the protein levels of ACE2 were increased in the TAD mouse model and in patients with TAD. For further evidence, ACE2 deficiency decelerated the formation of TAD. However, overexpression of ACE2 aggravated BAPN-induced aortic injury and VSMCs phenotypic switch via lowered SIRT3 expression and elevated inflammatory cytokine expression. Conclusion ACE2 deficiency prevented the development of TAD by inhibiting inflammation and VSMCs phenotypic switch in a SIRT3-dependent manner, suggesting that the ACE2/SIRT3 signaling pathway played a pivotal role in the pathological process of TAD and might be a potential therapeutical target. Highlights This study demonstrated for the first time that ACE2 deficiency attenuates the development of TAD induced by BAPN. The inhibitory effect of ACE2 deficiency on phenotypic transformation of VSMCs and inflammation may be through SIRT3 signaling pathway. Specific inhibition of SIRT3 can speed the exacerbation of TAD induced by BAPN and SIRT3 may be an important target for drug therapy of TAD.

Pulmonary arterial hypertension (PAH) is a progressive and rapidly fatal disease with an intricate etiology. Identifying biomarkers for early PAH lesions based on the exploration of subtle biological processes is significant for timely diagnosis and treatment. In the present study, nine distinct cell populations identified based on gene expression profiles revealed high heterogeneity in cell composition ratio, biological function, distribution preference, and communication patterns in PAH. Notably, compared to other cells, endothelial cells (ECs) showed prominent variation in multiple perspectives. Further analysis demonstrated the endothelial-to-mesenchymal transition (EndMT) in ECs and identified a subgroup exhibiting a contrasting phenotype. Based on these findings, a machine-learning integrated program consisting of nine learners was developed to create a PAH Endothelial-to-mesenchymal transition Signature (PETS). This study identified cell populations underlying EndMT and furnished a potential tool that might be valuable for PAH diagnosis and new precise therapies.

Background Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)–based model using PCD-related genes. Methods We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. Results The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. Conclusions The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research. To accurately reflect organ architecture, spatially resolved transcriptomics aims to provide spatial and expression information at the single cellular level for higher-order reconstruction.In silico methods combine single-cell RNA sequencing (scRNA-seq), in situ hybridization, and prior knowledge to reconstruct spatial transcriptomes of tissues but cannot match coordinates and tend to simplify.Laser capture microdissection (LCM)-based approaches allow full gene single-cell profiling plus position information, but assay only a few cells.RNA imaging provides the expression landscape for millions of cells in situ but detects only targeted transcripts.In situ sequencing provides spatial whole genome-wide expression at the micron level by combining barcoding with NGS but fails to describe individual cells.