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An Integrated Single-Cell Atlas of the Mouse Ileum Links Nutrient Metabolism with Epithelial and Immune Crosstalk.
BACKGROUND: The ileum integrates nutrient absorption with mucosal immunity, yet its cell-type-specific functions remain poorly defined. Disruption of epithelial or immune pathways contributes to nutrient deficiency, Crohn's disease, and impaired barrier integrity. Single-cell RNA sequencing (scRNA-seq) provides the resolution needed to uncover epithelial differentiation and immune crosstalk that bulk approaches cannot resolve. OBJECTIVES: This study aimed to map ileal cellular heterogeneity and define epithelial differentiation, nutrient metabolism programs, and epithelial-immune interactions relevant to health and disease. METHODS: scRNA-seq was performed on ileal cells from 8-wk-old male C57BL/6J mice. Gene expression and clustering were analyzed using Seurat, with pseudotime trajectory, cell-cell communication, and pathway enrichment analyses applied to characterize intestinal dynamics. RESULTS: A total of 32,076 ileal cells were identified, including 6 epithelial types and multiple immune populations. Enterocyte subclusters showed distinct nutrient-related functions: Ent_C1, C4, C7, and C8 were enriched for vitamin A absorption; Ent_C0, C1, C2 and C7 for carotenoid metabolism; and Ent_C1, C4, C7, C8, and C9 for vitamin B12 absorption. Coexpression of β-carotene oxygenase 2 (Bco2) and IL 18 (Il18) occurred across enterocytes, stem cells, and goblet cells, whereas noncanonical goblet cells exhibited high Bco2-Il18 expression together with signatures of fatty acid metabolism and stress responses. Plasmacytoid dendritic cells were identified as central regulators of immune-epithelial interactions. CONCLUSIONS: This study provides the first integrated single-cell atlas of the mouse ileum, profiling both epithelial and immune cells and revealing nutrient metabolism programs and epithelial-immune crosstalk relevant to intestinal health and disease.
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RNA‑seq analysis of predictive markers associated with glutamine metabolism in thyroid cancer.
The incidence of thyroid cancer (TC) increases year by year. It is necessary to construct a prognostic model for risk stratification and management of TC patients. Glutamine metabolism is essential for tumor progression and the tumor microenvironment. The present study aimed to develop a predictive model for TC using a glutamine metabolism gene set. Differentially expressed genes in cells with high glutamine metabolism levels from single cell RNA‑sequencing data were compared with genes differentially expressed between normal and TC tissues from The Cancer Genome Atlas Program data. Through Boruta feature selection methods and multivariate Cox regression, six crucial genes were identified for a risk‑scoring system to develop a prognostic model. The role of each gene was verified in TC cells in vitro. A risk‑scoring system was developed according to the glutamine gene set to forecast the overall survival of TC patients. This risk score could stratify TC patients and minimize unnecessary surgeries and invasive treatments. In addition, signal induced proliferation associated 1 like 2 (SIPA1L2), an important gene in the prognostic model, knockdown in TPC‑1 and BCPAP cell lines enhanced TC cell proliferation, migration and invasion. A risk model was developed based on a glutamine metabolism gene set. The model has reference values for TC stratification.
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Single-Microbe Transcriptomics Reveal Functional Heterogeneity in Sediment Microbiomes.
Sediment microbiomes drive global biogeochemical cycling, but their functional heterogeneity and transcriptional activity are hardly resolved at single-cell resolution. We optimized a scalable single-microbe RNA sequencing workflow for environmental muddy sediment samples, achieving high-throughput single cell sequencing across environmental gradients. The method integrates iohexol-based microbial enrichment, optimized enzymatic digestion step, droplet-based single-cell barcoding, and RNA sequencing, then generating a transcriptomic atlas of 55 859 high-quality microbial cells from 8 composite samples covering freshwater, estuarine and nearshore marine habitats. Comparison with metagenomic data showed a high consistency in species composition, supporting the reliability of single-microbe RNA sequencing in community profiling. Taxonomic analysis revealed site-specific microbial communities associated with methane oxidation, sulfur cycling and anaerobic nitrogen metabolism. 15 distinct functional clusters were identified transcriptionally, showing functional heterogeneity within sedimentary microbiome. Co-expression network analysis further resolved coordinated gene modules, with module activities varying across samples. Overall, this work establishes a practical framework for single-microbe transcriptomics in sediment systems and demonstrates the potential of single-cell resolution to uncover functional heterogeneity within complex microbial communities.
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A novel gene signature based on Like-Smith family members-related genes for predicting the prognosis of hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related deaths globally. The Like-Smith (LSM) family members are involved in RNA metabolism and tumor progression, but their role in HCC remains unclear. This study aims to construct a novel signature based on LSM family members-related genes and explore its clinical value in HCC. METHODS: Molecular patterns related to LSM family members were identified through clustering analysis. Differential expression analysis was used to identify genes with potential prognostic significance. Multivariate Cox regression analysis was performed to construct a signature with The Cancer Genome Atlas (TCGA) cohort. The International Cancer Genome Consortium (ICGC) cohort served as external validation. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive ability. Enrichment analysis, immune infiltration assessment, and single-cell RNA sequencing (scRNA-seq) data analysis were conducted to explore the underlying mechanisms. RESULTS: Two genes-paired-like homeodomain 2 (PITX2) and chromogranin A (CHGA)-were ultimately identified as a novel signature for HCC. Based on the risk score derived from the signature, samples were divided into high- and low-risk groups. Results indicated that the high-risk group had significantly poorer overall survival in both TCGA and ICGC cohorts. The ROC curves demonstrated that the signature exhibits stable predictive accuracy. Enrichment analysis showed that the high-risk group was associated with tumor-related pathways. Differences in immune infiltration were observed between high- and low-risk groups. scRNA-seq analysis indicated that PITX2 and CHGA were highly expressed in hepatocytes. CONCLUSIONS: The novel two-gene signature comprising PITX2 and CHGA effectively predicts survival outcomes in HCC patients and is closely associated with tumor metabolism and immune regulation. This signature may serve as a valuable tool for prognostic evaluation and guiding personalized treatment strategies for HCC patients.
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Integrative genomics elucidates the evolutionary, temporal, and developmental origins of a hydrocephalus risk gene.
INTRODUCTION: A prior integrative, multi-omics human genetics and functional genomics study identified maelstrom (MAEL), a gene involved in regulation of DNA transposon activity and genome structure, as a transcriptome-wide predictor of hydrocephalus (HC) in the brain cortex. Here we expand on this discovery and further characterize the evolutionary origin and expression of MAEL across developmental timescales and cell-lineages in the neonatal human brain towards a mechanistic understanding how variation in MAEL expression may cause HC. OBJECTIVE: To characterize the evolutionary, temporal, developmental, and lineages of MAEL expression in HC and the developing human brain. METHODS: Ensembl was used to delineate the evolution and taxonomy of MAEL across species. Analysis of single-cell RNA sequencing (scRNA-seq) of 49 brain regions across pre- and post-natal timescales from the Developing Human Brain Atlas (Allen Institute) identified temporal and spatial MAEL expression patterns. We quantified MAEL expression in primary cortical brain tissue obtained during the surgical treatment of HC. RESULTS: We performed taxonomic gene-mapping to define the evolutionary origin of MAEL to assess suitability for mechanistic characterization in vitro and in vivo across species. We find that MAEL is among the top 0.01% human-specific genes and < 50% sequence homology among commonly used model organisms with highly divergent functions, necessitating mechanistic validation in human tissue. scRNA-seq of the non-disease prenatal human brain identified MAEL expression enriched in cortical excitatory neurons, which was recapitulated in primary HC brain tissue obtained during surgery. Finally, using scRNA-seq of primary HC brain tissue, we functionally validated reduced MAEL expression, consistent with a prior human TWAS analysis. CONCLUSIONS: We identify the evolutionary, temporal, and developmental expression pattern of MAEL in the neonatal human brain. We also provide direct evidence for reduced MAEL expression in human HC brain tissue. These data, at least in part, implicate reduced MAEL expression underlying human HC across etiologies.
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Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution.
A high-resolution spatial physiological atlas of cortical neurons serves as an essential reference for studying neurological diseases and is crucial for a comprehensive understanding of the cortical functions and physiological mechanisms. Although previous studies have elucidated the cellular basis of individual cortex, the molecular characteristics and precise spatial organization of neural cells within multiple human cortex remain incompletely understood. Subcellular-level spatial transcriptomic sequencing and snRNA-seq enabled mapping of 14 human cortical regions, creating a single-cell atlas with transcriptomic data from 1,121,772 nuclei and spatial profiles from 1,888,306 cells to characterize neural cell organization. The atlas reveals distinct expression patterns and spatial arrangements of neural cell types. Glutamatergic neurons show precise laminar patterns, with similar expression in adjacent cortex. SST neurons fall into two transcriptional categories, corresponding to superficial and deep layer distributions. The atlas, integrated with functional networks, highlights correlations between neural cell types and cortical functions, uncovering cell-cell interactions and ligand-receptor patterns with regional differences in neuron-glia communication. It also deciphers transcriptomic differences and cellular composition in layer 4 and the stable subplate (layer 6b) across regions. Our findings offer insights into the cellular foundations of complex and intelligent regions within the human cortex.
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Unraveling the spatial and signaling dynamics and splicing kinetics of immune infiltration in osteoarthritis synovium.
INTRODUCTION: Osteoarthritis (OA), a debilitating joint disorder characterized by synovial inflammation and immune myeloid cell infiltration, currently lacks a comprehensive spatial and transcriptional atlas. This study investigates the spatial dynamics, splicing kinetics, and signaling pathways that drive immune infiltration in OA synovium. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data from 8 OA and 4 healthy synovial samples with spatial transcriptomics using Spatrio. Spatial transition tensor (STT) analysis decoded multistable spatial homeostasis, while splicing kinetics and non-negative matrix factorization (NMF) identified gene modules. CellPhoneDB and pyLIGER mapped ligand-receptor interactions and transcriptional networks. RESULTS: Re-annotation of scRNA-seq data resolved synovial cells into 27 subclasses. Spatial analysis revealed OA-specific attractors (8 in OA vs. 6 in healthy samples), including immune myeloid (Attractor3) and lymphoid infiltration (Attractor4). Key genes OLR1 (myeloid homeostasis) and CD69 (T-cell activation) exhibited dysregulated splicing kinetics, driving inflammatory pathways. Myeloid-specific transcription factors (SPI1, MAF, NFKB1) and lymphoid-associated BCL11B were identified as regulators. Computational drug prediction nominated ZILEUTON as a potential inhibitor of ALXN5 to mitigate myeloid infiltration. DISCUSSION: This study delineates the spatial and transcriptional landscape of OA synovium, linking immune cell dynamics to localized inflammation. The identification of OLR1 and CD69 as spatial homeostasis drivers, alongside dysregulated signaling networks, offers novel therapeutic targets. These findings advance strategies to modulate immune infiltration and restore synovial homeostasis in OA.
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Construction and validation of a regulatory T cells-based classification of renal cell carcinoma: an integrated bioinformatic analysis and clinical cohort study.
PURPOSE: Renal cell carcinoma (RCC), exhibiting remarkable heterogeneity, can be highly infiltrated by regulatory T cells (Tregs). However, the relationship between Treg and the heterogeneity of RCC remains to be explored. METHODS: We acquired single-cell RNA-seq profiles and 537 bulk RNA-seq profiles of TCGA-KIRC cohort. Through clustering, monocle2 pseudotime and prognostic analyses, we identified Treg states-related prognostic genes (TSRPGs), then constructing the RCC Treg states-related prognostic classification (RCC-TSC). We also explored its prognostic significance and multi-omics landmarks. Additionally, we utilized correlation analysis to establish regulatory networks, and predicted candidate inhibitors. More importantly, in Xinhua cohort of 370 patients with kidney neoplasm, we used immunohistochemical (IHC) staining for classification, then employing statistical analyses including Chi-square tests and multivariate Cox proportional hazards regression analysis to explore its clinical relevance. RESULTS: We defined 44 TSRPGs in four different monocle states, and identified high immune infiltration RCC (HIRC, LAG3+, Mki67+) as the highly exhausted subtype with the worst prognosis in RCC-TSC (p < 0.001). BATF-LAG3-immune cells axis might be its underlying metastasis-related mechanism. Immunotherapy and inhibitors including sunitinib potentially conferred best therapeutic effects for HIRC. Furthermore, we successfully validated HIRC subtype as an independent prognostic factor within the Xinhua cohort (OS, HR = 16.68, 95% CI = 1.88-148.1, p = 0.011; PFS, HR = 4.43, 95% CI = 1.55-12.6, p = 0.005). CONCLUSION: Through integrated bioinformatics analysis and a large-sample retrospective clinical study, we successfully established RCC-TSC and a diagnostic kit, which could stratify RCC patients with different prognosis and to guide personalized treatment.
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Hypoxia-associated genes as predictors of outcomes in gastric cancer: a genomic approach.
OBJECTIVE: To investigate the effects of hypoxia-related genes in stomach adenocarcinoma (STAD) and construct an excellent prognostic model. METHODS: RNA expression data and clinical details were retrieved from the TCGA and GEO database dataset. scRNA-seq analysis was conducted on primary gastric cancer samples from GSE183904. Cellular hypoxia status was predicted using the CHPF software. WGCNA and GO-BP/KEGG enrichment of module genes analyses were performed to identify gene modules associated with hypoxia and biological pathway enrichment. A prognostic model was developed employing the LassoCox algorithm. GES-1, AGS, BGC823, and MGC803 cell lines were obtained for qRT-PCR analysis to identify the expression of model genes. RESULTS: Single-cell atlas within STAD delineated that most of neoplastic cells, fibroblasts, endothelial cells, and myeloid cells were hypoxic. Further analysis of neoplastic cell subpopulations identified four hypoxic subpopulations (H1-H4) and four non-hypoxic subpopulations (N1-N4), with H1 subpopulation had the highest degree of hypoxia. The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients. qRT-PCR analysis determined the higher expression level of five H1-specific transcription factors in gastric cancer cell lines than that in normal gastric epithelial cell line. CONCLUSION: Hypoxia exerts a profound influence on STAD due to the overexpression of hypoxic cellular subpopulations-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2. The novel prognostic model developed by these hypoxia-associated genes presents a novel approach to risk stratification, exhibiting an excellent prognostic value for STAD patients.
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Combining Bulk RNA-seq and scRNA-seq data to identify RNA m5C methyltransferases NSUN1: a rising star as a biomarker for cancer diagnosis, prognosis and therapy.
BACKGROUND: RNA 5-methylcytosine (m5C) methyltransferases NSUN1 is a member of the NOP2/SUN (NSUN) RNA methyltransferase family. Studies have found that the expression of NSUN1 is elevated in breast and colon cancer and can predict poor prognosis. However, the NSUN1 gene has only been studied in a few tumors. METHODS: Single-cell RNA sequencing (scRNA-seq) and Bulk RNA-seq data were used for comprehensive analysis of NSUN1 in cancers. The Human Protein Atlas (HPA) database was used to identify the gene location. Immunofluorescence staining was used to detect NSUN1 subcellular distribution within the nucleus, endoplasmic reticulum (ER), and microtubules of A-431, U-2, U-251 cells. The cBioPortal tool was used to analyze the alteration frequency and mutation type. The epigenetic profile of NSUN1 also was analyzed by using the University of Alabama at Birmingham CANcer data analysis Portal (UCLCAN). Tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint expression in cancers were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform enrichment and visualization. The study was based on online resources and public databases. RESULTS: Elevated NSUN1 expression had been observed in most human cancers. Analysis of scRNA-seq data showed that NSUN1 was highly expressed in immune cells such as T cells, B cells, and dendritic (DC) cells. High NSUN1 expression indicated poor overall survival (OS) and disease-free survival (DFS). The characteristics of genetic alteration, methylation and phosphorylation of NSUN1 were analyzed and higher levels of phosphorylation in tumor tissues were found. In addition, the expression of NSUN1 was closely related to tumor-infiltrating immune cells. At the same time, the expression of NSUN1 was positively correlated with the expression of multiple immune checkpoints. CONCLUSIONS: The gene expression profile, survival status, genetic alteration, methylation, phosphorylation and infiltrating immune cells of NSUN1 in human cancers were comprehensively analyzed. The results herein implied that NSUN1 may be an effective biomarker for early cancer diagnosis, prognosis and therapy.