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A dynamic transcriptional cell atlas of testes development after birth in Hu sheep.
BACKGROUND: Testes development is a fundamental process in sexual development and reproduction. The testes undergo dramatic structural changes during development, including the proliferation and differentiation of somatic cells such as Sertoli cells, Leydig cells, and myoid cells, as well as the maturation of spermatogonia. However, little is known about the onset of spermatogenesis and cell proliferation and maturation in the spermatogonial niche in large animals. RESULTS: We used single-cell RNA sequencing (scRNA-seq) to profile nearly 100,000 cells from Hu sheep testes across seven developmental stages (birth, prepuberty, puberty, and adulthood). We constructed single-cell transcriptomic atlases and identified distinct spermatogonial subtypes, revealing dynamic gene expression patterns during spermatogenesis. Notably, we observed that two distinct Sertoli cell states converge into a mature population during puberty. Additionally, we identified a common prepubertal progenitor for Leydig and myoid cells, with Leydig cells transitioning through progenitor and immature stages before reaching maturity. CONCLUSIONS: Our study provides a comprehensive atlas of Hu sheep testes development, revealing key insights into the dynamic changes and regulatory mechanisms of spermatogenesis and somatic cell maturation from birth to adulthood. These findings offer new perspectives on testicular development in large mammals and support future research on reproductive biology and breeding strategies.
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Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma.
BACKGROUND: Glioblastoma (GB) is incurable with a dismal prognosis. Single-cell RNA sequencing (scRNA-seq) is a pivotal tool for studying tumor heterogeneity. The dysregulation of the urea cycle (UC) frequently occurs in tumors, but its characteristics in GB have not been illuminated. This study integrated scRNA-seq UC scores and bulk RNA-seq data to build a GB prognostic model. METHODS: Samples from 3 pairs of GB patients were collected for scRNA-seq analysis. GB-mRNA expression data, clinical data, and SNV mutation data were sourced from the Cancer Genome Atlas (TCGA). GB-mRNA expression data and clinical data were downloaded from the Chinese Glioma Genome Atlas (CGGA). GB RNA-seq data and clinical data were obtained from Gene Expression Omnibus (GEO) database. The R package Seurat was applied for scRNA-seq data processing. UMAP and TSNE were used for dimensionality reduction. UCell enrichment method was employed to score each astrocyte. Monocle algorithm was applied for pseudotime trajectory analysis. CellChat R package was applied for cell communication analysis. Cell labeling was performed on the results of the nine subclusters of astrocytes. The GSE138794 dataset was used to validate the results of single-cell classification. For bulk RNA-seq, univariate Cox and LASSO analyses were undertaken to screen prognostic genes, while multivariate Cox regression analysis was applied to set up a prognostic model. The differences between high-risk (HR) and low-risk (LR) groups were studied in terms of immune infiltration, sensitivity to anti-tumor drugs, etc. We verified the effect of the marker gene on the function of GB cells at the cellular level. RESULTS: The analysis of scRNA-seq data yielded 7 core cell types. Further clustering of the largest proportion of astrocytes resulted in 9 subclusters. UC score and pseudotime analysis revealed the heterogeneity and differentiation process among subclusters. Subcluster 8 was annotated as an immature astrocyte (marker: CXCL8), and this cell cluster had a higher UC score. The results were validated in the GSE138794 dataset. Combining UC scores, we performed univariate Cox and LASSO to select prognostic genes on bulk RNA-seq data. A prognostic model based on 5 feature genes (RGS4, CTSB, SERPINE2, ID1, and CALD1) was established through multivariate Cox analysis. In addition, patients in the HR group had higher immune infiltration and immune function. Final cell experiments demonstrated that 5 feature genes were highly expressed in GB cells. CALD1 promoted the malignant phenotype of GB cells. CONCLUSION: We set up a novel prognostic model for predicting the survival of GB patients by integrating bulk RNA-seq and scRNA-seq data. The risk score was closely correlated with immune infiltration and drug sensitivity, pinpointing it as a promising independent prognostic factor.
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Transcriptomic sequencing analysis of the tumor microenvironment atlas and potential mechanisms of metastasis in papillary thyroid carcinoma.
Papillary thyroid carcinoma (PTC) is the most prevalent thyroid malignancy and its incidence continues to rise. Although prognosis is generally favorable, overdiagnosis, overtreatment, and occasional lethal complications persist. To clarify the micro-environmental basis of PTC metastasis, we integrated single-cell RNA-seq from tumor (T), peritumoral thyroid (PT) and lymph-node (LN) tissues with bulk transcriptomes. Metastatic (M) and non-metastatic (NM) cases were compared, and machine-learning models were built to predict lymph-node status and outcome. Our atlas delineates highly heterogeneous ecosystems across T, PT and LN samples. Within T/NK lymphocytes, both immune-activating and immune-suppressive subsets coexist; NK and CD4+TNFRSF4 cells were selectively enriched in M tumors, implying heterogeneous anti-tumor responses. Among myeloid cells, a Mac-APOC1 subset displayed tumor-associated macrophage/M2 features, consistent with immune escape and dissemination. Dendritic cells differed markedly in antigen presentation and chemotaxis, while RGS5+ myCAFs, markedly expanded in M samples from T and LN, up-regulated metabolic and ribosomal pathways. Trajectory analysis uncovered dynamic CAF state transitions. Tumor epithelial cells followed multiple developmental branches, with those linked to LN invasion predicting poorer survival. A random-forest classifier achieved 0.98 accuracy for N-stage, and a lasso-Cox signature (C-index > 0.9) robustly stratified survival, underscoring the clinical relevance of the identified genes. Collectively, this work reveals the cellular and functional heterogeneity that drives PTC progression, identifies metastasis-associated subpopulations, and demonstrates the utility of machine learning for precise prediction of lymph-node metastasis and patient prognosis, offering a foundation for tailored therapeutic strategies.
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Single-cell transcriptome atlas reveals somatic cell embryogenic differentiation features during regeneration.
Understanding somatic cell totipotency remains a challenge facing scientific inquiry today. Plants display remarkable cell totipotency expression, illustrated by single-cell differentiation during somatic embryogenesis (SE) for plant regeneration. Determining cell identity and exploring gene regulation in such complex heterogeneous somatic cell differentiation have been major challenges. Here, we performed high-throughput single-cell sequencing assays to define the precise cellular landscape and revealed the modulation mode of marker genes during embryogenic differentiation in cotton (Gossypium hirsutum L.) as the crop for biotechnology application. We demonstrated that nonembryogenic calli (NEC) and primary embryogenic calli (PEC) tissues were composed of heterogeneous cells that could be partitioned into four broad populations with six distinct cell clusters. Enriched cell clusters and cell states were identified in NEC and PEC samples, respectively. Moreover, a broad repertoire of new cluster-specific genes and associated expression modules were identified. The energy metabolism, signal transduction, environmental adaptation, membrane transport pathways, and a series of transcription factors were preferentially enriched in cell embryogenic totipotency expression. Notably, the SE-ASSOCIATED LIPID TRANSFER PROTEIN (SELTP) gene dose-dependently marked cell types with distinct embryogenic states and exhibited a parabolic curve pattern along the somatic cell embryogenic differentiation trajectory, suggesting that SELTP could serve as a favorable quantitative cellular marker for detecting embryogenic expression at the single-cell level. In addition, RNA velocity and Scissor analysis confirmed the pseudo-temporal model and validated the accuracy of the scRNA-seq data, respectively. This work provides valuable marker-genes resources and defines precise cellular taxonomy and trajectory atlases for somatic cell embryogenic differentiation in plant regeneration.
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Cell-Type-Resolved Acetylation Regulator Atlas Defines Immune Endotypes and Druggable Vulnerabilities in Psoriasis.
Background: Psoriasis frequently relapses after treatment withdrawal, consistent with persistent epigenetic programs in lesional immune cells. Lysine acetylation is a reversible regulatory layer linking chromatin accessibility, transcription factor activity, and immune-cell effector programs; yet, its cell-type-resolved landscape and clinical stratification value in psoriasis remain incompletely defined. Methods: We integrated four bulk transcriptome cohorts of psoriatic and healthy skin (746 psoriasis, 515 controls) with two public skin scRNA-seq datasets. A diagnostic acetylation-regulator signature was derived from 33 curated acetylation regulators, and acetylation endotypes were defined by unsupervised clustering. The cell-type-specific expression was mapped at the single-cell resolution. Key regulators were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in an imiquimod-induced psoriasis-like mouse model, and further verified in an independent dataset (GSE136757). Motif enrichment and drug-target mining were used to prioritize transcriptional regulators and candidate epigenetic therapeutics. Results: Sixteen acetylation regulators were differentially expressed in bulk skin, with histone deacetylase (HDAC1) showing the strongest upregulation and lysine acetyltransferase (KAT2A) the strongest downregulation. A 13-gene acetylation signature discriminated psoriasis from controls (area under the curve, AUC 0.886) and separated lesional samples into two acetylation endotypes with divergent pathway states (hypoxia-glycolysis versus oxidative-stress-dominated programs). Single-cell mapping demonstrated immune-restricted acetylation modules, including CREB binding protein (CREBBP)-enriched neutrophils, histone deacetylase 1 (HDAC1)-high cluster of differentiation (CD)8+ T cells, and lysine acetyltransferase 6A (KAT6A)/lymphoid enhancer binding factor (LEF1)-enriched CD4+ and regulatory T cell (Treg) subsets, coincident with interleukin (IL)-17-related inflammatory programs. In mice, qRT-PCR confirmed the coordinated dysregulation of hub genes and highlighted Hnf1a and Kat6a as reproducible candidates. External validation using the GSE136757 dataset further supports their robust diagnostic performance. Motif analysis nominated interferon regulatory factor (IRF4), YY transcription factor (YY2), and zinc finger protein (ZNF404) as putative transcriptional mediators downstream of acetylation programs, and drug-target mining prioritized epigenetic compounds with subtype-relevant potential, including histone deacetylase (HDAC) inhibitors (e.g., entinostat) and the p300/CREB binding protein (CBP) inhibitor A485. Conclusions: This integrative atlas links acetylation regulators to specific immune compartments, defines acetylation endotypes associated with distinct inflammatory programs, and provides a rationale for stratified epigenetic target selection in psoriasis.
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Pig jejunal single-cell RNA landscapes revealing breed-specific immunology differentiation at various domestication stages.
BACKGROUND: Domestication of wild boars into local and intensive pig breeds has driven adaptive genomic changes, resulting in significant phenotypic differences in intestinal immune function. The intestine relies on diverse immune cells, but their evolutionary changes during domestication remain poorly understood at single-cell resolution. METHODS: We performed single-cell RNA sequencing (scRNA-seq) and marker gene analysis on jejunal tissues from wild boars, a Chinese local breed (Jinhua), and an intensive breed (Duroc). Then, we developed an immune cell evaluation system that includes immune scoring, gene identification, and cell communication analysis. Additionally, we mapped domestication-related clustering relationships, highlighting changes in gene expression and immune function. RESULTS: We generated a single-cell atlas of jejunal tissues, analyzing 26,246 cells and identifying 11 distinct cell lineages, including epithelial and plasma cells, and discovered shared and unique patterns in intestinal nutrition and immunity across breeds. Immune cell evaluation analysis confirmed the conservation and heterogeneity of immune cells, manifested by highly conserved functions of immune cell subgroups, but wild boars possess stronger immune capabilities than domesticated breeds. We also discovered four patterns of domestication-related breed-specific genes related to metabolism, immune surveillance, and cytotoxic functions. Lastly, we identified a unique population of plasma cells with distinctive antibody production in Jinhua pig population. CONCLUSIONS: Our findings provide valuable single-cell insights into the cellular heterogeneity and immune function evolution in the jejunum during pig at various domestication stages. The single-cell atlas also serves as a resource for comparative studies and supports breeding programs aimed at enhancing immune traits in pigs.
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Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.
BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.
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The Advances of Single-Cell RNA-Seq in Kidney Immunology.
Kidney diseases are highly prevalent and treatment is costly. Immune cells play important roles in kidney diseases; however, it has been challenging to investigate the contribution of each cell type in kidney pathophysiology. Recently, the development of single-cell sequencing technology has allowed the extensive study of immune cells in blood, secondary lymphoid tissues, kidney biopsy and urine samples, helping researchers generate a comprehensive immune cell atlas for various kidney diseases. Here, we discuss several recent studies using scRNA-seq technology to explore the immune-related kidney diseases, including lupus nephritis, diabetic kidney disease, IgA nephropathy, and anti-neutrophil cytoplasmic antibody-associated glomerulonephritis. Application of scRNA-seq successfully defined the transcriptome profiles of resident and infiltrating immune cells, as well as the intracellular communication networks between immune and adjacent cells. In addition, the discovery of similar immune cells in blood and urine suggests the possibility of examining kidney immunity without biopsy. In conclusion, these immune cell atlases will increase our understanding of kidney immunology and contribute to novel therapeutics for patients with kidney diseases.
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Expression Atlas update: gene and protein expression in multiple species.
The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa.
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A ligand-centered framework for γδ T cell activation in colorectal cancer revealed by single-cell and transformer-based perturbation.
Understanding the activation mechanisms of γδ T cells in colorectal cancer (CRC) is critical for harnessing their therapeutic potential. Here, using an atlas of human CRC-infiltrating γδ T cells that we built by integrating multiple single-cell RNA-seq datasets, we developed a γδ T cell-refined ligand inference pipeline by combining differential gene expression, gene regulatory network prediction, ligand inference, and in silico perturbation analysis. This approach identified ligands, including IL-15 and TNFSF9 (4-1BBL), as candidates promoting γδ T cell effector function and highlighted NCR2 and KLRC3 (NKG2E), whose in silico overexpression was associated with γδ T cell activation. Ligand enrichment analyses further indicated that monocytes and dendritic cells are key contributors to γδ T cell activation in the tumor microenvironment. Our results also highlighted transcription factors IKZF1, FOSL2, and FOXO1 in the less activated γδ T cells and IRF1, KLF2, and BHLHE40 in the effector γδ T cells that plausibly regulated the differential activation state. Together, our results offer a systems-level view of the signaling and transcriptional programs governing γδ T cell phenotypes in CRC and provide a foundation for γδ T cell-based immunotherapies with enhanced antitumor functions.