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A single-cell transcriptomic atlas of the periventricular proliferative zone in the late gestation fetal brain in the pigtail macaque.
BACKGROUND: The fetal brain undergoes rapid cellular and structural changes in late gestation, when waves of neurogenesis and gliogenesis shape cortical circuitry. The ventricular zone (VZ), subventricular zone (SVZ), periventricular white matter (PVWM), and deep white matter (DWM) are enriched in neuroprogenitor cells, newborn neurons, and interneurons, which are regions challenging to study in the third-trimester human fetal brain. The nonhuman primate (NHP) provides a powerful translational model to overcome this limitation, given its close similarity to human neurodevelopmental trajectories. The study objective was to construct a single-cell RNA-Seq (scRNA-Seq) atlas of the late-gestation fetal brain of the pigtail macaque (Macaca nemestrina), focusing on cells in the periventricular proliferative zone. METHODS: A sample of the lateral ventricular wall, subventricular zone, and overlying white/gray matter was dissociated into single cells and processed through 10X Genomics sequencing, SoupX removal of ambient RNA, empty droplet removal, doublet exclusion, and Seurat's pipeline which consists of aggregation, unsupervised clustering, and cluster annotation to create a single-cell RNA-Seq (scRNA-Seq) atlas. We also investigated developmental trajectories using scVelo and CellRank2. RESULTS: This analysis captured diverse populations of neuroprogenitors, newborn neurons, developing lineages of excitatory and inhibitory neurons, oligodendrocytes, astrocytes, epithelial and vascular cells. CONCLUSIONS: Single-cell populations from the third-trimester nonhuman primate fetal brain are highly similar to those in the human fetus at the level of major lineages. This late-gestation single-cell atlas of the periventricular proliferative zone provides a unique reference for progenitor, neuronal, glial, vascular, and immune cell states during a critical window of primate neurodevelopment, enabling mechanistic interrogation of how inflammatory, infectious, or hypoxic insults disrupt vulnerable neurogenic niches.
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Prognostic value and potential mechanism of cellular senescence and tumor microenvironment in hepatocellular carcinoma: Insights from bulk transcriptomics and single-cell sequencing analysis.
The high mortality rate and postoperative recurrence of hepatocellular carcinoma (HCC) contribute to the burden on society and healthcare. The prognostic value and underlying mechanisms of cellular senescence and tumor microenvironment (TME) in HCC remain unclear. Bulk transcriptomic data were obtained from 368 HCC samples in The Cancer Genome Atlas-liver hepatocellular carcinoma cohort and 64 samples from the GSE116174 dataset. Single-cell RNA sequencing (scRNA-seq) data of HCC were obtained from the GSE149614 dataset, including 18 tumor samples from 10 patients. Prognosis-related cellular senescence genes and immune cells were identified through univariate analysis. Least absolute shrinkage and selection operator regression analysis was performed to construct the CellAge score and TME score, both of which were identified as independent prognostic factors for HCC based on multivariate Cox analysis. The combined CellAge and TME scores showed improved prognostic stratification for HCC patients, as confirmed by multivariate Cox analysis (p < .001). The gene set enrichment analysis (GSEA) revealed enrichment of the extracellular matrix receptor interaction signaling pathway in the group with high CellAge scores and low TME scores, which exhibited a worse prognosis. Single-cell sequencing results revealed higher expression activity of the cAMP response element modulator (CREM) extended transcription factor in HCC cells and most immune cells, indicating its involvement in TME remodeling. Finally, the tumor immune dysfunction and exclusion (TIDE) analysis demonstrated that the combined scores could predict the outcomes of immune therapy in patients with HCC. In conclusion, cellular senescence contributes to TME remodeling in HCC, and the developed CellAge and TME scores serve as independent prognostic factors and predictors of immune therapy in HCC.
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Multi-Omics Identification of UBE2C as a Prognostic Biomarker and Therapeutic Target Linked to Topotecan Sensitivity in Cervical Cancer.
Objectives: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) necessitate the discovery of novel biomarkers for prognostic and therapeutic advancement. This study aims to evaluate the clinical significance of ubiquitin-conjugating enzyme E2C (UBE2C) and its association with the tumor microenvironment (TME) in CESC. Methods: We meticulously sourced CESC data from renowned repositories such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO), leveraging cutting-edge techniques including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and pharmacogenomics. Through multifaceted data analysis, we endeavored to unravel the intricate role and potential value of UBE2C in CESC tumorigenesis and progression. Results: Analysis of public datasets confirms UBE2C elevation in CESC tumors, correlating with advanced stages, metastasis, and poor disease-free survival (DFS). Dependency screens and functional enrichment highlight UBE2C's critical role in cell viability and DNA replication. Notably, multi-omics and spatial transcriptomics reveal a strong link between UBE2C expression and macrophage infiltration (CD63+) in tumor regions. Finally, pharmacogenomic profiling and molecular docking identified Topotecan as a potent therapeutic agent with high UBE2C binding affinity. Conclusion: In conclusion, UBE2C expression is associated with cervical cancer progression and correlates with an immunosuppressive macrophage-enriched microenvironment, making it a promising candidate for further investigation in therapeutic intervention.
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Single-cell exploration of gonadal somatic cell lineage specification during human sex determination.
Gonad development is an exciting model to study cell fate commitment. A better understanding of sex determination requires the identification of all involved cell types and their dynamic expression programs. Here, we present an atlas of 128,000 single cells from human gonads between 5 and 12 post-conceptional weeks. A focused analysis of somatic cells uncovered a population of bipotential progenitors derived from the coelomic epithelium of both testes and ovaries, which may have the capacity to commit to either a steroidogenic or a supporting fate. Moreover, our analyses suggest that early supporting cells, prior to differentiation into Sertoli or pre-granulosa cells, also give rise to the rete testis/ovarii and that the ovary retains the capacity to feed the supporting cell pool for an extended period of time, directly from the surface epithelium. Finally, the potential involvement of the gonadotropin releasing hormone (GnRH) signaling pathway in regulating testis differentiation was assessed ex vivo.
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Integrative multi-omics reveals the POSTN+ CAF-APOE+ macrophage axis drives immunosuppression and progression in prostate cancer.
BACKGROUND: The progression of prostate cancer to lethal castration-resistant (CRPC) and metastatic (mCRPC) stages is driven by a profoundly remodeled tumor microenvironment (TME). However, the identity of key stromal cell populations, their developmental dynamics, and their precise crosstalk with immune cells remain incompletely understood, limiting our ability to target the TME therapeutically. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data from 222,529 cells across 10 studies, encompassing normal prostate, primary tumors, CRPC, and mCRPC. Fibroblast heterogeneity was resolved using unsupervised clustering, trajectory inference (Monocle2), and regulon analysis (pySCENIC). Intercellular communication was deciphered using CellChat. Spatial transcriptomic data were integrated via CellTrek and SpaGene for validation. Clinical associations were evaluated in multiple bulk transcriptomic cohorts (e.g., TCGA-PRAD, IMvigor210) and extended to a pan-cancer atlas of 11 tumor types. RESULTS: We identified a distinct POSTN+ CAF subset that was progressively enriched in advanced disease. Trajectory analysis positioned POSTN+ CAFs as a progenitor-like state potentially transitioning toward inflammatory or contractile subtypes, with STAT1-centered regulon activity associated with this process. POSTN+ CAFs created an extracellular matrix (ECM)-remodeled, immune-excluded niche, correlated with elevated T-cell exclusion scores. We further uncovered a predicted communication axis where POSTN+ CAFs are computationally inferred to interact with M2-like, immunoregulatory APOE+ macrophages via the MDK-NCL ligand-receptor pair. The co-occurrence of POSTN+ cancer-associated fibroblasts and APOE+ macrophages correlated with worse patient outcomes. In the IMvigor210 urothelial carcinoma cohort (as indirect cross-cancer evidence), high co-infiltration was associated with diminished anti-PD-L1 response, although this finding requires validation in prostate cancer-specific cohorts. Exploratory pan-cancer analysis suggested conserved enrichment and adverse prognostic impact of this stromal-immune axis across diverse tumor types, though tissue-specific context should be considered. CONCLUSIONS: Our integrated single-cell atlas defines a critical POSTN+ CAF-APOE+ macrophage unit that is associated with a fibrotic and immunosuppressive TME in advanced prostate cancer. If functionally validated, targeting this stromal-immune crosstalk axis could represent a promising therapeutic strategy to remodel the TME and overcome treatment resistance, although this remains speculative at present.
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Construction and clinical application of a risk model based on N6-methyladenosine regulators for colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) shows a high incidence in developed countries. This study established a prognosis signature based on N6-methyladenosine (m6A) regulators involved in CRC progression. METHOD: The bulk RNA-seq data from the Atlas and Compass of Immune-Colon cancer-Microbiome interactions (AC-ICAM) and GSE33113 CRC datasets were obtained from the cBioportal and GEO databases, and a total of 21 m6A regulators genes were collected from a previous study. The scRNA-seq analysis of the GSE146771 cohort was conducted applying the Seurat and harmony R packages. Consensus clustering based on the expressions of m6A regulators was performed with the ConsensusClusterPlus package. The ggGSEA package was used for the Gene Set Enrichment Analysis (GSEA). The un/multivariate and LASSO Cox analysis were performed applying the "survival" and "glmnet" packages for developing a risk model. The pRRophetic and GSVA packages were utilized to analyze potential drugs for CRC and immune infiltration in different risk groups, followed by the Kaplan-Meier (KM) survival and ROC analysis with the "survival" and "timeROC" packages. In vitro assays included the quantitative polymerase chain reaction (qPCR), wound healing and transwell were performed. RESULTS: CRC patients in the AC-ICAM cohort were assigned into three molecular subtypes (S1, 2 and 3) based on nine m6A regulator genes. Specifically, the prognostic outcome of the S3 was the most favorable, while that of the S1 was the worst and this subtype was associated with the activation of NF-kB, TNF-α and hypoxia pathways. Three key genes, namely, methyltransferase-like 3 (METTL3), insulinolike Growth Factor2 mRNA-Binding Protein 3 (IGF2BP3) and YTH domain-containing protein 2 (YTHDC2), selected from the 9 m6A regulator genes were combined into a RiskScore, which showed a high classification effectiveness in dividing the patients into high- and low-risk groups. Inhibition of the expression of METTL3A or that of IGF2BP3 suppressed the invasion and migration of CRC cells. Notably, the high-risk patients had higher immune cell infiltration to support the activation of multiple immune responses and exhibited significantly poor prognosis. Meanwhile, a nomogram with practical clinical value was developed based on the RiskScore and other clinical features. Finally, eight potential drugs associated with the RiskScore were identified, and CD4+ cells and Tregs were found to be closely associated with CRC progression. CONCLUSION: The RiskScore model developed based on m6A regulators played a critical role in CRC development and can be considered as a prognosis predictor for patients with the cancer. The present discoveries will facilitate the diagnosis and clinical management of CRC patients.
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Prediction of prognostic biomarkers for hepatocellular carcinoma and immune microenvironment infiltration based on single-cell sequencing and RNA-Seq integration.
OBJECTIVE: Early diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration. METHODS: In this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups. RESULTS: A total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4+ T cells, and 820 central memory CD8+ T cells. By integrating transcriptomic data from TCGA-LIHC and GEO datasets, we identified six prognostic biomarkers: LYZ, SPP1, EGR1, MARCO, FCN3, and PTTG1. A prognostic model was developed based on these biomarkers, enabling risk stratification into high- and low-risk groups. The model demonstrated robust predictive performance in estimating patient survival rates and immune cell infiltration levels within the tumor microenvironment. CONCLUSION: This study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.
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CLIC6 and ANLN: novel exosome-related prognostic markers and therapeutic targets in lung adenocarcinoma.
BACKGROUND: Exosomes can promote tumor development and regulate tumor immune responses, making them of significant value in Lung Adenocarcinoma (LUAD) management. In-depth exploration of exosome-related genes in LUAD is of great significance for expanding LUAD clinical treatment options. METHODS: Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. Differential expression analysis (limma package), consensus clustering (ConsensusClusterPlus), and Least Absolute Shrinkage and Selection Operator (LASSO) regression (glmnet package) were used to build a prognostic model. Immune infiltration was assessed with TIMER, MCPcounter, and single-sample gene set enrichment analysis (ssGSEA). Tumor Immune Dysfunction and Exclusion (TIDE) algorithm evaluated immunotherapy response. Single-cell RNA sequencing (scRNA-seq) data were processed using Seurat. LUAD cell lines (A549, NCI-H838) were used for quantitative real-time PCR (qRT-PCR), Cell Counting Kit-8 (CCK-8), Transwell, and wound-healing assays. RESULTS: Four genes associated with exosomes were identified as key genes significantly influencing LUAD prognosis, namely CLIC6, ANLN, FAM83A, and RHOV. A LUAD prognosis model was constructed based on these genes, and the ROC curve confirmed the model's excellent predictive performance. Immune infiltration analysis revealed immune cell infiltration differences between low- and high-RiskScore groups in LUAD, with significant differences in infiltration observed between groups for cells including eosinophils, T cells, myeloid dendritic cells, and B lineage (P < 0.05). CD274, PDCD1 and LAG3 were highly expressed in the high-risk group of LUAD (P < 0.01). Exosome-related pathways were significantly enriched in epithelial cells and monocyte/macrophage cells. Single-cell analysis reveals that CLIC6 exhibits higher expression levels in epithelial cells. ANLN regulates the malignant phenotype of LUAD cell lines. CONCLUSION: A four-gene exosome-related signature was identified that can effectively separate LUAD patients into risk subgroups having distinct immune characteristics and immunotherapeutic benefits. Which occurred when ANLN was validated as an oncogene, coupled to the model being significantly associated with immune evasion mechanisms, suggests these biomarkers could enhance prognostic assessment and allow clinicians to identify patients who may be less likely to respond to immunotherapy, thereby informing personalized treatment strategies.
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Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.
BACKGROUND: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. METHODS: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). RESULTS: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.
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Single-cell atlas of Leishmania development in sandflies reveals the heterogeneity of transmitted parasites and their role in infection.
Sandfly vectors transmit Leishmania through egestion of parasites into the host skin. The transmissible dose is shaped by Leishmania development in the sandfly gut, described as a sequential differentiation of promastigote morphotypes. Apart from isolated mammal-infective metacyclic promastigotes, little is known about the transcriptional programs and molecular markers for other stages coinhabiting the midgut in mature infections and cotransmitted by the sandfly bite. Here, we elucidate the single-cell transcriptomic complexity of Leishmania major colonizing its natural vector Phlebotomus duboscqi at early and late infection, providing markers for three transmissible stages. Contrary to prevailing models, our analyses indicate a nonlinear developmental progression, with bifurcation into either replicating early metacyclics or attached and detached haptomonads. We demonstrate that haptomonads constitute a key component of the transmitted inoculum and, along with nondividing late metacyclics, are infectious to and exacerbate the pathology in mice. Our single-cell analysis and validated markers will facilitate further studies on the Leishmania life cycle and its implications for vector-to-host transmission dynamics.