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An integrated analysis of human myeloid cells identifies gaps in in vitro models of in vivo biology.
The Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and pluripotent stem cell-derived myeloid cells. Three classes of tissue-resident macrophage were identified: Kupffer cells and microglia; monocyte-associated; and tumor-associated macrophages. Culture had a major impact on all primary cell phenotypes. Pluripotent stem cell-derived macrophages were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Myeloid subsets, and phenotypes associated with derivation, were reproducible across experimental series including data projected from single-cell studies, demonstrating that the atlas provides a robust reference for myeloid phenotypes. Implementation in Stemformatics.org allows users to visualize patterns of sample grouping or gene expression for user-selected conditions and supports temporary upload of your own microarray or RNA sequencing samples, including single-cell data, to benchmark against the atlas.
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Integrative Multi-Omics and Single-Cell Analysis Prioritizes YWHAB Within Ubiquitination-Related Transcriptional Programs in Endometrial Carcinoma.
BACKGROUND: Ubiquitination-related transcriptional programs may help characterize epithelial heterogeneity in endometrial carcinoma (EC). We aimed to identify candidates associated with these programs and epithelial plasticity. METHODS: We integrated a 43,613-cell atlas from five endometrioid EC samples and two normal endometrial samples with bulk transcriptomic, genetic-association and protein-expression data. Five scoring methods defined a consensus expression score, with HIGH and LOW quartiles defined within the Epithelial + EMT_like lineage. Differential expression and high-dimensional weighted gene co-expression network analysis (hdWGCNA) identified candidates for exploratory Mendelian randomization (MR) and survival-based prioritization among 541 patients from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) cohort. Repeated nested cross-validation and cross-model feature support were used to evaluate internal performance and candidate stability. Pseudobulk, independently curated ubiquitin-proteasome system (UPS) gene sets and scSurv input masking provided sensitivity analyses. RESULTS: The lineage-restricted screen identified 444 candidates, including 412 detectable in TCGA. GeneCards and curated Reactome and KEGG UCell scores were positively correlated (ρ = 0.519 and 0.579). YWHAB ranked fourth among 412 genes in the top-10% workflow analysis and fifth in the top-20% sensitivity analysis. Among the 12 nominal MR-associated genes, it had the highest machine-learning priority. Its single-nucleotide polymorphism (SNP) MR estimate was nominal (odds ratio (OR) = 1.732, p = 0.0488) and did not survive multiple-testing correction. Higher bulk YWHAB expression was associated with poorer overall survival (HR = 2.188, 95% CI 1.559-3.072). Western blotting showed higher YWHAB protein expression in EC than controls (nine versus nine tissues, p = 0.0027), consistent with descriptive Human Protein Atlas (HPA) staining. Single-cell analyses associated YWHAB with RNA processing, proteostasis-related expression and epithelial-state heterogeneity. CONCLUSIONS: Lineage-restricted discovery and cross-model prioritization identify YWHAB as an exploratory candidate associated with ubiquitination-related transcriptional programs in EC. Protein-expression evidence supports its further investigation, while causal function and clinical prognostic utility require independent validation.
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A single-cell atlas of Schwannoma across genetic backgrounds and anatomic locations.
BACKGROUND: Schwannomas are nerve sheath tumors arising at cranial and peripheral nerves, either sporadically or in patients with a schwannomatosis-predisposition syndrome. There is limited understanding of the transcriptional heterogeneity of schwannomas across genetic backgrounds and anatomic locations. METHODS: Here, we prospectively profile by single-cell full-length transcriptomics tumors from 22 patients with NF2-related schwannomatosis, non-NF2-related schwannomatosis, and sporadic schwannomas, resected from cranial and peripheral nerves. We profiled 11,373 cells (after QC), including neoplastic cells, fibroblasts, T cells, endothelial cells, myeloid cells, and pericytes. RESULTS: We characterize the intra-tumoral genetic and transcriptional heterogeneity of schwannoma, identifying six distinct transcriptional metaprograms, with gene signatures related to stress, myelin production, antigen presentation, interferon signaling, glycolysis, and extracellular matrix. We demonstrate the robustness of our findings with analysis of an independent cohort. CONCLUSIONS: Overall, our atlas describes the spectrum of gene expression across schwannoma entities at the single-cell level and will serve as an important resource for the community.
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NKX2‑1 copy number alterations are associated with oncogenic, immunological and prognostic remodeling in non‑small cell lung cancer.
NK2 homeobox 1 (NKX2-1) copy number alterations (CNAs) are frequently observed in lung cancer. However, little is known about the complete landscape of focal alterations in NKX2-1 copy number (CN), their clinical significance and their therapeutic implications in non-small cell lung cancer (NSCLC). The correlations between NKX2-1 expression and EGFR driver mutations and programmed death ligand 1 (PD-L1) co-expression were studied using immunohistochemistry and PCR from the tumors of recruited Filipino patients (n=45). Clinical features of NSCLC with NKX2-1 CNAs were resolved at the tumor and clonal levels using the molecular profiles of patients with lung adenocarcinoma and lung squamous cell carcinoma from The Cancer Genome Atlas (n=1,130), and deconvoluted single-cell RNA-seq data from the Bivona project (n=1,654), respectively. Despite a significant and positive correlation between expression and CN (r=0.264; P<0.001), NKX2-1 CNAs exerted a stronger influence on the combined EGFR and PD-L1 status of NSCLC tumors than expression. NKX2-1 CN gain was prognostic of favorable survival (P=0.018) and a better response to targeted therapy. NKX2-1 CN loss predicted a worse survival (P=0.041). Mutational architecture in the Y-chromosome differentiated the two prognostic groups. There were 19,941 synonymous mutations and 1,408 genome-wide CN perturbations associated with NKX2-1 CNAs. Tumors with NKX2-1 CN gain expressed lymphocyte markers more heterogeneously than those with CN loss. Higher expression of tumor-infiltrating lymphocyte gene signatures in CN gain was prognostic of longer disease-free survival (P=0.005). Tumors with NKX2-1 CN gain had higher B-cell (P<0.001) and total T-cell estimates (P=0.003). NKX2-1 CN loss was associated with immunologically colder tumors due to higher M2 macrophage infiltrates (P=0.011) and higher expression of immune checkpoint proteins, CD274 (P=0.025), VTCN1 (P<0.001) and LGALS9 (P=0.002). In conclusion, NKX2-1 CNAs are associated with tumors that exhibit clinically diverse characteristics, and with unique oncogenic, immunological and prognostic signatures.
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Spatial transcriptomic landscape and cellular neighborhood heterogeneity in cervical cancer: integrative single-cell and spatial RNA sequencing analysis.
BACKGROUND: Cervical cancer ranks as the fourth most common malignancy among women worldwide, with approximately 604,000 new cases and 342,000 deaths annually. Despite advances in HPV vaccination and screening programs, significant challenges remain in diagnostic accuracy, therapeutic efficacy, and prognostic assessment. Traditional bulk RNA sequencing methods average gene expression data across entire tissue specimens, masking cellular diversity and spatial architectural patterns that influence tumor progression. Telomere maintenance mechanisms play crucial roles in cervical cancer development, with over 90% of cervical cancers exhibiting telomerase reactivation. METHODS: This study integrated single-cell RNA sequencing and spatial transcriptomics technologies to construct the first comprehensive spatial molecular atlas of cervical cancer. Single-cell RNA sequencing analysis utilized public datasets from the GEO database with systematic quality control through a multidimensional assessment framework. Spatial transcriptomics analysis employed the 10x Genomics Visium platform, successfully identifying 38 distinct cellular neighborhoods (N1-N38). Spatial expression patterns of 10 key genes were analyzed, including epithelial markers (MUC1, CDH1, KRT16), stromal markers (COL1A1, COMP, DCN), and immune markers (CD3G, FCGR1A). In vitro validation was performed using HeLa and SiHa cell lines. RESULTS: Multiple distinct cell subpopulations and 38 cellular neighborhoods with unique molecular characteristics were successfully identified. MKI67 demonstrated spatial heterogeneity with proliferative "hotspots"; COMP was primarily expressed in stromal regions and participated in tumor-stroma interactions; KRT16 exhibited patterns reflecting epithelial differentiation gradients. Different neighborhoods showed enrichment for various cell types, including immune hotspots, stromal-rich regions, and epithelial-dominant areas. Immunoglobulin-related genes (IGLC2, IGHG1, IGHG2, etc.) displayed unique spatial expression characteristics. Quantitative PCR validation confirmed differential expression patterns between cell lines. CONCLUSIONS: This study established the first comprehensive spatial transcriptomic atlas of cervical cancer, revealing unprecedented insights into tumor microenvironment organization and cellular spatial relationships.
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Single-cell analysis of the cellular landscape of vulvar melanoma provides new insight for immunotherapy administration.
BACKGROUND: Vulvar and vaginal melanoma (VuM & VaM) is a rare gynecologic malignancy with high mortality but low effectiveness to checkpoint immunotherapy compared to cutaneous melanoma. This article aims to elucidate the role of the disordered immune microenvironment in cancer progression in VuM. METHODS: At first, this article applied single-cell RNA sequencing (scRNA-seq) to the VuM obtained from a 68-year-old female patient, and constructed a single-cell atlas of VuM consist of 12,243 single cells. Then this article explores the genomic complexity and core signal channel in VuM microenvironment. RESULTS: This article provides new insights about the pathogenesis of VuM based on single-cell resolution data. It was found that the activation of CD8+ T cell contributed to induce tumor angiogenesis and immune escape, and the activation of the antigen-presenting molecular function participated in melanoma metastasis. CONCLUSION: This article provided new insights into underlining VuM molecular regulation and potential signaling involved in immunotherapy, which would benefit the clinical practice and administration.
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Single-cell technology grows up: Leveraging high-resolution omics approaches to understand neurodevelopmental disorders.
The identification of hundreds of neurodevelopmental disorder (NDD) genes in the last decade led to numerous genetic models for understanding NDD gene mutation consequences and delineating putative neurobiological mediators of disease. In parallel, single-cell and single-nucleus genomic technologies have been developed and implemented to create high-resolution atlases of cell composition, gene expression, and circuit connectivity in the brain. Here, we discuss the opportunities to leverage mutant models (or human tissue, where available) and genomics approaches to systematically define NDD etiology at cellular resolution. We review progress in applying single-cell and spatial transcriptomics to interrogate developmental trajectories, cellular composition, circuit activity, and connectivity across human tissue and NDD models. We discuss considerations for implementing these approaches at scale to maximize insights and facilitate reproducibility. Finally, we highlight how standardized application of these technologies promises to not only define etiologies of individual disorders but also identify molecular, cellular, and circuit level convergence across NDDs.
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Computational approaches for characterizing the tumor immune microenvironment.
Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.
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Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles.
Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing "just-in-time" expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.
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A single-nucleus multiome profiling reveals the regulatory programs driving plant nematode-induced syncytium.
Plant-parasitic cyst nematodes induce the formation of specialized feeding structures, known as syncytia, within host roots by orchestrating extensive transcriptional and epigenetic reprogramming. Here, we generated parallel single-nucleus RNA-seq and ATAC-seq libraries from fully developed Heterodera schachtii-induced syncytia on Arabidopsis roots. We analyzed high-quality transcriptome and chromatin profiles independently and in an integrated framework. We identified multiple transcriptomic cell states, several of which were highly syncytium-enriched and spanned vascular and cortical identities. Differential expression analysis identified hundreds of syncytium-enriched genes, and pseudotime trajectory inference points to potential compartmentalization or functional heterogeneity of syncytium nuclei, consistent with the possibility of distinct transcriptional programs within or between mature syncytia. Joint clustering of snATAC-seq with snRNA-seq delineated syncytium-unique chromatin accessibility landscapes, with several cell states exhibiting extensive open chromatin linked to upregulated genes. Motif enrichment across these accessible regions highlighted cis-regulatory elements mainly for AP2, bZIP, bHLH, MYB, and TCP transcription factors and identified functionally validated members of these families as candidate regulators of syncytium identity. Our study establishes a multiome atlas of syncytial cells and uncovers the regulatory programs driving nematode feeding site maintenance, as well as transcriptionally distinct nuclear states that may reflect functional specialization within or between the syncytia.