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HMGA2 Expression Predicts Subtype, Survival, and Treatment Outcome in Pancreatic Ductal Adenocarcinoma.
PURPOSE: The purpose of this study was to establish HMGA2 as a marker of basal-like disease in pancreatic ductal adenocarcinoma (PDAC) and explore its use as a biomarker for prognosis and treatment resistance. EXPERIMENTAL DESIGN: We identified high-mobility group A2 (HMGA2) protein expression in basal PDAC cells in a single-cell RNA sequencing (RNA-seq) atlas of 172 patient samples. We then analyzed HMGA2 expression, along with expression of the classic marker GATA-binding factor 6 (GATA6), in a cohort of 580 PDAC samples with multiplex IHC. We further supplemented these data with an additional 30 diverse patient samples and multiple independent single-cell RNA-seq databases. RESULTS: We found that expression of HMGA2, but not previously described basal markers cytokeratins 5 or 17, predicted overall survival in our cohort. Combining HMGA2 and GATA6 statuses allowed for the identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, low tumor-infiltrating CD8+ T cells, increased FAP+ fibroblasts, and poorer response to gemcitabine-based chemotherapies (n = 94, median survival = 11.2 months after surgery) and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T-cell infiltrate, decreased FAP+ fibroblasts, and improved survival with gemcitabine-based chemotherapy (n = 198, median survival = 21.7 months after surgery). HMGA2 was also prognostic for overall survival in RNA-seq from an independent cohort. CONCLUSIONS: IHC stratification of primary tumors by HMGA2 and GATA6 statuses in pancreatic cancer is associated with differential outcomes, survival following chemotherapy, and tumor microenvironments. As a nuclear marker for basal disease, HMGA2 complements GATA6 to identify disease subtypes in PDAC.
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Embryo-scale, single-cell spatial transcriptomics.
Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.
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Single-cell, single-nucleus and xenium-based spatial transcriptomics analyses reveal inflammatory activation and altered cell interactions in the hippocampus in mice with temporal lobe epilepsy.
BACKGROUND: Temporal lobe epilepsy (TLE) is among the most common types of epilepsy and often leads to cognitive, emotional, and psychiatric issues due to the frequent seizures. A notable pathological change related to TLE is hippocampal sclerosis (HS), which is characterized by neuronal loss, gliosis, and an increased neuron fibre density. The mechanisms underlying TLE-HS development remain unclear, but the reactive transcriptomic changes in glial cells and neurons of the hippocampus post-epileptogenesis may provide insights. METHODS: To induce TLE, 200 nl of kainic acid (KA) was stereotactically injected into the hippocampal CA1 region of mice, followed by a 7-day postinjection period. Single-cell RNA sequencing (ScRNA-seq), single-nucleus RNA sequencing (SnRNA-seq), and Xenium-based spatial transcriptomics analyses were employed to evaluate the changes in mRNA expression in glial cells and neurons. RESULTS: From the ScRNA-seq and SnRNA-seq data, 31,390 glial cells and 48,221 neuronal nuclei were identified. Analysis of the differentially expressed genes (DEGs) revealed significant transcriptomic alterations in the hippocampal cells of mice with TLE, affecting hundreds to thousands of mRNAs and their signalling pathways. Enrichment analysis indicated notable activation of stress and inflammatory pathways in the TLE hippocampus, while pathways related to axonal development and neural support were suppressed. Xenium analysis demonstrated the expression of all 247 genes across mouse brain sections, revealing the spatial distributions of their expression in 27 cell types. Integrated analysis of the DEGs identified via the three sequencing techniques revealed that Spp1, Trem2, and Cd68 were upregulated in all glial cell types and in the Xenium data; Penk, Sorcs3, and Plekha2 were upregulated in all neuronal cell types and in the Xenium data; and Tle4 and Sipa1l3 were downregulated in all glial cell types and in the Xenium data. CONCLUSION: In this study, a high-resolution single-cell transcriptomic atlas of the hippocampus in mice with TLE was established, revealing potential intrinsic mechanisms driving TLE-associated inflammatory activation and altered cell interactions. These findings provide valuable insights for further exploration of HS development and epileptogenesis.
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Single cell RNA sequencing and its promise in reconstructing plant vascular cell lineages.
In the last decade, recent advances in single-cell RNA sequencing coupled with computational algorithms have opened new avenues to study the cell type composition of tissues and organs as well as to infer cell developmental trajectories. These technologies have been used to resolve and map atlases of tissues and organs in many animal species as well as to further order cell developmental trajectories. Despite these advances in animals, many of the current plant cell type expression profiles confound multiple developmental stages preventing an accurate monitoring of cell lineage. In this review, we propose how the application of single-cell sequencing will improve our molecular understanding of cell type differentiation. Using root vascular cells as a model, we highlight the potential of single cell transcriptomics as well as its limitations to monitor the progression of vascular maturation. By comparing cell morphology, functionality and gene expression, we aim to provide a new perspective of plant cell type differentiation.
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Single Cell Transcriptomic Atlas Reveals Cellular Heterogeneity and Molecular Mechanisms in Pediatric Airway Hyperresponsiveness Epithelium.
BACKGROUND: Airway hyperresponsiveness (AHR) is a clinically important feature of chronic pediatric inflammatory airway disease, but its cellular architecture remains incompletely resolved. Because bulk transcriptomic approaches can mask signals from rare or state-specific cell populations, single-cell RNA sequencing (scRNA-seq) offers a useful framework for examining epithelial and immune cell diversity in hyperresponsive airways. METHODS: Publicly available scRNA-seq data (GEO: GSM8722221) from bronchial brushing specimens of pediatric AHR patients (10x Genomics Chromium platform) were processed with Cell Ranger, quality-filtered, and analyzed using PCA (15 PCs), UMAP/tSNE embedding, and Leiden clustering (resolution = 0.8). Cell types were annotated by canonical markers and validated via Seurat label transfer. Pseudotime trajectories were inferred with Monocle (DDRTree) and validated by scVelo, Slingshot, and PAGA. Macrophage subpopulations were resolved at resolution = 1.2 and profiled by hdWGCNA. GO and KEGG enrichment used clusterProfiler (Benjamini-Hochberg; adjusted p <0.05). Network pharmacology integrated TCM BTMAN 2.0, Swiss Target Prediction, SEA, and PPI topology to identify targets and herbal compounds from Xiao Qing Long Tang. RESULTS: Quality control supported the analytical suitability of the dataset (mitochondrial fraction vs. nCount_RNA r = -0.31; inter-dataset r = 0.96). Actb, Gapdh, Tuba1b, Hmga2, Vim, Col1a1, Zeb1, Snai2, Twist1, and Mmp9 were among the top variable genes, and the first 15 PCs were retained after JackStraw testing. UMAP and tSNE resolved eight airway mucosal cell populations with concordant separation. Cell-type-specific expression of TSLP, IL33, CCL26, S100A8, POSTN, CLCA1, FCGR3B, TNFRSF8, FOXJ1, and SCGB1A1 was consistent with known inflammatory, remodeling, and mucociliary programs. High-resolution macrophage analysis identified 23 putative subpopulations, which were interpreted cautiously as computationally defined states. Enrichment analysis highlighted cytoplasmic translation, ribosome biogenesis, oxidative phosphorylation, and related mitochondrial signatures. Network pharmacology identified VEGFA, PTGS2, and ESR1 among prioritized targets, yielding 83 shared candidates between disease-associated and compound-associated target sets. CONCLUSIONS: This study provides a pediatric AHR single-cell atlas and an integrated, hypothesis-generating view of epithelial-immune heterogeneity, macrophage state diversity, and candidate therapeutic networks. The findings should be interpreted as computational evidence that requires validation in healthy pediatric controls, independent cohorts, and functional experiments.
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scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.
Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.
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Deciphering the impact of aging on splenic endothelial cell heterogeneity and immunosenescence through single-cell RNA sequencing analysis.
BACKGROUND: Aging is associated with significant structural and functional changes in the spleen, leading to immunosenescence, yet the detailed effects on splenic vascular endothelial cells (ECs) and their immunomodulatory roles are not fully understood. In this study, a single-cell RNA (scRNA) atlas of EC transcriptomes from young and aged mouse spleens was constructed to reveal age-related molecular changes, including increased inflammation and reduced vascular development and also the potential interaction between splenic endothelial cells and immune cells. RESULTS: Ten clusters of splenic endothelial cells were identified. DEGs analysis across different EC clusters revealed the molecular changes with aging, showing the increase in the overall inflammatory microenvironment and the loss in vascular development function of aged ECs. Notably, four EC clusters with immunological functions were identified, suggesting an Endothelial-to-Immune-like Cell Transition (EndICLT) potentially driven by aging. Pseudotime analysis of the Immunology4 cluster further indicated a possible aging-induced transitional state, potentially initiated by Ctss gene activation. Finally, the effects of aging on cell signaling communication between different EC clusters and immune cells were analyzed. CONCLUSIONS: This comprehensive atlas elucidates the complex interplay between ECs and immune cells in the aging spleen, offering new insights into endothelial heterogeneity, reprogramming, and the mechanisms of immunosenescence.
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A single-cell atlas of murine reproductive tissues during preterm labor.
Preterm birth, the leading cause of perinatal morbidity and mortality worldwide, frequently results from the syndrome of preterm labor. The best-established causal link to preterm labor is intra-amniotic infection, which involves premature activation of the parturition cascade in the reproductive tissues. Herein, we utilize single-cell RNA sequencing (scRNA-seq) to generate a single-cell atlas of the murine uterus, decidua, and cervix in a model of infection-induced preterm labor. We show that preterm labor affects the transcriptomic profiles of specific immune and non-immune cell subsets. Shared and tissue-specific gene expression signatures are identified among affected cells. Determination of intercellular communications implicates specific cell types in preterm labor-associated signaling pathways across tissues. In silico comparison of murine and human uterine cell-cell interactions reveals conserved signaling pathways implicated in labor. Thus, our scRNA-seq data provide insights into the preterm labor-driven cellular landscape and communications in reproductive tissues.
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Single-Cell and Single-Nuclei transcriptomics profiling reveals dynamic cellular features in tumor-related adipose microenvironment of breast cancer patients with high BMI.
OBJECTIVES: High body mass index (BMI), encompassing overweight and obesity, is a well-established risk factor for developing breast cancer (BC). The underlying mechanisms linking elevated BMI to increased BC risk involve metabolic reprogramming and chronic inflammatory microenvironments regulated by cellular networks within breast white adipose tissue (WAT). However, the complicated landscape and specific cell chat leading to BC-related adipose microenvironment remained unclear. MATERIALS AND METHODS: We unveiled a comprehensive cell atlas by employing single-cell (N = 27) and single-nuclei (N = 6) transcriptomics to address dynamic changes of immune and stromal cell components within WAT in high BMI population. Bulk RNA-seq data sets were used for validation. RESULTS: Characteristics of adipose-infiltrating tissue-resident macrophages (PVMs), APOD+γδ T cells, and mature FKBP5+ adipocytes in breast cancer women with high BMI were revealed, in terms of transcriptional genes, metabolism features, developmental trajectories and gene set enrichment analysis (GSEA). PVMs upregulated c-Maf combined with its co-activator CREB1 to increase TCA cycles. APOD+γδ T cells were found to elevate intracellular lipid metabolism, leading to poor clinical prognosis. Mature FKBP5+adipocytes served as an advanced adipogenesis mediator to promote tumor aggressiveness. In-depth analysis of cell-cell interactions uncovered a remodeling trend towards metabolic dysfunction and chronic inflammation in WAT with weight gain via EGF, CXCL, and CCL signalings. CONCLUSION: These results provided a novel understanding of detailed and unbiased cellular landscape of WAT in breast cancer with high BMI from single-cell atlas perspective, uncovering interplays between breast adipose-infiltrating immune cells and stromal cells that promote progression of BC under high BMI conditions.
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Homologous recombination deficiency serves as a prognostic biomarker in clear cell renal cell carcinoma.
Kidney renal clear cell carcinoma (KIRC) is a frequent malignant tumor characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficit (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is not known, however, whether the molecular characteristics linked with HRD have a predictive role in KIRC. The discovery cohort comprised 501 KIRC patients from The Cancer Genome Atlas database. Genome and transcriptome data of HRD patients were used for comprehensive analysis. Single cell RNA sequencing (scRNA-seq) was used to verify the test results of bulk RNA-seq. In the present study, patients with a high HRD score had a worse prognosis compared with those with a low HRD score. The DNA damage response signaling pathways and immune-related signaling pathways were notably enriched in the HRD-positive subgroup. Further comprehensive analysis of the tumor microenvironment (TME) revealed that the signal of exhausted CD8+ T cells was enriched in the HRD-positive subgroup. Finally, scRNA-seq analyses confirmed that the immune-related signaling pathways were upregulated in HRD-positive patients. In conclusion, the present study not only demonstrated that a high HRD score is a valid prognostic biomarker in KIRC patients, but also revealed the TME in HRD-positive tumors.