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Evolutionary convergence and divergence of hippocampal cytoarchitecture between rodents and primates revealed by single-cell spatial transcriptomics.
The hippocampus comprises subregions of distinct cell types critical for memory and cognition, but their gene expression profiles and spatial distribution patterns remain to be clarified. Using single-cell spatial transcriptomic analysis and single-nucleus RNA sequencing, we obtained transcriptome-based atlases for the macaque, marmoset and mouse hippocampus. Cross-species comparison revealed primate- and lamina-specific glutamatergic cell types in the subicular complex, as well as enrichment of VIP-expressing GABAergic cells from mice to primates, including humans. Furthermore, we found reduced transcriptomic differences between CA3 and CA4 subregions and distinct longitudinal distributions of various cell types and expression of ion-channel genes, correlated with differences in electrophysiological properties of CA3, CA4 and CA1 neurons revealed by slice recording from marmosets and mice. Collectively, this cross-species study provides a molecular and cellular basis for understanding the evolution and function of the hippocampus.
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Gamma-Glutamyl Transferase and Cognitive Outcomes: A Cross-Species Analysis and Hepatic Transcriptomic Signature.
Cognitive impairment is a major public health concern, and the liver-brain axis has attracted growing interest. Gamma-glutamyl transferase (GGT) is a serum marker of liver function, but its association with cognitive outcomes remains uncertain. We conducted a cross-species analysis. In 5xFAD mice (n = 52), we examined serum liver markers and spatial cognition using the Morris water maze. In NHANES 2011-2014 (n = 3181, aged ≥60 years), we evaluated liver markers and low cognitive performance (LCP) using multivariable logistic regression. We also performed virtual hepatic GGT1 knockout in a human liver single-cell atlas and integrated GTEx liver co-expression, transcription factor enrichment, and WGCNA analyses. In mice, GGT-cognition associations largely attenuated after strain adjustment, but a positive association with platform crossings emerged (r = 0.321, p = 0.02). In NHANES, serum GGT was inversely associated with LCP (Q4 vs. Q1 OR = 0.54, p < 0.001), with a nonlinear association and an inflection point near 19 U/L. Virtual hepatic GGT1 knockout identified eight immunoglobulin-related genes associated with SPI1 and oxidative stress-related pathways. These parallel findings are hypothesis-generating. A testable hypothesis is that lower hepatic GGT1 activity or serum GGT is associated with upregulated cholangiocyte immunoglobulin-related programs and poorer cognition.
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Comparative analysis of human and mouse ovaries across age.
The mouse is a tractable model for human ovarian biology; however, its utility is limited by incomplete understanding of how transcription and signaling differ interspecifically and with age. We compared ovaries between species using three-dimensional imaging, single-cell transcriptomics, and functional studies. In mice, we mapped declining follicle numbers and oocyte competence during aging; in human ovaries, we identified cortical follicle pockets and decreases in density. Oocytes had species-specific gene expression patterns during growth that converged toward maturity. Age-related transcriptional changes were greater in oocytes than in granulosa cells across species, although mature oocytes change more in humans. We identified ovarian sympathetic nerves and glia; axon density increased in aged ovaries and, when ablated in mice, perturbed folliculogenesis. This comparative atlas defines shared and species-specific hallmarks of ovarian biology.
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Multi-omics Analysis Identifies Hypoxia Subtypes and S100A2 as an Immunosuppressive Factor in Cervical Cancer.
Cervical cancer is a common gynecological oncology. Growing evidence indicates hypoxia plays an important role in tumor progression and immunity. However, no study has examined the hypoxia landscape in cervical cancer. In this study, using hierarchical clustering, we identified three hypoxia subtypes in cervical cancer samples from The Cancer Genome Atlas dataset according to formerly described hypoxia-related genes. The overall survival time, hypoxic features, genomics, and immunological characteristics of these subtypes existed distinct differences. We also created a hypoxia score by principle component analysis for dimension reduction. The hypoxiaScore was an effective prognostic biomarker validated by GSE44001 and was associated with immunotherapy response. Furthermore, combined with single-cell RNA-sequence (scRNA-seq) and experiments, S100A2 was identified as an immunosuppressive factor induced by hypoxia and regulated expression of PD-L1. S100A2 also served as an oncogene promoting the proliferation and migration of cervical cancer cells. These findings depicted a new hypoxia-based classification and identified S100A2 as a potential therapeutic target for cervical cancer, thereby advancing the understanding of immunotherapy resistance mechanisms and cervical cancer genetic markers.
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Single-cell atlas of murine adrenal glands reveals immune-adrenal crosstalk during systemic Candida albicans infection.
Fungal sepsis remains a major health threat with high mortality, where the adrenal gland stress response has been rarely reported. Candida albicans (C.albicans) is the most common opportunistic fungal pathogen of life-threatening disseminated candidiasis and fungal sepsis. In the present study, we performed single-cell RNA sequencing (scRNA-Seq) using the 10x Genomics platform to analyze the changes in murine adrenal transcriptome following systemic C.albicans infection. A total of 16 021 cells were categorized into 18 transcriptionally distinct clusters, representing adrenocortical cells, endothelial cells, various immune cells, mesenchymal cells, smooth muscle cells, adrenal capsule, chromaffin cells, neurons and glials. As the main cell component in the adrenal gland responsible for steroidogenesis, the adrenocortical cells dramatically diminished and were further grouped into 10 subclusters, which differently distributed in the infected and uninfected samples. Pseudo-time analysis revealed transitions of the adrenocortical cells from the initial normal states to active or dysfunctional states following systemic C.albicans infection via two trajectory paths. Endothelial cells in the highly vascularized organ of adrenal gland further proliferated following infection, with the upregulation of genes positively regulating angiogenesis and downregulation of protective genes of endothelial cells. Immune cells were also excessively infiltrated in adrenal glands of C.albicans-infected mice. Macrophages dominated the immune microenvironments in murine adrenal glands both before and after C.albicans infection, mediating the crosstalk among the steroid-producing cells, endothelial cells and immune cells within the adrenal gland. NLR family, pyrin domain containing 3 (NLRP3, encoded by Nlrp3) and complement receptor 3 (CR3, encoded by Itgam) were found to be significantly upregulated on the adrenal macrophages upon systemic C.albicans infection and might play critical roles in mediating the myeloid response. Meanwhile, the number and strength of the interactions between the infiltrating immune cells and adrenal resident cells were unveiled by cell-cell communication analysis to be dramatically increased after systemic C.albicans infection, indicating that the immune-adrenal crosstalk might contribute to the compromised functions of adrenal cells. Overall, our comprehensive picture of the murine adrenal gland microenvironment in systemic C.albicans infection provides deeper insights into the immune-adrenal cell communications during fungal sepsis.
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Single-Cell Transcriptomic Analyses Define Distinct Peripheral B Cell Subsets and Discrete Development Pathways.
Separation of B cells into different subsets has been useful to understand their different functions in various immune scenarios. In some instances, the subsets defined by phenotypic FACS separation are relatively homogeneous and so establishing the functions associated with them is straightforward. Other subsets, such as the "Double negative" (DN, CD19+CD27-IgD-) population, are more complex with reports of differing functionality which could indicate a heterogeneous population. Recent advances in single-cell techniques enable an alternative route to characterize cells based on their transcriptome. To maximize immunological insight, we need to match prior data from phenotype-based studies with the finer granularity of the single-cell transcriptomic signatures. We also need to be able to define meaningful B cell subsets from single cell analyses performed on PBMCs, where the relative paucity of a B cell signature means that defining B cell subsets within the whole is challenging. Here we provide a reference single-cell dataset based on phenotypically sorted B cells and an unbiased procedure to better classify functional B cell subsets in the peripheral blood, particularly useful in establishing a baseline cellular landscape and in extracting significant changes with respect to this baseline from single-cell datasets. We find 10 different clusters of B cells and applied a novel, geometry-inspired, method to RNA velocity estimates in order to evaluate the dynamic transitions between B cell clusters. This indicated the presence of two main developmental branches of memory B cells. A T-independent branch that involves IgM memory cells and two DN subpopulations, culminating in a population thought to be associated with Age related B cells and the extrafollicular response. The other, T-dependent, branch involves a third DN cluster which appears to be a precursor of classical memory cells. In addition, we identify a novel DN4 population, which is IgE rich and closely linked to the classical/precursor memory branch suggesting an IgE specific T-dependent cell population.
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ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications.
For nearly a decade, the difficulties associated with both the determination and reproducibility of Ras-dependency indexes (RDIs) have limited their application and further delineation of the biology underlying Ras dependency. In this report, we describe the application of a computational single sample gene set enrichment analysis (ssGSEA) method to derive RDIs with gene expression data. The computationally derived RDIs across the Cancer Cell Line Encyclopedia (CCLE) cell lines show excellent agreement with the experimentally derived values and high correlation with a previous in-house siRNA effector node (siREN) study and external studies. Using EMT signature-derived RDIs and data from cell lines representing the extremes in RAS dependency, we identified enriched pathways distinguishing these classes, including the Fas signaling pathway and a putative Ras-independent pathway first identified in NK cells. Importantly, extension of the method to patient samples from The Cancer Genome Atlas (TCGA) showed the same consensus differential expression patterns for these two pathways across multiple tissue types. Last, the computational RDIs displayed a significant association with TCGA cancer patients' survival outcomes. Together, these lines of evidence confirm that our computationally derived RDIs faithfully represent a measure of Ras dependency in both cancer cell lines and patient samples. The application of such computational RDIs can provide insights into Ras biology and potential clinical applications.
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Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis.
BACKGROUND: Synovial fibroblasts (SFs) are specialized cells of the synovium that provide nutrients and lubricants for the proper function of diarthrodial joints. Recent evidence appreciates the contribution of SF heterogeneity in arthritic pathologies. However, the normal SF profiles and the molecular networks that govern the transition from homeostatic to arthritic SF heterogeneity remain poorly defined. METHODS: We applied a combined analysis of single-cell (sc) transcriptomes and epigenomes (scRNA-seq and scATAC-seq) to SFs derived from naïve and hTNFtg mice (mice that overexpress human TNF, a murine model for rheumatoid arthritis), by employing the Seurat and ArchR packages. To identify the cellular differentiation lineages, we conducted velocity and trajectory analysis by combining state-of-the-art algorithms including scVelo, Slingshot, and PAGA. We integrated the transcriptomic and epigenomic data to infer gene regulatory networks using ArchR and custom-implemented algorithms. We performed a canonical correlation analysis-based integration of murine data with publicly available datasets from SFs of rheumatoid arthritis patients and sought to identify conserved gene regulatory networks by utilizing the SCENIC algorithm in the human arthritic scRNA-seq atlas. RESULTS: By comparing SFs from healthy and hTNFtg mice, we revealed seven homeostatic and two disease-specific subsets of SFs. In healthy synovium, SFs function towards chondro- and osteogenesis, tissue repair, and immune surveillance. The development of arthritis leads to shrinkage of homeostatic SFs and favors the emergence of SF profiles marked by Dkk3 and Lrrc15 expression, functioning towards enhanced inflammatory responses and matrix catabolic processes. Lineage inference analysis indicated that specific Thy1+ SFs at the root of trajectories lead to the intermediate Thy1+/Dkk3+/Lrrc15+ SF states and culminate in a destructive and inflammatory Thy1- SF identity. We further uncovered epigenetically primed gene programs driving the expansion of these arthritic SFs, regulated by NFkB and new candidates, such as Runx1. Cross-species analysis of human/mouse arthritic SF data determined conserved regulatory and transcriptional networks. CONCLUSIONS: We revealed a dynamic SF landscape from health to arthritis providing a functional genomic blueprint to understand the joint pathophysiology and highlight the fibroblast-oriented therapeutic targets for combating chronic inflammatory and destructive arthritic disease.
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An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia.
The basal ganglia regulate motor, cognitive, and affective behaviors, and their dysfunction underlies diverse neurological and psychiatric disorders. Comprehensive, accessible multi-omics resources are needed to understand the regulatory mechanisms governing basal ganglia cell types. Here we present an open, interactive web-based platform for exploring single-cell multi-omics datasets from basal ganglia, generated using 10X Multiome, snm3C-seq, and Paired-Tag technologies from the BICAN (NIH BRAIN Initiative Cell Atlas Network) consortium. The platform is available at https://basalganglia.epigenomes.net/ and enables integrated visualization of gene expression, chromatin accessibility, DNA methylation, histone modifications, and chromatin conformation across cell types and human, macaque, marmoset, and mouse species, with direct genome browser support and comparative epigenomic functionality. Representative analyses demonstrate cell-type-specific regulatory landscapes, conserved and species-specific regulatory elements, and links between epigenomic regulation and transcription. This resource provides a scalable, community-oriented foundation for advancing basal ganglia biology and interpreting regulatory mechanisms relevant to brain function and disease.
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Roles of GFPT2 Expression Levels on the Prognosis and Tumor Microenvironment of Colon Cancer.
BACKGROUND: Recently, increasing evidence has suggested that Glutamine-fructose-6-phosphate transaminase 2 (GFPT2) is related to carcinogenesis. However, the potential roles of GFPT2 in colon cancer still need to be fully investigated. METHODS: We examined the protein levels of GFPT2 by immunohistochemistry (IHC) in tissues collected from 83 patients with colon cancer. We further detected GFBPT2 protein levels by Western Blot assay. We checked the relationship between GFPT2 expression levels and overall survival (OS), stromal and immune scores and immune components from The Cancer Gene Atlas (TCGA) database. GFBP2-related pathways were validated in the Cancer Cell Line Encyclopedia (CCLE) database. Expression of GFPT2 in single cell subpopulations was calculated from The Tumor Immune Single Cell Center (TISCH). The levels of GFPT2 and drug sensitivity data were performed from CellMiner dataset. RESULTS: GFPT2 was highly expressed and correlated with poor pathological features in 83 colon cancer patients. Moreover, increased GFPT2 expression was significantly associated with poorer OS in 329 colon adenocarcinoma (COAD) patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the differentially expressed genes of GFPT2 were mostly enriched in focal adhesion, ECM receptor interaction, JAK/STAT signaling pathway and immune related pathways. In addition, GFPT2 expression was correlated with the tumor microenvironment (TME). GFPT2 expression was linked to cancer-associated fibroblasts (CAFs)-associated factors and epithelial-mesenchymal transition (EMT)-related factors. GFPT2 was positively correlated with immunosuppressive cells and regulated immunosuppressive factors and T-cell exhaustion. Finally, our data suggested that the expression of GFPT2 may be a judgment of the sensitivity of a certain class of drugs. CONCLUSIONS: Our work reveals the roles of GFPT2 in tumorigenesis, particularly in immune response, TME and drug resistance, which are crucial for the development of customized cancer therapies.