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Neuronal subtype-specific ribosomal protein mRNA expression.
Current understanding recognizes that ribosomal proteins (RPs) have regulatory roles beyond their canonical structural functions in translation, raising the question of how their expression is organized across cell types. Given the diversity of neuronal cell types, understanding RP gene expression at the neuronal subtype level is an important and previously inaccessible question. Here, leveraging advances in single-cell transcriptomics, we analyzed single-cell RNA-seq data sets from the mouse cerebral cortex and hippocampus to examine RP mRNA expression across neuronal subtypes. We observed distinct RP mRNA expression profiles between excitatory and inhibitory neurons and found that higher Rps27 transcript levels in inhibitory neurons corresponded to increased RPS27 protein abundance. Beyond excitatory-inhibitory differences, RP mRNA expression further segregated across well-defined neuronal subclasses, with 59 of 84 RP genes differentially expressed, including enrichment of Rpl21 in Lamp5 and Rps27 in Vip interneurons. These patterns were consistent across cortical regions and reproducible across two independent single-cell technologies (Smart-seq2 and 10x Genomics). Analysis of aging- and stress-associated data sets revealed stable RP expression signatures, with limited phenotype-linked changes. Together, we present a comprehensive atlas of ribosomal protein gene expression at neuronal subclass resolution, revealing robust subclass-specific transcriptional signatures, suggesting an underestimated regulatory layer.
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Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder driven by complex cellular changes. To identify transcriptional signatures involved in AD pathology, we first analyzed nine single-cell/nucleus RNA-seq datasets from 67 high-pathology cases (Braak stages V/VI), generating an atlas of 363 243 cells. Using a multi-sample integration strategy, we identified 51 cross-sample meta-programs (MPs) across seven cell types that collectively capture key AD-related processes, including synaptic dysfunction and neuroinflammation. By screening bulk transcriptomes from 1208 ad and 725 normal samples, we found that 26 MPs (13 pathogenic and 13 protective) displayed differential activity in AD, characterized by up-regulation of microglia MPs and down-regulated of neuron MPs. Furthermore, spatial transcriptomics revealed that these MPs form spatially coherent communities associated with distinct cellular neighborhoods. Finally, we performed an integrative drug repurposing screen to identify candidate drugs predicted to regulate pathogenic MPs. In conclusion, we conducted an integrated multi-omics study to identify AD cell-type-specific MPs, and this framework can be applied to deconvolve cellular heterogeneity and screen candidate drugs for AD.
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Single-cell and spatial transcriptomic profiling reveals distinct immune landscapes in murine lungs infected with H1N1 versus H5N1 influenza viruses.
Influenza A viruses of divergent pathogenicity elicit distinct host immune responses, yet the underlying cellular and spatial dynamics remain poorly defined. Here, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to systematically compare the immune landscapes in murine lungs following infection with seasonal H1N1 or a highly pathogenic avian influenza H5N1 virus belonging to clade 2.3.2.1. Analysis of 84,162 immune cells identifies 12 distinct cell types, suggesting that H1N1 infection is associated with robust interferon and inflammatory responses, characterized by alveolar macrophage depletion and extensive infiltration of monocytes and neutrophils. In contrast, H5N1 clade 2.3.2.1 infection is characterized by attenuated interferon signaling, delayed monocyte activation, dysregulated neutrophil maturation, and impaired intercellular communication-features that may be distinct from previously characterized H5N1 strains. Spatial mapping indicates distinct distribution and interaction patterns of immune cells between the two infections. Functional validation through myeloid cell depletion supports the critical role of the monocyte-macrophage axis in disease progression. These findings provide a high-resolution atlas of pulmonary immune responses to influenza viruses of differing pathogenicity and highlight cell-type-specific mechanisms of immune modulation.IMPORTANCESeasonal H1N1 influenza virus causes annual epidemics, while highly pathogenic avian H5N1 virus has a high mortality rate and pandemic potential. Understanding why H5N1 causes more severe disease is critical for developing better treatments. In this study, we used advanced single-cell and spatial technologies to create a detailed map of the immune response in the lungs of mice infected with either H1N1 or a clade 2.3.2.1 H5N1 virus. We discovered that H1N1 triggers a strong, well-organized immune response that controls the virus. In contrast, H5N1 infection leads to a disorganized and weakened response where key immune cells fail to activate and communicate properly. These findings suggest that the H5N1 virus may evade and suppress the host's immune system. Our study provides a high-resolution immune atlas and identifies potential targets for new therapies against severe influenza.
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Single-cell RNA sequencing data analysis reveals differential expressions of ion channels in immunity response to pulmonary COVID-19 infection.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by immune dysregulation that contributes to disease severity and systemic inflammation. Ion channels are critical regulators of immune cell activation, signaling, and effector functions; however, their regulation across heterogeneous immune populations during COVID-19 remains poorly understood. In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and healthy controls to systematically profile the expression of immune-related ion channels at single-cell resolution. Our analysis revealed notably disease-associated shifts in the expression of key ion channels involved in potassium, calcium, proton, and anion channels across immune cell populations in COVID-19 patients. The findings show that COVID-19 infection is associated with broad remodeling of ion channel-mediated immune function in circulating immune cells. The study provides a single-cell atlas of immune ion channel dysregulation in COVID-19 and highlights ion channels as potential contributors to immune dysfunction and therapeutic targets in COVID-19 infection.
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Single-cell sequencing reveals that neutrophils mediate the inflammatory response in the placenta of women with GDM.
Gestational diabetes mellitus (GDM) is a pregnancy-associated metabolic disorder characterized by insulin resistance and hyperglycemia, posing significant risks to maternal-fetal health. While placental inflammation contributes to GDM pathology, its cellular mechanisms remain incompletely understood. Leveraging public single-cell RNA sequencing (scRNA-seq) data, we constructed a cellular atlas of GDM and control placental tissues, identifying 14 transcriptionally distinct populations. Neutrophil_1 emerged as the dominant pro-inflammatory subset, exhibiting amplified interactions with macrophages and T cells that exacerbate inflammatory responses. Pseudotime trajectory analysis revealed Neutrophil_2 as a precursor state differentiating toward the Neutrophil_1 lineage through transcriptionally regulated programming. Critically, Arginine catabolism and Nicotinate/nicotinamide metabolism were found to modulate Neutrophil_1's pro-inflammatory functions. Our findings implicate pathogenic neutrophil subsets in GDM progression and nominate arginase and NAMPT inhibition as potential therapeutic strategies for mitigating neutrophil-mediated placental inflammation.
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Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.
BACKGROUND: The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. Using the kidney as an emblematic example of a complex organ, we perform a systematic evaluation of multimodal single-cell integration strategies, with heart tissue used for additional methodological validation. RESULTS: We generate a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we develop the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assess integration strategies. "Horizontal" integration of scRNA and snRNA-seq improves cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq has an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration is especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. CONCLUSIONS: Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.
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Single-cell dissection of cholesterol metabolism reveals cell-type-specific genes and hypercholesterolemia-associated remodeling.
This study was aimed at systematically characterizing cell-type-specific cholesterol metabolism in human blood samples and investigating the impact of hypercholesterolemia on the composition and gene expression of immune and stromal populations. Single-cell RNA sequencing (scRNA-seq) data from 31 human blood samples, encompassing 472,204 high-quality circulating cells, were analyzed. Pathway activities were assessed via gene set variation analysis (GSVA), and cell-type-specific markers were identified using differential expression analysis. Clinical cholesterol levels were used to stratify samples for comparative analysis of cell abundance and transcriptional changes. Monocytes and macrophages were found to exhibit heightened activity in cholesterol transport and dynamic response pathways. Five monocyte-enriched cholesterol metabolism genes (colony-stimulating factor 3 receptor, formyl peptide receptor 1, myeloid cell nuclear differentiation antigen, macrophage-expressed gene 1, and versican) were identified. Hypercholesterolemia was associated with increased abundance of macrophages, T cells, and natural killer cells, as well as decreased B cells and monocytes. Significant upregulation of proinflammatory genes such as VCAM1 in high-cholesterol conditions was revealed by differential gene expression analysis. Our study provided a comprehensive cell-type-specific atlas of cholesterol metabolism in human blood cells, highlighting metabolic heterogeneity and hypercholesterolemia-induced immune remodeling in the circulation. This resource offered novel insights into cholesterol-mediated immunomodulation.NEW & NOTEWORTHY Monocytes and macrophages display elevated activity in cholesterol transport and dynamic response pathways. Five monocyte-enriched genes (CSF3R, FPR1, MNDA, MPEG1, VCAN) are implicated in cholesterol metabolism regulation. Hypercholesterolemia reshapes circulating immune cell composition and upregulates proinflammatory genes (e.g., VCAM1), mediating immunomodulatory effects.
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Single-cell transcriptomic analysis of plant quiescent center by third-generation sequencing reveals developmental trajectories.
BACKGROUND: The quiescent center of plant roots plays a critical role in maintaining stemness and proliferative activity of surrounding meristem cells that drive root growth. However, it remains difficult to distinguish and isolate quiescent center cells for transcriptomic analysis. RESULTS: To overcome the challenges, we develop a protocol for isolating intact quiescent center cells from Arabidopsis root tips and perform single-cell long-read RNA sequencing of quiescent center cells across two developmental stages. The analysis reveals a transition from hormone signaling and proliferation toward differentiation between 5 and 10 days after germination. We capture 6,713 previously unannotated transcripts, including several isoform types. We validate ROOT MERISTEM GROWTH FACTOR 10 (RGF10) as a quiescent center specific marker gene. We also detect enrichment in transcripts associated with cell growth, auxin response, and cell division at 5 days, but lost at 10 days, after germination. Functional analysis suggests that the quiescent center marker, TERMINAL EAR1-like (TEL1), is involved in maintaining stem cell identity via regulation of WUSCHEL-RELATED HOMEOBOX 5 (WOX5). Comparative analysis of rice quiescent center transcriptomes reveals distinct auxin biosynthesis and signaling pathways compared to that in Arabidopsis. CONCLUSIONS: This study provides a method for isolating rare cell types and generates long- and short-read transcriptomic atlases, offering novel mechanistic insights into stem cell maintenance in monocot and eudicot roots. The findings have implications for improving crop root systems.
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A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma.
BACKGROUND: Liver zonation is a unique phenomenon in which the liver exhibits distinct functions among hepatocytes along the radial axis of the lobule. This phenomenon can cause the sectionalized initiation of several liver diseases, including hepatocellular carcinoma (HCC). However, few studies have explored the zonation features of HCC. METHODS: Four single-cell RNA sequencing datasets were used to identify hepatocyte-specific zonation markers. Integrative analysis was then performed with a training RNA-seq cohort (616 HCC samples) and an external validating microarray cohort (285 HCC samples) from the International Cancer Genome Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, and EMBL's European Bioinformatics Institute for clustering using non-negative matrix factorization consensus clustering based on zonation genes. Afterward, we evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses. RESULTS: A total of 94 human hepatocyte-specific zonation markers (39 central markers and 55 portal markers) were identified for the first time. Subsequently, three subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity toward immune blockade therapy. Cluster3 was intensively correlated with a portal-like signature with the best prognosis. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples. CONCLUSION: The current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study provided new perspectives on the heterogeneity of HCC and shed new light on delivering precision medicine for HCC patients.
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Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.
Subcellular spatial transcriptomics can resolve tissue architecture at cellular scale, but sparse gene panels and limited detection sensitivity constrain downstream analysis. Existing enhancement methods often require tissue-matched single-cell RNA sequencing (scRNA-seq) references and dataset-specific retraining. Here we show that stPainter, a conditional generative model pretrained on a pan-cancer scRNA-seq atlas, can enhance spatial transcriptomics data without matched references or retraining. Using a latent diffusion architecture guided by Stochastic Differential Equations (SDE), stPainter reconstructs expanded expression profiles from sparse measurements and produces latent representations for clustering and cell-state analysis. When we apply stPainter upon 6 spatial transcriptomics datasets of different cancer types, we demonstrate that our model empowers downstream biological analyses, including fine-grained subpopulation clustering and pathway enrichment. Comparison with spatially resolved proteomics (CODEX) provided independent support for regional agreement between imputed cellular compositions and protein-level tissue organization. These results establish stPainter as a scalable approach for analyzing tumor microenvironments without auxiliary sequencing data.