Single-cell RNA Sequencing Datasets Research Articles

Resting natural killer cells promote the progress of colon cancer liver metastasis by elevating tumor-derived stem cell factor.

The abundance and biological contribution of natural killer (NK) cells in cancer are controversial. Here, we aim to uncover clinical relevance and cellular roles of NK cells in colon cancer liver metastasis (CCLM). Here, we integrated single-cell RNA-sequencing, spatial transcriptomics (ST), and bulk RNA-sequencing datasets to investigate NK cells' biological properties and functions in the microenvironment of primary and liver metastatic tumors. Results were validated through an in vitro co-culture experiment based on bioinformatics analysis. Useing single-cell RNA-sequencing and ST, we mapped the immune cellular landscape of colon cancer and well-matched liver metastatic cancer. We discovered that GZMK+ resting NK cells increased significantly in tumor tissues and were enriched in the tumor regions of both diseases. After combining bulk RNA and clinical data, we observed that these NK cell subsets contributed to a worse prognosis. Meanwhile, KIR2DL4+ activated NK cells exhibited the opposite position and relevance. Pseudotime cell trajectory analysis revealed the evolution of activated to resting NK cells. In vitro experiments further confirmed that tumor-cell-co-cultured NK cells exhibited a decidual-like status, as evidenced by remarkable increasing CD9 expression. Functional experiments finally revealed that NK cells exhibited tumor-activating characteristics by promoting the dissociation of SCF (stem cell factor) on the tumor cells membrane depending on cell-to-cell interaction, as the supernatant of the co-culture system enhanced tumor progression. In summary, our findings revealed resting NK cells exhibited a clinical relevance with CCLM, which may be exploited for novel strategies to improve therapeutic outcomes for patients with CCLM.

Resting natural killer cells promote the progress of colon cancer liver metastasis by elevating tumor-derived stem cell factor

The abundance and biological contribution of natural killer (NK) cells in cancer are controversial. Here, we aim to uncover clinical relevance and cellular roles of NK cells in colon cancer liver metastasis (CCLM). Here, we integrated single-cell RNA-sequencing, spatial transcriptomics (ST), and bulk RNA-sequencing datasets to investigate NK cells’ biological properties and functions in the microenvironment of primary and liver metastatic tumors. Results were validated through an in vitro co-culture experiment based on bioinformatics analysis. Useing single-cell RNA-sequencing and ST, we mapped the immune cellular landscape of colon cancer and well-matched liver metastatic cancer. We discovered that GZMK+ resting NK cells increased significantly in tumor tissues and were enriched in the tumor regions of both diseases. After combining bulk RNA and clinical data, we observed that these NK cell subsets contributed to a worse prognosis. Meanwhile, KIR2DL4+ activated NK cells exhibited the opposite position and relevance. Pseudotime cell trajectory analysis revealed the evolution of activated to resting NK cells. In vitro experiments further confirmed that tumor-cell-co-cultured NK cells exhibited a decidual-like status, as evidenced by remarkable increasing CD9 expression. Functional experiments finally revealed that NK cells exhibited tumor-activating characteristics by promoting the dissociation of SCF (stem cell factor) on the tumor cells membrane depending on cell-to-cell interaction, as the supernatant of the co-culture system enhanced tumor progression. In summary, our findings revealed resting NK cells exhibited a clinical relevance with CCLM, which may be exploited for novel strategies to improve therapeutic outcomes for patients with CCLM.

Identification of a senescence-related transcriptional signature to uncover molecular subtypes and key genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer caused by abnormal cell growth due to faulty signal transduction. Cells secrete tumor suppressor factors in response to potential carcinogenic signals, inducing cellular senescence (CS) as a countermeasure. However, accurately measuring CS levels in different types of tumors is challenging due to tumor heterogeneity and the lack of universal and specific CS markers. Machine learning has revealed unique molecular traits in HCC patients, leading to clinical advantages. More research is needed to understand senescence-related molecular features in these patients. In this study, the gene expression profile features of patients with HCC were analyzed by integrating single-cell RNA sequencing and bulk RNA-seq datasets from HCC samples. The analysis identified the senescence-related pathways exhibiting HCC specificity. Subsequently, genes from these pathways were used to identify senescence-related molecular subtypes in HCC, showing significant variations in biological and clinical attributes. An HCC-specific CS risk model developed in this study revealed substantial associations between the patients' CS scores and prognosis grouping, clinical staging, immune infiltration levels, immunotherapy response, and drug sensitivity levels. Within the constructed model, G6PD was identified as a key gene, potentially serving as a senescence-related target in liver cancer. Molecular biology experiments demonstrated that overexpression of G6PD effectively promotes the proliferative, invasive, and migration capacities of HepG2 and SK-HEP-1 cells. In conclusion, this analysis offers a valuable framework for understanding senescence in HCC and introduces a new biomarker. These findings improve our understanding of senescence in HCC and have potential for future research.

An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development.

Single-cell technologies have enhanced comprehensive knowledge regarding the human brain by facilitating an extensive transcriptomic census across diverse brain regions. Nevertheless, understanding the cellular and temporal specificity of neurological disorders remains ambiguous due to developmental variations. To address this gap, we illustrated the dynamics of disorder risk gene expression under development by integrating multiple single-cell RNA sequencing datasets. We constructed a comprehensive single-cell atlas of the developing human brain, encompassing 393,060 single cells across diverse developmental stages. Temporal analysis revealed the distinct expression patterns of disorder risk genes, including those associated with autism, highlighting their temporal regulation in different neuronal and glial lineages. We identified distinct neuronal lineages that diverged across developmental stages, each exhibiting temporal-specific expression patterns of disorder-related genes. Lineages of nonneuronal cells determined by molecular profiles also showed temporal-specific expression, indicating a link between cellular maturation and the risk of disorder. Furthermore, we explored the regulatory mechanisms involved in early brain development, revealing enriched patterns of fetal cell types associated with neuronal disorders indicative of the prenatal stage's influence on disease determination. Our findings facilitate unbiased comparisons of cell type‒disorder associations and provide insight into dynamic alterations in risk genes during development, paving the way for a deeper understanding of neurological disorders.

Interleukin-11 causes alveolar type 2 cell dysfunction and prevents alveolar regeneration

In lung disease, persistence of KRT8-expressing aberrant basaloid cells in the alveolar epithelium is associated with impaired tissue regeneration and pathological tissue remodeling. We analyzed single cell RNA sequencing datasets of human interstitial lung disease and found the profibrotic Interleukin-11 (IL11) cytokine to be highly and specifically expressed in aberrant KRT8+ basaloid cells. IL11 is similarly expressed by KRT8+ alveolar epithelial cells lining fibrotic lesions in a mouse model of interstitial lung disease. Stimulation of alveolar epithelial cells with IL11 causes epithelial-to-mesenchymal transition and promotes a KRT8-high state, which stalls the beneficial differentiation of alveolar type 2 (AT2)-to-AT1 cells. Inhibition of IL11-signaling in AT2 cells in vivo prevents the accumulation of KRT8+ cells, enhances AT1 cell differentiation and blocks fibrogenesis, which is replicated by anti-IL11 therapy. These data show that IL11 inhibits reparative AT2-to-AT1 differentiation in the damaged lung to limit endogenous alveolar regeneration, resulting in fibrotic lung disease.

Fam3a-mediated prohormone convertase switch in α-cells regulates pancreatic GLP-1 production in an Nr4a2-Foxa2-dependent manner

BackgroundFam3a has been demonstrated to regulate pancreatic β-cell function and glucose homeostasis. However, the role and mechanism of Fam3a in regulating α-cell function remains unexplored. MethodsGlucagon and glucagon-like peptide-1 (GLP-1) levels in pancreas and plasma were measured in global Fam3a knockout (Fam3a−/−) mice. Human islet single-cell RNA sequencing (scRNA-seq) datasets were utilized to analyze gene expression correlations between FAM3A and PCSK1 (encoding PC1/3, which processes proglucagon into GLP-1). Mouse pancreatic α-cell line αTC1.9 cells were transfected with Fam3a siRNA or plasmid for Fam3a knockdown or overexpression to explore the effects of Fam3a on PC1/3 expression and GLP-1 production. The downstream mediator (including Nr4a2) was identified by transcriptomic analysis, and its role was confirmed by Fam3a knockdown or overexpression in αTC1.9 cells. Based on the interacted protein of Nr4a2 and the direct binding to Pcsk1 promoter, the transcription factor Foxa2 was selected for further verification. Nuclear translocation assay and dual-luciferase reporter assay were used to clarify the involvement of Fam3a-Nr4a2-Foxa2 pathway in PC1/3 expression and GLP-1 production. Moreover, α-cell-specific Fam3a knockout (Fam3aα−/−) mice were constructed to evaluate the metabolic variables and hormone levels under normoglycemic, high-fat diet (HFD)-fed and streptozotocin (STZ)-induced diabetic conditions. Exendin 9–39 (Ex9), a GLP-1 receptor antagonist, was used to investigate GLP-1 paracrine effects in Fam3aα−/− mice and in their primary islets. ResultsCompared with wild-type mice, pancreatic and plasma active GLP-1 levels were increased in Fam3a−/− mice. Analysis of human islet scRNA-seq datasets showed a significant negative correction between FAM3A and PCSK1 in α-cells. Fam3a knockdown upregulated PC1/3 expression and GLP-1 production in αTC1.9 cells, while Fam3a overexpression displayed inverse effects. Transcriptomic analysis identified Nr4a2 as a key downstream mediator of Fam3a, and Nr4a2 expression in αTC1.9 cells was downregulated and upregulated by Fam3a knockdown and overexpression, respectively. Nr4a2 silencing increased PC1/3 expression, albeit Nr4a2 did not directly bind to Pcsk1 promoter. Instead, Nr4a2 formed a complex with Foxa2 to facilitate Fam3a-mediated Foxa2 nuclear translocation. Foxa2 negatively regulated PC1/3 expression and GLP-1 production. Besides, Foxa2 inhibited the transcriptional activity of Pcsk1 promoter at specific binding sites 10 and 6, and this inhibition was intensified by Nr4a2 in αTC1.9 cells. Compared with Flox/cre littermates, improved glucose tolerance, increased active GLP-1 level in pancreas and plasma, upregulated plasma insulin level in response to glucose, and decreased plasma glucagon level were observed in Fam3aα−/− mice. Primary islets isolated from Fam3aα−/− mice also showed an increase in active GLP-1 and insulin release. In addition, the insulinotropic effect of intra-islet GLP-1 were blocked by Ex9 in Fam3aα−/− mice and in their primary islets. Similarly, HFD-fed Fam3aα−/− mice also exhibited an improved glucose tolerance. Both HFD-fed and STZ-induced diabetic Fam3aα−/− mice showed an increased pancreatic active GLP-1 level, an elevated plasma insulin level and a reduced plasma glucagon level. ConclusionsFam3a deficiency in α-cells enhances pancreatic GLP-1 production to improve β-cell function via paracrine signaling in an Nr4a2-Foxa2-PC1/3-dependent manner. Our study unveils a novel strategy for reprogramming α-cell proglucagon processing output from glucagon to GLP-1 and deepen the understanding of crosstalk between α-cells and β-cells.

Catching the Big Fish in Big Data: A Meta-Analysis of Zebrafish Kidney scRNA-Seq Datasets Highlights Conserved Molecular Profiles of Macrophages and Neutrophils in Vertebrates

The innate immune system (IIS) is an ancient and essential defense mechanism that protects animals against a wide range of pathogens and diseases. Although extensively studied in mammals, our understanding of the IIS in other taxa remains limited. The zebrafish (Danio rerio) serves as a promising model organism for investigating IIS-related processes, yet the immunogenetics of fish are not fully elucidated. To address this gap, we conducted a meta-analysis of single-cell RNA sequencing (scRNA-seq) datasets from zebrafish kidney marrow, encompassing approximately 250,000 immune cells. Our analysis confirms the presence of key genetic pathways in zebrafish innate immune cells that are similar to those identified in mammals. Zebrafish macrophages specifically express genes encoding cathepsins, major histocompatibility complex class II proteins, integral membrane proteins, and the V-ATPase complex and demonstrate the enrichment of oxidative phosphorylation ferroptosis processes. Neutrophils are characterized by the significant expression of genes encoding actins, cytoskeleton organizing proteins, the Arp2/3 complex, and glycolysis enzymes and have demonstrated their involvement in GnRH and CLR signaling pathways, adherents, and tight junctions. Both macrophages and neutrophils highly express genes of NOD-like receptors, phagosomes, and lysosome pathways and genes involved in apoptosis. Our findings reinforce the idea about the existence of a wide spectrum of immune cell phenotypes in fish since we found only a small number of cells with clear pro- or anti-inflammatory signatures.

Transcriptomic correlates of state modulation in GABAergic interneurons: A cross-species analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical and functional properties. In mouse visual cortex, they also differ in their modulation with an animal’s behavioural state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalises across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissim­ilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.Significance StatementThe brain is a complex network of many different cell types. A particularly diverse group of cells is that of inhibitory interneurons, named for their suppressive effect. These interneurons change their activity depending on an animal’s behavioural state—at least in mice. Here, we investigate if this finding generalises to other species by comparing gene expression patterns of human, turtle, and zebra finch interneurons. Despite sharing an evolutionary past, we find that only human and mouse interneurons have similar gene expression patterns associated with state modulation. A mathematical model suggests which expression differences in individual cells translate into functional differences at the network level.

Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in schizophrenia.

Mitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated. Single-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on key mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore). We found 7 key mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis. We identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.

Associations between cuprotosis-related genes and the spectrum of metabolic dysfunction-associated fatty liver disease: An exploratory study.

To explore the associations between cuprotosis-related genes (CRGs) across different stages of liver disease in metabolic dysfunction-associated fatty liver disease (MAFLD), including hepatocellular carcinoma (HCC). We analysed several bulk RNA sequencing datasets from patients with MAFLD (n = 331) and MAFLD-related HCC (n = 271) and two MAFLD single-cell RNA sequencing datasets. To investigate the associations between CRGs and MAFLD, we performed differential correlation, logistic regression and functional enrichment analyses. We also validated the findings in an independent Wenzhou PERSONS cohort of MAFLD patients (n = 656) used for a genome-wide association study (GWAS). GLS, GCSH and ATP7B genes showed significant differences across the MAFLD spectrum and were significantly associated with liver fibrosis stages. GLS was closely associated with fibrosis stages in patients with MAFLD and those with MAFLD-related HCC. GLS is predominantly expressed in monocytes and T cells in MAFLD. During the progression of metabolic dysfunction-associated fatty liver to metabolic-associated steatohepatitis, GLS expression in T cells decreased. GWAS revealed that multiple single nucleotide polymorphisms in GLS were associated with clinical indicators of MAFLD. GLS may contribute to liver inflammation and fibrosis in MAFLD mainly through cuprotosis and T-cell activation, promoting the progression of MAFLD to HCC. These findings suggest that cuprotosis may play a role in MAFLD progression, potentially providing new insights into MAFLD pathogenesis.

Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings

Probabilistic-based non-linear dimensionality reduction (PB-NL-DR) methods, such as t-SNE and UMAP, are effective in unfolding complex high-dimensional manifolds, allowing users to explore and understand the structural patterns of data. However, due to the trade-off between global and local structure preservation and the randomness during computation, these methods may introduce false neighborhood relationships, known as distortion errors and misleading visualizations. To address this issue, we first conduct a detailed survey to illustrate the design space of prior layout enrichment visualizations for interpreting DR results, and then propose a node-link visualization technique, ManiGraph. This technique rethinks the neighborhood fidelity between the high- and low-dimensional spaces by constructing dynamic mesoscopic structure graphs and measuring region-adapted trustworthiness. ManiGraph also addresses the overplotting issue in scatterplot visualization for large-scale datasets and supports examining in unsupervised scenarios. We demonstrate the effectiveness of ManiGraph in different analytical cases, including generic machine learning using 3D toy data illustrations and fashion-MNIST, a computational biology study using a single-cell RNA sequencing dataset, and a deep learning-enabled colorectal cancer study with histopathology-MNIST.

The opposite aging effect to single cell transcriptome profile among cell subsets.

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

Epigenetic regulation of cell state by H2AFY governs immunogenicity in high-risk neuroblastoma.

Childhood neuroblastoma with MYCN-amplification is classified as high-risk and often relapses after intensive treatments. Immune checkpoint blockade therapy against the PD-1/L1 axis shows limited efficacy in neuroblastoma patients and the cancer intrinsic immune regulatory network is poorly understood. Here, we leverage genome-wide CRISPR/Cas9 screens and identify H2AFY as a resistance gene to the clinically approved PD-1 blocking antibody, nivolumab. Analysis of single-cell RNA sequencing datasets reveals that H2AFY mRNA is enriched in adrenergic cancer cells and is associated with worse patient survival. Genetic deletion of H2afy in MYCN-driven neuroblastoma cells reverts in vivo resistance to PD-1 blockade by eliciting activation of the adaptive and innate immunity. Mapping of the epigenetic and translational landscape demonstrates that H2afy deletion promotes cell transition to a mesenchymal-like state. With a multi-omics approach, we uncover H2AFY-associated genes that are functionally relevant and prognostic in patients. Altogether, our study elucidates the role of H2AFY as an epigenetic gatekeeper for cell states and immunogenicity in high-risk neuroblastoma.

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Hepatic stellate cell single cell atlas reveals a highly similar activation process across liver disease aetiologies

Background & AimsThe progression of chronic liver disease (CLD) is characterized by excessive extracellular matrix deposition, disrupting hepatic architecture and function. Upon liver injury, hepatic stellate cells (HSCs) differentiate towards myofibroblasts and become inflammatory, proliferative and fibrogenic. To date it is still unclear whether HSC activation is driven by similar mechanisms in different aetiologies. MethodsHSCs from multiple publicly available single-cell RNA-sequencing datasets were annotated and merged into a single-cell HSC activation atlas. Spheroid co-cultures of primary mouse hepatocytes/HSCs (n=5) and ELISAs on patient plasma samples (n=80) were performed to validate the mechanistic insight obtained from the HSC atlas. ResultsWe established an HSC activation atlas in which HSCs are clearly divided into 3 distinct transcriptomic profiles: quiescent HSCs, initiatory HSCs and myofibroblasts. These transcriptomic profiles are present in each of the investigated mouse liver injury models as well as in human chronic liver diseases, indicating that HSC activation is a conserved process. This activation process is driven by a core set of transcription factors independent of liver injury or species. Furthermore, we reveal novel ligands associated with activation of HSCs in multiple liver injury models and validate the profibrotic effect of parathyroid hormone. Finally, we identify COLEC10 as a conserved marker for quiescent HSCs and a biomarker of liver fibrosis in patients with different CLDs (p<0.0001). ConclusionsWe reveal unexpected similarities in the regulatory mechanisms of HSCs across diverse liver injury settings and species. The HSC activation atlas has the potential to provide novel insights into liver fibrosis and steer novel treatment options. Impact and implicationsThis study establishes a single-cell atlas of hepatic stellate cells across various liver injuries, highlighting a conserved activation process between different injuries and across species. The discovery of novel activating ligands and the biomarker COLEC10 in human plasma enhances diagnostic and therapeutic strategies. Additionally, the conserved activation process supports the use of any mouse model for mechanistic studies and testing of new anti-fibrotic compounds, streamlining preclinical research efforts.

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis

BackgroundPostmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers. MethodsThe GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models. ResultsUsing machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated. ConclusionJUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Full-length nanopore sequencing of circular RNA landscape in peripheral blood cells following sequential BNT162b2 mRNA vaccination

Circular RNAs (circRNA) lack 5′ or 3′ ends; their unique covalently closed structures prevent RNA degradation by exonucleases. These characteristics provide circRNAs with high pharmaceutical stability and biostability relative to current standard-of-care linear mRNAs. CircRNA levels are reportedly associated with certain human diseases, making them novel disease biomarkers and a noncanonical class of therapeutic targets. In this study, the endogenous circRNAs underlying the response to BNT162b2 mRNA vaccination were evaluated. To this end, peripheral blood samples were subjected to full-length sequencing of circRNAs via nanopore sequencing and transcriptome sequencing. Fifteen samples, comprising pre-, first, and second vaccination cohorts, were obtained from five healthcare workers with no history of SARS-CoV-2 infection or previous vaccination. A total of 4706 circRNAs were detected; following full-length sequencing, 4217 novel circRNAs were identified as being specifically expressed during vaccination. These circRNAs were enriched in the binding motifs of stress granule assemblies and SARS-CoV-2 RNA binding proteins, namely poly(A) binding protein cytoplasmic 1 (PABPC1), pumilio RNA binding family member 1 (PUM1), and Y box binding protein 1 (YBX1). Moreover, 489 circRNAs were identified as previously reported miRNA sponges. The differentially expressed circRNAs putatively originated from plasma B cells compared to circRNAs reported in human blood single-cell RNA sequencing datasets. The pre- and post-vaccination differences observed in the circRNA expression landscape in response to the SARS-CoV-2 BNT162b2 mRNA vaccine.

Comprehensive Characterization of a Subfamily of Ca2+-Binding Proteins in Mouse and Human Retinal Neurons at Single-Cell Resolution.

Ca2+-binding proteins (CaBPs; CaBP1-5) are a subfamily of neuronal Ca2+ sensors with high homology to calmodulin. Notably, CaBP4, which is exclusively expressed in rod and cone photoreceptors, is crucial for maintaining normal retinal functions. However, the functional roles of CaBP1, CaBP2, and CaBP5 in the retina remain elusive, primarily due to limited understanding of their expression patterns within inner retinal neurons. In this study, we conducted a comprehensive transcript analysis using single-cell RNA sequencing datasets to investigate the gene expression profiles of CaBPs in mouse and human retinal neurons. Our findings revealed notable similarities in the overall expression patterns of CaBPs across both species. Specifically, nearly all amacrine cell, ganglion cell, and horizontal cell types exclusively expressed CaBP1. In contrast, the majority of bipolar cell types, including rod bipolar (RB) cells, expressed distinct combinations of CaBP1, CaBP2, and CaBP5, rather than a single CaBP as previously hypothesized. Remarkably, mouse rods and human cones exclusively expressed CaBP4, whereas mouse cones and human rods coexpressed both CaBP4 and CaBP5. Our single-cell reverse transcription polymerase chain reaction analysis confirmed the coexpression CaBP1 and CaBP5 in individual RBs from mice of either sex. Additionally, all three splice variants of CaBP1, primarily L-CaBP1, were detected in mouse RBs. Taken together, our study offers a comprehensive overview of the distribution of CaBPs in mouse and human retinal neurons, providing valuable insights into their roles in visual functions.

Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment.

Galectin-9 is a multifaceted regulator of various pathophysiological processes that exerts positive or negative effects in a context-dependent manner. Here, we elucidated the distinctive functional properties of galectin-9 on myeloid cells within the brain tumor microenvironment. Galectin-9-expressing cells were abundant at the hypoxic tumor edge in the tumor-bearing ipsilateral hemisphere compared to the contralateral hemisphere in an intracranial mouse brain tumor model. Galectin-9 was highly expressed in microglia and macrophages in tumor-infiltrating cells. In primary glia, both the expression and secretion of galectin-9 were influenced by tumors. Analysis of a human glioblastoma bulk RNA-sequencing dataset and a single-cell RNA-sequencing dataset from a murine glioma model revealed a correlation between galectin-9 expression and glial cell activation. Notably, the galectin-9high microglial subset was functionally distinct from the galectin-9neg/low subset in the brain tumor microenvironment. Galectin-9high microglia exhibited properties of inflammatory activation and higher rates of cell death, whereas galectin-9neg/low microglia displayed a superior phagocytic ability against brain tumor cells. Blockade of galectin-9 suppressed tumor growth and altered the activity of glial and T cells in a mouse glioma model. Additionally, glial galectin-9 expression was regulated by Hif-2α in the hypoxic brain tumor microenvironment. Myeloid-specific Hif-2α deficiency led to attenuated tumor progression. Together, these findings reveal that galectin-9 on myeloid cells is an immunoregulator and putative therapeutic target in brain tumors.