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VirusCircBase: a database of virus circular RNAs.
Circular RNAs (circRNAs) are covalently closed long noncoding RNAs critical in diverse cellular activities and multiple human diseases. Several cancer-related viral circRNAs have been identified in double-stranded DNA viruses (dsDNA), yet no systematic study about the viral circRNAs has been reported. Herein, we have performed a systematic survey of 11 924 circRNAs from 23 viral species by computational prediction of viral circRNAs from viral-infection-related RNA sequencing data. Besides the dsDNA viruses, our study has also revealed lots of circRNAs in single-stranded RNA viruses and retro-transcribing viruses, such as the Zika virus, the Influenza A virus, the Zaire ebolavirus, and the Human immunodeficiency virus 1. Most viral circRNAs had reverse complementary sequences or repeated sequences at the flanking sequences of the back-splice sites. Most viral circRNAs only expressed in a specific cell line or tissue in a specific species. Functional enrichment analysis indicated that the viral circRNAs from dsDNA viruses were involved in KEGG pathways associated with cancer. All viral circRNAs presented in the current study were stored and organized in VirusCircBase, which is freely available at http://www.computationalbiology.cn/ViruscircBase/home.html and is the first virus circRNA database. VirusCircBase forms the fundamental atlas for the further exploration and investigation of viral circRNAs in the context of public health.
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Mechanism and endoscopic-treatment-induced evolution of biliary non-anastomotic stricture after liver transplantation revealed by single-cell RNA sequencing.
BACKGROUND: Biliary complications, especially non-anastomotic stricture (NAS), are the main complications after liver transplantation. Insufficient sampling and no recognized animal models obstruct the investigation. Thus, the mechanisms and alterations that occur during endoscopic treatment (ET) of NAS remain unclear. METHODS: Samples were obtained with endoscopic forceps from the hilar bile ducts of NAS patients receiving continuous biliary stent implantation after diagnosis. Retrospective analysis of multiple studies indicated that the duration of ET for NAS was approximately 1-2 years. Thus, we divided the patients into short-term treatment (STT) and long-term treatment (LTT) groups based on durations of less or more than 1 year. Samples were subjected to single-cell RNA sequencing. Transcriptomic differences between STT and normal groups were defined as the NAS mechanism. Similarly, alterations from STT to LTT groups were regarded as endoscopic-treatment-induced evolution. RESULTS: In NAS, inflammation and immune-related pathways were upregulated in different cell types, with nonimmune cells showing hypoxia pathway upregulation and immune cells showing ATP metabolism pathway upregulation, indicating heterogeneity. We confirmed a reduction in bile acid metabolism-related SPP1+ epithelial cells in NAS. Increases in proinflammatory and profibrotic fibroblast subclusters indicated fibrotic progression in NAS. Furthermore, immune disorders in NAS were exacerbated by an increase in plasma cells and dysfunction of NK and NKT cells. ET downregulated multicellular immune and inflammatory responses and restored epithelial and endothelial cell proportions. CONCLUSIONS: This study reveals the pathophysiological and genetic mechanisms and evolution of NAS induced by ET, thereby providing preventive and therapeutic insights into NAS. HIGHLIGHTS: For the first time, single-cell transcriptome sequencing was performed on the bile ducts of patients with biliary complications. scRNA-seq analysis revealed distinct changes in the proportion and phenotype of multiple cell types during Nonanastomotic stricture (NAS) and endoscopic treatment. A reduction in bile acid metabolism-related SPP1+ epithelial cells and VEGFA+ endothelial cells, along with explosive infiltration of plasma cells and dysfunction of T and NK cells in NAS patients. SPP1+ macrophages and BST2+ T cells might serve as a surrogate marker for predicting endoscopic treatment.
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SNAC: A Single-Nuclei Atlas of Capsid Distribution in Nonhuman Primate Eye.
Adeno-associated virus (AAV) vectors have emerged as the leading gene therapy vehicle due to their favorable safety profile and sustained payload expression. Approved therapies such as voretigene neparvovec (Luxturna) and omnasemnogene abeparvovec (Zolgensma) rely on the tropism of natural AAV variants. The majority of discovered natural AAVs and engineered AAV capsids have not been comprehensively profiled for their biodistribution, especially at single-cell resolution. Recent advances in single nuclei sequencing can enable further refinement of AAV cell-type specificity and reduce off-target effects. However, low levels of transduction and muted sensitivity of current single-cell detection methods make screening pooled capsids at single-cell resolution challenging. Here, we develop SNAC (Single-Nuclei Atlas of Capsid distribution), an improved method for single-nuclei profiling of AAV transduction at multiplex scale. We provide proof of concept using the nonhuman primate eye as a model system, showing that we can accurately identify and quantify vector expression in all major retinal cell types. Furthermore, the ranking of capsids by SNAC agrees with that from pre-established tissue sampling protocols. Our method promises to reduce the time, effort, and cost of accurate cell-type-specific profiling of AAV capsids.
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Integrated Single-Cell Transcriptomic Atlas of Human Kidney Endothelial Cells.
KEY POINTS: We created a comprehensive reference atlas of normal human kidney endothelial cells. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney. BACKGROUND: Kidney endothelial cells are exposed to different microenvironmental conditions that support specific physiologic processes. However, the heterogeneity of human kidney endothelial cells has not yet been systematically described. METHODS: We reprocessed and integrated seven human kidney control single-cell/single-nucleus RNA sequencing datasets of >200,000 kidney cells in the same process. RESULTS: We identified five major cell types, 29,992 of which were endothelial cells. Endothelial cell reclustering identified seven subgroups that differed in molecular characteristics and physiologic functions. Mapping new data to a normal kidney endothelial cell atlas allows rapid data annotation and analysis. We confirmed that endothelial cell types in the human kidney were also highly conserved in the mouse kidney and identified endothelial marker genes that were conserved in humans and mice, as well as differentially expressed genes between corresponding subpopulations. Furthermore, combined analysis of single-cell transcriptome data with public genome-wide association study data showed a significant enrichment of endothelial cells, especially arterial endothelial cells, in BP heritability. Finally, we identified M1 and M12 from coexpression networks in endothelial cells that may be deeply involved in BP regulation. CONCLUSIONS: We created a comprehensive reference atlas of normal human kidney endothelial cells that provides the molecular foundation for understanding how the identity and function of kidney endothelial cells are altered in disease, aging, and between species. Finally, we provide a publicly accessible online tool to explore the datasets described in this work (https://vascularmap.jiangnan.edu.cn).
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Single-cell analysis reveals the regulatory role of ECT2 in HPV-driven cervical carcinogenesis process.
BACKGROUND: This study attempted to investigate possible mechanisms of human papillomavirus (HPV) infection from high-grade squamous intraepithelial lesions (HSIL) to cervical cancer (CESC) by combining bulk RNA-seq and scRNA-seq data of CESC. METHODS: scRNA-seq (GSE208653) and bulk RNA-seq (GSE63514) of CESC were obtained from GEO database. scRNA-seq and clinical data were obtained from the The Cancer Genome Atlas (TCGA) database. mRNA sequencing was performed on cervical samples from HPV-infected patients with histologically normal inflammation (IA), HSIL patients, and CESC patients. Cell-cell communication and hdWGCNA analysis were performed on GSE208653 data. Differentially upregulated genes in HSIL.vs.IA and CESC.vs.HSIL were identified. The intersection of them with the module genes were obtained by hdWGCNA to obtain intersection genes. Immunological analysis was conducted using CIBERSORT in TCGA data. Using MsigDB database and literature research, key signaling pathways were determined, and the relationship between ECT2 and glycolysis metabolism was analyzed. RESULTS: scRNA-seq data analysis identified 13 distinct cell clusters, including B cells, epithelial cells, and cancer stem cells, etc. Cell-cell communication analysis revealed that changes in epithelial cells may affect CESC progression. Key gene ECT2 was obtained through hdWGCNA analysis and clinical sequencing analysis. Validation of multiple datasets revealed that ECT2 may participate in epithelial cell repair during the early inflammatory response to HPV infection through glycolysis, and later glycolysis became a pro-cancer condition, promoting tumor stemness and inhibiting immune activity through lactate accumulation. CONCLUSION: This study probed into relationships among HPV infection, HSIL, and CESC, and identified potential oncogene ECT2 in epithelial cells and its possible regulatory mechanisms. These findings provided new insights into understanding HPV infection and CESC progression.
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Integration of single-cell regulon atlas and bulk RNA-seq for individualized prognostic prediction in stomach adenocarcinoma.
Transcriptional regulators reflect cellular heterogeneity and are key for prognostic modeling. Given the poor prognosis of stomach adenocarcinoma (STAD), regulator-derived signatures are vital for risk stratification. Using multi-stage scRNA-seq data, we delineated the transcriptional regulatory landscape of STAD and identified Helicobacter pylori-associated epithelial heterogeneity in intestinal metaplasia. We then developed a 23-regulator machine learning-based STAD prognostic signature (SPS) from malignant epithelial cells to predict overall survival (OS). Patients with high-SPS exhibited significantly worse OS than patients with low-SPS (hazard ratio [HR] = 1.50, 95% CI: 1.09-2.09, log rank p = 7.11 × 10-3). Notably, SPS outperformed other established STAD prognosis models across various independent datasets and remained an independent prognostic factor after adjusting for clinical and pathologic factors. Moreover, integrating SPS with tumor stage and age showed superior accuracy to stage alone. Collectively, our study establishes a robust regulator-based prognostic signature, holding potential to facilitate precision prognostication in STAD.
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MatriCom: a scRNA-Seq data mining tool to infer ECM-ECM and cell-ECM communication systems.
The ECM is a complex and dynamic meshwork of proteins that forms the framework of all multicellular organisms. Protein interactions within the ECM are critical to building and remodeling the ECM meshwork, while interactions between ECM proteins and cell surface receptors are essential for the initiation of signal transduction and the orchestration of cellular behaviors. Here, we report the development of MatriCom, a web application (https://matrinet.shinyapps.io/matricom) and a companion R package (https://github.com/Izzilab/MatriCom), devised to mine scRNA-Seq datasets and infer communications between ECM components and between different cell populations and the ECM. To impute interactions from expression data, MatriCom relies on a unique database, MatriComDB, that includes over 25,000 curated interactions involving matrisome components, with data on 80% of the ~1,000 genes that compose the mammalian matrisome. MatriCom offers the option to query open-access datasets sourced from large sequencing efforts (Tabula Sapiens, The Human Protein Atlas, HuBMAP) or to process user-generated datasets. MatriCom is also tailored to account for the specific rules governing ECM protein interactions and offers options to customize the output through stringency filters. We illustrate the usability of MatriCom with the example of the human kidney matrisome communication network. Last, we demonstrate how the integration of 46 scRNA-Seq datasets led to the identification of both ubiquitous and tissue-specific ECM communication patterns. We envision that MatriCom will become a powerful resource to elucidate the roles of different cell populations in ECM-ECM and cell-ECM interactions and their dysregulations in the context of diseases such as cancer or fibrosis.
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Exploring the impact of cuproptosis on prostate cancer prognosis via RNA methylation regulation based on single cell and bulk RNA sequencing data.
BACKGROUND: Cuproptosis, along with RNA methylation regulators, has recently come to the fore as innovative mechanisms governing cell death, exerting profound impact on the onset and progression of multiple cancers. Nonetheless, the prognostic implications and underlying regulatory mechanisms of them associated with prostate cancer (PCa) remain to be thoroughly investigated. METHODS: Genomic and clinical data for PCa from The Cancer Genome Atlas datasets were analyzed to identify a prognostic model through univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses that were validated utilizing external datasets. We used receiver operating characteristic curves and C-index to evaluate the accuracy of our prognostic model. In conjunction with this, we conducted single-cell RNA sequencing (scRNA-seq) analyses to investigate underlying mechanisms and evaluate the degree of immune infiltration, as well as to assess patients' responses to diverse chemotherapy agents. Especially, qPCR assay was utilized to unveil the expression of signature genes in PCa. RESULTS: We meticulously selected six Cuproptosis-Associated RNA Methylation Regulators (CARMRs) to establish a risk prognosis model, which was further verified to obtain enhanced predictive capacity in external validation cohorts. Insights from immune infiltration and scRNA-seq analyses have elucidated the immune characteristics of PCa, and highlighted the immunosuppressive role of regulatory T cells on immune response. Additionally, drug susceptibility analysis demonstrated that patients with PCa in the low-risk category derived better benefit from bicalutamide treatment, whereas those in the high-risk group exhibited a favor response to adriamycin and docetaxel treatments. The qPCR and immunohistochemistry (IHC) staining assays also reveal the a dramatically altered expression pattern of TRDMT1 and ALYREF in PCa tissues. CONCLUSION: In general, we established a model involving CARMRs that can better predict the risk of recurrence of PCa and have identified the possible mechanisms affecting PCa progression, thereby promoting further research in this field.
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Residual-stream geometry of single-cell foundation models carries incremental gene-regulatory signal across tissues.
BACKGROUND: Single-cell foundation models such as scGPT and Geneformer learn rich representations of gene expression programs, but whether these representations encode gene regulatory relationships beyond expression-level confounds remains unclear. Attention patterns in these models have been shown to capture co-expression rather than direct regulation, leaving open the question of whether deeper representations-particularly the residual stream-contain genuine regulatory information. RESULTS: We systematically investigated residual-stream geometry in scGPT and Geneformer across four tissue contexts from the Tabula Sapiens atlas, evaluating whether geometric proximity between gene vectors provides incremental predictive value for curated TRRUST transcription factor-target edges beyond expression confounds. Under repeated stratified cross-validation, geometric features provided significant incremental signal in kidney and immune settings, validated by label-permutation and geometry-shuffle null controls; centered-cosine similarity, PCA projection and multi-layer bundling recovered comparable signal in lung tissues, and the multi-layer bundle improved every domain (kidney ΔAUROC = + 0.122, immune + 0.042, lung + 0.028, external lung + 0.027; geometry-augmented AUROC 0.60-0.69). The effect was fully robust to leave-TF-out and leave-target-out cross-validation and to harder degree- and expression-matched negative edges, but under the stricter leave-both-out split-no transcription factor and no target shared between folds-it collapsed to near-zero (ΔAUROC at most + 0.003, and not statistically significant in kidney or immune), marking the ceiling of out-of-entity generalization. With a comparable per-layer residual-stream extraction applied to both models, the apparent Geneformer advantage mostly disappeared (small residual gaps remained in three of four domains), indicating it largely reflected representation-construction choices rather than a substantial architectural difference. Asymmetric geometric features predicted regulatory edge orientation (AUROC 0.80-0.90), and the geometric signal added incremental value on top of expression-based gene regulatory network (GRN) inference (GENIE3, co-expression). CONCLUSION: Foundation model residual streams carry incremental, regulatory-relevant geometric signal that is distributed across layers and that complements expression-based GRN inference for retrospective edge prioritization. The signal is statistical enrichment rather than a stand-alone regulatory classifier: absolute performance is modest and out-of-entity generalization is limited, so its practical role is as an orthogonal evidence channel for edge re-ranking and hypothesis prioritization in multi-evidence frameworks.
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Differentiation Trajectory of Virus-Induced Tumour Cells in Rice Revealed by Single-Cell RNA Sequencing.
Many plant viruses trigger abnormal differentiation and development of host cells, causing distinct symptoms. Here, in a single-cell RNA sequence analysis, we find transcriptional heterogeneity between cells from rice leaf sheaths that are infected with rice black streaked dwarf virus (RBSDV) and from those that are virus-free. Using 106 973 cells, we construct a single-cell transcriptome atlas with 12 cell types and find that parenchyma and vascular parenchyma cells are the two major cell types affected by RBSDV. Notably, in RBSDV-infected plants, virus-induced tumours are observed to proliferate and show to differentiate from vascular parenchyma cells in a pseudotime analysis. During the differentiation of RBSDV-induced tumour cells, genes that involved in such processes as nucleosome assembly and chromatin remodeling are upregulated, and those involved in photosynthesis and energy metabolism are downregulated. A gene module of DGP1-GLK1-LHCBs that functions in regulating chloroplast development is repressed in the process of tumour cell formation. Moreover, a jasmonate-inducible pathogenesis-related gene, JiPR10, is expressed specifically in RBSDV-induced tumour cells and endows host resistance to RBSDV by inhibiting the proliferation and expansion of tumours. Our cell-type-specific atlas for plant responses to viral infection provides a valuable resource for further study of virus-induced abnormal development of host plants.