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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.
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Multi-Omics Integrative Analysis Identifies the NK Cell-STAT3 Axis as a Shared Immunogenetic Hub Underlying the Comorbidity of Primary Sclerosing Cholangitis and Ulcerative Colitis.
Primary sclerosing cholangitis (PSC) and ulcerative colitis (UC) exhibit a striking clinical comorbidity, with 60-80% of PSC patients concurrently harboring UC, yet the shared immunogenetic mechanisms remain poorly understood. Here, we constructed a multi-omics integrative framework to systematically dissect the cellular and molecular basis of this comorbidity. GWAS meta-analyses were performed for each disease, followed by tissue-level enrichment assessment using QTLEnrich, MAGMA, and gsMap spatial mapping. Single-cell transcriptomic atlases were constructed, and cell-type prioritization was conducted using four complementary methods. Core genes were identified through cross-validation of five algorithms, with subsequent genomic fine-mapping via FUMA and GCTA-COJO. Tissue-level analyses consistently identified the intestine and immune-related tissues as commonly affected. Multi-dimensional evidence integration prioritized natural killer (NK) cells as the core effector cell type for both diseases, supported principally by CELLECT (Cell-type Expression-specific Integration for Complex Traits) heritability enrichment and single-cell differential analysis. Convergence of five gene-level algorithms pinpointed STAT3 as the sole high-confidence comorbidity gene, broadly expressed across immune cell populations and exhibiting tissue-differential alternative splicing. Colocalization identified a high-risk variant (rs3736161) within the STAT3 locus, with conditional analysis revealing 35 additional independent signals. These findings identify the NK cell-STAT3 axis as a central immunogenetic hub connecting PSC and UC, offering potential therapeutic targets for comorbidity management.
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From COVID to fibrosis: lessons from single-cell analyses of the human lung.
The increased resolution of single-cell RNA-sequencing technologies has led to major breakthroughs and improved our understanding of the normal and pathologic conditions of multiple tissues and organs. In the study of parenchymal lung disease, single-cell RNA-sequencing has better delineated known cell populations and identified novel cells and changes in cellular phenotypes and gene expression patterns associated with disease. In this review, we aim to highlight the advances and insights that have been made possible by applying these technologies to two seemingly very different lung diseases: fibrotic interstitial lung diseases, a group of relentlessly progressive lung diseases leading to pulmonary fibrosis, and COVID-19 pneumonia, an acute viral disease with life-threatening complications, including pulmonary fibrosis. We discuss changes in cell populations and gene expression, highlighting potential common features, such as alveolar cell epithelial injury and aberrant repair and monocyte-derived macrophage populations, as well as relevance and implications to mechanisms of disease and future directions.
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Single-cell and spatial transcriptome sequencing analysis reveals characteristics of a unique subpopulation in high-grade IDH-mutant astrocytoma.
INTRODUCTION: (Isocitrate dehydrogenase) IDH-mutant astrocytoma is classified as World Health Organization (WHO) grade 2-4 and is second only to IDH wild-type glioblastoma in the incidence of adult glioma. However, few studies use single-cell and spatial transcriptome sequencing to analyze its malignant progression. METHODS: Intraoperative navigation and yellow fluorescence visualization were utilized to accurately isolate high-grade (WHO grade 3-4) and low-grade (WHO grade 2) samples of IDH-mutant astrocytoma for single-cell and spatial transcriptome sequencing. By combining single-cell, spatial transcriptome, The Cancer Genome Atlas (TCGA), and The Chinese Glioma Genome Atlas (CGGA) data, analyses of survival, enriched pathways, transcription factors, intercellular communication, differentiation trajectories, and immune response were performed to identify the characteristics of a unique subpopulation of high-grade IDH-mutant astrocytoma. RESULTS: Our single-cell RNA sequencing analysis identified a distinct subpopulation (Cluster 7) present in high-grade IDH-mutant astrocytoma, which was localized to the terminus of the pseudotime trajectory. Importantly, this cluster not only exhibited an immunosuppressive phenotype correlated with poor clinical prognosis, but also demonstrated significant enrichment in Developmental Biology and Calcium Signaling pathways. Furthermore, this subpopulation engaged in prominent ligand-receptor interactions, particularly through PTN_PTPRZ1 and MIF_CD74 pairs. Notably, comparative analysis revealed that high-grade astrocytoma displayed both quantitatively and qualitatively enhanced communication networks when compared to their low-grade counterparts. CONCLUSIONS: Our single-cell RNA sequencing analysis identifies a distinct tumor cell subpopulation present in high-grade (WHO grade 3-4) adult IDH-mutant astrocytoma. This cluster, which likely arises from malignant progression in adult astrocytoma, may provide new insights for developing therapeutic strategies against this clinically challenging disease.
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SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples.
High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor.
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Single-nucleus RNA sequencing reveals heterogenous microenvironments and specific drug response between cervical squamous cell carcinoma and adenocarcinoma.
BACKGROUND: Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAde) are two major pathological types of cervical cancer (CC), but their high-resolution heterogeneity of tumor and immune microenvironment remains elusive. METHODS: Here, we performed single-nucleus RNA sequencing (snRNA-seq) from five CSCC and three CAde samples, and systematically outlined their specific transcriptome atlas. FINDINGS: We found CD8+ T cells in CSCC were more cytotoxic but lower exhausted compared to those in CAde, and phagocytic MRC1+ macrophages were specifically enriched in CSCC. Interestingly, we discovered that pro-tumoral cancer-associated myofibroblasts (myoCAFs) and cancer-associated vascular-fibroblasts (vCAFs) were more abundant in CSCC, and further verified their pro-metastatic roles in vitro. Furthermore, we also identified some specific chemotherapy drugs for CSCC (Dasatinib and Doramapimod) and CAde (Pyrimethamine and Lapatinib) by revealing their heterogeneity in transcriptomic profiles of malignant epithelial cells, and further verified their specific sensitivity in cell lines and constructed CC-derived organoids. Cell-cell communication networks revealed that the pathways of NRG1-ERBB2, and FN1-ITAG3 were specific for CAde and CSCC, respectively, which may partly explain the specificities of identified chemotherapy drugs. INTERPRETATION: Our study described the immune heterogeneity and specific cellular interactions between CSCC and CAde, which could provide insights for uncovering pathogenesis and designing personalized treatment. FUNDINGS: National Key R&D Program of China (2021YFC2701201), National Natural Science Foundation of China (82072895, 82141106, 82103134, 81903114).