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An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.
BACKGROUND: SERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a key regulator in regulating inflammatory responses and immune homeostasis. SERPING1 is downregulated in various disease, this downregulation occurs through the body's negative feedback resulting from the overactivation of the complement system in diseases such as infections and acute inflammatory responses. Additionally, SERPING1 is vital for tumor immunomodulation. Diffuse large B-cell lymphoma (DLBCL) is a common and aggressive type of non-Hodgkin lymphoma. More treatment options are becoming available for this disease. However, some patients still experience recurrence or even disease progression during treatment. Consequently, elucidating the molecular underpinnings of DLBCL's malignant behavior and identifying novel prognostic markers and therapeutic targets are paramount for improving patient outcomes. METHODS: This investigation utilized multi-omics integration combined with machine learning algorithms to identify CD8 + T cell-associated hub genes and clarify their roles in DLBCL pathogenesis. Data were obtained from public repositories including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Utilizing multi-omics and machine learning techniques such as differential analysis, WGCNA, feature selection, scRNA-seq analysis, molecular docking, and Mendelian randomization, we identified four key hub genes (MAFB, TMEM176A, SERPING1, and C1QB) linked to CD8 + T cells, highlighting SERPING1 as central. They have potential value in the diagnosis, prognosis, and immunotherapy of DLBCL. RESULTS: Analysis of six GEO datasets (GSE56315, GSE25638, GSE12453, GSE12195, GSE32018, GSE83632) and TCGA-DLBC RNA-sequencing data, following batch correction, revealed 209 differentially expressed genes (DEGs). CIBERSORT revealed significant immune infiltration disparities, with CD8 + T cells notably enriched in DLBCL. WGCNA identified the yellow module (151 genes) as strongly correlated with CD8 + T cell infiltration, yielding 40 core DEGs upon intersection with DEGs. Functional enrichment analysis highlighted their involvement in chemokine signaling and complement cascades. A machine learning framework utilizing 12 algorithms identified eight key genes: ANKRD22, C1QB, C1QC, CD163, MAFB, SERPING1, TMEM176A, and TNFSF13B. The combination of Least Absolute Shrinkage and Selection Operator (LASSO) with Linear Discriminant Analysis (LDA) demonstrated the highest diagnostic efficacy, achieving an area under the curve (AUC) of 0.799. Four hub genes (MAFB, TMEM176A, SERPING1, and C1QB) were identified from the eight key genes through the intersection of eight feature selection methods and validated using protein-protein interaction networks and receiver operating characteristic curve analysis, achieving an AUC greater than 0.9. SHapley Additive exPlanations analysis underscored the predictive dominance of MAFB, while a prognostic nomogram incorporating these genes demonstrated high accuracy (1-/3-/5-year calibration). scRNA-sequencing revealed hub gene enrichment in non-classical monocytes, and CellChat implicated their roles in intercellular communication via the APP/GALECTIN pathways. Molecular docking and dynamics confirmed the stable binding of SERPING1-doxorubicin (ΔG = - 7.6 kcal/mol), supported by the root mean square deviation/Rg stability. CONCLUSION: Summary-data-based Mendelian randomization analysis identified a closed positive genetic association between SERPING1 overexpression and DLBCL incidence risk (P < 0.05), with no statistical evidence of horizontal pleiotropy, suggesting that SERPING1 overexpression may be a potential risk-related factor for DLBCL onset. SERPING1 is significantly upregulated in DLBCL tissues, suggesting that its expression is regulated in a disease-specific manner. SERPING1 exhibits a pattern characterized by "local selective activation and overall inhibition" through the unbalanced regulation of the complement system in the tumor microenvironment, acting as a pro-tumor hub gene in synergistic regulation with C1QB.
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Meta single-cell atlas and xQTL post-GWAS analysis revealed the pathogenic features of thyroid cancer for target therapy: A multi-omics study.
Thyroid cancer (TC) is an endocrine malignancy characterized by metabolic abnormalities, with its incidence continually on the rise. Understanding the pathogenesis of this cancer would help develop better diagnostic and therapeutic methods. We aimed to integrate single-cell transcriptomics, bulk transcriptomics, and GWAS data to identify causal associations with thyroid cancer at the gene level. We intended to utilize single-cell atlases to identify malignant cells and their characteristics, and employed SMR to search for genetic loci causally associated with thyroid cancer. We validated the expression differences of the genes at the single-cell level and bulk level, as well as through immunohistochemistry experimental results. We investigated the tumor immune microenvironment of patients, attempting to find immune subgroups with differential proportions. Based on these subgroups, we conducted multi-machine learning modeling to predict the likelihood of disease and developed a corresponding interactive web application. HMGA2, SDCCAG8, DLG5, MT1E, RABL2B, RERE, and NDUFA12 all demonstrated to varying degrees their roles in promoting or inhibiting the occurrence and development of thyroid cancer, with HMGA2 showing consistency across all analyses. We also identified some immune subtypes significantly associated with TC and chose markers of T_cell_C8_STMN1 to construct patient diagnostic models. Through various combinations of machine learning feature selection and model construction, we ultimately built 178 diagnostic models, with the combination of glmBoost+RF having the best diagnostic performance (Average AUPR: 0.9915). The predictive web pages ( https://zclab-cnp.shinyapps.io/TC-WEB/ ) can provide convenience and reference for clinical personnel.
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Pan-cancer atlas of cellular architecture reveals a nearest-neighbour distance-associated biomechanical-immune axis involving CD4+ memory T cells.
BACKGROUND: The systematic link between cellular spatial organization and its biomechanical consequences remains a critical knowledge gap in human oncology. OBJECTIVE: To quantitatively map the cellular architecture of solid tumours and elucidate its mechanistic impact on mechanical signalling, immune infiltration, and patient survival. METHODS: We developed a scalable digital pathology framework, processing 7910 H&E whole-slide images across 21 solid tumours. A deep learning pipeline was employed to segment over 4.7 billion nuclei, enabling the calculation of cell density and nearest neighbour distance (NND) as key spatial metrics. To bridge morphology and function, we integrated these metrics with bulk RNA-seq data from 19 TCGA cohorts (n = 7401) using rigorous histological matching. Furthermore, to resolve microenvironmental heterogeneity, we performed unsupervised clustering of over 60 000 T-cell transcriptomes from independent cohorts. These findings were validated through high-resolution Visium-HD spatial transcriptomics to correlate physical proximity with localized gene expression. RESULTS: While tumours exhibited significant heterogeneity, NND, but not cell density, emerged as a primary determinant of biomechanical and immune signatures. A trend-level association was observed between lower NND and higher Hippo/YAP/TAZ pathway activity across nine matched cancer types (Spearman's ρ = -.65, p = .058, n = 9). In addition, lower NND was significantly correlated with increased CD4+ memory T-cell (CD4+ TMem cell) abundance (Spearman's ρ = -.86, p < .01). Single-cell analyses confirmed that CD4+ TMem cells intrinsically express mechanical stress signalling markers, which spatial transcriptomics localized to CD4+ TMem cell aggregation zones characterized by high pathway activity. Clinically, this spatial-mechanical-CD4+ TMem cell axis was associated with prognosis in breast, oesophageal, liver, and lung adenocarcinomas, where high mechanical signalling generally predicted poor outcomes but could be modulated by CD4+ TMem infiltration levels. CONCLUSION: Our study identifies low NND as a spatial correlate of biomechanical crowding that is associated with CD4+ TMem cell programming and adverse clinical outcomes. By integrating deep learning-based spatial metrics with multi-omics, we highlight spatial mechanics as a critical, potentially targetable dimension of the tumour microenvironment for future immunotherapies.
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Multiomic spatial atlas shows deleted in malignant brain tumors 1 (DMBT1) glycoprotein is lost in colonic dysplasia.
Colorectal cancer (CRC) is responsible for over 900,000 annual deaths worldwide. Emerging evidence supports pro-carcinogenic bacteria in the colonic microbiome are at least promotional in CRC development and may be causal. We previously showed toxigenic C. difficile from human CRC-associated bacterial biofilms accelerates tumorigenesis in ApcMin/+ mice, both in specific pathogen-free mice and in gnotobiotic mice colonized with a defined consortium of bacteria. To further understand host-microbe interactions during colonic tumorigenesis, we combined single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics, and immunofluorescence to define the molecular spatial organization of colonic dysplasia in our consortium model with or without C. difficile. Our data show a striking bipartite regulation of Deleted in Malignant Brain Tumors 1 (DMBT1) in the inflamed versus dysplastic colon. From scRNA-seq, differential gene expression analysis of normal absorptive colonocytes at 2 weeks postinoculation showed DMBT1 upregulated by C. difficile compared to colonocytes from mice without C. difficile exposure. In contrast, our spatial transcriptomic analysis showed DMBT1 dramatically downregulated in dysplastic foci compared with normal-adjacent tissue. We further integrated our datasets to generate custom colonic dysplasia scores and ligand-receptor mapping. Validation with immunofluorescence showed DMBT1 protein downregulated in dysplastic foci from three mouse models of colonic tumorigenesis and in adenomatous dysplasia from human samples. Finally, we used mouse and human organoids to implicate WNT signaling in the downregulation of DMBT1 mRNA and protein. Together, our data reveal cell type-specific regulation of DMBT1, a potential mechanistic link between bacteria and colonic tumorigenesis. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.
Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. Here, we present Unified Variational Inference (UniVI), a scalable mixture-of-experts β-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or preannotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Genomics Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a nonhematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to trimodal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, trimodal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.
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Why Peri-Implant Tissues Are Susceptible to Inflammatory Breakdown-Single-Cell Transcriptomics Reveals Immune Priming in Clinically Healthy Peri-Implant Mucosa and CXCR2+ Neutrophil Activation in Peri-Implantitis.
AIM: Peri-implant tissues are more susceptible than gingiva to rapid inflammatory breakdown, but the cellular basis of this vulnerability remains unclear. This study investigated (1) whether clinically healthy peri-implant mucosa already displays an immune landscape distinct from healthy gingiva, and (2) which cellular states and molecular pathways characterize the transition from peri-implant health to peri-implantitis. METHODS: An integrative single-cell RNA-sequencing analysis was performed using clinically healthy pre-loading peri-implant mucosa (n = 3), healthy gingiva (n = 17), and peri-implantitis tissues (n = 3). Healthy peri-implant mucosa was first compared with a healthy gingival reference atlas to define the baseline peri-implant immune niche. Disease-associated changes were then examined by comparing healthy peri-implant and peri-implantitis tissues. Key findings were validated in independent tissue cohorts comprising healthy gingiva, clinically healthy peri-implant mucosa, and peri-implantitis (n = 6 participants per group) using multiplex immunofluorescence, immunohistochemistry, and reactive oxygen species (ROS) detection. RESULTS: Clinically healthy peri-implant mucosa showed greater immune-cell enrichment than healthy gingiva, most notably within the myeloid compartment. Among myeloid cells, a CXCR2+ neutrophil subset exhibited the highest baseline signature for neutrophil extracellular trap (NET) formation, indicating an immune-enriched state with enhanced NET-associated transcriptional potential, despite clinical health. In peri-implantitis, this CXCR2+ neutrophil subset expanded further and displayed increased oxidative, chemotactic, and NET-associated programs, supported by tissue validation of MPO, CXCR2, CitH3, and ROS signals. Cell-cell communication analysis nominated fibroblast-derived CXCL signaling as a potential contributor to CXCR2+ neutrophil recruitment. NSUN6, an RNA modification enzyme, was enriched in disease-associated CXCR2+ neutrophils, and NSUN6 knockdown in neutrophil-like cells attenuated CXCR2+ cell enrichment and NET-associated marker expression. CONCLUSION: Clinically healthy peri-implant mucosa before functional loading is not immunologically equivalent to gingiva, but represents an immune-primed niche that may lower the threshold for inflammatory breakdown. These findings may not be directly generalized to long-term functionally loaded implants. Peri-implantitis is characterized by amplification of an oxidative CXCR2+ neutrophil state, with fibroblast-derived CXCL-CXCR2 signaling and NSUN6-associated neutrophil activation emerging as candidate pathways for future diagnostic and therapeutic investigation.
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Machine Learning-Guided Stress Atlases Reveal Co-Expression Rewiring and Divergent Cellular Deployment of Abiotic Stress Programs in Rice and Wheat.
Abiotic stress limits cereal productivity, yet whether conserved stress-responsive genes retain similar transcriptional network organization and cellular deployment across cereal species remains unknown. Here, we developed a machine learning-guided comparative framework to integrate public leaf transcriptomes of rice (Oryza sativa) and wheat (Triticum aestivum) under major abiotic perturbations. Using harmonized compendia comprising 787 rice and 337 wheat samples, we found that rice samples resolved into more discrete stress-associated transcriptional states, whereas wheat samples formed a more continuous landscape with partial overlap among stress responses. Supervised learning prioritized compact sets of stress-predictive candidate genes, including heat-shock/chaperone-related, ABA-biosynthetic and membrane-associated features. Orthology-guided co-expression analysis further showed that homologous stress-predictive genes, particularly heat-associated candidates, can retain stress responsiveness while occupying divergent module neighborhoods in rice and wheat. Leaf single-cell projection resolved this divergence at cellular resolution: several rice predictors showed compartmentalized expression in parenchyma and vascular parenchyma, whereas their wheat counterparts were more broadly deployed across mesophyll, epidermal and vascular cell types. Our findings reveal context-dependent redeployment of homologous stress-associated genes in cereals and offer a scalable strategy for prioritizing candidates for functional validation and climate-resilience breeding.
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An atlas of neuropathic pain-associated molecular pathological characteristics in the mouse spinal cord.
Peripheral nerve injury (PNI)-induced neuropathic pain (NP) is a severe disease with high prevalence in clinics. Gene reprogramming and tissue remodeling in the dorsal root ganglia (DRG) and spinal cord (SC) drive the development and maintenance of neuropathic pain (NP). However, our understanding of the NP-associated spatial molecular processing landscape of SC and the non-synaptic interactions between DRG neurons and SC cells remains limited. We here integrate spatial transcriptomics (ST) with single-nucleus RNA-sequencing (snRNA-seq) and bulk RNA-sequencing (bulk RNA-seq) to characterize regional pathological heterogeneity of the SC under NP conditions. First, the SC of NP mice manifests unique spatial atlases of genes, cell populations, cell-cell cross-talks, signaling pathways, and transcriptional regulatory networks compared to sham mice. We further report that injured DRG sensory neurons and the corresponding ventral horn of the SC show similar expression patterns after PNI. In addition, for the first time, we systematically exhibit "cross-talk omics" between the DRG neurons and SC dorsal horn neurons and glial cells, indicating an altered communication profile under NP conditions. Together, our findings decode the spatial and cellular heterogeneity of molecular pathological mechanisms underlying NP, providing a foundation for designing therapeutic targets for this disorder.
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Single-Cell Profiles of Retinal Ganglion Cells Differing in Resilience to Injury Reveal Neuroprotective Genes.
Neuronal types in the central nervous system differ dramatically in their resilience to injury or other insults. Here we studied the selective resilience of mouse retinal ganglion cells (RGCs) following optic nerve crush (ONC), which severs their axons and leads to death of ∼80% of RGCs within 2 weeks. To identify expression programs associated with differential resilience, we first used single-cell RNA-seq (scRNA-seq) to generate a comprehensive molecular atlas of 46 RGC types in adult retina. We then tracked their survival after ONC; characterized transcriptomic, physiological, and morphological changes that preceded degeneration; and identified genes selectively expressed by each type. Finally, using loss- and gain-of-function assays in vivo, we showed that manipulating some of these genes improved neuronal survival and axon regeneration following ONC. This study provides a systematic framework for parsing type-specific responses to injury and demonstrates that differential gene expression can be used to reveal molecular targets for intervention.
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Deterministic genetic barcoding for multiplexed behavioral and single-cell transcriptomic studies.
Advances in single-cell sequencing technologies have provided novel insights into the dynamics of gene expression and cellular heterogeneity within tissues and have enabled the construction of transcriptomic cell atlases. However, linking anatomical information to transcriptomic data and positively identifying the cell types that correspond to gene expression clusters in single-cell sequencing data sets remains a challenge. We describe a straightforward genetic barcoding approach that takes advantage of the powerful genetic tools in Drosophila to allow in vivo tagging of defined cell populations. This method, called Targeted Genetically-Encoded Multiplexing (TaG-EM), involves inserting a DNA barcode just upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct so that the barcode sequence can be read out during single-cell sequencing, labeling a cell population of interest. By creating many such independently barcoded fly strains, TaG-EM enables positive identification of cell types in cell atlas projects, identification of multiplet droplets, and barcoding of experimental timepoints, conditions, and replicates. Furthermore, we demonstrate that TaG-EM barcodes can be read out using next-generation sequencing to facilitate population-scale behavioral measurements. Thus, TaG-EM has the potential to enable large-scale behavioral screens in addition to improving the ability to multiplex and reliably annotate single-cell transcriptomic experiments.