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At least 19 recordsLinked to original sources
PubMed2025

Integration of single cell multiomics data by deep transfer hypergraph neural network.

Multi-omics characterization of individual cells offers remarkable potential for analyzing the dynamics and relationships of gene regulatory states across millions of cells. How to integrate multimodal data is an open problem, existing integration methods struggle with accuracy and modality-specific biological variation retention. In this paper, we present scHyper (scalable, interpretable machine learning for single cell integration), a low-code and data-efficient deep transfer model designed for integrating paired and unpaired single-cell multimodal data. We benchmark scHyper against datasets from different multimodal data. ScHyper learns a low-dimensional representation and aligns the covariance matrices of the measured modalities, achieving high accuracy even with large scale atlas-level datasets with low memory and computational time across different cell lines, shedding light on regulatory relationships between different types of omics. Altogether, we show that scHyper is a versatile and robust tool for cell-type label transfer and integration from multimodal single-cell datasets.

Single-Cell Analysis↗
PubMed2026

Atlas-level single-cell integration and clustering-free differential expression analysis with GEDI 2.0.

MOTIVATION: GEDI is a generative framework for multi-sample, multi-condition single-cell analysis that performs batch correction, latent representation learning, and clustering-free differential expression within a unified model. However, the original implementation suffered from prohibitive memory use and runtime, preventing its application to modern atlas-scale datasets. RESULTS: We present GEDI 2.0, a complete high-performance reimplementation featuring a standalone C++ computational core with pre-allocated workspaces, strict sparse-matrix preservation, optimized BLAS routines, and multi-threaded block-coordinate descent. Across extensive benchmarks spanning up to 500 000 cells and 10 000 features, GEDI 2.0 achieves 40%-63.6% mean reduction in peak memory, 2.98× mean single-threaded speedups, and up to 11.5× acceleration with parallel execution, while maintaining full numerical equivalence to the original method. These improvements enable GEDI 2.0 to analyze million-cell datasets, a scale not achievable with the legacy implementation. GEDI 2.0 provides R and Python interfaces and seamless interoperability with common single-cell workflows. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, reproducible codebase, and tutorials are available at https://github.com/csglab/gedi2.

Single-Cell Analysis↗
PubMed2026

Coordinated inflammatory macrophage and vascular smooth muscle cell remodeling signatures in human atherosclerosis: An integrative single-cell and bulk transcriptomic analysis.

Atherosclerotic plaque progression is shaped by coordinated inflammatory and remodeling programs involving immune cells and vascular wall cells. Inflammatory macrophage activation and vascular smooth muscle cell (VSMC) phenotypic remodeling are central features of human atherosclerosis, but their transcriptomic relationships during plaque progression remain incompletely characterized. This study integrated single-cell and bulk transcriptomic datasets to examine highly inflammatory macrophage states, VSMC remodeling-related transcriptional programs, and candidate ligand-receptor expression patterns in human atherosclerotic plaques. Human atherosclerotic plaque single-cell RNA sequencing data from GSE260657 and bulk transcriptomic data from GSE28829 were analyzed. After quality control, 7628 cells were retained for single-cell analysis. Major cell types were annotated using canonical markers, followed by reclustering of macrophages and VSMC-related cells. Functional module scoring, differential expression analysis, Gene Ontology biological process enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to characterize macrophage transcriptional states. Slingshot was applied to infer VSMC pseudotime ordering. CellChat and NicheNet were used to prioritize candidate ligand-receptor expression patterns and ligand-associated VSMC target gene programs. External bulk transcriptomic analysis was performed to examine whether single-cell-derived inflammatory and remodeling signatures were represented at the tissue-transcriptome level during plaque progression. Macrophage reclustering identified a highly inflammatory macrophage state characterized by prominent inflammatory activation, cytokine-response, and stress-response features. Genes upregulated in this population were enriched in pathways related to tumor necrosis factor (TNF) response, nuclear factor kappa B signaling, leukocyte activation, cytokine signaling, lipid and atherosclerosis, toll-like receptor signaling, and inflammasome-associated inflammation. VSMC reclustering revealed contractile VSMCs, PTHLH+ synthetic VSMCs, KRT7+ VSMC-like cells, interferon-responsive VSMCs, pericyte-like mural cells, and osteogenic/modulated VSMCs. Pseudotime analysis showed a broad contractile-to-osteogenic/modulated transcriptional continuum accompanied by increased expression of remodeling-associated genes and selected inflammatory or remodeling-associated receptor genes. CellChat and NicheNet analyses prioritized candidate ligand-receptor and ligand-associated target gene expression patterns involving SPP1-CD44, TNF-TNFRSF1A, IL1B-IL1R1/IL1RAP, MIF-ACKR3, PDGFB-PDGFRB, and FN1-SDC1/ITGB1. In GSE28829, inflammatory macrophage-, osteogenic/modulated VSMC-, candidate ligand-receptor expression-, SPP1-CD44 candidate axis-, and NicheNet-prioritized target program-related signatures were more prominent in advanced plaques and were positively correlated with each other. This integrative transcriptomic analysis identified a highly inflammatory macrophage state and a VSMC remodeling continuum in human atherosclerotic plaques. Candidate ligand-receptor and ligand-associated target gene expression patterns linked inflammatory macrophage activation with osteogenic/modulated VSMC remodeling at the computational level. External bulk data further showed coordinated enrichment of inflammatory and remodeling signatures in advanced plaques. These findings provide a descriptive and hypothesis-generating transcriptomic framework for understanding inflammatory macrophage activation and VSMC remodeling in human atherosclerosis.

atherosclerosis↗
PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗
PubMed2026

Interpretable data integration for single-cell and spatial multi-omics.

Integrating single-cell or spatial transcriptomic and epigenomic data enables scrutinizing the transcriptional regulatory mechanisms controlling cell fate. Current integration methods usually align multi-omics data into a shared latent space but fail to reveal the underlying connections between genes and regulatory elements. The correlation- or regression-based regulatory inference methods cannot dissect different transcriptional regulation codes for cells under different spatial and temporal states. To address both problems, we develop a feature-guided optimal transport (FGOT) method, which simultaneously uncovers cellular heterogeneity and their associated transcriptional regulatory links. FGOT also provides post hoc interpretability for existing integration methods. FGOT is applicable for paired/unpaired single-cell multi-omics data and paired spatial multi-omics data. Benchmarking and validating via histone modification data or three-dimensional (3D) genomics data show good robustness and accuracy in integration and inference of regulatory links. The method allows systematic screening of cell-state and spatial-location-specific regulatory elements in diseases at the single-cell level. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗
PubMed2026

Integrating single-cell transcriptomics to construct an oncogene-driven prognostic model and elucidate metabolic-immune crosstalk in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell "oncogene scoring" system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.

Hepatocellular carcinoma↗
PubMed2026

An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.

The immunoregulatory architecture of human oral tissues remains poorly defined. We present an integrated single-cell and spatial proteotranscriptomic atlas profiling >250,000 single-cell transcriptomes and >4 million spatially resolved cells across 13 niches. Using our AI-enabled AstroSuite, we defined neighborhoods and interaction modules, revealing peri-epithelial fibroblast-centered hubs enriched in effector cytokines. We harmonized fibroblast subtypes (universal, immune, peri-epithelial, peri-vascular, peri-neural, antigen-presenting cell [APC]-like, stress responsive, and myofibroblasts) with stress-responsive subtypes partitioning between mucosae and glands (type I and II). Spatial multiomics mapped ligand-receptor programs and identified mucosal stress-responsive fibroblasts as putative immunoregulatory hubs. Niche-aware integration of healthy and diseased datasets revealed fibroblast rewiring into inflammatory and reparative niches. Disease neighborhoods exhibited expansion of major histocompatibility complex (MHC)-I+, MHC-II+, and programmed cell death ligand 1 (PD-L1)+ fibroblasts and predicted spatial engagement with T cells at tertiary lymphoid structures. Together, this atlas identifies fibroblasts as central regulators of structural immunity and provides a scalable framework to target stromal-immune interactions across barrier organs.

Journal Article↗
PubMed2026

Integrated single-cell and spatial transcriptomic analyses reveal malignant epithelial glycolytic heterogeneity and spatial niche remodeling during colorectal cancer progression.

Colorectal cancer (CRC) progression is shaped by metabolic reprogramming and complex interactions within the tumor microenvironment. However, the cellular heterogeneity, spatial organization, and clinical relevance of glycolytic activity in CRC remain incompletely understood. In this study, we integrated single-cell RNA sequencing, bulk transcriptomics, and spatial transcriptomics data to systematically characterize glycolytic heterogeneity in CRC. Glycolytic activity was quantified using five independent scoring methods, consistently showing that epithelial cells exhibited the highest glycolytic activity across the two single-cell cohorts. Stratification of CopyKAT-verified aneuploid malignant epithelial cells into high-glycolysis (HG) and low-glycolysis (LG) subgroups by glycolysis scores revealed that HG cells exhibited higher stemness scores and chromosomal copy number variations. Cell-cell communication analysis revealed that, compared with LG cells, HG cells exhibited increased interaction frequency and strength with immune and stromal populations, indicating enhanced malignant epithelial-microenvironment crosstalk. Spatial transcriptomics analyses further revealed that glycolytic activity varied across normal colorectal tissue, primary CRC, and colorectal liver metastases, accompanied by progressive remodeling of epithelial-associated spatial niches and MIF-mediated intercellular communication. Bulk transcriptomic analysis identified a glycolysis-related prognostic signature with robust predictive performance, which served as an independent prognostic factor for overall survival in CRC cohorts. Collectively, these findings indicate that glycolytic heterogeneity is a key feature of CRC malignant epithelial cells and is closely associated with tumor progression, microenvironmental remodeling, and clinical outcomes.

Humans↗
PubMed2026

Integrative single-cell and genomic analysis reveals NMB as a driver of metastatic adaptation in esophageal squamous cell carcinoma via metabolic rewiring and immune evasion.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) has high mortality, and metastasis is the leading cause of patient death. Neuromedin B (NMB) promotes tumor development in various cancers, yet its role in ESCC metastasis remains unclear. METHODS: We integrated single-cell transcriptomic data from matched primary and metastatic ESCC lesions (GSE309392) with bulk transcriptomic cohorts from TCGA and GSE53624. In silico gene perturbation, ligand-receptor communication analysis, and single-cell prognostic model construction were performed, followed by functional validation through siRNA-mediated NMB knockdown in TE-1 and KYSE30 cell lines. RESULTS: NMB was identified as a key gene enriched in metastatic ESCC lesions, and its high expression was associated with coordinated upregulation of oxidative phosphorylation pathway genes and aldo-keto reductase family antioxidant enzymes (AKR1C1, AKR1C2, AKR1B10). Genomic analysis revealed that NMB-high tumors carried a higher clonal mutation burden and a markedly increased frequency of NFE2L2 activating mutations (23% vs. 8%, P = 0.04). In silico knockout and correlation analysis identified AKR1C1 as a downstream effector of NMB. NMB expression was negatively correlated with CD8+ T cell and activated NK cell infiltration. CellChat analysis revealed communication between NMB-positive cells and monocytes via the TGM2-ADGRG1 axis, and specifically detected IFNG signaling. In the single-cell prognostic model, NMB-positive cells accounted for 50% of the high-risk group but only 20% of the low-risk group. TCGA-based survival analysis demonstrated that high NMB expression was associated with shorter overall survival (HR = 2.98, P = 0.03). In vitro NMB-targeted RNA interference markedly inhibited proliferation, colony formation, and migration in TE-1 and KYSE30 cells. CMap screening identified the endothelin-PDE5-cGMP axis as a potential therapeutic target. CONCLUSION: NMB serves as a key driver of metastatic adaptation in ESCC, conferring a survival advantage to tumor cells during metastatic colonization through genomic evolution and immune remodeling, with metabolic adaptation as a downstream consequence of genomic alterations.

NMB↗
PubMed2026

Next-generation brain proteomics: Integrating single-cell, spatial, and multi-omics for clinical biomarker discovery.

The mammalian brain's functional complexity arises from the sophisticated architecture of neurons and glia. This network is essentially defined by its dynamic proteome, which reveals the functional execution underlying neural computation and disease. This review integrates the technological leap in neuroproteomics. It has moved beyond bulk tissue proteome cataloguing to high-sensitivity single-cell and spatial resolution. We detail how next-generation platforms, such as TIMS-PASEF and Orbitrap-Astral, have enabled deeper and faster phenotypic profiling of limited brain samples. However, the proteome coverage remains constrained by dynamic range, sample loss, ionisation bias and incomplete detection of low-abundance regulatory proteins. We further examine how such studies have revealed the proteomic remodelling that drives lineage specification and synaptic plasticity by linking temporal protein expression waves to biological function. Crucially, we delineate the clinical translational trajectory, illustrating how aberrant signatures are verified in cerebrospinal fluid (CSF) and validated in plasma to support precision medicine. Finally, we argue for the necessity of "fused" multi-omics integration and Artificial Intelligence (AI) to decode the non-linear molecular logic of brain pathology.

Humans↗
PubMed2026

Integrated single-cell transcriptomics, Mendelian randomization, and machine learning identify CEBPZ as an immune-related biomarker in oral lichen planus.

BACKGROUND: Oral lichen planus (OLP) is a chronic, immune-mediated oral mucosal disease with complex pathophysiology and potential for malignant transformation. Understanding its molecular basis is critical for the development of precise diagnostic and therapeutic strategies. OBJECTIVES: We aimed to identify key immune-related biomarkers and characterize cellular dynamics in OLP, with a particular focus on the role of CEBPZ in disease pathogenesis. MATERIAL AND METHODS: We analyzed single-cell RNA sequencing (scRNA-seq) data from OLP lamina propria samples (GSE211630) to identify disease-specific T-cell subpopulations using high-dimensional weighted gene co-expression network analysis (hdWGCNA) for oxidative stress-related gene modules.-data-based Mendelian randomization (SMR) integrated FinnGen genome-wide association study (GWAS; 342,499 Europeans) data with Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) data to identify causal genes. Machine learning (ML) models (least absolute shrinkage and selection operator (LASSO) and convolutional neural network (CNN)) were developed using bulk RNA-seq datasets (GSE52130 and GSE38616) for diagnostic purposes. RESULTS: We identified OLP-specific T-cell populations (clusters 0, 3, 5, 7, 13, and 15) with enhanced migration inhibition factor (MIF) pathway signaling toward B cells and monocytes. Two oxidative stress-associated modules contained hub genes, including CEBPZ. Summary-data-based Mendelian randomization analysis identified 231 OLP-associated genes, with CEBPZ uniquely intersecting LASSO-selected markers (odds ratio (OR) = 1.057, 95% confidence interval (95% CI) = 1.013-1.102, p = 0.010). Machine learning models achieved area under the curve (AUC) values ranging from 0.653 to 0.745, with the CNN model reaching a validation accuracy of 0.735. CEBPZ showed elevated expression in OLP T cells and correlated with enhanced MIF-(CD74+CXCR4) signaling. CONCLUSIONS: This integrative approach identifies CEBPZ as a pivotal biomarker linking genetic susceptibility, oxidative stress, and immune dysregulation in OLP. Our diagnostic models offer promising tools for OLP management.

CEBPZ↗
PubMed2026

Integrated single-cell RNA sequencing and mendelian randomization analysis identifies causal immune-related driver genes in the heart failure inflammatory microenvironment.

BACKGROUND: Heart failure (HF) is a major global cause of cardiovascular death and disability. Chronic inflammation and immune dysregulation are critical in its development. The cardiac immune microenvironment, especially macrophages, drives HF progression, yet its molecular mechanisms and prognostic impact are not fully clear. This study aimed to identify causal immune-related driver genes in the HF inflammatory microenvironment. METHODS: We combined single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR) to study how the inflammatory immune microenvironment affects HF risk. Using two public scRNA-seq datasets, we identified differentially expressed genes (DEGs) in HF heart tissues and selected 489 candidate genes. Causal relationships between these genes and HF were tested using expression quantitative trait loci (eQTL) data and HF genome-wide association study (GWAS) summary statistics. RESULTS: MR analysis showed that 65 genes were causally linked to HF risk. These genes were enriched in pathways related to cardiomyopathy, leukocyte migration, natural killer (NK) cell cytotoxicity, neutrophil extracellular traps, and NF-κB signaling. HF hearts displayed increased levels of macrophages, T cells, B cells, lymphoid cells, and mast cells, while neutrophils were reduced. CONCLUSIONS: Our integrated analysis reveals the central role of the cardiac inflammatory immune microenvironment in HF and identifies 65 key genes causally associated with HF susceptibility. These genes influence specific immune pathways and cell infiltration, shaping HF progression, and provide a basis for developing new biomarkers and immune-targeted therapies.

Heart failure (HF)↗
PubMed2026

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma↗
PubMed2025

A Multi-omics Exploration Revealing SLIT2 as a Prime Therapeutic Target for Peripheral Facial Paralysis: Integrating Single-Cell Transcriptomics and Plasma Proteome Data.

Peripheral facial paralysis (PFP) is a common neurological disorder characterized by facial-nerve dysfunction. Identifying therapeutic targets and understanding the molecular and cellular mechanisms underlying PFP are crucial for developing effective treatment strategies. This study combined Mendelian randomization (MR) analysis and single-cell RNA sequencing (scRNA-seq) to explore potential therapeutic candidates and their roles in PFP pathophysiology. The MR analysis included 1925 publicly available plasma protein cis-heritability instruments. Instrumental variables were selected for MR analysis to identify plasma proteins associated with PFP, followed by colocalization analysis to evaluate shared genetic variants between the identified proteins and PFP. After the initial identification of plasma proteins associated with Bell's palsy using MR analysis, a rat model of facial-nerve injury was established to further dissect underlying mechanisms at cellular and molecular levels. Using scRNA-seq technology, we delved deeply into cellular Heterogeneity and dynamic changes in gene expression in the facial-nerve nucleus tissues under both injured and control conditions, thereby achieving a systematic study ranging from macroscopic genetic associations to microscopic cellular functions. Finally, expression patterns were preliminarily validated by performing in vitro immunofluorescence analysis on the facial-nerve nucleus samples of SD rats. The MR analysis results identified 30 plasma proteins significantly associated with PFP, with nine target genes showing differential expression in the scRNA-seq data. Colocalization analysis demonstrated that slit guidance Ligand 2 (SLIT2), semaphorin 4D (SEMA4D), EGF containing fibulin extracellular matrix protein 1 (EFEMP1), and sprouty related EVH1 domain containing 2 (SPRED2) shared causal variants with PFP. SLIT2 was highly expressed in the microglia and inhibitory neurons in the experimental group, whereas SEMA4D showed elevated expression across multiple glial cell types in the same group. In contrast, EFEMP1 and SPRED2 showed distinct expression patterns in fibroblasts and oligodendrocytes. The role of SLIT2 has been previously well-documented in many central nervous system diseases. However, for the first time, this study detected SLIT2 alteration after facial-nerve injury. Altered intercellular signaling, particularly enhanced SLIT2-ROBO signaling between neurons and glial cells, was observed in the PFP group. Pseudotime analysis revealed dynamic SLIT2 expression during microglia and inhibitory neuron differentiation, mirroring changes in ROBO1 expression. Immunofluorescence analysis of rat facial-nerve nucleus samples verified that SLIT2 protein levels were significantly increased in the facial-nerve nuclei of injured samples. In conclusion, despite the fact that this study is primarily founded on animal models and despite notable differences existing between animals and humans in terms of the facial motor nucleus, this study successfully identified SLIT2 as potential therapeutic targets for PFP. The SLIT2-ROBO axis stands out as a particularly promising candidate. SLIT2 may play a role in modulating neuroimmune interactions and promoting nerve repair. These findings provide a foundation for future clinical studies and targeted interventions to enhance recovery from PFP. Future research should focus on human sample validation to enhance clinical translation.

Animals↗
PubMed2026

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations.

Although well-differentiated thyroid carcinoma (WDTC) is characterized by a robust treatment response, aggressive subtypes, such as anaplastic thyroid carcinoma (ATC), remain highly lethal. To understand thyroid cancer evolution in both children and adults, we analyzed single-cell transcriptomes of 423,733 cells from 81 samples and spatially resolved key tumor and microenvironment populations across 28 tumors with spatial transcriptomics, including rare and unique composite WDTC/ATC tumors and pediatric diffuse sclerosing thyroid carcinomas. Additionally, we identified gene signatures of stromal cell populations in 5 large thyroid cancer bulk RNA-sequencing cohorts. Through this multi-institutional effort, we defined a population of POSTN+ myofibroblast cancer-associated fibroblasts (myCAFs) that are intimately associated with invasive tumor cells and correlate with poor prognosis, lymph node metastasis, and disease progression in thyroid carcinoma. We also revealed a population of inflammatory CAFs that are distant to tumor cells and are found in the inflammatory stromal microenvironment of autoimmune thyroiditis. Together, our study provides spatial profiling of thyroid cancer evolution in samples with mixed WDTC/ATC histopathology and identifies a prognostic myCAF subtype with potential clinical utility in predicting aggressive disease in both children and adults.

Humans↗
PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗