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Integration of single-cell and bulk RNA sequencing data identified an aortic dissection-enriched ANGPTL4+ macrophage subpopulation.
BACKGROUND: Macrophages play an indispensable role in the pathogenesis of aortic dissection (AD). However, the underlying molecular and cellular mechanisms remain incompletely understood. METHODS: We integrated six publicly available single-cell RNA sequencing (scRNA-seq) datasets and constructed an atlas of AD, identifying an AD-enriched ANGPTL4+ macrophage subpopulation. Subsequent comprehensive bioinformatic analyses explored its potential mechanisms in AD. Using machine learning (ML) algorithms on integrated bulk RNA sequencing (bulk RNA-seq) data, we screened the signature genes of ANGPTL4+ macrophage and constructed a clinical prediction model. The correlation between the proportion of ANGPTL4+ macrophage and clinical features was investigated after CIBERSORTx deconvolution. Finally, we performed in vitro experiments to validate our findings. RESULTS: Bioinformatic analyses revealed that ANGPTL4+ macrophage was predominantly present in AD, exhibited a distinct glycolytic phenotype, represented an early monocyte-derived macrophage state in the dissected aorta, predicted to be regulated by PPARG, and showed inferred ligand-receptor interactions with structural cells through ANGPTL and SPP1 signaling pathways. ML algorithms identified ANGPTL4, PHLDA1, and SERPINE1 as signatures of ANGPTL4+ macrophage. The proportion of ANGPTL4+ macrophage was positively associated with clinical metrics like monocyte count. In vitro experiments validated the remarkable enrichment of ANGPTL4+ macrophage and considerable up-regulation and co-localization of the signatures. CONCLUSIONS: In-depth investigation into the potential mechanisms of ANGPTL4+ macrophage, an AD-enriched macrophage subpopulation, and its signatures ANGPTL4, PHLDA1, and SERPINE1, provides novel insights into the pathogenesis and prospective therapeutic targets of AD.
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Long-Read Single-Cell RNA Sequencing Reveals Dynamic Isoform Changes During Corneal Epithelial Wound Healing in Cynomolgus Monkeys.
PURPOSE: Corneal epithelial wound healing is essential for visual recovery after injury, yet the molecular mechanisms remain incompletely understood. Different RNA isoforms from the same gene may have distinct functions not captured by gene-level analyses. This study characterized isoform-level transcriptional changes during corneal epithelial wound healing in cynomolgus monkeys using long-read single-cell RNA sequencing (RNA-seq). METHODS: We profiled corneal limbal tissues from cynomolgus monkeys under uninjured conditions and at 1 day and 3 days post-injury using HIT-scISOseq, a long-read single-cell approach detecting full-length transcripts. Bioinformatic analyses examined differential isoform expression, usage, diversity, and switching across epithelial cell populations. Key isoforms were validated by RT-qPCR in scratch-wounded HCE-T cells and RNA-FISH in monkey corneal tissue. RESULTS: Analysis revealed widespread isoform-level changes during corneal epithelial repair that were not evident from gene expression alone. Cytoskeletal remodeling genes (TPM1 and DST) and stress-response genes (ERICH5 and CTSL) showed altered isoform usage despite relatively stable total expression. ARID5B and GSN exhibited cell-type-specific isoform switching across repair stages. We identified 27 genes with dynamic isoform diversity changes and 62 genes with significant isoform switching. RT-qPCR and RNA-FISH validation confirmed injury-induced expression dynamics and cell-type-specific localization of key isoforms. CONCLUSIONS: Isoform-level regulation represents an important and independent layer of transcriptional control during corneal epithelial wound healing. This single-cell isoform atlas provides a resource for identifying injury-associated transcripts and candidate regulatory pathways, offering a framework for future functional studies and isoform-focused therapeutic strategies.
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Single-Cell Transcriptomics Reveal Microenvironment Alterations in Canine Peri-Implantitis.
BACKGROUND: Peri-implantitis, a major contributor to dental implant failure, lacks comprehensive insights into tissue-specific heterogeneity as current researches predominantly focus on the whole peri-implant tissue rather than distinct molecular and cellular dynamics in gingiva and alveolar bone microenvironments. Furthermore, ethical challenges hinder the acquisition of healthy peri-implant tissues, limiting our understanding of peri-implantitis progression and the development of targeted therapies. METHODS: We established a controlled peri-implantitis model in beagle dogs, enabling ethical collection of healthy control tissues. Single-cell RNA sequencing (scRNA-seq) transcriptomics profiling was conducted on gingiva and alveolar bone tissues from diseased and healthy controls. Additionally, flow cytometry was utilized to further verify the identified subclusters and their involvement in peri-implantitis. RESULTS: Single-cell transcriptomic profiling unveiled a pronounced expansion of inflammation-associated cellular subsets in both gingival and alveolar bone microenvironments during peri-implantitis. Gingival tissues exhibited marked expansions in IL6+/IL18BP+ endothelial cell and CXCL8+ fibroblast, whereas APOD+ fibroblast dominated in peri-implantitis bone tissues. Gene-level profiling further identified upregulated pro-inflammatory chemokines (CXCL8, CXCL17, CCL24) within gingiva IL18BP+ endothelial cells. Notably, we discovered a unique ligand-receptor interaction C3 (APOD+ fibroblast)-C3AR1 (monocyte/macrophage) in alveolar bone tissue, implicating complement-dependent signaling in immune crosstalk. CONCLUSIONS: Our study provides the first comparative atlas of soft/hard tissue remodeling in peri-implantitis at single-cell resolution. The expansion of IL6+/IL18BP+ endothelial cell and CXCL8+ fibroblast in gingiva, alongside APOD+ fibroblast-driven C3-C3AR1 signaling in alveolar bone, highlights distinct microenvironmental reprogramming between soft and hard tissues. These findings not only identify potential therapeutic targets but also validate the translational relevance of the canine model for peri-implantitis research.
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Single-cell multiomic human brain atlas reveals regulatory drivers of cortical regionality.
Distinct regional functionality of the human cortex is orchestrated by diverse cellular and molecular processes, yet the underlying regulatory mechanisms remain poorly understood. We performed multiomic single-cell and spatial characterization of nine regions of the human cortex to define the gene regulatory networks and transcription factors that govern cell-type and region specificity. With the combined data of over three million cells, two striking patterns of cortical neuron specialization were uncovered: a rostral-caudal spatial pattern of calcium regulatory machinery, and subunit switching of multiple signaling receptor families across the transmodal-sensory axis. Gene regulatory network analysis revealed putative transcriptional regulators of cortical neuron specialization with cell-type- and region-specific gene regulation patterns. While regionalization was observed in gene expression, chromatin accessibility, and spatial distributions, these modalities exhibited distinct cortical patterns. Our findings illuminate critical neuronal pathways that vary throughout the cortex and the gene regulatory networks that establish cortical regionalization in the human brain.
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Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.
BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.
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Construction of a Single-cell Atlas of Thyroid Cancer.
INTRODUCTION: Differentiated thyroid cancer generally has a favorable prognosis; however, the cure rate remains low for patients with metastatic or undifferentiated thyroid cancer. Moreover, this group of patients exhibits diverse responses to different treatments. To address this, single- cell RNA sequencing (scRNA-seq) offers an unbiased approach to reveal the heterogeneity within and between tumor cells. Using, scRNA-seq, we aimed to explore the intricate ecosystem of thyroid cancer, potentially providing novel insights into clinical cancer staging and treatment strategies. METHODS: We conducted a thorough analysis by screening thyroid cancer and paraneoplastic tissues from 20 patients sourced from the Gene Expression Omnibus database. The dataset comprised 11 primary tumor tissues, 6 paraneoplastic tissues, 8 metastatic lymph nodes, and 2 distant metastases of papillary thyroid cancer. Through comprehensive bioinformatic analyses, we constructed a panoramic single-cell atlas of thyroid cancer (THCA). RESULTS: Our findings revealed significant heterogeneity in gene expression among tumor cells from different patients with THCA, contributing to the development of a comprehensive single- cell landscape. Notably, the long noncoding RNA (lncRNA) gene XIST exhibited higher abundance in anaplastic thyroid cancer (ATC) tumor cells. Additionally, we identified an enriched m6A locus in lncRNA XIST and observed high expression of the m6A “reader” IGF2BP3, as well as low expression of the “encoder” VIRMA. Based on these observations, we hypothesized that IGF2BP3 and VIRMA could augment the expression of lncRNA XIST, thereby promoting the malignant proliferation and invasion of ATC. CONCLUSION: By leveraging scRNA-seq technology, our study sheds light on the intricate molecular characteristics of THCA lesions. These findings have the potential to revolutionize our understanding of thyroid cancer pathogenesis and pave the way for innovative therapeutic interventions.
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Integrated single-cell and spatial transcriptomic atlas of multi-etiology acute lung injury reveals etiology-dependent neutrophil fate decisions and a prognostic neutrophil-monocyte/macrophage interaction signature.
Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a life-threatening syndrome with heterogeneous etiologies and no effective pharmacological therapy. Although neutrophils are central mediators of lung injury, their functional diversity and interplay with monocyte/macrophage (Mo/MΦ) populations across etiologies remain poorly defined. Here, we integrated single-cell RNA-sequencing profiles from 180,031 murine lung cells across seven ALI models spanning infectious, sterile, and extrapulmonary insults to construct a multi-etiology neutrophil atlas. Augur identified neutrophils as the most transcriptionally perturbed immune population. Gene-set variation analysis revealed etiology-specific programs, including NF-κB/IL-6-driven inflammation in infection/sepsis, dual interferon responses in influenza, tissue-remodeling and stress-adaptive programs in bleomycin injury, and ROS-dominated injury with metabolic reprogramming after radiation. Spatial transcriptomics further mapped the spatiotemporal dynamics of neutrophil reprogramming during influenza-induced lung injury. High-resolution re-clustering resolved 13 neutrophil subtypes organized into four macro-states, and pseudotime analysis uncovered an etiology-dependent bifurcation from a bone marrow-like immature origin toward either an IFN/inflammasome-associated effector branch or an NF-κB-driven inflammatory branch, with sepsis and bacterial infection retaining neutrophils near the immature origin. In parallel, we defined 12 Mo/MΦ subtypes and derived a 26-gene Neutrophil-Monocyte/Macrophage Interaction Index with prognostic value for sepsis mortality. Importantly, immunobiological validation showed that conditioned media from LPS-, P. aeruginosa- and CLP-derived neutrophils contained elevated THBS1 and activated NF-κB-MAPK-NLRP3 signaling in macrophages, whereas blockade of THBS1-CD36 with CD36 peptide P(93-110) attenuated inflammatory activation, promoted reparative polarization, and reduced circulating neutrophils in vivo. Together, these findings define an etiology-resolved atlas of ALI and identify THBS1-CD36 as a mechanistically relevant therapeutic target in infection- and endotoxin-driven ALI.
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Systematic evaluation of the isolated effect of tissue environment on the transcriptome using a single-cell RNA-seq atlas dataset.
BACKGROUND: Understanding cellular diversity throughout the body is essential for elucidating the complex functions of biological systems. Recently, large-scale single-cell omics datasets, known as omics atlases, have become available. These atlases encompass data from diverse tissues and cell-types, providing insights into the landscape of cell-type-specific gene expression. However, the isolated effect of the tissue environment has not been thoroughly investigated. Evaluating this isolated effect is challenging due to statistical confounding with cell-type effects, which arises from the highly limited subset of tissue-cell-type combinations that are biologically realized compared to the vast number of theoretical possibilities. RESULTS: This study introduces a novel data analysis framework, named the Combinatorial Sub-dataset Extraction for Confounding Reduction (COSER), which addresses statistical confounding by using graph theory to enumerate appropriate sub-datasets. COSER enables the assessment of isolated effects of discrete variables in single cells. Applying COSER to the Tabula Muris Senis single-cell transcriptome atlas, we characterized the isolated impact of tissue environments. Our findings demonstrate that some genes are markedly affected by the tissue environment, particularly in modulating intercellular diversity in immune responses and their age-related changes. CONCLUSION: COSER provides a robust, general-purpose framework for evaluating the isolated effects of discrete variables from large-scale data mining. This approach reveals critical insights into the interplay between tissue environments and gene expression.
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Single-cell transcriptomic atlas of ovarian endometriosis patient's follicular fluid: unraveling novel insights into abnormal follicular development.
In brief: We present the first single-cell transcriptomic atlas of follicular fluid from an ovarian endometrioma (OMA) patient, revealing a novel TCR+ macrophage subtype, aberrant granulosa cell developmental trajectories, immune dysregulation, and enhanced MIF/MK signaling, providing new insights into mechanisms underlying impaired follicular development in OMA. Abstract: Ovarian endometrioma (OMA), an endometriosis subtype, involves hemorrhagic cysts formed by ectopic endometrial-like tissue, causing pelvic pain, inflammation, and infertility by disrupting ovarian function. Despite its impact, the single-cell dynamics in OMA follicular fluid are poorly understood. We performed single-cell RNA sequencing (10x Genomics) on follicular fluid from a patient's affected (2,793 cells) and unaffected (4,699 cells; control) ovaries. Analysis revealed four cell types, including a novel TCR+ macrophage subtype. Affected-side granulosa cells showed abnormal developmental trajectories linked to dysregulated steroid pathways. Immune cells exhibited heightened inflammation and reduced macrophage phagocytosis. Cell-cell communication highlighted prominent Macrophage Migration Inhibitory Factor and Midkine signaling from granulosa cells. This study provides the first single-cell transcriptomic profile of OMA follicular fluid, elucidating aberrant granulosa cell development, immune dysregulation, and distinctive cellular interactions, enhancing understanding of OMA's impact on follicular development and suggesting potential diagnostic/therapeutic targets.
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Identification of a lactylation-related gene signature in microsatellite stable gastric cancer based on bulk and single-cell RNA-seq.
BACKGROUND: Microsatellite stable (MSS) gastric cancer (GC) responds poorly to immunotherapy and exhibits heterogeneous outcomes. Histone lactylation plays a critical role in cancer progression. However, the prognostic and therapeutic potential of a lactylation-related gene signature (LRGS) in MSS GC remains largely unexplored. METHODS: Data of MSS GC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering based on lactylation-related gene expression profiles was performed to stratify patients. We further constructed the LRGS using machine learning algorithms. We also assessed its correlations with clinicopathological parameters, the tumor microenvironment, and chemosensitivity. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was employed to predict potential responses to immunotherapy. Single-cell analysis, cell-cell communication analysis, in silico knockout, and immunohistochemical validation were integrated to explore the functional roles of key genes. RESULTS: Consensus clustering uncovered two clusters with significantly different overall survival outcomes. A nine-gene LRGS was established and effectively stratified patients into high- and low-risk groups. The high-risk group displayed an immunosuppressive microenvironment, reduced chemosensitivity, and higher TIDE scores. Single-cell analysis revealed that LRGS scores were highest in cancer-associated fibroblasts (CAFs), with APOD and SERPINE1 highly expressed in PDGFRA+ CAFs and CA9+ CAFs, respectively. Cell-cell communication analysis showed that these two CAF subtypes exhibited distinct signaling patterns via the COLLAGEN and MIF pathways. In silico knockout further validated that APOD and SERPINE1 are essential for matrix remodeling and hypoxia adaptation in their respective CAF subtypes. Immunohistochemical analysis confirmed elevated protein levels of APOD and SERPINE1 in MSS GC. CONCLUSIONS: This study developed and validated a robust LRGS for MSS GC. This signature facilitates accurate prognosis prediction and shows potential to predict responses to both chemotherapy and immunotherapy.