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Fibroblast Heterogeneity Links Inflammatory Microenvironments to Tissue Remodeling in Chronic Rhinosinusitis.
INTRODUCTION: Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disorder with distinct endotypes and tissue remodeling. Fibroblasts contribute to extracellular matrix remodeling and immune regulation, but their heterogeneity in CRS remains unclear. METHODS: Public single-cell transcriptomic data from 21 CRS and healthy control samples were integrated with bulk RNA-sequencing data from primary fibroblasts derived from nasal polyps stimulated with phosphate-buffered saline, interferon (IFN)-γ, interleukin (IL)-4, or IL-17A. Fibroblast subsets, pseudotime trajectories, and inferred communication between fibroblasts and immune cells were analyzed. Cytokine-induced signatures were projected onto the fibroblast atlas. A subset of longitudinal RNA-seq data from the COMPASS cohort (NCT05598424) was used to validate predefined candidate marker genes, including IL-4-associated markers of the C8 subset and IL-17A-associated markers of the C5/C10 subsets, during glucocorticoid treatment across V1 (baseline), V2 (after 4 weeks of intranasal corticosteroid treatment), and V3 (after subsequent systemic glucocorticoid therapy). Temporal expression changes were assessed using linear mixed-effects models with V1-V2 and V2-V3 comparisons. RESULTS: Twelve transcriptionally distinct fibroblast subsets (C0-C11) were identified across CRS subtypes. C8 fibroblasts were enriched in eosinophilic CRSwNP and associated with type 2 inflammation-related genes, including CCL26 and POSTN, whereas C5 and C10 were associated with non-eosinophilic CRSwNP- and IL-17A-related inflammatory features. Pseudotime analysis suggested two trajectories from C4 toward C8 and C10, respectively. CellChat revealed bidirectional communication between fibroblasts and immune cell subsets. Cytokine stimulation induced distinct transcriptional responses: IFN-γ mainly induced antigen presentation and interferon response; IL-4 induced contraction- and cytoskeleton-related features; and IL-17A activated IL-17, nuclear factor κB, and tumor necrosis factor signaling pathways. Signature projection linked IL-4-related features mainly to C8 fibroblasts and IL-17A-related features mainly to C5 and C10 fibroblasts. Longitudinal analysis of glucocorticoid-treated patients identified a stable C8-associated transcriptional signature characterized by sustained downregulation of CCL26, POSTN, CISH, and CCL13 across V1-V3 time points. CONCLUSIONS: Fibroblasts in CRS exhibit marked heterogeneity and may contribute to tissue remodeling and immune regulation in response to distinct inflammatory stimuli. The IL-4-associated C8 state and IL-17A-associated C5/C10 states may represent distinct fibroblast states associated with different CRS inflammatory microenvironments. The C8-associated transcriptional module shows stable and sustained suppression under glucocorticoid treatment.
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Age-determined expression of priming protease TMPRSS2 and localization of SARS-CoV-2 in lung epithelium.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) novel coronavirus 2019 (COVID-19) global pandemic has led to millions of cases and hundreds of thousands of deaths. While older adults appear at high risk for severe disease, hospitalizations and deaths due to SARS-CoV-2 among children have been relatively rare. Integrating single-cell RNA sequencing (scRNA-seq) of developing mouse lung with temporally resolved immunofluorescence in mouse and human lung tissue, we found that expression of SARS-CoV-2 Spike protein primer TMPRSS2 was highest in ciliated cells and type I alveolar epithelial cells (AT1), and TMPRSS2 expression increased with aging in mice and humans. Analysis of autopsy tissue from fatal COVID-19 cases detected SARS-CoV-2 RNA most frequently in ciliated and secretory cells in airway epithelium and AT1 cells in peripheral lung. SARS-CoV-2 RNA was highly colocalized in cells expressing TMPRSS2. Together, these data demonstrate the cellular spectrum infected by SARS-CoV-2 in lung epithelium and suggest that developmental regulation of TMPRSS2 may underlie the relative protection of infants and children from severe respiratory illness.
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Multiple pathways of CD34+ cell differentiation during embryogenesis.
CD34 has long been defined as a canonical marker for endothelial progenitors as well as hematopoietic stem cells, implicating its role in vascular development and hematopoiesis. However, the precise developmental hierarchy and lineage potential of CD34+ cells remain controversial. In this study, we integrated inducible genetic lineage tracing techniques, proteomics and single-cell RNA-seq (scRNA-seq) analyses to elucidate the dynamic developmental trajectory of CD34+ cells during various embryonic periods in both humans and mice. Remarkably, our analyses indicated that the progeny of CD34+ cells marked distinct, spatiotemporally restricted progenitor waves with divergent fates, at which point cells adopted endothelial, hematopoietic and fibroblastic fates, respectively. During gastrulation (E6.5-E8.5), an initial wave of CD34+ progenitors predominantly orchestrates vasculogenesis via a Kdr-dependent mechanism. Subsequently, from E9.5 to E14.5, cell cycle activation serves as a molecular switch, facilitating the endothelial-to-hematopoietic transition (EHT) of CD34+ progenitors. Unexpectedly, we identify a wave of CD34+ progenitors in late embryogenesis that gives rise to fibroblasts, distinct from earlier endothelial or hematopoietic lineages. Furthermore, because umbilical cord blood is a valuable source of different circulating stem/progenitor cells, we distinguish circulating endothelial progenitors from fibroblast progenitors in human cord blood by unique molecular signatures, with GFPT2 specifically marking the fibroblast progenitors. Collectively, our study provides a high-resolution spatiotemporal atlas of CD34+ cells during embryogenesis, redefining the temporal shifts of CD34+ cells in cell states and offering a precise framework for manipulating CD34+ cells in regenerative medicine.
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HOSCA: Human Ocular Single-Cell Atlas for Decoding Ocular Biology and Disease Complexity.
The human eye is a highly specialized organ composed of different tissue types, each with a unique cellular microenvironment critical to visual function. Recent advances in single-cell RNA sequencing (scRNA-seq) have provided unprecedented opportunities to explore the cellular heterogeneity of ocular tissues and diseases. However, the rapid accumulation of scRNA-seq data in ophthalmology poses challenges in integrating, aligning, and effectively reusing different datasets. Here, we present the Human Ocular Single-Cell Atlas (HOSCA), an atlas-scale curated database and analysis platform that integrates and harmonizes more than 3.88 million single-cell transcriptomic profiles from 704 human samples and 60 high-quality scRNA-seq datasets, covering 16 anatomical regions of the eye and 15 associated diseases. HOSCA provides user-friendly, interactive tools for conducting cross-tissue, cross-disease, and demographic comparative analyses using a unified bioinformatics pipeline for data standardization, cross-platform batch correction, and accurate cell type annotation. As the largest ocular single-cell resource to date, HOSCA bridges the gap between atlas-scale data and precision ophthalmology, providing a powerful platform for advancing our understanding of ocular biology and disease. HOSCA is publicly available at http://www.bio-data.cn/HOSCA/.
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Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.
BACKGROUND: Colon adenocarcinoma (COAD) is a common digestive system malignancy with high mortality and poor prognosis. Accumulating evidence indicates that choline metabolism is closely related to tumorigenesis and development. However, the efficacy of choline metabolism-related signature in predicting patient prognosis, immune microenvironment and chemotherapy response has not been fully clarified. METHODS: Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. Two choline metabolism-related genes (CHKB and PEMT) were identified by univariate and multivariate Cox regression analyses. TCGA-COAD was the training cohort, and GSE17536 was the validation cohort. Patients in the high- and low-risk groups were distinguished according to the optimal cutoff value of the risk score. A nomogram was used to assess the prognostic accuracy of the choline metabolism-related signature. Calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were used to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs in the high- and low-risk groups were performed. KEGG cluster analysis was conducted by the KOBAS-i database. The distribution and expression of CHKB and PEMT in various types of immune cells were analyzed based on single-cell RNA sequencing (scRNA-seq). The CIBERSORT and ESTIMATE algorithms evaluated tumor immune cell infiltration in the high- and low-risk groups. Evaluation of the half maximal inhibitory concentration (IC50) of common chemotherapeutic drugs based on the choline metabolism-related signature was performed. Small molecule compounds were predicted using the Connectivity Map (CMap) database. Molecular docking is used to simulate the binding conformation of small molecule compounds and key targets. By immunohistochemistry (IHC), Western blot, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiments, the expression levels of CHKB and PEMT in human, mouse, and cell lines were detected. RESULTS: We constructed and validated a choline metabolism-related signature containing two genes (CHKB and PEMT). The overall survival (OS) of patients in the high-risk group was significantly worse than that of patients in the low-risk group. The nomogram could effectively and accurately predict the OS of COAD patients at 1, 3, and 5 years. The DCA curve and CIC demonstrate the clinical utility of the nomogram. scRNA-seq showed that CHKB was mainly distributed in endothelial cells, while PEMT was mainly distributed in CD4+ T cells and CD8+ T cells. In addition, multiple types of immune cells expressing CHKB and PEMT differed significantly. There were significant differences in the immune microenvironment, immune checkpoint expression and chemotherapy response between the two risk groups. In addition, we screened five potential small molecule drugs that targeted treatment for COAD. Finally, the results of IHC, Western blot, and qRT-PCR consistently showed that the expression of CHKB in human, mouse, and cell lines was elevated in normal samples, while PMET showed the opposite trend. CONCLUSION: In conclusion, we constructed a choline metabolism-related signature in COAD and revealed its potential application value in predicting the prognosis, immune microenvironment, and chemotherapy response of patients, which may lay an important theoretical basis for future personalized precision therapy.
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Cancer cell-specific PD-L1 expression is a predictor of poor outcome in patients with locally advanced oral cavity squamous cell carcinoma.
BACKGROUND: Locally advanced oral cavity squamous cell carcinoma (OCSCC) presents a significant clinical challenge despite being partially responsive to standard treatment modalities. This study investigates the prognostic implications of programmed death-ligand 1 (PD-L1) expression in these tumors, focusing on its association with treatment outcomes and the immune microenvironment. METHODS: We assessed tumor-infiltrating lymphocytes (TILs) in 132 patients with OCSCC to evaluate their impact on survival. Multiplex immunohistochemistry staining for CD3, CD68, CD11c, PD-L1, and P40 was used to explore correlations with clinical outcomes in patients with early-stage (n=22) and locally advanced (n=36) OCSCC. These initial findings were validated through differential gene expression analysis, gene set enrichment, and immune cell deconvolution in a The Cancer Genome Atlas cohort of 163 locally advanced OCSCC tumors. Additionally, single-cell RNA sequencing (scRNA-seq) on a smaller cohort (n=10) further characterized the PD-L1hi or PD-L1lo cancer cells in these tumors. RESULTS: Elevated PD-L1 expression was associated with poor outcomes in patients with locally advanced OCSCC undergoing standard adjuvant therapy, irrespective of "hot" or "cold" classification based on TILs assessment. PD-L1hi tumors exhibited an active immune response phenotype, enriched with M1 macrophages, CD8+ T cells and T regulatory cells in the tumor microenvironment. Notably, the negative impact of PD-L1 expression on outcomes was primarily attributed to its expression by cancer cells, rather than immune cells. Furthermore, scRNA-seq revealed that immune interactions were not essential for PD-L1 upregulation in cancer cells, instead, complex regulatory networks were involved. Additionally, PD-L1lo locally advanced tumors exhibited more complex pathway enrichment and diverse T-cell populations compared with those in the early-stage. CONCLUSION: Our findings underscore the prognostic significance of PD-L1 expression in locally advanced OCSCC, and unveil the complex interplay between PD-L1 expression, immune responses, and molecular pathways in the tumor microenvironment. This study provides insights that may inform future therapeutic strategies, including the possibility of tailored immunotherapeutic approaches for patients with PD-L1hi locally advanced OCSCC.
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Population-level single-cell genomics reveals conserved gene programs in systemic juvenile idiopathic arthritis.
Systemic autoimmune and autoinflammatory diseases are characterized by genetic and cellular heterogeneity. While current single-cell genomics methods provide insights into known disease subtypes, these analysis methods do not readily reveal novel cell-type perturbation programs shared among distinct patient subsets. Here, we performed single-cell RNA-Seq of PBMCs of patients with systemic juvenile idiopathic arthritis (SJIA) with diverse clinical manifestations, including macrophage activation syndrome (MAS) and lung disease (LD). We introduced two new computational frameworks called UDON and SATAY-UDON, which define patient subtypes based on their underlying disrupted cellular programs as well as associated biomarkers or clinical features. Among twelve independently identified subtypes, this analysis uncovered what we believe to be a novel complement and interferon activation program identified in SJIA-LD monocytes. Extending these analyses to adult and pediatric lupus patients found new but also shared disease programs with SJIA, including interferon and complement activation. Finally, supervised comparison of these programs in a compiled single-cell pan-immune atlas of over 1,000 healthy donors found a handful of normal healthy donors with evidence of early inflammatory activation in subsets of monocytes and platelets, nominating possible biomarkers for early disease detection. Thus, integrative pan-immune single-cell analysis resolved what we believe to be new conserved gene programs underlying inflammatory disease pathogenesis and associated complications.
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Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.
The rapid growth of single-cell and spatial omics has outpaced computational methods capable of unifying these data into a cohesive framework for tissue atlas construction and cross-sample analysis. A critical bottleneck lies in the inability of existing tools to co-embed cells from diverse technologies-spanning transcriptomics, epigenomics, and proteomics-into a shared reference space while preserving spatial architecture and molecular specificity. Here, we present Garfield (Graph-based Contrastive Learning Enables Fast Single-Cell Embedding), a geometric deep-learning framework that addresses these challenges through spatially or molecularly aware cell embedding. Leveraging a graph contrastive learning framework, Garfield learns a shared embedding space for data generated by diverse technologies, enabling seamless construction and querying of spatial reference atlases. Our results show that Garfield consistently outperforms state-of-the-art benchmark models in identifying spatial niches across multiple datasets. We further demonstrate Garfield's versatility by applying it to multimodal spatial data, including gene expression and chromatin accessibility, where it successfully identifies distinct niches in the mouse brain. Notably, Garfield reveals tumor microenvironment heterogeneity in non-small cell lung cancer and breast cancer, uncovered conserved, barrier-like immune niches at tumor margins orchestrating CD80-mediated T cell-B cell-dendritic cell interactions and IFN-$\gamma$/B cell activation pathways, forming spatially coordinated immune surveillance hubs. These findings underscore Garfield's potential to advance spatial omics research by offering a robust, scalable solution for integrating and interpreting complex spatial data across diverse tissue types and modalities.
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Opportunities and challenges in the application of single-cell transcriptomics in plant tissue research.
Single-cell transcriptomics overcomes the limitations of conventional transcriptome methods by isolating and sequencing RNA from individual cells, thus capturing unique expression values for each cell. This technology allows unprecedented precision in observing the stochasticity and heterogeneity of gene expression within cells. However, single-cell RNA sequencing (scRNA-seq) experiments often fail to capture all cells and genes comprehensively, and single-modality data is insufficient to explain cell states and systemic changes. To address this, the integration of multi-source scRNA-seq and single-cell multi-modality data has emerged, enabling the construction of comprehensive cell atlases. These integration methods also facilitate the exploration of causal relationships and gene regulatory mechanisms across different modalities. This review summarizes the fundamental principles, applications, and value of these integration methods in revealing biological changes, and analyzes the advantages, disadvantages, and future directions of current approaches.
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Single-cell atlas of the liver myeloid compartment before and after cure of chronic viral hepatitis.
BACKGROUND & AIMS: Chronic viral infections present serious public health challenges; however, direct-acting antivirals (DAAs) are now able to cure nearly all patients infected with hepatitis C virus (HCV), representing the only cure of a human chronic viral infection to date. DAAs provide a valuable opportunity to study immune pathways in the reversal of chronic immune failures in an in vivo human system. METHODS: To leverage this opportunity, we used plate-based single-cell RNA-seq to deeply profile myeloid cells from liver fine needle aspirates in patients with HCV before and after DAA treatment. We comprehensively characterised liver neutrophils, eosinophils, mast cells, conventional dendritic cells, plasmacytoid dendritic cells, classical monocytes, non-classical monocytes, and macrophages, and defined fine-grained subpopulations of several cell types. RESULTS: We discovered cell type-specific changes post-cure, including an increase in MCM7+STMN1+ proliferating CD1C+ conventional dendritic cells, which may support restoration from chronic exhaustion. We observed an expected downregulation of interferon-stimulated genes (ISGs) post-cure as well as an unexpected inverse relationship between pre-treatment viral load and post-cure ISG expression in each cell type, revealing a link between viral loads and sustained modifications of the host's immune system. We found an upregulation of PD-L1/L2 gene expression in ISG-high neutrophils and IDO1 expression in eosinophils, pinpointing cell subpopulations crucial for immune regulation. We identified three recurring gene programmes shared by multiple cell types, distilling core functions of the myeloid compartment. CONCLUSIONS: This comprehensive single-cell RNA-seq atlas of human liver myeloid cells in response to cure of chronic viral infections reveals principles of liver immunity and provides immunotherapeutic insights. CLINICAL TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov (NCT02476617). IMPACT AND IMPLICATIONS: Chronic viral liver infections continue to be a major public health problem. Single-cell characterisation of liver immune cells during hepatitis C and post-cure provides unique insights into the architecture of liver immunity contributing to the resolution of the first curable chronic viral infection of humans. Multiple layers of innate immune regulation during chronic infections and persistent immune modifications after cure are revealed. Researchers and clinicians may leverage these findings to develop methods to optimise the post-cure environment for HCV and develop novel therapeutic approaches for other chronic viral infections.