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Transcriptomic profiles of single-cell autophagy-related genes (ATGs) in lung diseases.
Autophagy related genes (ATGs) play essential roles in maintaining cellular functions, although biological and pathological alterations of ATG phenotypes remain poorly understood. To address this knowledge gap, we utilized the single-cell sequencing technology to elucidate the transcriptomic atlas of ATGs in lung diseases, with a focus on lung epithelium and lymphocytes. This study conducted a comprehensive investigation into RNA profiles of ATGs in the lung tissues obtained from healthy subjects and patients with different lung diseases through single-cell RNA sequencing (scRNA-seq), including COVID-19 related acute lung damage, idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), systemic sclerosis (SSC), and lung adenocarcinoma (LUAD). Our findings revealed significant variations of ATGs expression across lung epithelial cell subsets, e.g., over-expression of MAPK8 in basal cells, ATG10 in club cells, and BCL2 in a goblet cell subset. The changes of autophagy-related pathways varied between lung epithelial and lymphocyte subsets. We identified the disease-associated changes in ATG expression, including significant alterations in BCL2, BCL2L1, PRKCD, and PRKCQ in inflammatory lung diseases (COPD and IPF), and MAP2K7, MAPK3, and RHEB in lung cancer (LUAD), as compared to normal lung tissues. Key ligand-receptor pairs (e.g., CD6-ALCAM, CD99-CD99) and signaling pathways (e.g., APP, CD74) might serve as biomarkers for lung diseases. To evaluate ATGs responses to external challenges, we examined ATGs expression in different epithelial cell lines exposed to cigarette smoking extract (CSE), lysophosphatidylcholine (lysoPC), lipopolysaccharide (LPS), and cholesterol at various doses and durations. Notable changes were observed in CFLAR, EIF2S1, PPP2CA, and PPP2CB in A549 and H1299 against CSE and LPS. The heterogeneity of ATGs expression was dependent on cell subsets, pathologic conditions, and challenges, as well as varied among cellular phenotypes, functions, and behaviors, and the severity of lung diseases. In conclusion, our data might provide new insights into the roles of ATGs in epithelial biology and pulmonary disease pathogenesis, with implications for disease progression and prognosis.
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Single-cell atlas in the living fossil Yangtze sturgeon provides insight into the evolution of fish.
BACKGROUND: Fish are the largest group of vertebrates. Studying the characteristics, functions, and interactions of different fish cells is important for understanding their roles in disease and evolution. However, most single cell RNA-seq studies in fish are restricted to a few specific organs, leaving a comprehensive cell landscape that aims to characterize the heterogeneity and connections among body-wide organs largely unexplored. RESULTS: We construct a single-cell atlas from the Yangtze sturgeon, Acipenser dabryanus, containing 82,401 high-quality cells and identify 72 major cell types. We compare differentially expressed genes among enterocytes in each part of the intestines and reveal the differences in absorption function of pyloric caecum, duodenum, spiral intestine, and rectum. Best4/Otop2 cells, newly identified in human, are also found in the intestines of sturgeon, indicating that these cells originated in an ancient period. We confirm the status of Müller glia as resident stem cells in fish nervous systems. We also characterize the different immune function between mucosa- and systemic-associated tissues. We detect several unexpected types of cells in Yangtze sturgeon, confirming that sturgeon is basal to teleosts. Cross-species analysis further indicates that sturgeon might occupy an ancient evolutionary position bridging the transition from cartilaginous to bony fish. In addition, novel cell markers, transcription factors, and intercellular communications are identified and found to be associated with the biological functions of specific types of cells. CONCLUSION: We generate the cellular landscape of an ancient fish and provide a valuable resource for revealing the cellular heterogeneity and biology of vertebrates.
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scImmOmics: a manually curated resource of single-cell multi-omics immune data.
Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.
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NR2F6, a new immune checkpoint that acts as a potential biomarker of immunosuppression and contributes to poor clinical outcome in human glioma.
INTORODUCTION: Nuclear receptor subfamily 2 group F member 6 (NR2F6) is a promising checkpoint target for cancer immunotherapy. However, there has been no investigation of NR2F6 in glioma. Our study systematically explored the clinical characteristics and biological functions of NR2F6 in gliomas. METHODS: We extracted RNA sequencing (RNA-seq) data of 663 glioma samples from The Cancer Genome Atlas (TCGA) as the training cohort and 325 samples from the Chinese Glioma Genome Atlas (CGGA) as the validation cohort. We also confirmed the NR2F6 gene expression feature in our own cohort of 60 glioma patients. R language and GraphPad Prism softwares were mainly used for statistical analysis and graphical work. RESULTS: We found that NR2F6 was significantly related to high tumor aggressiveness and poor outcomes for glioma patients. Functional enrichment analysis demonstrated that NR2F6 was associated with many biological processes that are related to glioma progression, such as angiogenesis, and with multiple immune-related functions. Moreover, NR2F6 was found to be significantly correlated with stromal and immune infiltration in gliomas. Subsequent analysis based on Gliomas single-cell sequencing datasets showed that NR2F6 was expressed in immune cells, tumor cells, and stromal cells. Mechanistically, results suggested that NR2F6 might act as a potential immunosuppression-mediated molecule in the glioma microenvironment through multiple ways, such as the recruitment of immunosuppressive cells, secretion of immunosuppressive cytokines, M2 polarization of macrophages, in addition to combining with other immune checkpoint inhibitors. CONCLUSION: Our findings indicated that intracellular targeting of NR2F6 in both immune cells and tumor cells, as well as stromal cells, may represent a promising immunotherapeutic strategy for glioma. Stromal cells, may represent a promising immunotherapeutic strategy for glioma.
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Single cell approaches to address adipose tissue stromal cell heterogeneity.
A central function of adipose tissue is in the management of systemic energy homeostasis that is achieved through the co-ordinated regulation of energy storage and mobilization, adipokine release, and immune functions. With the dramatic increase in the prevalence of obesity and obesity-related metabolic disease over the past 30 years, there has been extensive interest in targeting adipose tissue for therapeutic benefit. However, in order for this goal to be achieved it is essential to establish a comprehensive atlas of adipose tissue cellular composition and define mechanisms of intercellular communication that mediate pathologic and therapeutic responses. While traditional methods, such as fluorescence-activated cell sorting (FACS) and genetic lineage tracing, have greatly advanced the field, these approaches are inherently limited by the choice of markers and the ability to comprehensively identify and characterize dynamic interactions among stromal cells within the tissue microenvironment. Single cell RNA sequencing (scRNAseq) has emerged as a powerful tool for deconvolving cellular heterogeneity and holds promise for understanding the development and plasticity of adipose tissue under normal and pathological conditions. scRNAseq has recently been used to characterize adipose stem cell (ASC) populations and has provided new insights into subpopulations of macrophages that arise during anabolic and catabolic remodeling in white adipose tissue. The current review summarizes recent findings that use this technology to explore adipose tissue heterogeneity and plasticity.
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Single-cell transcriptomic dissection of the cellular and molecular events underlying the triclosan-induced liver fibrosis in mice.
BACKGROUND: Triclosan [5-chloro-2-(2,4-dichlorophenoxy) phenol, TCS], a common antimicrobial additive in many personal care and health care products, is frequently detected in human blood and urine. Therefore, it has been considered an emerging and potentially toxic pollutant in recent years. Long-term exposure to TCS has been suggested to exert endocrine disruption effects, and promote liver fibrogenesis and tumorigenesis. This study was aimed at clarifying the underlying cellular and molecular mechanisms of hepatotoxicity effect of TCS at the initiation stage. METHODS: C57BL/6 mice were exposed to different dosages of TCS for 2 weeks and the organ toxicity was evaluated by various measurements including complete blood count, histological analysis and TCS quantification. Single cell RNA sequencing (scRNA-seq) was then carried out on TCS- or mock-treated mouse livers to delineate the TCS-induced hepatotoxicity. The acquired single-cell transcriptomic data were analyzed from different aspects including differential gene expression, transcription factor (TF) regulatory network, pseudotime trajectory, and cellular communication, to systematically dissect the molecular and cellular events after TCS exposure. To verify the TCS-induced liver fibrosis, the expression levels of key fibrogenic proteins were examined by Western blotting, immunofluorescence, Masson's trichrome and Sirius red staining. In addition, normal hepatocyte cell MIHA and hepatic stellate cell LX-2 were used as in vitro cell models to experimentally validate the effects of TCS by immunological, proteomic and metabolomic technologies. RESULTS: We established a relatively short term TCS exposure murine model and found the TCS mainly accumulated in the liver. The scRNA-seq performed on the livers of the TCS-treated and control group profiled the gene expressions of > 76,000 cells belonging to 13 major cell types. Among these types, hepatocytes and hepatic stellate cells (HSCs) were significantly increased in TCS-treated group. We found that TCS promoted fibrosis-associated proliferation of hepatocytes, in which Gata2 and Mef2c are the key driving TFs. Our data also suggested that TCS induced the proliferation and activation of HSCs, which was experimentally verified in both liver tissue and cell model. In addition, other changes including the dysfunction and capillarization of endothelial cells, an increase of fibrotic characteristics in B plasma cells, and M2 phenotype-skewing of macrophage cells, were also deduced from the scRNA-seq analysis, and these changes are likely to contribute to the progression of liver fibrosis. Lastly, the key differential ligand-receptor pairs involved in cellular communications were identified and we confirmed the role of GAS6_AXL interaction-mediated cellular communication in promoting liver fibrosis. CONCLUSIONS: TCS modulates the cellular activities and fates of several specific cell types (including hepatocytes, HSCs, endothelial cells, B cells, Kupffer cells and liver capsular macrophages) in the liver, and regulates the ligand-receptor interactions between these cells, thereby promoting the proliferation and activation of HSCs, leading to liver fibrosis. Overall, we provide the first comprehensive single-cell atlas of mouse livers in response to TCS and delineate the key cellular and molecular processes involved in TCS-induced hepatotoxicity and fibrosis.
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Single-cell spatial transcriptomics of tertiary lymphoid organ-like structures in human atherosclerotic plaques.
Tertiary lymphoid organs have been identified in the arterial adventitia in both mouse models of atherosclerosis and patients with atherosclerosis, yet their role in the disease remains insufficiently explored. Here we present a spatially resolved single-cell transcriptome atlas of human atherosclerotic plaques, identifying 14 distinct cell types and providing evidence of plaque tertiary lymphoid organs (PTLOs). The development of PTLOs was associated with the expression of lymphangiogenic chemokine genes and the adhesion molecule gene in fibroblast-like smooth muscle cells. PTLOs harbor abundant B cells with expanded and diversified B cell receptors, suggesting substantial immune involvement. We also observed that B cells may be exchanged between PTLOs and perivascular adipose tissues. The presence of PTLO-like structures correlates with cerebrovascular events, which may be mediated by PTLO-derived IgG antibodies enhancing macrophage functional activity. Our findings suggest the existence and characteristics of PTLOs in human atherosclerosis, elucidating their cellular functions and clinical implications and offering avenues for understanding, diagnosing and treating this condition.
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A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.
Single-cell RNA sequencing has provided unprecedented insights into the cellular heterogeneity of acute and chronic pulmonary injury. Lack of data-driven analytical frameworks remains a significant barrier to reproducible and biologically robust analyses. To address this challenge, we developed a modular, interactive analysis platform for single-cell RNA sequencing data processing and visualization. As a case study, we applied the framework to an immune-enriched murine lung dataset containing samples from different batches and across four conditions: control, inflammation and remodeling, fibrosis, and fibrotic lung with ozone exposure. Manual annotations performed on individual samples were used as ground-truth labels to evaluate automated cell-type annotations generated using the Tabula Muris Senis and LungMAP reference atlases. These annotations were also utilized to benchmark the performance of data preprocessing and integration combinations on batch-effect removal and the preservation of biological identity. Within the LIANA framework, we evaluated eight inference algorithms and 18 references for cell-cell communication analysis. In our results, we outlined the framework of the analysis pipeline and interactive platform. Through manual annotation, we identified 36 distinct cell populations, highlighting the limitations of automated reference-based annotations in resolving cell states missing from underlying references - particularly injury-induced monocyte-derived macrophages and eosinophils. In our dataset, Log-normalization outperformed SCTransform in both preserving cell-type separation and batch mixing, while FastMNN provided the best balance between technical batch correction and the retention of cell subtype identities. Cell-cell communication analysis revealed that permutation-based algorithms used by CellChat and CellPhoneDB improved prediction confidence, while MouseConsensus and OmniPath resources increased interaction coverage. These findings demonstrate that preprocessing, integration, and cell annotation strategies should be selected in a context-dependent, data-driven manner rather than relying on default workflows. The annotated murine lung dataset expands the existing atlas, and the modular interactive platform offers a practical framework to improve accessibility, reproducibility, and analytical precision in pulmonary research.
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A senescence-based machine learning model prognosticates and personalizes therapy in cervical cancer.
BACKGROUND: Cervical cancer remains a major global health burden. This study aimed to characterize the role of cellular senescence in the tumor microenvironment (TME) and its clinical implications, filling the critical gap in understanding senescence landscapes in cervical cancer. METHODS: We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and multi-omics analyses to profile the senescence landscape in cervical cancer. A 17-gene senescence-related signature (SRS) was constructed from 266 key genes via Boruta feature selection, and a combined random survival forest (RSF) + gradient boosting machine (GBM) machine learning model was developed for prognostic stratification and validated in The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and GSE44001 cohorts. We further analyzed immune profiles, genomic alterations, and drug sensitivity across SRS-defined risk groups, and performed transcriptome RNA-seq, in vitro assays, and in vivo xenograft models to investigate the anti-tumor efficacy of LY3177833 alone or with paclitaxel in SiHa and HeLa cells. RESULTS: Single-cell analysis identified fibroblasts and cancer cells as the primary senescent populations linked to stromal remodeling. The SRS model stratified patients into high- and low-risk groups with distinct survival outcomes; high-risk patients showed metabolic reprogramming, enhanced pro-tumorigenic interactions, increased co-mutations, reduced immune checkpoint expression, and poorer therapy responses. Transcriptome analyses confirmed that the LY3177833-paclitaxel combination inhibited proliferation-related pathways (MITOTIC_SPINDLE and G2M_CHECKPOINT) in both cell lines, with in vitro assays showing LY3177833 induced senescence and synergized with paclitaxel via apoptosis, and in vivo models validating suppressed tumor growth. CONCLUSIONS: This study defines a senescence-rich microenvironment linked to aggressive cervical cancer biology. The SRS serves as a robust prognostic and predictive biomarker, providing a framework for personalized risk assessment. Furthermore, the LY3177833-paclitaxel combination exhibits synergistic anti-tumor effects, offering a promising therapeutic strategy.
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A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types.
The paraventricular hypothalamus (PVH) controls behavioral and physiologic processes, including appetite, social behavior, autonomic outflow, and pituitary hormone secretion. However, molecular markers for centrally projecting PVH neuron populations remain largely undefined, and a complete census of PVH cell types has not been established. Therefore, we performed extensive single-cell/nucleus RNA sequencing to catalog PVH neuron subtypes and multiplexed error-robust fluorescence in situ hybridization (MERFISH) to map them spatially. Our spatial transcriptomic atlas resolves 26 Sim1+ and 29 GABAergic neuron populations from the PVH and surrounding areas. Additionally, projection-based profiling identified neurons that project to the parabrachial region (PB) and spinal cord, helping to determine PVH populations that regulate satiety and sympathetic nervous system activity, respectively. Notably, activation of PB-projecting PVH neurons expressing Brs3 reduces food intake, and silencing them causes obesity. Together, this atlas contributes high-resolution PVH spatial and circuit-based gene expression profiles, representing a valuable resource for the field of homeostasis.