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The injured sciatic nerve atlas (iSNAT), insights into the cellular and molecular basis of neural tissue degeneration and regeneration.
Upon trauma, the adult murine peripheral nervous system (PNS) displays a remarkable degree of spontaneous anatomical and functional regeneration. To explore extrinsic mechanisms of neural repair, we carried out single-cell analysis of naïve mouse sciatic nerve, peripheral blood mononuclear cells, and crushed sciatic nerves at 1 day, 3 days, and 7 days following injury. During the first week, monocytes and macrophages (Mo/Mac) rapidly accumulate in the injured nerve and undergo extensive metabolic reprogramming. Proinflammatory Mo/Mac with a high glycolytic flux dominate the early injury response and rapidly give way to inflammation resolving Mac, programmed toward oxidative phosphorylation. Nerve crush injury causes partial leakiness of the blood-nerve barrier, proliferation of endoneurial and perineurial stromal cells, and entry of opsonizing serum proteins. Micro-dissection of the nerve injury site and distal nerve, followed by single-cell RNA-sequencing, identified distinct immune compartments, triggered by mechanical nerve wounding and Wallerian degeneration, respectively. This finding was independently confirmed with Sarm1-/- mice, in which Wallerian degeneration is greatly delayed. Experiments with chimeric mice showed that wildtype immune cells readily enter the injury site in Sarm1-/- mice, but are sparse in the distal nerve, except for Mo. We used CellChat to explore intercellular communications in the naïve and injured PNS and report on hundreds of ligand-receptor interactions. Our longitudinal analysis represents a new resource for neural tissue regeneration, reveals location- specific immune microenvironments, and reports on large intercellular communication networks. To facilitate mining of scRNAseq datasets, we generated the injured sciatic nerve atlas (iSNAT): https://cdb-rshiny.med.umich.edu/Giger_iSNAT/.
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Coordinated transcriptional shifts in microglia and oligodendrocytes from neurodevelopment to inflammaging.
Glial cells shape brain development and aging, yet whether distinct glial lineages undergo coordinated lifespan transitions remains unclear. Although microglial aging states and oligodendrocyte vulnerability in neurodegeneration are well described, it remains unclear whether these programs evolve independently or in a temporally related manner. Here, we analyzed Mouse Cell Atlas single-cell RNA-seq data spanning embryogenesis to 24 months. Microglia and oligodendrocytes were the most transcriptionally dynamic glial populations, whereas astrocyte module activity was primarily observed during development. Microglia and oligodendrocytes exhibited similar late-life increases in inflammatory and senescence-associated modules, supported by cross-correlation (Pearson r ≈ 0.98; Max_CCF ≈ 0.98), dynamic time warping (DTW ≈ 6.16), and a large effect size (Cohen's d ≈ 11.08). These patterns were also supported by mixed glial contexts (p = 1.85 × 10⁻³). Together, these results identify a parallel transcriptional shift in microglia and oligodendrocytes from neurodevelopment to inflammaging and suggest that shared temporal programs across glial lineages may contribute to neurodegenerative vulnerability. This quantitative framework for assessing temporal similarity may be broadly applicable to other biological systems involving dynamic cellular state transitions.
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Single-cell analysis of TIGD genes in hepatocellular carcinoma: Prognostic value and functional characterization.
BACKGROUND: Hepatocellular carcinoma (HCC) is an aggressive cancer with limited therapies. Although transposable element-derived genes are increasingly linked to tumorigenesis, the role of the TIGD family in HCC remains unclear. This study examined the expression, clinical significance, and stemness-related features of TIGDs in HCC, emphasizing their prognostic and therapeutic potential. METHOD: TIGD expression was analyzed using TCGA data via GEPIA2, cBioPortal, and Kaplan-Meier Plotter, with protein validation from the Human Protein Atlas. Single-cell RNA-seq data (GSE242889) were used to examine TIGD expression across hepatic cell types. Survival and Cox regression analyses assessed prognostic value, while functional enrichment and immune infiltration analyses explored biological roles. TIGD5-high and TIGD5-low epithelial subsets were compared for functional features and cell-cell communication. RESULTS: TIGD1, 3, 4, 5, 6, and 7 were significantly upregulated in HCC. High TIGD4, TIGD5, and TIGD6 expression correlated with poorer overall survival and served as independent prognostic markers. TIGD5 exhibited the broadest and highest expression across immune and stromal compartments. Stratification of epithelial cells into TIGD5-high and TIGD5-low subsets revealed distinct functional phenotypes: TIGD5-high cells showed enrichment in extracellular matrix organization, growth factor signaling, and immune regulatory pathways, with enhanced communication probability within the tumor microenvironment. Notably, TIGD5+ epithelial cells demonstrated increased interaction strength and MIF signaling pathway engagement compared to TIGD5- counterparts. Functional analyses indicated roles in RNA splicing, DNA replication, and cell-cycle regulation. TIGDs also showed associations with immune infiltration, particularly Th2 cells. CONCLUSION: TIGD4, TIGD5, and TIGD6 exhibit oncogenic potential and may serve as prognostic biomarkers and therapeutic targets in HCC. Their stem-cell-associated expression highlights a novel connection between transposable element-derived genes and liver cancer stemness.
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An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation.
Characterizing senescent cells and identifying corresponding senescence-associated genes within complex tissues is critical for our understanding of aging and age-related diseases. We present DeepSAS, an intrinsic-hoc framework for elucidating the heterogeneity encoded in senescent cells and their associated genes from single-cell RNA-seq data using deep graph representation learning. Applied to both healthy eye cell atlas and in-house idiopathic pulmonary fibrosis datasets with Xenium spatial transcriptomics validation, DeepSAS reveals robust and biologically grounded senotypes and demonstrates superior benchmarking performance compared with existing methods.
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Fatty acid metabolism affects hepatocellular carcinoma progression via the PPAR-γ signaling pathway and fatty acid β-oxidation.
PURPOSE: This study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism. METHODS: RNA-seq and clinical data of HCC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatic analyses were employed to identify differentially expressed genes (DEGs) related to prognosis. A signature was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to classify HCC patients from the TCGA database into low-risk and high-risk groups. The predictive performance of the signature was evaluated through principal components analysis (PCA), Kaplan Meier (KM) survival analysis, receiver operating characteristics (ROC) curves, nomogram, genetic mutations, drug sensitivity analysis, immunological correlation analysis, and enrichment analysis. Single-cell maps were constructed to illustrate the distribution of core genes. Immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blot were employed to verify the expression of core genes. The function of one core gene was validated through a series of in vitro assays, including cell viability, colony formation, wound healing, trans-well migration, and invasion assays. The results were analyzed in the context of relevant signaling pathways. RESULTS: Bioinformatic analyses identified 15 FAMGs that were related to prognosis. A 4-gene signature was constructed, and patients were divided into high- and low-risk groups according to the signature. The high-risk group exhibited a poorer prognosis compared to the low-risk group in both the training (P < 0.001) and validation (P = 0.020) sets. Furthermore, the risk score was identified as an independent predictor of OS (P < 0.001, HR = 8.005). The incorporation of the risk score and clinicopathologic features into a nomogram enabled the effective prediction of patient prognosis. The model was able to effectively predict the immune microenvironment, drug sensitivity to chemotherapy, and gene mutation for each group. Single-cell maps demonstrated that FAMGs in the model were distributed in tumor cells. Enrichment analyses revealed that the cell cycle, fatty acid β oxidation and PPAR signaling pathways were the most significant pathways. Among the four key prognostically related FAMGs, Spermine Synthase (SMS) was selected and validated as a potential oncogene affecting cell cycle, PPAR-γ signaling pathway and fatty acid β oxidation in HCC. CONCLUSIONS: The risk characteristics based on FAMGs could serve as independent prognostic indicators for predicting HCC prognosis and could also serve as evaluation criteria for gene mutations, immunity, and chemotherapy drug therapy in HCC patients. Meanwhile, targeted fatty acid metabolism could be used to treat HCC through related signaling pathways.
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CTLA4-Mediated Immunosuppression in Glioblastoma is Associated with the Infiltration of Macrophages in the Tumor Microenvironment.
PURPOSE: CTLA4, the immune checkpoint, has been widely reported to contribute to immune evasion in anti-tumor activity. The inhibitors of CTLA4 provide a novel strategy to improve the outcome of peripheral cancer, but their clinical effects are limited in glioblastoma (GBM), thus the comprehensive role of CTLA4 needs to be addressed. PATIENTS AND METHODS: A total of 471 GBM cases were enrolled in this study from 5 cohorts. In our works, the Cancer Genome Atlas (TCGA) cohort was divided into the training set, and the Chinese Glioma Genome Atlas (CGGA), REMBRANDT, and GSE84465 cohorts were divided into validation sets. Tissues from our cohort were collected for histopathologic validation. Then, the role of CTLA4 in the TME of GBM was comprehensively investigated. RESULTS: Significant differences exist in immunological characteristics between the low and high CTLA4 expression groups. Mutation analysis found different genomic patterns associated with CTLA4 expression. Next, network analysis found the module named c1-1562 including CTLA4 correlated with over survival (OS) in GBM. We also developed a predictive model to calculate the risk score for every GBM case and the risk score was independently related to OS. Furthermore, the expression of CTLA4 was positively related to the infiltration level and function of macrophage in GBM TME based on seven independent algorithms, single-cell RNA-seq data and immunohistochemistry. CONCLUSION: These findings implicate that CTLA4 could serve as a novel target for prognosis and therapy in GBM patients. CTLA4-mediated immune suppression may be attributed to the infiltration of macrophages in the tumor microenvironment.
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A unified single-cell atlas of human lung provides insights into pulmonary diseases.
BACKGROUND: The human lung is a highly complex organ characterised by extensive cellular heterogeneity, making it susceptible to a broad range of diseases. Single-cell transcriptomics has shed light on disease-specific cellular features, but previous studies have been fragmented, limiting a unified understanding of cellular mechanisms across various lung diseases. Furthermore, high-resolution reference atlases for lung cells are lacking, impeding the effective integration of spatial omics data and the exploration of shared pathogenic mechanisms. METHODS: We constructed uniLUNG, the most comprehensive single-cell RNA sequencing atlas of the human lung, by integrating 62 published datasets comprising 9.2 million cells from 1807 donors across health and 17 disease conditions. We leveraged this high-resolution cell atlas to seek distinct cell types across diverse lung pathologies. By integrating spatial transcriptomics, we also identified transitional cell populations in lung cancer, and provided new insights into tumour evolution and the associated microenvironment. FINDINGS: We present a comprehensive lung cell atlas, encompassing cellular data across major lung diseases and health states. Using this resource, we identified distinct cell populations, such as Lym-monocytes and T-like B cells, which are specifically enriched in certain lung diseases and linked to immune dysregulation. Furthermore, our spatially resolved multi-omics analysis revealed a transitional malignant subpopulation, NSCLC-like SCLC, which plays a key role in the transformation from non-small cell lung cancer (NSCLC) to small cell lung cancer (SCLC), driving tumour microenvironment remodelling. INTERPRETATION: We offered a high-resolution, cross-disease lung cell reference that uncovers distinct cell types and cellular transitions critical to disease progression and therapeutic resistance. This resource provides essential insights into lung disease mechanisms and has important implications for the development of targeted therapeutic strategies, particularly in the context of lung cancer. FUNDING: This work was supported by the grants of National Key R&D Program of China (2021YFF1200900 and 2021YFF1200903), National Natural Science Foundation of China (92474107), Guangdong Basic and Applied Basic Research Foundation of China (2022B1515120077), Major Project of Guangzhou National Laboratory of China (GZNL2024A01003), and Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship of China (2020007).
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Single-cell transcriptomic analysis deciphers key transitional signatures associated with oncogenic evolution in human intramucosal oesophageal squamous cell carcinoma.
BACKGROUND AND AIMS: The early diagnosis and intervention of oesophageal squamous cell carcinoma (ESCC) are particularly important because of the lack of effective therapies and poor prognosis. Comprehensive research on early ESCC at the single-cell level is rare due to the need for fresh and high-quality specimens obtained from ESD. This study aims to systematically describe the cellular atlas of human intramucosal ESCC. METHODS: Five paired samples of intramucosal ESCC, para-ESCC oesophageal tissues from endoscopically resected specimens and peripheral blood mononuclear cells were adopted for scRNA-seq analysis. Computational pipeline scMetabolism was applied to quantify the metabolic diversity of single cells. RESULTS: A total of 164 715 cells were profiled. Epithelial cells exhibited high intra-tumoural heterogeneity and two evolutionary trajectories during ESCC tumorigenesis initiated from proliferative cells, and then through an intermediate state, to two different terminal states of normally differentiated epithelial cells or malignant cells, respectively. The abundance of CD8+ TEX s, Tregs and PD1+ CD4+ T cells suggested an exhausted and suppressive immune microenvironment. Several genes in immune cells, such as CXCL13, CXCR5 and PADI4, were identified as new biomarkers for poor prognosis. A new subcluster of malignant cells associated with metastasis and angiogenesis that appeared at an early stage compared with progressive ESCC was also identified in this study. Intercellular interaction analysis based on ligand-receptor pairs revealed the subcluster of malignant cells interacting with CAFs via the MDK-NCL pathway, which was verified by cell proliferation assay and IHC. This indicates that the interaction may be an important hallmark in the early change of tumour microenvironment and serves as a sign of CAF activation to stimulate downstream pathways for facilitating tumour invasion. CONCLUSION: This study demonstrates the changes of cell subsets and transcriptional levels in human intramucosal ESCC, which may provide unique insights into the development of novel biomarkers and potential intervention strategies.
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Intercellular communication atlas reveals Oprm1 as a neuroprotective factor for retinal ganglion cells.
The progressive death of mature neurons often results in neurodegenerative diseases. While the previous studies have mostly focused on identifying intrinsic mechanisms controlling neuronal survival, the extracellular environment also plays a critical role in regulating cell viability. Here we explore how intercellular communication contributes to the survival of retinal ganglion cells (RGCs) following the optic nerve crush (ONC). Although the direct effect of the ONC is restricted to the RGCs, we observed transcriptomic responses in other retinal cells to the injury based on the single-cell RNA-seq, with astrocytes and Müller glia having the most interactions with RGCs. By comparing the RGC subclasses showing distinct resilience to ONC-induced cell death, we found that the high-survival RGCs tend to have more ligand-receptor interactions with other retinal cells, suggesting that these RGCs are intrinsically programmed to foster more communication with their surroundings. Furthermore, we identified top 47 interactions that are stronger in the high-survival RGCs, likely representing neuroprotective interactions. We performed functional assays on one of the receptors, μ opioid receptor (Oprm1), a receptor known to play roles in regulating pain, reward, and addictive behavior. Although Oprm1 is preferentially expressed in intrinsically photosensitive retinal ganglion cells (ipRGCs), its neuroprotective effect could be transferred to multiple RGC subclasses by specific overexpressing Oprm1 in pan-RGCs in ONC, excitotoxicity, and glaucoma models. Lastly, manipulating Oprm1 activity improved visual functions and altered pupillary light response in mice. Our study provides an atlas of cell-cell interactions in both intact and post-ONC retina and an effective strategy to predict molecular mechanisms in neuroprotection, underlying the principal role played by extracellular environment in supporting neuron survival.
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Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response.
Diverse subtypes of renal cell carcinomas (RCCs) display a wide spectrum of histomorphologies, proteogenomic alterations, immune cell infiltration patterns, and clinical behavior. Delineating the cells of origin for different RCC subtypes will provide mechanistic insights into their diverse pathobiology. Here, we employed single-cell RNA sequencing (scRNA-seq) to develop benign and malignant renal cell atlases. Using a random forest model trained on this cell atlas, we predicted the putative cell of origin for more than 10 RCC subtypes. scRNA-seq also revealed several attributes of the tumor microenvironment in the most common subtype of kidney cancer, clear cell RCC (ccRCC). We elucidated an active role for tumor epithelia in promoting immune cell infiltration, potentially explaining why ccRCC responds to immune checkpoint inhibitors, despite having a low neoantigen burden. In addition, we characterized an association between high endothelial cell types and lack of response to immunotherapy in ccRCC. Taken together, these single-cell analyses of benign kidney and RCC provide insight into the putative cell of origin for RCC subtypes and highlight the important role of the tumor microenvironment in influencing ccRCC biology and response to therapy.