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Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues.
The Tabula Sapiens is a reference human cell atlas containing single cell transcriptomic data from more than two dozen organs and tissues. Here we report Tabula Sapiens 2.0 which includes data from nine new donors, doubles the number of cells in Tabula Sapiens, and adds four new tissues. This new data includes four donors with multiple organs contributed, thus providing a unique data set in which genetic background, age, and epigenetic effects are controlled for. We analyzed the combined Tabula Sapiens data for expression of transcription factors, thereby providing putative cell type specificity for nearly every human transcription factor and as well as new insights into their regulatory roles. We analyzed the molecular phenotypes of senescent cells across the entire data set, providing new insight into both the universal attributes of senescence as well as those aspects of human senescence that are specific to particular organs and cell-types. Similarly, we analyzed sex-specific gene expression across all of the identified cell types and discovered which cell types and genes have the most distinct sex based gene expression profiles. Finally, to enable accessible analysis of the voluminous medical records of Tabula Sapiens donors, we created a web application powered by a large language model that allows users to ask general questions about the health history of the donors.
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Identification of a Gene Signature for Renal Cell Carcinoma-Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis.
Background: Cancer-associated fibroblasts (CAFs) are mainly involved in cancer progression and treatment failure. However, the specific signature of CAFs and their related clinicopathological parameters in renal cell carcinoma (RCC) remain unclear. Here, methods to recognize gene signatures were employed to roughly assess the infiltration of CAFs in RCC, based on the data from The Cancer Genome Atlas (TCGA). Weighted Gene Coexpression Network Analysis (WGCNA) was used to cluster transcriptomes and correlate with CAFs to identify the gene signature. Single-cell and cell line sequencing data were used to verify the expression specificity of the gene signature in CAFs. The gene signature was used to evaluate the infiltration of CAFs in each sample, and the clinical significance of each key gene in the gene signature and CAFs was analyzed. We observed that the CAF infiltration was higher in kidney cancer and advanced tumor stage and grade than in normal tissues. The seven key genes of the CAF gene signature identified using WGCNA showed high expression of CAF-related characteristics in the cell clustering landscape and fibroblast cell lines; these genes were found to be associated with extracellular matrix function, collagen synthesis, cell surface interaction, and adhesion. The high CAF infiltration and the key genes were verified from the TCGA and Gene Expression Omnibus data related to the advanced grade, advanced stage, and poor prognosis of RCC. In summary, our findings indicate that the clinically significant gene signature may serve as a potential biomarker of CAFs in RCC, and the infiltration of CAFs is associated with the pathological grade, stage, and prognosis of RCC.
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Spatiotemporal mapping reveals Ccl8hi macrophages as key drivers of testicular inflammaging.
BACKGROUND: Testicular macrophages (TMs) are key regulators of testicular immune privilege and endocrine function in the testis. However, their age-related heterogeneity and role in testicular degeneration remain poorly characterized. METHODS: We performed spatial transcriptomics and FACS-enriched single-cell RNA sequencing (scRNA-seq) to characterize testicular macrophage heterogeneity across ageing. Key findings were validated through intratesticular injection of recombinant CCL8 protein, organotypic culture of seminiferous tubules, and immunofluorescence analysis. RESULTS: Using spatial transcriptomics, we identified pronounced Leydig niche senescence in aged testes, mechanistically linked to TM-mediated inflammatory remodelling. Coupling FACS-enriched TM isolation with scRNA-seq resolved seven transcriptionally distinct subpopulations, including ageing-associated subsets (Ccl8hi and Cxcl13hi). These subsets exhibited inflammatory signalling rewiring (e.g., CCL8-CCR2 axis) and activation of senescence transcriptional regulators (ASCL2, SPI1, CEBPB, JUNB), with conserved mediators (CCL8, TREM2, IL1β, and CXCL2) across murine and human testes. Functional validation showed that intratesticular injection of recombinant CCL8 protein in 3-month-old mice recapitulated ageing phenotypes, such as germ cell apoptosis and steroidogenic decline. CONCLUSIONS: Our multi-omics atlas highlights TM heterogeneity as a driver of testicular inflammaging and identifies CCL8 as a conserved target for therapeutic interventions aimed at mitigating age-associated male reproductive decline. KEY POINTS: Spatiotemporal multi‑omics establish inflammaging as a defining feature of testicular ageing. A refined CD74‑based sorting strategy improves enrichment of testicular macrophages. scRNA-seq resolves seven TM subsets with age‑dependent shifts. TM‑secreted inflammatory mediators-especially CCL8-drive testicular inflammaging.
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LncRNAs specifically overexpressed in endocervical adenocarcinoma are associated with an unfavorable recurrence prognosis and the immune response.
BACKGROUND: Cervical cancer is the fourth most common gynecological tumor in terms of both the incidence and mortality of females worldwide. Cervical squamous cell carcinoma (CSCC) accounts for 70-80% of cervical cancers, and endocervical adenocarcinoma (EAC) accounts for 20-25%. Unlike CSCC, EAC has worse clinical outcomes and prognosis. In this study, we explored the relationship between various types of long noncoding RNAs (lncRNAs) and pathological types of cervical cancer. METHODS: RNA sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) were used in this study. A single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE package were used to assess lncRNA activity and immune responses, respectively. RT-qPCR was performed to verify our findings. RESULTS: We explored the relationship between various types of lncRNAs and pathological types of cervical cancer. A series of long intergenic noncoding RNAs (lincRNAs) and antisense RNAs, which are the major types of lncRNAs, were identified to be specifically expressed in EAC and associated with a poor recurrence prognosis in patients with cervical cancer, suggesting that they might serve as independent prognostic markers of recurrence in patients with cervical cancer. RT-qPCR was performed to verify the 10 EAC-specific lncRNAs in cervical cancer samples we collected. Furthermore, the overexpression of these lncRNAs was positively correlated with EAC pathology levels but negatively correlated with immune responses in the microenvironment of cervical cancer. CONCLUSIONS: These lncRNAs potentially represent new biomarkers for the prediction of the recurrence prognosis and help obtain deeper insights into potential immunotherapeutic approaches for treating cervical cancer.
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Insights into the E2F target genes in breast cancer: associations of pathway genes with prognosis and immune cell filtration based on in silico and ex vivo analyses.
BACKGROUND: E2F transcription factors are crucial in various biological processes, including cell proliferation, differentiation, and apoptosis. However, the exact role of E2F target genes in breast cancer (BC), as well as their influence on survival and immune response, remains poorly understood. METHODS: To investigate the differential expression of E2F target genes and their relationship with patient prognosis and immune cell infiltration, transcriptomic data from the Cancer Genome Atlas database were analyzed. A risk model was developed to identify genes associated with survival. BC samples were clustered into high-expression (C1) and low-expression (C2) groups of E2F target genes. The correlation between gene expression and factors such as survival, immune cell infiltration (CD4 + and CD8 + T cells), and immune checkpoint inhibitors (PD-L1 and PD-L2) was analyzed. We analyzed the link between clusters and clinical characteristics using the chi-squared test. For further investigation, single-cell data from GSE243526 were utilized. For validation, the expression levels of JPT1 and TBRG4 were assessed using RT-qPCR in clinical samples. RESULTS: Genes targeting E2F, such as AURKB, JPT1, TBRG4, and KIF4A, showed increased expression linked to poor patient prognosis, regardless of clinical features. Kaplan-Meier survival analysis revealed that elevated expression of these genes correlated significantly with decreased survival rates and heightened mortality risk. Single-cell data confirmed that candidate genes exhibited higher expression in tumor-associated epithelial cells than healthy ones. Furthermore, samples from group C1 exhibited a lower survival rate than C2. Immune cell infiltration analysis determined that high expression of E2F target genes in the C1 subgroup was associated with diminished T cell infiltration and increased PD-L1 and PD-L2 expression. A strong and significant association was also identified between triple-negative breast cancer and the C1 cluster. RT-qPCR validation confirmed a significant elevation of JPT1 and TBRG4 expression levels relative to adjacent healthy tissues in BC. CONCLUSION: These findings suggest that E2F target genes, including JPT1 and TBRG4, may act as prognostic biomarkers and contribute to immune evasion in BC. E2F target genes can also offer good potential for classifying and treating patients.
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SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells.
The SARS-CoV-2 pandemic affecting the human respiratory system severely challenges public health and urgently demands for increasing our understanding of COVID-19 pathogenesis, especially host factors facilitating virus infection and replication. SARS-CoV-2 was reported to enter cells via binding to ACE2, followed by its priming by TMPRSS2. Here, we investigate ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue (twelve donors, 39,778 cells) and in cells derived from subsegmental bronchial branches (four donors, 17,521 cells) by single nuclei and single cell RNA sequencing, respectively. While TMPRSS2 is strongly expressed in both tissues, in the subsegmental bronchial branches ACE2 is predominantly expressed in a transient secretory cell type. Interestingly, these transiently differentiating cells show an enrichment for pathways related to RHO GTPase function and viral processes suggesting increased vulnerability for SARS-CoV-2 infection. Our data provide a rich resource for future investigations of COVID-19 infection and pathogenesis.
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Decoding the Lymphangioleiomyomatosis (LAM) Niche Microenvironment via Integrative Analysis of Single Cell Multiomics and Spatial Transcriptomics.
Lymphangioleiomyomatosis (LAM) is a rare, destructive lung disease caused by mutations in TSC1 or TSC2, leading to mTORC1 hyperactivation. While mTOR inhibitor sirolimus, the only FDA approved drug for this disease, stabilizes lung function in most LAM patients, the drug does not eliminate LAM cells, underscoring a critical gap in our understanding of the tumor microenvironment and cellular heterogeneity that drive disease progression. This study provides the first comprehensive multiomics atlas of the human LAM niche, integrating single-cell/nucleus RNA-seq, single-nucleus ATAC-seq, and spatial transcriptomics to deconvolute its complex architecture. We elucidate LAM cellular heterogeneity by identifying three distinct subtypes: the canonical, uterine smooth muscle-like, mTORC1-hyperactive LAMCORE1; a novel, fibroblast-like LAMCORE2 subtype with potent extracellular matrix (ECM) remodeling activity; and LAMCORE3, a substate of LAMCORE1 that shares LAM and myogenic signatures but is characterized by a lower transcriptional activity and specific functional enrichment in protein translation.Our analysis reveals the transcriptomic heterogeneity of the LAM subtypes, orchestrated by distinct transcriptional drivers and networks. Furthermore, we uncover spatially resolved LAM-associated fibroblast (LAF) states, LAF-seed and LAF-niche, that orchestrate TGF-β signaling, ECM deposition and remodeling, and niche expansion. Spatial mapping uncovers a structured ecosystem where LAMCORE1 cells form a central core enmeshed with the lymphatic endothelium, which is surrounded by LAFs, LAMCORE2 cells, and reprogrammed immune and epithelial cells. Findings were validated through multimodal imaging technologies. Present work advances the field by providing the first high-resolution blueprint of the LAM niche microenvironment, revealing novel cell states and crosstalk that identify promising therapeutic targets.
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A platform-independent AI tumor lineage and site (ATLAS) classifier.
Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.
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Integrating Clinical and Genetic Analysis of Perineural Invasion in Head and Neck Squamous Cell Carcinoma.
Introduction: Perineural invasion (PNI), a key pathological feature of head and neck squamous cell carcinoma (HNSCC), predicts poor survival. However, the associated clinical characteristics remain uncertain, and the molecular mechanisms are largely unknown. Materials and methods: HNSCC gene expression and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA). Prognostic subgroup analysis was performed, and potential PNI risk factors were assessed with logistic regression. PNI-associated gene coexpression modules were identified with weighted gene coexpression network analysis (WGCNA), and key module gene functions and the roles of non-malignant cells in PNI were evaluated with a single-cell transcriptomic dataset (GSE103322). Results: PNI was significantly inversely associated with overall survival (HR, 2.08; 95% CI, 1.27 to 3.40; P = 0.004), especially in advanced patients (HR, 2.62; 95% CI, 1.48 to 4.64; P < 0.001). Age, gender, smoking history, and alcohol history were not risk factors. HPV-positive cases were less likely than HPV-negative cases to develop PNI (OR, 0.28; 95% CI, 0.09 to 0.76; P = 0.017). WGCNA identified a unique significantly PNI-associated coexpression module containing 357 genes, with 12 hub genes (TIMP2, MIR198, LAMA4, FAM198B, MIR4649, COL5A1, COL1A2, OLFML2B, MMP2, FBN1, ADAM12, and PDGFRB). Single-cell transcriptomic data analysis revealed that the genes in the PNI-associated module correlated with the signatures "EMT," "metastasis," and "invasion." Among non-malignant cells, fibroblasts had relatively high expression of the key genes. Conclusion: At the molecular and omic levels, we verified that PNI in HNSCC is a process of invasion rather than simple diffusion. Fibroblasts probably play an important role in PNI. Novelty & Impact Statements The study is a thorough analysis of PNI in HNSCC from the clinical level to the molecular level and presents the first description of cancer-related PNI from the omics perspective to date as far as we know. We verified that PNI in HNSCC is a process of invasion rather than simple diffusion, at the molecular and omic levels. Fibroblasts were found to probably play an important role in PNI by analyzing single-cell transcriptomic data.
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Efferocytosis-related signatures identified via Single-cell analysis and machine learning predict TNBC outcomes and immunotherapy response.
Triple-negative breast cancer (TNBC) is characterized by poor prognosis and limited targeted treatment options. Efferocytosis, an essential immune mechanism for the clearance of apoptotic cells, is increasingly recognized as a key contributor to tumor immune evasion. This study aimed to identify key efferocytosis-related genes in TNBC, investigate their impact on the tumor microenvironment and immunotherapy responses, and construct a prognostic model to inform and optimize treatment strategies. RNA sequencing data and clinical information for patients with TNBC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Machine learning models were employed to derive efferocytosis-related signatures to predict clinical outcomes and immunotherapy responses. Eight efferocytosis-related genes were identified, considered efferocytosis-related gene signatures herein: P2RX1, IFNG, IL1A, CD93, XKR8, SIAH2, F2RL1, and TLR4. Using the individual risk scores derived from this model, patients were stratified into high- and low-risk groups, revealing significant differences in immune infiltration and immuno-therapy response. Our study highlights the predictive significance of efferocytosis in assessing chemotherapy sensitivity, emphasizing the pivotal role of the immune microenvironment in mediating drug resistance. Moreover, we identified potential targets for immunotherapeutic strategies in the treatment of TNBC.