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Comprehensive profiling of migratory primordial germ cells reveals niche-specific differences in non-canonical Wnt and Nodal-Lefty signaling in anterior vs posterior migrants.
Mammalian primordial germ cells (PGCs) migrate asynchronously through the embryonic hindgut and dorsal mesentery to reach the gonads. We previously found that interaction with different somatic niches regulates PGC proliferation along the migration route. To characterize transcriptional heterogeneity of migrating PGCs and their niches, we performed single-cell RNA sequencing of 13,262 mouse PGCs and 7,868 surrounding somatic cells during migration (E9.5, E10.5, E11.5) and in anterior versus posterior locations to enrich for leading and lagging migrants. Analysis of PGCs by position revealed dynamic gene expression changes between faster or earlier migrants in the anterior and slower or later migrants in the posterior at E9.5; these differences include migration-associated actin polymerization machinery and epigenetic reprogramming-associated genes. We furthermore identified changes in signaling with various somatic niches, notably strengthened interactions with hindgut epithelium via non-canonical WNT (ncWNT) in posterior PGCs compared to anterior. Reanalysis of a previously published dataset suggests that ncWNT signaling from the hindgut epithelium to early migratory PGCs is conserved in humans. Trajectory inference methods identified putative differentiation trajectories linking cell states across timepoints and from posterior to anterior in our mouse dataset. At E9.5, we mainly observed differences in cell adhesion and actin cytoskeletal dynamics between E9.5 posterior and anterior migrants. At E10.5, we observed divergent gene expression patterns between putative differentiation trajectories from posterior to anterior including Nodal signaling response genes Lefty1, Lefty2, and Pycr2 and reprogramming factors Dnmt1, Prc1, and Tet1. At E10.5, we experimentally validated anterior migrant-specific Lefty1/2 upregulation via whole-mount immunofluorescence staining for LEFTY1/2 and phosphorylated SMAD2/3, suggesting that elevated autocrine Nodal signaling in migrating PGCs occurs as they near the gonadal ridges. Together, this positional and temporal atlas of mouse PGCs supports the idea that niche interactions along the migratory route elicit changes in proliferation, actin dynamics, pluripotency, and epigenetic reprogramming.
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Deciphering immune heterogeneity in lung adenocarcinoma via machine learning-based Differential Phenotype Immune Score: TPX2 as a key biomarker for immunotherapy resistance.
BACKGROUND: Immune heterogeneity is a major determinant of clinical outcome and immunotherapy responsiveness in lung adenocarcinoma (LUAD). However, the tumor-intrinsic transcriptional programs that drive immune divergence across patients remain insufficiently characterized. METHODS: We constructed an integrated immune landscape of LUAD by combining bulk transcriptomic data, multi-omics profiling, and a large-scale single-cell atlas of non-small cell lung cancer. Immune subtypes were identified through integrative clustering approaches. A machine learning-derived Differential Phenotype Immune Score (DPIS) was developed to quantify immune-related phenotypic variation. Single-cell mapping, regulatory network inference, pan-cancer analyses, protein-level validation, and functional assays were conducted to interrogate key molecular drivers. RESULTS: Three recurrent immune states were identified, including the Wound Healing, IFN-γ Dominant, and Inflammatory subtypes, each exhibiting distinct immune compositions, metabolic features, signalling activities, and clinical trajectories. Although tumors classified as IFN-γ Dominant or Inflammatory showed comparable sensitivity to immune checkpoint blockade, their baseline prognoses differed substantially, suggesting that immune activation alone does not fully explain outcome heterogeneity. DPIS consistently stratified overall survival across six independent cohorts and was predominantly localized to highly proliferative malignant cells at single-cell resolution. Regulatory network analysis revealed that DPIS-high tumors were governed by cell cycle-associated transcriptional programs. Among the DPIS components, TPX2 emerged as a central regulator linking proliferative signalling to immune suppression, characterized by impaired antigen presentation, reduced immune cell infiltration, and unfavorable immunotherapy responses. Functional experiments further demonstrated that TPX2 promotes tumor cell proliferation, migration, and resistance to apoptosis. CONCLUSION: This study identifies a proliferation-driven immune suppression program in LUAD, establishes DPIS as a robust and clinically applicable framework for immune stratification, and highlights TPX2 as a potential therapeutic target for overcoming immune resistance.
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FAP+ fibroblasts orchestrate tumor microenvironment remodeling in renal cell carcinoma with tumor thrombus.
Tumor thrombus (TT) worsens prognosis and complicates surgery in renal cell carcinoma (RCC), yet its formation mechanisms remain unclear. Here, we perform integrative single-cell and spatial transcriptomic analyses on 71 tissues and 48 sections from RCC patients with or without TT. The cellular and spatial atlas reveals distinct TT-associated tumor microenvironment remodeling characterized by the enrichment of FAP+ fibroblasts. These FAP+ fibroblasts are spatially contiguous to aggressive cancer cells and promote their malignant phenotypes in vitro. Their abundance inversely correlates with functional NK cells, suggesting roles in tumor invasion and immune evasion. Furthermore, single-cell multiomics analysis identifies tumor pericytes as a source of FAP+ fibroblasts and delineates transcription factor dynamics underlying pericyte-fibroblast transition. Finally, high levels of FAP+ fibroblasts are associated with poor prognosis and predict a weaker response to anti-VEGF-based therapy. In conclusion, our study highlights FAP+ fibroblasts as drivers of aggressive RCC with TT, suggesting potential therapeutic targets.
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An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors.
Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.
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Angiogenesis-Informed Preoperative CT Radiogenomics Predicts Overall Survival in Clear Cell Renal Cell Carcinoma: Development and External Validation.
BACKGROUND/OBJECTIVES: We aimed to identify angiogenesis-related prognostic biomarkers and develop a radiogenomics model to predict overall survival (OS) in clear cell renal cell carcinoma (ccRCC), supporting risk stratification and potential therapeutic target discovery. METHODS: Bulk transcriptomes from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma cohort (TCGA-KIRC), seven Gene Expression Omnibus (GEO) microarrays, and a single-cell RNA sequencing (scRNA-seq) dataset were integrated to identify angiogenesis-related prognostic genes. Preoperative contrast-enhanced computed tomography (CT) images from The Cancer Imaging Archive Kidney Renal Clear Cell Carcinoma collection (TCIA-KIRC) were used for radiomics feature extraction, and a radiogenomics signature was constructed by linking radiomic features with transcriptomic risk patterns. Nine machine learning models were trained to predict OS; the best model was further evaluated in an independent external retrospective cohort. PDLIM1 (PDZ and LIM domain protein 1) was validated at the protein level, and conditioned medium from stable ccRCC cell lines was applied to human umbilical vein endothelial cells (HUVECs) for Matrigel tube formation assays. RESULTS: Five angiogenesis-related hub genes (PDLIM1, EMCN, ARPC1B, PLAT, and TIMP1) were identified. The extreme gradient boosting (XGBoost)-based radiogenomics model showed the best performance, with time-dependent concordance index (C-index) values of 0.880, 0.816, and 0.789 at 1, 3, and 5 years in the training set and 0.864, 0.758, and 0.736 in the internal validation set, respectively. In the external validation cohort, C-index values were 0.800, 0.726, and 0.703 at 1, 3, and 5 years. PDLIM1 protein was upregulated in ccRCC versus normal tissues. Functionally, PDLIM1 overexpression suppressed, whereas PDLIM1 knockdown promoted tube formation. CONCLUSIONS: This study developed and validated an angiogenesis-related radiogenomics model that accurately predicts OS in ccRCC patients and provides potential therapeutic targets for anti-angiogenic therapy.
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Grid2 interacting protein is a potential biomarker related to immune infiltration in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) is one of the three deadliest malignant tumors in the world, posing a severe hazard to human health. Nonetheless, the 5-year survival rate for advanced CRC remains unsatisfactory. Grid2 interacting protein (GRID2IP) is a Purkinje fiber postsynaptic scaffold protein implicated in a number of signal transduction pathways in the nervous system. Previous studies have shown that Grid2 is closely related to the occurrence and prognosis of gastric cancer and many other diseases. Therefore, we aim to identify the relationship between GRID2IP and the occurrence and prognosis of CRC. METHODS: Transcriptome data were retrieved from The Cancer Genome Atlas (TCGA) database to analyze the differential expression of GRID2IP in a variety of malignant tumors and then validate it by quantitative real time polymerase chain reaction(Q-PCR) and Western Blot in HT29 and SW480 cells. "DESeq2" package was used to analyze the differentially expressed genes (DEGs) between the high- and low-GRID2IP subgroups. In relation to DEGs, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. In addition, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were employed to examine DEGs-associated signaling pathways and GRID2IP-associated immune cell infiltration levels. Besides, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were compared between the two subgroups using a Kaplan-Meier analysis. In addition, a prognostic model for GRID2IP and clinical characteristics was developed using the univariate Cox regression method. The "pRRophetic" package was applied to predict the drug sensitivity of different subgroups. Moreover, we also performed single-cell analysis of GRID2IP using the TISCH database. RESULTS: GRID2IP is upregulated in CRC patients. The rise of GRID2IP inhibits the invasion of tumor-associated immune cells resulting in a lower immune score. In addition, high GRID2IP expression was associated with poor prognosis in different clinical subgroups. Analysis of single cells revealed that GRID2IP was predominantly expressed in immune cells, myofibroblasts, and cancerous cells. In terms of chemotherapy drug sensitivity, the subgroup with high GRID2IP expression was less sensitive to gemcitabine. CONCLUSIONS: Our results suggest that rising GRID2IP promotes tumor-associated immune cell infiltration and suggests adverse outcomes in CRC patients, which may be a useful biomarker for determining the prognosis of CRC and a potential target molecule for CRC therapy.
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Cell-type-specific transposon demethylation and TAD remodeling in aging mouse brain.
Aging is a major risk factor for neurodegenerative diseases, yet the underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that transposable element (TE) methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in topologically associated domain (TAD) boundary strength with enhanced accessibility at CCCTC-binding factor (CTCF) binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed deep-learning models that reliably predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. Age-related comparisons use a 2-month baseline reflecting the late-adolescent/early-young adult stage. This dataset advances our understanding of brain aging and offers potential translational applications.
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Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women.
The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates every menstrual cycle or upon tissue damage. Here, we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of five healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells, representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and messenger Ribonucleic Acid (mRNA) patterns of literature-based markers as a shared community resource. We identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type and potential cross-lineage multipotent stromal progenitors that may replenish the epithelial, stromal, and endothelial compartments. Furthermore, many cell types and subtypes exhibit shifts in cell number and transcriptomes across different phases of the menstrual cycle. Finally, comparisons between premenopausal, postpartum, and postmenopausal samples revealed substantial alterations in tissue composition, particularly in the proportions of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders.
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Tissue signatures of human macrophages during homeostasis and activation.
Human macrophages (MΦs) reside in tissues and develop tissue-specific identities. While studies in mice have identified molecular signatures for site-specific MΦ differentiation, less is known about the transcriptional profiles of human MΦs in distinct sites, including mucosal tissues and lymphoid organs during homeostasis and activation. Here, we use multimodal single-cell sequencing and ex vivo stimulation assays to define tissue signatures for populations of human MΦs isolated from lungs, small intestine, spleen, bone marrow, and lymph nodes obtained from individual organ donors. Our results reveal distinct tissue-adapted gene and protein profiles of metabolic, adhesion, and immune interaction pathways, which are specific to MΦs and not monocytes isolated from the same sites and exhibit homology to murine MΦs from the same sites. Tissue-adapted MΦs remained responsive to polarizing cytokine stimuli ex vivo, with upregulation of expected transcripts and secreted proteins, while retaining tissue-specific profiles. Patterns of chromatin accessibility in tissue MΦs identified from single-nucleus assay for transposase-accessible chromatin by sequencing reflected gene expression signatures and indicate that differential utilization of transcription factors may drive stable tissue-adapted profiles. Together, our findings show how human MΦ identity is coupled to their site of residence for mucosal and lymphoid organs and is intrinsically maintained during activation and polarization.
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A multi-dimensional omics framework identifies GPR35 as a driver of M2 macrophage activation and poor prognosis in colorectal cancer.
INTRODUCTION: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, with therapeutic outcomes heavily reliant on the tumor microenvironment (TME). While immunotherapy has revolutionized treatment for distinct subsets, the mechanisms driving immune evasion in the majority of patients remain elusive. METHODS: In this study, we constructed a comprehensive single-cell atlas of the CRC TME by integrating multi-cohort scRNA-seq data. RESULTS: Through non-negative matrix factorization (NMF), we identified nine intratumoral heterogeneity meta-programs (MPs), among which MP8 was robustly linked to M2 macrophage activation. High-dimensional WGCNA further pinpointed GPR35 as a master regulator within the MP8-associated gene network. Clinical analysis across four independent cohorts validated GPR35 as a significant predictor of poor prognosis. Functionally, GPR35 knockdown in vitro markedly impaired CRC cell proliferation, migration, and invasion. Mechanistically, high GPR35 expression orchestrated an immune-excluded microenvironment characterized by diminished cytotoxic T cell and NK cell recruitment, yet paradoxically elevated immune checkpoint expression. Furthermore, GPR35 expression was negatively correlated with eight established immunotherapy response signatures and associated with aggressive mutational landscapes. DISCUSSION: Collectively, our findings identify GPR35 as a novel cancer cell-intrinsic driver of immune evasion and immunotherapy resistance, positioning it as a promising therapeutic target to sensitize "cold" CRC tumors to immune checkpoint blockade.