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Single cell RNA sequencing reveals ferritin as a key mediator of autoimmune pre-disposition in a mouse model of systemic lupus erythematosus.
Systemic lupus erythematosus (SLE) is a devastating autoimmune disorder characterized by failure of self-tolerance with resultant production of autoreactive antibodies. The etiology of this syndrome is complex, involving perturbations in immune cell signaling and development. The NZBWF1 mouse spontaneously develops a lupus-like syndrome and has been widely used as a model of SLE for over 60 years. The NZBWF1 model represents the F1 generation of a cross between New Zealand Black (NZB) and New Zealand White (NZW) mice. In order to better understand the factors that contribute to the development of autoimmunity, single cell RNA sequencing was conducted using the bone marrow from female NZBWF1 mice prior to the development of overt disease. The results were contrasted with single cell RNA sequencing results from the two parental strains. The expected findings of B cell abundance and upregulation, and evidence of interferon signaling were validated in this model. In addition, several novel areas of inquiry were identified. Most notably, the data showed a marked upregulation of the ferritin light chain across all cell types in the NZBWF1 mice compared to parental controls. This data can serve as a gene expression atlas of all hematopoietic cells in the NZBWF1 bone marrow prior to the development of autoimmunity.
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Integration of 117 machine learning algorithms and single-cell transcriptomics identifies macrophage polarization and ER stress signatures for cancer prognosis and precision therapy.
BACKGROUND: Macrophage polarization and endoplasmic reticulum (ER) stress play critical yet incompletely understood roles in cancer progression and therapeutic resistance. METHODS: Here, we conduct a systematic pan-cancer analysis of macrophage polarization and ER stress-related genes (MPERSRGs) by integrating multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), and single-cell RNA sequencing datasets across 33 cancer types. RESULTS: We identify distinct expression patterns of seven core MPERSRGs (CEBPB, NUPR1, ATF3, CASP3, TNFSF10, BRSK2, NOD2) that correlate significantly with tumor stage, immune infiltration, and patient prognosis. Employing 117 machine learning algorithm combinations, we develop a robust five-gene prognostic signature (FAM83A, RHOV, CPS1, STRIP2, SLC2A1) for lung adenocarcinoma (LUAD) with area under the curve values of 0.692, 0.688, and 0.614 for 1-, 3-, and 5-year overall survival, respectively. Single-cell transcriptomic analysis of 86,378 cells reveals three functionally distinct fibroblast subpopulations (MFAP5+, MATK+, HP+) with differential MPERSRG expression profiles, with MFAP5 + fibroblasts showing the highest enrichment in epithelial-mesenchymal transition and angiogenesis pathways. Cell-cell communication analysis identifies fibroblasts and epithelial cells as the most interactive populations, with the CLEC2C-KLRB1 ligand-receptor pair mediating the strongest signaling between mast cells and NK cells. Drug sensitivity predictions across multiple databases identify vorinostat, nilotinib, olaparib, and paclitaxel as potential therapeutic agents showing differential efficacy based on MPERSRG expression stratification. CONCLUSIONS: These findings establish MPERSRGs as key determinants of tumor-immune interactions and provide actionable biomarkers for risk stratification and precision therapy selection in cancer.
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Dissecting the dynamic cellular transcriptional atlas of adult teleost testis development throughout the annual reproductive cycle.
Teleost testis development during the annual cycle involves dramatic changes in cellular compositions and molecular events. In this study, the testicular cells derived from adult black rockfish at distinct stages - regressed, regenerating and differentiating - were meticulously dissected via single-cell transcriptome sequencing. A continuous developmental trajectory of spermatogenic cells, from spermatogonia to spermatids, was delineated, elucidating the molecular events involved in spermatogenesis. Subsequently, the dynamic regulation of gene expression associated with spermatogonia proliferation and differentiation was observed across spermatogonia subgroups and developmental stages. A bioenergetic transition from glycolysis to mitochondrial respiration of spermatogonia during the annual developmental cycle was demonstrated, and a deeper level of heterogeneity and molecular characteristics was revealed by re-clustering analysis. Additionally, the developmental trajectory of Sertoli cells was delineated, alongside the divergence of Leydig cells and macrophages. Moreover, the interaction network between testicular micro-environment somatic cells and spermatogenic cells was established. Overall, our study provides detailed information on both germ and somatic cells within teleost testes during the annual reproductive cycle, which lays the foundation for spermatogenesis regulation and germplasm preservation of endangered species.
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Multiomics and deep learning dissect regulatory syntax in human development.
Transcription factors establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility and regulating gene expression1. Mapping accessible chromatin offers critical insights into transcriptional control, but available datasets for human development are restricted to bulk tissue, single organs or single modalities2. Here we present the Human Development Multiomic Atlas, a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types and more than 1 million candidate cis-regulatory elements, many of which exhibit organ-specific in vivo enhancer activity. Deep learning models trained to predict accessibility from local DNA sequence unravel a comprehensive lexicon of motifs that influence accessibility, including composite motifs exhibiting distinct syntactic constraints that are predicted to mediate transcription factor cooperativity. We identify 'hard' syntactic rules requiring precise motif spacing and orientation, 'soft' rules allowing flexible motif arrangements, and ubiquitous motifs inhibiting accessibility. Model-based interpretation of genetic variants reveals that disruption of motifs with positive and negative effects is associated with concordant effects on gene expression. Our work delineates how motif syntax governs cell-type-specific chromatin accessibility and provides a foundational resource for decoding cis-regulatory logic and interpreting genetic variation during human development.
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Integrative analyses of bulk and single-cell transcriptomics reveals the infiltration and crosstalk of cancer-associated fibroblasts as a novel predictor for prognosis and microenvironment remodeling in intrahepatic cholangiocarcinoma.
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood. METHODS: To address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue. RESULTS: Through the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs. CONCLUSIONS: This study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.
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Single-nucleus profiling of the left ventricle of the mouse heart after chronic stress.
It is widely accepted that stress and cardiac disease are related, but the exact mechanism is still up for debate. Although stress has been scientifically confirmed as a causative factor, there is still a lack of qualitative and quantitative indicator systems for stress-induced cardiac injury. The forensic evidence frequently indicates that cases of sudden cardiac death are often preceded by prolonged exposure to chronic stress factors. However, the cellular responses and mechanisms that trigger and regulate these activities in the pathological and physiological processes of chronic stress are still poorly understood. The left ventricle (LV), as a critical target organ in cardiovascular diseases, still has unclear cellular heterogeneity under chronic stress. Using single-nucleus RNA sequencing (snRNA-seq), we established a cellular atlas of 99,154 LV cells (42,491 stress and 56,663 control), comprehensively profiling all major cardiac cell types. The resulting dataset not only provides a foundation for deciphering the molecular mechanisms underlying chronic stress in cardiovascular dysfunction but also enables the identification of potential biomarkers for future diagnostic and therapeutic strategies.
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Comprehensive analysis of the potential cuproptosis-related biomarker LIAS that regulates prognosis and immunotherapy of pan-cancers.
Lipoic acid synthetase (LIAS) has been demonstrated to play a crucial role in the progression of cancer. Exploring the underlying mechanisms and biological functions of LIAS could have potential therapeutic guidance for cancer treatment. Our study has explored the expression levels and prognostic values of LIAS in pan-cancer through several bioinformatics platforms, including TIMER2.0, Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2.0), and Human Protein Atlas (HPA). We found that a high LIAS expression was related to the good prognosis in patients with kidney renal clear cell carcinoma (KIRC), rectum adenocarcinoma (READ), breast cancer, and ovarian cancer. Inversely, a high LIAS expression showed unfavorable prognosis in lung cancer patients. In addition, the genetic alteration, methylation levels, and immune analysis of LIAS in pan-cancer have been evaluated. To elucidate the underlying molecular mechanism of LIAS, we conduct the single-cell sequencing to implicate that LIAS expression was related to hypoxia, angiogenesis, and DNA repair. Thus, these comprehensive pan-cancer analyses have conveyed that LIAS could be potentially significant in the progression of various cancers. Moreover, the LIAS expression could predict the efficacy of immunotherapy in cancer patients.
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Systematic CRISPRi perturbation of 1,408 autism risk genes maps multilevel transcriptional convergence in human cortical neurons.
Genetic heterogeneity in autism spectrum disorder (ASD) complicates the identification of shared molecular pathways amenable to therapeutic intervention. Here, we perform a CRISPRi Perturb-seq screen targeting 1,408 ASD risk genes in human embryonic stem cell-derived immature cortical neurons and profile the resulting transcriptomic effects by single-cell RNA sequencing. We identify 215 ASD risk genes whose repression induces significant global transcriptomic dysregulation. Leveraging this functional atlas, we characterize candidate genes based on transcriptional similarity to established ASD hub genes. We identify gene programs recurrently dysregulated across perturbations, anchored by processes governing microtubule dynamics, neuron differentiation, cell migration, and transmembrane transport. We further identify CHAMP1 as a previously unrecognized regulator of Wnt signaling and define specific ASD risk genes that modulate the rate of cortical neuron differentiation. Analysis of differentially expressed genes reveals both convergent and perturbation-specific downstream transcriptional responses. Together, these findings provide a multilevel map of transcriptomic convergence in ASD and establish a framework for identifying both pathway-level and genotype-specific therapeutic strategies.
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Integrative bulk and single-cell transcriptomic analysis identifies a migrasome-associated lncRNA signature predictive of prognosis and immune landscape in clear cell renal cell carcinoma.
INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is characterized by high recurrence and metastasis rates, leading to poor prognosis. Migrasomes, a class of organelles mediating intercellular communication, and long noncoding RNAs (lncRNAs) both play critical roles in tumor progression; however, the prognostic significance of migrasome-associated lncRNAs in ccRCC remains unclear. METHODS: Migrasome-associated lncRNAs were identified using The Cancer Genome Atlas (TCGA) dataset, and a prognostic risk signature was constructed. The associations between the model and overall survival (OS), functional enrichment, tumor mutation burden (TMB), tumor microenvironment (TME) characteristics, immune evasion, and drug sensitivity were evaluated. Single-cell transcriptomic analysis was performed to determine cell type-specific expression patterns and intercellular communication networks. Functional roles of key lncRNAs were validated in vitro using qRT-PCR, CCK-8 proliferation assays, wound-healing assays, Transwell assays, colony formation assays, immunofluorescence, and Western blotting. RESULTS: The risk signature stratified patients into high- and low-risk groups with significantly different survival outcomes. High-risk patients exhibited elevated TMB and enhanced immune evasion potential. Drug sensitivity analysis revealed distinct therapeutic response profiles between the groups. Single-cell transcriptomic analysis uncovered pronounced cellular heterogeneity and TME characteristics associated with the prognostic signature. High-risk cells were predominantly enriched within tumor epithelial clusters and displayed distinct intercellular communication patterns. Knockdown of FOXD2-AS1 markedly suppressed tumor cell proliferation and migration and reduced the expression of migrasome marker proteins. DISCUSSION: This study presents a novel migrasome-associated lncRNA risk signature with significant prognostic and therapeutic implications for ccRCC. The signature captures distinct immune, genomic, and pharmacologic features, and its core lncRNAs may promote tumor progression through migrasome-mediated signaling pathways, warranting further mechanistic investigation.
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Single-cell deconstruction of medulloblastoma microenvironment elucidates subtype-specific immune architectures and prognostic molecular signatures.
Medulloblastoma (MB), the most common malignant pediatric brain tumor, exhibits molecular heterogeneity classified into WNT, SHH, GP3, and GP4 subtypes, each with distinct prognoses and therapeutic vulnerabilities. Despite advances in multimodal therapies, treatment-related morbidity and incomplete understanding of tumor microenvironment (TME) heterogeneity impede precision medicine. Through integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic profiles from 38 MB specimens, we systematically mapped subtype-specific TME features. WNT MB demonstrated sparse immune infiltration but enriched supportive stromal cells (pericytes, astrocytes/oligodendrocytes) and elevated angiogenesis, correlating with superior prognosis. In contrast, SHH, GP3, and GP4 subtypes exhibited immunosuppressive TMEs dominated by tumor-associated macrophages (TAMs) and T cells. Myeloid subtyping revealed 11 functionally distinct TAM clusters: WNT-enriched microglia-derived M1-polarized subsets with anti-tumor activity, SHH-specific M2-like classical TAMs promoting immune evasion, and GP4-associated ECM-remodeling TAMs driving malignancy. T cell profiling identified CD8+ exhaustion and CD4+ regulatory T cell enrichment in non-WNT subtypes, underpinning immunosuppression. Pseudotime trajectory analysis uncovered myeloid differentiation routes from proliferative TAMs to terminal immunosuppressive subsets, regulated by subtype-specific transcription factors (XBP1, STAT6). CellChat analysis highlighted MIF-CD74 and MDK-LRP1 ligand-receptor pairs as key mediators of tumor-TME crosstalk in non-WNT subtypes. Secretome-receptor clustering stratified MB into three TME-driven subtypes: SHH (matrix remodeling), GP3/GP4 (neuronal differentiation), and WNT (angiogenesis), with tumor cells as primary secretory sources. Prognostic core genes (SHH: EMILIN3, CD163; GP3/GP4: SEMA3A, TULP1) predicted survival outcomes and demonstrated diagnostic specificity. This first comprehensive TME atlas of MB subtypes elucidate mechanisms of immunosuppressive niche formation and provides actionable core targets for precision immunotherapy.