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In Silico Single-Cell Frame work for Modeling Intestinal Stem and Transit-Amplifying Progenitor Cells Dynamics.
Single-cell RNA sequencing (scRNA-seq) has revolutionized the ability to resolve cellular heterogeneity within complex tissues, enabling the identification of discrete cell states. Here, we present an in silico analytical pipeline designed to characterize intestinal stem cells (ISC), transit-amplifying (TA) progenitors, and BEST4⁺ enterocyte precursors from human scRNA-seq datasets, with a focus on inflammatory contexts such as inflammatory bowel disease (IBD). The pipeline integrates dataset acquisition, quality control, normalization, dimensionality reduction, unsupervised clustering, and cell type annotation using a reference cell atlas. We implemented iterative subsetting and re-clustering of ISC and TA compartments to identify inflammation-associated subpopulations and epithelial biomarkers. While demonstrated in the context of IBD, this computational framework is broadly applicable to other tissues and pathological conditions where stem/progenitor dynamics underpin disease progression and tissue repair.
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Mendelian Randomization and Single-Cell Transcriptomic Analyses Implicate PPP1R1B as a Risk Factor of Intrahepatic Cholangiocarcinoma.
Background:PPP1R1B has been implicated in tumor progression in several malignancies; however, population-level genetic evidence supporting its role in intrahepatic cholangiocarcinoma (ICC) remains limited. This study aimed to investigate the association between PPP1R1B and ICC risk and prognosis using genetic and multi-omics analyses. Methods: Multiple public datasets were integrated, including finn-b-C3_LIVER_INTRAHEPATIC_BILE_DUCTS, ebi-a-GCST90018583, eqtl-a-ENSG00000131771, GSE138709, The Cancer Genome Atlas (TCGA)-ICC, and FU-iCCA. Two-sample Mendelian randomization (MR) was performed to evaluate the genetic association between PPP1R1B expression and ICC risk. PPP1R1B expression patterns were further characterized using single-cell and bulk transcriptomic analyses. Expression validation was conducted using TCGA-ICC data and tissue microarray immunohistochemistry. Survival analyses were performed using the FU-iCCA cohort and institutional clinical samples. Results: MR analysis identified PPP1R1B as significantly associated with increased ICC risk (p < 0.05, OR > 1), and sensitivity analyses supported the robustness of the results. Single-cell analysis demonstrated enrichment of PPP1R1B expression in malignant cell populations. Bulk transcriptomic datasets and immunohistochemical validation confirmed elevated PPP1R1B expression in tumor tissues. High PPP1R1B expression was significantly associated with poorer overall survival. Conclusions: Genetic and multi-omics evidence supports PPP1R1B as a risk-associated and potential prognostic biomarker in ICC, providing a translational genomics framework for future biomarker development. These findings complement previously reported functional studies and strengthen the biological relevance of PPP1R1B in ICC.
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A prognostic risk prediction model based on ferroptosis-related long non-coding RNAs in bladder cancer: A bulk RNA-seq research and scRNA-seq validation.
BACKGROUND: To construct a prognostic risk model of bladder cancer (BC) from the perspective of long non-coding RNAs (lncRNAs) and ferroptosis, in order to guide clinical prognosis and identify potential therapeutic targets. METHODS: In-hours BC samples were collected from 4 patients diagnosed with BC, who underwent radical cystectomy. Single cell transcriptome sequencing was performed and Seurat package were used for quality control and secondary analysis. LncRNAs expression profiles of BC samples were extracted from The Cancer Genome Atlas database. And sex, age, tumor, node, metastasis stage and other clinical data was downloaded at the same time. Ferroptosis-related lncRNAs were identified by co-expression analysis. We constructed a risk model by Cox regression and least absolute shrinkage and selection operator regression analyses. The predictive strength of the risk model for overall survival (OS) of patients with BC was evaluated by the log-rank test and Kaplan-Meier method. Finally, the enrichment analysis was performed and visualized. RESULTS: We identified and included 15 prognostic ferroptosis-related lncRNAs (AL356740.1, FOXC2AS1, ZNF528AS1, LINC02535, PSMB8AS1, AL590428.1, AP000347.2, OCIAD1-AS1, AP001347.1, AC104986.2, AC018926.2, LINC00867, AC099518.4, USP30-AS1, and ARHGAP5-AS1), to build our ferroptosis-related lncRNAs risk model. Using this risk model, BC patients were divided into high and low-risk groups, and their respective survival lengths were calculated. The results showed that the OS of the low-risk group was significantly longer than that of the high-risk group. A nomogram was utilized to predict the survival rate of BC patients. As indicated in the nomogram, risk score was the most important indicator of OS in patients with BC. The ferroptosis-related lncRNAs risk model is an independent tool for prognostic risk assessment in patients with BC. Single cell transcriptome sequencing suggests that ferroptosis-related lncRNAs express specifically in BC tumor microenvironment. AL356740.1, LINC02535 and LINC00867 were mainly expressed in tumor cells. CONCLUSION: The risk model based on the ferroptosis-related lncRNAs and the genomic clinico-pathological nomogram could be used to accurately predict the prognosis of patients with BC. The lncRNAs used to build this model might become potential therapeutic targets in the future.
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SAD: Sparse-Aware Diffusion Model for Single-Cell Gene Expression Completion.
Single-cell RNA sequencing (scRNA-seq) is entering an era of foundation models that accept the complete gene atlas as input, yet most current datasets cover only 10-12 k genes and contain numerous technical zeros, severely limiting the generalization of these models in downstream tasks. To address this, we pioneer the gene-completion task for scRNA-seq and present SAD, a diffusion-based framework tailored to extremely sparse data, capable of completing genes and correcting sparsity bias under high missing rates. Unlike imputation or reconstruction methods that rely on the i.i.d. assumption, SAD's completion paradigm can generate gene entries originally absent from the expression profile, be aware of and rectify sparsity-distribution bias, and supply foundation models with consistent, reliable inputs of more than 30 k genes. Extensive benchmarks show that SAD significantly outperforms existing methods across multiple completion metrics, particularly in extreme scenarios with missing rates above 80%. This provides a data foundation for reusing missing scRNA-seq information and for precision-medicine applications.
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ScRNA-seq reveals the spatiotemporal distribution of camptothecin pathway and transposon activity in Camptotheca acuminata shoot apexes and leaves.
Camptotheca acuminata Decne., a significant natural source of the anticancer drug camptothecin (CPT), synthesizes CPT through the monoterpene indole alkaloid (MIA) pathway. In this study, we used single-cell RNA sequencing (scRNA-seq) to generate datasets encompassing over 60,000 cells from C. acuminata shoot apexes and leaves. After cell clustering and annotation, we identified five major cell types in shoot apexes and four in leaves. Analysis of MIA pathway gene expression revealed that most of them exhibited heightened expression in proliferating cells (PCs) and vascular cells (VCs). In contrast to MIA biosynthesis in Catharanthus roseus, CPT biosynthesis in C. acuminata did not exhibit multicellular compartmentalization. Some putative genes encoding enzymes and transcription factors (TFs) related to the biosynthesis of CPT and its derivatives were identified through co-expression analysis. These include 19 cytochrome P450 genes, 8 O-methyltransferase (OMT) genes, and 62 TFs. Additionally, these pathway genes exhibited dynamic expression patterns during VC and EC development. Furthermore, by integrating gene and transposable element (TE) expression data, we constructed novel single-cell transcriptome atlases for C. acuminata. This approach significantly facilitated the identification of rare cell types, including peripheral zone cells (PZs). Some TE families displayed cell type specific, tissue specific, or developmental stage-specific expression patterns, suggesting crucial roles for these TEs in cell differentiation and development. Overall, this study not only provides novel insights into CPT biosynthesis and spatial-temporal TE expression characteristics in C. acuminata, but also serves as a valuable resource for further comprehensive investigations into the development and physiology of this species.
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Gene-Chronos: parameter-efficient developmental time inference using a pretrained single-cell foundation model.
Large-scale single-cell and spatial transcriptomic atlases enable the study of developmental processes at high resolution. However, most datasets capture only static snapshots of cells, making it difficult to infer continuous biological time from transcriptomic profiles. Existing temporal inference methods often show limited robustness across heterogeneous datasets, and recent single-cell foundation models, although powerful for representation learning, are not designed to capture continuous temporal relationships. We present Gene-Chronos, a parameter-efficient framework for developmental time inference built on a frozen pretrained Geneformer backbone. The model introduces learnable temporal prompt tokens and a temporal contrastive objective to extract time-informative signals and encourage temporally coherent organization of cell representations. Across multiple benchmark datasets spanning diverse species and developmental stages, Gene-Chronos outperforms existing approaches and demonstrates strong generalization to previously unseen samples. Attention-based analyses further identify genes associated with developmental progression, providing interpretable insights into temporal gene expression dynamics.
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Single-cell profiling defines cellular ecosystems and malignant cell state coupling across pan-cancer tumor microenvironments.
Tumors are embedded within complex microenvironments composed of malignant, immune and stromal cells, yet how coordinated cellular organization shapes tumor biology across cancers remains incompletely understood. Here we integrate single-cell transcriptomic profiles from over two million cells spanning 19 human epithelial cancers to construct a comprehensive pan-cancer atlas of tumor microenvironment (TME) organization. We identify cell subsets selectively enriched in tumors compared with normal tissues and show that their relative abundance is associated with clinical outcomes. By clustering tumors according to their cellular composition, we define eight recurrent cellular ecosystem subtypes that capture coordinated variation in immune and stromal architectures across cancer types. Comparative analyses reveal that these ecosystem states are associated with distinct tumor-intrinsic molecular programs, including differences in signaling pathway activity, transcriptional regulation and copy number alterations. We further uncover subtype-specific patterns of ligand-receptor interactions linking malignant cells with immune and stromal compartments and provide independent spatial support for these interactions using spatial transcriptomic data. Together, this work provides a unified framework for tumor ecosystem stratification and highlights cellular organization as a key dimension of tumor heterogeneity with potential implications for precision cancer therapy.
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Single-cell transcriptomics of Pacific white shrimp (Litopenaeus vannamei) hepatopancreas reveal immune and metabolic responses to AHPND-causing Vibrio parahaemolyticus.
BACKGROUND: The shrimp aquaculture industry is severely impacted by acute hepatopancreatic necrosis disease (AHPND) caused by the bacterium Vibrio parahaemolyticus. The hepatopancreas is a multi-functional organ with roles in digestion, immunity, molting and reproduction. The mechanism by which the pathogen causes disease, and the host immune response is not completely understood. Therefore, we set out to characterize the cells of the hepatopancreas and host response to bacterial infection at single-cell resolution. METHODS: First, hepatopancreas from three healthy Pacific white shrimp (Litopenaeus vannamei) were sampled to produce a single-cell transcriptomic atlas. Then, the hepatopancreas from three V. parahaemolyticus infected and two mocked treated shrimp were sampled for an infection study. Primary cell suspensions were produced, and single-cell libraries were generated using the 10x Genomics Chromium X controller with GEM-X 3' gene expression reagents. Libraries were sequenced and data aligned to the shrimp reference genome using Cell Ranger. Seurat and clusterProfiler were used for downstream analyses. RESULTS: The atlas consists of 11,006 quality cells that were grouped into nine clusters, and represent the hepatopancreas epithelial cells, myocytes and hemocytes. The infection study generated 16,368 quality cells and was integrated with the atlas for 27,374 cells grouped into nine clusters. Cells from the infected shrimp exhibited expression of immune related genes including diverse pathogen recognition receptors and humoral proteins, including hemocyanin, proteases and C-type lectins. We also found that cells expressed factors that the PirA/B toxins present in the infective V. parahaemolyticus may bind to, such as fatty acid binding protein (Fabp). In response to infection, energy metabolism (oxidative phosphorylation) was altered in a cluster-dependent manner which may reflect immune or pathogenic processes. CONCLUSIONS: We characterized the cells types of the hepatopancreas and examined the transcriptomic response to a virulent isolate of V. parahaemolyticus, the causative agent of AHPND. Cells exhibited significant humoral immune responses suggesting the role of these genes in immune responsiveness to the pathogen. The outcomes will inform future functional studies and provide insights toward novel preventative measures or treatments.
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Mapping the Regulatory Landscape of Sheep Pituitary Gland Associated With Puberty at Single-Cell Resolution.
The pituitary gland plays a pivotal role in regulating puberty and reproductive physiology; however, the precise cellular and molecular mechanisms driving the pubertal transition in large animal, such as ewes, remain poorly understood. Here, we generated a comprehensive single-cell transcriptomic atlas of the ovine anterior pituitary, specifically comparing the pre-pubertal (3 month) and post-pubertal (6 month) stages. We identified 30 335 cells classified into ten distinct clusters. Comparative analysis revealed a global transcriptional reprogramming during puberty, characterized by a marked upregulation of genes associated with ribosome biogenesis, unfolded protein response, and hormone secretion across endocrine cells, reflecting an expanded biosynthetic capacity. Specifically, we identified SCG2 as a critical regulator of gonadotroph maturation. Functional validation demonstrated that SCG2 facilitates the biogenesis of secretory granules, thereby promoting FSH synthesis and secretion. Furthermore, intercellular communication analysis uncovered a distinct shift in the pituitary microenvironment: the 6 month pituitary exhibited enhanced regulatory networks, including IGF signaling mediated by non-endocrine cells and NT signaling (e.g., BDNF-NTRK2) driven by multiple cell types. These findings suggest that the onset of puberty relies on a coordinated "endocrine-to-endocrine" and "non-endocrine-to-endocrine" crosstalk. This study provides a high-resolution molecular blueprint of the pubertal transition, highlighting the key roles of biosynthetic machinery upgrades and microenvironmental remodeling in establishing the high reproductive performance of Hu sheep.
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OLFM4 orchestrates the immunosuppressive microenvironment and drives gallbladder cancer progression.
PURPOSE: Gallbladder cancer (GBC) is the most common biliary tract malignancy, characterized by complex microenvironment and poor prognosis. Olfactomedin-4 (OLFM4) has been identified as a key regulator of GBC progression. This study aims to comprehensively elucidate the molecular profiling, tumor microenvironment (TME) and underlying mechanisms through which OLFM4 drives GBC progression. METHODS: Single-cell RNA sequencing (scRNA-seq) was performed on tumor tissues and adjacent non-tumor tissues (ANT) from clinical GBC samples. We analyzed scRNA-seq data to characterize the landscape of heterogeneous TME and intricate intercellular communication networks of the single cells within GBC. Findings were validated through in vitro experiments. RESULTS: Compared with ANT, GBC exhibited a distinct remodeling of the tumor microenvironment. Specifically, epithelial cells were markedly enriched in GBC (18.2% vs. 11.6% in ANT), accompanied by a pronounced reduction in endothelial cells (2.4% vs. 9.7%). In parallel, myeloid cells decreased from 16.8% in ANT to 11.5% in GBC, while fibroblasts remained comparably abundant in both conditions (21.1%). Within the OLFM4+epithelium, there was a significant enrichment of genes and pathways related to malignant progression, including signatures driving cell proliferation, stemness maintenance (WNT/β-catenin), and metastasis, as well as crucial molecules involved in TME remodeling (SPP1, TGF-β). Pseudotemporal trajectory analysis revealed an upregulation of OLFM4 expression correlating with disease progression, suggesting its close association with malignant transition. Cell-cell communication analysis identified OLFM4+epithelial cells as central signaling hubs, primarily communicating with stromal and immune cells via the SPP1-CD44 and SPP1-Integrin axes. Importantly, OLFM4 knockdown inhibited cell proliferation, migration, invasion, and gemcitabine chemoresistance in vitro, underscoring its central role in GBC progression. CONCLUSION: These findings offer a comprehensive insight into the atlas of molecular signatures and TME within GBC, identifying OLFM4 as a potential biomarker and therapeutic target for GBC.