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Found: 4 877
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The Single-Cell and Spatial Transcriptomics Atlas of Epithelial-Fibroblast Interactions in Colorectal Cancer.
BACKGROUND: Colorectal cancer (CRC) exhibits extensive cellular heterogeneity and complex tumor microenvironment (TME) interactions, which influence tumor progression and treatment response. However, the precise interplay between epithelial cells and cancer-associated fibroblasts and its prognostic relevance remain incompletely understood. METHODS: We performed single-cell RNA sequencing on CRC and matched normal tissues, integrating the data with spatial transcriptomics. Cellular heterogeneity, developmental trajectories, and intercellular communication were analyzed to identify key epithelial and fibroblast subpopulations. RESULTS: Nine epithelial and nine fibroblast subpopulations were identified, with FGGY + epithelial cells and COL11A1 + fibroblasts markedly enriched in tumors. Intercellular communication analysis revealed a tumor-specific epithelial-CAF axis mediated by PPIA-BSG signaling, accompanied by strong spatial co-localization. Trajectory and copy number variation (CNV) analyses indicated progenitor-like PLK1 + epithelial cells contribute to tumor plasticity. The FGCS score, derived from six genes from these two subsets, stratified patients by stage and survival, acting as an independent prognostic factor. CONCLUSION: This study provides a comprehensive, high-resolution view of CRC cellular and microenvironmental organization, revealing how epithelial-fibroblast interactions shape tumor progression and spatial niches, offering insights into CRC biology and potential therapeutic targets.
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Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.
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GeneExt: a gene model extension tool for enhanced single-cell RNA-seq analysis.
MOTIVATION: Incomplete gene models negatively impact single-cell gene expression quantification. This is particularly true in non-model species where often gene 3' ends are inaccurately annotated, while most scRNA-seq methods only capture the 3' transcript region. This results in many genes being incorrectly quantified or not detected. RESULTS: GeneExt leverages scRNA-seq data to refine gene annotations. We exemplify GeneExt usage and its impact on the gene expression quantification of eight non-model organism single-cell atlases. By extending and homogenizing gene annotations, our tool will help improve biological interpretation and cross-species comparisons of cell type expression atlases. AVAILABILITY: GeneExt is available at https://github.com/sebepedroslab/GeneExt (DOI: https://doi.org/10.5281/zenodo.18712940) under a GNU General Public license, together with test data and usage instructions.
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Decoding plants cell by cell: opportunities and challenges in single-cell and spatial biology.
Recent advances in single-cell and spatial omics technologies are transforming plant research by enabling cell-resolved analyses of gene expression, proteomics, chromatin organization, and metabolism at the level of individual cells and at spatial resolution. These approaches have revealed extensive cellular heterogeneity, rare and transient cell states, and previously unrecognized developmental and physiological programs. However, they also introduce conceptual, technical, and computational challenges related to data quality and integration, and biological interpretation. In this review, we summarize the emerging opportunities and key bottlenecks in plant single-cell and spatial biology. We discuss how embedding single-cell data within evolutionary developmental frameworks can accelerate the discovery of functionally relevant genes, and how spatial transcriptomics may enable comparative study designs, the study of cell-to-cell communication, and multi-organism spatial analyses. We also explore how integrating multiple datatypes and gene regulatory network (GRN) inference can move the field from descriptive atlases toward mechanistic insights. Finally, we highlight the importance of visualization strategies across spatial, temporal, environmental, and evolutionary scales. Overall, we argue that careful experimental design, robust data integration, and a clear understanding of cell identity are essential to obtain the full potential of plant single-cell and spatial omics in basic research and crop improvement.
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Single-Cell RNA Sequencing of Uterine Tissue from a 70-Day-Old Female Rabbit: Cellular Composition and Fibroblast-Associated Networks.
Background/Objectives: The cellular composition and molecular features of the rabbit uterus remain incompletely understood. This study aimed to provide an initial single-cell characterization of the cell populations, transcriptional features, and predicted communication networks identified in one rabbit uterine-body specimen. Methods: Single-cell RNA sequencing was performed on uterine-body tissue from one 70-day-old female Kangda V-line rabbit. Following quality control, cells were clustered and annotated using canonical markers. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, descriptive functional module scoring, CellChat, and high-dimensional weighted gene co-expression network analysis were used to characterize cell-associated expression patterns, predicted intercellular communication, and fibroblast co-expression modules. Results: A total of 9251 cells from this single specimen were retained and resolved into 16 preliminary clusters; 15 clusters were assigned to nine major cell types, whereas one small cluster remained unclassified. Fibroblasts were the predominant cell population among the analyzed cells (76.6%), followed by smooth muscle cells (10.9%). Functional analyses highlighted expression programs related to extracellular matrix organization, contractility, vascular function, epithelial barrier maintenance, and immune responses. CellChat analysis using an unadjusted empirical permutation p value < 0.05 predicted prominent COLLAGEN, LAMININ, CD99, MIF, and PTN signaling. Integration with fibroblast co-expression modules identified 21 candidate genes, including COL1A1, COL1A2, COL6A2, LAMC1, PTN, BMP2, and PDGFRA, linking fibroblast-associated expression programs with predicted extracellular-matrix, adhesion, vascular, and developmental relationships. Conclusions: This single-specimen study provides a preliminary cell-level reference for rabbit uterine tissue. The relatively high proportion of fibroblasts and the predicted communication patterns generate testable hypotheses for future studies using independent animals and functional validation.
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Exploring the regulatory mechanisms of paraptosis-related prognostic genes in gastric cancer using single-cell sequencing and transcriptome analysis.
Previous studies have suggested a potential role for paraptosis-related genes (PRGs) in cancer pathogenesis, yet their prognostic significance and therapeutic efficacy in gastric cancer (GC) remain largely unexplored. This study aims to investigate the function of PRGs in GC and offer valuable insights for clinical assessment of patient prognosis. Single-cell RNA sequencing (scRNA-seq) and the cancer genome atlas (TCGA)-GC transcriptomic data were acquired from public databases, with PRGs derived from Literature. Differential expressed genes 1 (DEGs1) were identified through scRNA-seq differential analysis. Prognostic genes were determined via regression analysis, followed by construction of prognostic models and nomograms to validate risk scores as independent predictors. Immune infiltration analysis assessed prognostic genes' impact on the tumor microenvironment. Cell trajectory analysis elucidated critical differentiation patterns between GC and normal groups, while cell communication analysis revealed interactions between key cellular subpopulations. In this study, 3,177 DEGs were identified from scRNA-seq. A risk model using 8 prognostic genes predicted GC patient survival outcomes effectively. These genes were also validated as independent prognostic factors in a nomogram. High-risk patients showed significantly increased immunotherapy resistance. T cells and fibroblasts were more differentiated in GC patients, while neutrophils had the highest interaction intensity in both GC and normal groups, with the CXCL8-CXCR2 interaction being most significant (p < 0.01). This study identified 8 prognostic genes (ABRACL, RANBP1, RAMP1, CCT2, AKR1A1, NPTN, ITGA8, CLU) and combined single-cell and transcriptome analysis to offer new insights into potential GC treatment strategies.
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Single-cell RNA and ATAC sequencing reveals cell subsets characteristics in adipose-derived stem cells.
The inherent heterogeneity of adipose-derived stem cells (ADSCs) complicates their characterization, as aggregated data obscure the nuanced states of individual cells and are skewed by dominant functional subpopulations affecting genetic and biological profiles. This heterogeneity presents a significant challenge for the effective deployment of ADSCs in clinical and research settings, highlighting the necessity for precise identification and isolation of specific subsets according to stringent criteria. Using single-cell RNA + ATAC multi-omics technology, we sequenced ADSCs derived from human adipose tissue through extraction, culture, and purification. This analysis identified three distinct subsets-proliferative, functional, and senescent-each exhibiting unique stemness properties. Notably, within the functional subset, cluster 6 emerged as a prime candidate for cellular engineering, showcasing robust stemness, high proliferative capacity, and low senescence. Our findings also reveal ADSCs' predisposition toward neuro-lineage differentiation, with their spatial distribution reflecting developmental trajectories and biological functions. In-depth analysis uncovered subset-specific genes with unique chromatin accessibility patterns, critical for targeted differentiation. Significantly, stemness markers BNC2 and HMGA2 were identified as indicators of non-senescent ADSCs. Through single-cell multi-omics sequencing, we have mapped a comprehensive cellular atlas of ADSCs, elucidating their transcriptional and chromatin profiles to unravel their complex heterogeneity. This atlas not only elucidates variations in composition, function, stemness, and developmental stages across subsets but also identifies essential biomarkers for ADSCs quality control, establishing a robust foundation for advancing ADSC-based therapeutic strategies.
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Single-cell and single-nucleus transcriptomics of the dorsal root ganglion in neuropathic pain: cell-state remodeling and translational prospects.
Neuropathic pain remains a major unmet clinical challenge, as current therapies provide limited efficacy and poor tolerability. A major obstacle to developing effective analgesics is the pronounced cellular and molecular heterogeneity of the dorsal root ganglion (DRG), which integrates neuronal, glial, immune, and stromal responses to injury. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have enabled cell-type-resolved analyses of these responses and revealed disease-associated cellular states that are obscured by bulk tissue profiling, thereby offering new opportunities to redefine disease mechanisms and therapeutic priorities. This review synthesizes evidence from single-cell studies of peripheral nerve injury, diabetic painful neuropathy, and chemotherapy-induced neuropathy. Although these conditions share common biological processes, including neuronal state remodeling, disrupted glial homeostatic support, context-dependent immune responses, and extracellular matrix reorganization, they do not converge on a single conserved molecular program. Peripheral nerve injury is characterized by neuronal injury and repair states, reactive and metabolic glial remodeling, and neuroimmune recruitment and crosstalk. Diabetic neuropathy is associated with altered sensory coding, impaired satellite glial lipid support, and neurodegenerative remodeling. Chemotherapy-induced neuropathy exhibits substantial agent-specific heterogeneity, including subtype-selective neuronal vulnerability and metalloproteinase-dysregulated satellite glial states following paclitaxel treatment, as well as sex-associated fibrotic remodeling following bortezomib treatment. These cellular states may serve adaptive, maladaptive, or degenerative functions. However, because most available studies rely on single-time-point or repeated cross-sectional sampling, they cannot directly establish temporal progression or causal relationships. We further examine how human DRG atlases bridge experimental models and human disease by determining whether candidate molecular targets and cell states are conserved and by identifying their cellular localization. These datasets also highlight species-specific differences in neuronal organization and non-neuronal transcriptional programs that may limit the direct translation of findings from rodent models. Consequently, the primary translational value of DRG single-cell studies lies in elucidating disease mechanisms and prioritizing candidate therapeutic targets for further investigation. Advancing these discoveries toward clinical application will require complementary evidence from human genetics, human DRG transcriptomic datasets, functional validation studies, and clinical pharmacology. Collectively, these complementary approaches may facilitate the development of mechanism-based and cell-type-informed analgesic strategies.
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Single-cell transcriptomic atlas of shrimp hepatopancreas reveals an endoplasmic reticulum stress-dependent immune response to Vibrio parahaemolyticus infection.
Innate immunity represents the sole immune defense system of crustaceans. Single-cell RNA sequencing (scRNA-Seq) has greatly advanced the exploration of invertebrate immune mechanisms, enabling a more detailed understanding of immune responses and their underlying molecular pathways. In this study, we employed single-cell nuclear sequencing (snRNA-Seq) to characterize the cellular atlas of the hepatopancreas in the whiteleg shrimp Litopenaeus vannamei in response to Vibrio parahaemolyticus infection. We captured over 20,000 high-quality cells and classified them into five distinct cell types (B, F, E, M, and R cells). Approximately 1400 to 2800 differentially expressed genes (DEGs) were identified in each of the five cell types. Although R cells exhibited the highest number of DEGs in response to bacterial infection, a substantial proportion of DEGs (approximately 33%) were shared across all five cell types. Further analysis revealed that these common DEGs were primarily enriched in endoplasmic reticulum (ER) stress and cell death-related processes. Concurrently, pseudotime trajectory analysis identified a cluster of DEGs highly associated with the antibacterial response, which showed a close relationship with integrin molecules. Finally, protein-level assessment (Western blot and immunohistochemistry) confirmed the presence and activation of ER stress, protein ubiquitination, apoptosis, and autophagy in hepatopancreatic cells during bacterial infection. In summary, this study enhances our understanding of antibacterial immunity in crustaceans and suggests potential evolutionary parallels of ER stress-induced immunomodulation, a finding that is consistent with stress-associated immune regulatory patterns reported in diverse taxa.
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Transcriptome-based classification in mice with ASD-risk mutations.
Autism spectrum disorder (ASD) is a neurodevelopmental condition with a strong genetic component. Large-scale human genetic studies have identified >1200 ASD-risk genes. We report a sex-balanced atlas of 1008 prefrontal RNA sequencing (RNA-seq) profiles from 17 mouse lines carrying ASD-risk mutations. Our analysis identified two opposing transcriptomic states. The two groups differed in sex bias, regional specificity, developmental stability, cell type remodeling, and responses to fluoxetine and lithium. Single-nucleus RNA-seq revealed broader cell type remodeling in group 1 than in group 2, and cell type-specific modules showed reciprocal associations that mirrored bulk transcriptomic signatures. The framework classifies independent mouse lines and identifies subgroups with conserved synaptic directionality, supporting molecular stratification.