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Single-Cell Mapping of the Gut Epithelium Reveals Paneth Cell Dysfunction and Lysozyme Deficiency in Peanut Allergy.
BACKGROUND & AIMS: A growing body of evidence links intestinal epithelial cell barrier dysfunction to the development of food allergies. We previously showed that the peanut allergy-susceptible CC027 mouse has increased intestinal permeability. To understand the heterogeneity of the intestinal barrier in peanut allergy susceptibility, we used single-cell RNA sequencing to generate a high-resolution single-cell atlas of the small intestinal epithelium in the naïve CC027 mouse. This can be used to inform patient findings and provide a resource for understanding how epithelial cells contribute to peanut allergy pathogenesis. METHODS: We generated a high-resolution single-cell atlas of the intestinal epithelium of allergy-susceptible, naïve CC027 mice and allergy-prone but orally resistant, naïve C3H/HeJ mice. Histology studies in gastrointestinal tissues from mice and human patients with or without peanut allergy corroborated our findings. RESULTS: Naïve CC027 mice exhibited a reduced proportion of interferon-responsive absorptive enterocytes compared with naïve C3H/HeJ mice, reduced numbers of enteroendocrine cells, and increased numbers of goblet and tuft cells. We found a complete absence of the Paneth cell antimicrobial peptide lysozyme 1 transcript and enrichment of mitochondrial stress pathways in peanut unexposed CC027 mice. Paneth cells from CC027 mice exhibited signs of cell stress by electron microscopy. In patients with peanut allergy, small intestinal biopsies demonstrated decreased Paneth cell-specific lysozyme 1 protein expression. CONCLUSIONS: We establish a single-cell atlas of the intestinal epithelium in a translationally relevant peanut allergy-susceptible mouse model and show a link between intestinal permeability, epithelial-derived interferon responses, and Paneth cell dysfunction in maintaining immune tolerance to dietary antigens.
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Widespread sex-dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues.
The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism to study vertebrate aging. Here we describe the first multi-tissue, single-cell gene expression atlas of female and male turquoise killifish tissues comprising immune and metabolic cells from the blood, kidney, liver, and spleen. We were able to annotate 22 distinct cell types, define associated marker genes, and infer differentiation trajectories. Using this dataset, we found pervasive sex-dimorphic gene expression across cell types, especially in the liver. Sex-dimorphic genes tended to be involved in processes related to lipid metabolism, and indeed, we observed clear differences in lipid storage in female vs. male turquoise killifish livers. Importantly, we use machine-learning to predict sex using single-cell gene expression in our atlas and identify potential transcriptional markers for molecular sex identity in this species. As proof-of-principle, we show that our atlas can be used to deconvolute existing liver bulk RNA-seq data in this species to obtain accurate estimates of cell type proportions across biological conditions. We believe that this single-cell atlas can be a resource to the community that could notably be leveraged to identify cell type-specific genes for cell type-specific expression in transgenic animals.
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Evaluation of Stem-Cell Embryo Models by Integration with a Human Embryo Single-Cell Transcriptome Atlas.
Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of the molecular processes of early development and provided us with the means to capture biological heterogeneity and assess the cellular composition in early embryos. Comparative analysis of the transcriptional landscapes of embryos with single-cell resolution allows us to better understand and improve stem-cell-based embryo models. However, proper comparison between different single-cell datasets acquired by different laboratories and through different technologies is imperative for adequate analysis and findings. In this chapter, we focus on the analysis of human blastoids, which model the blastocyst, and their integrative analysis with human embryo datasets and a 2D in vitro early development model system dataset, which models epiblast, extraembryonic mesoderm, and trophoblast cells.
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PalmaClust: A graph-fusion framework leveraging the Palma ratio for robust ultra-rare cell type detection in scRNA-seq data.
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is routinely used to build atlases of tissues, resolve developmental trajectories, and characterize disease microenvironments. Yet many biologically and clinically meaningful populations-including transient progenitors, therapy-resistant tumor subclones, and antigen-specific lymphocytes-occur at very low frequencies (<1%) and are easily missed by standard clustering pipelines. Existing approaches often require extensive manual curation, rely on known marker genes, or trade sensitivity for unacceptable false positive rates due to the insensitivity of metrics like the Gini index to heavy-tailed distributions. A scalable, statistically grounded method is needed to sensitively detect rare populations while providing calibrated confidence and interpretable molecular signatures. RESULTS: We present PalmaClust, a graph-fusion clustering framework that repurposes Palma ratio-a tail-sensitive inequality metric in sociology-to identify marker genes driven by extreme sparsity. PalmaClust constructs and fuses multiple K-Nearest Neighbor (KNN) graphs derived from complementary gene-selection statistics including the Palma ratio, Gini index, and Fano factor. It employs a local refinement strategy that re-prioritizes Palma-ranked genes within parent clusters. Benchmarking across diverse public scRNA-seq datasets confirms that PalmaClust consistently outperforms state-of-the-art baselines, improving rare-class F1 scores by at least 20% (absolute) while maintaining high global clustering stability. Further studies demonstrate that the Palma ratio-derived graph layer is essential for capturing ultra-rare signatures that other views miss. AVAILABILITY: https://github.com/wan-mlab/PalmaClust.
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A unified rodent atlas reveals the cellular complexity and evolutionary divergence of the dorsal vagal complex.
The dorsal vagal complex (DVC) is a region in the brainstem comprised of an intricate network of specialized cells responsible for sensing and propagating many appetite-related cues. Understanding the dynamics controlling appetite requires deeply exploring the cell types and transitory states harbored in this brain site. We generated a multi-species DVC cell atlas using single-nuclei RNA-sequencing, by curating and harmonizing mouse and rat data, which includes >180,000 cells and 123 cell identities at 5 granularities of cellular resolution. We report unique DVC features such as Kcnj3 expression in Ca+-permeable astrocytes as well as new cell populations like neurons co-expressing Th and Cck, and a leptin receptor-expressing neuron population in the rat area postrema which is marked by expression of the progenitor marker, Pdgfra. In summary, our findings demonstrate a high degree of complexity within the DVC and provide a valuable tool for the study of this metabolic center.
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Transcriptional landscape of direct reprogramming toward the hematopoietic lineage.
Direct reprogramming of human fibroblasts into hematopoietic stem cells (HSCs) offers a promising strategy for generating autologous cells to treat blood and immune disorders. Current protocols are limited by low efficiency and insufficient tools for evaluating reprogramming outcomes. Although functional assays are the standard for confirming cell identity, they require fully reprogrammed cells, limiting their utility during protocol development. To address this, we assembled a single-cell transcriptomic reference atlas of hematopoietic reprogramming and tested an algorithmically-predicted transcription factor recipe for HSC induction. Long-read single-cell RNA sequencing of CD34+ reprogrammed cells revealed progressive loss of fibroblast identity alongside induction of early hematopoietic and endothelial programs, with reference-atlas benchmarking placing reprogrammed cells in an intermediate transcriptomic state between fibroblasts, endothelial cells, and HSCs. Isoform-level analysis further revealed transcriptional remodeling not captured by gene-level analyses. This experimental-computational framework offers a generalizable strategy for characterizing partially reprogrammed states and guiding optimization of reprogramming protocols.
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A spatiotemporal gene expression and cell atlases of the developing rat ovary.
Normal ovarian development is necessary for the production of healthy oocytes. However, the characteristics of oocytes development at different stages and the regulatory relationship between oocytes and somatic cells remain to be fully explained. Here, we combined scRNA-seq and spatial transcriptomic sequencing to profile the transcriptomic atlas of developing ovarian of the rat. We identified four components from developing granulosa cells including cumulus, primitive, mural, and luteal cells, and constructed their differential transcriptional regulatory networks. Several novel growth signals from oocytes to cumulus cells were identified, such as JAG1-NOTCH2 and FGF9-FGFR2. Moreover, we observed three cumulus sequential phases during follicle development determined by the key transcriptional factors in each cumulus phase (Bckaf1, Gata6, Cebpb, etc.), as well as the potential pinpointed roles of macrophages in luteal regression. Altogether, the single-cell spatial transcriptomic profile of the ovary provides not only a new research dimension for temporal and spatial analysis of ovary development, but also valuable data resources and a research basis for in-depth excavation of the mechanisms of mammalian ovary development.
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Fast and memory-efficient scRNA-seq k-means clustering with various distances.
Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicore's novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattachaiyya distance, which can be directly applied to count data and probability distributions. Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (<2-fold) speed differences among all distances. We show that a minicore pipeline consisting of k-means++, localsearch++ and mini-batch k-means can cluster a 4-million cell dataset in minutes, using less than 10GiB of RAM. This memory-efficiency enables atlas-scale clustering on laptops and other commodity hardware. Finally, we report findings on which distance measures give clusterings that are most consistent with known cell type labels.
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The mouse Gene Expression Database (GXD): 2026 update.
The Gene Expression Database (GXD; https://www.informatics.jax.org/expression.shtml) is an extensive, well-curated community resource that provides detailed information about gene expression patterns in mouse strains and mutants, with a particular emphasis on development. For over 25 years, GXD has systematically curated the scientific literature and collaborated with large-scale expression projects to compile and integrate detailed expression data from multiple assay types, including RNA in situ hybridization, immunohistochemistry, in situ reporter (knock-in), RT-PCR, northern blot, and western blot experiments. In recent years, GXD has expanded its scope to include bulk RNA-Seq data, imported from the EMBL-EBI Expression Atlas. Since our last report in 2021, we continued to add data to GXD on a daily basis, and we implemented new search and display features. These include: (i) searches for qualitative differential gene expression and expression profiles that include both classical types of expression data and RNA-Seq data; (ii) browsing, searching, and filtering for cell-type-specific gene expression information; and (iii) an enhancement of our RNA-Seq and microarray experiment metadata index and search to allow users to quickly and reliably find bulk, single-cell, or spatial RNA-Seq data in the public repositories.
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Simultaneous profiling of RNA isoforms and chromatin accessibility of single cells of human retinal organoids.
Single-cell multi-omics sequencing is a powerful approach to analyze complex mechanisms underlying neuronal development and regeneration. However, current methods lack the ability to simultaneously profile RNA alternative splicing and chromatin accessibility at the single-cell level. We develop a technique, single-cell RNA isoform and chromatin accessibility sequencing (scRICA-seq), which demonstrates higher sensitivity and cost-effectiveness compared to existing methods. scRICA-seq can profile both isoforms and chromatin accessibility for up to 10,000 single cells in a single run. Applying this method to human retinal organoids, we construct a multi-omic cell atlas and reveal associations between chromatin accessibility, isoform expression of fate-determining factors, and alternative splicing events in their binding sites. This study provides insights into integrating epigenetics, transcription, and RNA splicing to elucidate the mechanisms underlying retinal neuronal development and fate determination.