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Emerging frontiers for AI in single-cell genomics.
We are just over a decade into the single-cell revolution in biology, which has been coupled to the growing use of artificial intelligence (AI) in single-cell genomics. Most early single-cell RNA embedding models and subsequent "foundation models"-representational models pretrained on large single-cell datasets-did not gain wide traction among experimental biologists. However, as massive single-cell RNA-sequencing (scRNA-seq) atlas efforts ramp up, there is a clear rationale for exploiting AI models to interpret new datasets in the context of vast existing data resources. Meanwhile, the advent of single-cell epigenetic/multiomic assays and Perturb-seq technologies presents new opportunities for AI modeling. Here, we review AI embedding and foundation models for scRNA-seq and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), extensive machine-learning efforts to exploit single-cell multiome in regulatory genomics, and AI strategies to learn "virtual cell" models from Perturb-seq, ending with a description of current limitations of single-cell AI models and an outlook for future advances.
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Dynamic single-cell transcriptomics reveals lsamp-guided neural network formation in male S. japonicum driving female reproduction.
Sexual maturation and egg production in schistosomes require sustained male-female pairing, yet the cellular dynamics underlying this process remain poorly understood. Here, we generate a high-resolution single-cell transcriptomic atlas of 104,671 cells from male and female Schistosoma japonicum across four developmental stages, identifying 76 distinct cell populations and mapping lineage trajectories in reproductive and neural systems. Notably, we discover a male-specific neuron subtype - N4.3 neuron located in the gynecophoric canal, and identify lsamp as a critical gene co-expressed with nrps in N4.3 neurons. Loss of lsamp impairs axonal integrity and disrupts the male ventral nerve network, reducing the targeted delivery of pheromone BATT precursor to N4.3 neurons, thereby hindering female reproductive maturation. This work maps schistosome male-female interactions at single-cell resolution, revealing a male ventral nerve network that facilitates intersex communication and promotes female reproductive development, offering strategies to disrupt parasite reproduction and combat schistosomiasis.
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Single-cell expression QTL analyses of the human cerebellum reveal vulnerability of oligodendrocytes in essential tremor.
Essential tremor, a movement disorder characterized by an upper-limb postural and action tremor, is among the most common neurological disorders, affecting 1% of the population worldwide. Despite strong evidence for genetic factors driving the etiology of essential tremor, the underlying pathophysiology remains poorly understood. To understand the effects of genetic risk factors in essential tremor on the cerebellum, the brain region suspected to be affected by the disease, we built a population-scale single-cell atlas of the human cerebellar cortex comprising more than 1 million cells from 109 individuals. Here, using single-cell expression quantitative trait loci and Mendelian randomization, we show that essential-tremor-associated variants in the BACE2 locus are causally linked to its downregulation in cerebellar oligodendrocytes. We highlight a genetically vulnerable population of BACE2-expressing immature oligodendrocytes, suggestive of demyelination. We also identify dysfunctional processes affecting interactions between neuronal populations and oligodendrocytes in essential tremor. Our findings suggest a crucial role for cerebellar oligodendrocytes in the pathogenesis of essential tremor.
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Spatially guided in vivo single-cell functional genomics of postnatal heart.
Understanding how spatial organization and cell-cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues.
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Single-cell RNA sequencing reveals depot-specific characteristics of adipose progenitor cells in ducks.
Depot-specific fat deposition influences carcass yield and meat quality in ducks. To explore regulatory mechanisms, we profiled adipogenic progenitor cells (APCs) from pectoral muscle (PM), subcutaneous (SAT), and visceral adipose tissue (VAT) using single-cell RNA sequencing. Nine major cell types were identified, with APCs being most abundant in VAT and least abundant in PM, consistent with histological findings. Transcriptomic comparisons revealed depot-specific microenvironments: PM was enriched with myogenic-related pathways, SAT with immune and inflammatory responses, and VAT with pro-adipogenic signaling. Next, APCs were regrouped into 5 subclusters. Cluster 0 (PI16high PDGFRBhigh) enriched in SAT/VAT showed activated signaling of BMP and glycolysis, indicating strong adipogenic potential. Cluster 1 (PDGFRAhigh F3high) enriched in PM was associated with Wnt signaling, which maintains APCs in a progenitor state. Cell cycle, RNA velocity, and pseudotime analyses indicated that APCs of SAT/VAT were more advanced in differentiation, while APCs of PM retained stronger proliferative capacity. Cell-cell communication analysis further revealed more active paracrine regulation within SAT/VAT, while APCs from PM showed limited signaling interactions. Key depot-specific regulators were identified, including NEGR1 (PM), CD36 (SAT), and RSPO2 (VAT), which may contribute to regulations of the adipogenic process. This study provides the first single-cell atlas of duck APCs across depots, revealing molecular heterogeneity, lineage dynamics, and niche-specific regulation. These findings advance understanding of depot-specific adipogenesis and offer targets to enhance IMF while limiting excessive SAT and VAT, thereby improving both meat quality and production efficiency.
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Integrative Multiomics Analysis Reveals Tumor-Associated Macrophage Heterogeneity and a Prognostic Signature in Gastric Cancer.
Gastric cancer (GC) is characterized by a complex tumor microenvironment (TME) with substantial cellular heterogeneity. Tumor-associated macrophages (TAMs) represent the most abundant immune cell population in the TME and exhibit remarkable functional plasticity. This study integrated single-cell RNA-sequencing (scRNA-seq) data, bulk transcriptomics, and spatial transcriptomics to systematically characterize TAM heterogeneity and identify prognostic biomarkers in GC. ScRNA-seq analysis revealed nine major cell types (T cells, plasma cells, epithelial cells, fibroblasts, macrophages, endothelial cells, B cells, smooth muscle cells, and mast cells) and distinct macrophage subpopulations with tumor-specific expansion patterns. High-dimensional weighted gene coexpression network analysis identified coexpression modules enriched in GC-associated macrophages. Machine learning algorithms were employed to construct a prognostic signature, and the CoxBoost model demonstrated superior predictive performance across multiple cohorts. The seven-gene signature, including UPP1, VCAN, ELL2, ABCA1, TUBA1A, MX2, and TSPO, showed robust prognostic value in survival prediction. Spatial transcriptomic analysis further revealed distinct metabolic profiles and extensive cellular interaction networks mediated by UPP1-expressing TAMs. These findings provide a comprehensive atlas of TAM heterogeneity and establish novel prognostic biomarkers with potential therapeutic implications in GC.
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Temporal single-cell transcriptional dynamics of murine pancreatic islet remodeling during hyperglycaemia progression.
Pancreatic islets undergo coordinated cellular remodeling during obesity-induced insulin resistance. However, longitudinal changes across endocrine and non-endocrine compartments remain largely unexplored. We present a comprehensive high-resolution atlas using longitudinal single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) on islets from C57BL/6 mice subjected to high-fat diet (HFD) feeding for 8, 16, and 24 weeks, along with age-matched controls on regular chow (RC). We mapped dynamic changes in islet cell composition and transcriptional states. Trajectory inference indicated diversification of beta-cell programs into adaptive and inflammatory states under HFD. Progression of insulin resistance induced shrinkage and transcriptional remodeling of glucagon-secreting alpha-cells, marked by upregulation of genes related to intracellular transport and oxidative stress, accompanied by the emergence of a polyhormonal alpha-cell subpopulation. Similarly, we identified delta-cell subpopulations exhibiting beta-like transcriptional signatures and polyhormonal identity under nutritional stress, suggesting adaptive delta-cell plasticity that may partially compensate for beta-cell loss during insulin resistance. The islet microenvironment exhibited robust expansion of proinflammatory M1 macrophages, reaching a plateau by 16 weeks of HFD, indicating niche saturation. Cell-cell communication analyses revealed disruption of key signaling pathways within endocrine and between endocrine and non-endocrine cells under HFD conditions. Notably, CCL27a-chemokine receptor signaling between beta-cells and M1 macrophages was significantly reduced in HFD islets, likely driven by reduced Ccl27a expression and chromatin accessibility in a distinct beta cell subpopulation, which we further validated using INS-1 cells exposed to HFD-like conditions. Comparative analysis with scRNA seq of human islets confirmed conserved stress signatures. Furthermore, genetic variants at the CCL27 locus were associated with increased T2D risk and HOMA-IR in human populations, establishing a novel link between beta-cell stress and systemic inflammation. This resource provides a hierarchical framework for understanding islet failure and identifies potential therapeutic nodes for type 2 diabetes.
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Single-cell RNA sequencing reveals the characteristics of porcine viral diseases, development, and immune repertoires: Current status and challenges.
Pigs are globally important agricultural and economic animals, and due to their physiological and anatomical structures similar to humans in terms of skin, respiratory, intestinal, and urinary systems, they have become an ideal model for studying human viral diseases. Single-cell RNA sequencing (scRNA-seq) is capable of revealing the specific functions and dynamic changes of different cell subpopulations in tissues, emerging as a powerful tool for dissecting disease mechanisms, developmental trajectories, and immune responses in pigs, and it has been increasingly applied in porcine research. This article systematically reviews the research progress of scRNA-seq technology in three aspects of pig research: decoding disease mechanisms, exploring developmental biology, and analyzing immunological characteristics. It deciphers the heterogeneous responses of porcine cell subpopulations and the dynamic regulation of cell transcriptomes following viral infection, investigates the mechanisms underlying cell lineage differentiation and development during pig growth as well as their associations with disease susceptibility, and elaborates on the dynamic changes of immune cells and immune repertoires along with their correlations with viral infection and vaccine responses. Furthermore, we analyze the technical challenges that this technology still faces, including the incompleteness of pig reference genome annotation, difficulties in sample acquisition and processing, and the complexity of data analysis. We also discuss future application prospects, aiming to provide research insights for scRNA-seq to play a greater role in the prevention and control of pig infectious diseases, genetic breeding, and the development of biomedical models.
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Construction of a Prognostic Model for Lysosome-dependent Cell Death in Gastric Cancer Based on Single-cell RNA-seq and Bulk RNA-seq Data.
OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION: C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
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A P4HA2 hypoxia signature derived from single cell atlas stratifies conserved subtypes with prognostic significance in cervical squamous cell carcinoma.
BACKGROUND: Cervical cancer, especially cervical squamous cell carcinoma (CSCC), is a major public health issue in low- and middle-income countries, with advanced recurrence and metastasis linked to poor prognosis. It shows significant intra-tumoral phenotypic heterogeneity and plasticity. METHODS: We analyzed the single-cell RNA sequencing data of cervical squamous cell carcinoma available in the Gene Expression Database (GEO) and identified the tumor cell subtype exhibiting hypoxic characteristics. We extracted differentially expressed genes (HRDEGs) between this hypoxia-related cluster and other tumor cells. Based on the CSCC bulk RNA sequencing data from the Cancer Genome Atlas (TCGA), this subtype was confirmed to be closely associated with poor prognosis in CSCC.101 combinations consisting of 10 machine learning models were used for screening prognostic biomarkers in HRDEGs, and a hypoxia signature was established by multivariate COX regression. RESULTS: The hypoxia signature, validated using external GEO datasets, was significantly correlated with tumor invasiveness. Further analysis demonstrated that immune infiltration and responses to both chemotherapy and immunotherapy are associated with the hypoxia signature. In addition, the key gene P4HA2 in the hypoxia signature has been demonstrated to be involved in the regulation of malignant phenotypes of tumor cells and the regulation of HIF-1α stability. CONCLUSIONS: Overall, this hypoxia signature is a promising independent prognostic factor, provides potential biomarkers for the prognosis of CSCC and may guide future investigations into patient stratification.