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A population of stem cells with strong regenerative potential discovered in deer antlers.
The annual regrowth of deer antlers provides a valuable model for studying organ regeneration in mammals. We describe a single-cell atlas of antler regrowth. The earliest-stage antler initiators were mesenchymal cells that express the paired related homeobox 1 gene (PRRX1+ mesenchymal cells). We also identified a population of "antler blastema progenitor cells" (ABPCs) that developed from the PRRX1+ mesenchymal cells and directed the antler regeneration process. Cross-species comparisons identified ABPCs in several mammalian blastema. In vivo and in vitro ABPCs displayed strong self-renewal ability and could generate osteochondral lineage cells. Last, we observed a spatially well-structured pattern of cellular and gene expression in antler growth center during the peak growth stage, revealing the cellular mechanisms involved in rapid antler elongation.
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Developmental trajectory of prehematopoietic stem cell formation from endothelium.
Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.
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A spatial transcriptomics dataset of the endometrium from repeated implantation failure patients.
Repeated implantation failure (RIF) presents a significant challenge to in vitro fertilization (IVF), and abnormalities in the endometrium often play a crucial role in RIF. To enhance and deepen our understanding of tissue context in the endometrium underlying RIF, we applied spatial transcriptomics (ST) sequencing using the 10x Visium platform to 8 endometrial tissues from 4 normal individuals (CTR) and 4 RIF patients during the mid-luteal phase in this study. We achieved 10131 high-quality spots, with a median detected gene number of 3156. Seven distinct cellular niches (Niche 1-7) with specific characteristics were identified in our data. Deconvolution of ST data was performed by integrating with a public single-cell RNA (scRNA) dataset, and it was found that unciliated Epithelia were the dominant components in our data. To the best of our knowledge, this is the first spatial transcriptomics atlas of endometrial tissue in normal and RIF conditions, and the spatial data can provide a valuable source for further studies investigating the RIF mechanism.
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Single-Cell Analysis Reveals the Immune Characteristics of Myeloid Cells and Memory T Cells in Recovered COVID-19 Patients With Different Severities.
Despite many studies on the immune characteristics of Coronavirus disease 2019 (COVID-19) patients in the progression stage, a detailed understanding of pertinent immune cells in recovered patients is lacking. We performed single-cell RNA sequencing on samples from recovered COVID-19 patients and healthy controls. We created a comprehensive immune landscape with more than 260,000 peripheral blood mononuclear cells (PBMCs) from 41 samples by integrating our dataset with previously reported datasets, which included samples collected between 27 and 47 days after symptom onset. According to our large-scale single-cell analysis, recovered patients, who had severe symptoms (severe/critical recovered), still exhibited peripheral immune disorders 1-2 months after symptom onset. Specifically, in these severe/critical recovered patients, human leukocyte antigen (HLA) class II and antigen processing pathways were downregulated in both CD14 monocytes and dendritic cells compared to healthy controls, while the proportion of CD14 monocytes increased. These may lead to the downregulation of T-cell differentiation pathways in memory T cells. However, in the mild/moderate recovered patients, the proportion of plasmacytoid dendritic cells increased compared to healthy controls, accompanied by the upregulation of HLA-DRA and HLA-DRB1 in both CD14 monocytes and dendritic cells. In addition, T-cell differentiation regulation and memory T cell-related genes FOS, JUN, CD69, CXCR4, and CD83 were upregulated in the mild/moderate recovered patients. Further, the immunoglobulin heavy chain V3-21 (IGHV3-21) gene segment was preferred in B-cell immune repertoires in severe/critical recovered patients. Collectively, we provide a large-scale single-cell atlas of the peripheral immune response in recovered COVID-19 patients.
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A pathomics-based pyroptosis signature predicts survival in clear cell renal cell carcinoma.
Better prognostic tools are needed to improve the clinical management of clear cell renal cell carcinoma (ccRCC). To address this, we developed a novel prognostic model by integrating pathomics features with pyroptosis-related signaling, a strategy not previously explored in ccRCC. Analysis of The Cancer Genome Atlas (TCGA) whole-slide images identified 59 quantitative image features significantly correlated with a pyroptosis gene set. Based on these features, a prognostic risk score was developed using the StepCox[forward]+Lasso algorithm and validated as an independent predictor of patient survival. This model demonstrated robust predictive performance, with time-dependent AUCs of 0.744, 0.729, and 0.716 for 1-, 3-, and 5-year survival and C-indexes of 0.71 and 0.64 in the training and validation sets. This model implicates key pyroptosis-related genes (e.g., GSDMD, GSDME, CASP5, and several CHMP family genes), whose expression links pathological phenotypes to patient outcomes. Single-cell sequencing revealed their specific expression patterns in the ccRCC microenvironment, and functional exploration highlighted GSDMD's potential role. By providing a novel, biologically integrated signature, this model offers a refined tool for prognostic assessment that complements conventional clinical parameters. Ultimately, this pyroptosis-based pathomics model could help guide personalized treatment strategies for ccRCC patients in the future.
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Single-Cell Atlas of Renal Cell Carcinoma Brain Metastasis Uncovers Mechanisms of Immune Dysfunction and Resistance.
Brain metastasis (BM) in renal cell carcinoma (RCC) remains poorly understood and often resistant to immune checkpoint inhibitors. We generated a large single-nucleus RNA-seq data of RCC BM, profiling 14 BM samples alongside matched extracranial metastases and primary tumors. Tumor cells in BM displayed neuronal infiltration, neural-like adaptation, and marked remodeling of the microenvironment, including expansion of immunosuppressive myeloid cells and depletion of antigen-presenting dendritic cells. Tumor, immune, and stromal cells exhibited metabolic rewiring characterized by fatty-acid metabolism, oxidative phosphorylation, and MYC-driven programs. CD8 T cells showed terminal exhaustion and impaired proliferative capacity, and tertiary lymphoid structures were absent. Spatial profiling of 12 BM samples (13,128 cells) validated key cellular interactions, while ligand-receptor analysis revealed immunoregulatory circuits between tumor, stromal, and immune cells. These findings define BM-specific adaptations that promote immune evasion and resistance, revealing therapeutic vulnerabilities in RCC BM.
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Identification of therapeutic targets for giant cell arteritis through integrated analysis of multi-omics datasets.
BACKGROUND: Giant cell arteritis (GCA), the most common systemic vasculitis affecting elderly individuals, currently lacks specific therapies. This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets. METHODS: We constructed a multi-stage analytical framework encompassing 32 proteomic datasets (covering 2914 unique plasma proteins) and 6 transcriptomic datasets. Multi-omics integration strategies, including two-sample Mendelian randomization, colocalization analysis, and functional enrichment analysis, were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts. Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships. RESULTS: We identified 43 plasma proteins causally associated with GCA [false discovery rate (FDR) < 0.05], with 17 representing novel therapeutic targets. Through dual validation using proteome-wide association studies and transcriptome-wide association studies, we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns. Unc-51 like kinase 3 (ULK3) emerged as the strongest protective factor (odds ratio = 0.47, 95% confidence interval: 0.37-0.71) through autophagy regulation, while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab. Five targets have existing approved drugs (SLAMF7, ICAM1, IL18, IL6ST, CTSS). Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients, with cell-type-specific alterations in inflammatory gene expression, and TYMP as the most critical hub gene. CONCLUSIONS: This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA, identifying novel mechanisms including autophagy modulation and metabolic reprogramming, with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns. These findings require experimental validation before clinical translation.
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Multiomics profiling of mouse polycystic kidney disease progression at a single-cell resolution.
Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and causes significant morbidity, ultimately leading to kidney failure. PKD pathogenesis is characterized by complex and dynamic alterations in multiple cell types during disease progression, hampering a deeper understanding of disease mechanism and the development of therapeutic approaches. Here, we generate a single-nucleus multimodal atlas of an orthologous mouse PKD model at early, mid, and late timepoints, consisting of 125,434 single-nucleus transcriptomic and epigenetic multiomes. We catalog differentially expressed genes and activated epigenetic regions in each cell type during PKD progression, characterizing cell-type-specific responses to Pkd1 deletion. We describe heterogeneous, atypical collecting duct cells as well as proximal tubular cells that constitute cyst epithelia in PKD. The transcriptional regulation of the cyst lining cell marker GPRC5A is conserved between mouse and human PKD cystic epithelia, suggesting shared gene regulatory pathways. Our single-nucleus multiomic analysis of mouse PKD provides a foundation to understand the earliest changes molecular deregulation in a mouse model of PKD at a single-cell resolution.
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Barcoding Notch signaling in the developing brain.
Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control, while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER-seq with an expanded juvenile brain atlas to identify cell types derived from Notch-active founders. Our data reveal rare examples where differential Notch activities in ancestral progenitors are detected in terminally differentiated neuronal subtypes. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.
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Integrative profiling of diverse post-translational modifications for prognostic stratification and personalized therapy in papillary thyroid cancer.
Post-translational modifications (PTMs) are pivotal in tumor biology, yet their role in papillary thyroid cancer (PTC) remains unclear. We integrated bulk and single-cell transcriptomes with clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to analyze 17 PTMs and construct a prognostic model using 12 machine learning algorithms for predicting the thyroid cancer-free interval (TCFi). Enrichment analysis, single-cell analysis, and immune-related analysis were performed to elucidate the biological role of PTMs. Therapeutic responses of PTC patients were predicted based on the model. We validated the expression of model genes and identified the key signature associated with the malignant phenotypes of PTC. We filtered out 12 genes to construct a post-translational modification index (PTMI) and identified three molecular clusters of PTC. Shapley additive explanations (SHAP) and nomogram models confirmed the predictive efficacy of PTMI. Integrated analyses revealed significant associations between PTMI and immune features. High-PTMI patients showed sensitivity to FDA-approved drugs and chemotherapeutics but resistance to radioactive iodine therapy. Notably, we identified TYMS as a key functional PTMI signature. The PTMI proposed in this study holds strong potential as a prognostic biomarker and therapeutic predictor, offering valuable insights for personalized management of PTC patients.