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Single-cell atlas comparison across vertebrates reveals auditory cell evolution and mechanisms for hair cell regeneration.
Mammals suffer permanent hearing impairment from the loss of auditory hair cells due to their inability to regenerate. In contrast, lower vertebrates exhibit extraordinary capacity for hair cell regeneration and hearing restoration, but the mechanisms remain unclear. Here we characterize the single-cell atlas of Xenopus laevis inner ear and perform a comprehensive comparison with mouse model. An exceptionally conserved inner ear neuronal cell type is discovered. The results reveal that the outer hair cells (OHCs) exist exclusively in mammals. Importantly, our analyses reveal an orchestrated gene expression program in Xenopus, characterized by upregulation of hair cell regeneration-related genes, coupled with downregulation of proliferation inhibitory genes. These findings unveil a natural feature of regenerative capacity in Xenopus, and provide molecular and evolutionary evidences for differential regenerative capacities across vertebrates. This work offers insights from amphibians into developing strategies to solve the challenges of hair cell regeneration in humans.
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Temporal single-cell atlas of full-length Huntington's disease mouse model defines stage-specific signatures of corticostriatal dysfunction.
BACKGROUND: Huntington's disease (HD) involves progressive corticostriatal dysfunction, yet the temporal dynamics and cell type-specific vulnerability patterns remain incompletely understood. While recent single-cell studies in rapidly progressing models have revealed early developmental and regional changes, temporal profiling distinguishing pathogenic mechanisms from normal aging in full-length HTT models remains lacking. Resolving stage-specific temporal dynamics across interconnected striatal and cortical neuronal populations over protracted time is essential for identifying drivers of cellular dysfunction. METHODS: A temporal single-nucleus transcriptomic atlas was generated from striatum and motor cortex from heterozygous zQ175 knock-in mice at early symptomatic (6 months) and late symptomatic (18 months) stages. This full-length huntingtin model enables staging of progressive circuit dysfunction alongside physiological aging. The high inherited CAG repeat length of the zQ175 model places cells beyond the somatic expansion threshold associated with transcriptional dysregulation and identity erosion in vulnerable human neuronal populations, yet prior to the de-repression crisis and cell loss observed at the most extreme expansions in HD, providing a tractable window into the progressive molecular pathogenic cascade. Genotype-dependent effects were modeled to distinguish cell type-specific signatures of disease mechanisms from age-related and compensatory changes. Integration of weighted gene co-expression and transcription factor regulatory networks with protein-protein interaction databases predicted candidate regulators of stage-specific programs. Findings were validated across human HD datasets and the rapidly progressive R6/2 mouse model. RESULTS: Temporal gene and network analysis revealed diverging, converging and biphasic patterns of transcriptional changes, distinguishing progressive disease and neuronal identity loss from aging. 21 cell type-specific gene co-expression modules were validated in human HD and R6/2 mice datasets, revealing stage-specific shifts in cellular stress, proteostasis, and synaptic programs. Disease modules enriched for CAG repeat length-dependent genes resolved their temporal progression. Shared vulnerability across cortical and striatal projection neurons implicated epigenetic regulator Zswim6 and splicing factors Rbfox1 and Celf2 in corticostriatal dysfunction. Integrative network analysis identified Foxo1, Neurod2, and Npas2 as stage-specific transcriptional regulators. Cross-species validation established conserved gene regulatory modules in human HD, establishing generalizable cell type-specific gene modules of translational relevance. CONCLUSIONS: This temporally resolved atlas reveals stage-specific transcriptional dynamics of disease-relevant gene expression programs and physiological trajectories in vulnerable neuronal populations. distinguished from aging alone. This work establishes an important framework for understanding the temporal and regional coordination of pathogenic mechanisms, providing molecular insights into stage-specific therapeutic intervention.
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Establishment of a Machine Learning Model for the Risk Assessment of Perineural Invasion in Head and Neck Squamous Cell Carcinoma.
Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.
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Single-cell and spatial RNA sequencing reveal the spatiotemporal trajectories of fruit senescence.
The senescence of fruit is a complex physiological process, with various cell types within the pericarp, making it highly challenging to elucidate their individual roles in fruit senescence. In this study, a single-cell expression atlas of the pericarp of pitaya (Hylocereus undatus) is constructed, revealing exocarp and mesocarp cells undergoing the most significant changes during the fruit senescence process. Pseudotime analysis establishes cellular differentiation and gene expression trajectories during senescence. Early-stage oxidative stress imbalance is followed by the activation of resistance in exocarp cells, subsequently senescence-associated proteins accumulate in the mesocarp cells at late-stage senescence. The central role of the early response factor HuCMB1 is unveiled in the senescence regulatory network. This study provides a spatiotemporal perspective for a deeper understanding of the dynamic senescence process in plants.
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How Single-Cell Transcriptomics of Hydractinia Is Informing the Evolution of Cnidarian Sensory Systems.
For over a century, the colonial cnidarian Hydractinia has been employed as a research organism to study stem cells, germ cells, regeneration, and coloniality. For the last 70 years, it has also been used in allorecognition research within the field of comparative immunology. More recently, other aspects of Hydractinia biology, including sensory biology, have been explored. Hydractinia colonies are composed of a limited number of repeating structural units: polyps and the stolon tissue that connects polyps. Polyps are divided into three major types used for feeding, reproduction, or defense. Clonal lines grown in the lab provide unlimited material from a single genetic unit. Colonies have separate sexes and spawn regularly with exposure to light. Recently, genomic and transcriptomic resources have been released for two species of Hydractinia: H. symbiolongicarpus and H. echinata. Tools for gene expression manipulation have been developed for this organism, including CRISPR/Cas9 knockout, shRNA knockdown, and overexpression via synthetic RNA. Fluorescent transgenic reporter lines have been created via random integration of circular DNA plasmids and CRISPR/Cas9-mediated gene knockin. We recently constructed an updated single-cell transcriptomic atlas of adult Hydractinia colonies to explore the cellular biology and cell-type expression profiles of the animal. We are investigating known and novel cell types and validating spatial expression patterns of cell-type specific markers to enable further understanding of the animal's cellular biology. This includes gaining a deeper understanding of the genetic control of cell differentiation of specific cell types from progenitor populations and uncovering the diversity of transcriptional subtypes that may be relevant to specific functions. Since Hydractinia is a model for whole-body regeneration, the identification and validation of new cell type and cell state markers will now allow for the elucidation of potential pathways involved in regenerating specific cell types, including testing alternative pathways for regeneration that include dedifferentiation and transdifferentiation. Hydractinia is poised to become a model for sensory biology research, as we can now fully explore their sensory cell types, including cnidocytes and neurons, and the expression and evolution of their gene complement with modern approaches and tools.
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cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.
MOTIVATION: The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. RESULTS: Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. AVAILABILITY AND IMPLEMENTATION: Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.
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Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH.
Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells1-5. Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells-with high accuracy and sub-diffraction-limit resolution-in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand-receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ.
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Gene Expression Variations Induced by Sevoflurane in Glia and Glioma Cells and the Role of EPHA3 in Sevoflurane's Anti-Glioma Effect.
INTRODUCTIONS: Sevoflurane, a commonly used anesthetic in surgery, can produce anesthetic effects such as sedation and muscle relaxation upon inhalation, which may lead to postoperative cognitive dysfunction. However, the molecular mechanism by which sevoflurane affects nervous system diseases like glioma remains unclear. METHODS: In this study, we analyzed high-throughput single-cell transcriptome data of sevoflurane treatment and identified immune cells that play crucial roles in biological processes such as the immunoinflammatory response and the maintenance of the blood- -brain barrier. A total of 11,244 genes and their variations were obtained by analyzing the cell development trajectory. Additionally, 10 key genes affected by sevoflurane were manually retrieved, and their protein-protein interaction network was constructed. The Chinese Glioma Genome Atlas (CGGA) dataset was utilized for survival analysis. RESULTS: This article reveals that sevoflurane can generate anesthetic effects through signaling pathways such as the immunoinflammatory response, neuronal differentiation, and the maintenance of the blood-brain barrier, all of which have implications for organ injury and protection. Meanwhile, genes that play a key role in these immune pathways, such as the DAAM2 and EPHA3 genes, are significantly associated with the survival of glioma patients. This association implies that sevoflurane's influence on these genes might potentially affect postoperative cognitive dysfunction and the overall outcome of organ-organ interactions during and after surgery. Moreover, the experimental results demonstrated that sevoflurane inhibits the invasion and migration of glioma cells by suppressing the expression of EPHA3. CONCLUSION: The discovery of this new target may pave the way for identifying causative gene regulation events and emphasize personalized treatments for nervous system diseases.
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KRAS modulates immune infiltration levels and survival outcomes in patients with lung adenocarcinoma.
The murine sarcoma virus oncogene (KRAS) is a key gene associated with tumorigenesis and chemotherapy resistance. However, little is known about the molecular mechanisms and immune infiltration of RASs in lung adenocarcinoma. Gene Expression Profiling Interaction Analysis was used for RASs expression analysis, and Kaplan-Meier analysis was used to analyze the potential of RASs in clinical prognosis. The effect of KRAS on immune infiltration was analyzed by TIMER. In addition, the correlation between KRAS expression and molecular mechanisms was investigated by TIMER and Cancer Single-cell State Atlas (Cancer SEA). KRAS expression levels were associated with good prognosis and tumor progression. Furthermore, KRAS expression correlates with several immune cell markers and regulates tumorigenesis. KRAS expression is involved in the regulation of multiple oncogenes and tumorigenesis, especially in the prognosis and immune infiltration of lung adenocarcinoma.
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Inference of cell-type composition and single-cell spatial maps from spatial transcriptomics data with SWOT.
Spatially resolved single-cell transcriptomics is crucial for mapping the cellular atlas of organisms, but many spatial transcriptomics data lack single-cell resolution. Most cell-type deconvolution methods are limited to estimating cell-type proportions, and they cannot further identify the exact cells needed to reconstruct a single-cell spatial map. To overcome this limitation, we introduce a spatially weighted optimal transport method, named SWOT, for learning a mapping from cells to spots to infer both cell-type composition and single-cell spatial maps from spot-based spatial transcriptomics data. Experimental results demonstrate that the learned cell-to-spot mapping offers advantages in estimating cell-type proportions, cell numbers per spot, and spatial coordinates per cell. SWOT also depicts cell-type spatial distributions and maps single cells to their spatial locations in different morphological tissues. We further showcase the utility of SWOT in assistance of accurately identifying and functionally annotating cellular neighborhoods for deciphering tissue architecture. In summary, SWOT represents a useful tool for transforming abundant spot-resolution spatial transcriptomics data into single-cell resolution, thereby facilitating cell-level discoveries within tissues.