Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration

Long non-coding RNA H19 regulates neurogenesis of induced neural stem cells in a mouse model of closed head injury

Stem cell-based therapies have been proposed as a potential treatment for neural regeneration following closed head injury. We previously reported that induced neural stem cells exert beneficial effects on neural regeneration via cell replacement. However, the neural regeneration efficiency of induced neural stem cells remains limited. In this study, we explored differentially expressed genes and long non-coding RNAs to clarify the mechanism underlying the neurogenesis of induced neural stem cells. We found that H19 was the most downregulated neurogenesis-associated lncRNA in induced neural stem cells compared with induced pluripotent stem cells. Additionally, we demonstrated that H19 levels in induced neural stem cells were markedly lower than those in induced pluripotent stem cells and were substantially higher than those in induced neural stem cell-derived neurons. We predicted the target genes of H19 and discovered that H19 directly interacts with miR-325-3p, which directly interacts with Ctbp2 in induced pluripotent stem cells and induced neural stem cells. Silencing H19 or Ctbp2 impaired induced neural stem cell proliferation, and miR-325-3p suppression restored the effect of H19 inhibition but not the effect of Ctbp2 inhibition. Furthermore, H19 silencing substantially promoted the neural differentiation of induced neural stem cells and did not induce apoptosis of induced neural stem cells. Notably, silencing H19 in induced neural stem cell grafts markedly accelerated the neurological recovery of closed head injury mice. Our results reveal that H19 regulates the neurogenesis of induced neural stem cells. H19 inhibition may promote the neural differentiation of induced neural stem cells, which is closely associated with neurological recovery following closed head injury.

Gene regulation supporting sociality shared across lineages and variation in complexity

Across evolutionary lineages, insects vary in social complexity, from those that exhibit extended parental care to those with elaborate divisions of labor. Here, we synthesize the sociogenomic resources from hundreds of species to describe common gene regulatory mechanisms in insects that regulate social organization across phylogeny and levels of social complexity. Different social phenotypes expressed by insects can be linked to the organization of co-expressing gene networks and features of the epigenetic landscape. Insect sociality also stems from processes like the emergence of parental care and the decoupling of ancestral genetic programs. One underexplored avenue is how variation in a group's social environment affects the gene expression of individuals. Additionally, an experimental reduction of gene expression would demonstrate how the activity of specific genes contributes to insect social phenotypes. While tissue specificity provides greater localization of the gene expression underlying social complexity, emerging transcriptomic analysis of insect brains at the cellular level provides even greater resolution to understand the molecular basis of social insect evolution.

The spatial transcriptomic landscape of human gingiva in health and periodontitis

The gingiva is a key oral barrier that protects oral tissues from various stimuli. A loss of gingival tissue homeostasis causes periodontitis, one of the most prevalent inflammatory diseases in humans. The human gingiva exists as a complex cell network comprising specialized structures. To understand the tissue-specific pathophysiology of the gingiva, we applied a recently developed spatial enhanced resolution omics-sequencing (Stereo-seq) technique to obtain a spatial transcriptome (ST) atlas of the gingiva in healthy individuals and periodontitis patients. By utilizing Stereo-seq, we identified the major cell types present in the gingiva, which included epithelial cells, fibroblasts, endothelial cells, and immune cells, as well as subgroups of epithelial cells and immune cells. We further observed that inflammation-related signalling pathways, such as the JAK-STAT and NF-κB signalling pathways, were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals. Additionally, we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva. In conclusion, our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.

High-Plex Assessment of Biomarkers in Tumors

The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data

MOTIVATION

The rapid development of spatial transcriptome technologies has enabled researchers to acquire single-cell-level spatial data at an affordable price. However, computational analysis tools, such as annotation tools, tailored for these data are still lacking. Recently, many computational frameworks have emerged to integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. While some frameworks can utilize well-annotated scRNA-seq data to annotate spatial expression patterns, they overlook critical aspects. First, existing tools do not explicitly consider cell type mapping when aligning the two modalities. Second, current frameworks lack the capability to detect novel cells, which remains a key interest for biologists.

RESULTS

To address these problems, we propose an annotation method for spatial transcriptome data called SPANN. The main tasks of SPANN are to transfer cell-type labels from well-annotated scRNA-seq data to newly generated single-cell resolution spatial transcriptome data and discover novel cells from spatial data. The major innovations of SPANN come from two aspects: SPANN automatically detects novel cells from unseen cell types while maintaining high annotation accuracy over known cell types. SPANN finds a mapping between spatial transcriptome samples and RNA data prototypes and thus conducts cell-type-level alignment. Comprehensive experiments using datasets from various spatial platforms demonstrate SPANN's capabilities in annotating known cell types and discovering novel cell states within complex tissue contexts.

AVAILABILITY

The source code of SPANN can be accessed at https://github.com/ddb-qiwang/SPANN-torch.

CONTACT

dengmh@math.pku.edu.cn.

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

SCAN: Spatiotemporal Cloud Atlas for Neural cells

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

Mechanisms of kidney fibrosis and routes towards therapy

Kidney fibrosis is the final common pathway of virtually all chronic kidney diseases (CKDs) and is therefore considered to be a promising therapeutic target for these conditions. However, despite great progress in recent years, no targeted antifibrotic therapies for the kidney have been approved, likely because the complex mechanisms that initiate and drive fibrosis are not yet completely understood. Recent single-cell genomic approaches have allowed novel insights into kidney fibrosis mechanisms in mouse and human, particularly the heterogeneity and differentiation processes of myofibroblasts, the role of injured epithelial cells and immune cells, and their crosstalk mechanisms. In this review we summarize the key mechanisms that drive kidney fibrosis, including recent advances in understanding the mechanisms, as well as potential routes for developing novel targeted antifibrotic therapeutics.

Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

Spatial transcriptomics drives a new era in plant research

The plant community lags far behind the animal and human fields concerning the application of single‐cell methodologies. This is primarily due to the challenges associated with plant tissue dissection and the limitations of the available technologies. However, recent advances in spatial transcriptomics enable the study of single‐cells derived from plant tissues from a spatial perspective. This technology is already successfully used to identify cell types, reconstruct cell‐fate lineages, and reveal cell‐to‐cell interactions. Future technological advancements will overcome the challenges in sample processing, data analysis, and the integration of multiple‐omics technologies. Thanks to spatial transcriptomics, we anticipate several plant research projects to significantly advance our understanding of critical aspects of plant biology.

Cellular and molecular mechanisms of fibrosis and resolution in bleomycin-induced pulmonary fibrosis mouse model revealed by spatial transcriptome analysis

The bleomycin-induced pulmonary fibrosis mouse model is commonly used in idiopathic pulmonary fibrosis research, but its cellular and molecular changes and efficiency as a model at the molecular level are not fully understood. In this study, we used spatial transcriptome technology to investigate the cellular and molecular changes in the lungs of bleomycin-induced pulmonary fibrosis mouse models. Our analyses revealed cell dynamics during fibrosis in epithelial cells, mesenchymal cells, immunocytes, and erythrocytes with their spatial distribution available. We confirmed the differentiation of the alveolar type II (AT2) cell type expressing Krt8, and we inferred their trajectories from both the AT2 cells and club cells. In addition to the fibrosis process, we also noticed evidence of self-resolving, especially to identify possible self-resolving related genes, including Prkca. Our findings provide insights into the cellular and molecular mechanisms underlying fibrosis resolution and represent the first spatiotemporal transcriptome dataset of the bleomycin-induced fibrosis mouse model.

Dbh+ catecholaminergic cardiomyocytes contribute to the structure and function of the cardiac conduction system in murine heart

The heterogeneity of functional cardiomyocytes arises during heart development, which is essential to the complex and highly coordinated cardiac physiological function. Yet the biological and physiological identities and the origin of the specialized cardiomyocyte populations have not been fully comprehended. Here we report a previously unrecognised population of cardiomyocytes expressing Dbhgene encoding dopamine beta-hydroxylase in murine heart. We determined how these myocytes are distributed across the heart by utilising advanced single-cell and spatial transcriptomic analyses, genetic fate mapping and molecular imaging with computational reconstruction. We demonstrated that they form the key functional components of the cardiac conduction system by using optogenetic electrophysiology and conditional cardiomyocyte Dbh gene deletion models. We revealed their close relationship with sympathetic innervation during cardiac conduction system formation. Our study thus provides new insights into the development and heterogeneity of the mammalian cardiac conduction system by revealing a new cardiomyocyte population with potential catecholaminergic endocrine function.

stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics

With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.

High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq

Spatial resolution of the T cell repertoire is essential for deciphering cancer-associated immune dysfunction. Current spatially resolved transcriptomic technologies are unable to directly annotate T cell receptors (TCR). We present spatially resolved T cell receptor sequencing (SPTCR-seq), which integrates optimized target enrichment and long-read sequencing for highly sensitive TCR sequencing. The SPTCR computational pipeline achieves yield and coverage per TCR comparable to alternative single-cell TCR technologies. Our comparison of PCR-based and SPTCR-seq methods underscores SPTCR-seq's superior ability to reconstruct the entire TCR architecture, including V, D, J regions and the complementarity-determining region 3 (CDR3). Employing SPTCR-seq, we assess local T cell diversity and clonal expansion across spatially discrete niches. Exploration of the reciprocal interaction of the tumor microenvironmental and T cells discloses the critical involvement of NK and B cells in T cell exhaustion. Integrating spatially resolved omics and TCR sequencing provides as a robust tool for exploring T cell dysfunction in cancers and beyond.

A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

Teleost fish form the largest group of vertebrates and show a tremendous variety of adaptive behaviors, making them critically important for the study of brain evolution and cognition. The neural basis mediating these behaviors remains elusive. We performed a systematic comparative survey of the goldfish telencephalon. We mapped cell types using single-cell RNA sequencing and spatial transcriptomics, resulting in de novo molecular neuroanatomy parcellation. Glial cells were highly conserved across 450 million years of evolution separating mouse and goldfish, while neurons showed diversity and modularity in gene expression. Specifically, somatostatin interneurons, famously interspersed in the mammalian isocortex for local inhibitory input, were curiously aggregated in a single goldfish telencephalon nucleus but molecularly conserved. Cerebral nuclei including the striatum, a hub for motivated behavior in amniotes, had molecularly conserved goldfish homologs. We suggest elements of a hippocampal formation across the goldfish pallium. Last, aiding study of the teleostan everted telencephalon, we describe substantial molecular similarities between goldfish and zebrafish neuronal taxonomies.

The Dynamic Landscapes of Circular RNAs in Axolotl, a Regenerative Medicine Model, with Implications for Early Phase of Limb Regeneration

Circular RNAs (circRNAs) are of relevance to regenerative medicine and play crucial roles in post-transcriptional and translational regulation of biological processes. circRNAs are a class of RNA molecules that are formed through a unique splicing process, resulting in a covalently closed-loop structure. Recent advancements in RNA sequencing technologies and specialized computational tools have facilitated the identification and functional characterization of circRNAs. These molecules are known to exhibit stability, developmental regulation, and specific expression patterns in different tissues and cell types across various organisms. However, our understanding of circRNA expression and putative function in model organisms for regeneration is limited. In this context, this study reports, for the first time, on the repertoire of circRNAs in axolotl, a widely used model organism for regeneration. We generated RNA-seq data from intact limb, wound, and blastema tissues of axolotl during limb regeneration. The analysis revealed the presence of 35,956 putative axolotl circRNAs, among which 5331 unique circRNAs exhibited orthology with human circRNAs. In silico data analysis underlined the potential roles of axolotl circRNAs in cell cycle, cell death, and cell senescence-related pathways during limb regeneration, suggesting the participation of circRNAs in regulation of diverse functions pertinent to regenerative medicine. These new observations help advance our understanding of the dynamic landscape of axolotl circRNAs, and by extension, inform future regenerative medicine research and innovation that harness this model organism.

Revealing Academic Evolution and Frontier Pattern in the Field of Uveitis Using Bibliometric Analysis, Natural Language Processing, and Machine Learning

PURPOSE

Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field.

METHODS

We divided the 15,994 uveitis papers into four consecutive time periods for bibliometric analysis, and applied latent Dirichlet allocation topic modeling and machine learning techniques to the latest period. .

RESULTS

The yearly publication pattern fitted the curve: 1.21335x2 - 4,848.95282x + 4,844,935.58876 (R2 = 0.98311). The USA, the most productive country/region, focused on topics like ankylosing spondylitis and biologic therapy, whereas China (mainland) focused on topics like OCT and Behcet disease. The logistic regression showed the highest accuracy (71.6%) in the test set.

CONCLUSION

In this century, a growing number of countries/regions/authors/journals are involved in the uveitis study, promoting the scientific output and thematic evolution. Our pioneering study uncovers the evolving academic trends and frontier patterns in this field using bibliometric analysis and AI algorithms.

ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data

MOTIVATION

Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement.

RESULTS

In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive.

AVAILABILITY AND IMPLEMENTATION

Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).

Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics

Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.

Spatial transcriptomics in neuroscience

The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.

A lamprey neural cell type atlas illuminates the origins of the vertebrate brain

The vertebrate brain emerged more than ~500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey-a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits-based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of cell types, tissue structures and gene expression programs of the ancestral vertebrate brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral brain was probably devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors in the jawed vertebrate lineage. Altogether, our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.

A scATAC-seq atlas of chromatin accessibility in axolotl brain regions

Axolotl (Ambystoma mexicanum) is an excellent model for investigating regeneration, the interaction between regenerative and developmental processes, comparative genomics, and evolution. The brain, which serves as the material basis of consciousness, learning, memory, and behavior, is the most complex and advanced organ in axolotl. The modulation of transcription factors is a crucial aspect in determining the function of diverse regions within the brain. There is, however, no comprehensive understanding of the gene regulatory network of axolotl brain regions. Here, we utilized single-cell ATAC sequencing to generate the chromatin accessibility landscapes of 81,199 cells from the olfactory bulb, telencephalon, diencephalon and mesencephalon, hypothalamus and pituitary, and the rhombencephalon. Based on these data, we identified key transcription factors specific to distinct cell types and compared cell type functions across brain regions. Our results provide a foundation for comprehensive analysis of gene regulatory programs, which are valuable for future studies of axolotl brain development, regeneration, and evolution, as well as on the mechanisms underlying cell-type diversity in vertebrate brains.

Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.

SIMVI reveals intrinsic and spatial-induced states in spatial omics data

Spatial omics analyze gene expression and interaction dynamics in relation to tissue structure and function. However, existing methods cannot model the intrinsic and spatial-induced variation in spatial omics data, thus failing to identify true spatial interaction effects. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free framework that disentangles cell intrinsic and spatial-induced latent variables for modeling gene expression in spatial omics data. SIMVI enables novel downstream analyses, such as clustering and differential expression analysis based on disentangled representations, spatial effect (SE) identification, SE interpretation, and transfer learning on new measurements / modalities. We benchmarked SIMVI on both simulated and real datasets and show that SIMVI uniquely generates highly accurate SE inferences in synthetic datasets and unveils intrinsic variation in complex real datasets. We applied SIMVI to spatial omics data from diverse platforms and tissues (MERFISH human cortex / mouse liver, Slide-seqv2 mouse hippocampus, Spatial-ATAC-RNA-seq) and revealed various region-specific and cell-type-specific spatial interactions. In addition, our experiments on MERFISH human cortex and spatial-ATAC-RNA-seq showcased SIMVI's power in identifying SEs for new samples / modalities. Finally, we applied SIMVI on a newly collected CosMx melanoma dataset. Using SIMVI, we identified immune cells associated with spatial-dependent interactions and revealed the underlying spatial variations associated with patient outcomes.

SnRNA-seq reveals the heterogeneity of spinal ventral horn and mechanism of motor neuron axon regeneration

Spinal motor neurons, the distinctive neurons of the central nervous system, extend into the peripheral nervous system and have outstanding ability of axon regeneration after injury. Here, we explored the heterogeneity of spinal ventral horn cells after rat sciatic nerve crush via single-nuclei RNA sequencing. Interestingly, regeneration mainly occurred in a Sncg⁺ and Anxa2⁺ motor neuron subtype (MN2) surrounded by a newly emerged microglia subtype (Mg6) after injury. Subsequently, microglia depletion slowed down the regeneration of sciatic nerve. OPCs were also involved into the regeneration process. Knockdown of Cacna2d2 in vitro and systemic blocking of Cacna2d2 in vivo improved the axon growth ability, hinting us the importance of Ca²⁺. Ultimately, we proposed three possible phases of motor neuron axon regeneration: preparation stage, early regeneration stage, and regeneration stage. Taken together, our study provided a resource for deciphering the underlying mechanism of motor neuron axon regeneration in a single cell dimension.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

The salamander blastema within the broader context of metazoan regeneration

Throughout the animal kingdom regenerative ability varies greatly from species to species, and even tissue to tissue within the same organism. The sheer diversity of structures and mechanisms renders a thorough comparison of molecular processes truly daunting. Are "blastemas" found in organisms as distantly related as planarians and axolotls derived from the same ancestral process, or did they arise convergently and independently? Is a mouse digit tip blastema orthologous to a salamander limb blastema? In other fields, the thorough characterization of a reference model has greatly facilitated these comparisons. For example, the amphibian Spemann-Mangold organizer has served as an amazingly useful comparative template within the field of developmental biology, allowing researchers to draw analogies between distantly related species, and developmental processes which are superficially quite different. The salamander limb blastema may serve as the best starting point for a comparative analysis of regeneration, as it has been characterized by over 200 years of research and is supported by a growing arsenal of molecular tools. The anatomical and evolutionary closeness of the salamander and human limb also add value from a translational and therapeutic standpoint. Tracing the evolutionary origins of the salamander blastema, and its relatedness to other regenerative processes throughout the animal kingdom, will both enhance our basic biological understanding of regeneration and inform our selection of regenerative model systems.

Spatial Transcriptomic Technologies

Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.

Machine learning discriminates P2X7-mediated intracellular calcium sparks in human-induced pluripotent stem cell-derived neural stem cells

Adenosine triphosphate (ATP) is an extracellular signaling molecule that mainly affects the pathophysiological situation in the body and can be sensed by purinergic receptors, including ionotropic P2X7. Neuronal stem cells (NSCs) remain in adult neuronal tissues and can contribute to physiological processes via activation by evoked pathophysiological situations. In this study, we revealed that human-induced pluripotent stem cell-derived NSCs (iNSCs) have ATP-sensing ability primarily via the purinergic and ionotropic receptor P2X7. Next, to develop a machine learning (ML)-based screening system for food-derived neuronal effective substances and their effective doses, we collected ATP-triggered calcium responses of iNSCs pretreated with several substances and doses. Finally, we discovered that ML was performed using composite images, each containing nine waveform images, to achieve a better ML model (MLM) with higher precision. Our MLM can correctly sort subtle unidentified changes in waveforms produced by pretreated iNSCs with each substance and/or dose into the positive group, with common mRNA expression changes belonging to the gene ontology signatures.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Spatial mapping of the DNA adducts in cancer

DNA adducts and strand breaks are induced by various exogenous and endogenous agents. Accumulation of DNA damage is implicated in many disease processes, including cancer, aging, and neurodegeneration. The continuous acquisition of DNA damage from exogenous and endogenous stressors coupled with defects in DNA repair pathways contribute to the accumulation of DNA damage within the genome and genomic instability. While mutational burden offers some insight into the level of DNA damage a cell may have experienced and subsequently repaired, it does not quantify DNA adducts and strand breaks. Mutational burden also infers the identity of the DNA damage. With advances in DNA adduct detection and quantification methods, there is an opportunity to identify DNA adducts driving mutagenesis and correlate with a known exposome. However, most DNA adduct detection methods require isolation or separation of the DNA and its adducts from the context of the nuclei. Mass spectrometry, comet assays, and other techniques precisely quantify lesion types but lose the nuclear context and even tissue context of the DNA damage. The growth in spatial analysis technologies offers a novel opportunity to leverage DNA damage detection with nuclear and tissue context. However, we lack a wealth of techniques capable of detecting DNA damage in situ. Here, we review the limited existing in situ DNA damage detection methods and examine their potential to offer spatial analysis of DNA adducts in tumors or other tissues. We also offer a perspective on the need for spatial analysis of DNA damage in situ and highlight Repair Assisted Damage Detection (RADD) as an in situ DNA adduct technique with the potential to integrate with spatial analysis and the challenges to be addressed.

E3 ubiquitin ligases in cancer stem cells: key regulators of cancer hallmarks and novel therapeutic opportunities

BACKGROUND

Human malignancies are composed of heterogeneous subpopulations of cancer cells with phenotypic and functional diversity. Among them, a unique subset of cancer stem cells (CSCs) has both the capacity for self-renewal and the potential to differentiate and contribute to multiple tumor properties. As such, CSCs are promising cellular targets for effective cancer therapy. At the molecular level, hyper-activation of multiple stemness regulatory signaling pathways and downstream transcription factors play critical roles in controlling CSCs establishment and maintenance. To regulate CSC properties, these stemness pathways are controlled by post-translational modifications including, but not limited to phosphorylation, acetylation, methylation, and ubiquitination.

CONCLUSION

In this review, we focus on E3 ubiquitin ligases and their roles and mechanisms in regulating essential hallmarks of CSCs, such as self-renewal, invasion and metastasis, metabolic reprogramming, immune evasion, and therapeutic resistance. Moreover, we discuss emerging therapeutic approaches to eliminate CSCs through targeting E3 ubiquitin ligases by chemical inhibitors and proteolysis-targeting chimera (PROTACs) which are currently under development at the discovery, preclinical, and clinical stages. Several outstanding issues such as roles for E3 ubiquitin ligases in heterogeneity and phenotypical/functional evolution of CSCs remain to be studied under pathologically and clinically relevant conditions. With the rapid application of functional genomic and proteomic approaches at single cell, spatiotemporal, and even single molecule levels, we anticipate that more specific and precise functions of E3 ubiquitin ligases will be delineated in dictating CSC properties. Rational design and proper translation of these mechanistic understandings may lead to novel therapeutic modalities for cancer procession medicine.

Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research

Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Transcriptome analyses of betta fish (Betta splendens) provide novel insights into fin regeneration and color-related genes

The betta fish (Betta splendens), an important ornamental fish, haswell-developed and colorful fins.After fin amputation, betta fish can easily regenerate finssimilar to the originalsin terms of structureand color. The powerful fin regeneration ability and a variety of colors in the betta fish are fascinating. However, the underlying molecular mechanisms are still not fully understood. In this study, tail fin amputation and regeneration experiments were performed on two kinds of betta fish: red and white color betta fish. Then, transcriptome analyseswere conducted to screen out fin regeneration and color-relatedgenes in betta fish. Through enrichment analyses of differentially expressed genes (DEGs), we founda series of enrichment pathways and genes related to finregeneration, including cell cycle (i.e. plcg2), TGF-beta signaling pathway (i.e. bmp6), PI3K-Akt signaling pathway (i.e. loxl2aand loxl2b), Wnt signaling pathway(i.e. lef1), gap junctions (i.e. cx43), angiogenesis (i.e. foxp1), and interferon regulatory factor (i.e. irf8). Meanwhile, some fin color-related pathways and genes were identified in betta fish, especially melanogenesis (i.e. tyr, tyrp1a, tyrp1b, and mc1r) and carotenoid color genes (i.e. pax3, pax7, sox10, and ednrba). In conclusion, this studycan not only enrich the research onfish tissue regeneration, but also has a potential significance for the aquaculture and breeding of the betta fish.

Cholangiokines: undervalued modulators in the hepatic microenvironment

The biliary epithelial cells, also known as cholangiocytes, line the intra- and extrahepatic bile ducts, forming a barrier between intra- and extra-ductal environments. Cholangiocytes are mostly known to modulate bile composition and transportation. In hepatobiliary diseases, bile duct injury leads to drastic alterations in cholangiocyte phenotypes and their release of soluble mediators, which can vary depending on the original insult and cellular states (quiescence, senescence, or proliferation). The cholangiocyte-secreted cytokines (also termed cholangiokines) drive ductular cell proliferation, portal inflammation and fibrosis, and carcinogenesis. Hence, despite the previous consensus that cholangiocytes are bystanders in liver diseases, their diverse secretome plays critical roles in modulating the intrahepatic microenvironment. This review summarizes recent insights into the cholangiokines under both physiological and pathological conditions, especially as they occur during liver injury-regeneration, inflammation, fibrosis and malignant transformation processes.

Comparisons between Plant and Animal Stem Cells Regarding Regeneration Potential and Application

Regeneration refers to the process by which organisms repair and replace lost tissues and organs. Regeneration is widespread in plants and animals; however, the regeneration capabilities of different species vary greatly. Stem cells form the basis for animal and plant regeneration. The essential developmental processes of animals and plants involve totipotent stem cells (fertilized eggs), which develop into pluripotent stem cells and unipotent stem cells. Stem cells and their metabolites are widely used in agriculture, animal husbandry, environmental protection, and regenerative medicine. In this review, we discuss the similarities and differences in animal and plant tissue regeneration, as well as the signaling pathways and key genes involved in the regulation of regeneration, to provide ideas for practical applications in agriculture and human organ regeneration and to expand the application of regeneration technology in the future.

Thyroid hormone receptor knockout prevents the loss of Xenopus tail regeneration capacity at metamorphic climax

BACKGROUND

Animal regeneration is the natural process of replacing or restoring damaged or missing cells, tissues, organs, and even entire body to full function. Studies in mammals have revealed that many organs lose regenerative ability soon after birth when thyroid hormone (T3) level is high. This suggests that T3 play an important role in organ regeneration. Intriguingly, plasma T3 level peaks during amphibian metamorphosis, which is very similar to postembryonic development in humans. In addition, many organs, such as heart and tail, also lose their regenerative ability during metamorphosis. These make frogs as a good model to address how the organs gradually lose their regenerative ability during development and what roles T3 may play in this. Early tail regeneration studies have been done mainly in the tetraploid Xenopus laevis (X. laevis), which is difficult for gene knockout studies. Here we use the highly related but diploid anuran X. tropicalis to investigate the role of T3 signaling in tail regeneration with gene knockout approaches.

RESULTS

We discovered that X. tropicalis tadpoles could regenerate their tail from premetamorphic stages up to the climax stage 59 then lose regenerative capacity as tail resorption begins, just like what observed for X. laevis. To test the hypothesis that T3-induced metamorphic program inhibits tail regeneration, we used TR double knockout (TRDKO) tadpoles lacking both TRα and TRβ, the only two receptor genes in vertebrates, for tail regeneration studies. Our results showed that TRs were not necessary for tail regeneration at all stages. However, unlike wild type tadpoles, TRDKO tadpoles retained regenerative capacity at the climax stages 60/61, likely in part by increasing apoptosis at the early regenerative period and enhancing subsequent cell proliferation. In addition, TRDKO animals had higher levels of amputation-induced expression of many genes implicated to be important for tail regeneration, compared to the non-regenerative wild type tadpoles at stage 61. Finally, the high level of apoptosis in the remaining uncut portion of the tail as wild type tadpoles undergo tail resorption after stage 61 appeared to also contribute to the loss of regenerative ability.

CONCLUSIONS

Our findings for the first time revealed an evolutionary conservation in the loss of tail regeneration capacity at metamorphic climax between X. laevis and X. tropicalis. Our studies with molecular and genetic approaches demonstrated that TR-mediated, T3-induced gene regulation program is responsible not only for tail resorption but also for the loss of tail regeneration capacity. Further studies by using the model should uncover how T3 modulates the regenerative outcome and offer potential new avenues for regenerative medicines toward human patients.

Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy

Ischemic heart disease including myocardial infarction is still the leading cause of death worldwide. Although the survival early after myocardial infarction has been significantly improved by the introduction of percutaneous coronary intervention, long-term morbidity and mortality remain high. The elevated long-term mortality is mainly driven by cardiac remodeling processes triggering ischemic heart failure and electric instability. Despite the new developments in pharmaco-therapy of heart failure, we still lack targeted therapies for cardiac remodeling and fibrosis. Single-cell and genomic technologies allow us to map the human heart at unprecedented resolution and allow to gain insights into cellular and molecular heterogeneity. However, these technologies rely on digested tissue and isolated cells or nuclei and thus lack spatial information. Spatial information is critical to understand tissue homeostasis and disease and can be utilized to identify disease-driving cell populations and mechanisms including cellular cross-talk. Here, we discuss recent advances in single-cell and spatial genomic technologies that give insights into cellular and molecular mechanisms of cardiac remodeling after injury and can be utilized to identify novel therapeutic targets and pave the way toward new therapies in heart failure.

Establishing an Efficient Electroporation-Based Method to Manipulate Target Gene Expression in the Axolotl Brain

The tetrapod salamander species axolotl (Ambystoma mexicanum) is capable of regenerating injured brain. For better understanding the mechanisms of brain regeneration, it is very necessary to establish a rapid and efficient gain-of-function and loss-of-function approaches to study gene function in the axolotl brain. Here, we establish and optimize an electroporation-based method to overexpress or knockout/knockdown target gene in ependymal glial cells (EGCs) in the axolotl telencephalon. By orientating the electrodes, we were able to achieve specific expression of EGFP in EGCs located in dorsal, ventral, medial, or lateral ventricular zones. We then studied the role of Cdc42 in brain regeneration by introducing Cdc42 into EGCs through electroporation, followed by brain injury. Our findings showed that overexpression of Cdc42 in EGCs did not significantly affect EGC proliferation and production of newly born neurons, but it disrupted their apical polarity, as indicated by the loss of the ZO-1 tight junction marker. This disruption led to a ventricular accumulation of newly born neurons, which are failed to migrate into the neuronal layer where they could mature, thus resulted in a delayed brain regeneration phenotype. Furthermore, when electroporating CAS9-gRNA protein complexes against TnC (Tenascin-C) into EGCs of the brain, we achieved an efficient knockdown of TnC. In the electroporation-targeted area, TnC expression is dramatically reduced at both mRNA and protein levels. Overall, this study established a rapid and efficient electroporation-based gene manipulation approach allowing for investigation of gene function in the process of axolotl brain regeneration.