Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis

A contrastive learning approach to integrate spatial transcriptomics and histological images

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Pharmacology of Hydrogen Sulfide and Its Donors in Cardiometabolic Diseases

Cardiometabolic diseases (CMDs) are major contributors to global mortality, emphasizing the critical need for novel therapeutic interventions. Hydrogen sulfide (H2S) has garnered enormous attention as a significant gasotransmitter with various physiological, pathophysiological, and pharmacological impacts within mammalian cardiometabolic systems. In addition to its roles in attenuating oxidative stress and inflammatory response, burgeoning research emphasizes the significance of H2S in regulating proteins via persulfidation, a well known modification intricately associated with the pathogenesis of CMDs. This review seeks to investigate recent updates on the physiological actions of endogenous H2S and the pharmacological roles of various H2S donors in addressing diverse aspects of CMDs across cellular, animal, and clinical studies. Of note, advanced methodologies, including multiomics, intestinal microflora analysis, organoid, and single-cell sequencing techniques, are gaining traction due to their ability to offer comprehensive insights into biomedical research. These emerging approaches hold promise in characterizing the pharmacological roles of H2S in health and diseases. We will critically assess the current literature to clarify the roles of H2S in diseases while also delineating the opportunities and challenges they present in H2S-based pharmacotherapy for CMDs. SIGNIFICANCE STATEMENT: This comprehensive review covers recent developments in H2S biology and pharmacology in cardiometabolic diseases CMDs. Endogenous H2S and its donors show great promise for the management of CMDs by regulating numerous proteins and signaling pathways. The emergence of new technologies will considerably advance the pharmacological research and clinical translation of H2S.

Voltage-gated ion channel's gene expression in the myocardium of embryo and adult chickens

The functioning of the cardiovascular system is critical for embryo survival. Cardiac contractions depend on the sequential activation of different classes of voltage-gated ion channels. Understanding the fundamental features of these interactions is important for identifying the mechanisms of pathologies development in the myocardium. However, at present there is no consensus on which ion channels are involved in the formation of automaticity in the early embryonic stages. The aim of this study was to elucidate the expression of genes encoding various types of ion channels that are involved in the generation of electrical activity chicken heart at different stages of ontogenesis. We analyzed the expression of 14 genes from different families of ion channels. It was revealed that the expression profiles of ion channel genes change depending on the stages of ontogenesis. The HCN4, CACNA1D, SCN1A, SCN5A, KCNA1 genes have maximum expression at the tubular heart stage. In adult, a switch occurs to the higher expression of CACNA1C, KCNH6, RYR and SLC8A1 genes. This data correlated with the results obtained by the microelectrode method. It can be assumed that the automaticity of the tubular heart is mainly due to the mechanism of the «membrane-clock» (hyperpolarization-activated current (If), Ca2+-current L-type (ICaL), Na+-current (INa) and the slow component of the delayed rectifier K+-current (IKs)). Whereas in adult birds, the mechanism for generating electrical impulses is determined by both « membrane- clock» and «Ca2+-clock».

Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization

Imputation of missing features in spatial transcriptomics is urgently needed due to technological limitations. However, most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. To fill this research gap, we introduce a computational model, TransImpute, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference data. We derive a set of attributes that can accurately predict imputation uncertainty, enabling us to select reliably imputed genes. In addition, we introduce a spatial autocorrelation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms demonstrate that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImpute offers a reliable approach to spatial analysis of missing features for both matched and unseen modalities, such as nascent RNAs.

Cardiac regeneration in goldfish (Carassius auratus) associated with increased expression of key extracellular matrix molecules

Cardiac regeneration is a natural phenomenon that occurs in many species outside of humans. The goldfish (Carassius auratus) is an understudied model of cardiac wound response, despite its ubiquity as pets as well as its relationship to the better-studied zebrafish. In this study, we examined the response of the goldfish heart to a resection injury. We found that by 70 days post-injury, goldfish scarlessly heal cardiac wounds under a certain size, with local cardiomyocyte proliferation driving the restoration of the myocardial layer. We also found the upregulation of extracellular matrix components related to cardiac regeneration in the injury site. This upregulation correlated with the level of cardiomyocyte proliferation occurring in the injury site, indicating an association between the two that warrants further exploration.

Advances in single-cell transcriptomics in animal research

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health.

Bone mineral density affects tumor growth by shaping microenvironmental heterogeneity

Breast cancer bone metastasis is the leading cause of mortality in patients with advanced breast cancer. Although decreased mineral density is a known risk factor for bone metastasis, the underlying mechanisms remain poorly understood because studying the isolated effect of bone mineral density on tumor heterogeneity is challenging with conventional approaches. Here, we investigate how bone mineral content affects tumor growth and microenvironmental complexity in vivo by combining single-cell RNA-sequencing with mineral-containing or mineral-free decellularized bone matrices. We discover that the absence of bone mineral significantly influences fibroblast and immune cell heterogeneity, promoting phenotypes that increase tumor growth and alter the response to injury or disease. Importantly, we observe that the stromal response to matrix mineral content depends on host immunocompetence and the murine tumor model used. Collectively, our findings suggest that bone mineral density affects tumor growth by altering microenvironmental complexity in an organism-dependent manner.

Epicardial EMT and cardiac repair: an update

Epicardial epithelial-to-mesenchymal transition (EMT) plays a pivotal role in both heart development and injury response and involves dynamic cellular changes that are essential for cardiogenesis and myocardial repair. Specifically, epicardial EMT is a crucial process in which epicardial cells lose polarity, migrate into the myocardium, and differentiate into various cardiac cell types during development and repair. Importantly, following EMT, the epicardium becomes a source of paracrine factors that support cardiac growth at the last stages of cardiogenesis and contribute to cardiac remodeling after injury. As such, EMT seems to represent a fundamental step in cardiac repair. Nevertheless, endogenous EMT alone is insufficient to stimulate adequate repair. Redirecting and amplifying epicardial EMT pathways offers promising avenues for the development of innovative therapeutic strategies and treatment approaches for heart disease. In this review, we present a synthesis of recent literature highlighting the significance of epicardial EMT reactivation in adult heart disease patients.

Exploring the Function of Epicardial Cells Beyond the Surface

The epicardium, previously viewed as a passive outer layer around the heart, is now recognized as an essential component in development, regeneration, and repair. In this review, we explore the cellular and molecular makeup of the epicardium, highlighting its roles in heart regeneration and repair in zebrafish and salamanders, as well as its activation in young and adult postnatal mammals. We also examine the latest technologies used to study the function of epicardial cells for therapeutic interventions. Analysis of highly regenerative animal models shows that the epicardium is essential in regulating cardiomyocyte proliferation, transient fibrosis, and neovascularization. However, despite the epicardium's unique cellular programs to resolve cardiac damage, it remains unclear how to replicate these processes in nonregenerative mammalian organisms. During myocardial infarction, epicardial cells secrete signaling factors that modulate fibrotic, vascular, and inflammatory remodeling, which differentially enhance or inhibit cardiac repair. Recent transcriptomic studies have validated the cellular and molecular heterogeneity of the epicardium across various species and developmental stages, shedding further light on its function under pathological conditions. These studies have also provided insights into the function of regulatory epicardial-derived signaling molecules in various diseases, which could lead to new therapies and advances in reparative cardiovascular medicine. Moreover, insights gained from investigating epicardial cell function have initiated the development of novel techniques, including using human pluripotent stem cells and cardiac organoids to model reparative processes within the cardiovascular system. This growing understanding of epicardial function holds the potential for developing innovative therapeutic strategies aimed at addressing developmental heart disorders, enhancing regenerative therapies, and mitigating cardiovascular disease progression.

Single-cell RNA sequencing of the holothurian regenerating intestine reveals the pluripotency of the coelomic epithelium

In holothurians, the regenerative process following evisceration involves the development of a "rudiment" or "anlage" at the injured end of the mesentery. This regenerating anlage plays a pivotal role in the formation of a new intestine. Despite its significance, our understanding of the molecular characteristics inherent to the constituent cells of this structure has remained limited. To address this gap, we employed state-of-the-art scRNA-seq and HCR-FISH analyses to discern the distinct cellular populations associated with the regeneration anlage. Through this approach, we successfully identified thirteen distinct cell clusters. Among these, two clusters exhibit characteristics consistent with putative mesenchymal cells, while another four show features akin to coelomocyte cell populations. The remaining seven cell clusters collectively form a large group encompassing the coelomic epithelium of the regenerating anlage and mesentery. Within this large group of clusters, we recognized previously documented cell populations such as muscle precursors, neuroepithelial cells and actively proliferating cells. Strikingly, our analysis provides data for identifying at least four other cellular populations that we define as the precursor cells of the growing anlage. Consequently, our findings strengthen the hypothesis that the coelomic epithelium of the anlage is a pluripotent tissue that gives rise to diverse cell types of the regenerating intestinal organ. Moreover, our results provide the initial view into the transcriptomic analysis of cell populations responsible for the amazing regenerative capabilities of echinoderms.

Single-cell transcriptomic reveals a cell atlas and diversity of chicken amygdala responded to social hierarchy

Amygdala serves as a highly cellular, heterogeneous brain region containing excitatory and inhibitory neurons and is involved in the dopamine and serotoninergic neuron systems. An increasing number of studies have revealed the underpinned mechanism mediating social hierarchy in mammal and vertebrate, however, there are rare studies conducted on how amygdala on social hierarchy in poultry. In this study, we conducted food competition tests and determined the social hierarchy of the rooster. We performed cross-species analysis with mammalian amygdala, and found that cell types of human and rhesus monkeys were more closely related and that of chickens were more distant. We identified 26 clusters and divided them into 10 main clusters, of which GABAergic and glutamatergic neurons were associated with social behaviors. In conclusion, our results provide to serve the developmental studies of the amygdala neuron system and new insights into the underpinned mechanism of social hierarchy in roosters.

Optimizing the design of spatial genomic studies

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies-one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor-our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

A Bibliometric Analysis of the Spatial Transcriptomics Literature from 2006 to 2023

In recent years, spatial transcriptomics (ST) research has become a popular field of study and has shown great potential in medicine. However, there are few bibliometric analyses in this field. Thus, in this study, we aimed to find and analyze the frontiers and trends of this medical research field based on the available literature. A computerized search was applied to the WoSCC (Web of Science Core Collection) Database for literature published from 2006 to 2023. Complete records of all literature and cited references were extracted and screened. The bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, Bibliometrix R Package software, and Scimago Graphica. A total of 1467 papers and reviews were included. The analysis revealed that the ST publication and citation results have shown a rapid upward trend over the last 3 years. Nature Communications and Nature were the most productive and most co-cited journals, respectively. In the comprehensive global collaborative network, the United States is the country with the most organizations and publications, followed closely by China and the United Kingdom. The author Joakim Lundeberg published the most cited paper, while Patrik L. Ståhl ranked first among co-cited authors. The hot topics in ST are tissue recognition, cancer, heterogeneity, immunotherapy, differentiation, and models. ST technologies have greatly contributed to in-depth research in medical fields such as oncology and neuroscience, opening up new possibilities for the diagnosis and treatment of diseases. Moreover, artificial intelligence and big data drive additional development in ST fields.

Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation

MOTIVATION

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information.

RESULTS

To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods.

AVAILABILITY AND IMPLEMENTATION

The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

Spatial transition tensor of single cells

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

The subcommissural organ regulates brain development via secreted peptides

The subcommissural organ (SCO) is a gland located at the entrance of the aqueduct of Sylvius in the brain. It exists in species as distantly related as amphioxus and humans, but its function is largely unknown. Here, to explore its function, we compared transcriptomes of SCO and non-SCO brain regions and found three genes, Sspo, Car3 and Spdef, that are highly expressed in the SCO. Mouse strains expressing Cre recombinase from endogenous promoter/enhancer elements of these genes were used to genetically ablate SCO cells during embryonic development, resulting in severe hydrocephalus and defects in neuronal migration and development of neuronal axons and dendrites. Unbiased peptidomic analysis revealed enrichment of three SCO-derived peptides, namely, thymosin beta 4, thymosin beta 10 and NP24, and their reintroduction into SCO-ablated brain ventricles substantially rescued developmental defects. Together, these data identify a critical role for the SCO in brain development.

Nanoscale Methods for Longitudinal Extraction of Intracellular Contents

Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

The subcommissural organ regulates brain development via secreted peptides

The subcommissural organ (SCO) is a gland located at the entrance of the aqueduct of Sylvius in the brain. It exists in species as distantly related as amphioxus and humans, but its function is largely unknown. To explore its function, we compared transcriptomes of SCO and non-SCO brain regions and found three genes, Sspo, Car3, and Spdef, that are highly expressed in the SCO. Mouse strains expressing Cre recombinase from endogenous promoter/enhancer elements of these genes were used to genetically ablate SCO cells during embryonic development, resulting in severe hydrocephalus and defects in neuronal migration and development of neuronal axons and dendrites. Unbiased peptidomic analysis revealed enrichment of three SCO-derived peptides, namely thymosin beta 4, thymosin beta 10, and NP24, and their reintroduction into SCO-ablated brain ventricles substantially rescued developmental defects. Together, these data identify a critical role for the SCO in brain development.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Multi-slice spatial transcriptome domain analysis with SpaDo

With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.

Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study.

METHODS

In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis.

RESULTS

The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment.

CONCLUSIONS

The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.

Macrophage lineages in heart development and regeneration

During development, macrophage subpopulations derived from hematopoietic progenitors take up residence in the developing heart. Embryonic macrophages are detectable at the early stages of heart formation in the nascent myocardium, valves and coronary vasculature. The specific subtypes of macrophages present in the developing heart reflect the generation of hematopoietic progenitors in the yolk sac, aorta-gonad-mesonephros, fetal liver, and postnatal bone marrow. Ablation studies have demonstrated specific requirements for embryonic macrophages in valve remodeling, coronary and lymphatic vessel development, specialized conduction system maturation, and myocardial regeneration after neonatal injury. The developmental origins of macrophage lineages change over time, with embryonic lineages having more reparative and remodeling functions in comparison to the bone marrow derived myeloid lineages of adults. Here we review the contributions and functions of cardiac macrophages in the developing heart with potential regenerative and reparative implications for cardiovascular disease.

A spatiotemporal molecular atlas of the ovulating mouse ovary

Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell type- and ovulation stage-specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte-cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.

Bioresponsive Cytokine Delivery Responding to Matrix Metalloproteinases

Cytokines are regulated in acute and chronic inflammation, including rheumatoid arthritis (RA) and myocardial infarction (MI). However, the dynamic windows within which cytokine activity/inhibition is desirable in RA and MI change timely and locally during the disease. Therefore, traditional, static delivery regimens are unlikely to meet the idiosyncrasy of these highly dynamic pathophysiological and individual processes. Responsive delivery systems and biomaterials, sensing surrogate markers of inflammation (i.e., matrix metalloproteinases - MMPs) and answering with drug release, may present drug activity at the right time, manner, and place. This article discusses MMPs as surrogate markers for disease activity in RA and MI to clock drug discharge to MMP concentration profiles from MMP-responsive drug delivery systems and biomaterials.

Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction

Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.

Transcriptome studies of congenital heart diseases: identifying current gaps and therapeutic frontiers

Congenital heart disease (CHD) are genetically complex and comprise a wide range of structural defects that often predispose to - early heart failure, a common cause of neonatal morbidity and mortality. Transcriptome studies of CHD in human pediatric patients indicated a broad spectrum of diverse molecular signatures across various types of CHD. In order to advance research on congenital heart diseases (CHDs), we conducted a detailed review of transcriptome studies on this topic. Our analysis identified gaps in the literature, with a particular focus on the cardiac transcriptome signatures found in various biological specimens across different types of CHDs. In addition to translational studies involving human subjects, we also examined transcriptomic analyses of CHDs in a range of model systems, including iPSCs and animal models. We concluded that RNA-seq technology has revolutionized medical research and many of the discoveries from CHD transcriptome studies draw attention to biological pathways that concurrently open the door to a better understanding of cardiac development and related therapeutic avenue. While some crucial impediments to perfectly studying CHDs in this context remain obtaining pediatric cardiac tissue samples, phenotypic variation, and the lack of anatomical/spatial context with model systems. Combining model systems, RNA-seq technology, and integrating algorithms for analyzing transcriptomic data at both single-cell and high throughput spatial resolution is expected to continue uncovering unique biological pathways that are perturbed in CHDs, thus facilitating the development of novel therapy for congenital heart disease.

Isolation of planarian viable cells using fluorescence-activated cell sorting for advancing single-cell transcriptome analysis

Preparing viable single cells is critical for conducting single-cell RNA sequencing (scRNA-seq) because the presence of ambient RNA from dead or damaged cells can interfere with data analysis. Here, we developed a method for isolating viable single cells from adult planarian bodies using fluorescence-activated cell sorting (FACS). This method was then applied to both adult pluripotent stem cells (aPSCs) and differentiating/differentiated cells. Initially, we employed a violet instead of ultraviolet (UV) laser to excite Hoechst 33342 to reduce cellular damage. After optimization of cell staining conditions and FACS compensation, we generated FACS profiles similar to those created using a previous method that employed a UV laser. Despite successfully obtaining high-quality RNA sequencing data for aPSCs, non-aPSCs produced low-quality RNA reads (i.e., <60% of cells possessing barcoding mRNAs). Subsequently, we identified an effective FACS gating condition that excluded low-quality cells and tissue debris without staining. This non-staining isolation strategy not only reduced post-dissociation time but also enabled high-quality scRNA-seq results for all cell types (i.e., >80%). Taken together, these findings imply that the non-staining FACS strategy may be beneficial for isolating viable cells not only from planarians but also from other organisms and tissues for scRNA-seq studies.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Understanding Epicardial Cell Heterogeneity during Cardiogenesis and Heart Regeneration

The outermost layer of the heart, the epicardium, is an essential cell population that contributes, through epithelial-to-mesenchymal transition (EMT), to the formation of different cell types and provides paracrine signals to the developing heart. Despite its quiescent state during adulthood, the adult epicardium reactivates and recapitulates many aspects of embryonic cardiogenesis in response to cardiac injury, thereby supporting cardiac tissue remodeling. Thus, the epicardium has been considered a crucial source of cell progenitors that offers an important contribution to cardiac development and injured hearts. Although several studies have provided evidence regarding cell fate determination in the epicardium, to date, it is unclear whether epicardium-derived cells (EPDCs) come from specific, and predetermined, epicardial cell subpopulations or if they are derived from a common progenitor. In recent years, different approaches have been used to study cell heterogeneity within the epicardial layer using different experimental models. However, the data generated are still insufficient with respect to revealing the complexity of this epithelial layer. In this review, we summarize the previous works documenting the cellular composition, molecular signatures, and diversity within the developing and adult epicardium.

A Bayesian multivariate mixture model for high throughput spatial transcriptomics

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

A Spatiotemporal Molecular Atlas of the Ovulating Mouse Ovary

Ovulation is essential for reproductive success, yet the underlying cellular and molecular mechanisms are far from clear. Here, we applied high-resolution spatiotemporal transcriptomics to map out cell-type- and ovulation-stage-specific molecular programs as function of time during follicle maturation and ovulation in mice. Our analysis revealed dynamic molecular transitions within granulosa cell types that occur in tight coordination with mesenchymal cell proliferation. We identified new molecular markers for the emerging cumulus cell fate during the preantral-to-antral transition. We describe transcriptional programs that respond rapidly to ovulation stimulation and those associated with follicle rupture, highlighting the prominent roles of apoptotic and metabolic pathways during the final stages of follicle maturation. We further report stage-specific oocyte-cumulus cell interactions and diverging molecular differentiation in follicles approaching ovulation. Collectively, this study provides insights into the cellular and molecular processes that regulate mouse ovarian follicle maturation and ovulation with important implications for advancing therapeutic strategies in reproductive medicine.

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.

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

The heterocellular heart: identities, interactions, and implications for cardiology

The heterocellular nature of the heart has been receiving increasing attention in recent years. In addition to cardiomyocytes as the prototypical cell type of the heart, non-myocytes such as endothelial cells, fibroblasts, or immune cells are coming more into focus. The rise of single-cell sequencing technologies enables  identification of ever more subtle differences and has reignited the question of what defines a cell's identity. Here we provide an overview of the major cardiac cell types, describe their roles in homeostasis, and outline recent findings on non-canonical functions that may be of relevance for cardiology. We highlight modes of biochemical and biophysical interactions between different cardiac cell types and discuss the potential implications of the heterocellular nature of the heart for basic research and therapeutic interventions.

nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG .

Single-Cell RNA Sequencing: Opportunities and Challenges for Studies on Corneal Biology in Health and Disease

The structure and major cell types of the multi-layer human cornea have been extensively studied. However, various cell states in specific cell types and key genes that define the cell states are not fully understood, hindering our comprehension of corneal homeostasis, related diseases, and therapeutic discovery. Single-cell RNA sequencing is a revolutionary and powerful tool for identifying cell states within tissues such as the cornea. This review provides an overview of current single-cell RNA sequencing studies on the human cornea, highlighting similarities and differences between them, and summarizing the key genes that define corneal cell states reported in these studies. In addition, this review discusses the opportunities and challenges of using single-cell RNA sequencing to study corneal biology in health and disease.

Identification of a protective microglial state mediated by miR-155 and interferon-γ signaling in a mouse model of Alzheimer's disease

Microglia play a critical role in brain homeostasis and disease progression. In neurodegenerative conditions, microglia acquire the neurodegenerative phenotype (MGnD), whose function is poorly understood. MicroRNA-155 (miR-155), enriched in immune cells, critically regulates MGnD. However, its role in Alzheimer's disease (AD) pathogenesis remains unclear. Here, we report that microglial deletion of miR-155 induces a pre-MGnD activation state via interferon-γ (IFN-γ) signaling, and blocking IFN-γ signaling attenuates MGnD induction and microglial phagocytosis. Single-cell RNA-sequencing analysis of microglia from an AD mouse model identifies Stat1 and Clec2d as pre-MGnD markers. This phenotypic transition enhances amyloid plaque compaction, reduces dystrophic neurites, attenuates plaque-associated synaptic degradation and improves cognition. Our study demonstrates a miR-155-mediated regulatory mechanism of MGnD and the beneficial role of IFN-γ-responsive pre-MGnD in restricting neurodegenerative pathology and preserving cognitive function in an AD mouse model, highlighting miR-155 and IFN-γ as potential therapeutic targets for AD.

ScRNA-seq and spatial transcriptomics: exploring the occurrence and treatment of coronary-related diseases starting from development

Single-cell RNA sequencing (scRNA-seq) is a new technology that can be used to explore molecular changes in complex cell clusters at the single-cell level. Single-cell spatial transcriptomic technology complements the cell-space location information lost during single-cell sequencing. Coronary artery disease is an important cardiovascular disease with high mortality rates. Many studies have explored the physiological development and pathological changes in coronary arteries from the perspective of single cells using single-cell spatial transcriptomic technology. This article reviews the molecular mechanisms underlying coronary artery development and diseases as revealed by scRNA-seq combined with spatial transcriptomic technology. Based on these mechanisms, we discuss the possible new treatments for coronary diseases.

Single-cell analysis of human fetal epicardium reveals its cellular composition and identifies CRIP1 as a modulator of EMT

The epicardium plays an essential role in cardiogenesis by providing cardiac cell types and paracrine cues to the developing myocardium. The human adult epicardium is quiescent, but recapitulation of developmental features may contribute to adult cardiac repair. The cell fate of epicardial cells is proposed to be determined by the developmental persistence of specific subpopulations. Reports on this epicardial heterogeneity have been inconsistent, and data regarding the human developing epicardium are scarce. Here we specifically isolated human fetal epicardium and used single-cell RNA sequencing to define its composition and to identify regulators of developmental processes. Few specific subpopulations were observed, but a clear distinction between epithelial and mesenchymal cells was present, resulting in novel population-specific markers. Additionally, we identified CRIP1 as a previously unknown regulator involved in epicardial epithelial-to-mesenchymal transition. Overall, our human fetal epicardial cell-enriched dataset provides an excellent platform to study the developing epicardium in great detail.

Spatiotemporal single-cell transcriptomic profiling reveals inflammatory cell states in a mouse model of diffuse alveolar damage

Diffuse alveolar damage (DAD) triggers neutrophilic inflammation in damaged tissues of the lung, but little is known about the distinct roles of tissue structural cells in modulating the recruitment of neutrophils to damaged areas. Here, by combining single-cell and spatial transcriptomics, and using quantitative assays, we systematically analyze inflammatory cell states in a mouse model of DAD-induced neutrophilic inflammation after aerosolized intratracheal inoculation with ricin toxin. We show that homeostatic resident fibroblasts switch to a hyper-inflammatory state, and the subsequent occurrence of a CXCL1-CXCR2 chemokine axis between activated fibroblasts (AFib) as the signal sender and neutrophils as the signal receiver triggers further neutrophil recruitment. We also identify an anatomically localized inflamed niche (characterized by a close-knit spatial intercellular contact between recruited neutrophils and AFib) in peribronchial regions that facilitate the pulmonary inflammation outbreak. Our findings identify an intricate interplay between hyper-inflammatory fibroblasts and neutrophils and provide an overarching profile of dynamically changing inflammatory microenvironments during DAD progression.

Quantitative 4D imaging of biomechanical regulation of ventricular growth and maturation

Abnormal cardiac development is intimately associated with congenital heart disease. During development, a sponge-like network of muscle fibers in the endocardium, known as trabeculation, becomes compacted. Biomechanical forces regulate myocardial differentiation and proliferation to form trabeculation, while the molecular mechanism is still enigmatic. Biomechanical forces, including intracardiac hemodynamic flow and myocardial contractile force, activate a host of molecular signaling pathways to mediate cardiac morphogenesis. While mechanotransduction pathways to initiate ventricular trabeculation is well studied, deciphering the relative importance of hemodynamic shear vs. mechanical contractile forces to modulate the transition from trabeculation to compaction requires advanced imaging tools and genetically tractable animal models. For these reasons, the advent of 4-D multi-scale light-sheet imaging and complementary multiplex live imaging via micro-CT in the beating zebrafish heart and live chick embryos respectively. Thus, this review highlights the complementary animal models and advanced imaging needed to elucidate the mechanotransduction underlying cardiac ventricular development.

Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine

Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients' induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.

Insights into the genetic architecture of congenital heart disease from animal modeling

Congenital heart disease (CHD) is observed in up to 1% of live births and is one of the leading causes of mortality from birth defects. While hundreds of genes have been implicated in the genetic etiology of CHD, their role in CHD pathogenesis is still poorly understood. This is largely a reflection of the sporadic nature of CHD, as well as its variable expressivity and incomplete penetrance. We reviewed the monogenic causes and evidence for oligogenic etiology of CHD, as well as the role of de novo mutations, common variants, and genetic modifiers. For further mechanistic insight, we leveraged single-cell data across species to investigate the cellular expression characteristics of genes implicated in CHD in developing human and mouse embryonic hearts. Understanding the genetic etiology of CHD may enable the application of precision medicine and prenatal diagnosis, thereby facilitating early intervention to improve outcomes for patients with CHD.

SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation

The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these technologies poses a significant challenge for a comprehensive delineation of cell type-specific spatial patterns. Here, we propose SPADE (SPAtial DEconvolution), an in-silico method designed to address this challenge by incorporating spatial patterns during cell type decomposition. SPADE utilizes a combination of single-cell RNA sequencing data, spatial location information, and histological information to computationally estimate the proportion of cell types present at each spatial location. In our study, we showcased the effectiveness of SPADE by conducting analyses on synthetic data. Our results indicated that SPADE was able to successfully identify cell type-specific spatial patterns that were not previously identified by existing deconvolution methods. Furthermore, we applied SPADE to a real-world dataset analyzing the developmental chicken heart, where we observed that SPADE was able to accurately capture the intricate processes of cellular differentiation and morphogenesis within the heart. Specifically, we were able to reliably estimate changes in cell type compositions over time, which is a critical aspect of understanding the underlying mechanisms of complex biological systems. These findings underscore the potential of SPADE as a valuable tool for analyzing complex biological systems and shedding light on their underlying mechanisms. Taken together, our results suggest that SPADE represents a significant advancement in the field of spatial transcriptomics, providing a powerful tool for characterizing complex spatial gene expression patterns in heterogeneous tissues.

Spatial transcriptomics using multiplexed deterministic barcoding in tissue

Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm² with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.

SODB facilitates comprehensive exploration of spatial omics data

Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .