Spatially resolved multiomics of human cardiac niches

Integrating single-cell data with biological variables

Constructing single-cell atlases requires preserving differences attributable to biological variables, such as cell types, tissue origins, and disease states, while eliminating batch effects. However, existing methods are inadequate in explicitly modeling these biological variables. Here, we introduce SIGNAL, a general framework that leverages biological variables to disentangle biological and technical effects, thereby linking these metadata to data integration. SIGNAL employs a variant of principal component analysis to align multiple batches, enabling the integration of 1 million cells in approximately 2 min. SIGNAL, despite its computational simplicity, surpasses state-of-the-art methods across multiple integration scenarios: 1) heterogeneous datasets, 2) cross-species datasets, 3) simulated datasets, 4) integration on low-quality cell annotations, and 5) reference-based integration. Furthermore, we demonstrate that SIGNAL accurately transfers knowledge from reference to query datasets. Notably, we propose a self-adjustment strategy to restore annotated cell labels potentially distorted during integration. Finally, we apply SIGNAL to multiple large-scale atlases, including a human heart cell atlas containing 2.7 million cells, identifying tissue- and developmental stage-specific subtypes, as well as condition-specific cell states. This underscores SIGNAL's exceptional capability in multiscale analysis.

Immune-mediated cardiac development and regeneration

The complex interplay between the immune and cardiovascular systems during development, homeostasis and regeneration represents a rapidly evolving field in cardiac biology. Single cell technologies, spatial mapping and computational analysis have revolutionised our understanding of the diversity and functional specialisation of immune cells within the heart. From the earliest stages of cardiogenesis, where primitive macrophages guide heart tube formation, to the complex choreography of inflammation and its resolution during regeneration, immune cells emerge as central orchestrators of cardiac fate. Translating these fundamental insights into clinical applications represents a major challenge and opportunity for the field. In this Review, we decode the immunological blueprint of heart development and regeneration to transform cardiovascular disease treatment and unlock the regenerative capacity of the human heart.

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene expression within tissue context, revealing cellular interactions and microenvironments. To fully leverage this potential, effective computational models are crucial. We evaluate KanCell on both simulated and real datasets from technologies such as STARmap, Slide-seq, Visium, and Spatial Transcriptomics. Our results demonstrate that KanCell outperforms existing methods across metrics like PCC, SSIM, COSSIM, RMSE, JSD, ARS, and ROC, with robust performance under varying cell numbers and background noise. Real-world applications on human lymph nodes, hearts, melanoma, breast cancer, dorsolateral prefrontal cortex, and mouse embryo brains confirmed its reliability. Compared with traditional approaches, KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN, providing an accurate and efficient tool for ST. By improving data accuracy and resolving cell type composition, KanCell reveals cellular heterogeneity, clarifies disease microenvironments, and identifies therapeutic targets, addressing complex biological challenges.

Harnessing omics data for drug discovery and development in ovarian aging

BACKGROUND

Ovarian aging occurs earlier than the aging of many other organs and has a lasting impact on women's overall health and well-being. However, effective interventions to slow ovarian aging remain limited, primarily due to an incomplete understanding of the underlying molecular mechanisms and drug targets. Recent advances in omics data resources, combined with innovative computational tools, are offering deeper insight into the molecular complexities of ovarian aging, paving the way for new opportunities in drug discovery and development.

OBJECTIVE AND RATIONALE

This review aims to synthesize the expanding multi-omics data, spanning genome, transcriptome, proteome, metabolome, and microbiome, related to ovarian aging, from both tissue-level and single-cell perspectives. We will specially explore how the analysis of these emerging omics datasets can be leveraged to identify novel drug targets and guide therapeutic strategies for slowing and reversing ovarian aging.

SEARCH METHODS

We conducted a comprehensive literature search in the PubMed database using a range of relevant keywords: ovarian aging, age at natural menopause, premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), genomics, transcriptomics, epigenomics, DNA methylation, RNA modification, histone modification, proteomics, metabolomics, lipidomics, microbiome, single-cell, genome-wide association studies (GWAS), whole-exome sequencing, phenome-wide association studies (PheWAS), Mendelian randomization (MR), epigenetic target, drug target, machine learning, artificial intelligence (AI), deep learning, and multi-omics. The search was restricted to English-language articles published up to September 2024.

OUTCOMES

Multi-omics studies have uncovered key mechanisms driving ovarian aging, including DNA damage and repair deficiencies, inflammatory and immune responses, mitochondrial dysfunction, and cell death. By integrating multi-omics data, researchers can identify critical regulatory factors and mechanisms across various biological levels, leading to the discovery of potential drug targets. Notable examples include genetic targets such as BRCA2 and TERT, epigenetic targets like Tet and FTO, metabolic targets such as sirtuins and CD38+, protein targets like BIN2 and PDGF-BB, and transcription factors such as FOXP1.

WIDER IMPLICATIONS

The advent of cutting-edge omics technologies, especially single-cell technologies and spatial transcriptomics, has provided valuable insights for guiding treatment decisions and has become a powerful tool in drug discovery aimed at mitigating or reversing ovarian aging. As technology advances, the integration of single-cell multi-omics data with AI models holds the potential to more accurately predict candidate drug targets. This convergence offers promising new avenues for personalized medicine and precision therapies, paving the way for tailored interventions in ovarian aging.

REGISTRATION NUMBER

Not applicable.

Heart Rate Mystery Unveiled: Sex Differences in Human Sinoatrial Node Genes and Female Tachycardia

BACKGROUND

Despite over a century of clinical electrocardiographic studies showing that females exhibit a faster resting heart rate (HR), the mechanisms underlying sex differences in HR remain unresolved. Moreover, inappropriate sinus tachycardia primarily affects females, whereas males are at a higher risk for conduction block and atrial fibrillation. We hypothesized that the sexual dimorphism of genes responsible for sinoatrial node (SAN) pacemaking and signaling pathways may contribute to the sex differences in HR and susceptibility to arrhythmias.

METHODS

Human SAN central pacemaker and right atrial tissue were isolated from nondiseased ex vivo donor hearts. Gene expressions were quantified and validated using the transcriptomic panel and quantitative polymerase chain reaction. Gene set enrichment analysis, Ingenuity Pathway Analysis, and human-specific SAN models were utilized to define regulatory mechanisms and functional impacts of sex-biased gene transcription.

RESULTS

We identified differentially expressed region- and sex-specific genes, with gene sets enriched in HR regulation (eg, TBX3, HCN1) and metabolism (eg, ADIPOQ, LEP) pathways in female SAN. In contrast, differential genes and gene sets involved in collagen biosynthetic processes, fibrogenesis (eg, EGR1), and immune response (eg, IL6, CXCL8) pathways were enriched in males SAN and right atrial. Ingenuity Pathway Analysis predicted significant roles for TBX3 and estradiol in the sex-specific expression of genes involved in SAN function. Computational simulations showed that the sex-specific SAN differences in If (HCN1) and ICa,L (CACNA1D) can explain the faster HR in females, with females having a lower threshold for inappropriate sinus tachycardia, whereas males are more vulnerable to sinus arrest.

CONCLUSIONS

The human SAN exhibits region-specific sexual dimorphism in pacemaking gene sets. Higher expression of TBX3 and HCN1 in females may underlie their faster HR and increased susceptibility to inappropriate sinus tachycardia, whereas enriched gene sets related to inflammation and collagen biosynthesis in males may predispose them to conduction impairments and atrial fibrillation risk.

An organ-wide spatiotemporal transcriptomic and cellular atlas of the regenerating zebrafish heart

Adult zebrafish robustly regenerate injured hearts through a complex orchestration of molecular and cellular activities. However, this remarkable process, which is largely non-existent in humans, remains incompletely understood. Here, we utilize integrated spatial transcriptomics (Stereo-seq) and single-cell RNA-sequencing (scRNA-seq) to generate a spatially-resolved molecular and cellular atlas of regenerating zebrafish heart across eight stages. We characterize the cascade of cardiomyocyte cell states responsible for producing regenerated myocardium and explore a potential role for tpm4a in cardiomyocyte re-differentiation. Moreover, we uncover the activation of ifrd1 and atp6ap2 genes as a unique feature of regenerative hearts. Lastly, we reconstruct a 4D "virtual regenerating heart" comprising 569,896 cells/spots derived from 36 scRNA-seq libraries and 224 Stereo-seq slices. Our comprehensive atlas serves as a valuable resource to the cardiovascular and regeneration scientific communities and their ongoing efforts to understand the molecular and cellular mechanisms underlying vertebrate heart regeneration.

Spatial genomics: Mapping the landscape of fibrosis

Organ fibrosis causes major morbidity and mortality worldwide. Treatments for fibrosis are limited, with organ transplantation being the only cure. Here, we review how various state-of-the-art spatial genomics approaches are being deployed to interrogate fibrosis across multiple organs, providing exciting insights into fibrotic disease pathogenesis. These include the detailed topographical annotation of pathogenic cell populations and states, detection of transcriptomic perturbations in morphologically normal tissue, characterization of fibrotic and homeostatic niches and their cellular constituents, and in situ interrogation of ligand-receptor interactions within these microenvironments. Together, these powerful readouts enable detailed analysis of fibrosis evolution across time and space.

Glial-immune interactions in barrier organs

Neuro-immune interactions within barrier organs, such as lung, gut, and skin, are crucial in regulating tissue homeostasis, inflammatory responses, and host defence. Our rapidly advancing understanding of peripheral neuroimmunology is transforming the field of barrier tissue immunology, offering a fresh perspective for developing therapies for complex chronic inflammatory disorders affecting barrier organs. However, most studies have primarily examined interactions between the peripheral nervous system and the immune system from a neuron-focused perspective, while glial cells, the nonneuronal cells of the nervous system, have received less attention. Glial cells were long considered as mere bystanders, only supporting their neuronal neighbours, but recent discoveries mainly on enteric glial cells in the intestine have implicated these cells in immune-regulation and inflammatory disease pathogenesis. In this review, we will highlight the bi-directional interactions between peripheral glial cells and the immune system and discuss the emerging immune regulatory functions of glial cells in barrier organs.

Quantitative characterization of cell niches in spatially resolved omics data

Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.

Glucagon-like peptide-1 receptor agonism and end-organ protection

Identification of exendin-4 (a glucagon-like peptide-1 receptor agonist, GLP-1RA) in Gila monster venom may be regarded as one of the most serendipitous discoveries of recent times. GLP-1RAs are now an established therapeutic approach in type 2 diabetes (T2D), body weight management, and cardiovascular (CV) risk protection. Furthermore, there is a growing platform of evidence that GLP-1RA has extended benefit in renal, hepatic, respiratory, and neurological diseases. One can speculate on the biological advantage of exendin-4 to the Gila monster, but for humankind GLP-1RAs are peptides with significant potential to improve disease-related outcomes. We report on the latest evidence and mechanisms for GLP-1RA-mediated end-organ protection that uniquely highlight its future development potential across multiple disease areas.

Tissue elasticity modulates cardiac pacemaker cell automaticity

Tissue elasticity is essential to a broad spectrum of cell biology and organ function including the heart. Routine cell culture models on rigid polystyrene dishes are limited in studying the impact of tissue elasticity in distinct regions of the myocardium such as the cardiac conduction system. Gelatin, a derivative of collagen, is a simple and tunable platform for modeling tissue elasticity. We sought to study the effects of increasing tissue stiffness on cardiac pacemaker cell function by using transcription factor-reprogrammed pacemaker cells cultured on gelatin hydrogels with specific elasticity. Our data indicate that automaticity of the pacemaker cells, measured in rhythmic contractions and oscillating intracellular Ca2+ transients, was enhanced when cultured on a stiffer matrix of 14 kPa. This was accompanied by increased expression of cardiac pacemaker ion channel, Hcn4, and a reciprocal decrease in Cx43 expression compared with control conditions. Propagation of Ca2+ transients was slower in the pacemaker cell monolayers compared with control, which recapitulates a hallmark feature in the native pacemaker tissue. Ca2+ transient propagation of pacemaker cell monolayer was slower on stiffer than on softer hydrogel, and this was dependent on enhanced proliferation of cardiac fibroblasts rather than differences in gap junctional coupling. Culturing the pacemaker cells on rigid plastic plates led to irregular or loss of synchronous contractions as well as unusually long Ca2+ transient durations. Taken together, our data demonstrate that automaticity of pacemaker cells is augmented by stiffer extracellular matrix substrates within the elasticity range of the healthy myocardium. This simple approach presents a physiological in vitro model to study mechanoelectric feedback of cardiomyocytes including the conduction system cells.NEW & NOTEWORTHY The major achievement of this work is development of a robust and straightforward approach to model cardiac conduction system cells with a range of cardiac tissue elasticity with a goal to understand the impact of tissue stiffness on cardiac pacing. Our data provide a framework for further investigation of the heart rhythm in health and disease in the context of fibrosis.

Alpha-actinin-1 stabilizes focal adhesions to facilitate sarcomere assembly in cardiac myocytes

Cardiac sarcomere assembly is a highly orchestrated process requiring integration between intracellular contractile components and extracellular adhesions. While α-actinin-2 (ACTN2) is well known for its structural role at Z-discs, the function of the "non-muscle" paralog α-actinin-1 (ACTN1) in cardiomyocytes remains unclear. Using human induced pluripotent stem cell-derived cardiac myocytes (hiCMs), we demonstrate that ACTN1 is essential for sarcomere assembly. siRNA-mediated depletion of ACTN1 disrupted Z-line formation and impaired sarcomere organization, defects that were rescued by exogenous ACTN1 but not ACTN2, revealing non-redundant functions. Unlike ACTN2, ACTN1 localized predominantly to focal adhesions and was required for adhesion maturation, as evidenced by reduced adhesion size and number following ACTN1 depletion. Live-cell imaging of vinculin dynamics showed decreased stability of adhesion-associated vinculin in ACTN1-deficient cells, whereas paxillin dynamics were unaffected. These results suggest that ACTN1 stabilizes focal adhesions to promote effective force transmission during sarcomere assembly.

CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Cell-cell communication is essential for tissue development, function and regeneration. The revolution of single-cell genomics technologies offers an unprecedented opportunity to uncover how cells communicate in vivo within their tissue niches and how disruption of these niches can lead to diseases and developmental abnormalities. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with methods to integrate these interactions with single-cell genomics data. Here we present a protocol for the latest version of CellPhoneDB (v5), offering several new features. First, the repository has been expanded by one-third with the addition of new interactions, including ~1,000 interactions mediated by nonpeptidic ligands such as steroidogenic hormones, neurotransmitters and small G-protein-coupled receptor (GPCR)-binding ligands. Second, we outline a new way of using the database that allows users to tailor queries to their experimental designs. Third, the update incorporates novel strategies to prioritize specific cell-cell interactions, leveraging information from other modalities such as tissue microenvironments derived from spatial transcriptomics technologies or transcription factor activities derived from a single-cell assay for transposase accessible chromatin assays. Finally, we describe the new CellPhoneDBViz module to interactively visualize and share results. Altogether, CellPhoneDB v5 enhances the precision of cell-cell communication inference, offering new insights into tissue biology in physiological microenvironments. This protocol typically takes ~15 min and requires basic knowledge of python.

Integrating -Omic Technologies across Modality, Space, and Time to Decipher Remodeling in Cardiac Disease

PURPOSE OF REVIEW

Despite significant efforts to understand pathophysiological processes underlying cardiac diseases, the molecular causes for the most part remain unresolved. Rapid advancements in -omics technologies, and their application in cardiac research, offer new insight into cardiac remodeling in disease states. This review aims to provide an accessible overview of recent advances in omics approaches for studying cardiac remodeling, catering to readers without extensive prior expertise.

RECENT FINDINGS

We provide a methodologically focused overview of current methods for performing transcriptomics and proteomics, including their extensions for single-cell and spatial measurements. We discuss approaches to integrate data across modalities, resolutions and time. Key recent applications within the cardiac field are highlighted. Each -omics modality can provide insight, yet each existing experimental method has technical or conceptual limitations. Integrating data across multiple modalities can leverage strengths and mitigate weaknesses, ultimately enhancing our understanding of cardiac pathophysiology.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

Charting Postnatal Heart Development Using In Vivo Single-Cell Functional Genomics

The transition at birth, marked by increased circulatory demands and rapid growth, necessitates extensive remodeling of the heart's structure, function, and metabolism. This transformation requires precise spatial and temporal coordination among diverse cardiac cell types; central to this process is cardiomyocyte maturation, yet the regulatory mechanisms driving these changes remain poorly understood. Here, we present a temporal and spatial atlas of postnatal hearts by integrating single-nucleus transcriptomics with image-based spatial transcriptomics, which uncovers the dynamic regulatory networks of cardiomyocyte maturation. To functionally interrogate candidate regulators in vivo , we developed Probe-based Indel-detectable Perturb-seq (PIP-seq), a high-throughput platform that uses probe-based chemistry to directly capture sgRNA expression, perturbation status, and transcriptomic profiles at single-nucleus resolution. Applying PIP-seq to postnatal cardiac development identified 21 novel regulators of cardiomyocyte maturation, highlighting critical nodal points in this process. Our study establishes a high-resolution framework for dissecting postnatal heart development, underscoring the integrative and highly ordered roles of microenvironment and intercellular communication in cardiomyocyte maturation. Importantly, PIP-seq enables systematic, high-throughput exploration of gene function and networks underlying complex biological processes in their native in vivo context.

Thor: a platform for cell-level investigation of spatial transcriptomics and histology

Spatial transcriptomics integrates transcriptomics data with histological tissue images, offering deeper insights into cellular organization and molecular functions. However, existing computational platforms mainly focus on genomic analysis, leaving a gap in the seamless integration of genomic and image analysis. To address this, we introduce Thor, a comprehensive computational platform for multi-modal analysis of spatial transcriptomics and histological images. Thor utilizes an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptomes from spot-level data, effectively integrating cell morphology with spatial transcriptomics. The platform features 10 modules designed for cell-level genomic and image analysis. Additionally, we present Mjolnir, a web-based tool for interactive tissue analysis using vivid gigapixel images that display information on histology, gene expression, pathway enrichment, and immune response. Thor's accuracy was validated through simulations and ISH, MERFISH, Xenium, and Stereo-seq datasets. To demonstrate its versatility, we applied Thor for joint genomic-histology analysis across various datasets. In in-house heart failure patient samples, Thor identified a regenerative signature in heart failure, with protein presence confirmed in blood vessels through immunofluorescence staining. Thor also revealed the layered structure of the mouse olfactory bulb, performed unbiased screening of breast cancer hallmarks, elucidated the heterogeneity of immune responses, and annotated fibrotic regions in multiple heart failure zones using a semi-supervised approach. Furthermore, Thor imputed high-resolution spatial transcriptomics data in an in-house bladder cancer sample sequenced using Visium HD, uncovering stronger spatial patterns that align more closely with histology. Bridging the gap between genomic and image analysis in spatial biology, Thor offers a powerful tool for comprehensive cellular and molecular analysis.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Multi-modal refinement of the human heart atlas during the first gestational trimester

Forty first-trimester human hearts were studied to lay groundwork for further studies of the mechanisms underlying congenital heart defects. We first sampled 49,227 cardiac nuclei from three fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw) for single-nucleus RNA sequencing, enabling the distinction of six classes comprising 21 cell types. Improved resolution led to the identification of previously unappreciated cardiomyocyte populations and minority autonomic and lymphatic endothelial transcriptomes, among others. After integration with 5-7 pcw heart single-cell RNA-sequencing data, we identified a human cardiomyofibroblast progenitor preceding the diversification of cardiomyocyte and stromal lineages. Spatial transcriptomic analysis (six Visium sections from two additional hearts) was aided by deconvolution, and key spatial markers validated on sectioned and whole hearts in two- and three-dimensional space and over time. Altogether, anatomical-positional features, including innervation, conduction and subdomains of the atrioventricular septum, translate latent molecular identity into specialized cardiac functions. This atlas adds unprecedented spatial and temporal resolution to the characterization of human-specific aspects of early heart formation.

Towards deciphering the bone marrow microenvironment with spatial multi-omics

The tissue microenvironment refers to a localised tissue area where a complex combination of cells, structural components, and signalling molecules work together to support specific biological activities. A prime example is the bone marrow microenvironment, particularly the hematopoietic stem cell (HSC) niche, which is of immense interest due to its critical role in supporting lifelong blood cell production and the growth of malignant cells. In this review, we summarise the current understanding of HSC niche biology, highlighting insights gained from advanced imaging and genomic techniques. We also discuss the potential of emerging technologies such as spatial multi-omics to unravel bone marrow architecture in unprecedented detail.

Radiotherapy-Associated Cellular Senescence and EMT Alterations Contribute to Distinct Disease Relapse Patterns in Locally Advanced Cervical Cancer

A notable number of locally advanced cervical carcinoma (LACC) patients experience local or distant disease relapse following radiotherapy. The contribution of tumor microenvironment (TME) to tumor recurrence at different sites remains unclear. Here, single-nucleus RNA sequencing data from 28 pre- and on-treatment LACC samples from patients with different disease relapse patterns is analyzed. The findings revealed opposing alterations in the expression levels of the cellular senescence pathway after radiotherapy in patients with local and distant relapses. In contrast, an increase in the expression of the epithelial-mesenchymal transition module after radiotherapy in both relapse groups is observed. Cell-cell interactions, drug-target expression analyses in malignant cells after radiation, and multiplex immunofluorescence of tumor tissue identified interleukin-1 receptor type I (IL1R1) as a potential therapeutic target. It is demonstrated that combining the IL1R1 inhibitor anakinra with radiation can mitigate the effects of radiation on tumor cells. This study highlights the distinct roles of cellular senescence and EMT in tumor recurrence.

Proteomic profile of human sinoatrial and atrioventricular nodes in comparison to working myocardium

The proteomic profile of the human cardiac conduction system: the sinoatrial node (SAN) and atrioventricular node (AVN), remains poorly understood. The aim of the current study is to identify proteomic characteristic of the human SAN and AVN in the comparison to working myocardium of the right atrium (RAM) and right ventricle (RVM). The proteomic analysis was performed on 10 autopsied human heart specimens collected from healthy adults. During the data-independent acquisition proteomics analysis 2752 different proteins were identified in all sample sets. In both nodal tissues (compared to working myocardium), the following pathways were upregulated: regulation of Insulin-like Growth Factor transport and uptake by Insulin-like Growth Factor Binding Proteins, post-translational protein phosphorylation, glutathione metabolism, metabolism of carbohydrates, glycolysis and gluconeogenesis. Other common for nodal tissue pathways were these related to immune system and related to extracellular matrix. The pathways related to cardiac muscle contraction were more abundant in RAM and RVM samples. The current study presents extensive comparative analysis of protein abundance in the human SAN and AVN. Few key differences may be found in the nodal proteome in comparison to working cardiomyocytes, including involvement of immune system and upregulated pathways related to extracellular matrix. The SAN exhibits enrichment in the PPAR signaling and pentose phosphate pathways, as well as prostaglandin synthesis and regulatory proteins, compared to the AVN.

Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo's utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.

Sex difference in TRPM3 channel functioning in nociceptive and vascular systems: an emerging target for migraine therapy in females?

Transient Receptor Potential Melastatin 3 (TRPM3) channels are Ca2+ permeable ion channels that act as polymodal sensors of mechanical, thermal, and various chemical stimuli. TRPM3 channels are highly expressed in the trigeminovascular system, including trigeminal neurons and the vasculature. Their presence in dural afferents suggests that they are potential triggers of migraine pain, which is originating from the meningeal area. This area is densely innervated by autonomous and trigeminal nerves that contain the major migraine mediator calcitonin gene-related peptide (CGRP) in peptidergic nerve fibers. Co-expression of TRPM3 channels and CGRP receptors in meningeal nerves suggests a potential interplay between both signalling systems. Compared to other members of the TRP family, TRPM3 channels have a high sensitivity to sex hormones and to the endogenous neurosteroid pregnenolone sulfate (PregS). The predominantly female sex hormones estrogen and progesterone, of which the levels drop during menses, act as natural inhibitors of TRPM3 channels, while PregS is a known endogenous agonist of these channels. A decrease in sex hormone levels has also been suggested as trigger for attacks of menstrually-related migraine. Notably, there is a remarkable sex difference in TRPM3-mediated effects in trigeminal nociceptive signalling and the vasculature. In line with this, the relaxation of human isolated meningeal arteries induced by the activation of TRPM3 channels is greater in females. Additionally, the sex-dependent vasodilatory responses to CGRP in meningeal arteries seem to be influenced by age-related hormonal changes, which could contribute to sex differences in migraine pathology. Consistent with these observations, activation of TRPM3 channels triggers nociceptive sensory firing much more prominently in female than male mouse meninges, suggesting that pain processing in female patients with migraine may differ. Overall, the combined TRPM3-related neuronal and vascular mechanisms could provide a possible explanation for the higher prevalence and even the more severe quality of migraine attacks in females. This narrative review summarizes recent data on the sex-dependent roles of TRPM3 channels in migraine pathophysiology, the potential interplay between TRPM3 and CGRP signalling, and highlights the prospects for translational therapies targeting TRPM3 channels, which may be of particular relevance for women with migraine.

Integrated Multiomics Reveals Alterations in Paucimannose and Complex Type N-Glycans in Cardiac Tissue of Patients with COVID-19

Coronavirus infectious disease of 2019 (COVID-19) can lead to cardiac complications, yet the molecular mechanisms driving these effects remain unclear. Protein glycosylation is crucial for viral replication, immune response, and organ function and has been found to change in the lungs and liver of patients with COVID-19. However, how COVID-19 impacts cardiac protein glycosylation has not been defined. Our study combined single nuclei transcriptomics, mass spectrometry (MS)-based glycomics, and lectin-based tissue imaging to investigate alterations in N-glycosylation in the human heart post-COVID-19. We identified significant expression differences in glycogenes involved in N-glycan biosynthesis and MS analysis revealed a reduction in high mannose and isomers of paucimannose structures post-infection, with changes in paucimannose directly correlating with COVID-19 independent of comorbidities. Our observations suggest that COVID-19 primes cardiac tissues to alter the glycome at all levels, namely, metabolism, nucleotide sugar transport, and glycosyltransferase activity. Given the role of N-glycosylation in cardiac function, this study provides a basis for understanding the molecular events leading to cardiac damage post-COVID-19 and informing future therapeutic strategies to treat cardiac complications resulting from coronavirus infections.

Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

Digital twins as global learning health and disease models for preventive and personalized medicine

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

Pathophysiological insights into HFpEF from studies of human cardiac tissue

Heart failure with preserved ejection fraction (HFpEF) is a major, worldwide health-care problem. Few therapies for HFpEF exist because the pathophysiology of this condition is poorly defined and, increasingly, postulated to be diverse. Although perturbations in other organs contribute to the clinical profile in HFpEF, altered cardiac structure, function or both are the primary causes of this heart failure syndrome. Therefore, studying myocardial tissue is fundamental to improve pathophysiological insights and therapeutic discovery in HFpEF. Most studies of myocardial changes in HFpEF have relied on cardiac tissue from animal models without (or with limited) confirmatory studies in human cardiac tissue. Animal models of HFpEF have evolved based on theoretical HFpEF aetiologies, but these models might not reflect the complex pathophysiology of human HFpEF. The focus of this Review is the pathophysiological insights gained from studies of human HFpEF myocardium. We outline the rationale for these studies, the challenges and opportunities in obtaining myocardial tissue from patients with HFpEF and relevant comparator groups, the analytical approaches, the pathophysiological insights gained to date and the remaining knowledge gaps. Our objective is to provide a roadmap for future studies of cardiac tissue from diverse cohorts of patients with HFpEF, coupling discovery biology with measures to account for pathophysiological diversity.

Transcriptional profile of the rat cardiovascular system at single-cell resolution

We sought to characterize cellular composition across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We performed single-nucleus RNA sequencing (snRNA-seq) in 78 samples in 10 distinct regions, including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins, which produced 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, with different cellular composition across cardiac regions and tissue-specific transcription for each cell type. Several cell subtypes were region specific, including a subtype of vascular smooth muscle cells enriched in the large vasculature. We observed tissue-enriched cellular communication networks, including heightened Nppa-Npr1/2/3 signaling in the sinoatrial node. The existence of tissue-restricted cell types suggests regional regulation of cardiovascular physiology. Our detailed transcriptional characterization of each cell type offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.

Microenvironmental determinants of endothelial cell heterogeneity

During development, endothelial cells (ECs) undergo an extraordinary specialization by which generic capillary microcirculatory networks spanning from arteries to veins transform into patterned organotypic zonated blood vessels. These capillary ECs become specialized to support the cellular and metabolic demands of each specific organ, including supplying tissue-specific angiocrine factors that orchestrate organ development, maintenance of organ-specific functions and regeneration of injured adult organs. Here, we illustrate the mechanisms by which microenvironmental signals emanating from non-vascular niche cells induce generic ECs to acquire specific inter-organ and intra-organ functional attributes. We describe how perivascular, parenchymal and immune cells dictate vascular heterogeneity and capillary zonation, and how this system is maintained through tissue-specific signalling activated by vasculogenic and angiogenic factors and deposition of matrix components. We also discuss how perturbation of organotypic vascular niche cues lead to erasure of EC signatures, contributing to the pathogenesis of disease processes. We also describe approaches that use reconstitution of tissue-specific signatures of ECs to promote regeneration of damaged organs.

Rossi F, Collins C, Bashir J, Laksman Z. Spatial Transcriptomics in Human Cardiac Tissue

Spatial transcriptomics has transformed our understanding of gene expression by preserving the spatial context within tissues. This review focuses on the application of spatial transcriptomics in human cardiac tissues, exploring current technologies with a focus on commercially available platforms. We also highlight key studies utilizing spatial transcriptomics to investigate cardiac development, electro-anatomy, immunology, and ischemic heart disease. These studies demonstrate how spatial transcriptomics can be used in conjunction with other omics technologies to provide a more comprehensive picture of human health and disease. Despite its transformative potential, spatial transcriptomics comes with several challenges that limit its widespread adoption and broader application. By addressing these limitations and fostering interdisciplinary collaboration, spatial transcriptomics has the potential to become an essential tool in cardiovascular research. We hope this review serves as a practical guide for researchers interested in adopting spatial transcriptomics, particularly those with limited prior experience, by providing insights into current technologies, applications, and considerations for successful implementation.

Role of GPCR Signaling in Anthracycline-Induced Cardiotoxicity

Anthracyclines are a class of chemotherapeutics commonly used to treat a range of cancers. Despite success in improving cancer survival rates, anthracyclines have dose-limiting cardiotoxicity that prevents more widespread clinical utility. Currently, the therapeutic options for these patients are limited to the iron-chelating agent dexrazoxane, the only FDA-approved drug for anthracycline cardiotoxicity. However, the clinical use of dexrazoxane has failed to replicate expectations from preclinical studies. A limited list of GPCRs have been identified as pathogenic in anthracycline-induced cardiotoxicity, including receptors (frizzled, adrenoreceptors, angiotensin II receptors) previously implicated in cardiac remodeling in other pathologies. The RNA sequencing of iPSC-derived cardiac myocytes from patients has increased our understanding of the pathogenic mechanisms driving cardiotoxicity. These data identified changes in the expression of novel GPCRs, heterotrimeric G proteins, and the regulatory pathways that govern downstream signaling. This review will capitalize on insights from these experiments to explain aspects of disease pathogenesis and cardiac remodeling. These data provide a cornucopia of possible unexplored potential pathways by which we can reduce the cardiotoxic side effects, without compromising the anti-cancer effects, of doxorubicin and provide new therapeutic options to improve the recovery and quality of life for patients undergoing chemotherapy.

Elucidating cardiomyocyte heterogeneity and maturation dynamics through integrated single-cell and spatial transcriptomics

The intricate development and functionality of the mammalian heart are influenced by the heterogeneous nature of cardiomyocytes (CMs). In this study, single-cell and spatial transcriptomics were utilized to analyze cells from neonatal mouse hearts, resulting in a comprehensive atlas delineating the transcriptional profiles of distinct CM subsets. A continuum of maturation states was elucidated, emphasizing a progressive developmental trajectory rather than discrete stages. This approach enabled the mapping of these states across various cardiac regions, illuminating the spatial organization of CM development and the influence of the cellular microenvironment. Notably, a subset of transitional CMs was identified, characterized by a transcriptional signature marking a pivotal maturation phase, presenting a promising target for therapeutic strategies aimed at enhancing cardiac regeneration. This atlas not only elucidates fundamental aspects of cardiac development but also serves as a valuable resource for advancing research into cardiac physiology and pathology, with significant implications for regenerative medicine.

T cells in cardiac health and disease

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, with inflammation playing a pivotal role in its pathogenesis. T lymphocytes are crucial components of the adaptive immune system that have emerged as key mediators in both cardiac health and the development and progression of CVD. This Review explores the diverse roles of T cell subsets, including Th1, Th17, γδ T cells, and Tregs, in myocardial inflammatory processes such as autoimmune myocarditis and myocardial infarction. We discuss the contribution of T cells to myocardial injury and remodeling, with emphasis on specific immune receptors, e.g., CD69, that have a critical role in regulating immune tolerance and maintaining the balance between T cell subsets in the heart. Additionally, we offer a perspective on recent advances in T cell-targeted therapies and their potential to modulate immune responses and improve clinical outcomes in patients with CVD and in heart transplant recipients. Understanding the intricate interplay between T cells and cardiovascular pathology is essential for developing novel immunotherapeutic strategies against CVD.

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and cell-type annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate cell-type annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

Ion channel traffic jams: the significance of trafficking deficiency in long QT syndrome

A well-balanced ion channel trafficking machinery is paramount for the normal electromechanical function of the heart. Ion channel variants and many drugs can alter the cardiac action potential and lead to arrhythmias by interfering with mechanisms like ion channel synthesis, trafficking, gating, permeation, and recycling. A case in point is the Long QT syndrome (LQTS), a highly arrhythmogenic disease characterized by an abnormally prolonged QT interval on ECG produced by variants and drugs that interfere with the action potential. Disruption of ion channel trafficking is one of the main sources of LQTS. We review some molecular pathways and mechanisms involved in cardiac ion channel trafficking. We highlight the importance of channelosomes and other macromolecular complexes in helping to maintain normal cardiac electrical function, and the defects that prolong the QT interval as a consequence of variants or the effect of drugs. We examine the concept of "interactome mapping" and illustrate by example the multiple protein-protein interactions an ion channel may undergo throughout its lifetime. We also comment on how mapping the interactomes of the different cardiac ion channels may help advance research into LQTS and other cardiac diseases. Finally, we discuss how using human induced pluripotent stem cell technology to model ion channel trafficking and its defects may help accelerate drug discovery toward preventing life-threatening arrhythmias. Advancements in understanding ion channel trafficking and channelosome complexities are needed to find novel therapeutic targets, predict drug interactions, and enhance the overall management and treatment of LQTS patients.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

Considerations for building and using integrated single-cell atlases

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.

The molecular mechanisms of cardiac development and related diseases

Cardiac development is a complex and intricate process involving numerous molecular signals and pathways. Researchers have explored cardiac development through a long journey, starting with early studies observing morphological changes and progressing to the exploration of molecular mechanisms using various molecular biology methods. Currently, advancements in stem cell technology and sequencing technology, such as the generation of human pluripotent stem cells and cardiac organoids, multi-omics sequencing, and artificial intelligence (AI) technology, have enabled researchers to understand the molecular mechanisms of cardiac development better. Many molecular signals regulate cardiac development, including various growth and transcription factors and signaling pathways, such as WNT signaling, retinoic acid signaling, and Notch signaling pathways. In addition, cilia, the extracellular matrix, epigenetic modifications, and hypoxia conditions also play important roles in cardiac development. These factors play crucial roles at one or even multiple stages of cardiac development. Recent studies have also identified roles for autophagy, metabolic transition, and macrophages in cardiac development. Deficiencies or abnormal expression of these factors can lead to various types of cardiac development abnormalities. Nowadays, congenital heart disease (CHD) management requires lifelong care, primarily involving surgical and pharmacological treatments. Advances in surgical techniques and the development of clinical genetic testing have enabled earlier diagnosis and treatment of CHD. However, these technologies still have significant limitations. The development of new technologies, such as sequencing and AI technologies, will help us better understand the molecular mechanisms of cardiac development and promote earlier prevention and treatment of CHD in the future.

Association of Metabolic Diseases and Moderate Fat Intake with Myocardial Infarction Risk

BACKGROUND

Myocardial infarction (MI) can range from mild to severe cardiovascular events and typically develops through complex interactions between genetic and lifestyle factors.

OBJECTIVES

We aimed to understand the genetic predisposition associated with MI through genetic correlation, colocalization analysis, and cells' gene expression values to develop more effective prevention and treatment strategies to reduce its burden.

METHODS

A polygenic risk score (PRS) was employed to estimate the genetic risk for MI and to analyze the dietary interactions with PRS that affect MI risk in adults over 45 years (n = 58,701). Genetic correlation (rg) between MI and metabolic syndrome-related traits was estimated with linkage disequilibrium score regression. Single-cell RNA sequencing (scRNA-seq) analysis was performed to investigate cellular heterogeneity in MI-associated genes.

RESULTS

Ten significant genetic variants associated with MI risk were related to cardiac, immune, and brain functions. A high PRS was associated with a threefold increase in MI risk (OR: 3.074, 95% CI: 2.354-4.014, p < 0.001). This increased the risk of MI plus obesity, hyperglycemia, dyslipidemia, and hypertension by about twofold after adjusting for MI-related covariates (p < 0.001). The PRS interacted with moderate fat intake (>15 energy percent), alcohol consumption (<30 g/day), and non-smoking, reducing MI risk in participants with a high PRS. MI was negatively correlated with the consumption of olive oil, sesame oil, and perilla oil used for cooking (rg = -0.364). MI risk was associated with storkhead box 1 (STOX1) and vacuolar protein sorting-associated protein 26A (VPS26A) in atrial and ventricular cardiomyocytes and fibroblasts.

CONCLUSIONS

This study identified novel genetic variants and gene expression patterns associated with MI risk, influenced by their interaction with fat and alcohol intake, and smoking status. Our findings provide insights for developing personalized prevention and treatment strategies targeting this complex clinical presentation of MI.

Self-organizing human heart assembloids with autologous and developmentally relevant cardiac neural crest-derived tissues

Neural crest cells (NCCs) are a multipotent embryonic cell population of ectodermal origin that extensively migrate during early development and contribute to the formation of multiple tissues. Cardiac NCCs play a critical role in heart development by orchestrating outflow tract septation, valve formation, aortic arch artery patterning, parasympathetic innervation, and maturation of the cardiac conduction system. Abnormal migration, proliferation, or differentiation of cardiac NCCs can lead to severe congenital cardiovascular malformations. However, the complexity and timing of early embryonic heart development pose significant challenges to studying the molecular mechanisms underlying NCC-related cardiac pathologies. Here, we present a sophisticated functional model of human heart assembloids derived from induced pluripotent stem cells, which, for the first time, recapitulates cardiac NCC integration into the human embryonic heart in vitro . NCCs successfully integrated at developmentally relevant stages into heart organoids, and followed developmental trajectories known to occur in the human heart. They demonstrated extensive migration, differentiated into cholinergic neurons capable of generating nerve impulses, and formed mature glial cells. Additionally, they contributed to the mesenchymal populations of the developing outflow tract. Through transcriptomic analysis, we revealed that NCCs acquire molecular features of their cardiac derivatives as heart assembloids develop. NCC-derived parasympathetic neurons formed functional connections with cardiomyocytes, promoting the maturation of the cardiac conduction system. Leveraging this model's cellular complexity and functional maturity, we uncovered that early exposure of NCCs to antidepressants harms the development of NCC derivatives in the context of the developing heart. The commonly prescribed antidepressant Paroxetine disrupted the expression of a critical early neuronal transcription factor, resulting in impaired parasympathetic innervation and functional deficits in cardiac tissue. This advanced heart assembloid model holds great promise for high-throughput drug screening and unraveling the molecular mechanisms underlying NCC-related cardiac formation and congenital heart defects.

IN BRIEF

Human neural crest heart assembloids resembling the major directions of neural crest differentiation in the human embryonic heart, including parasympathetic innervation and the mesenchymal component of the outflow tract, provide a human-relevant embryonic platform for studying congenital heart defects and drug safety.

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Genetic and Molecular Underpinnings of Atrial Fibrillation

Atrial fibrillation (AF), the most common sustained arrhythmia, increases stroke and heart failure risks. Here we review genes linked to AF and mechanisms by which they alter AF risk. We highlight gene expression differences between atrial and ventricular cardiomyocytes, regulatory mechanisms responsible for these differences, and their potential contribution to AF. Understanding AF mechanisms through the lens of atrial gene regulation is crucial to improving AF treatment.

Decoding the molecular, cellular, and functional heterogeneity of zebrafish intracardiac nervous system

The proper functioning of the heart relies on the intricate interplay between the central nervous system and the local neuronal networks within the heart itself. While the central innervation of the heart has been extensively studied, the organization and functionality of the intracardiac nervous system (IcNS) remain largely unexplored. Here, we present a comprehensive taxonomy of the IcNS, utilizing single-cell RNA sequencing, anatomical studies, and electrophysiological techniques. Our findings reveal a diverse array of neuronal types within the IcNS, exceeding previous expectations. We identify a subset of neurons exhibiting characteristics akin to pacemaker/rhythmogenic neurons similar to those found in Central Pattern Generator networks of the central nervous system. Our results underscore the heterogeneity within the IcNS and its key role in regulating the heart's rhythmic functionality. The classification and characterization of the IcNS presented here serve as a valuable resource for further exploration into the mechanisms underlying heart functionality and the pathophysiology of associated cardiac disorders.

Cutaneous T cell lymphoma atlas reveals malignant TH2 cells supported by a B cell-rich tumor microenvironment

Cutaneous T cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides, can be difficult to diagnose, resulting in treatment delay. We performed single-cell and spatial transcriptomics analysis of skin from patients with mycosis fungoides-type CTCL and an integrated comparative analysis with human skin cell atlas datasets from healthy and inflamed skin. We revealed the co-optation of T helper 2 (TH2) cell-immune gene programs by malignant CTCL cells and modeling of the tumor microenvironment to support their survival. We identified MHC-II+ fibroblasts and dendritic cells that can maintain TH2 cell-like tumor cells. CTCL tumor cells are spatially associated with B cells, forming tertiary lymphoid structure-like aggregates. Finally, we validated the enrichment of B cells in CTCL and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.

An organotypic atlas of human vascular cells

The human vascular system, comprising endothelial cells (ECs) and mural cells, covers a vast surface area in the body, providing a critical interface between blood and tissue environments. Functional differences exist across specific vascular beds, but their molecular determinants across tissues remain largely unknown. In this study, we integrated single-cell transcriptomics data from 19 human organs and tissues and defined 42 vascular cell states from approximately 67,000 cells (62 donors), including angiotypic transitional signatures along the arterial endothelial axis from large to small caliber vessels. We also characterized organotypic populations, including splenic littoral and blood-brain barrier ECs, thus clarifying the molecular profiles of these important cell states. Interrogating endothelial-mural cell molecular crosstalk revealed angiotypic and organotypic communication pathways related to Notch, Wnt, retinoic acid, prostaglandin and cell adhesion signaling. Transcription factor network analysis revealed differential regulation of downstream target genes in tissue-specific modules, such as those of FOXF1 across multiple lung vascular subpopulations. Additionally, we make mechanistic inferences of vascular drug targets within different vascular beds. This open-access resource enhances our understanding of angiodiversity and organotypic molecular signatures in human vascular cells, and has therapeutic implications for vascular diseases across tissues.