Search Results (98)

Background/Objectives: Traditional protein contact analysis often fails to distinguish between local, sequence-driven motifs and global, tertiary scaffolding, which ensures structural determinism. While deep-learning models do not fully elucidate the ‘why’, they do reveal the underlying directional rules of the stability landscape. In this study, we analyzed 475 non-redundant Protein Data Bank (PDB) structures categorized into SCOP classes (all-α, all-β, α/β, α+β) of the hydrolase superfamily. Methods: To isolate the structural anchors of the global fold, we applied a sequence separation filter of ∣i − j∣ ≥ 6 and a precise spatial cutoff of 3–5 Å between Cα-only to construct asymmetric 20 × 20 frequency matrices, both raw and normalized, then present the former using a violin diagram. We developed a Pearson Correlation (PC) framework to analyze these matrices, providing high correlation when considered as vectors and giving the directionality (N-to-C vs. C-to-N) in protein folding when considered as matrices. Results: Our results reveal a hierarchical organization of tertiary determinism. Initial visualization of Residue–Residue Contact Frequency Matrices (RRCFMs), Z-score normalization (NRRCFM), and violin plots reveal the Universal Structural Grammar (USG) of interaction. Furthermore, a near-perfect PC (r = 0.99) as determined via inter-class Z-score correlation and inter-class PC demonstrates shared statistical interaction laws. In addition, PC Stage 1 (intra-class) analysis identified high symmetry, with around 80% of contacts exhibiting a very strong to strong positive correlations, while PC Stage 2 (inter-class) analysis demonstrated that around 50% of contacts exhibited very strong to strong positive correlations. Finally, we identified universal druggable pockets for drug discovery. Conclusions: This powerful mathematical framework provides a robust analytical tool for structure-based drug design. Full article

Land capitalization has become one of the central issues in contemporary China’s economic development and land system reform. Existing scholarship has predominantly approached this topic from the perspectives of effects, governance, and property rights, while a spatial analytical lens remains conspicuously absent. This study draws on the theoretical perspective of the production of space (spatial politics) and selects Xunliao Bay, a coastal tourism destination currently undergoing rapid land capitalization, as a typical case. Based on qualitative methods, including three-phase, five-time interviews and non-participatory observation conducted in Xunliao Bay, it investigates the spatial logic and restructuring processes of land capitalization in coastal tourism areas. The findings reveal that: (1) Land capitalization in coastal tourism destinations is essentially a process of the spatialization of capital, following a logical sequence of “spatial occupation–spatial planning–spatial production.” (2) In Xunliao Bay, land capitalization has generated multifaceted spatial consequences, leading to the reconfiguration of land property rights, land functional attributes, and land morphology. (3) Far from being a purely economic value-adding endeavor, land capitalization in coastal tourism destinations constitutes a spatial political process fraught with power struggles, interest negotiations, and conflicts. In this process, capital forges “growth coalitions” with local governments to complete land consolidation and property rights restructuring, subsequently redefines land attributes through planning mechanisms to safeguard its own interests, and ultimately engages in selective land use to carry out landscape construction and spatial production, thereby profoundly reshaping the local socio-spatial fabric. This study extends the spatial perspective and tourism context within land capitalization research and deepens the theoretical understanding of land capitalization as a socio-spatial and political process. Full article

Fibroblast growth factor receptor 2b (FGFR2b) has become an increasingly important therapeutic target in gastric and gastroesophageal junction cancer, particularly with the clinical development of FGFR2b-directed antibody therapy. However, its translation into routine treatment selection is not straightforward. FGFR2b is usually assessed as a protein biomarker by immunohistochemistry, and a positive result may reflect different biological situations depending on staining intensity, percentage of positive tumor cells, sample type and spatial distribution. In addition, FGFR2b protein expression, FGFR2 amplification, transcript-level activity and true pathway dependency are related but not interchangeable. This review examines FGFR2b-positive gastric cancer from the perspective of biomarker reliability rather than target presence alone. We discuss the biological basis of FGFR2b targeting, the reasons for variability in reported positivity rates, the implications of intratumoral and inter-lesion heterogeneity, the current clinical evidence for FGFR2b-directed and broader FGFR-targeted approaches, and the emerging challenges of safety, resistance and treatment sequencing. Particular attention is given to the gap between detecting FGFR2b and identifying tumors in which this target is sufficiently expressed, representative and biologically relevant to guide therapy. We also consider how FGFR2b should be interpreted alongside HER2, CLDN18.2, immune biomarkers and other receptor tyrosine kinase alterations. As FGFR2b-directed strategies move forward, their success will depend not only on drug efficacy, but also on standardized testing, careful reporting, and selective reassessment when disease biology changes. FGFR2b therefore offers a useful model for how protein biomarkers can be developed in gastric cancer: not as isolated positive-or-negative labels, but as clinically interpreted variables within a changing therapeutic landscape. Full article

Alzheimer’s Disease (AD), the most common form of dementia, is pathologically defined by extracellular beta-amyloid (Aβ) plaques and intraneuronal neurofibrillary tangles (NFTs), accompanied by chronic neuroinflammation. Recent advances in single-cell RNA sequencing (scRNA-seq/snRNA-seq) and spatial transcriptomics have provided unprecedented resolution for unraveling the cellular and molecular landscape of neuroinflammation in AD. While scRNA-seq enables high-throughput profiling of cellular heterogeneity across brain regions, spatial transcriptomics preserves tissue architecture to map cell-type-specific gene expression within its anatomical context. This review synthesizes the neuroinflammatory mechanisms of AD, outlines the technical evolution and comparative capabilities of single-cell and spatial omics platforms, including resolution, throughput, and compatibility with multiple sample types, and critically evaluates findings from studies in both animal models and human brain tissues. These approaches have revealed state-specific transitions in microglia and astrocytes, including shifts in transcriptional programs, metabolic reprogramming, and pro-inflammatory polarization across disease stages. Notably, spatial transcriptomic analyses demonstrate pronounced regional heterogeneity: periplaque microenvironments exhibit distinct immune-cell compositions and gene expression signatures. Collectively, these omics technologies are redefining the cellular basis of AD progression and hold the potential to impact the discovery of early diagnostic biomarkers and precision therapeutic targets. Full article

Intrahepatic cholangiocarcinoma (ICC) is an aggressive liver malignancy with a rising global incidence and limited therapeutic options. Vascular invasion (VI) is a hallmark of advanced disease, correlating with early recurrence and dismal prognosis, yet its tumor microenvironment (TME) drivers remain elusive. We analyzed single-cell RNA sequencing (scRNA-seq) data from 25 ICC samples to systematically characterize the cellular composition and molecular features related to VI. By integrating bulk RNA-seq data, spatial transcriptomics, and multiplex immunofluorescence, we identified a distinct subset of tumor-like cancer-associated fibroblasts (CAFs), termed tCAFs, enriched in VI-positive tumors. Functional enrichment analyses revealed that tCAFs were prominently associated with hypoxia and angiogenesis pathways, findings corroborated by the significant upregulation of tCAF markers (MME and NT5E) in ICC-derived CAFs under hypoxic conditions in vitro. Cell–cell communication analysis and spatial mapping uncovered that tCAFs might promote VI primarily through VEGF signaling interactions with endothelial cells. Integrative bioinformatics and RT-qPCR validation identified three key functional genes in tCAFs: SLC2A1, PTGS2, and PLOD2. In endothelial sprouting assays, pharmacological inhibition of SLC2A1 exerted a pronounced suppressive effect. Consistently, sprouting assays using ICC-derived CAFs with SLC2A1 knockdown confirmed that its downregulation significantly reduced endothelial sprouting capacity. Importantly, administration of the SLC2A1 inhibitor BAY-876 effectively suppressed tumor progression and intrahepatic metastasis in the orthotopic ICC mouse model. Our findings define a VI-associated cellular ecosystem and molecular landscape in ICC, unveiling a novel hypoxia–tCAFs–endothelial cells axis. Furthermore, we identify SLC2A1 as a clinically relevant therapeutic target, offering new insights into tumor VI. Full article

Pulmonary arterial hypertension (PAH) is a progressive, fatal disease of the pulmonary vasculature characterized by obliterative remodeling of small pulmonary arteries, leading to sustained elevation of pulmonary vascular resistance, right ventricular failure, and premature death. The diagnostic gold standard remains right heart catheterization, requiring a mean pulmonary artery pressure greater than 20 mmHg at rest, a pulmonary arterial wedge pressure of 15 mmHg or below, and a pulmonary vascular resistance exceeding 2 Wood units. PAH is an autosomal dominant disorder with markedly incomplete penetrance of approximately 20–30%, indicating that germline mutations alone are insufficient to cause disease. Disease manifestation requires additional “second hits”, including chronic hypoxia, systemic inflammation, hemodynamic stress, hormonal influences, and common genetic modifiers such as single-nucleotide polymorphisms (SNPs). This genetic and environmental complexity underpins the broad clinical heterogeneity observed across PAH subtypes, which include idiopathic PAH, heritable PAH, and disease associated with connective tissue disorders, HIV infection, portal hypertension, congenital heart disease, schistosomiasis, and drug or toxin exposure. This review provides a comprehensive and critical appraisal of the molecular-genetic architecture of PAH. Thirty genes have now been implicated in disease pathogenesis, spanning seven functional categories: receptors of the TGF-β/BMP signaling family (BMPR2, ACVRL1, ENG, BMPR1B); circulating BMP ligands (GDF2, BMP10); transcription factors (TBX4, SOX17, KLF4, FOXF1, SMAD1, SMAD4, SMAD9); membrane and polyamine transporters (ATP13A3, AQP1); potassium channel regulators (KCNA5, KCNK3, ABCC8); metabolic and mitochondrial genes (EIF2AK4, NFU1, GGCX); signaling receptors and structural proteins (NOTCH3, KDR, CAV1, PLEKHH2); vasoactive and extracellular matrix regulators (KLK1, CBLN2, CD248); and epigenetic regulators (TET2, TOPBP1). Among these, BMPR2 is the dominant contributor, accounting for 53–86% of heritable PAH and 14–35% of idiopathic cases. The remaining genes each account for fewer than 5% of cases individually, collectively reflecting a broad landscape of rare and ultra-rare genetic contributions. For each gene, we critically evaluate the strength of genetic evidence, pathogenic mechanisms, degree of mechanistic resolution, and clinical relevance. We further discuss the contribution of emerging technologies, including whole-genome sequencing, single-cell and spatial transcriptomics, multi-omics integration, iPSC-derived vascular models, and artificial intelligence, to expanding the PAH genetic architecture beyond single-gene discovery. A key theme across this landscape is convergence: despite mechanistic diversity at the gene level, most PAH-associated variants ultimately impair endothelial quiescence, promote smooth muscle proliferation, and drive apoptosis resistance through disruption of BMP signaling amplitude, transcriptional stability, ion channel homeostasis, metabolic integrity, or epigenetic regulation. This convergence supports both a unified therapeutic rationale and a precision medicine framework for genotype-stratified intervention in PAH. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification. Full article

Breast cancer is one of the most devastating malignancies in women worldwide. A growing body of evidence has linked neoplastic growth, invasion, metastasis, immune escape, and therapeutic resistance to infiltrating tumor-associated macrophages. In a breast cancer mass, macrophages are largely polarized to two main subtypes, M1 and M2, albeit with continuum intermediates, based on their immunological behaviors, gene signatures, and functional roles. While the former portrays proinflammatory and anti-cancer effects, the latter elicits the opposite impacts. M2 macrophages have gained rising attention as they are largely involved in fostering an immune-suppressive, cancer-promoting landscape and are imperative for malignant features across breast cancer subtypes. Through a positive feedback paracrine loop, M2 macrophages can be enriched by a plethora of dysregulated oncogenic signaling mediators, exemplified by CSF1/CSF1R, STAT3, IL-6, YAP, PI3K, PDK1, and AKT. These modulators could be released from or activated by surrounding malignant cells, fibroblasts, secreted extracellular vesicles, cell fragments generated after chemotherapies, hypoxia, dysregulated immune checkpoint pathways or oncometabolites. This review aims to discern the molecular cues fortifying M2 subpopulations. Moreover, recent advances in single-cell sequencing, spatial, and computational approaches have refined the understanding of TAM heterogeneity, while clinical translation remains limited by low therapeutic specificity, compensatory signaling, and differences between murine and human macrophage biology. Future therapeutic regimens should include strategies aimed at correcting aberrations that favor M2 polarization and are justified with divergences between humans and mice. Full article

Colorectal cancer (CRC) is a biologically heterogeneous disease in which single-omics analyses incompletely capture the cross-layer mechanisms underlying tumor progression, immune evasion, and therapeutic resistance. This review critically examines how the integration of genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling is redefining CRC biology and precision oncology. Landmark integrative efforts, including TCGA analyses of 276 colorectal cancer samples, CPTAC proteogenomic profiling of 95 tumors, and recent whole-genome sequencing studies of 2023 CRC cases, have refined molecular subtyping, expanded the driver landscape, and revealed clinically relevant discordance between mRNA abundance and protein activity. Integrative studies further show that oncogenic signaling may be driven by post-transcriptional and post-translational regulation, while spatially resolved profiling and microbiome–metabolite analyses are uncovering previously obscured tumor–microenvironment interactions. We also discuss how artificial intelligence-based approaches, including factor analysis, deep learning, graph-based models, and explainable AI, are improving subtype classification, biomarker discovery, and treatment-response prediction, with particular relevance to microsatellite instability-high and early-onset CRC. Finally, we critically evaluate the principal barriers to clinical translation, including batch effects, cross-platform variability, limited external validation, regulatory constraints, and cost, and outline priorities for building reproducible, clinically deployable multi-omics pipelines for CRC management. Full article

Understanding how agricultural soil management affects soil fungal communities is essential for assessing the resilience of biodiversity hotspots such as the Brazilian Cerrado. In this study, we characterized fungal community structure across three contrasting land-use systems within the same agricultural landscape: a native Cerrado remnant, a cover-cropping system, and a spatially isolated potato monoculture field. The soil’s chemical and enzymatic characteristics differed from one another and were clustered by area. However, the same pattern was not observed for the fungal community. Alpha-diversity indices did not differ significantly among sites, although native Cerrado soils showed slightly higher richness and evenness. Beta-diversity analyses based on Bray–Curtis and Jaccard distances, supported by NMDS, ANOSIM, beta-dispersion, and PERMANOVA, indicated no significant compositional differences among communities. Core-mycobiota analysis identified 157 shared ASVs, including genera such as Fusarium, Cladosporium, Chrysosporium, Trichoderma, and Clonostachys. As a preliminary assessment based on a limited spatial design and sequencing-based inference, these findings should be interpreted with caution. These results underscore the need for further research on the mechanisms driving fungal dispersal, edge effects, and the long-term impacts of agricultural land-use on fungal diversity and ecological integrity in the Cerrado. Full article

The evolution and spatial dissemination of SARS-CoV-2 Omicron subvariants have been characterized by rapid lineage replacement and complex transmission dynamics influenced by regional connectivity. This study presents a comprehensive discrete phylogeographic analysis of 90,136 SARS-CoV-2 sequences collected in Poland from 2022 to 2024 to reconstruct the dispersal dynamics of major Omicron lineages, including BA.1, BA.2, BA.5, CH.1, XBB.1, and JN.1. Utilizing Bayesian statistical frameworks, we identified significant viral transitions between the 16 Polish voivodeships and established variant-specific dominance windows ranging from 2 to 4 months. Our findings reveal a highly dynamic epidemic landscape with shifting regional epicenters. The initial BA.1 wave was primarily driven by the Mazovian voivodeship, accounting for 36.1% of outward migration events. This pattern shifted dramatically with the rise in BA.2, which was centered in the industrial Silesian region in the south-west, a densely populated area with strong economic ties to neighboring countries, potentially reflecting a different introduction or transmission dynamic. Furthermore, the epidemic landscape continued to reconfigure during the BA.5 wave, marked by the emergence of new transmission hubs in eastern border regions such as Lublin. Subsequent lineages exhibited distinct geographic signatures: BA.5 spread broadly along the Baltic-central corridor, CH.1 was centered in the north-east, XBB.1 re-emerged in the west-central region of Greater Poland, and JN.1 was driven overwhelmingly by Lesser Poland. These transitions highlight that regional transmission hubs are transient and influenced by local factors such as population density, cross-border mobility, and socio-economic connectivity. This study underscores the critical value of dense genomic surveillance in identifying evolving dispersal routes to inform adaptive, region-specific public health interventions. Full article

Background: Recent advances in spatial transcriptomic technologies enable in situ gene expression profiling while preserving spatial context. This capability is particularly important for studying the tumor microenvironment (TME), where diverse and admixed cell populations interact within highly organized spatial niches that influence tumor progression and therapeutic response. However, the limited resolution of early spatial transcriptomic platforms results in each spatial spot capturing transcripts from multiple cell types, making accurate spot deconvolution or annotation a critical yet challenging step in downstream data analysis. The level of complexity will be particularly prominent in heterogeneous samples like the tumor microenvironments where multiple cell types are highly admixed and reliable single-cell reference atlases may usually be unavailable. Methods: In this paper, we developed our method called STEA, which is a novel and accurate reference-independent enrichment-based annotation algorithm for major cell type. Unlike the existing approaches, STEA does not require single-cell RNA sequencing datasets as reference, offering both flexibility and computational efficiency in execution. Results: We performed comprehensive benchmarking using a variety of simulated datasets across different platforms and scenarios and demonstrated the superior accuracy of STEA. Apart from synthetic data, we also evaluated multiple real datasets to further exemplify its practical applicability on both oncology-related and oncology-unrelated data. More importantly, we could confidently demonstrate the high concordance between prediction of STEA and histological classification by experienced pathologist. Conclusion: Our STEA algorithm provides a practical reference-independent framework to complement the cutting-edge spatial transcriptomics in genomics studies, facilitating accurate downstream high-dimensional spatial characterization of cellular and molecular landscapes, reconstruction of tissue architecture as well as cell–cell communication in malignant and non-malignant scenarios. Taken together, our comprehensive evaluation demonstrates the robustness and reliability of STEA, highlighting its potential as a valuable tool for studying complex tissue organization, particularly within heterogeneous TME. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article

Addressing the prevalent issue of “physical preservation but spiritual silence” in the revitalisation of China’s national industrial heritage, this study proposes and empirically validates a “dual-track narrative” design framework that systematically translates cultural values into spatial experiences. The framework integrates a “figure–history” narrative, which crystallises historical lineage and symbolic spirit through spatial sequences, commemorative landmarks, and authentic remains, with a “scene–activity” narrative, which transforms former production spaces into dynamic, culturally vibrant stages through ecological restoration displays, industrial landscape transformation, and flexible activity implantation. Using Nantong Guangsheng Oil Mill as a single-case study, the research employs qualitative methods including archival analysis, field observation, and semi-structured interviews to examine how the dual-track framework operates in practice. The findings reveal that the “figure–history” narrative manifests in a walkable “time corridor” along the north–south axis, where architectural remnants from different eras are organised to materialise Zhang Jian’s industrial salvation ethos and the collective memory of generations of workers. Meanwhile, the “scene–activity” narrative activates underutilised spaces—such as the repurposing of acid treatment ponds into constructed wetlands and paved grounds into public stages—enabling ongoing cultural production, community interaction, and ecological healing. The study demonstrates that the dual-track framework bridges the historical and contemporary dimensions often treated separately in heritage practice, establishing a systematic “translation mechanism” from cultural decoding to design intervention. Theoretically, it contributes to industrial heritage research by integrating narratology, memory studies, heritage interpretation, and situationism into a coherent design methodology. Practically, it offers decision-makers evaluation criteria beyond the preservation-versus-development binary, provides designers with a mode of creative transformation grounded in material authenticity, and suggests to operators a content-driven, event-based model for sustaining heritage spaces. By spatialising and eventising narratives, the dual-track approach enables industrial heritage to function as a catalyst for cultural identity, social vitality, and economic sustainability, offering a transferable paradigm for the adaptive reuse of industrial heritage in contemporary urban contexts. Full article

Background/Objectives: Cardiac embryonic development is a highly coordinated and dynamic process governed by precise spatiotemporal gene regulation. Increasing evidence indicates that cellular heterogeneity and lineage specification during heart development are tightly controlled by complex gene regulatory networks (GRNs) and epigenetic mechanisms. Recent advances in single-cell multi-omics technologies provide unprecedented resolution to dissect these regulatory processes. This review aims to summarise current applications of single-cell multi-omics approaches to elucidate gene regulatory mechanisms underlying cardiac embryogenesis and their implications for congenital heart disease (CHD). Methods: We systematically reviewed recent literature on single-cell RNA sequencing (scRNA-seq), single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), spatial transcriptomics, and integrative multi-omics analyses applied to embryonic heart development. Studies were analysed to evaluate how these technologies contribute to cell-type identification, lineage trajectory reconstruction, GRN inference, and epigenetic landscape characterisation. Results: Single-cell multi-omics approaches have enabled the construction of high-resolution cardiac cell atlases, revealing previously unrecognised cellular heterogeneity and transitional states during heart development. Integrative analyses of transcriptomic and chromatin accessibility data have provided insights into lineage commitment, key transcription factors, enhancer–promoter interactions, and dynamic GRNs. These findings have advanced understanding of developmental genetics in cardiac morphogenesis and offered new perspectives on the molecular mechanisms underlying CHD. Conclusions: Single-cell multi-omics technologies provide a powerful framework for investigating gene regulatory mechanisms during cardiac embryogenesis. Continued methodological refinement and integrative analyses are expected to further clarify developmental processes and facilitate translational insights into CHD. Full article