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Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.
Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.
Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi‐omics data analysis and machine learning techniques. Single‐cell RNA sequencing disclosed a heightened presence of macrophages and CD8‐positive alpha‐beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.
External constraints, such as development, disease, and environment, can induce changes in epigenomic patterns that may profoundly impact the health trajectory of fetuses and neonates into adulthood, influencing conditions like obesity. Epigenetic modifications encompass processes including DNA methylation, covalent histone modifications, and RNA-mediated regulation. Beyond forward cellular differentiation (cell programming), terminally differentiated cells are reverted to a pluripotent or even totipotent state, that is, cellular reprogramming. Epigenetic modulators facilitate or erase histone and DNA modifications both in vivo and in vitro during programming and reprogramming. Noticeably, obesity is a complex metabolic disorder driven by both genetic and environmental factors. Increasing evidence suggests that epigenetic modifications play a critical role in the regulation of gene expression involved in adipogenesis, energy homeostasis, and metabolic pathways. Hence, we discuss the mechanisms by which epigenetic interventions influence obesity, focusing on DNA methylation, histone modifications, and non-coding RNAs. We also analyze the methodologies that have been pivotal in uncovering these epigenetic regulations, i.e., Large-scale screening has been instrumental in identifying genes and pathways susceptible to epigenetic control, particularly in the context of adipogenesis and metabolic homeostasis; Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of gene expression patterns at the individual cell level, revealing the heterogeneity and dynamics of epigenetic regulation during cellular differentiation and reprogramming; Chromatin immunoprecipitation (ChIP) assays, focused on candidate genes, have been crucial for characterizing histone modifications and transcription factor binding at specific genomic loci, thereby elucidating the epigenetic mechanisms that govern cellular programming; Somatic cell nuclear transfer (SCNT) and cell fusion techniques have been employed to study the epigenetic reprogramming accompanying cloning and the generation of hybrid cells with pluripotent characteristics, etc. These approaches have been instrumental in identifying specific epigenetic marks and pathways implicated in obesity, providing a foundation for developing targeted therapeutic interventions. Understanding the dynamic interplay between epigenetic regulation and cellular programming is crucial for advancing mechanism and clinical management of obesity.
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