Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming

Molecular Therapy — Nucleic Acids, Journal Year: 2023, Volume and Issue: 34, P. 102044 - 102044

Published: Oct. 5, 2023

Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of fates transition. Only partial intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher underlying decisive factors driving fate, we identified induced pluripotent stem cells or stromal-like (iPSCs/SLCs) and iPSCs trophoblast-like (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition activation pluripotency networks are main series in successful reprogramming. Correspondingly, diverge into SLCs accompanied inhibition cycle genes extracellular matrix genes, whereas TLCs characterized up-regulation placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) three key (Cdc5l, Klf4, Nanog) were individually as drivers triggering downstream during iPSCs/SLCs iPSCs/TLCs bifurcation. Conversely, 11 (Cebpb, Sox4, Junb, four (Gata2, Jund, Ctnnb1, drive respectively. Our study sheds new light on understanding which helpful for improving efficiency through manipulating avoid alternative fates.

Language: Английский

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SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(4)

Published: May 23, 2024

Abstract The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension the properties gene expression within tissues. However, due challenges high dimensionality, pronounced noise and dynamic limitations in ST data, integration information accurately identify domains remains challenging. This paper proposes SpaNCMG algorithm for purpose achieving precise domain description localization based on neighborhood-complementary mixed-view graph convolutional network. enables better adaptation data at different resolutions by integrating local from KNN global structure r-radius into complementary neighborhood graph. It also introduces an attention mechanism achieve adaptive fusion reconstructed expressions, utilizes KPCA method dimensionality reduction. application five datasets four sequencing platforms demonstrates superior performance eight existing advanced methods. Specifically, achieved highest ARI accuracies 0.63 0.52 human dorsolateral prefrontal cortex mouse somatosensory cortex, respectively. identified locations marker genes olfactory bulb tissue inferred biological functions regions. When handling larger such as embryos, not only main structures but explored unlabeled domains. Overall, good generalization ability scalability make it outstanding tool understanding disease mechanisms. Our codes are available https://github.com/ZhihaoSi/SpaNCMG.

Language: Английский

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Review and new insights into the catalytic structural domains of the Fe(ll) and 2-Oxoglutarate families

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 278, P. 134798 - 134798

Published: Aug. 15, 2024

Language: Английский

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A composite scaling network of EfficientNet for improving spatial domain identification performance

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 25, 2024

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast noisy image data in transcriptomics (ST) for precise recognition of domains remains a challenge. In this study, we propose method EfNST recognizing domains, which employs an efficient composite scaling network EfficientNet to learn multi-scale features. Compared with other relevant algorithms on six sets from three sequencing platforms, exhibits higher accuracy discerning fine tissue structures, highlighting its strong scalability operational efficiency. Under limited computing resources, testing results multiple show that algorithm runs faster maintaining accuracy. The ablation studies model demonstrate significant effectiveness EfficientNet. Within annotated sets, showcases ability finely identify subregions within structure discover corresponding marker genes. unannotated successfully identifies minute regions complex tissues elucidated their patterns biological processes. summary, presents novel approach inferring cellular organization discrete spots implications exploration function.

Language: Английский

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Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi‐omics data and machine learning analysis

IET Systems Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Abstract Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of abdominal aorta, leading to rupture if untreated. The objective this study was identify key biomarkers and decipher immune mechanisms underlying AAA utilising multi‐omics data analysis machine learning techniques. Single‐cell RNA sequencing disclosed heightened presence macrophages CD8‐positive alpha‐beta T cells in AAA, highlighting their critical role disease pathogenesis. Analysis cell–cell communication highlighted augmented interactions between dendritic derived from monocytes. Enrichment differential expression gene indicated substantial involvement metabolic pathways Machine techniques identified CCR7 CBX6 as candidate biomarkers. In upregulated, whereas downregulated, both showing significant correlations with cell infiltration. These findings provide valuable insights into molecular suggest potential for diagnosis therapeutic intervention.

Language: Английский

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Inference and analysis of cell‐cell communication of non‐myeloid circulating cells in late sepsis based on single‐cell RNA‐seq

IET Systems Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 22, 2024

Abstract Sepsis is a severe systemic inflammatory syndrome triggered by infection and leading cause of morbidity mortality in intensive care units (ICUs). Immune dysfunction hallmark sepsis. In this study, the authors investigated cell‐cell communication among lymphoid‐derived leucocytes using single‐cell RNA sequencing (scRNA‐seq) to gain deeper understanding underlying mechanisms late‐stage The authors’ findings revealed that both number strength cellular interactions were elevated septic patients compared healthy individuals, with several pathways showing significant alterations, particularly conventional dendritic cells (cDCs) plasmacytoid (pDCs). Notably, such as CD6‐ALCAM more activated sepsis, potentially due T cell suppression. This study offers new insights into immunosuppression provides potential avenues for clinical intervention

Language: Английский

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Research on Design Method of Man-Machine- Environment System in Product Processing Based on MMESE

Lecture notes in electrical engineering, Journal Year: 2021, Volume and Issue: unknown, P. 878 - 884

Published: Sept. 21, 2021

Language: Английский

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