Single-cell transcriptome atlases of soybean root and mature nodule reveal new regulatory programs that control the nodulation process

Plant Communications, Год журнала: 2024, Номер 5(8), С. 100984 - 100984

Опубликована: Июнь 6, 2024

The soybean root system is complex. In addition to being composed of various cell types, the includes primary root, lateral roots, and nodule, an organ in which mutualistic symbiosis with N-fixing rhizobia occurs. A mature nodule characterized by a central infection zone where atmospheric nitrogen fixed assimilated symbiont, resulting from close cooperation between plant bacteria. To date, transcriptome individual cells isolated developing nodules has been established, but transcriptomic signatures have not yet characterized. Using single-nucleus RNA-seq Molecular Cartography technologies, we precisely signature types revealed co-existence different sub-populations B. diazoefficiens–infected including those actively involved fixation engaged senescence. Mining single-cell-resolution atlas associated gene co-expression network confirmed role known nodulation-related genes identified new that control nodulation process. For instance, functionally GmFWL3, plasma membrane microdomain-associated protein controls rhizobial infection. Our study reveals unique cellular complexity helps redefine concept when considering nodule.

Язык: Английский

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scMGATGRN: a multiview graph attention network–based method for inferring gene regulatory networks from single-cell transcriptomic data

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(6)

Опубликована: Сен. 23, 2024

Abstract The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex relationships, exploring disease mechanisms. Recently, deep learning (DL)–based methods have been proposed to infer GRNs from single-cell transcriptomic data achieved impressive performance. However, these do not fully utilize graph topological information high-order neighbor multiple receptive fields. To overcome those limitations, we propose novel model based on multiview attention network, namely, scMGATGRN, GRNs. scMGATGRN mainly consists GAT, multiview, view-level mechanism. GAT can extract essential features network. simultaneously local feature nodes mechanism dynamically adjusts relative importance node embedding representations efficiently aggregates two views. verify effectiveness compared its performance with 10 (five shallow algorithms five state-of-the-art DL-based methods) seven benchmark RNA sequencing (scRNA-seq) datasets cell lines (two human three mouse) four different kinds ground-truth networks. experimental results only show that outperforms competing but also demonstrate potential this inferring code are made freely available GitHub (https://github.com/nathanyl/scMGATGRN).

Язык: Английский

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Universal data-driven models to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide: Correlation development and machine learning modeling

Journal of CO2 Utilization, Год журнала: 2025, Номер 92, С. 103021 - 103021

Опубликована: Янв. 13, 2025

Язык: Английский

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Optimal Sizing and Techno-economic Analysis of Combined Solar Wind Power System, Fuel Cell and Tidal Turbines Using Meta-heuristic Algorithms: A Case Study of Lavan Island

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 27, 2025

Язык: Английский

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A novel semi-local centrality to identify influential nodes in complex networks by integrating multidimensional factors

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 145, С. 110177 - 110177

Опубликована: Фев. 8, 2025

Язык: Английский

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Robust self supervised symmetric nonnegative matrix factorization to the graph clustering

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 1, 2025

Graph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities. Traditional Nonnegative Matrix Factorization (NMF) methods have shown promise tasks by providing low-dimensional representations data. However, most existing NMF-based approaches are highly sensitive to noise outliers, leading suboptimal performance real-world scenarios. Additionally, these often struggle capture the underlying nonlinear structures complex networks, which can significantly impact accuracy. To address limitations, this paper introduces Robust Self-Supervised Symmetric NMF (R3SNMF) improve graph clustering. The proposed algorithm leverages robust principal component model handle outliers effectively. By incorporating self-supervised learning mechanism, R3SNMF iteratively refines process, enhancing quality learned increasing resilience data imperfections. symmetric factorization ensures preservation structures, while approach allows adaptively its over successive iterations. In addition, integrates graph-boosting method how relationships within represented. Extensive experimental evaluations various datasets demonstrate that outperforms state-of-the-art terms both accuracy robustness.

Язык: Английский

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Modeling of parameters affecting the removal of chromium using polysulfone/graphene oxide membrane via response surface methodology

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Янв. 22, 2025

Язык: Английский

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HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer

BMC Bioinformatics, Год журнала: 2025, Номер 26(1)

Опубликована: Фев. 11, 2025

Gene regulatory networks (GRNs) involve complex relationships between genes and play important roles in the study of various biological systems diseases. The introduction single-cell sequencing (scRNA-seq) technology has allowed gene regulation studies to be carried out on specific cell types, providing opportunity accurately infer networks. However, sparsity noise problems data pose challenges for network inference, although many inference methods have been proposed, they often fail eliminate transitive interactions or do not address multilevel nonlinear features graph well. On basis above limitations, we propose a framework named HGATLink. HGATLink combines heterogeneous attention simplified transformer capture effectively low-dimensional space via matrix decomposition techniques, which only enhances ability model structures alleviate interactions, but also captures long-range dependencies ensure more accurate prediction. Compared with 10 state-of-the-art GRN 14 scRNA-seq datasets under two metrics, AUROC AUPRC, shows good stability accuracy tasks.

Язык: Английский

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scMFG: a single-cell multi-omics integration method based on feature grouping

BMC Genomics, Год журнала: 2025, Номер 26(1)

Опубликована: Фев. 11, 2025

Recent advancements in methodologies and technologies have enabled the simultaneous measurement of multiple omics data, which provides a comprehensive understanding cellular heterogeneity. However, existing methods limitations accurately identifying cell types while maintaining model interpretability, especially presence noise. We propose novel method called scMFG, leverages feature grouping group integration techniques for single-cell multi-omics data. By organizing features with similar characteristics within each layer through grouping. Furthermore, scMFG ensures consistent approach across different layers, promoting comparability diverse data types. Additionally, incorporates matrix factorization-based to enable integrated results remain interpretable. comprehensively evaluated scMFG's performance on four complex real-world datasets generated using sequencing technologies, highlighting its robustness Notably, exhibited superior deciphering heterogeneity at finer resolution compared when applied simulated datasets. our proved highly effective rare types, showcasing robust suitability detecting low-abundance populations. The interpretability was successfully validated specific association outputs or states observed neonatal mouse cerebral cortices dataset. Moreover, we demonstrated that is capable developmental trajectories even batch effects. Our work presents framework analysis advancing interpretable manner.

Язык: Английский

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SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication

BMC Biology, Год журнала: 2025, Номер 23(1)

Опубликована: Фев. 12, 2025

Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis cellular demands consideration not only binding between ligands and receptors but also a series downstream signal transduction reactions within cells. Thanks to advancements in spatial transcriptomics technology, we are now able better decipher process microenvironment. Nevertheless, majority existing cell–cell algorithms fail take into account signals In this study, put forward SpaCcLink, method that takes influence individual cells systematically investigates patterns as well networks. Analyses conducted on real datasets derived from humans mice have demonstrated SpaCcLink can help identifying more relevant receptors, thereby enabling us decode genes signaling pathways influenced by communication. Comparisons with other methods suggest identify closely associated biological processes discover reliable ligand-receptor relationships. By means profound all-encompassing comprehension mechanisms underlying be achieved, which turn promotes deepens our understanding intricate complexity

Язык: Английский

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