sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.
sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.
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DOI：
10.1371/journal.pcbi.1011785
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发表时间：
2024-01
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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作者：
单细胞 RNA 测序 (scRNA-seq) 是一项强大的技术，可研究基质细胞、免疫细胞和疾病细胞中的转录程序，例如阿尔茨海默病 (AD) 大脑或肿瘤微环境 (ME) 或微环境中的肿瘤细胞或神经元。 ME 内的细胞间通讯在疾病进展和免疫治疗反应中发挥着重要作用，并且是新颖且关键的治疗靶点。尽管已经开发了许多 scRNA-seq 分析工具来研究细胞的异质性和亚群，但很少有工具是为了揭示 ME 的细胞间通讯并预测抑制通讯的潜在有效药物而设计的。此外，利用scRNA-seq数据发现信号通讯网络和有效药物的数据分析过程非常复杂，涉及一套关键的分析流程和外部支持数据资源，这对于没有强大计算背景和scRNA-seq数据分析训练的研究人员来说是困难的。为了应对这些挑战，在本研究中，我们开发了一种新颖的开源计算工具 sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/)。它是专门设计的，使用 scRNA-seq 数据来识别疾病 ME 内的细胞类型，揭示单个细胞类型内功能失调的信号通路以及不同细胞类型之间的相互作用，并预测可能破坏细胞间信号传导通讯的有效药物。 sc2MeNetDrug 提供了一个用户友好的图形用户界面来封装数据分析模块，这可以促进基于 scRNA-seq 数据的新型细胞间信号通信和新型治疗方案的发现。单细胞基因组学数据正在改变我们对疾病微环境 (ME) 中不同细胞类型以及复杂的细胞内和细胞间复杂生物过程的理解。它可以指导新型精确靶向和免疫疗法的开发，这些疗法可以有效地扰乱疾病 ME 内复杂的多细胞信号相互作用。然而，进行复杂的单细胞基因组学数据分析任务仍然具有挑战性且不平凡，这些任务通常由多个复杂的分析模块和不同的支持数据集组成。在此，我们的目标是通过开发可公开访问的开源工具 sc2MeNetDrug 来促进生物医学研究人员进行单细胞基因组学数据驱动的研究。它提供了一个用户友好的图形界面，封装了逐步分析模块、各种支持数据集、可视化功能和新颖的分析模块，用于识别细胞间通信网络，并预测可能扰乱疾病 ME 内多细胞信号相互作用的有效药物。它使用户能够交互式地执行全面的单细胞 RNA-seq 数据分析任务。
Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer’s Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens. Single cell genomics data have been transforming our understanding of the diverse cell types, and complex intra- and inter-cellular complex biological processes in disease microenvironments (ME). It can guide the development of novel precise targeted and immunotherapy treatments that can effectively perturb the complex multi-cell signaling interactions within disease ME. However, it remains challenging and nontrivial to conduct complex single cell genomics data analysis tasks, which often consist of multiple complex analysis modules and diverse supportive datasets. Herein, our goal is to facilitate biomedical researchers conducting single cell genomics data-driven studies, by developing a publicly accessible open-source tool, sc2MeNetDrug. It provides a user-friendly graphical interface, encapsulating step-wise analysis modules, diverse supportive datasets, visualization functions, and novel analysis modules for identifying cell-cell communication networks, and predicting effective drugs that can potentially perturb the multi-cell signaling interactions within disease ME. It enables users to interactively conduct comprehensive single cell RNA-seq data analysis tasks.
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DOI：
10.1016/j.bbamcr.2021.118997
发表时间：
2021-05
期刊：
Biochimica et biophysica acta. Molecular cell research
影响因子：
--
作者：
Callens M;Kraskovskaya N;Derevtsova K;Annaert W;Bultynck G;Bezprozvanny I;Vervliet T
通讯作者：
Vervliet T
DOI：
10.1073/pnas.1320318110
发表时间：
2013-12-10
影响因子：
11.1
作者：
Feig, Christine;Jones, James O.;Fearon, Douglas T.
通讯作者：
Fearon, Douglas T.
影响因子：
48
作者：
Browaeys, Robin;Saelens, Wouter;Saeys, Yvan
通讯作者：
Saeys, Yvan
影响因子：
5.9
作者：
Kalecký K;Ashcraft P;Bottiglieri T
通讯作者：
Bottiglieri T
影响因子：
14.8
作者：
Efremova, Mirjana;Vento-Tormo, Miquel;Vento-Tormo, Roser
通讯作者：
Vento-Tormo, Roser