A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation.
A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation.
复制标题
一条机器学习管道，揭示了对药物扰动对血管平滑肌细胞球体形态和形成的不同反应。
DOI：
10.1038/s41598-021-02683-4
复制
发表时间：
2021-12-02
影响因子：
4.6
通讯作者：
Bae Y
中科院分区：
文献类型：
--
作者：
Vaidyanathan K;Wang C;Krajnik A;Yu Y;Choi M;Lin B;Jang J;Heo SJ;Kolega J;Lee K;Bae Y
机器学习方法在生物学和医学领域显示出巨大的前景，可以发现隐藏的信息，以进一步了解复杂的生物和病理过程。在这项研究中，我们开发了一种基于深度学习的机器学习算法，以有意义地处理图像数据，并促进血管生物学和病理学的研究。血管损伤和动脉粥样硬化的特征在于血管壁内血管平滑肌细胞（VSMCs）的异常积聚和增殖引起的新生内膜形成。了解如何控制VSMC的行为将促进血管疾病治疗靶点的开发。然而，具有相同病变血管状况的VSMC对药物治疗的反应通常是异质的。在这里，为了识别药物治疗的异质性反应，我们使用VSMC球体创建了一个体外实验模型系统，并开发了一种基于机器学习的计算方法，称为HETERisk（异质球体）。首先，我们建立了一个模拟血管内膜样增生和动脉结构的VSMC球体模型。然后，为了识别药物治疗的VSMC球体的形态亚群，我们使用了一种机器学习框架，该框架结合了基于深度学习的球体分割和形态聚类分析。我们的机器学习方法成功地表明，FAK，Rac，Rho和Cdc42抑制剂差异影响球体形态，表明存在VSMC球体形成的多种药物反应。总体而言，我们的HETERFACE管道能够通过单球体分析对体内发生的新生内膜形成的形态学变化进行详细的定量药物表征。
Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.
登录
查看更多内容
影响因子：
5.6
作者：
Antoni D;Burckel H;Josset E;Noel G
通讯作者：
Noel G
DOI：
10.1161/atvbaha.111.232231
发表时间：
2011-10
期刊：
Arteriosclerosis, thrombosis, and vascular biology
影响因子：
--
作者：
Cheng Z;Sundberg-Smith LJ;Mangiante LE;Sayers RL;Hakim ZS;Musunuri S;Maguire CT;Majesky MW;Zhou Z;Mack CP;Taylor JM
通讯作者：
Taylor JM
影响因子：
7.3
作者：
Bae YH;Mui KL;Hsu BY;Liu SL;Cretu A;Razinia Z;Xu T;Puré E;Assoian RK
通讯作者：
Assoian RK
影响因子：
1.2
作者：
Foty, Ramsey
通讯作者：
Foty, Ramsey
影响因子：
--
作者：
Genkel, Vadim V;Shaposhnik, Igor I
通讯作者：
Shaposhnik, Igor I