Computer vision reveals hidden variables underlying NF-κB activation in single cells.
Computer vision reveals hidden variables underlying NF-κB activation in single cells.
复制标题
DOI：
10.1126/sciadv.abg4135
复制
发表时间：
2021-10-22
期刊：
影响因子：
13.6
通讯作者：
Tay S
中科院分区：
文献类型：
--
作者：
Patel P;Drayman N;Liu P;Bilgic M;Tay S
一种基于图像的机器学习方法发现了单个细胞中NF-κB通路可变激活的机制。个体细胞对环境信号的反应是异质的。在外部信号下，某些细胞激活基因调控途径，而其他细胞则完全忽略该信号。细胞异质性背后的机制往往难以理解，因为研究分子状态所需的实验破坏了我们需要研究的状态。在这里，我们开发了一个基于图像的支持向量机器学习模型来揭示控制免疫途径核因子κB （NF-κB）激活的变量。计算机视觉分析预测了对细胞因子刺激作出反应的细胞的身份，并表明激活是由微量的“泄漏”NF-κB （p65:p50）定位到细胞核预先确定的。机制模型显示，NF-κB与NF-κB抑制剂的比例预先决定了细胞的渗漏和活化概率。当细胞在分子状态之间转换时，它们保持NF-κB激活的总体概率。我们的研究结果展示了计算机视觉如何在促炎刺激下发现异质性单细胞激活背后的机制。
An image-based machine learning approach finds mechanisms underlying variable activation of NF-κB pathway in single cells. Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor κB (NF-κB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of “leaky” NF-κB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-κB to inhibitor of NF-κB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-κB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.
登录
查看更多内容
影响因子：
48
作者：
Buggenthin F;Buettner F;Hoppe PS;Endele M;Kroiss M;Strasser M;Schwarzfischer M;Loeffler D;Kokkaliaris KD;Hilsenbeck O;Schroeder T;Theis FJ;Marr C
通讯作者：
Marr C
影响因子：
9.9
作者：
Cheong, Raymond;Hoffmann, Alexander;Levchenko, Andre
通讯作者：
Levchenko, Andre
影响因子：
14.8
作者：
Kellogg, Ryan A.;Gomez-Sjoeberg, Rafael;Tay, Savas
通讯作者：
Tay, Savas
影响因子：
7.3
作者：
Chakraborty, Arup K.;Das, Jayajit;Roose, Jeroen
通讯作者：
Roose, Jeroen
影响因子：
7.7
作者：
Kellogg, Ryan A.;Tian, Chengzhe;Tay, Savas
通讯作者：
Tay, Savas