FRET imaging and statistical signal processing reveal positive and negative feedback loops regulating the morphology of randomly migrating HT-1080 cells
FRET imaging and statistical signal processing reveal positive and negative feedback loops regulating the morphology of randomly migrating HT-1080 cells
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DOI：
10.1242/jcs.096859
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发表时间：
2012-05-15
影响因子：
4
通讯作者：
Aoki, Kazuhiro
中科院分区：
文献类型：
--
作者：
Kunida, Katsuyuki;Matsuda, Michiyuki;Aoki, Kazuhiro
细胞迁移在许多生理过程中起着重要作用。Rho GTP酶（Rac 1、Cdc 42、RhoA）和磷脂酰肌醇在定向细胞迁移中已被广泛研究。然而，Rho GTP酶和磷脂酰肌醇如何在空间和时间上调节随机细胞迁移仍不清楚。我们试图解决这个问题，使用荧光共振能量转移（FRET）成像和统计信号处理。首先，我们获得了表达Rho GTPases和磷脂酰肌醇FRET生物传感器的HT-1080纤维肉瘤细胞随机迁移的延时图像。我们开发了一种图像处理算法，以提取FRET值和迁移细胞的前沿速度。自相关和互相关分析表明Rac 1、磷脂酰肌醇和膜突起之间存在反馈调节。为了验证反馈调节，我们采用了药物抑制剂对信号通路的急性抑制。肌动蛋白聚合的抑制降低了Rac 1的活性，表明肌动蛋白聚合对Rac 1存在正反馈。此外，用PI 3-激酶抑制剂处理诱导Rac 1活性的适应，即Rac 1活性的瞬时降低，随后恢复到基础水平。在模拟，再现适应预测存在一个负反馈回路，从Rac 1肌动蛋白聚合。最后，我们确定MLCK可能是负反馈的控制因素。这些研究结果定量地证明了涉及肌动蛋白，Rac 1和MLCK的正反馈和负反馈回路，并解释了在随机迁移细胞中观察到的膜动力学的有序模式。
Cell migration plays an important role in many physiological processes. Rho GTPases (Rac1, Cdc42, RhoA) and phosphatidylinositols have been extensively studied in directional cell migration. However, it remains unclear how Rho GTPases and phosphatidylinositols regulate random cell migration in space and time. We have attempted to address this issue using fluorescence resonance energy transfer (FRET) imaging and statistical signal processing. First, we acquired time-lapse images of random migration of HT-1080 fibrosarcoma cells expressing FRET biosensors of Rho GTPases and phosphatidyl inositols. We developed an image-processing algorithm to extract FRET values and velocities at the leading edge of migrating cells. Auto-and cross-correlation analysis suggested the involvement of feedback regulations among Rac1, phosphatidyl inositols and membrane protrusions. To verify the feedback regulations, we employed an acute inhibition of the signaling pathway with pharmaceutical inhibitors. The inhibition of actin polymerization decreased Rac1 activity, indicating the presence of positive feedback from actin polymerization to Rac1. Furthermore, treatment with PI3-kinase inhibitor induced an adaptation of Rac1 activity, i.e. a transient reduction of Rac1 activity followed by recovery to the basal level. In silico modeling that reproduced the adaptation predicted the existence of a negative feedback loop from Rac1 to actin polymerization. Finally, we identified MLCK as the probable controlling factor in the negative feedback. These findings quantitatively demonstrate positive and negative feedback loops that involve actin, Rac1 and MLCK, and account for the ordered patterns of membrane dynamics observed in randomly migrating cells.