The spatiotemporal pattern of Src activation at lipid rafts revealed by diffusion-corrected FRET imaging.
The spatiotemporal pattern of Src activation at lipid rafts revealed by diffusion-corrected FRET imaging.
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DOI：
10.1371/journal.pcbi.1000127
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发表时间：
2008-07-25
影响因子：
4.3
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Lu S;Ouyang M;Seong J;Zhang J;Chien S;Wang Y
基于荧光共振能量转移（FRET）的基因编码生物传感器已被广泛应用于以高时空分辨率可视化活细胞中的分子活动。然而，生物传感器蛋白的快速扩散阻碍了对实际分子激活图谱的精确重建。基于光漂白后荧光恢复（FRAP）实验，我们开发了一种有限元（FE）方法来分析、模拟并减除可移动生物传感器的扩散效应。该方法已被应用于分析被设计定位于活细胞不同亚区室的Src FRET生物传感器的移动性。结果表明，位于细胞质中的Src生物传感器比锚定在质膜不同区室（在脂筏中：0.11±0.01 µm²/秒，在脂筏外：0.18±0.02 µm²/秒）的移动速度快4 - 8倍（0.93±0.06 µm²/秒）。生物传感器在脂筏中的移动性比在脂筏外慢，且主要受二维扩散控制。当从FRET比率图像中减除这种扩散效应时，在靠近细胞周边的聚集区域观察到脂筏处的高Src活性，在表皮生长因子（EGF）刺激时这些区域保持相对静止。这一结果表明EGF诱导了脂筏处具有良好时空协调性的Src激活。我们基于有限元的方法还为研究分子移动性以及重建活细胞中信号分子的时空激活图谱提供了一个综合的图像分析平台。
荧光生物传感器已被广泛用于报告活细胞中目标分子的时空活动。然而，生物传感器可以独立于目标分子移动，并将其信号携带到其他亚细胞位置。因此，观察到的图像似乎是目标分子活动和生物传感器移动（主要由于扩散）所引入的伪影的组合。有趣的问题是如何从观察到的荧光图像中估计并排除生物传感器的移动效应，以及如何重建目标分子的真实活动图谱。Src分子在细胞黏附、迁移和癌症侵袭中起重要作用。在本文中，我们开发了一种新的计算方法来分析和模拟Src生物传感器的移动，然后从原始荧光图像中减除该移动效应。利用这种计算方法，我们在质膜上相对静止的位置观察到了高Src活性的离散簇。因此，我们的结果强调了分子活动在空间和时间上的协调性。除了Src，我们的计算方法还可用于重建其他信号分子的活动图谱。
Genetically encoded biosensors based on fluorescence resonance energy transfer (FRET) have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution. However, the rapid diffusion of biosensor proteins hinders a precise reconstruction of the actual molecular activation map. Based on fluorescence recovery after photobleaching (FRAP) experiments, we have developed a finite element (FE) method to analyze, simulate, and subtract the diffusion effect of mobile biosensors. This method has been applied to analyze the mobility of Src FRET biosensors engineered to reside at different subcompartments in live cells. The results indicate that the Src biosensor located in the cytoplasm moves 4–8 folds faster (0.93±0.06 µm2/sec) than those anchored on different compartments in plasma membrane (at lipid raft: 0.11±0.01 µm2/sec and outside: 0.18±0.02 µm2/sec). The mobility of biosensor at lipid rafts is slower than that outside of lipid rafts and is dominated by two-dimensional diffusion. When this diffusion effect was subtracted from the FRET ratio images, high Src activity at lipid rafts was observed at clustered regions proximal to the cell periphery, which remained relatively stationary upon epidermal growth factor (EGF) stimulation. This result suggests that EGF induced a Src activation at lipid rafts with well-coordinated spatiotemporal patterns. Our FE-based method also provides an integrated platform of image analysis for studying molecular mobility and reconstructing the spatiotemporal activation maps of signaling molecules in live cells. Fluorescence biosensors have been widely used to report the spatial and temporal activity of target molecules in live cells. However, biosensors can move independently of the target molecule and carry its signal to other subcellular locations. Therefore, the observed images appear to be the combination of the target molecular activity and the artifacts introduced by the movement of the biosensors (mainly due to diffusion). The intriguing question is how to estimate and exclude the movement effect of biosensors from the observed fluorescent images and to reconstruct the real activity map of the target molecules. The Src molecule plays important roles in cell adhesion, migration, and cancer invasion. In this paper, we developed a novel computational method to analyze and simulate the movement of the Src biosensor, which was then subtracted from the original fluorescent images. With this computational method, we observed discrete clusters of high Src activity at relatively stationary locations on the plasma membrane. Therefore, our results highlight the coordination of molecular activities in space and time. In addition to Src, our computational method can be used to reconstruct the activity map of other signaling molecules.
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DOI：
10.1073/pnas.0503658102
发表时间：
2005-08-02
影响因子：
11.1
作者：
Hamamura, K;Furukawa, K;Furukawa, K
通讯作者：
Furukawa, K
影响因子：
4.8
作者：
Arcaro, Alexandre;Aubert, Muriel;Seckl, Michael J.
通讯作者：
Seckl, Michael J.
影响因子：
4.1
作者：
Calleja, V;Ameer-Beg, SM;Larijani, B
通讯作者：
Larijani, B
影响因子：
3
作者：
Farla, P;Hersmus, R;Houtsmuller, AB
通讯作者：
Houtsmuller, AB
影响因子：
56.9
作者：
Houtsmuller, AB;Rademakers, S;Vermeulen, W
通讯作者：
Vermeulen, W