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动态纤毛跳动频率的稳健估计

Robust estimation of the motile cilia beating frequency.

作者信息

Meste O, Brau F, Guyon A

机构信息

Lab I3S, UMR 7271, CNRS, University of Nice-Sophia Antipolis, Sophia Antipolis, France.

IPMC, UMR 7275, CNRS, University of Nice-Sophia Antipolis, Valbonne, France.

出版信息

Med Biol Eng Comput. 2015 Oct;53(10):1025-35. doi: 10.1007/s11517-015-1345-0. Epub 2015 Jul 28.

Abstract

The estimation of the cilia beating frequency (CBF) is of great interest in understanding how the CBF modulates liquid fluxes and how it is controlled by the ciliated cell intra- and/or extracellular medium composition in physiological processes. Motion artifacts and camera defaults may hinder the computation of the frequency variations during long-lasting experiments. We have developed a new analysis approach consisting of a preliminary corrective step (removal of a grid pattern on the image sequence and shift compensation), followed by a harmonic model of the observed cilia using a maximum likelihood estimator framework. It is shown that a more accurate estimation of the frequency can be obtained by averaging the squared Fourier transform of individual pixels followed by a particular summation over the different frequencies, namely the compressed spectrum. Robustness of the proposed method over traditional approaches is shown by several examples and simulations. The method is then applied to images of samples containing ciliated ependymal cells located in the third cerebral ventricle of mouse brains, showing that even small variations in CBF in response to changes in the amount of oxygenation, pH or glucose were clearly visible in the computed frequencies. As a conclusion, this method reveals a fine metabolic tuning of the cilia beating in ependimocytes lining the third cerebral ventricle. Such regulations are likely to participate in homeostatic mechanisms regulating CSF movements and brain energy supply.

摘要

在理解纤毛摆动频率(CBF)如何调节液体通量以及在生理过程中它如何由纤毛细胞的细胞内和/或细胞外介质组成所控制方面,对CBF的估计具有重要意义。运动伪影和相机缺陷可能会妨碍在长期实验中对频率变化的计算。我们开发了一种新的分析方法,该方法包括一个初步的校正步骤(去除图像序列上的网格图案并进行偏移补偿),随后使用最大似然估计框架对观察到的纤毛建立谐波模型。结果表明,通过对各个像素的平方傅里叶变换进行平均,然后对不同频率进行特定求和,即压缩频谱,可以获得更准确的频率估计。通过几个例子和模拟展示了所提出方法相对于传统方法的稳健性。然后将该方法应用于包含位于小鼠脑第三脑室的纤毛室管膜细胞的样本图像,结果表明,即使是由于氧合、pH值或葡萄糖含量变化而导致的CBF的微小变化,在计算出的频率中也清晰可见。总之,该方法揭示了第三脑室衬里室管膜细胞中纤毛摆动的精细代谢调节。这种调节可能参与调节脑脊液流动和脑能量供应的稳态机制。

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