Smith Claire M, Djakow Jana, Free Robert C, Djakow Petr, Lonnen Rana, Williams Gwyneth, Pohunek Petr, Hirst Robert A, Easton Andrew J, Andrew Peter W, O'Callaghan Christopher
Department of Infection, Immunity and Inflammation, University of Leicester, University Road, Leicester LE1 9HN, UK.
Department of Paediatrics, Second Faculty of Medicine, University Hospital Motol, Prague, Czech Republic.
Cilia. 2012 Aug 1;1:14. doi: 10.1186/2046-2530-1-14. eCollection 2012.
Analysis of ciliary function for assessment of patients suspected of primary ciliary dyskinesia (PCD) and for research studies of respiratory and ependymal cilia requires assessment of both ciliary beat pattern and beat frequency. While direct measurement of beat frequency from high-speed video recordings is the most accurate and reproducible technique it is extremely time consuming. The aim of this study was to develop a freely available automated method of ciliary beat frequency analysis from digital video (AVI) files that runs on open-source software (ImageJ) coupled to Microsoft Excel, and to validate this by comparison to the direct measuring high-speed video recordings of respiratory and ependymal cilia. These models allowed comparison to cilia beating between 3 and 52 Hz.
Digital video files of motile ciliated ependymal (frequency range 34 to 52 Hz) and respiratory epithelial cells (frequency 3 to 18 Hz) were captured using a high-speed digital video recorder. To cover the range above between 18 and 37 Hz the frequency of ependymal cilia were slowed by the addition of the pneumococcal toxin pneumolysin. Measurements made directly by timing a given number of individual ciliary beat cycles were compared with those obtained using the automated ciliaFA system.
The overall mean difference (± SD) between the ciliaFA and direct measurement high-speed digital imaging methods was -0.05 ± 1.25 Hz, the correlation coefficient was shown to be 0.991 and the Bland-Altman limits of agreement were from -1.99 to 1.49 Hz for respiratory and from -2.55 to 3.25 Hz for ependymal cilia.
A plugin for ImageJ was developed that extracts pixel intensities and performs fast Fourier transformation (FFT) using Microsoft Excel. The ciliaFA software allowed automated, high throughput measurement of respiratory and ependymal ciliary beat frequency (range 3 to 52 Hz) and avoids operator error due to selection bias. We have included free access to the ciliaFA plugin and installation instructions in Additional file 1 accompanying this manuscript that other researchers may use.
分析纤毛功能以评估疑似原发性纤毛运动障碍（PCD）的患者，并用于呼吸和室管膜纤毛的研究，需要同时评估纤毛摆动模式和摆动频率。虽然从高速视频记录中直接测量摆动频率是最准确且可重复的技术，但极其耗时。本研究的目的是开发一种可免费使用的自动化方法，用于从数字视频（AVI）文件中分析纤毛摆动频率，该方法运行于与Microsoft Excel联用的开源软件（ImageJ）上，并通过与呼吸和室管膜纤毛的直接测量高速视频记录进行比较来验证。这些模型允许对3至52Hz之间的纤毛摆动进行比较。
使用高速数字视频记录器捕获运动性纤毛室管膜细胞（频率范围34至52Hz）和呼吸道上皮细胞（频率3至18Hz）的数字视频文件。为了涵盖18至37Hz之间的范围，通过添加肺炎球菌毒素肺炎溶血素来减慢室管膜纤毛的频率。将通过对给定数量的单个纤毛摆动周期计时直接进行的测量与使用自动化纤毛FA系统获得的测量结果进行比较。
纤毛FA系统与直接测量高速数字成像方法之间的总体平均差异（±标准差）为-0.05±1.25Hz，相关系数显示为0.991，呼吸纤毛的布兰德-奥特曼一致性界限为-1.99至1.49Hz，室管膜纤毛的为-2.55至3.25Hz。
开发了一个用于ImageJ的插件，该插件可提取像素强度并使用Microsoft Excel执行快速傅里叶变换（FFT）。纤毛FA软件允许对呼吸和室管膜纤毛摆动频率（范围3至52Hz）进行自动化、高通量测量，并避免了因选择偏差导致的操作员误差。我们已在本手稿的附加文件1中提供了对纤毛FA插件的免费访问和安装说明，其他研究人员可以使用。
