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Updated: May 14, 2026
Collection, Expansion, and Differentiation of Primary Human Nasal Epithelial Cell Models for Quantification of Cilia Beat Frequency
Published on: November 10, 2021
ciliaFA: a research tool for automated, high-throughput measurement of ciliary beat frequency using freely available
Claire M Smith1, Jana Djakow2, Robert C Free3
1Department of Infection, Immunity and Inflammation, University of Leicester, University Road, Leicester LE1 9HN, UK.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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