Cilia dynamics creates a dynamic barrier to penetration of the periciliary layer in human airway epithelia

This dataset supports the analysis of fluid flow within the pericilary layer (PCL) of differentiated human bronchial epithelial cells (MucilAir™, Epithelix) by tracking the displacement of a caged fluorescent dye activated within the ciliary layer. The experiments were carried out at physiological temperature (37 °C) using a 60x water immersion objective.  The repository includes 14 out of the 15 total experiments used for the paper dataset; one experiment could not be uploaded due to storage limitations.

Each .zip folder contains the set of raw videos recorded on a specific area (cilia patch) on the epithelium. The experiments are from 3 distinct inserts from the same donors. For each position, labeled with coordinates indicating the displacement vector along the axis perpendicular to the epithelium, a bright field 'bf' and epifluorescent videos 'fluo' are recorder. The first one serves to identify the position and shape of the epithelium and measure the ciliary beating frequency (CBF), the second records dye displacement after activation, enabling analysis of fluid flow. The experimental configuration ensures that the dye is activated at consistent, spatially defined locations progressing from the cilia base toward the tips.

Videos are provided in .movie format, which can be read using the script moviereader.m found in the associated GitHub repository.

The full analysis pipeline is provided at https://github.com/erikacausa/PCL_caged_dye. The repository includes all necessary scripts to extract fluid flow and diffusion parameters, as well as CBF from the video data.

The main script, Caged_dye_manual_line.m, begins by loading the brightfield video for a specific position and allowing the user to manually trace the epithelial contour. This contour is then  translated to define a measuring line that intersects the dye activation point, which remains fixed in the field of view due to the stationary laser configuration. The fluorescence video is subsequently loaded, and the activation frame is detected automatically. A Gaussian fit is applied to the intensity profile along the measurement line in each frame, extracting the center of mass (mean) and spread (standard deviation) of the dye cloud over time. These values are saved in the file CagedResults.mat.

A second script, Caged_dye_fit_speed.m, analyzes the temporal evolution of the dye cloud. The displacement of the mean is used to estimate flow velocity, while the change in standard deviation over time is used to get information about diffusion properties.

In parallel, the CBF is measured using the script CBF_profile_autopoints.m. This algorithm identifies three regions within the PCL and applies a Fast Fourier Transform (FFT)-based approach to estimate the local beating frequency from the brightfield video signal.

This dataset is intended for use with the provided analysis scripts and may be adapted for extended studies or new formats.