Cilia density and flow velocity affect alignment of motile cilia from brain cells

Here we store video and code used for the publication Cilia density and flow velocity affect alignment of motile cilia from brain cells. We provide raw images and codes to support the article.The complete dataset of raw images is more than 1 Tb. Here we are limited to 50Gb. The full dataset is available upon request. <br> We choose to provide a full dataset of two culture at DIV 16, one treated with shear flow and a control without flow. <br> For each of the two cultures, the videos with propelled particles are in the directory FL,<br> The bright field images without particles are stored in BF. Unfortunately we uploaded only few videos because of their large size. The results of the analysis of this dataset is reported in the directory analysis (available for each culture). Moreover we provide the code to analyse these data.<br> The analysis routine: Step 1: for each field of view (fov) getting the cilia beating direction from the FL images. This is done with PIV. The code is Step1_PIVanalysis.mat Step 2: for each fov getting ciliated cell position and CBF from the BF movies. Gather the cilia beating direction and cilia posion and frequency in a unique figure and matlab class (Res.mat). This is done in Step2_gatherResults.mat The results of these analysis are stored in the analysis folder for each culture. These routines are repeated for each experiment and results are then plotted to get trends. In the folder code4figures we report the code that we used to make the figures in the papers starting from a matlab file "all_results*.mat", where are gathered all the analysis. The code may improve in the future with more comments. please check Nicola's github page for the latest update. Please contact us for any problem. https://github.com/NicolaPellicciotta/Code4-Cilia-density-and-flow-velocity-affect-alignment-of-motile-cilia-from-brain-cells All the raw videos and code are in the archive.本数据集收录了用于发表论文《纤毛密度与流速影响脑细胞运动纤毛排列》的视频与代码，提供支撑该论文的原始图像与配套代码。原始图像完整数据集容量超过1太字节（Tb），本次上传仅限定50吉字节（Gb），完整数据集可通过申请获取。 本次我们提供两份体外培养至第16天（DIV 16）的完整数据集：一份接受剪切流处理，另一份为无流动的对照组。 两份细胞培养样本中，带有示踪粒子的视频文件均存储于FL目录；不含粒子的明场图像存储于BF目录。受限于文件体积过大，本次仅上传了少量视频。 本数据集的分析结果存储于analysis目录（每份培养样本均对应相关结果），此外我们还提供了用于分析上述数据的代码。 分析流程如下： 步骤1：针对每个视场（field of view, FOV），从FL目录的图像中提取纤毛摆动方向，该步骤通过粒子图像测速法（Particle Image Velocimetry, PIV）完成，对应代码为Step1_PIVanalysis.mat。 步骤2：针对每个视场，从BF目录的影片中提取纤毛细胞的位置与纤毛摆动频率（Ciliary Beat Frequency, CBF）；将纤毛摆动方向、细胞位置及摆动频率整合至单幅图表与Matlab类文件Res.mat中，该步骤对应代码为Step2_gatherResults.mat。 上述分析的结果均存储于每份培养样本对应的analysis文件夹中。该分析流程可重复应用于所有实验，最终通过绘图得到变化趋势。 在code4figures文件夹中，我们提供了用于绘制论文图表的代码，该代码基于整合了所有分析结果的Matlab文件"all_results*.mat"生成。后续该代码将补充更多注释以完善功能，请访问Nicola的GitHub页面获取最新版本。 如有任何问题，请联系我们。数据集仓库地址：https://github.com/NicolaPellicciotta/Code4-Cilia-density-and-flow-velocity-affect-alignment-of-motile-cilia-from-brain-cells 所有原始视频与代码均收纳于本归档文件中。