Image processing and analysis

A short description of the image processing and analysis of the raw images are provided here, a more detailed explanation has beenpublished previously byour research group. In short, a database is created to handle the generated images, containing filename, time point, Z-stack and fluorescence channel (this information was obtained from the full filenames provided by the micromanager software used for image acquisition). A difference of Gaussian approach is used for background reduction of the images, this is performed on individual image planes in a three-dimensional (3D) volume (the Z-stack images for each time point are handled as a volume). The images are thresholded to discriminate the platelets from the background, and the threshold is set using a probe to reduce the influence of the operator. Platelets are detected in the volume and their respective x, y and z position is determined. The data from the analysis and in the original database are joined into a data frame fromwhich information and statistics from the experiments can be derived. The script used for image processing and analysis was developed in Python. The above-mentioned steps are built using packages and modules that are readily available for download online and named in the published method. In this article, we have used the positional information about the detected platelets to enable tracking of the platelets throughout the experiment. The platelet positions in 3D for each time frame were used as an input in the Trackpy Python package (v 0.3.2, available online via https:// soft-matter.github.io/trackpy/v0.3.2/). The platelets were tracked between consecutive frames provided that the maximum displacement did not exceed 3 μm between two time frames. From this analysis, the tracked platelets were also indexed so it is possible to distinguish the platelet throughout the experiment. With the tracking, it is possible to determine the size of themovement for each tracked platelet along each axis (dvx, dvy, dvz) and the total length (dv). The contractile component was deduced by projecting each platelet movement vector onto a vector towards the thrombus centre of mass. The processing of the data, obtaining mean values, graphs and plots have been performed using several different Python modules, NumPy (for scientific computing), pandas (processing of data frames), matplotlib (2D-plotting) and Seaborn (statistical data visualization). Thrombus formation on collagen at 400 s , 10 minutes time-lapse. Experiments were performed in a polydimethylsiloxane (PDMS) flow chamber with 5% CD42a-labelled platelets (red) and annexin V (green). Time-lapse Z-stack images were captured with wide-field fluorescence microscopy. The video shows the first 600 seconds of a 1,200-second experiment and the flow is directed downwards in the video. Online content including video sequences viewable at: www.thieme-connect.com/ejournals/ html/doi/10.1055/s-0038-1668151.