2D-GolgiTrack-a semi-automated tracking system to quantify morphological changes and dynamics of the Golgi apparatus...

The Golgi apparatus and membrane tubules derived from this organelle play essential roles in membrane trafficking in eukaryotic cells. High-resolution live cell imaging is one highly suitable method for studying the molecular mechanisms of dynamics of organelles during membrane trafficking events. Due to the complex morphological changes and dynamic movements of the Golgi apparatus and associated membrane tubules during membrane trafficking, it is challenging to accurately quantify them. In this study, a semi-automated 2D tracking system, 2D-GolgiTrack, has been established for quantifying morphological changes and movements of Golgi elements, specifically encompassing the Golgi apparatus and its associated tubules, the fission and fusion of Golgi tubules, and the kinetics of formation of Golgi tubules and redistribution of the Golgi-associated protein Rab6A to the endoplasmic reticulum. The Golgi apparatus and associated tubules are segmented by a combination of Otsu's method and adaptive local normalization thresholding. Curvilinear skeletons and tips of skeletons of segmented tubules are used for calculating tubule length by the Geodesic method. The k-nearest neighbor is applied to search the possible candidate objects in the next frame and link the correct objects of adjacent frames by a tracking algorithm to calculate changes in morphological features of each Golgi object or tubule, e.g., number, length, shape, branch point and position, and fission or fusion events. Tracked objects are classified into morphological subtypes, and the Track-Map function of morphological evolution visualizes events of fission and fusion. Our 2D-GolgiTrack not only provides tracking results with 95% accuracy, but also maps morphological evolution for fast visual interpretation of the fission and fusion events. Our tracking system is able to characterize key morphological and dynamic features of the Golgi apparatus and associated tubules, enabling biologists to gain a greater understanding of the molecular mechanisms of membrane traffic involving this essential organelle. Graphical Abstract Overview of the semi-automated 2D tracking system. There are two main parts to the system, namely detection and tracking. The workflow process requires a raw sequence of images (a), which is filtered by the Gaussian filter method (c), and threshold intensity (b) to segment elements of Golgi cisternae (d) and tubules (e). Post-processing outputs are binary images of the cisternae area and tubule skeletons. The tubules are classified into three lengths, namely short, medium, and long tubules (f). Outputs of segmentation are calculated as morphological features (g). The tracking processing starts by loading the segmented outputs (h) and key-inputs of direction reference (i; (DR)) and interval setting of the start ((S)) and end ((E)) frame numbers (j). A tubule of interest is selected by the user (k; (GT_{interest, S}) as the tubule input ((GT_IN)) at the current frame ((i = S)). The tracking algorithm tracks and links the correct tubules at each subsequent frame ((i = i + 1)). The locations of tubule tips are determined for detecting tubule branches using the (DR) to identify the direction of tubule growth (l: (1); (GT_{tipBr, i}); Golgi cisternae: white area; Golgi tubule: white skeleton; tubule tips: green dots; branched tubules: two branches due to the (DR) of growth of the simulated tubule moving from left-to-right away from the Golgi cisternae location). According to the position of the (GT_IN), five candidates ((GT_{candidates, i})) are searched using the k-nearest neighbor method (l: (2)). Matching of tubules between the (GT_IN) and those (GT_{candidates, i}) uses the bounding box technique to check the amount of tubule-overlap based on the tracking conditions (l: (3)). If there is tubule-overlap, the system collects that tubule as the final output ((GT_{OUT, i})). By contrast, shape (see the Extent feature in Table reftab:1) and distance features are used to generate the tracked output, which has a priority of a minimum of both of these features ((Min_{DIST, EXTENT})); otherwise, it is from the minimum of the distance ((Min_DIST)). Once a loop of the interval track to the last frame is finished ((i = E + 1)), a Track-Map is generated allowing visualization of the morphological pattern of tubule formation and movement, including identification of fission and fusion events (m). Dynamic features are calculated (n). Related outputs are saved, and all features obtained from the detection and tracking processing are exported as MS Excel files (o).