DYNAMIK: a software environment for cell DYNAmics, Motility, and Information tracKing, with an application to Ras pathways

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

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).

Automated tracking of label-free cells with enhanced recognition of whole tracks

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

Integrative analysis of T cell motility from multi-channel microscopy data using TIAM

Integrative analytical approaches are needed to study and understand T cell motility as it is a highly coordinated and complex process. Several computational algorithms and tools are available to track motile cells in time-lapse microscopy images. In contrast, there has only been limited effort towards the development of tools that take advantage of multi-channel microscopy data and facilitate integrative analysis of cell-motility. We have implemented algorithms for detecting, tracking, and analyzing cell motility from multi-channel time-lapse microscopy data. We have integrated these into a MATLAB-based toolset we call TIAM (Tool for Integrative Analysis of Motility). The cells are detected by a hybrid approach involving edge detection and Hough transforms from transmitted light images. Cells are tracked using a modified nearest-neighbor association followed by an optimization routine to join shorter segments. Cell positions are used to perform local segmentation for extracting features from transmitted light, reflection and fluorescence channels and associating them with cells and cell-tracks to facilitate integrative analysis. We found that TIAM accurately captures the motility behavior of T cells and performed better than DYNAMIK, Icy, Imaris, and Volocity in detecting and tracking motile T cells. Extraction of cell-associated features from reflection and fluorescence channels was also accurate with less than 10% median error in measurements. Finally, we obtained novel insights into T cell motility that were critically dependent on the unique capabilities of TIAM. We found that 1) the CD45RO subset of human CD8 T cells moved faster and exhibited an increased propensity to attach to the substratum during CCL21-driven chemokinesis when compared to the CD45RA subset; and 2) attachment area and arrest coefficient during antigen-induced motility of the CD45A subset is correlated with surface density of integrin LFA1 at the contact.

The green tea catechin epigallocatechin gallate (EGCG) blocks cell motility, chemotaxis and development in Dictyostelium discoideum

Catechins, flavanols found at high levels in green tea, have received significant attention due to their potential health benefits related to cancer, autoimmunity and metabolic disease, but little is known about the mechanisms by which these compounds affect cellular behavior. Here, we assess whether the model organism Dictyostelium discoideum is a useful tool with which to characterize the effects of catechins. Epigallocatechin gallate (EGCG), the most abundant and potent catechin in green tea, has significant effects on the Dictyostelium life cycle. In the presence of EGCG aggregation is delayed, cells do not stream and development is typically stalled at the loose aggregate stage. The developmental effects very likely result from defects in motility, as EGCG reduces both random movement and chemotaxis of Dictyostelium amoebae. These results suggest that catechins and their derivatives may be useful tools with which to better understand cell motility and development in Dictyostelium and that this organism is a useful model to further characterize the activities of catechins.

Potential vulnerability markers within the affective domain in subjects at genetic and clinical high risk for schizophrenia

BACKGROUND

Relative to ample high-risk studies on neurocognitive function, only a few high-risk studies have examined affective functioning components as possible vulnerability markers. In this study, we comprehensively assessed baseline affective functioning in subjects at clinical high risk (CHR) and genetic high risk (GHR) for schizophrenia, and healthy controls (HC), and compared the results to elucidate possible vulnerability markers in the affective domain.

METHODS

We studied 3 groups of subjects: those with CHR (n = 28) or GHR (n = 28) and a HC group (n = 24). Affective-process- and affective-content-related functioning were assessed using 5 emotion-related scales.

RESULTS

In affective process, CHR subjects showed impairments in emotional awareness and mood repair, with some trend of impaired emotional expressivity as well as aggression control relative to either HC or GHR subjects, whereas GHR subjects showed only a trend of impairment in mood repair. In affective content, CHR subjects had less positive and more negative affect scores than the other 2 groups.

CONCLUSIONS

These results correspond to previous findings of prodrome studies of schizophrenia and chronic schizophrenia and suggest that impaired mood repair and emotional awareness, as well as less positive and more negative affect may be potential candidates of vulnerability markers.