Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes.

Dynamic Measurement of Endosome-Lysosome Fusion in Neurons Using High-Content Imaging.

Endocytosis is a dynamic cellular process that actively transports particles into a cell. Late endosome fusion with the lysosome is a crucial step in the delivery of newly synthesized lysosomal proteins and endocytosed cargo for degradation. Disturbing this step in neurons is associated with neurological disorders. Thus, studying endosome-lysosome fusion in neurons will provide new insight into the mechanisms of these diseases and open new possibilities for therapeutic treatment. However, measuring endosome-lysosome fusion is challenging and time consuming, which limits the research in this area. Here we developed a high throughput method using pH-insensitive dye-conjugated dextrans and the Opera Phenix® High Content Screening System. By using this method, we successfully separated endosomes and lysosomes in neurons, and time-lapse images were collected to capture endosome-lysosome fusion events in hundreds of cells. Both assay set-up and analysis can be completed in an expeditious and efficient manner.

Tracking Moving Cells in 3D Time Lapse Images Using 3DeeCellTracker.

Recent advancements in 3D microscopy have enabled scientists to monitor signals of multiple cells in various animals/organs. However, segmenting and tracking the moving cells in three-dimensional time-lapse images (3D + T images), to extract their dynamic positions and activities, remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline called 3DeeCellTracker, which precisely tracks cells with large movements in 3D + T images, obtained from different animals or organs, using highly divergent optical systems. In this protocol, we explain how to set up the computational environment, the required data, and the procedures to segment and track cells with 3DeeCellTracker. Our protocol will help scientists to analyze cell activities/movements in 3D + T image datasets that have been difficult to analyze. Graphic abstract: The flowchart illustrating how to use 3DeeCellTracker. See the Equipment and Procedure sections for detailed explanations.

Myosin V-mediated transport of Snc1 and Vps10 toward the trans-Golgi network.

Retrieval of cargo proteins from the endosome towards the trans-Golgi network (TGN) is a crucial intracellular process for cellular homeostasis. Its dysfunction is associated with pathogenesis of Alzheimer and Parkinson's diseases. Myosin family proteins are cellular motors walking along actin filaments by utilizing the chemical energy from ATP hydrolysis, known to involve in pleiotropic cellular trafficking pathways. However, the question of whether myosins play a role in the trafficking of Snc1 and Vps10 has not been addressed yet. The present study assesses the potential roles of all five yeast myosins in the recycling of two membrane cargo, Snc1 and Vps10. It appears that all myosins except Myo2 are not required for the Snc1 traffic, while it was found that Myo1 and 2 play important roles for Vps10 retrieval from the endosome and the vacuole. Multiple myo2 mutants harboring a point mutation in the actin binding or the cargo binding tail domain were characterized to demonstrate abnormal Vps10-GFP and GFP-Snc1 distribution phenotypes, suggesting a severe defect in their sorting and trafficking at the endosome. Furthermore, Vps10-GFP patches in all tested myo2 mutants were found to be near stationary with quantitative live cell imaging. Finally, we found that actin cables in the myo2 mutant cells were considerably disrupted, which may aggravate the trafficking of Vps10 from the endosome. Together, our results provide novel insights into the function of Myo-family proteins in the recycling traffic of Vps10 and Snc1 destined for the TGN.

Development of an automated two pronuclei detection system on time-lapse embryo images using deep learning techniques.

PURPOSE: To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. METHODS: An algorithm was developed by explicitly incorporating human observation where the outline around pronuclei is being observed in determining the number of pronuclei. Supervised data were selected from the time-lapse images of 300 pronuclear stage embryos per class (total 900 embryos) clearly classified by embryologists as 0PN, 1PN, and 2PN. One-hundred embryos per class (a total of 300 embryos) were used for verification data. The verification data were evaluated for the performance of detection in the number of pronuclei by regarding the results consistent with the judgment of the embryologists as correct answers. RESULTS: The sensitivity rates of 0PN, 1PN, and 2PN were 99%, 82%, and 99%, respectively, and the overlapping 2PN being difficult to determine by microscopic observation alone could also be appropriately assessed. CONCLUSIONS: This study enabled the establishment of the automated pronuclei determination system with the precision almost equivalent to highly skilled embryologists.

Tracking-assisted Detection of Dendritic Spines in Time-Lapse Microscopic Images.

Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. To detect dendritic spines in a time point image we employ an SVM classifier trained by pre-labeled SIFT feature descriptors in combination with a dot enhancement filter. Second, to track the growth or loss of spines, we apply a SIFT-based rigid registration method for the alignment of time-series images. This step takes into account both the structure and the movement of objects, combined with a robust dynamic scheme to link information about spines that disappear and reappear over time. Next, we improve spine detection by employing a probabilistic dynamic programming approach to search for an optimum solution to accurately detect missed spines. Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.

Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation.

Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.