CellTracksColab - T cell dataset (full)

Dataset used in the manuscript "CellTracksColab—A platform for compiling, analyzing, and exploring tracking data" This Zenodo archive contains: The raw video (Tracks.zip) The tracking files as XML and CSV files (Tracks.zip) The CellTracksColab dataframes storing the dataset (CellTracksColab_results.zip) The CellTracksColab outputs used to make the figures in the paper (CellTracksColab_results.zip) In brief: In summary, Lab-Tek 8 chamber slides (ThermoFisher) were prepared by overnight coating with either 2 μg/mL ICAM-1 or VCAM-1 at a temperature of 4°C. Subsequently, activated primary mouse CD4+ T cells were cleansed and suspended in L-15 media, enriched with 2 mg/mL D-glucose. These T cells were then placed into the chamber slides and incubated for 20 minutes. Post-incubation, a gentle wash was performed to eliminate all unattached cells. The imaging process was conducted using a 10x phase contrast objective at 37°C, utilizing a Zeiss Axiovert 200M microscope equipped with an automated X-Y stage and a Roper EMCCD camera. Time-lapse imaging was executed at intervals of 1 minute over 10 minutes, employing SlideBook 6 software from Intelligent Imaging Innovations. Cells were automatically tracked using StarDist, directly implemented within TrackMate. The StarDist model was trained using ZeroCostDL4Mic and is publicly available on Zenodo. This model generated excellent segmentation results on our test dataset (F1 score > 0.99). In TrackMate, the StarDist detector custom model (score threshold = 0.41 and overlap threshold = 0.5) and the Simple LAP tracker (linking max distance = 30 µm; gap closing max distance = 15 µm, gap closing max frame gap = 2 frames) were used. In CellTracksColab, we conducted a dimensionality reduction analysis employing Uniform Manifold Approximation and Projection (UMAP). The UMAP settings were as follows: number of neighbors (n_neighbors) set to 20, minimum distance (min_dist) to 0, and number of dimensions (n_dimension) to 2. This analysis utilized an array of track metrics, including: NUMBER_SPOTS, NUMBER_GAPS, NUMBER_SPLITS, NUMBER_MERGES, NUMBER_COMPLEX, LONGEST_GAP, TRACK_DISPLACEMENT, TRACK_MEAN_QUALITY, MAX_DISTANCE_TRAVELED, CONFINEMENT_RATIO, MEAN_STRAIGHT_LINE_SPEED, LINEARITY_OF_FORWARD_PROGRESSION, MEAN_DIRECTIONAL_CHANGE_RATE, Track Duration, Mean Speed, Median Speed, Max Speed, Min Speed, Speed Standard Deviation, Total Distance Traveled, Directionality, Tortuosity, MEAN_CIRCULARITY, MEAN_SOLIDITY, MEAN_SHAPE_INDEX, MEDIAN_CIRCULARITY, MEDIAN_SOLIDITY, MEDIAN_SHAPE_INDEX, STD_CIRCULARITY, STD_SOLIDITY, STD_SHAPE_INDEX, MIN_CIRCULARITY, MIN_SOLIDITY, MIN_SHAPE_INDEX, MAX_CIRCULARITY, MAX_SOLIDITY, MAX_SHAPE_INDEX Subsequently, clustering analysis was performed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). The parameters included clustering_data_source set to UMAP, min_samples at 20, min_cluster_size at 200, and the metric employed was Euclidean.