Raw and annotated data for Shape2Fate

Datasets acquired and generated for the manuscript "Shape2Fate: a morphology-aware deep learning framework for tracking endocytic and exocytic carriers at nanoscale". The datasets include test, training and time series datasets each containing the raw data and the annotated data where it applies. Overview This archive contains live-cell time-lapse TIRF-SIM datasets used in the development of Shape2Fate. Each dataset includes raw SIM data (all phases/orientations/frames in a single file). Depending on the experiment, additional files include SIM reconstructions (-recon), binary masks (-mask.tif), and manual annotations (-annotations.csv). Unless noted otherwise, data is provided in .dv format. This dataset is related to the following paper:Shape2Fate: a morphology-aware deep learning framework for tracking endocytic and exocytic carriers in super-resolution TIRF-SIM. Biological systems Datasets were acquired from engineered mammalian cell lines expressing fluorescent reporters of clathrin-mediated endocytosis and cargo exocytosis. The following reporters are represented: EGFP-CLCa, SNAP-CLCa (JF-dye labelled), SBP-mEmerald-LAMP1, pHluorin-GLUT4, and Dynamin2-mRuby3. RUSH assays (_STIM) were stimulated with biotin; adipocyte GLUT4 datasets (_STIM) were stimulated with insulin. Further methodological details are provided in the associated manuscript. Imaging Most TIRF-SIM experiments were carried out at the Micron Bioimaging Facility, University of Oxford, using a DeltaVision OMX SR inverted wide-field microscope equipped with a 60× 1.5-NA TIRF UPLAPO oil-immersion objective (Olympus) and a PCO Edge 4.2 sCMOS camera. Acquisition parameters are stored in the raw files metadata. Channel registration Multicolour datasets were registered using 0.1 µm TetraSpeck™ fluorescent microspheres imaged under identical conditions as the experimental datasets. Registration was performed using the sub-pixel alignment method of Thevenaz et al. (IEEE Trans. Image Process., 1998; doi: 10.1109/83.650848), applying a similarity (scaled rotation) transformation. For each dataset, a corresponding transformation matrix is provided (registration_transform.json), mapping coordinates from the target channel to the source channel (i.e., inverse transform). Masks Each image file is accompanied by a corresponding binary mask that delineates the in-focus, well-adhered regions of cells within the field of view. These masks were used to restrict all quantitative analyses to areas containing reliably imaged cells. Dataset groups Below is a summary of the included datasets: Dataset Cell type Format Extras Adipocytes-CME coupling 3T3-L1 adipocytes pHluorin-GLUT4 SNAP-CLCa .dv Separate CME channel (_subset), Exocytosis productivity annotations, Masks CME tracking testing RPE-1 EGFP-CLCa .nd2 Reconstruction, Tracking annotations CME tracking validation RPE-1 EGFP-CLCa .mrc Reconstruction, 3x tracking annotations Dynamin productivity validation RPE-1 Dyn2-mRuby3 SNAP-CLCa .dv Masks Exocytosis tracking validation HeLa SBP-mEmerald-LAMP1 .dv Reconstruction, Tracking annotations RUSH-CME global coupling SH-SY5Y SNAP-CLCa .dv Masks RUSH-CME local coupling SH-SY5Y SBP-mEmerald-LAMP1 SNAP-CLCa .dv Separate CME channel (_subset), Exocytosis productivity annotations, Masks