DOI: 10.6078/d13h6n
title: Dataset in support of: Recovering mixtures of fast diffusing states from short single particle trajectories

description: Single particle tracking (SPT) directly measures the dynamics of proteins
 in living cells and is a powerful tool to dissect molecular mechanisms of
 cellular regulation. Interpretation of SPT with fast-diffusing proteins in
 mammalian cells, however, is complicated by technical limitations imposed
 by fast image acquisition. These limitations include short trajectory
 length due to photobleaching and shallow depth of field, high localization
 error due to the low photon budget imposed by short integration times, and
 cell-to-cell variability. To address these issues, we developed methods to
 infer distributions of diffusion coefficients from SPT data with short
 trajectories, variable localization accuracy, and absence of prior
 knowledge about the number of underlying states. We discuss advantages and
 disadvantages of these approaches relative to other frameworks for SPT
 analysis.
description: This dataset collects all live cell single particle trajectories
 used in the manuscript "Recovering mixtures of fast diffusing states
 from short single particle trajectories". These trajectories are the
 paths of individual fluorescent emitters collected with an image modality
 called stroboscopic photoactivated single particle tracking (spaSPT) and
 analyzed with the software package quot (https://github.com/alecheckert/quot). The Materials and Methods in that manuscript describe the protocol used to collect the data in detail; we briefly summarize this protocol here. spaSPT: spaSPT experiments were performed with a custom-built Nikon TI microscope equipped with a 100X/NA 1.49 oil-immersion TIRF objective (Nikon apochromat CFI Apo TIRF 100X Oil), an EMCCD camera (Andor iXon Ultra 897), a perfect focus system to account for axial drift, an incubation chamber maintaining a humidified 37˚C atmosphere with 5% carbon dioxide, and a laser launch with 405 nm (140 mW, OBIS, Coherent), 488 nm, 561 nm, and 633 nm (all 1 W, Genesis Coherent) laser lines. Laser intensities were controlled by an acousto-optic Tunable Filter (AA Opto-Electronic, AOTFnC-VIS-TN) and triggered with the camera TTL exposure output signal. Lasers were directed to the microscope by an optical fiber, reflected using a multi-band dichroic (405 nm/488 nm/561 nm/633 nm quad-band, Semrock) and focused in the back focal plane of the objective. The angle of incident laser was adjusted for highly inclined laminated optical sheet (HiLo) conditions. Emission light was filtered using single band-pass filters (Semrock 593/40 nm for PAJFX549 and Semrock 676/37 nm for PAJF646). Hardware was controlled with the Nikon NIS-Elements software. For stroboscopic illumination, the excitation laser (561 nm or 633 nm) was pulsed for 1 millisecond at maximum (1 W) power at the beginning of the frame interval, while the photoactivation laser (405 nm) was pulsed during the ~447 microsecond camera transition time, so that the background contribution from the photoactivation laser is not integrated. For all spaSPT, we used an EMCCD vertical shift speed of 0.9 microseconds and conversion gain setting 2. On our setup, the pixel size after magnification is 160 nm and the photon-to-grayscale gain is 109. 15000-30000 frames with this sequence were collected per nucleus, during which the 405 nm intensity was manually tuned to maintain low density of fluorescent particles per frame. Fluorescent labeling: For spaSPT experiments, cells were labeled with one of three distinct photoactivatable fluorescent dyes (PAJF646, PAJF549, or PAJFX549) at a concentration of 100 nM for 10 min, followed by four washes in cell culture medium at 37˚C. Detection and tracking: To produce trajectories from raw spaSPT movies, we used a custom tracking tool publicly available on GitHub (quot; https://github.com/alecheckert/quot). This tool provides several options for detection and tracking algorithms. In all trajectories produced for this dataset, for detection, we used a generalized log likelihood ratio test with a 2D Gaussian kernel with fixed radius of 190 nm (detection method "llr" in quot), window size 15 pixels, and threshold 16.0. For subpixel localization, we used a Levenberg-Marquardt fitting routine with a 2D integrated Gaussian point spread function model. For tracking, we used a custom Hungarian algorithm with a 1.2–2.0 µm search radius, depending on the target. Trajectories are stored in a CSV format described in detail in the README.md. The settings used to produce trajectories are described in detail in the Materials and Methods of the manuscript.
description: The format of the dataset and the meaning of each field is
 described in detail in the README.md document.