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9 results for “Single-particle tracking”
Experimental data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Overview of experimental spaSPT data</strong></p> <p>To comprehensively test Spot-On over many different conditions, we conducted 1064 spaSPT experiments. The raw data is freely available and the purpose of this ReadMe file is to describe the organization, acquisition parameters and format of the data. The data is for 4 different cell lines imaged over 15 different conditions yielding a total of 60 different conditions. The four cell lines were:</p> <ul> <li> <p>U2OS C32 Halo-CTCF</p> </li> <li> <p>U2OS H2B-Halo-SNAP</p> </li> <li> <p>U2OS Halo-3xNLS</p> </li> <li> <p>mESC (JM8.N4) C3 Halo-Sox2</p> </li> </ul> <p>The cell lines were constructed in different ways. U2OS C32 Halo-CTCF was made by homozygous endogenous N-terminal tagging of CTCF in human osteosarcoma U2OS cells using CRISPR/Cas9-mediated genome-editing as described (C32 refers to clone number 32)<sup>1</sup>. We note the CTCF is an essential gene and that N-terminal tagging did not appear to affect CTCF function or expression level according to a series of control experiments<sup>1</sup>. Moreover, C32 Halo-CTCF has been authenticated using Short Tandem Repeat (STR) profiling (performed by Dr. Alison N. Killilea at the UC Berkeley Cell Culture Facility) against the following loci: THO1, D5S818, D13S317, D7S820, D16S539, CSF1PO, AMEL, vWA and TPOX. The C32 Halo-CTCF cell line showed a 100% match with U2OS.</p> <p>U2OS H2B-Halo-SNAP was made through random integration of a H2B-HaloTag-SNAP-Tag transgene expressed using the EF1a promoter with an IRES-NeoR gene for drug selection. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>U2OS Halo-3xNLS was made through random integration of a FLAG-Halo-3xNLS (3x SV40 NLS: PKKKRKV) transgene expressed using the EF1a promoter. NeoR for drug selection was separately expressed using an SV40 promoter. After transfection, cells were selected using G418 until a pure cell population was obtained. This cell line has also been described previously<sup>1</sup>. The wild-type U2OS cell line used to make this cell line was also authenticated using STR profiling against the same loci as C32 and also showed a 100% match with U2OS.</p> <p>mESC C3 Halo-Sox2 was made through homozygous N-terminal tagging of Sox2 in JM8.N4<sup>2</sup> mouse embryonic stem cells using CRISPR/Cas9-mediated genome editing as previously described (C3 refers to clone number 3)<sup>3</sup>. The functionality of the C3 Halo-Sox2 knock-in was validated through control experiments and pluripotency through teratoma assays as described previously<sup>3</sup>.</p> <p>Each file contains single-molecule trajectories from a single cell imaged over 30,000 frames. Localization and tracking was performed using a custom-written Matlab implementation of the MTT-algorithm<sup>4</sup> and the following settings: Localization error: 10<sup>-6.25</sup>; deflation loops: 0; Blinking (frames): 1; max competitors: 3; max <em>D</em> (m<sup>2</sup>/s): 20.</p> <p>The same 15 conditions were used for each of the 4 cell lines.</p> <p><strong>ExpA PA-JF549</strong></p> <p>The purpose of this experiment was to test the effect of “motion-blurring” on the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub>. 5 different experimental conditions were considered. Full details are given in the Methods section. Briefly, cells were grown overnight on plasma-cleaned 25 mm circular coverslips either directly (U2OS) and MatriGel coated as described<sup>1</sup>. Cell were labeled with 5-50 nM PA-JF549<sup>5</sup> for around 15-30 min, washed twice and medium exchanged to phenol-red free medium. 30,000 frames were collected at a camera exposure time (Andor iXon Ultra 897; frame-transfer mode; vertical shift speed: 0.9 μs; -70C) of 9.5 ms which together with a ~447 μs camera integration time gave a frame rate of ~100 Hz. PA-JF549 dyes were photo-activated during the ~447 μs camera integration time using 405 nm pulses and the 405 nm pulse intensity optimized to achieve a mean density of 1 molecule per frame per nucleus. The JF549 dye was excited using a 561 nm laser and the total number of excitation photons kept constant but either delivered during a 1 ms pulse, a 2 ms pulse, a 4 ms pulse, a 7 ms pulse or with constant illumination.</p> <p>For each cell line and condition, 4 replicates were performed. We count a replicate as an independent experiment performed on a different day. For each replicate around 5 cells were imaged. Occasionally, fewer than 5 cells are available. To avoid tracking errors, we removed cells with too high a localization density from the analysis. All of this information is available in the file name. For example, “U2OS_C32_Halo-CTCF_PA-JF549_1ms-561nm_100Hz_rep2_cell03” refers to the third cell imaged in the second replicate of U2OS C32 Halo-CTCF using a 1 ms excitation pulse of 561 nm laser at a frame rate of 100 Hz. Similarly, “U2OS_C32_Halo-CTCF_PA-JF549_cont-561nm_100Hz_rep4_cell01” refers to the first cell imaged in the fourth replicate of U2OS C32 Halo-CTCF using constant 561 nm laser at a frame rate of 100 Hz.</p> <p>The five ExpA_PAJF549 conditions are separated by cell line such that each cell line is provided in a separate directory. E.g. the directory “U2OS_H2B_ExpA_PAJF549” contains all data for the U2OS H2B-Halo-SNAP cell line.</p> <p><strong>ExpA PA-JF646</strong></p> <p>This experiment was exactly identical to the “ExpA_PA-JF549” experiment except cell were labeled with PA-JF646<sup>5</sup> and excited using a 633 nm laser. The file names and data organization was otherwise the same and the same five excitation conditions were considered.</p> <p><strong>ExpB PA-JF646</strong></p> <p>The purpose of this experiment was to test if the Spot-On estimated <em>D</em><sub>FREE</sub> and <em>F</em><sub>BOUND</sub> values would depend on the frame rate. In particular, all four proteins exhibit some levels of apparent anomalous diffusion, which could cause a dependence on the frame rate. Cells were labeled with PA-JF646 and grown and imaged as described above. Photo-activation took place during the ~447 μs camera integration time and JF646 dyes were excited using 1 ms stroboscopic 633 nm excitation pulses. To change the frame rate, the camera exposure time was set to 4.5 ms (~201 Hz), 5.5 ms (~167 Hz), 7 ms (~134 Hz), 13 ms (~74 Hz) and 19.5 ms (~50 Hz) when also counting the ~447 μs camera integration time. All of this information is available in the file name. For example, “U2OS_Halo-3xNLS_PA-JF646_1ms-633nm_74Hz_rep2_cell04” refers to the fourth cell imaged in the second replicate of U2OS Halo-3xNLS using a 1 ms excitation pulse of 633 nm laser at a frame rate of 74 Hz. Similarly, “mESC_C3_Halo-Sox2_PA-JF646_1ms-633nm_201Hz_rep1_cell03” refers to the third cell imaged in the first replicate of mESC Halo-Sox2 using a 1 ms excitation pulse of 633 nm laser at a frame rate of 201 Hz.</p> <p><strong>Data format</strong></p> <p>All data is available in two different formats: CSV-files and Matlab MAT-files. Both file formats are readable by the web-version of Spot-On. The Matlab version of Spot-On is only able to read the MAT-files. The CSV format consists of comma-separated values and contains headers. If opened with Microsoft Excel, it should appear as shown:</p> <p>Here the “frame” column contains the frame number in which the molecule was detected. The “t” column contains the timestamp. The “trajectory” column contains the trajectory number. For example, trajectory number 1 was only detected in frame 13 after which it disappeared. In contrast, trajectory number 4 was detected in frames 20, 21 22, 23 and 24. Finally, the “x” and “y” columns contain the x,y coordinates of the localization in units of micrometers (μm).</p> <p>The MAT-files contain a structure array named “trackedPar”. trackedPar contains three variables:</p> <ul> <li> <p>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of localizations in that trajectory. The first column is the x-coordinate and the second column is the y-coordinate. The units are micrometers (μm).</p> </li> <li> <p>trackedPar.Frame: “Frame” is a column vector where each element is the frame where the particle was localized.</p> </li> <li> <p>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint where the particle was localized.</p> </li> </ul> <p>Each element in the structure array “trackedPar” correspond to a different trajectory.</p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments"
<p><strong>Generation of simulated data</strong></p> <p>To systematically evaluate the performance of Spot-On as well as other common analysis tools such as MSD<sub>i</sub> and vbSPT, we considered a comprehensive set of 3480 realistic SPT simulations spanning the range of plausible dynamics. The simulations were performed using simSPT, which is freely available at GitLab: https://gitlab.com/tjian-darzacq-lab/simSPT. The simulation methods are described in detail at GitLab. A full description of the parameters which allows exact reproduction of the simulations is available together with the data (see Data Availability section). Briefly, we parameterized simSPT to consider that particles diffuse inside a sphere (the nucleus) of 8 µm diameter illuminated using HiLo illumination (assuming a HiLo beam width of 4 µm), with an axial detection range of ~700 nm, centered at the middle of the HiLo beam. Molecules are assumed to have a half-life of 4 frames (when inside the HiLo beam) and of 40 frames when outside the HiLo beam. The localization error was set to 25 nm and the simulation was run until 100000 in-focus trajectories were recorded. More specifically, the effect of the exposure time (1 ms, 4 ms, 7 ms, 13 ms, 20 ms), the free diffusion constant (from 0.5 µm²/s to 14.5 µm²/s in 0.5 µm²/s increments) and the fraction bound (from 0 % to 95 % in 5 % increments) were investigated, yielding a dataset consisting of 3480 simulations. The advantage of simulations is that the ground truth is known. This allows a quantitative assessment of which method works the best.</p> <p><strong>Content of the archives:</strong></p> <ol> <li>170718_simSPT_simulations.zip the code and instructions to reproduce the simulations</li> <li>4um.tar.bz2 simulated data inside a 4 µm nucleus</li> <li>20um.tar.bz2 simulated data inside a 20 µm nucleus, in which virtually no confinement occurs.</li> <li>subsampled.tar.bz2 is a set of subsampled datasets, containing either 99999, 30000, 10000, 3000, 1000, 300, 100 or 30 trajectories. Each subsampling was done 50 times, yielding 50 files per subsmpling.</li> </ol> <p><strong>Formats:</strong></p> <p>The data is provided both in CSV and .mat formats. .mat files are provided in the following dataset: 10.5281/zenodo.835541</p>
Advances in volumetric super-resolution microscopy and single-particle tracking (associated codes and datasets)
<h2>Overview</h2> <p>This Zenodo repository contains datasets and code relating to the thesis entitled "Advances in volumetric super-resolution microscopy and single-particle tracking" by <a href="https://www.ch.cam.ac.uk/person/sgd46">Sam G. Daly</a> (Yusuf Hamied Department of Chemistry, University of Cambridge).</p> <p>Managed/updated versions my be avalible at <a href="https://github.com/TheLeeLab">https://github.com/TheLeeLab</a>.</p> <p>The Excel Workbook 'MicrolensRelayCalculator' is designed to help in the design of MLAs for SMLFM.</p> <h2>Available Datasets</h2> <h3>Chapter 4</h3> <ol> <li>Simulated localisation data for various PSFs: standard, astigmatism, double helix, SMLFM, and tetrapod; 4000 detected photons, 20 emitters per frame, 200 frames.</li> <li>Microtubule imaging in a fixed HeLa cell (dSTORM); 30 ms exposure, 640 nm excitation, 200 frames.</li> </ol> <h3>Chapter 5</h3> <ol> <li>B cell receptor imaging on a fixed B cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>SPT of the B cell receptor on a live B cell (PALM); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed Jurkat T cell embedded in agarose (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>PD-1 imaging on a fixed T cell (dSTORM);<em> 30 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> <li>Membrane imaging on a fixed T cell (resPAINT); <em>20 ms exposure, 640 nm excitation, 200 frames, fiducial: nanodiamonds.</em></li> </ol> <h3>Chapter 6</h3> <ol> <li>SPT of ACBD3 in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> <li>SPT of TMD mutant (length: 27) in a live HeLa cell (PALM); <em>20 ms exposure, 640 and 405 nm excitation, 200 frames.</em></li> </ol> <h2>Available Code</h2> <ol> <li><strong>Autofocus (BeanShell):</strong> Counteracts axial drift in SMLFM experiments.</li> <li><strong>Calibration (BeanShell):</strong> Controls the piezo scanner for axial calibrations in 3D-SMLM.</li> <li><strong>3D Reconstruction (Matlab):</strong> Reconstructs 2D-localised SMLFM data in 3D. Maintained version available on GitHub.</li> <li><strong>Fiducial correction (Matlab):</strong> Removes focal drift artifacts from 3D localisation data.</li> <li><strong>Temporal grouping (Python):</strong> Removes multiple single-molecule blinking events.</li> <li><strong>3D tracking (Matlab):</strong> Converts 3D localisations into tracks and calculates diffusion quantities.</li> <li><strong>Matching (Matlab):</strong> Determines PPV, sensitivity, and Jaccard index from localisation data.</li> <li><strong>Membrane curvature (Python):</strong> Determines the frequency of 3D localisations at a given membrane curvature.</li> </ol> <h3><em>Supported by The Royal Society (RGF\EA\181021) </em></h3>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Dataset for publication: ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks
<p>Dataset related to article </p> <p>Simon, F., J.-Y. Tinevez, S. van Teeffelen (2023) ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks. Journal of Cell Biology</p> <p>Movies: example movies for PBP1b and RodZ. G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>Tracks: resulting tracks from the replicates, each replicate is composed of several movies (one xml file per movie). G0 = 370% expression level, G1 = 130% expression level, G2 = 30% expression level.</p> <p>See the material and method section for more details on the experimental conditions and the analysis pipeline.<br> </p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)
<p>See 10.5281/zenodo.834787 for a more complete description.</p>
Data Repository: Fast Single-Particle Tracking of Membrane Proteins Combined with Super-Resolution Imaging of Actin Nanodomains
<p>This repository contains a collection of correlated 2D super-resolution images obtained through single molecule localization microscopy and 3D time series of single particle tracking data of membrane proteins. By utilizing high-speed fluorescent microscopy, we tracked a transmembrane protein – the high-affinity IgE receptor – and an outer-leaflet protein – GPI-anchored protein – at the frame rate of 490 Hz. Subsequently, actin structures of the same cells were captured using a super-resolution microscopy (dSTORM technique). Additionally, this dataset includes Technical Validation to support the transition from live-cell imaging to fixed-cell imaging when adding fixation buffers. </p> <p>The data was classified as “Class I” and “Class II” describing two categories of RBL-2H3 cells. In "Class I", the cells were untransfected. In “Class II”, the cells were transfected to express GFP-GPI-anchored fusion protein. The zip folder names that include “IgE Untreated” include image time series of fluorescently labeled IgE receptors in untreated RBL-2H3 cells and image series of corresponding super-resolution imaging of fluorescently labeled actin filaments of the same cell in “SRImage” folder.</p> <p>The data with folder names including with “IgE Treated” are image time series of fluorescently labeled IgE receptors in either phalloidin- or PMA-treated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>Similarly, the data with folder names starting with “GPI Untreated” are image time series of fluorescently labeled GPI-anchored proteins in untreated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>The data with folder names starting with “GPI Treated” are image time series of fluorescently labeled GPI-anchored proteins in phalloidin treated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>The number within each folder name represents an individual experiment and the corresponding data collected under the same conditions.</p> <p>For all experiments an IR movie was added to monitor the cell morphology during live-cell image and adding initial fixation buffer and it was saved in the tracking file. </p> <p>The HDF5 files of all data are available in the second version of this repository. For each sample, there are two files: "Tracking.h5" and "SuperResolution_actin.h5". The "Tracking" files include two groups: single-particle tracking data and IR images during tracking. The "SuperResolution_actin" files contain two groups: super-resolution images of actin filaments and IR reference images for image registeration and drift correction during data collection. </p> <p> </p> <p> </p> <p> </p>
Data belonging to "DNA-PAINT single-particle tracking (DNA-PAINT-SPT) enables extended single-molecule studies of membrane protein interactions"
<p>Source data for "DNA-PAINT single-particle tracking (DNA-PAINT-SPT) enables extended single-molecule studies of membrane protein interactions"</p>
The impact of DnaA titration on Escherichia coli DNA replication initiation elucidated through live-cell single-particle tracking
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