Find research datasets worth reusing

opencc-by-4.0Sep 2020View details →

Data for: Physics-based Reconstruction Methods for Magnetic Resonance Imaging

Magnetic Resonance Imaging measurement data used in our paper about 'Physics-based Reconstruction Methods for Magnetic Resonance Imaging' (DOI: 10.1098/rsta.2020.0196). (In version 2 the IR-FLASH data set was replaced with one which is from the same volunteer and slice as the ME-SE data set.)  The data is acquired from healthy volunteers and stored in the format of the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>). The acquisition parameters are shown in the following table: flip angle[◦] TR/TE/ Delta TE[ms] bandwidth [Hz/px] matrix spokes TA[s] FOV[mm] slice[mm] IR-FLASH 6 4.10/2.58 630 256 × 256 1020 4 192 5 ME-SE 90/180 2500/9.9/9.9 390 256 × 256 25 × 16 80 192 3 ME-FLASH 5 10.60/1.37/1.34 960 200× 200 33 × 7 0.35a 320 5 PC-FLASH 10 4.46/2.96 1250 210 × 210 2 × 7 15 320 5 fmSSFPb 15 4.5/2.25 840 192× 192 4 × 101 × 40 137 192 1

opencc-by-4.0Jun 2017View details →

Supporting data for 'Simultaneous maximum a posteriori longitudinal PET image reconstruction'

This dataset contains the data used to produce the paper: 'Simultaneous maximum a posteriori longitudinal PET image reconstruction' by Ellis and Reader, Physics in Medicine and Biology (2017). DOI: http://dx.doi.org/10.1088/1361-6560/aa7b49. Please see the article for a full description of methodology used to obtain this data. The dataset comprises a number of MATLAB data files (.mat), MATLAB scripts (.m), and plain text files (.txt), corresponding to each figure in the article. Running the .m script in MATLAB for each figure will reproduce that figure approximately as it appears in the article. Furthermore, the .txt files describe the contents of the .mat data files in order to allow independent exploration of the data. Note that the function plotSparseMarker is required to be able to run fig5.m. This work was funded by the King’s College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging (grant number EP/L015226/1) and supported by the EPSRC grant number EP/M020142/1. This data has been made available in accordance with the EPSRC's policy framework on research data.

opencc-by-4.0Nov 2023View details →

Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

This dataset contains the video clips used to produce the results presented in:Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

opencc-by-4.0May 2024View details →

Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome

<h2>Brief description</h2>   These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T1 weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T2* measurements. <h2>Animals</h2>   All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.   <h2>MR acquisition</h2>   MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were  acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm3, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm2, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm3, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms  (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm3, 24 averages, pulse angle shift 117°). <h2>Data structure</h2>   The repository contains the following data: -       READ_ME.txt: this file -       RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals. -       rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files: o   bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively. o   dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired. o   bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file. o   zerob: Text file containing the image numbers where images with no diffusion weighting were acquired. o   ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively. -       derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit. -       Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.

opencc-by-4.0Jan 2019View details →

LIDAR scan, photos and image-based reconstruction of sofa corner

This data set provides photographs and a continuous LIDAR scan (using the Velodyne VLP-16) of a sofa corner at Simula Research Laboratory that were created for the BUISAR project funded by the researcher council of Norway (project number 270951). A  3D reconstruction of the sofa corner was created using Meshroom (http://github.com/alicevision/meshroom) with support from the LADIO project. The LIDAR scan takes the form of a network trace (.pcap file). This file can be read and interpreted as a point cloud by the Point Cloud Library (PCL, https://github.com/PointCloudLibrary/pcl). Since the VLP-16 comes without motion sensors, the user of the LIDAR scan can either interpret the data as a series of frames, or attempt to align the recorded voxels in a global space. No ground truth for this is provided.

opencc-by-nc-sa-4.0Feb 2019View details →

3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images

3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT). It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers. The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known. All the images are of blocky 3D shapes floating in space in front of a black background. Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a> The dataset consists of: <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> (Training Dataset) <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> (Test Dataset) <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]. Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> (Validation Dataset) <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul>   Important: Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "unxz 3diqtt-v2-train.h5.xz" on Unix machines. If you use this dataset, please cite it.

opencc-by-4.0Apr 2023View details →

LASSO coherent seismic wavefield reconstruction and source imaging

Coherent wavefield reconstruction and source imaging has been performed for 4 cataloged seismic events recorded with the Large-N Seismic Survey in Oklahoma (LASSO). The array consists of almost 2,000 densely spaced seismic stations and the corresponding raw time sries data have been made freely accessible by the Incorporated Research Institutions for Seismology (IRIS). The results for the 4 seismic events are accompanied with results gained for controlled seismic simulations for two of these events Reconstruction results and source images are provided in HDF5 and MAT file formats, respectively. File names were giving according to the following pattern:  "LASSO_<event name>_<reconstruction mode>_<result type>" where <reconstruction mode> refers either to "enhancement" (reconstruction performed for the original station layout) or "regularization" (reconstruction perfomed for a new, sense and regular station layout). <result type> denotes either reconstructed waveforms ("wavefield"), waveform coherence ("coherence"), or spatial source images. For the HDF5 files, mportant meta information like spatial coordinates and temporal sampling parameters are stored in a symbolic dictionary named "META", whereas the time series data is saved as a 2D matrix. Important META fields include "ntrac" (number of traces), "nt" (number of time samples), "dt" (dampling interval), "gx" (stations x coordinates), "gy" (stations y coordinates). The MAT files (result type "images") contain raw waveform and STA/LTA images, which are stored as 3D regular arrays named "recm1z_Enh_5_raw" (enhancement) / "recm1z_Reg5_5_raw" (regularization) and "recm1z_Enh_5_slta" (enhancement) / "recm1z_Reg5_5_slta" (regularization), respectively. For comparison, source images generated for the raw field data (without reconstruction are included in every MAT file and can be accessed through fields "recm1z_Raw_raw" and "recm1z_Raw_slta".

opencc-by-4.0Feb 2023View details →

Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D)

Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However,  these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area.  To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository.  RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition,  thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and  between 58 and 65 degree fahrenheit respectively.  For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images,  similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories <a href="https://github.com/LBNL-ETA/a3dbr">https://github.com/LBNL-ETA/a3dbr</a>, and <a href="https://github.com/LBNL-ETA/AutoBFE">https://github.com/LBNL-ETA/AutoBFE</a> This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

opencc-by-4.0Jun 2015View details →

Reconstruction Algorithms in Undersampled AFM Imaging - results

This data set contains numerical simulation results from experiments for the paper "Review of compressed sensing reconstruction algorithms in AFM cell imaging", submitted to IEEE Journal of Selected Topics in Signal Processing. The data set consists of an HDF5 file containing the simulation results as well as MD5 and SHA checksums of the HDF5 database for validating the integrity of the data after download. The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://doi.org/10.5281/zenodo.18745. The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.

opencc-by-4.0Oct 2015View details →

Reconstruction Algorithms in Undersampled AFM Imaging - final results

This data set contains numerical simulation results from experiments for the paper "Reconstruction Algorithms in Undersampled AFM Imaging", published in IEEE Journal of Selected Topics in Signal Processing. The data set consists of a set of HDF5 files containing the simulation results as well as MD5 and SHA checksums of the HDF5 databases for validating the integrity of the data after download. The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://dx.doi.org/10.5281/zenodo.32959. The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.

opencc-by-4.0Apr 2017View details →

Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset

This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods. The deposition consists of: <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5).</li> <li>The Python script which was used to create the database (reconstruction_goblet.py).</li> <li>Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS).</li> </ol> The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) . The files are provided as-is with no warranty as detailed in the above mentioned licenses. The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset.

opencc-by-4.0Apr 2017View details →

Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"

Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging" The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690) <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "lattice-beads" and actin-labelled mESCs (files termed "lattice-phalloidin")</li> <li>An epifluorescence microscope: beads sample (files termed "epifluorescence")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "reference" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "compressed".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "**frames")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "PSF_model"). These are provided as numpy arrays.</li> </ol>

Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S5 and nuclear PI(4,5)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in control cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were control treated with 1:000 DMSO in the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S5 (Abcam ab5131) 3 µg/mL and mouse ascites IgM anti-PI(4,5)P2 2C11 (Z-A045; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with goat anti-mouse IgM (µ-chain) AF555 (Jackson ImmunoRes. A24126) 10 µg/mL; goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription elongation marker P-S2 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in THZ1 treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 3h with 1 µM THZ1 (MedChem HY80013) added to the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S2 (Abcam ab5095) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription elongation marker P-S2 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in control cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were control treated with 1:000 DMSO in the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S2 (Abcam ab5095) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S2 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in DRB treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 2h with 100 µM DRB (Sigma D1916) added to the cell culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S2 (Abcam ab5095) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S5 and nuclear PI(4,5)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in THZ1 treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 3h with 1 µM THZ1 (MedChem HY80013) added to the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S5 (Abcam ab5131) 3 µg/mL and mouse ascites IgM anti-PI(4,5)P2 2C11 (Z-A045; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with goat anti-mouse IgM (µ-chain) AF555 (Jackson ImmunoRes. A24126) 10 µg/mL; goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).

Ditrect STORM imaging and image reconstruction of the transcription initiation marker P-S5 and nuclear PI(3,4)P2 indirectly immunolabeled with AF647 (red) and AF555 (green) in THZ1 treated cells.

U-2 OS cells were grown in DMEM with 10% FBS at 37°C and 5% CO2. Cells were plated one day before staining in ~50% confluence on the high-precision 12 mm round coverslips treated with Hellmanex, sonicated, washed, dried and sterilized. Cells were treated for 3h with 1 µM THZ1 (MedChem HY80013) added to the culture media. U2OS cells were washed twice with PBS (pH 7.4) and fixed for 30 min in 2% PFA in PBS, washed 3-times for 5 min with PBS, then permeabilized in 0.1% Triton X-100 in PBS for 20 min, washed 3-times for 5 min by PBS and blocked in filtered 5% BSA in PBS for 30 min. Cells were incubated for 45 min with rabbit polyclonal IgG anti-RNAPII CTD P-S5 (Abcam ab5131) 3 µg/mL and mouse monoclonal IgG2 anti-PI(3,4)P2 (Z-P034; Echelon Biosci. Inc., USA) 5 µg/mL  in 5% BSA in PBS, washed 3-times for 5 min in PBS and incubated for 30 min with donkey anti-mouse IgG AF555 (Invitrogen A31570) 10 µg/mL and goat anti-rabbit IgG AF647 (Invitrogen A21245) 10 µg/mL diluted in 5% BSA in PBS. Then the cells were washed 3-times for 5 min in PBS, post-fixed for 15 min in 2% PFA in PBS and washed 3-times for 5 min in PBS. All procedures were performed at RT and the cells were stored in PBS in the fridge overnight prior imaging. Coverslips with cells were mounted in the Chamlide chamber (Live Cell Instrument, Korea) and covered with imaging buffer (PBS pH 7.4, 50 mM MEA). Single-molecule localizations (SMLs) data were acquired by Zeiss Elyra PS.1 equipped with HR Diode 642-150 and HR DPSS 561-200 lasers, Alpha Plan-Apochromat 100x/1.46 oil DIC M27 Elyra objective and Andor EM CCD iXon DU 897 camera and Zeiss ZEN Black 2.1 SP3 software (Zeiss). AF647 and AF555 photo-switching was achieved by HiLo illumination and TIRF HP FOV with 100% power of 642nm or 561nm laser, and the signal was acquired via MBS 642 + EF LP 655 and MBS 561 + EF BP 570-620 / LP 750 filters, respectively. Exposure time was 40 ms and EM gain was 300 for both channels. SMLs were calculated in 2D by Zeiss ZEN Black 2.1 SP3 software using x,y 2D Gauss fit with point spread function (PSF) half width 177.9 nm, peak mask size 9 pixels and peak intensity to noise 6 and accounted for overlap in 2D with max cluster size 10. SMLs were rendered in ZEN software with 10 nm/px resolution and 1x PSF expansion factor. The data were model-based drift corrected in ZEN. Two channels were aligned using tetraspec beads fiducial markers for affine calibration. Drift-corrected and aligned localization coordinates were exported as text files. Text files were converted into csv files and imported using self-written macro (Hoboth et al., 2021a) into the ImageJ2 (Rueden et al., 2017) plug-in ThunderSTORM, visualized by normalized Gaussian method (Ovesny et al., 2014).