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91 results for “fluorescence microscopy”
An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlines
<p>Here we present a benchmarking dataset of fluorescence microscopy images with EGFP-Galectin-3-stained cells together with annotations of their outlines. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. </p> <p>The dataset contains 60 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging2.</p> <p>For most of the images, nuclear staining and annotations have been published previously in the dataset Aitslab_bioimaging1 (https://doi.org/10.5281/zenodo.6657260). The conversion script to produce the png images from the C01 images was published together with this dataset.</p>
Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906</p> <p>Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Super-Resolved FRET Imaging by Confocal Fluorescence-Lifetime Single-Molecule Localization Microscopy
<p>FRET-based methods are a special tool for detecting interactions between (bio)molecules and their immediate environment. The spatial distribution of molecular interactions and functional states can be seen using FLIM (Fluorescence Lifetime IMaging) and FRET imaging. The spatial information, accuracy, and dynamic range of the observed signals are, however, constrained by the fact that conventional FLIM and FRET imaging only provides average information over an ensemble of molecules within a diffraction-limited volume. On the other hand, conventional Single Molecule Localization Microscopy (SMLM) relies on highly sensitive multi-pixel detectors (e.g. sCMOS or EM-CCD) whose time resolution is not suitable for fluorescence lifetime measurements.</p> <p>Here, we demonstrate a method for obtaining super-resolved FRET imaging using confocal fluorescence-lifetime single-molecule localization microscopy. The proof of concept was carried out using a DNA origami sample for performing DNA-PAINT measurements in combination with fluorogenic probes for reducing background signal. With this method, We show that FRET events separated by sub-diffraction distances can be distinguished based on lifetime modifications.</p>
Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1
<p>This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air.</p> <p>The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings.</p> <p>The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images.</p> <p>This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs.</p> <p>Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”.</p> <p>The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission.</p> <p>CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good.</p> <p>Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org</p> <p>Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317.</p> <p>This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.</p>
An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines
<p>Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.</p> <p>The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. <a href="https://doi.org/10.1016/j.dib.2022.108769">10.1016/j.dib.2022.108769</a> )</p> <p><strong>Dataset description:</strong></p> <p>Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale)</p> <p>Annotations: 24-bit .png format (RGB)</p> <p>Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it</p>
Insights into metabolic changes during epidermal differentiation as revealed by multiphoton microscopy with fluorescence lifetime imaging
<p>Rapid developments in the field of organotypic cultures has generated a growing need for effective quality control measures during tissue development. In this study, we correlate metabolic changes with epidermal differentiation and demonstrate that multiphoton microscopy with fluorescence lifetime imaging (MPM-FLIM) can be applied as a non-invasive approach to monitor epidermal differentiation of keratinocytes with respect to proliferative and differentiated states. Keratinocytes grown at 1.5 mM Ca2+ exhibited increased expression of differentiation markers KRT1 and KRT10 compared to 60 μM Ca2+, and a metabolic shift from glycolysis to mitochondrial respiration. Fitting the fluorescence decay with a biexponential model revealed a decreased relative fraction of intracellular NADH and FAD after high calcium treatment, consistent with increased oxidative phosphorylation. Using these two parameters, the epidermal differentiation process could be monitored over a 96 h period. Implementing discriminating analysis based on k-means clustering generated clusters that correlated well with culturing time, suggesting that this methodology can be employed as part of an automated pipeline for monitoring keratinocyte differentiation.</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <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 "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</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 "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</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 "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
AI4Life-MDC24 Challenge data: Fluorescence Microscopy Datasets for Training Deep Neural Networks
<p>This is a subset of the Supporting data for <em>Guy M Hagen, Justin Bendesky, Rosa Machado, Tram-Anh Nguyen, Tanmay Kumar, Jonathan Ventura, Fluorescence microscopy datasets for training deep neural networks, GigaScience, Volume 10, Issue 5, May 2021, giab032, <a href="https://doi.org/10.1093/gigascience/giab032">https://doi.org/10.1093/gigascience/giab032</a></em><br><br>The selected <strong>subset</strong> contains 79 images from Data Set 4 in the form of a single tiff file. <br><br>The paper describing the original dataset is available here: <a href="https://academic.oup.com/gigascience/article/10/5/giab032/6269106">https://academic.oup.com/gigascience/article/10/5/giab032/6269106</a><br>The original dataset is available here: <a href="http://gigadb.org/dataset/100888">http://gigadb.org/dataset/100888</a></p> <p><br>AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p>
Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy
<p>Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later). </p> <p>A. CFM</p> <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> <p>B. OT-LSM </p> <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> <p>C. Simulation </p> <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>
(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0008 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0001 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0003 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0002 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0007 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0009 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0006 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0005 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Gold Nanoparticles Synthesized in the Presence of Peptides - UV-Vis Spectra, Fluorescence, USAXS, Electron Microscopy
<p>Content Summary:</p> <ul> <li>Data from experiments in which gold nanoparticles were synthesized in the presence of peptides using a liquid-handling robot. Samples were analyzed using UV-Vis spectroscopy, fluorescence emission, USAXS, TEM, and SEM. </li> <li>Notebooks for loading and plotting data</li> <li>Code for synthesizing samples using an OT2 Opentrons liquid-handling robot.</li> </ul> <p>README:</p> <p><strong>/Data</strong></p> <p>Contains all UV-Vis, electron microscopy, fluorescence, and SAXS data for gold nanoparticles synthesized in the presence of peptides and HEPES.</p> <p><strong>/Data/2021_12_30_Prepared_UV_Vis_Data</strong></p> <p>The primary portion of the experimental dataset. UV-Vis spectroscopy data collected on a Biotek Epoch 2 microplate spectrophotometer 24 hours after samples were synthesized using a liquid handling robot (Opentrons OT2). The <strong>4x4x4_SI.csv </strong>file is the compilation of all sample information:</p> <ul> <li>Concentrations (M) of peptide, HAuCl4, and HEPES</li> <li>UID – unique ID based on date of synthesis, sample position, and peptide which was used to synthesize the sample.</li> <li>Peptide names: Z2: RMRMKMK; MZ2: myristoylated - RMRMKMK; MZ2R: myristoylated - KMKMRMR; PZ2: palmitoylated – RMRMKMK; Z2M6I: RMRMKIK; Z2M246I: RIRIKIK; AG3: AYSSGAPPMPPF.</li> </ul> <p>Each sample’s UID is a key to match with UV-Vis measurement result stored in the {<strong>UID}.txt </strong>files. Each of these files contains the wavelength, absorbance, and absorbance after subtraction of a water measurement.</p> <p><strong>/Data/2022_02_13_AuPeptide_Kinetics</strong></p> <p><strong>Measurement_Data.xlsx</strong> and <strong>Measurement_Times.xlsx </strong>contain UV-Vis spectra at several time points for each well measured, and the time corresponding to each time step, respectively. See <strong>/Notebooks/UV_Vis_Kinetics.ipynb</strong> for data plotting and sample concentration information.</p> <p><strong>/Data/ElectronMicroscopy</strong></p> <p>Scanning electron microscopy and transmission electron microscopy results of gold nanoparticles formed from the reduction of HAuCl4 in the presence or absence of different peptides.</p> <p>Fig A, B, C, D, E/F were prepared in the presence of Z2, Z2M6I, Z2M246I, no peptide, and MZ2R, respectively.</p> <p><strong>/Data/Fluorescence</strong></p> <p>Pyrene fluorescence data collected in the presence of different concentrations of lipidated peptides (MZ2, MZ2R, and PZ2) for estimation of the peptide critical micelle concentration.</p> <p><strong>/Data/SAXS</strong></p> <p>SAXS data of a high concentration of MZ2 which was fit using a cylindrical model form factor. The evaluated model is also shared in this directory.</p> <p><strong>/Data/USAXS</strong></p> <p>Similarly to the UV-Vis data directory, the <strong>USAXS_SI.csv</strong> file contains sample information for all of the USAXS measurements. The <strong>dsm_rg.csv</strong> file contains the output of AUTORG evaluated on the desmeared data after subtraction of a flat background at high-q. <strong>/DSM_Nexus, DSM_sub_AUTORG, </strong>and <strong>SMR_Nexus</strong> contain the desmeared, desmeared with background subtraction, and smeared versions of the USAXS data, respectively.</p> <p><strong>/Notebooks</strong></p> <p>Notebooks for plotting the shared data and estimating the CMC from the fluorescence data. See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. We recommend installing this environment by using:</p> <p>conda env create -f /environment.yml</p> <p>Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/USAXS.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</p> <p><strong>/Figures</strong></p> <p>Figures generated from <strong>/Notebooks</strong>.</p> <p><strong>/Synthesis_Protocol</strong></p> <p>Please read the instructions within <strong>/Synthesis_Procol/Example.ipynb</strong>. In short, this folder contains the code used to synthesize the samples in this dataset using an OT2 Opentrons liquid handling robot.</p> <p> </p>
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