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324 results for “fluorescent imaging”
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>
Fluorescence Microscopy Images of Hela Cell infected with Plasmodium Berghei parasite expressing mCherry in cytoplasm
<p>The purpose of our experiments was to delve into the liver stage development of the P. berghei parasite and examine the host-parasite interactions using HeLa cells. This research is primarily focused on in vitro analysis and does not extend to in vivo applications. Our study investigated the integration of fluorescent microscopy with artificial intelligence to <br>track and predict the developmental milestones of Plasmodium liver stage development. </p> <p>This is the dataset used in our study.</p>
(12)-Pereyra2024A-DS0001--0009 – Nine Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(12)-Pereyra2024A-DS0001--0009 – Nine <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Computer code accompanying Schraivogel, D. et al. "High-speed fluorescence image-enabled cell sorting" Science, 2022. doi: 10.1126/science.abj3013
<p>Computer code accompanying Schraivogel et al. "High-speed fluorescence image-enabled cell sorting". Details are provided in the manuscript's data and materials availability section and table 3.</p> <p> </p> <p>We provide three directories:</p> <p>(1) R code to reproduce figures (ICS2021_0.1.0.tar.gz)</p> <p>(2) Python code to reproduce figures (ICS_Fiji_Plugin.zip)</p> <p>(3) Code for ICS/CellView Fiji plugins (ICSPython.zip)</p> <p> </p> <p>Code for (1) and (3) has also been shared via Github:</p> <p>https://github.com/benediktrauscher/ICS</p> <p>https://github.com/embl-cba/ICS</p> <p> </p> <p>We recommend downloading the ICS Fiji plugins via Github or to install them using the Fiji update site to ensure you're using the most recent version.</p>
Fluorescence lifetime imaging of pH along the secretory pathway
<p>Many cellular processes are dependent on correct pH levels, and this is especially important for the secretory pathway. Defects in pH homeostasis in distinct organelles cause a wide range of diseases, including disorders of glycosylation and lysosomal storage diseases. Ratiometric imaging of the pH-sensitive mutant of green fluorescent protein (GFP), pHLuorin, has allowed for targeted pH measurements in various organelles, but the required sequential image acquisition is intrinsically slow and therefore the temporal resolution unsuitable to follow the rapid transit of cargo between organelles. We therefore applied fluorescence lifetime imaging microscopy (FLIM) to measure intraorganellar pH with just a single excitation wavelength. We first validated this method by confirming the pH in multiple compartments along the secretory pathway. Then, we analyze the dynamic pH changes within cells treated with Brefeldin A, a COPI coat inhibitor. Finally, we followed the pH changes of newly-synthesized molecules of the inflammatory cytokine tumor necrosis factor (TNF)-α while it was in transit from the endoplasmic reticulum via the Golgi to the plasma membrane. The toolbox we present here can be applied to measure intracellular pH with high spatial and temporal resolution, and can be used to assess organellar pH in disease models.</p>
Tracking breast cancer cells migrating collectively and imaged in fluorescence with TrackMate-Cellpose
<p>Breast cancer cells migrating collectively.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells.</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
Bayesian machine learning analysis of single-molecule fluorescence colocalization images
<p>Data files for the "Bayesian machine learning analysis of single-molecule fluorescence colocalization images" manuscript.</p>
Fluorescence microscopy image of invitrogen FluoCells #2 Slide
<p>Image recorded by Lennart Hilbert at Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology Images were acquired using a fluorescence confocal microscope based on the VT-iSIM high-speed super-resolution scanner. Channels show DNA, Actin, and microtubuli, and a blank camera image recorded for technical reasons.</p>
Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle"
<p>Supplementary information, datasets and fluorescence images related to the article "The role of NSP6 in the biogenesis of the SARS-CoV-2 replication organelle".<br> The PDF file entitled "Supplementary material" contains the uncropped original western blots and autoradiographs published in the article.<br> The PDF files entitled "Extended Data Fig.2,6,7,9,10 all panels" and "Figure 1,4 all panels" contain the original full-size confocal immunofluorescence images from which specific ROIs are published in the article.<br> The Excel files "Source Data Principal Figures" and "Source Data Extended Figures" contain all the original datasets used for calculation and graphical representation of data published in the article.</p>
Volumetric imaging of fluorescently labeled BPAE cells
<p>Using the Nanoimager-S microscope (ONI, Oxford Nanoimaging) with a sCMOS sensor (Hamamatsu, ORCA-Flash4.0 V2), FluoCells™ Prepared Slide #1 (Thermo, #F36924) were imaged with a 100X, 1.4 NA, oil-immersion objective (Olympus). DAPI, Alexa-488 and MitoTracker™ Red excitation was delivered by 405 nm, 473 nm and 561 nm lasers, respectively. Light was collected while using two emission filters (1: 525/50; 2: Band 1 575-616.5) and a Channel Splitter dichroic 561 LP. A 3D Z-stack of the sample was acquired for each channel. 22 frames were generated, each separated 50 nm from each other in Z.</p>
Fluorescence Microscopy Images
<p>Fluorescent microscopy images (orthogonal projections) labelling several protein targets in live, Saccharomyces cerevisiae cells</p>
Fluorescent images of actin and DAPI-labelled MCF10A, MCF7 and MDA-MB-231 cell lines
<p>This dataset of cell images was generated to understand the morphological changes between less and more metastic cancer cells and between normal and cancerous cells. They have been used in the linked publications.</p>
Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
Fluorescence images acquisition on cultures of human-derived cardiomyocytes
<p><span>These data are used to evaluate the performance of the SiMulTox platform in terms of performance of live-cell fluorescence imaging.</span></p>
Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF
<p>Software tools and image files needed to reproduce the results of the image classifier evaluation in the Current Alzheimer Research manuscript of the same title.</p>
(06)-He2019A-DS0001 – Tribolium castaneum foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0001 – <em>Tribolium castaneum</em> foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
REAVER Vascular Networks Fluorescent Image Dataset
<p><strong>Fluorescent Images of Vessel Networks from Various Murine Tissues</strong></p> <p> </p> <p><strong>Purpose</strong>: Image dataset of vascular networks with a diverse range of vessel architectures. Dataset is used to evaluate performance of several image processing programs (AngioQuant<sup>1</sup>, AngioTool<sup>2</sup>, RAVE<sup>3</sup>, REAVER). Manual analysis from ImageJ is used as ground truth to compare other programs against.</p> <ul> <li><strong>Labeling</strong>: IB4-Lectin with Alexa Flour 647</li> <li><strong>Modality</strong>: Confocal Microscope Nikon 80i CLSM</li> <li><strong>Objective</strong>: Mixture of 20x and 60x objective images</li> <li><strong>Image Format</strong>: Images originally acquired in Nikon IDS format, converted to 8-bit greyscale TIFs found in “_Original_Images” folder.</li> <li><strong>Questions</strong>: Email <a href="mailto:bac7wj@virginia.edu">bac7wj@virginia.edu</a> for inquiries.</li> </ul> <p> </p> <p><strong>External Links</strong></p> <ol> <li><strong>Manuscript</strong>:</li> <li><strong>Code repository: </strong><a href="https://github.com/bacorliss/REAVER_public">https://github.com/bacorliss/REAVER_public</a> for code to analyze this data (MATLAB 2019a).</li> </ol> <p> </p> <p><strong>Dataset Summary:</strong></p> <p>Each image folder contains 36 images. For each image:</p> <ol> <li>The first channel (red) is the segmented image with values of 0 or 255 (false or true).</li> <li>The second channel (green) is the skeleton image with values of 0 or 255 (false or true).</li> <li>The third channel (blue) is empty except for the Manual images where the third channel contains the original raw image.</li> </ol> <p> </p> <p><strong>Subfolders</strong></p> <ol> <li><strong>_Original_Images</strong>: contains raw input images.</li> <li><strong>AngioQuant_Auto</strong>: contains output images from automated analysis in AngioQuant.</li> <li><strong>AngioTool_Auto</strong>: contains output images from automated analysis in AngioTool.</li> <li><strong>ImageJ_Auto</strong>: contains output images from automated analysis in ImageJ.</li> <li><strong>ImageJ_Manual</strong>: contains output images from manual analysis in ImageJ.</li> <li><strong>RAVE_Auto</strong>: contains output images from automated analysis in RAVE.</li> <li><strong>REAVER_Auto</strong>: contains output images from automated analysis in REAVER.</li> </ol> <p> </p> <p><strong>Image Metadata and Output data</strong></p> <p>Each image folder has a .mat file called “Results.mat” containing the results of analysis in the form of the following variables all of which are 1x36 arrays (one entry for each image) unless specified otherwise:</p> <ol> <li><strong>branchpoint_RC</strong>: A 1x36 struct containing the row-column values for each branchpoint in the i<sup>th</sup> image (when organized in alphabetic order which is the order given everywhere else); Effectively the same as “BranchpointsByName.mat”</li> <li><strong>mean_diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>num_branchpts</strong>: The number of branchpoints in the image</li> <li><strong>threshold_false_neg</strong>: The number of false negative pixels – a pixel is a false negative if the program has it as “false” and the manual image has the pixel as “true”</li> <li><strong>threshold_false_pos</strong>: The number of false positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_neg</strong>: The number of true negative pixels – a pixel is a true negative if the program has it as “false” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_pos</strong>: The number of true positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “true”</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>vessel_area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>vessel_length</strong>: The number of “true” pixels in the skeleton image</li> </ol> <p> </p> <p><strong>Dataset Output Data</strong></p> <p>The file “image_quantification.csv” in the base folder contains the aggregated results from each image folder. Each row contains the results for a given (Program, Image) pair. The columns are described below:</p> <ol> <li><strong>Program</strong>: Designates the program used to calculate the data for that row</li> <li><strong>Tissue_Type</strong>: Gives the tissue type for the image</li> <li><strong>Image_Name</strong>: Gives the specific name of the given image</li> <li><strong>Vessel_Length</strong>: The number of “true” pixels in the skeleton image</li> <li><strong>Vessel_Area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>Mean_Diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>Num_Branchpoints</strong>: The number of branchpoints in the image</li> <li><strong>Sensitivity</strong>: (Number of True Positive pixels) / (Number of True Positive pixels + Number of False Negative pixels)</li> <li><strong>Specificity</strong>: (Number of True Negative pixels) / (Number of True Negative pixels + Number of False Positive pixels)</li> <li><strong>Accuracy</strong>: (Number of True Positive pixels + Number of True Negative pixels) / (Total number of pixels)</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>pix_dim</strong>: The edge length in pixels of the square image</li> </ol> <p> </p> <p><strong>References</strong></p> <p>1. Niemisto, A., Dunmire, V., Yli-Harja, O., Wei Zhang & Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. <em>IEEE Trans. Med. Imaging</em> <strong>24</strong>, 549–553 (2005).</p> <p>2. Zudaire, E., Gambardella, L., Kurcz, C. & Vermeren, S. A Computational Tool for Quantitative Analysis of Vascular Networks. <em>PLOS ONE</em> <strong>6</strong>, e27385 (2011).</p> <p>3. Seaman, M. E., Peirce, S. M. & Kelly, K. Rapid Analysis of Vessel Elements (RAVE): A Tool for Studying Physiologic, Pathologic and Tumor Angiogenesis. <em>PLoS ONE</em> <strong>6</strong>, e20807 (2011).</p>
Real‐time fiber‐based fluorescence lifetime imaging with synchronous external illumination: A new path for clinical translation
<p>Time-correlated single photon counting is the “gold-standard” method for fluorescence lifetime measurements and has demonstrated potential for clinical deployment. Its clinical adoption is hindered by the use of high gain detectors, which make the fluorescence acquisition impractical with bright lighting conditions such as in clinical settings. We address this limitation by interleaving periodic fluorescence detection with synchronous out-of-phase externally modulated light source, thus guaranteeing specimen illumination and a fluorescence signal free from bright background light upon temporal separation. Fluorescence lifetime maps are generated in real-time from single-point measurements by tracking a reference beam and using the phasor approach. We demonstrate the feasibility and practicality of this technique in a number of biological specimens, including real-time mapping of degraded articular cartilage. This method is compatible and can be integrated with existing clinical microscopic, endoscopic and robotic modalities, thus offering a new pathway towards label-free diagnostics and surgical guidance in a number of clinical applications.</p>
tttrlib: modular software for integrating fluorescence spectroscopy and imaging
<p>This repository contains scripts and datasets associated with the manuscript "tttrlib: modular software for integrating fluorescence spectroscopy and imaging". The software is designed to facilitate the analysis of fluorescence spectroscopy and imaging data to enable integration with molecular modeling.</p> <p>The provided scripts encompass various functionalities, including:</p> <ul> <li>Single-molecule FRET (smFRET) analysis for studying conformational dynamics, particularly focusing on human guanylate binding protein 1 (hGBP1).</li> <li>Comprehensive image spectroscopy workflows applicable to murine guanylate binding protein 2 (mGBP2), featuring intensity and time-resolved analyses.</li> <li>Tools for burst variance analysis (BVA), multiparameter fluorescence detection, and correlative analysis of fluorescence lifetime and intensity.</li> </ul> <p>Datasets for both hGBP1 and mGBP2 are included to exemplify the software's capabilities in real-world applications.</p>
Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor
<p>These are data related to the manuscript titled "Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor" whose preprint can be found here: https://doi.org/10.1101/2022.05.03.490515</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.