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10 results for “Expansion Microscopy”
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
Photo-thermal expansion of a PMMA nanosphere using mid-IR photo-induced force microscopy (PiF-IR)
<p>This dataset contains the raw data associated with our manuscript, <em>'Photo-thermal expansion of nanostructures in photo-induced force microscopy</em><strong>'</strong></p> <p>by Shohely Tasnim Anindo,1,2 Daniela Täuber,3,4 and Christin David*1,5</p> <div> <div> <div> <p>1 Institute of Condensed Matter Theory and Optics, Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, 07743 Jena, Germany<br>2 Abbe Center of Photonics, Albert-Einstein-Straße 6, 07745 Jena, Germany<br>3 Institute of Physical Chemistry, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany <br>4 Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany <br>5 University of Applied Sciences Landshut, Am Lurzenhof 1, 84036 Landshut, Germany</p> </div> </div> </div> <p>The raw data were acquired using a VistaScope (Molecular Vista, US) operated in the side-band mode of mid-infrared photo-induced force microscopy (PiF-IR). These data are associated with the experimental part in this manuscript. Details of the data acquisition and processing are described in the Methods section of the manuscript.</p> <p>The dataset is structured in the following:</p> <ul> <li>PiF-IR scans of a spherical PMMA nanoparticle with radius R = 50 nm (PMMA NP) at varied illumination power in the resonant condition using the illumination frequency: 1150 cm^-1</li> <li>PiF-IR scans of the same PMMA NP at varied illumination power in the non-resonant condition using the illumination frequency: 1300 cm^-1</li> <li>PiF-IR hyperspectral scan of the same PMMA NP over the spectral range 989 - 1349 cm^-1</li> </ul>
YeastExM: Expansion Microscopy on Saccharomyces cerevisiae
<p>Here we provide a video protocol showing how to proceed throughout the protocol of ExM on Yeast as described here:</p> <p><strong>"Expansion microscopy on Saccharomyces cerevisiae" </strong></p> <p>(DOI:<strong> </strong>10.20944/preprints202203.0146.v1)</p> <p>Artemis G. Korovesi<sup>§1</sup>, Leonor Morgado<sup>§1</sup>, Marco Fumasoni<sup>1</sup>, Ricardo Henriques<sup>1,2</sup>, Hannah S. Heil<sup>1*</sup>, Mario Del Rosario<sup>1*</sup></p> <p>Correspondance to H.S.H.: <a href="mailto:hsheil@igc.gulbenkian.pt">hsheil@igc.gulbenkian.pt</a> & M.D.R.: <a href="mailto:mrosario@igc.gulbenkian.pt">mrosario@igc.gulbenkian.pt</a></p>
Quantifying Expansion Microscopy with DNA Origami Expansion Nanorulers
<p>Expansion microscopy evolved as an interesting alternative in the field of super-resolution microscopy by physically expanding the target sample to increase its size by a multiple and to subsequently image the sample by conventional fluorescence microcopy. But the quantitative determination of the expansion factor remains challenging, since microscopic measurement standards are missing. Here we use DNA nanorulers in combination with expansion microscopy to expand the fluorescently marked rulers in 2D and to determine the intermark distances before and after expansion. We compare macroscopic and microscopic expansion factors where we found substantial deviations, which should be considered for the quantitative discussion of expansion microscopy data of biological cells and tissues. The demonstrated proof-of-principle opens a new field with the potential to answer questions with respect to quantitation of microscopic magnification and the introduced expansion nanorulers present a powerful tool to reveal the true origin of anisotropy and heterogeneity in expanded microscopy specimen.</p> <p> </p> <p>Here we show the raw data the publication is based on.</p>
Data from: Atlas of Plasmodium falciparum intraerythrocytic development using expansion microscopy
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Data for: High-throughput expansion microscopy enables scalable super-resolution imaging
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3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset2
<p>wide type planarian 6G10 staining taken with TLSM</p>
Mapping GPIIb/IIIa on murine blood platelets with expansion microscopy
<p><strong>Mapping GPIIb/IIIa on murine blood platelets with expansion microscopy</strong></p> <p>We visualize the membrane architecture of platelets by mapping platelet integrin GPIIb/IIIa by super-resolution fluorescence. Our approach combines colocalization and expansion microscopy and successfully interrogates highly abundant receptors and their interactions where conventional methods fail in such dense 3D scenarios (https://doi.org/10.1101/2021.02.16.431449)</p> <p>Here we provide a s<em>election of typical images</em> of receptor distributions on unexpanded and expanded murine platelets, the <em>code</em> for simulation of platelet receptor distributions including a <em>user manual </em>and a <em>video protocol </em>of 10x expansion microscopy with resting platelets.</p> <p><strong>Sample images: </strong></p> <p>Unexpanded, 4x expanded and 10x expanded resting murine platelets on glycine coated coverslips were imaged on a SP5 confocal microscope (detailed methods can be found in the manuscript).</p> <p>There are three different labeling combinations:</p> <ul> <li>Case I: directly immunolabeled with monoclonal antibodies antibodies carrying Alexa Fluor 488 (MWReg30, green channel) or Alexa Fluor 594 (JON6, magenta channel) can bind simultaneously on the same GPIIb/IIIa receptor -> high colocalization</li> <li>Case II: directly immunolabeled with a monoclonal antibody targeting GPIIb/IIIa (MWReg30) carrying either Alexa Fluor 594 or Alexa Fluor 488) -> low colocalizaiton</li> <li>Case III: directly immunolabeld with monoclonal antibodies carrying Alexa Fluor 488 targeting GPIIb/IIIa (JON6) and carrying Alexa Fluor 594 targeting GPIX (p0p6) -> low colocalization</li> </ul> <p>The provided sample images are .ims (Imaris) files contain the raw data, chromatic aberration corrected data & deconvolved data as consecutive time points, as well as the cropped image stack of a single platelet with the masked data used for colocalization analysis.</p> <p><strong>MatLab Scipts: 3D-Image simulation of Platelet Receptor Distributions</strong></p> <p>The provided MatLab scripts allow to create 2-color, 3D image stacks based on simulated platelet receptor distributions and the image resolution provided by confocal microscopy and sample expansion. Refer to the user manual for a detailed description of how to set up and run the simulation.</p> <p><strong>Video Protocol:</strong> <strong>10x expansion microscopy with resting platelets.</strong></p> <p>The provided video protocol describes the sample preperation for 10x expansion microscopy of resting platelets, including platelet washing, immunolabeling, gelation, digestion, expansion and sample mounting for imaging. Detailed step by step protocols are also provided in the supplementary material (https://doi.org/10.1101/2021.02.16.431449).</p>
Multiplexed single-cell profiling of chromatin states at genomic loci by expansion microscopy
GEO Series GSE160784. Homo sapiens. 3 samples. Type: Other.
3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset1
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