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642 results for “Multiplexing”
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
Multiplexed DNA-FISH imaging dataset, drosophila embryos, nuclear cycles 11-14
<p>Multiplexed DNA-FISH imaging dataset from Drosophila embryos at nuclear cycles 11-14.</p> <p>Examples on how to load and use this dataset can be found at this <a href="https://github.com/NollmannLab/Goetz_etal">GitHub repository</a>.</p> <p><strong>Data processing details</strong></p> <p>Barcodes were segmented using a neural network (<a href="https://github.com/stardist/stardist"><em>stardist</em></a>) specifically trained for the detection of 3D diffraction limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by <em>stardist</em> was performed with Big-FISH (<a href="https://github.com/fish-quant/big-fish">https://github.com/fish-quant/big-fish</a>). Barcode localizations with intensities lower than 1.5 times that of the background were filtered out.</p> <p>Nuclei were segmented from projected DAPI images using <em><a href="https://github.com/stardist/stardist">stardist</a> </em>with a neural network trained for detection of nuclei from <em>Drosophila</em> embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei. Finally, pairwise distance matrices were calculated for each single nucleus.</p> <p><strong>Processed data in Figures</strong></p> <p>This new version of the dataset contains the raw data for each of the figures in the manuscript:</p> <p><strong>Associated publication</strong></p> <p><strong>Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in </strong><em>Drosophila</em>.</p> <p>Markus Götz, Olivier Messina, Sergio Espinola, Jean-Bernard Fiche, Marcelo Nollmann</p> <p>Nature Communications (2022).</p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
Dataset for Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters
<p>This dataset contains the raw data for the figures (Fig. 5, Fig. 6 and Fig. 7) in the publication entitled "Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters" published by Optics and Laser Technology (DOI: 10.1016/j.optlastec.2023.109513). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. </p>
Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC tonsil data-set
<p><strong>53 marker MELC Run in human tonsil</strong>. Each image depicts the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies. Images contain 2048 x 2048 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p> <p> </p>
epiGBS2: Improvements and evaluation of highly multiplexed, epiGBS-based reduced representation bisulfite sequencing
<p>We present epiGBS2, a laboratory protocol based on epiGBS (Gurp et al., 2016) with a revised and user-friendly bioinformatics pipeline for a wide range of species with or without reference genome. Performance of several critical steps in epiGBS2 was evaluated against baseline data sets from <em>Arabidopsis thaliana</em> and Great tit (<em>Parus major</em>), which confirmed overall good performance of epiGBS2. We provide here the raw bisulfite sequencing data of the epiGBS2 run for <em>Arabidopsis thaliana.</em></p> <p>A detailed description of the laboratory protocol and an extensive manual of the bioinformatics pipeline are publicly accessible on github (<a href="https://github.com/nioo-knaw/epiGBS2">https://github.com/nioo-knaw/epiGBS2)</a> and zenodo (https://doi.org/10.5281/zenodo.4764652).</p> <p>Demultiplexed data were deposited on NCBI under the BioProject ID PRJNA764918</p>
Metadata on EUbOPEN multiplex chemogenomic compound screen, wave 1
<p>This is the metadata about EUbOPEN multiplex chemogenomic compound screen, wave 1. The corresponding image data is found at <a href="https://www.ebi.ac.uk/biostudies/studies/S-BIAD145">https://www.ebi.ac.uk/biostudies/studies/S-BIAD145</a>.</p> <p>To compile the metadata Excel file into filelists, please use the Python scripts at: <a href="https://doi.org/10.5281/zenodo.6325622">https://doi.org/10.5281/zenodo.6325622</a>.</p> <p> </p>
Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
<p>Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.</p>
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"
<p>Here we share the relevant data of the manuscript “Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel”.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>
Datasets for Ultra High-Capacity Band and Space Division Multiplexing Backbone EONs
<p>The datasets have been generated for the paper titled "Ultra High-Capacity Band and Space Division Multiplexing Backbone EONs: Multi-core vs. Multi-fiber." </p>
OMAP-23: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of vermiform appendix (FFPE) with MICS on MACSima
<p><strong>Description</strong></p> <p> OMAP-23 was designed for MICS (MACSima imaging cyclic staining) imaging of FFPE human vermiform appendix sample. Tissue fixation and antigen retrieval is described in (<a href="https://www.biorxiv.org/content/biorxiv/early/2023/11/07/2023.10.27.564191.full.pdf">Spatial protein and RNA analysis on the same tissue section using MICS technology</a>). The MACSima technology is described in detail in the following publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">MACSima imaging cyclic staining (MICS) technology reveals combinatorial target pairs for CAR T cell treatment of solid tumors</a>). All, antibodies in this panel are recombinant antibodies with a mutated human IgG1 constant region, removing Fc receptor binding capacity of human IgG1, eliminating the need for additional blocking steps and reducing non-specific binding. The use of human IgG1 recombinant antibodies allows for the addition of uncoupled monoclonal antibodies from other species followed by a fluorescence labelled secondary reagent specific for the species of the monoclonal antibody. The multiplex system has been described already for OMAP-10 and OMAP-21. A new dye VioB515 is used for one reagents. The fluorescence is removed by cleavage. The panel contains 28 antibodies and the nuclear marker DAPI for image alignment and nuclear segmentation. This OMAP provides a spatial context for six anatomical structures and most cell types present in the vermiform appendix (link to ASCT+B table added). OMAP-23 follows OMAP-21, with fewer antibodies but adding antibodies for non-immune cells.</p> <p>All reagents are from Miltenyi Biotec and have been rigorously tested through an internal quality control system to have minimal variation between lots. For this reason, lot information is not included in this table. Analysis was performed by an accompanied software package MACSIQ View Analysis also described in the MACSima publication (<a href="https://doi.org/10.1038/s41598-022-05841-4">https://doi.org/10.1038/s41598-022-05841-4</a>). The MACSima system is continuously evolving, this is the third OMAP for the MACSima system. A representative dataset created using OMAP-23 can be found here 10.5281/zenodo.14008816 .The AVRs for the dataset can be found here (to be added).</p>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base
<p>The Iterative Bleaching Extends Multiplexity (IBEX) imaging method is an iterative immunolabeling and chemical bleaching method that enables highly multiplexed imaging of diverse tissues. Development of the <a href="https://doi.org/10.1038/s41596-021-00644-9">IBEX method</a> and <a href="https://github.com/niaid/imaris_extensions">related software</a> was led by Dr. Andrea Radtke and Dr. Ziv Yaniv. <a href="https://doi.org/10.1073/pnas.2018488117">IBEX</a> and related methods, <a href="https://doi.org/10.1073/pnas.1708981114">Ce3D</a>, <a href="https://doi.org/10.1111/imr.13052">Ce3D-IBEX</a>, <a href="https://doi.org/10.1073/pnas.2018488117">Opal-plex</a>, were originally developed in the laboratory of <a href="https://www.niaid.nih.gov/research/ronald-n-germain-md-phd">Dr. Ronald N. Germain</a>, US National Institutes of Health.</p><p>The IBEX Imaging Community is an international group of scientists committed to sharing knowledge related to multiplexed imaging in a transparent and collaborative manner. This open, global repository is a central resource for reagents, protocols, panels, publications, software, and datasets. In addition to IBEX, we support standard, single cycle multiplexed imaging (Multiplexed 2D imaging), volume imaging of cleared tissues with clearing enhanced 3D (Ce3D), highly multiplexed 3D imaging (Ce3D-IBEX), and extension of the IBEX dye inactivation protocol to the Leica Cell DIVE (Cell DIVE-IBEX). This dataset contains the current state of knowledge with respect to the IBEX microscopy imaging protocol.</p><p>How to use the Knowledge-Base:</p><ol><li>Save a copy to your computer.</li><li>To find a reagent: Open the reagent_resources.csv file found in the data directory. Use a spreadsheet application to filter the columns based on target name, target species, vendor, etc.</li><li>To view a complete list of fluorescent probes tested by the IBEX imaging community: Open the fluorescent_probes.csv file. This file reports the spectral properties and inactivation conditions of each fluorescent probe.</li><li>To import publications cited in the Knowledge-Base, import the publications.bib file found in the data directory to your reference manager.</li><li>To view a local copy of the website: Open the index.md file found in the docs directory using a markdown editor such as the free <a href="https://code.visualstudio.com/">Visual Studio Code</a>.</li><li>To view supporting information for a reagent (images, publications, notes): Open a specific target-conjugate-orcid combination under the docs-supporting_material directory structure using a markdown editor. This can also be visualized from the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/reagent_resources.html">Reagent Resources page</a> and filtered using a catalog number or other unique identifier in your web browser.</li></ol><p></p><p>Join the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">online IBEX Imaging community</a> and contribute your knowledge. For more details on how to contribute, see <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/contrib.html">these instructions</a>.</p><p>This research was supported by:</p><ul><li>The Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases and National Cancer Institute, under grants 1ZIAAI001290-02, 1ZIAAI000545-33, 1ZIAAI000758-24, 1ZIAAI000974-16, 1ZIAAI001034-14.</li><li> The Wellcome Trust, under grant 224586/Z/21/Z.</li><li> The National Institute of Allergy and Infectious Diseases, NIH, under grant 1ZIAAI001343-01.</li></ul><p></p>
DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids
<p>This dataset contains the raw data that were used for the publication entitled, "DNA origami book biosensor for multiplex detection of cancer-associated nucleic acids" published in Nanoscale.</p> <p> </p> <p>Abstract</p> <p>DNA nanotechnology provides a promising approach for the development of biomedical point-of-care diagnostic nanoscale devices that are easy to use and cost-effective, highly sensitive and thus constitute an alternative to expensive, complex diagnostic devices. Moreover, DNA nanotechnology-based devices are particularly advantageous for applications in oncology, owing to being ideally suited for the detection of cancer-associated nucleic acids, including circulating tumor-derived DNA fragments (ctDNAs), circulating microRNAs (miRNAs) and other RNA species. Here, we present a dynamic DNA origami book biosensor that is precisely decorated with arrays of fluorophores acting as donors and acceptors and also fluorescence quenchers that produce a strong optical readout upon exposure to external stimuli for the single or dual detection of target oligonucleotides and miRNAs. This biosensor allowed the detection of target molecules either through the decrease of Förster resonance energy transfer (FRET) or an increase in the fluorescence intensity profile owing to a rotation of the constituent top layer of the structure. Single-DNA origami experiments showed that detection of two targets can be achieved simultaneously within 10 min with a limit of detection in the range of 1–10 pM. Overall, our DNA origami book biosensor design showed sensitive and specific detection of synthetic target oligonucleotides and natural miRNAs extracted from cancer cells. Based on these results, we foresee that our DNA origami biosensor may be developed into a cost-effective point-of-care diagnostic strategy for the specific and sensitive detection of a variety of DNAs and RNAs, such as ctDNAs, miRNAs, mRNAs, and viral DNA/RNAs in human samples.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV1
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 3 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
ScienceDex guides
Understand access before you commit
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.