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642 results for “multiplexed”
MIHIC: A multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification
<p>A cohort of 47 TMA sections from 114 patients was collected from Liaoning cancer hospital \& Institute, where each TMA section has the size of 188,416$\times$110,080 pixels (i.e., 42660.87um$\times$24924.15um) at 40$\times$ magnification. TMA sections contain different number of tissue cores, ranging from 28 to 48. After excluding poor quality TMA sections with tissue folding, missing or contamination, there are totally 114 patients. Each patient has tissue cores with 12 different IHC stains, including CD3, CD20, CD34, CD38, CD68, CDK4, cyclin-D1, D2-40, FAP, Ki67, P53, and SMA. Two pathologists have manually labeled clear tissue regions (i.e., without controversy) in TMA sections based on visual examination via Qupath software, where six tissue types including Alveoli, Immune cells, Nerosis, Other, Stroma, Tumor were annotated. Besides the annotated six tissue types, we added one more Background type.</p> <p>To build histological classification models, we split 309,698 image patches in MIHIC dataset into three sets: training, validation and test. Note that image patches extracted from the same annotated tissue region are distributed into the same set, which avoids data leakage during classification model optimization. According to the number of extracted ROIs, train, val and test accounted for 64\%, 16\% and 20\%.</p> <h1>if you use this dataset, please cite:</h1> <pre>@article{wang2024mihic, title={MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification}, author={Wang, Ranran and Qiu, Yusong and Wang, Tong and Wang, Mingkang and Jin, Shan and Cong, Fengyu and Zhang, Yong and Xu, Hongming}, journal={Frontiers in Immunology}, volume={15}, year={2024}, publisher={Frontiers Media SA} }</pre>
Imputed multiplexed DNA FISH data from mouse cells
<p>Imputed multiplexed DNA FISH data from the following studies:</p><ol><li>Huang et al 2021 (DOI: 10.1038/s41588-021-00863-6): 5kb resolution chromatin tracing data of mouse embryonic stem cells</li><li>Takei et al 2021 (DOI: 10.1038/s41586-020-03126-2): 5kb and 1Mb resolution DNA seqFISH+ data of mouse embryonic stem cells</li><li>Takei et al 2021 (DOI: 10.1126/science.abj1966): 1Mb resolution DNA seqFISH+ data of mouse brain cells (excitatory neurons not included)</li></ol>
Multiplex DNA fluorescence in situ hybridization to analyze maternal vs. paternal C. elegans chromosomes - Gutnik et al - Tracing Datasets
<p>Tracing datasets (MATLAB Structure Format) from <i>C.elegans</i> N2 and HI embryos, as well as N2:HI and HI:N2 hybrid embryos presented in Gutnik et al.2024 (<strong>Multiplex DNA fluorescence in situ hybridization to analyze maternal vs. paternal </strong><i><strong>C. elegans</strong></i><strong> chromosomes)</strong></p><p> </p><p> </p><p> </p>
Multiplex DNA fluorescence in situ hybridization to analyze maternal vs. paternal C. elegans chromosomes - Gutnik et al - Raw Imaging data
<p>Raw Imaging data for all figures presented in Gutnik et al.2024 (<strong>Multiplex DNA fluorescence in situ hybridization to analyze maternal vs. paternal </strong><i><strong>C. elegans</strong></i><strong> chromosomes)</strong></p>
Analysis of multiplexed whole slide images with QuPath and Cytomap
<p>This image was acquired during MIFOBIO 2023 and was used for workshop entitled "Analysis of multiplexed whole slide images with QuPath and Cytomap".</p>
Data and code for 'Fast and artifact-free excitation multiplexing using synchronized image scanning'
<p>Data and source code for the publication 'Fast and artifact-free excitation multiplexing using synchronized image scanning'.</p>
Data from "Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels"
<p>Source data for all figures; full table of all nucleic acid sequences used; Supplementary Video 1 showing rotating view of Fig. 4e.</p>
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
<p>This is the data repository for Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data.</p> <p>Catalog:</p> <ol> <li>Intermediate data used in plotting: CANVAS_source_data.zip <ol> <li>Single cell monocyte data: monocyte.h5ad</li> <li>qPCR table: qPCR_1013.csv</li> <li>NanoString GeoMx cell composition: fig6b.csv</li> </ol> </li> <li>Pretrained CANVAS model: checkpoint-1999.pth</li> </ol> <p>The source IMC data is avaiable at: https://zenodo.org/records/7760826</p>
Example data for "Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data"
<p>This repository includes two example dataset and configurations for running CANVAS (https://github.com/tanjimin/CANVAS).</p> <p>The repostory is structured as follows:</p> <p>├── Kim_2022<br>│ ├── configs<br>│ │ ├── config.yaml<br>│ │ └── preprocess<br>│ │ ├── channels_vis_strength.yaml<br>│ │ └── selected_channels_w_color.yaml<br>│ └── data<br>│ └── raw_data<br>│ ├── common_channels.txt<br>│ └── image_files<br>└── Sorin_2023<br> ├── configs<br> │ ├── config.yaml<br> │ └── preprocess<br> │ ├── channels_vis_strength.yaml<br> │ └── selected_channels_w_color.yaml<br> └── data<br> └── raw_data<br> ├── common_channels.txt<br> └── image_files</p> <p> </p> <p>The source IMC data from this repository are from Kim et al. 2022 (https://www.nature.com/articles/s41592-022-01657-2) and Sorin et al. 2023 (https://www.nature.com/articles/s41586-022-05672-3). They are avaiable at: https://zenodo.org/records/4110560 and https://zenodo.org/records/7760826.</p>
Raw IMC files for Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.
<p>Raw MCD files for the Stanford cohort of oral squamous cell carcinoma patients in this publication:</p> <p>Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data (DOI:<span> <a href="https://doi.org/10.1016/j.isci.2023.108486" target="_blank" rel="noopener">10.1016/j.isci.2023.108486</a>).</span></p>
Data to demonstrate : Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM
<p>Data to demonstrate :</p> <p>Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM - <a href="https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3290&id=545659">https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3290&id=545659</a></p> <p>Trung Duc Nguyen, Yuan-I Chen, Anh-Thu Nguyen, Limin H. Chen, Siem Yonas, Mitchell Litvinov, Yujie He, Yu-An Kuo, Soonwoo Hong, H. Grady Rylander, and Hsin-Chih Yeh, "Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM," Opt. Express <strong>32</strong>, 3290-3307 (2024)</p>
Multiplexed protein profiling reveals spatial subcellular signaling networks
<ul> <li>Supplementary videos for "<strong>Multiplexed protein profiling reveals spatial subcellular signaling networks</strong>"</li> <li>The videos were made using Volocity 3D visualization software. </li> </ul>
Reproducible, high-dimensional imaging in archival human tissue by Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)
<p>1. SingleChannelMIBI.zip: Single-channel MIBI-TOF images</p> <p>All folders are labeled as Slide[Number]Stain[Number]_Point[Number]_[TMACoreIndex], where the slide number and stain number correspond to the slide and day of staining, the point number corresponds to the order in which the images were collected for each slide, and the TMA core index corresponds to the ID of the tissue microarray core. Each folder contains single-channel TIFFs for each marker. See paper for details.</p> <p>2. SegmentationOutput.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021, https://www.deepcell.org/predict). Output of Mesmer that delineates the single cells in each of the images is included here. Naming convention is the same as above.</p> <p>3. DataTables.zip: Data tables that are needed to run mpi_ppp_ihc_regression.ipynb</p> <p>Contains MIBI-TOF data (ionpath_processed_data.csv), MIBI-TOF calibration data (calibration_data.csv), IHC data (ihc_data.csv), and a map of each sample to its tissue type (tissue_data.csv). Also includes cell table output from Mesmer with the cell clusters appended to the table (cell_table_size_normalized_clusters.csv).</p>
Dataset for Mistic: an open-source multiplexed image t-SNE viewer
<p>This link consists of 10 anonymized non-small cell lung cancer (NSCLC) field of Views (FoVs) to test Mistic.</p> <p><strong>Mistic</strong></p> <p>Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs.</p> <p><strong>Links</strong></p> <p><br> <a href="https://github.com/MathOnco/Mistic">Mistic code</a></p> <p><a href="https://mistic-rtd.readthedocs.io/">Mistic documentation</a></p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.10.08.463728v1">Paper</a></p> <p> </p>
Differences in local immune cell landscape between Q fever and atherosclerotic abdominal aortic aneurysms identified by multiplex immunohistochemistry
<p><strong>Background:</strong> Chronic Q fever is a zoonosis caused by the bacterium <em>Coxiella burnetii</em> which can manifest as infection of an abdominal aortic aneurysm (AAA). Antibiotic therapy often fails, resulting in severe morbidity and high mortality. Whereas previous studies have focused on inflammatory processes in blood, the aim of this study was to investigate local inflammation in aortic tissue.</p> <p><strong>Methods:</strong> Multiplex immunohistochemistry was used to investigate local inflammation in Q fever AAAs compared to atherosclerotic AAAs in aorta tissue specimen. Two six-plex panels were used to study both the innate and adaptive immune system.</p> <p><strong>Results: </strong>Q fever AAAs and atherosclerotic AAAs contained similar numbers of CD68<sup>+</sup> macrophages and CD3<sup>+</sup> T cells. However, in Q fever AAAs the number of CD68<sup>+</sup>CD206<sup>+</sup> M2 macrophages was increased, while expression of GM-CSF was decreased compared to atherosclerotic AAAs. Furthermore, Q fever AAAs showed an increase in both the number of CD8<sup>+</sup> cytotoxic T cells and CD3<sup>+</sup>CD8<sup>-</sup>FoxP3<sup>+</sup> regulatory T cells. Lastly, Q fever AAAs did not contain any well-defined granulomas.</p> <p><strong>Conclusions:</strong> These findings demonstrate that despite the presence of pro-inflammatory effector cells, persistent local infection with <em>C. burnetii</em> is associated with an immune suppressed micro environment.</p> <p><strong>Funding:</strong> This work was supported by SCAN consortium: European Research Area - CardioVascualar Diseases (ERA-CVD) grant [JTC2017-044] and TTW-NWO open technology grant [STW-14716].</p>
Quantifying and reducing cross-contamination in single- and multiplex hybridization capture of ancient DNA
<p>The use of hybridization capture has enabled a massive upscaling in sample sizes for ancient DNA studies, allowing the analysis of hundreds of skeletal remains (Mathieson et al., 2015; Narasimhan et al., 2019) or sediments (Vernot et al., 2021; Wang et al., 2021; Zavala et al., 2021) in single studies. Yet demands in throughput continue to grow, and hybridization capture has become a limiting step in sample preparation due to the large consumption of reagents, consumables and time. Here we explore the possibility of improving the economics of sample preparation via multiplex capture, i.e. the hybridization capture of pools of double-indexed ancient DNA libraries. We demonstrate that this strategy is feasible for small genomic targets, such as mitochondrial DNA, if the annealing temperature is increased and PCR cycles are limited in post-capture amplification to avoid index swapping by jumping PCR, which manifests as cross-contamination in resulting sequence data. We also show that the re-amplification of double-indexed libraries to PCR plateau before or after hybridization capture can sporadically lead to small, but detectable cross-contamination even if libraries are amplified in separate reactions. We provide protocols for both manual capture and automated capture in 384-well format that are compatible with single- and multiplex capture and effectively suppress cross-contamination and artefact formation. Last, we provide a simple computational method for quantifying cross-contamination due to index swapping in double-indexed libraries, which we recommend using for routine quality checks in studies that are sensitive to cross-contamination. </p>
Data from: How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience
Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities' response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., "multiplex networks"), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full "entangled bank" of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 24 sections of the human intestine from 3 donors (B004, B005, B006) using a panel of 47 oligonucleotide-barcoded antibodies. We also performed CODEX imaging on both human tonsil and Barrett's esophagus (BE) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. Output of this process were dataframes of 870,000 cells and 220,000 cells respectively with fluorescence values quantified from each marker.</p>
OMAP-19 Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Tonsil with IBEX
<p>Representative dataset acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in:</p> <ol> <li>“IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., <em>Proc. Natl. Acad. Sci. USA,</em> 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi.org/10.1073/pnas.2018488117</a>.</li> <li>"IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues", A. J. Radtke, et al., <em>Nat Protoc</em>, 2022, <a href="https://doi.org/10.1038/s41596-021-00644-9">doi.org/10.1038/s41596-021-00644-9</a>.</li> </ol> <p>This dataset accompanies the <a href="https://www.nature.com/articles/s41592-023-01846-7">Organ Mapping Antibody Panel (OMAP) effort</a> led by the <a href="https://commonfund.nih.gov/HuBMAP">Human BioMolecular Atlas Program</a> detailed <a href="https://humanatlas.io/omap">here</a>.</p> <p>OMAP-19 was designed for IBEX imaging of human FFPE tonsil samples sectioned onto glass slides. The panel consists of 6 cycles of 20 primary antibodies, 5 secondary antibodies, and the nuclear label Hoechst for image alignment and nuclear segmentation. This OMAP provides a spatial context for 10 anatomical structures and at least 16 cell types present in the <a href="https://cdn.humanatlas.io/hra-releases/v2.0/docs/asct-b/asct-b-vh-palatine-tonsil.copy.html">ASCT+B tonsil table</a>. OMAP-19 was designed to examine myeloid subsets (CD11b, CD11c, CD14, CD15, HLA-DR) and angiogenesis (VEGF-A and VEGF-C) in human ovarian cancer biopsies. The human tonsil was used as a positive control for antibody validation and panel development. Antigen retrieval was performed using a pressure cooker (Borg Decloaker BD1000, 110°C for 15 minutes) and a tris-based buffer (pH 9.5). Several custom antibodies were created using commercial labeling kits as indicated in the associated RRID entries for these antibodies. More details on the antigen retrieval protocol and step-by-step application of antibodies can be found on the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">IBEX Imaging Community knowlege-base</a> (current version) and on <a href="../records/7693279">Zenodo</a> (last official version).</p> <p>Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as a 405 nm laser. All images were captured at an 16-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 µm), y (0.379 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software.</p> <p>Image supplied as a .ims Imaris Format file and can be opened with the <strong>free</strong> <a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer</a> software or <a href="https://imagej.net/software/fiji/">ImageJ/Fiji</a>. Image channel and antibody meta-data supplied as an xlsx file.</p>
FIGURE 2 in A multiplex PCR method for identification of two common true cutworm species (Lepidoptera: Noctuidae) tested in the central plain of Guilan province, Iran
FIGURE 2. Multiplex PCR by combination of three equimolar primers for COI (IpsiloF1/ XestiaF1/LepR1). 1: 100 bp DNA ladder; 2,3: Agrotis ipsilon; 4,5: Xestia c-nigrum, 6–9, Agrotis ipsilon. 6,7: larvae specimens collected from Chaf (Langroud), 8,9: larvae specimens collected from Rasht.
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.