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73 results for “multiplexed imaging”
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>
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>
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>
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>
Multiplexed imaging reveals an IFN-γ-driven inflammatory state in Nivolumab-associated gastritis (data)
<p>MIBI-TOF images, segmentation output, cell phenotype maps, and extracted cell tables for Ferrian et al.</p>
Imaging Mass Cytometry of human normal colon mucosa (CLN1-6) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from each block of samples CLN1-CLN6 with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For CLN1-CLN6, 1 mm<sup>2 </sup>ROIs were selected to contain the full thickness of the colon mucosa, with epithelial crypts in longitudinal orientation. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p> <p>Twenty-eight images from 26 antibodies (Supplementary Table 2) and two DNA intercalators were obtained from the raw .txt files of the ablated regions in CLN1-CLN6 using the data extraction process.</p>
Image dataset from multiplex IHC stained TMA sections
<p><strong>IHC Cohort:</strong> Multiplex IHC stained histological slides were collected from Liaoning Cancer Hospital and Institute in China. The raw data collected are TMA sections, all of which are obtained from NSCLC patients. Non-overlapping image patches (256*256 pixels) are extracted from TMA sections and manually annotated by our collaborated pathologists via the Qupath software. Initially, seven TMA sections with multiplex stains including CD3, CD20, CD38, CDK4, Cyclin-D1, Ki67, and P53 were used. Except for CD3 stained TMA sections, we randomly cropped 25 image patches from each of these TMA sections, of which 17, 3 and 5 patches are correspondingly used for training, validation, and testing. Note that 18, 3 and 5 CD3 image patches are cropped for building dataset. To suppress over-fitting and enhance generalization of the SRSA-Net, we additionally included 81 annotated image patches from other five TMA sections with stains of CD34, CD68, D2-40, FAP, and SMA into training and validation set. In total, the IHC cohort includes 9,725 manually identified cell nuclei. The numbers of training, validation and testing image patches are 195, 36, and 35, respectively.</p> <p>For the masks, the first channel and the second channel denotes the negative and positive nuclei pixels, respectively. Note that each pixel is labelled from 0 to n, where n is the number of individual nuclei detected. 0 pixels indicate background. Pixel values i indicate that the pixel belongs to the ith nucleus. The last channel marks the information of all the nuclei pixels, where nuclei pixel are left as 0, otherwise 1.</p> <p><strong>fold1: training set</strong></p> <p><strong>fold2: validation set</strong></p> <p><strong>fold3: testing set</strong></p> <p> </p> <h2>if you use this dataset, please cite:</h2> <pre>@article{wang2024simultaneously, title={Simultaneously segmenting and classifying cell nuclei by using multi-task learning in multiplex immunohistochemical tissue microarray sections}, author={Wang, Ranran and Qiu, Yusong and Hao, Xinyu and Jin, Shan and Gao, Junxiu and Qi, Heng and Xu, Qi and Zhang, Yong and Xu, Hongming}, journal={Biomedical Signal Processing and Control}, volume={93}, pages={106143}, year={2024}, publisher={Elsevier} }</pre>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)
<p>MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering</p> <p>1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images</p> <p>Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details.</p> <p>2. segmentation.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included.</p> <p>3. source_data.zip: Source data files for figures</p> <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>
DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging
<p>Tiff images, single cell data, and cell masks for the publication "DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging". The code used to produce the results of this study is available at https://github.com/BodenmillerGroup/SABER-IMC_publication</p>
CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
Open the record for dataset details and reuse information.
OMAP-8: Multiplexed Antibody-Based Imaging of Placenta with Imaging Mass Cytometry (IMC), v1.0
<p>OMAP-8 was designed for Imaging Mass Cytometry (IMC) (<a href="https://pubmed.ncbi.nlm.nih.gov/24584193/">https://pubmed.ncbi.nlm.nih.gov/24584193/</a>) of formalin-fixed paraffin-embedded (FFPE) human term-placenta samples. The tissue slides were prepared with a two-step antigen retrieval process (pH 6 and pH 9, as described <a href="https://dx.doi.org/10.17504/protocols.io.bpwumpew">https://dx.doi.org/10.17504/protocols.io.bpwumpew</a>). OMAP antibodies validated by immunohistochemistry and IMC were conjugated to polymers containing metal isotopes. Conjugated antibodies were used to stain processed human term-placenta tissue simultaneously. Regions of the processed tissue were then acquired on the imaging mass cytometer (Hyperion; Standard BioTools) by laser ablation and visualized. The panel contains 26 antibodies conjugated to unique metal isotopes and iridium marks the DNA. This OMAP provides a spatial context for key placenta cell types in the <a href="https://doi.org/10.48539/HBM446.WGLG.755">ASCT+B v.1.0 table</a>. Single-cell RNA sequencing data were used to guide marker selection for multiplexed tissue imaging. For example, ASCL2, HLA-G, PD-L1, CD68 and LYVE1 allow functionally specialized cell types to be visualized and quantified in the placenta. Note that one of our core antibodies is to LYVE1 but, unlike in other tissues where it is used to mark lymphatic vasculature, here we use it to mark the macrophage of the placenta (Hofbauer cells) – there should be no lymphatics in the placenta.</p>
Processed CODEX multiplexed imaging data of cellular microenvironment around T cell stimulating hydrogels
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into the cellular microenvironment surrounding T cell stimulating hydrogels. These hydrogels were engineered with signals that could locally expand antigen-specific T cells for use in tumor immunotherapy. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. 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.</p> <p>Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type, defined by x and y coordinates representing pixel locations in the original image. </p> <p>We then used this data to investigate how different proportions of the cell types change over time in response to the stimulating hydrogel injection with antigen-specific T cells. These data could be used to understand the cellular interactions, composition, and structure of T cell stimulating biomaterials for antigen-specific immunotherapy and with adoptive T cell transfer. These datasets offer valuable insights for researchers interested in engineering T cell stimulating microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and hydrogel environment in a murine melanoma model. We injected antigen-specific T cells with microparticle T cell stimulating hydrogels into mice subcutaneously. Injection sites were take out at different time points day=0 (just after injection), day=3, and day=9 (n=3-6 per time point). Our 51-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, and stromal cell types, resulting in a rich dataset of 241,685 cells across 51 marker channels.</p>
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma patients undergoing immune checkpoint inhibitor therapy by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 42 HCC patients that received immune checkpoint inhibtor therapy after tumor biopsy or resection. </p> <ul> <li>ICI_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file.</li> <li>ICI_cellmasks: Masks identifying individual cells on the images.</li> <li>ICI_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>ICICohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient etadata may be found in the supplementary table 3 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer">10.1136/gutjnl-2024-332837</a>.</p>
Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX
<p>Representative dataset using OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0. See https://humanatlas.io/omap.</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.