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73 results for “multiplexing imaging”
Human intestine processed CODEX multiplexed images for donors B009-B012 (Part 2/2)
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Processed single cell data from CODEX multiplexed imaging of the human intestine
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Antibody panel used for multiplexed antibody-based imaging of Human Pancreas Analysis Program (HPAP) samples by CODEX
<p>This data file details antibodies applied to human pancreas tissue samples from the Human Pancreas Analysis Program (HPAP; RRID:SCR_016202) of the <a href="https://hirnetwork.org/">Human Islet Research Network</a> (HIRN; RRID:SCR_014393). Images will be uploaded for interactive analysis on <a href="https://pancreatlas.org/datasets">Pancreatlas</a> (RRID:SCR_018567) and made available for download via <a href="https://hpap.pmacs.upenn.edu/">PANC-DB</a>. Workflow is documented on protocols.io: <a href="https://dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1">dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1</a>.</p><p>Table format adapted from Radtke AJ, Quardokus EM, Saunders DC (2022), <a href="https://doi.org/10.5281/zenodo.7386417">SOP: Construction of Organ Mapping Antibody Panels for Multiplexed Antibody-Based Imaging of Human Tissues</a>. See also: Saunders D; Reihsmann R. <a href="https://doi.org/10.48539/HBM754.BHVR.258">OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0</a>.</p>
Multiplexed imaging analysis of human pancreatic islets from donors with and without type 2 diabetes
<p>This record contains tabular data from traditional and multiplexed immunohistochemistry experiments presented in the manuscript <i>Genetic risk converges on regulatory networks mediating early type 2 diabetes</i> (<a href="https://doi.org/10.1038/s41586-023-06693-2">Walker, Saunders & Rai et al., <i>Nature</i> 2023</a>), a body of work that includes tissue imaging, <a href="https://theparkerlab.shinyapps.io/Islet-RNAseq-WGCNA/">sorted islet cell transcriptomics</a>, and islet functional analysis of donors with early-stage type 2 diabetes (T2D) and control donors. Images can be viewed interactively on Pancreatlas (RRID:SCR_018567): <a href="https://pancreatlas.org/datasets/904/explore">https://pancreatlas.org/datasets/904/explore</a>.</p><p>All immunohistochemistry was performed on lightly PFA-fixed human pancreatic tissue (sample characteristics available in Supplementary Table 1). For traditional immunohistochemistry, islets were imaged at 20× with 2× digital zoom using a FV3000 confocal laser scanning microscope (Olympus) or full cross-sections were scanned on a ScanScope FL (Leica/Aperio). Quantitative analysis was carried out using HALO™ (Indica Labs) or Metamorph (Molecular Devices) software. For multiplexed immunohistochemistry, images were acquired using the PhenoCycler (CODEX) Open system (Akoya Biosciences) integrated with a BZ-X810 epifluorescence microscope (Keyence) with a CFI plan Apo I 20x/0.75 objective (Nikon). Image alignment, stitching, background subtraction, and deconvolution were performed using the CODEX Processor v1.7.0.6 (Akoya Biosciences). Cell segmentation and cell type annotations were generated using the HALO HighPlex FL v3.2.1 module (Indica Labs). For cell neighborhood (CN) analysis, two methods were applied in parallel to CODEX data from annotated islets: a community detection method, termed <i>Dynamic CF-IDF</i>, and a <i>k</i>-means approach. Packages used for cell neighborhood analyses are published in <a href="http://github.com/liu-bioinfo-lab/Cellular-Neighborhood-Analysis">Github</a>.</p>
Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer
<p>All data supporting the publication: "Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer."</p><p>1. Fully_Processed_OME.TIFF: This folder contains the OME.TIFF files with all markers after compensation and hot pixel removal for visualization of the data. These can be opened with QuPath and other software. </p><p>2. PDAC_IMC_Seurat_FINAL.rds: Seurat object of all cells included in the analysis with cell type and neighborhood annotations, and unintegrated and rPCA-integrated UMAP reductions. </p><p>3. Raw_Data_TIFF_Files: All raw individual TIFF files from the image acquisition</p><p>4. ROI_Selection: Brightfield and IHC images of individual samples showing where the ROIs for each sample are collected </p><p>5. Segmentation_Files: All relevant segmentation files from Mesmer for nuclear and whole cell segmentation. </p><p>6. H&E Images for each case scanned at 40x </p>
CODEX multiplexed imaging of immunotherapy in human and mouse melanomas
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into melanoma tumors in both murine models and human samples. 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 and cellular neighborhood, defined by x and y coordinates representing pixel locations in the original image. Refer to the table in the "Usage Notes" section below for further details. The CODEX multiplexed imaging data is organized into three distinct files, each representing key aspects of our research and the studies detailed in our manuscript.</p> <p>We then used this data investigate the major cellular organization of the tumor sections we imaged, with downstream spatial statistics and analyses like cellular neighborhood analysis and cell-cell interaction analysis. These data could be used to understand the cellular interactions, composition, and structure of anti-tumor melanoma responses induced by antigen-specific immunotherapy either with adoptive T cell transfer for checkpoint blockade immunotherapy. These datasets offer valuable insights for researchers interested in anti-tumor microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p><em>1. Time-course of tumor microenvironment following antigen-specific T cell therapy in mice</em></p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and tumor progression in a murine melanoma model. We activated PMEL CD8+ T cells with cognate antigen gp100 and IL-2 for 10 days ex vivo and transferred into mice with established B16-F10 tumors. Tumors were harvested and imaged with CODEX imaging at 0-, 1-, 3-, 5-, and 12-days post-treatment (n=3-7 per time point). Our 42-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, stromal cell types, and tumor cell phenotypes, resulting in a rich dataset of 1,052,125 cells across 42 marker channels.</p> <p><em>2. Tumor microenvironment following antigen-specific T cell therapies with different phenotypes in mice</em></p> <p>We delve deeper into the modulation of the tumor microenvironment by manipulating T cell phenotypes. By comparing activated T cells stimulated with and without 2-hydroxycitrate (2HC), a metabolic inhibitor of acetyl CoA production, we explore the impact of phenotype on tumor progression. Our datasets from mice treated with 2HC T cells or T cells provide insights into the role of T cell phenotype manipulation in the tumor microenvironment (n=4-7 per group).</p> <p><em>3. Tumor microenvironment before and after checkpoint blockade in human melanoma patients of both responders and non-responders</em></p> <p>Our research extends to human melanoma patients with advanced, metastatic, stage IV tumors. We examine 12 FFPE tumor samples from six patients, each with samples taken before and after checkpoint inhibitor therapy. Our CODEX multiplexed imaging, using a panel of 58 antibodies, reveals changes in immune, stromal, and tumor compartments. We segmented 5,019,159 individual cells from the 12 CODEX images, facilitating unsupervised clustering to identify 39 major cell types based on their expression profiles. Our accompanying donor metadata table links donor IDs to essential clinical information, including treatment response, demographics, and sample details.</p>
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 54 HCC patients. </p> <ul> <li>DC_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file. ROIs are located in the tumor, interface and adjacent liver as indicated in the file name.</li> <li>DC_cellmasks: Masks identifying individual cells on the images.</li> <li>DC_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>DCCohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient metadata may be found as supplementary table 2 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer"> 10.1136/gutjnl-2024-332837 </a>.</p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 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), and single cell segmentation. Output of this process was a dataframe of nearly 130,000 cells with fluorescence values quantified from each marker. We used this dataframe as input to 1 of the 5 normalization techniques of which we compared z, double-log(z), min/max, and arcsinh normalizations to the original unmodified dataset. We used these normalized dataframes as inputs for 4 unsupervised clustering algorithms: k-means, leiden, X-shift euclidian, and X-shift angular.</p> <p>From the clustering outputs, we then labeled the clusters that resulted for cells observed in the data producing 20 unique cell type labels. We also labeled cell types by hiearchical hand-gating data within cellengine (cellengine.com). We also created another gold standard for comparison by overclustering unormalized data with X-shift angular clustering. Finally, we created one last label as the major cell type call from each cell from all 21 cell type labels in the dataset. </p> <p>Consequently the dataset has individual cells segmented out in each row. Then there are columns for the X, Y position in pixels in the overall montage image of the dataset. There are also columns to indicate which region the data came from (4 total). The rest are labels generated by all the clustering and normalization techniques used in the manuscript and what were compared to each other. These also were the data that were used for neighborhood analysis for the last figure of the manuscript. These are provided at all four levels of cell type level granularity (from 7 cell types to 35 cell types). </p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 26 as continuation of dataset <strong>10.5281/zenodo.7494413.</strong> The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p> <p>Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R.</p> <p>The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file.</p> <p>The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p>
Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
<p><span class="pre-line-wrapping ng-binding">Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. Here we introduce MANTIS (Multiplexed Annotated Tissue Imaging System), a flexible analytic pipeline compatible with routine practice, specifically designed for spatially-resolved immune phenotyping of the skin in experimental or clinical samples. Based on phenotype attribution matrices coupled to alpha-shape algorithms, MANTIS projects a representative digital immune landscape, while enabling automated detection of major inflammatory clusters and concomitant single-cell data quantification of biomarkers. We observed that severe pathological lesions from systemic lupus erythematosus, Kawasaki syndrome, or COVID-19-associated skin manifestations share common quantitative immune features, while displaying a non-random distribution of cells with the formation of disease-specific dermal immune structures. Given its accuracy and flexibility, MANTIS is designed to solve the spatial organization of complex immune environments to better apprehend the pathophysiology of skin manifestations.</span></p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
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Cell type labels for all clustering and normalization combinations compared for CODEX multiplexed imaging
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CODEX multiplexed imaging of immunotherapy in human and mouse melanomas
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Data from: 3-D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System (MANTIS)
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Data from: Protocol optimization improves the performance of multiplexed RNA imaging
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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>
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>
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