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111 results for “IMC”
IMC Segmentation Pipeline results of example IMC data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at <a href="http://10.5281/zenodo.5949116">10.5281/zenodo.5949116</a> using the IMC Segmentation Pipeline available at <a href="https://github.com/BodenmillerGroup/ImcSegmentationPipeline">https://github.com/BodenmillerGroup/ImcSegmentationPipeline</a> (DOI: <a href="http://10.5281/zenodo.6402666">10.5281/zenodo.6402666</a>) v3.6. Please refer to <a href="https://github.com/BodenmillerGroup/steinbock">https://github.com/BodenmillerGroup/steinbock</a> as alternative processing framework and <a href="http://10.5281/zenodo.6043600">10.5281/zenodo.6043600</a> for the data generated by <i>steinbock</i>.</p><p>The following files are part of the <strong>analysis.zip</strong> folder when running the IMC Segmentation Pipeline:</p><ul><li><strong>cpinp</strong>: contains input files for the segmentation pipeline</li><li><strong>cpout</strong>: contains all final output files of the pipeline: <i>cell.csv</i> containing the single-cell features; <i>Experiment.csv</i> containing CellProfiler metadata; <i>Image.csv</i> containing acquisition metadata; <i>Object relationships.csv</i> containing an edge list indicating interacting cells; <i>panel.csv</i> containing channel information; <i>var_cell.csv</i> containing cell feature information; <i>var_Image.csv</i> containing acquisition feature information; <i>images </i>containing the hot pixel filtered multi-channel images and the channel order; <i>masks</i> containing the segmentation masks; <i>probabilities </i>containing the pixel probabilities.</li><li><strong>histocat</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">https://bodenmillergroup.github.io/histoCAT/</a>)</li><li><strong>crops</strong>: contains upscaled image crops in .h5 format for ilastik (<a href="https://www.ilastik.org/">https://www.ilastik.org/</a>) training</li><li><strong>ometiff</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide</li><li><strong>ilastik</strong>: multi channel images for ilastik pixel classification (<i>_ilastik.full</i>) and their channel order (<i>_ilastik.csv</i>); upscaled multi channel images for ilastik pixel prediction (<i>_ilastik_s2.h5</i>); upscaled 3 channel images containing ilastik pixel probabilities (<i>_ilastik_s2_Probabilities.tiff</i>).</li></ul><p>The remaining files are part of the root directory:</p><ul><li><strong>docs.zip: </strong>Documentation of the pipeline in markdown format</li><li>I<strong>MCWorkflow.ilp: </strong>Ilastik pixel classifier pre-trained on the example data</li><li><strong>resources.zip: </strong>The CellProfiler pipelines and CellProfiler plugins used for the analysis</li><li><strong>scripts.zip: </strong>Python notebooks used for pre-processing and downloading the example data</li><li><strong>src.zip: </strong>Scripts for the imcsegpipe python package</li></ul>
steinbock results of IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a> using the <i>steinbock </i>framework available at <a href="https://github.com/BodenmillerGroup/steinbock">github.com/BodenmillerGroup/steinbock</a>. Please refer to <strong>steinbock.sh </strong>for how these data were generated from the raw data.</p><p>The following files are part of this repository:</p><ul><li><strong>panel.csv</strong>: contains channel information regarding the used antibodies in <i>steinbock</i> format</li><li><strong>img.zip</strong>: contains hot pixel filtered multi-channel images derived from the IMC raw data. One file per acquisition is generated</li><li><strong>images.csv</strong>: contains metadata per acquisition</li><li><strong>pixel_classifier.ilp</strong>: ilastik pixel classifier (same as the one in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_crops.zip</strong>: image crops on which the ilastik classifier was trained (same as the ones in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_img.zip</strong>: contains multi-channel images (one per acquisition) in .h5 format for ilastik pixel classification</li><li><strong>ilastik_probabilities.zip</strong>: 3 channel images containing the pixel probabilities after pixel classification</li><li><strong>masks_ilastik.zip</strong>: segmentation masks derived from the ilastik pixel probabilities using the <strong>cell_segmentation.cppipe</strong> pipeline</li><li><strong>masks_deepcell.zip</strong>: segmentation masks derived by <i>deepcell</i> segmentation</li><li><strong>intensities.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the mean pixel intensity per cell and channel based on the files in <strong>img.zip </strong>and <strong>masks_deepcell.zip</strong>.</li><li><strong>regionprops.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the morphological features and location of cells based on <strong>masks_deepcell.zip</strong>.</li><li><strong>neighbors.zip</strong>: Contains one .csv file per acquisition. Each file contains an edge list of cell IDs indicating cells in close proximity based on <strong>masks_deepcell.zip</strong>.</li><li><strong>ome.zip</strong>: contains .ome.tiff files derived from img.zip; one file per acquisition</li><li><strong>histocat.zip</strong>: contains single-channel .tiff files with segmentation masks derived from <strong>masks_deepcell.zip</strong> for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">bodenmillergroup.github.io/histoCAT</a>)</li><li><strong>cells.csv</strong>: contains intensity and regionprop measurements of all cells</li><li><strong>cells_csv.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition</li><li><strong>cells.fcs</strong>: contains intensity and regionprop measurements of all cells in fcs format</li><li><strong>cells_fcs.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition in fcs format</li><li><strong>cells.h5ad</strong>: contains intensity, regionprop and neighbor measurements of all cells in <i>anndata </i>format</li><li><strong>cells_h5ad</strong>: contains intensity regionprop and neighbor measurements of all cells per acquisition in <i>anndata </i>format</li><li><strong>graphs.zip</strong>: contains spatial object graphs in .graphml format; one file per acquisition</li></ul>
Single-cell information extracted from IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a></p><ul><li><strong>gated_cells.zip:</strong> contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li><strong>spe.rds:</strong> SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li><strong>images.rds:</strong> CytoImageList object containing the spillover-corrected images.</li><li><strong>masks.rds:</strong> CytoImageList object containing the segmentation masks.</li></ul>
The spatial landscape of lung pathology during COVID-19 progression - raw IMC data
<p>Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19). However thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking. We use high parameter imaging mass cytometry9 targeting the expression of 36 proteins, to investigate at single cell resolution, the cellular composition and spatial architecture of human acute lung injury including SARS-CoV-2. This spatially resolved, single-cell data unravels the disordered structure of the infected and injured lung alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyper-inflammatory cell state associated with lung damage. By leveraging the temporal range of COVID-19 severe fatal disease in relation to the time of symptom onset, we observe increased macrophage extravasation, mesenchymal cells, and fibroblasts abundance concomitant with increased proximity between these cell types as the disease progresses, possibly as an attempt to repair the damaged lung tissue. This spatially resolved single-cell data allowed us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. This spatial single-cell landscape enabled the pathophysiological characterization of the human lung from its macroscopic presentation to the single-cell, providing an important basis for the understanding of COVID-19, and lung pathology in general.</p>
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>
Double-negative B cells and DNASE1L3 colocalise with microbiota in gut-associated lymphoid tissue - IMC+RNAScope images
<p><span>Intestinal homeostasis is maintained by the response of gut-associated lymphoid tissue to bacteria transported across the follicle associated epithelium into the subepithelial dome. The initial response to antigens and how bacteria are handled is incompletely understood. By iterative application of spatial transcriptomics and multiplexed single-cell technologies, we identify that the double negative 2 subset of B cells, previously associated with autoimmune diseases, is present in the subepithelial dome in health. We show that in this location double negative 2 B cells interact with dendritic cells co-expressing the lupus autoantigens DNASE1L3 and C1q and microbicides.<span> </span>We observe that in humans, but not in mice, dendritic cells expressing DNASE1L3 are associated with sampled bacteria but not DNA derived from apoptotic cells. We propose that fundamental features of autoimmune diseases are microbiota-associated, interacting components of normal intestinal immunity.</span></p>
Tumor-Immune Microenvironment Revealed by Imaging Mass Cytometry in a Metastatic Sarcomatoid Urothelial Carcinoma with a Prolonged Response to Pembrolizumab - IMC data
<blockquote> <p>Sarcomatoid urothelial carcinoma (SUC) is a rare subtype of urothelial carcinoma (UC), that typically presents at an advanced stage compared to more common variants of UC. Locally advanced and metastatic UC have a poor long-term survival following progression on first-line platinum-based chemotherapy. Antibodies directed against the programmed cell death 1 protein (PD-1) or its ligand (PD-L1) are now approved to be used in these scenarios. The need for reliable biomarkers for treatment stratification is still under research. Here we present a novel case report of the first Image Mass Cytometry (IMC) analysis done in SUC to investigate the immune cell repertoire and PD-L1 expression in a patient who presented with metastatic SUC and experienced a prolonged response to the anti-PD1 immune checkpoint inhibitor pembrolizumab after progression on first line chemotherapy. This case report provides an important platform for translating these findings to a larger cohort of UC and UC variants.</p> </blockquote> <p>We make available TIFF files containing imaging mass cytometry data for 4 regions of interest of a sample of metastatic sarcomatoid urothelial carcinoma. The order of the axis in the image stacks is "CYX". The CSV files indicate the identity of the channels.</p>
Imaging Mass Cytometry (IMC) data for TNBC Samples
<p>Imaging mass cytometry (IMC) data was collected on multiple regions of interest (ROIs) from a racially balanced and clinically matched cohort of 57 surgically resected tissues, primarily TNBC, as identified by H&E images. This cohort consisted of 26 self-reported Black American (BA) women and 31 self-reported White American (WA) women. ROIs were selected from both the tumor center and tumor periphery, and were categorized as either immune-rich or immune-poor.</p>
IMC dataset: duodenal biopsies of COVID-19 and Control patients
<p>An Imaging Mass Cytometry dataset containing .mcd files of duodenal biopsies from COVID-19 and Control patients. The peer-reviewed publication for this dataset has been published in Mucosal Immunology, and can be accessed here: https://www.nature.com/articles/s41385-021-00437-z. Please cite this when using the dataset.</p>
Breast cancer IMC dataset for InterSTELLAR training
<p> <strong>graph_data.npy</strong>: preprocessed Breast cancer IMC dataset for InterSTELLAR training, saved as a<strong> .npy</strong> file.</p> <ol> <li>Read this file with python and get a list named <strong>all_graph</strong>. This list includes <strong>368 </strong>elements. The first <strong>366 </strong>elements are the information from each tissue, the last two elements are the <strong>mean </strong>and<strong> standard deviation</strong> of the cell feature data after log-transformation with shape <strong>30x1</strong>.</li> <li>Each sub-element <strong>all_graph[i]</strong> is alone a new list with three elements, including <strong>cell feature matrix all_graph[i][0]</strong>,<strong> cell locations all_graph[i][1],</strong> <strong>tissue labels all_graph[i][2] </strong>and <strong>cell phenotypes</strong> <strong>all_graph[i][3]</strong>.</li> <li><strong>all_graph[i][0]</strong> is a <strong>Nx30</strong> matrix, corresponding to<strong> 30 cell markers</strong>; <strong>all_graph[i][1]</strong> is a<strong> Nx2</strong> matrix, corresponding to <strong>x </strong>and <strong>y </strong>locations of a single cell; <strong> all_graph[i][2]</strong> is a list with<strong> 5</strong> elements, corresponding to <strong>tissue phenotypes</strong> (0: healthy, 1: TNBC, 2: Non-TNBC cancers), <strong>tissue area</strong> (um<sup>2</sup>),<strong> tumor grade</strong>, <strong>overall survival time </strong>(month) and <strong>patient status</strong> (alive or death); <strong>all_graph[i][3] </strong>is a list with <strong>N </strong>elements corresponding to the <strong>cell phenotypes</strong>.</li> </ol> <p><strong>cell_maskes.npy</strong>: the cell segmentation maskes corresponding to the 366 tissues.</p>
TRACERx PHLEX test dataset, model weights & TRACERx nuclear IMC segmentation dataset
<p>This repository contains three archives pertaining to the TRACERx PHLEX multiplexed imaging pipelines.</p> <ol> <li>Weights for the deep-imcyto neural network models</li> <li>The PHLEX test dataset consisting of two .ome.tiff files of imaging mass cytometry data</li> <li>The TRACERx Nuclear IMC Segmentation dataset, a training dataset of IMC image tiles and ~42,000 expert labelled nuclei from diverse lung, normal and other tissue types.</li> </ol>
A Study of IMC-A12 in Participants With Tumors Who No Longer Respond to Treatment or For Whom No Treatment is Available
ClinicalTrials.gov study NCT00785538. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Icrucumab (IMC-18F1) or Ramucirumab Drug Product (DP) in Combination With Capecitabine or Capecitabine on Previously Treated Breast Cancer Patients
ClinicalTrials.gov study NCT01234402. IPD Sharing: YES. Countries: 2. Publications: 1.
Phase II Study of IMC-A12 in Patients With Mesothelioma Who Have Been Previously Treated With Chemotherapy
ClinicalTrials.gov study NCT01160458. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
A Study of IMC-A12 or Ramucirumab Plus Mitoxantrone and Prednisone in Prostate Cancer
ClinicalTrials.gov study NCT00683475. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Olaratumab (IMC-3G3) in Previously Treated Participants With Unresectable and/or Metastatic Gastrointestinal Stromal Tumors
ClinicalTrials.gov study NCT01316263. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Study of IMC-1121B in Patients With Advanced Solid Tumors
ClinicalTrials.gov study NCT01005355. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of IMC-A12 in Advanced Solid Tumors
ClinicalTrials.gov study NCT01007032. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Liposomal Doxorubicin With or Without Olaratumab (IMC-3G3) in Platinum-Refractory or Resistant Advanced Ovarian Cancer
ClinicalTrials.gov study NCT00913835. IPD Sharing: YES. Countries: 1. Publications: 2.
Study of Ramucirumab (IMC-1121B) Therapy and Corrected QT (QTc) Interval Changes
ClinicalTrials.gov study NCT01017731. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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