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68 results for “Mass cytometry”
Example imaging mass cytometry raw 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 imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the <strong>I</strong>ntegrated i<strong>MMU</strong>noprofiling of large adaptive <strong>CAN</strong>cer patient cohorts (IMMUcan) project (<a href="https://immucan.eu">immucan.eu</a>) using the Hyperion imaging system (<a href="https://www.fluidigm.com/products-services/instruments/hyperion">www.fluidigm.com/products-services/instruments/hyperion</a>). To get an overview on the technology and available analysis strategies, please visit <a href="https://bodenmillergroup.github.io/IMCWorkflow/">bodenmillergroup.github.io/IMCWorkflow</a>. The individual data files are described below:</p><ul><li><strong>Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip</strong>: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.</li><li><strong>compensation.zip</strong>: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: <a href="https://doi.org/10.1016/j.cels.2018.02.010">Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry</a></li><li><strong>panel.csv</strong>: This file contains metadata for each antibody/channel used in the experiment. The <i>full</i> column indicates which channel should be analysed. The <i>ilastik</i> column specifies which channels were used for ilastik pixel classification and the <i>deepcell</i> column indicates the channels used for deepcell segmentation.</li><li><strong>sample_metadata.csv</strong>: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).</li></ul>
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>
Imaging Mass Cytometry Dataset of exhausted and non-exhausted breast cancer microenvironments
<p>A cohort of human breast tumor samples were annotated as having an "exhausted" or "non-exhausted" immune environment based on CyTOF characterization of T cell phenotypes (see Wagner et al. 2019). 12 samples (6 exhausted, 6 non-exhausted) were then selected for further analysis by Imaging Mass Cytometry (IMC) with the goal to compare the two immune environment types and to comprehensively characterize exhaustion-associated spatial features of the tumor microenvironment. For IMC, two consecutive FFPE sections of each sample were stained with two different antibody panels (Protein Panel and RNAscope Panel), and 4-10 regions of interest (ROIs, 1mm x 1mm) were measured on each section. ROIs on consecutive sections were registered manually to be as spatially close as possible.</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>
Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma
<p><strong>- melanoma_IMC_data.zip</strong></p> <p>The zip file contains the raw IMC images (in the raw_tiff folder) and corresponding single cell masks (in the mask folder) associated with the paper "Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma". The MCD files by CyTOF IMC were exported to a multi-channel TIFF file including 41 channels, and the order of the channel was provided in the <strong>Melanoma_panel.csv</strong>. </p> <p><strong>- Melanoma_code_data.zip</strong></p> <p>The zip file contains the 4 folders described as follows: </p> <ul> <li>Folder ”data“: the processed data for the result shown in paper<br> - Folder "input": <br> - sc_data.csv: the single cell protein expression data;<br> - Folder "abundance": the cell type abundance files;<br> - Folder "clidata": the response and survival data for 4 melanoma datasets used in the paper; <br> - Folder "hc_result": TME archetypes annotation for each sample/ROI from hierarchical clustering;<br> - Folder "ICB": data for ICB analysis (presented in FigS3);<br> - Folder "meta": panel file for clustering;<br> - Folder "RNAseq_data": the RNAseq data for 4 melanoma datasets used in the paper;<br> - Folder "RNAseq_deconv": the result of cell type deconvolution from bulk RNAseq; <br> - Folder "spatial": data for neighbourhood analysis (presented in Fig3, FigS4).<br> - Folder "output": intermediate result for analysis.</li> <li>Folder "Rscript": R scripts for reproducing results in the paper.<br> - generate_Figs.Rmd: ploting figures presented in the paper;<br> - functions.R: functions used for analysis;<br> - Clustering.Rmd: determining cell types based on marker intensities;<br> - Spatial_analysis.Rmd: neighbourhood analysis to get significant interction/avoidance cell relationships.</li> <li>Folder "Figs": figures presented in paper.</li> <li>Folder "HE_figs": the H&E image and the ROIs distribution for each sample.</li> </ul>
Imaging Mass Cytometry Images (APP1) 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 the APP1 FFPE block 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 APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
Characterizing Highly Cited Papers in Mass Cytometry through H-Classics: WoS dataset and citation report
<p>Dataset and citation report extracted from Web of Science (WoS) used to characterize highly cited papers in mass cytometry research field from 2010 to 2019.</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>
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>
Exploratory mass cytometry analysis reveals immunophenotypes of cancer treatment-related pneumonitis
<p>Anti-cancer treatments can result in various adverse effects, including infections due to immune suppression/dysregulation and drug-induced toxicity in the lung. One of the major opportunistic infections is <em>Pneumocystis jirovecii</em> pneumonia (PCP), which can cause severe respiratory complications and high mortality rates. Cytotoxic drugs and immune-checkpoint inhibitors (ICIs) can induce interstitial lung diseases (ILDs). Nonetheless, the differentiation of these diseases can be difficult, and the pathogenic mechanisms of such diseases are not yet fully understood. To better comprehend the immunophenotypes, we conducted an exploratory mass cytometry analysis of immune cell subsets in bronchoalveolar lavage fluid from patients with PCP, cytotoxic drug-induced ILD (DI-ILD), and ICI-associated ILD (ICI-ILD) using two panels containing 64 markers. In PCP, we observed an expansion of the CD16<sup>+</sup> T cell population, with the highest CD16<sup>+</sup> T proportion in a fatal case. In ICI-ILD, we found an increase in CD57<sup>+</sup> CD8<sup>+</sup> T cells expressing immune checkpoints (TIGIT<sup>+</sup> LAG3<sup>+</sup> TIM-3<sup>+</sup> PD-1<sup>+</sup>), FCRL5<sup>+</sup> B cells, and CCR2<sup>+</sup> CCR5<sup>+</sup> CD14<sup>+</sup> monocytes. These findings uncover the diverse immunophenotypes and possible pathomechanisms of cancer treatment-related pneumonitis.</p>
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
Mass Cytometry (CyTOF) FCS files from Priest et al. 2024. Human PBMC from longitudinal analysis of COVID-19, Bacterial Sepsis, mRNA vaccination cohorts.
<p>Mass Cytometry (CyTOF) FCS files from Priest et al. "Non-classical CD45RB<sup>lo</sup> memory B-cells are the majority of circulating antigen-specific B-cells following mRNA vaccination and COVID-19 infection." Research Square 2024. </p> <p>Files are already normalised, debarcoded, gated, batch corrected and compensated as described in Priest et al. </p> <p>Data is from Human PBMCs of londitudanal cohorts of Severe COVID-19, Sepsis and mRNA vaccine recipients. </p> <p>Samples were barcoded, mixed and then split magnetically before staining with seperate antibody panels for CD3+ (CD4, Treg, Tfh, CD8, gdT) or CD3- (B cells, DC, NK, Monocytes) to give approximatly 1280 FCS files from 218 individuals. </p> <p>A follow up experiment with a B-cell specific panel and Tetramers is included. </p> <p>Patient level metadata and antibody panel details are included. </p> <p> </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>
Mass cytometry files related to Figure 4 in A. Tomic et al
<p>Mass cytometry files related to Figure 4 in A. Tomic et al.</p> <p>Preprint available @BioRxiv:</p> <p>Adriana Tomic, Ivan Tomic, Yael Rosenberg-Hasson, Cornelia L. Dekker, Holden T. Maecker and Mark M. Davis. (2019). SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. (<a href="https://www.biorxiv.org/content/10.1101/545186v1">link</a>)</p> <p>Files have been compressed using 7-Zip available at https://www.7-zip.org/.</p>
Multivariate mixed model application to mass cytometry data (processed data)
<p>This bachelor thesis demonstrates the results of mass cytometry data re-analysis using multivariate regression. I reanalyse a dataset by Palgen et al. (2019) using two models: a Poisson log-normal mixed model and a logistic linear mixed model from the R package ‘cytoeffect’ (Seiler et al., 2019). By exposing multivariate patterns and the associated uncertainty profiles in the data, the aim of this analysis is to replicate biological conclusions and uncover new biological findings. </p>
Imaging Mass Cytometry for high-dimensional tissue profiling in the eye
<p>Imaging mass cytometry data (folders containing single tiffs + cell masks) generated for the analysis of healthy conjunctiva and conjunctival melanoma.</p>
A computational workflow for cell line profiling by Imaging Mass Cytometry.
<p>Imaging Mass Cytometry Data as 32-bit single TIFF with computational analysis from the manuscript: <strong>A computational workflow for cell line profiling by Imaging Mass Cytometry.</strong></p> <p><strong><span lang="EN-US">Breast cancer cell lines SKBR3 MCF7 HCC1143 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">Elongated cell lines HeLa SKOV3 BJ IMC data and CellProfiler pipelines.zip:</span></strong></p> <p><strong><span lang="EN-US">Small cell lines A431 HT29 BxPC3 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">U937 PMA-differentiated cells IMC data and CellProfiler pipeline.zip</span></strong></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data and CellProfiler pipeline.zip</span></strong></p> <p><span lang="EN-US">Contains 1 folder per cell line or drug treatment of single TIFF 32-bit markers exported from MCD/txt files (including Xe131 channel) and their respective cpproj. pipeline file for IMC Cell Line Profiler workstream reproducible analysis</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler high dimensional and correlation analysis R scripts.zip</span></strong></p> <p><span lang="EN-US">Contains three adaptable R scripts for high dimensional analysis, correlation analysis and combination of both scripts for Machine Learning classified datasets.</span></p> <p><strong><span lang="EN-US">Breast cancer cell lines nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, nuclear classes visual rendering, and classifier model files with outputs for two machine learning classifiers (Random Forest and Fast Gentle Boosting) per breast cancer cell line for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, classifier model with outputs for Fast Gentle Boosting and Random Forest per treatment for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler pseudo-color images with Ki-67 marker Cytoplasm marker and Cell-ID nuclei (Fig2 Fig3), visual nuclei and whole-cell segmentation contours rendered images (Fig4).</span></strong></p> <p><strong><span lang="EN-US">Non-compensated and compensated multiTIFF 32-bit cells lines with Cellprofiler masks SCE objects and FCS files and Datatables.zip</span></strong></p> <p>Contains publicly available compensation matrix (<a href="https://zenodo.org/records/7575859">https://zenodo.org/records/7575859</a>) , R compensation script (<strong>Compensation IMC data with CATALYST.R)</strong>, compensated and non-compensated multiTIFF stacks 32-bit per cell line experiment, exported CellProfiler 16-bit masks per cell line dataset, R single cell experiment script (<strong>Conversion IMC data to Single Cell Experiments Objects and FCS.R)</strong> with inputs and outputs (fcs files, sce files, panel files, metadata files),R<strong> </strong>conversion single cell experiment to datatable script<strong> (Conversion SCE to Datatable and analysis.R)</strong>.</p> <p><strong><span lang="EN-US">Step-by-step guide to assist users with the IMC Cell Line Profiler computational workflow.</span></strong></p>
Mass cytometry immunophenotyping data of two-week-old mouse pups' spleens depleted of maternal cells
<div> <div> <div> <p>The maternal cells transferred into the fetus during gestation persist long after birth in the progeny. These maternal cells have been hypothesized to promote the maturation of the fetal immune system in utero but there are still significant gaps in our knowledge of their potential roles after birth. To provide insights into these maternal cells' postnatal functional roles, we set up a transgenic mouse model to specifically eliminate maternal cells in the neonates by diphtheria toxin injection and confirmed significant depletion in the spleens. We then performed immunophenotyping of the spleens of two-week-old pups by mass cytometry to pinpoint the immune profile differences driven by the depletion of maternal cells in early postnatal life. We observed a heightened expression of markers related to activation and maturation in some natural killer and T cell populations. We hypothesize these results to indicate a potential postnatal regulation of lymphocytic responses by maternal cells. Together, our findings highlight an immunological influence of maternal microchimeric cells postnatally, possibly protecting against adverse hypersensitivity reactions of the neonate at a crucial time of new encounters with self and environmental antigens.</p> </div> </div> </div>
Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
<p>In contrast to the rich single-cell transcriptomic and epigenetic data, the corresponding protein level information of human hematopoietic stem and progenitor cell (HSPC) populations is still missing. We used a highly-multiplexed single-cell screen to quantify the protein expression of 353 surface molecules and 79 functional intracellular molecules (TFs, chromatin regulators, and metabolic enzymes) with mass cytometry. In doing this, we created a core panel with probes against functional protein molecules associated with specific lineage potentials to better illuminate the differentiation potentials of the progenitors. In total, we analyzed 556,226 CD34+ bone marrow HSPCs across three individuals. Our analysis identified ten distinct clusters among HSPCs by unsupervised method and defined their unique proteomic composition. We compare our data-driven populations to the canonical HSPC cell types identified by cell surface proteins and observe discrepancies, especially in the lympho-myeloid axis. Overall, we supply a quantified summary of the proteomes of human HSPCs and create a framework to redefine progenitor populations with unique functional states along hematopoiesis. </p>
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