Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
68
datasets available to search
ShareScore release 0.9.0
Dataset results
68 results for “Mass cytometry”
Figure S4. Mass cytometry data pre-processing
<p><em>Figure S4: Mass cytometry data pre-processing. Paired myocardial and blood samples were collected from patients undergoing cardiac surgery and who underwent DNA sequencing. Each sample was barcoded and samples were acquired in bulk with one sample was rerun for each CyTOF run in order to correct batch effect. A gating on CD45<sup>+</sup> live cells was then performed. A dimension reduction algorithm was then applied and the clustering algorithm ClusterX was applied on the entire data set. Cells clusters identification was then performed according to Heatmap based on cell markers associated with each cluster. </em></p>
Exploratory mass cytometry analysis reveals immunophenotypes of cancer treatment-related pneumonitis
Open the record for dataset details and reuse information.
Mass cytometry immunophenotyping data of two-week-old mouse pups' spleens depleted of maternal cells
Open the record for dataset details and reuse information.
Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
Open the record for dataset details and reuse information.
Imaging Mass Cytometry: p173
<p>Imaging Mass Cytometry: p173</p> <p>Raw acquisitions for the paper:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p>
Imaging Mass Cytometry: p165
<p>Imaging Mass Cytometry: p165</p> <p>Raw acquisitions for the paper:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p>
Imaging Mass Cytometry: p176
<p>Imaging Mass Cytometry: p176</p> <p>Raw acquisitions for the paper:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p>
Raw single cell mass cytometry data from breast cancer patient derived xenografts
<p>Raw single cell mass cytometry data from breast cancer patient derived xenografts.</p> <p>Data is organised in an expressioset R object</p>
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Imaging Mass cytometry of pediatric liver biopsies in patients with AHUO and possible PASC
<p>IMC Dataset as described in Roettele et al. "<strong><span>Characteristic immune cell interactions in livers of children with acute hepatitis revealed by spatial single-cell analysis identify a possible </span></strong><strong><span>post-acute sequel of COVID-19"</span></strong><strong><span> </span></strong></p>
Imaging mass cytometry data from IDH wildtype glioblastomas
<p>Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. In this dataset, we have used imaging mass cytometry to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME) using known markers for myeloid and neoplastic cells in GBM. Our analyses of these data found that different myeloid populations had distinct and reproducible compartmentalization patterns in the GBM TME that were driven by tissue hypoxia and varied homotypic and heterotypic cellular interactions. This dataset consists of imaging mass cytometry data (16-bit TIFF images) for 8 glioblastomas and 1 tonsil sourced from the Salford Royal NHS Trust Biobank.</p>
Single cell data from Imaging Mass Cytometry of mouse lung tumours treated with KRAS-G12C and immune checkpoint inhibitors (Dataset 3)
Open the record for dataset details and reuse information.
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>
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>
Imaging mass cytometry data from IDH wildtype glioblastomas
Open the record for dataset details and reuse information.
Hematopathologist-annotated mass cytometry dataset of acute myeloid leukemia diagnostic specimens
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>
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>
Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data (Processed Data)
<p>Processed data computed using R packages <a href="https://christofseiler.github.io/CytoGLMM">CytoGLMM</a> and <a href="https://christofseiler.github.io/cytoeffect">cytoeffect</a>. Raw data available <a href="http://flowrepository.org/id/FR-FCM-ZY3Q">here</a>.</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.