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278 results for “cytometry”
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>
Application of flow cytometry using advanced chromatin analyses for assessing changes in the sperm structure and DNA integrity in a porcine model
<p><span>Chromatin status is critical for sperm fertility. We tested a multivariate approach for studying pig sperm chromatin, aiming to capture the chromatin structure's complexity with a set of quick and simple techniques, not only DNA damage. Sperm doses from 36 boars (3 ejaculates/boar) were analyzed at days 0 and 11 (cooled storage). Analyses were: CASA (motility) and flow cytometry to assess sperm functionality and chromatin structure by SCSA (DNA fragmentation %DFI and chromatin maturity %HDS), monobromobimane (mBBr, tiol status/disulfide bridges between protamines), chromomycin A3 (CMA3, protamination) and 8-hydroxy-2'-deoxyguanosine (8-oxo-dG, DNA oxidative damage). Data were analyzed by linear models for effects of boar and storage, correlations, and multivariate analysis as hierarchical clustering and principal component analysis (PCA). Storage reduced sperm quality parameters, mainly motility, with no critical oxidative stress increases, while chromatin status worsened slightly (%DFI and 8-oxo-dG increased while mBBr MFI and disulfide bridges decreased). Boar significantly affected most chromatin variables except for CMA3, with storage affecting most except %HDS. At day 0, sperm chromatin variables clustered closely, except for CMA3, and %HDS and 8-oxo-dG correlated with many variables (notably, mBBr). After storage, the relation between %HDS and 8-oxo-dG remained, but correlations among other techniques disappeared, and mBBr variables clustered separately. The PCA suggested a considerable influence of mBBr on sample variance, especially regarding storage, with SCSA and 8-oxo-dG affecting between-sample variability. Overall, CMA3 was the least informative, in contrast with results in other species. The combination of DNA fragmentation, DNA oxidation, chromatin compaction, and tiol status seems a good candidate for obtaining a complete picture of the pig sperm nucleus status, raising many questions for future molecular studies and deserving further research to establish its usefulness as fertility predictors in multivariate models. The meaning of CMA3 should be clarified.</span></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>
S1_Flow_Cytometry_Data
Open the record for dataset details and reuse information.
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>
Ultra-high scale cytometry-based cellular interaction mapping - Data repository
<p>This is the repository for datasets used in Vonficht, Jopp-Saile, Yousefian, Flore <em>et al. </em>Ultra-high scale cytometry-based cellular interaction mapping, <em>Nature Methods </em>(2025) <a href="https://doi.org/10.1038/s41592-025-02744-w" rel="nofollow">https://doi.org/10.1038/s41592-025-02744-w</a>. Associated analysis code can be found at <a href="https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping">https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping</a>, and the repository for the accompanying R package is hosted at <a href="https://github.com/agSHaas/PICtR">https://github.com/agSHaas/PICtR</a>. </p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Dataset for "In silico-labeled ghost cytometry"
<p>Data and code for the analyses in Ugawa <em>et al.</em> "In silico<em>-</em>labeled ghost cytometry" eLife.</p>
Multicolor flow cytometry of monocultures and co-cultures of Bacteroides species
<p>Dataset of FCS (Flow Cytometry Standard) files, along with meta-data, related to a flow cytometry analysis of monocultures and co-cultures of <em>Bacteroides </em>species under several different conditions. </p> <p><strong>Data Collection. </strong>This<strong> </strong>dataset accompanies a journal artcle which was published in <em>Frontiers in Microbiology</em> (<a href="https://doi.org/10.3389/fmicb.2022.910390">https://doi.org/10.3389/fmicb.2022.910390</a>). The "Methods and Materials" section in this article fully describes the biological nature of these samples and how the samples were processed for flow analysis and analyzed with flow cytometry. </p> <p><strong>Data Organization. </strong>Dataset includes 1832 samples. See mapping.xlsx and mapping_key.xlsx for list of samples and their meta-data. Folders are formatted as {run_data}_{time_point} and contains only samples belonging to either a run performed on 2018/07/17 or 2018/07/21 for time points of either 0, 24, 48, 72, or 102 hours. </p> <p><strong>Data Analysis. </strong>Code used for manipulating and analyzing these samples is publicly available (<a href="https://github.com/firasmidani/BacteroidesFlowCytometry">https://github.com/firasmidani/BacteroidesFlowCytometry</a>).</p> <p><strong>Data Integrity</strong>. "hardac-hashes.txt" stores the MD5 hashes of the original folders created by the authors prior to uploading data to Zenodo.</p>
Data and code for "High-speed 3D imaging flow cytometry with optofluidic spatial transformation"
<p>Data and codes used in Ugawa & Ota, "High-speed 3D imaging flow cytometry with optofluidic spatial transformation".</p>
Viscoelastic properties of suspended cells measured with shear flow deformation cytometry
<p>Numerous cell functions are accompanied by phenotypic changes in viscoelastic properties, and measuring them can help elucidate higher-level cellular functions in health and disease. We present a high-throughput, simple and low-cost microfluidic method for quantitatively measuring the elastic (storage) and viscous (loss) modulus of individual cells. Cells are suspended in a high-viscosity fluid and are pumped with high pressure through a 5.8 cm long and 200 μm wide microfluidic channel. The fluid shear stress induces large, near ellipsoidal cell deformations. In addition, the flow profile in the channel causes the cells to rotate in a tank-treading manner. From the cell deformation and tank treading frequency, we extract the frequency-dependent viscoelastic cell properties based on a theoretical framework developed by R. Roscoe that describes the deformation of a viscoelastic sphere in a viscous fluid under steady laminar flow. We confirm the accuracy of the method using atomic force microscopy-calibrated polyacrylamide beads and cells. Our measurements demonstrate that suspended cells exhibit power-law, soft glassy rheological behavior that is cell cycle-dependent and mediated by the physical interplay between the actin filament and intermediate filament networks.</p>
Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering
<p>This dataset contains all relevant data for the manuscript (in submission) "<em>Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering</em>".</p> <p>Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/):</p> <p>[Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth]</p> <p>e.g: Allparameters_2014-07-31 08u08_1.0m</p> <p>The date, time and depth indicate the location and time at which the measurement was taken.</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>
Bacterial and phytoplankton abundances by flow cytometry - collected from the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p>Seawater surface samples (5 m) were collected every 6 hours from the ship’s underway pump. In addition, vertical profiles (6 depths, generally from 5 to 100-150 m) were sampled from CTD casts using a SBE 911 Plus attached to a rosette of 24 12-L PVC Niskin bottles. This dataset presents the abundances of high-DNA containing and low-DNA containing bacteria, pico and nanophytoplankton from seawater samples collected from the ship’s underway pump and CTDs. Samples were fixed with paraformaldehyde and glutaraldehyde and stored at -80ºC. In the lab, they were thawed, and one replicate, for bacteria, was stained with SYBR-Green and counted in a Cube 8 flow cytometer (SYSMEX PARTEC) based on green fluorescence. Another replicate was analyzed without staining for phytoplankton, and counted based on the red and orange autofluorescences. Samples were collected around the Southern Ocean on the R/V Akademik Tryoshnikov in the austral summer of 2016/2017, as part of the Antarctic Circumnavigation Expedition (ACE).</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>
Flow Cytometry Data from "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing"
<p>This record contains the flow cytometry data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing."</p> <p>Files are in <a href="https://docs.flowjo.com/flowjo/advanced-features/fj-acs/">Archive Cytometry Standard (ACS) format</a> . Each <code>.acs</code> file is a zip container which holds both the raw <code>.fcs</code> files and a FlowJo workspace (<code>.wsp</code>) file.</p> <p>Keywords in the workspace file identify which primary antibody (<code>primary</code>) was used and which cell genotype (<code>strain</code>) was used for each sample. The workspace also encodes the gating scheme and compensation matrix applied to each sample. Plots in the manuscript are exported from Layout views in the workspace.</p>
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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)
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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.