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185 results for “flow cytometry”
Flow Cytometry data from: "The EMT transcription factor Zeb1 is essential for HSPC differentiation that acts synergistically with Zeb2 in fine-tuning hematopoietic lineage fidelity"
<p>Abstract:</p> <p>The Zeb2 transcription factor has been demonstrated to play important roles in hematopoiesis and leukemic transformation. Zeb1 is a close family member of Zeb2 but has remained more enigmatic concerning its roles in hematopoiesis. Here we show using conditional loss of function approaches and bone marrow reconstitution experiments that Zeb1 plays cell autonomous role in hematopoietic lineage differentiation, particularly as a positive regulator of monocyte development in addition to its previously reported important role in T-cell differentiation. Analysis of existing single cell RNAseq data of early hematopoiesis has revealed distinctive expression differences between Zeb1 and Zeb2 in HSPC differentiation with Zeb2 being more highly and broadly expressed that Zeb1 except at a key transition point (ST-HSCàMPP1) whereby Zeb1 appears to be the dominantly expressed family member. Inducible deletion of both Zeb1 and Zeb2 using a tamoxifen inducible Cre-mediated approach leads to acute bone marrow failure at this transition point with increased long-term and shortterm hematopoietic stem cell numbers and an accompanying decrease in all hematopoietic lineage differentiation. Bioinformatics analysis of RNAseq data has revealed that Zeb2 acts predominantly as a transcriptional repressor involved in restraining mature hematopoietic lineage gene expression programs from being expressed too early in hematopoietic stem and progenitor cells (HSPCs). Zeb1 appears to fine tune this repressive role during hematopoiesis to ensure hematopoietic lineage fidelity. Analysis of ROSA26 locus based transgenic models has revealed that Zeb1 as well as Zeb2 overexpression within the hematopoietic system can drive extramedullary hematopoiesis/splenomegaly and enhanced monocyte development. Finally, deletion of Zeb2 alone or Zeb1/2 together was found to enhance survival in secondary MLL-AF9 AML models attesting to the oncogenic role of Zeb1/2 in AML.</p> <p> </p> <p>Flow cytometric and Hematocrit analysis methods: </p> <p><br> Cells were stained with antibodies listed in the provided Supplemental Table (Antibodies.xlsx) according to the manufacturer guidelines. Flow cytometric analyses were performed on the LSRII and Fortessa X-20 cytometer (BD Biosciences) and the results were analysed by FACSDiva or FlowJo software (BD Biosciences). Cells for MLL-AF9 experiments and RNA-seq were stained and sorted on Influx or FACSAria Fusion sorters (BD Biosciences) at AMREP Flow Cytometry Core Facility and FlowCore, Monash University. <br> Submandibular blood samples were collected into EDTA-coated tubes, and hematology parameters were measured using a HemaVet 950FS automated blood analysis machine (Drew Scientific).</p>
Merging and imputation of flow cytometry data: a critical assessment
<p>This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>.</p> <p>Scripts for reproducing our analyses are available at:</p> <p>https://github.com/AUMC-HEMA/imputation-manuscript</p>
Flow cytometry of smooth muscle cells (SMCs)
<p>"SMC phenotype assessment by flow cytometry for positive SMC intracellular markers. SMC were isolated from bladder tissue samples and cultured in vitro in xenogeneic-free culture medium"</p>
Discrete Flow Cytometry of Underway Samples from Gradients 5 (2023) Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 µm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Flow cytometry data for "Vitamin B12 conveys a protective advantage to phycosphere-associated bacteria at high temperatures"
<p>More details—including the analysis pipeline—are available in the GitHub repository: <a href="http://github.com/maggimars/bactB12">https://github.com/maggimars/bactB12</a>. </p> <p>Direct link to analysis pipeline interactive document: <a href="https://maggimars.github.io/bactB12/Flow_Cytometry_Analysis.html">https://maggimars.github.io/bactB12/Flow_Cytometry_Analysis.html</a></p> <p> </p> <p> </p>
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 4 (2021) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2021 (Gradients 4/TN397) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Discrete Flow Cytometry of Underway Samples from TN414 Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the transit from Fiji to New Zealand (TN414) in the South Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
CyTOF and Flow Cytometry dataset assocaited with "Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females"
<p>This dataset contains all the raw mass cytometry (CyTOF) and flow cytometry fcs files associated with Capelle <i>et al</i>. '<i>Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females',</i> <i><strong>Nature Communications</strong>, <strong>2023</strong>,</i> In Press.</p><p>The dataset contains the following information:</p><p>1, The folder " CoPImmunoPD Flow Zenodo V2.zip " contains all the raw fcs files of flow cytometry analysis and the excel table with marker information of five staining panels in the initial discovery analysis using fresh blood samples. The folder also includes the fcs files of analyzing cytotoxicity potential within CD8 T cells and of validation analyses using cryopreserved samples. Single-color/fluorochrome staining files have also been provided for the relevant experiments in the given subfolders for compensation.</p><p>2, The folder "<a href="https://zenodo.org/api/files/75c910aa-3615-4eff-a201-9d2d46e33ea0/CoPImmunoPD_CyTOF_Zenodo.zip">CoPImmunoPD_CyTOF_Zenodo.zip</a>" contains all the raw fcs files generated from the CyTOF measurements in the initial discovery analysis.</p><p><strong>To reproduce our published Figures, please be assure to first read all the accompanied readme/excel information annotation files deposited in the corresponding folders within the zip files, all the Source Data files of different main and supplementary Figure subpanels, Methods and/or any other relevant sections in our manuscript.</strong></p>
An imaging flow cytometry dataset for profiling the immunological synapse of therapeutic antibodies
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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>
S1_Flow_Cytometry_Data
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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>
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>
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>
Flow cytometry data and image data -A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice
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Flow cytometry data of "The impact of storage on extracellular vesicles: a systematic study" experiments
<p>Flow cytometry raw data acquired for the pubblication of "The impact of storage on extracellular vesicles: a systematic study"</p>
Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
<p><span>Copy-number and point mutations form the basis for most evolutionary novelty through the process of gene duplication and divergence. While a plethora of genomic sequence data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic system that allows us to distinguish copy-number and point mutations, we study their early and transient adaptive dynamics in real-time in <em>Escherichia</em> <em>coli</em>. We find two qualitatively different routes of adaptation depending on the level of functional improvement selected for: In conditions of high gene expression demand, the two types of mutations occur as a combination. Under</span><span> low gene expression demand, negative epistasis between the two types of mutations renders them mutually exclusive. Thus, owing to their higher frequency, adaptation is dominated by copy-number mutations. Ultimately, due to high rates of reversal and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation but also <a>constrain</a></span><span> sequence divergence over evolutionary time scales.</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.