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278 results for “cytometry”
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>
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>
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>
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>
Ki-67 and Bcl-2 data by flow cytometry in non-malignant bone marrow aspirates and aspirates from patients with myeloid malignancies.
<p>This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferation and anti-apoptosis in hematopoietic cells. This dataset includes analysis of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell population in non-malignant BM, and the BM disorders myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. As gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 50 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research about the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations based on cell biological characteristics were identified, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells to identify minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology.</p>
T-cell activity - Imaging flow cytometry experiment data
<p>Raw image data from the imaging flow cytometry experiments for correlating receptor localization and cytokine production.</p>
A streamlined approach for fluorescence labelling of low copy-number plasmids for determination of conjugation frequency by flow cytometry
<p><span>Bacterial conjugation plays a major role in the dissemination of antibiotic resistance and virulence traits through horizontal transfer of plasmids. </span>Robust <span>measurement</span> of<span> the conjugation frequency of plasmids between bacterial strains and species </span>is therefore important <span>to understand the transfer dynamics </span>and epidemiology <span>of conjugative plasmids. In this study, we present a streamlined experimental approach for fluorescence labelling of low copy-number conjugative plasmids that allows plasmid transfer frequency during filter mating to be measured by flow cytometry. A blue fluorescence gene is inserted into a conjugative plasmid of interest using a simple homologous recombineering procedure. </span><span>A small non-conjugative plasmid, which carries a red fluorescence gene with a toxin-antitoxin system that functions as a plasmid stability module, is used to label the recipient bacterial strain. This offers the dual advantage of circumventing chromosomal modifications of recipient strains and ensuring that the red fluorescence gene-bearing plasmid can be stably maintained in recipient cells in an antibiotic-free environment during conjugation. A strong constitutive promoter allows the two fluorescence genes to be strongly and constitutively expressed from the plasmids, thus allowing flow cytometers to clearly distinguish between donor, recipient and transconjugant populations in a conjugation mix for monitoring conjugation frequencies more precisely over time. </span></p>
Neuromorphic Particle Flow Cytometry Dataset
<p>Flow cytometry dataset for two different particles (A and B) from four different experiments, recorded with an event-based camera. </p>
Ki-67 and Bcl-2 data by flow cytometry in non-malignant bone marrow aspirates and patients with myeloid malignancies
<p>This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferative and anti-apoptotic activity in hematopoietic cells. This dataset includes analyses of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell populations in non-malignant BM, and in BM disorders, i.e. myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. Because gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 27 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research into the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations were identified based on cell biological characteristics, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells in the identification of minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology.</p>
Antimicrobial MMs - Flow Cytometry
<p>Flow cytometry data subset. Acquired using a Sony SA3800 spectral analyzer (Sony Biotechnology, CA, USA).</p>
Flow cytometry data (Multiclonal Experiments 1-3)
<p>Flow cytometry data from publication:</p> <blockquote> <p>Claus-Peter Stelzer, Maria Pichler, Peter Stadler, Genome streamlining and clonal erosion in nutrient-limited environments: a test using genome-size variable populations, <em>Evolution</em>, Volume 77, Issue 11, November 2023, Pages 2378–2391, <a href="https://doi.org/10.1093/evolut/qpad144">https://doi.org/10.1093/evolut/qpad144</a></p> </blockquote> <p>Please cite this study when using this data.</p> <p><strong>Multiclonal experiments</strong></p> <p>Experiment 1: 11.10.2017 - 22.11.2017</p> <p>Experiment 2: 15.02.2018 - 19.04.2018</p> <p>Experiment 3: 18.04.2018 - 05.07.2018</p>
Flow cytometry data (Clone triplets experiment)
<p>Flow cytometry data of publication:</p> <blockquote> <p>Stelzer, C.P., M. Pichler, P. Stadler, Genome streamlining and clonal erosion in nutrient-limited environments: a test using genome-size variable populations, <em>Evolution</em>, Volume 77, Issue 11, November 2023, Pages 2378–2391, <a href="https://doi.org/10.1093/evolut/qpad144">https://doi.org/10.1093/evolut/qpad144</a></p> </blockquote> <p>Please cite this study if you use the data.</p> <p> </p> <p>Experiments with three clones differing in genome size</p> <p>First Run (05.07.2017-09.08.2017)</p> <p>Second Run (17.08.2017-27.09.2017)</p>
Tara Nutrient and Flow Cytometry Data
<p>"Tara Oceans systematically collected ~35,000 samples for morphological, genetic, and environmental analyses using standardized protocols across multiple depths at global scale, aiming to facilitate a holistic study on how environmental factors and biogeochemical cycles affect oceanic life. ... Tara Oceans collected seawater samples within the epipelagic layer, both from the surface water and the deep chlorophyll maximum (DCM) layers, as well as the mesopelagic zone." (1)<br> Data provided for time, lat, and lon are mean values based on CTD casts that matched closest in location and depth of the actual sampling locations. This dataset includes environmental, nutrient, diversity, and flow cytometry data associated with samples found in the Tara Eukaryote Annotated 18s OTU Counts and Tara Prokaryote Annotated 16s OTU Counts datasets.</p> <p>(1) https://www-science-org.offcampus.lib.washington.edu/doi/full/10.1126/science.1261359</p>
From cameras to confocal to cytometry: measuring tumbling rates is a general way to reveal protein binding - Raw Data
<p>Molecular interactions are central to understanding any biological system, but they are labor-intensive to measure with existing optical approaches. Tracking "tumbling" (rotational diffusion) is a generalizable strategy for determining particle size, from which the presence and size of binding partners in a complex can be inferred. However, fluorescence-based tumbling measurements have historically been limited to small targets (<30 kDa) due to the short (nanosecond) lifetime of typical fluorophores. This size limit leaves most of the proteome inaccessible, severely limiting the usefulness of the approach. To reveal larger complexes and realize the full potential of optical tumbling measurements, several groups have explored longer-lived states, such as <a href="https://doi.org/10.1016/S0006-3495(88)83137-0">photobleached molecules</a>, <a href="https://doi.org/10.1038/s41587-022-01489-7">reversibly photoswitched proteins</a>, or <a href="https://doi.org/10.1021/acs.jpcb.3c01236">triggerable triplets</a>. Here, we describe four combinations of photophysics and hardware for measuring tumbling with longer-lived states, gearing each toward a candidate biological use case. We explore these four measurement schemes with simulations and, in one case, experimentally validate the approach. Our work suggests that, with minor reconfiguring, most fluorescence approaches to detect the presence of proteins could also reveal their binding state.</p><p>This repository holds the raw data for the main text figures. For the contents of the paper, please see: <a href="https://doi.org/10.5281/zenodo.10028433">doi.org/10.5281/zenodo.10028432</a></p>
A streamlined approach for fluorescence labelling of low copy-number plasmids for determination of conjugation frequency by flow cytometry
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Data and code from: Imaging flow cytometry enables label-free cell sorting of morphological variants from populations of the unculturable bacterium <em>Pasteuria ramosa</em>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.