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185 results for “flow cytometry”
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>
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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Viscoelastic properties of suspended cells measured with shear flow deformation cytometry
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Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
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CellCognize: a neural network pipeline for cell type classification from flow cytometry data
<p>Readme file content</p> <p>The files stored here contain the following material as supplementary and source data for the publication</p> <p>Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data</p> <p>Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1</p> <p>1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland<br> 2 Biotechnology Department, Firmenich SA, Geneva, Switzerland</p> <p>%%%%%%%<br> Flow cytometry data<br> %%%%%%</p> <p>FCM_files:</p> <p>.mat files with cleaned data as described in the supplementary methods section</p> <p>Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater</p> <p>MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods.</p> <p>PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods.</p> <p>%%%%%%%<br> Neural network data<br> %%%%%%</p> <p>NN_file_example: three ANN functions, to be used in conjunction with the SI methods section</p> <p>Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data</p> <p>%%%%%%%<br> 16S sequencing data<br> %%%%%%</p> <p>raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse.</p> <p>Readme_16S_sequence_files.txt: sample description of the read files</p>
Discrete flow cytometry from the Gradients 2017 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations and heterotrophic bacteria from discrete flow cytometry data collected during the Gradients 2017 (Gradients 2/MGL1704) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including bacteria, picoeukaryotes and the cyanobacteria Prochlorococcus and Synechococcus. 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 from the Gradients 2016 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations and heterotrophic bacteria from discrete flow cytometry data collected during the Gradients 2016 (Gradients 1/KOK1606) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including bacteria, picoeukaryotes, and the cyanobacteria Prochlorococcus and Synechococcus. 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>
Data from: The ORFIUS complex regulates ORC2 localization at replication origins (Flow cytometry)
<p>In this work, we use bromodeoxyuridine (BrdU)/propidium iodide cell cycle flow cytometry profiling to demonstrate that treatment of ovarian cancer cell lines with gene specific siRNAs, shRNAs, or drugs leads to minimal or no cell cycle arrest which might otherwise influence observed phenotypes. All raw FCS files for flow cytometry data presented in Figures 4E, 6G, S3A, S4C, S5B, S5F, S8C, S8D, S8E, S9C, S10C, and S12B are available here.</p>
UVAE toy data (flow cytometry sample)
<p>A sample of flow cytometry data containing 3 panels of 3 batches with artificial batch effects.</p>
Flow cytometry data of NEREA Augmented Observatory
<p>FCM sample were processed using a Becton-Dickinson FACSVerse flow cytometer, equipped with standard laser (488 nm) and filter set. Volumes were estimated using TruCount Beads run before every batch run or estimated from the volumetric count of the instrument.</p> <p> </p>
Bacterial 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 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, stained with SYBR-Green, and counted in a Cube 8 flow cytometer (SYSMEX PARTEC) based on green fluorewscence. 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>
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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.