A database of flow cytometry experiments where users can query and download data collected and annotated according to the MIFlowCyt data standard.

This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty. By separating these two components, the signal and noise can be compared independently to evaluate measurement quality across different experimental conditions. The files contain experiments from a single stock of Escherichia coli cells that was diluted to different concentrations, stained with Hoechst33342, and acquired on a CytoFLEX LX under the same acquisition conditions. ?Control_Hoechst? is a biologic control sample stained only with Hoechst. ?RainbowBeads? is a control of hard-dyed fluorescent beads with 8 distinct peaks of known fluorescent intensities per manufacturer documentation. ?Test_double? indicates test samples with double fluorescent probe staining, the fractional number (e.g. 0.7) indicates the dilution factor from the stock, and the integer at the end represents the technical replicate.The downloaded Exp_20230921_1_Cyto-A-journal.zip file contains 14 files in .fcs format, which requires suitable software to read/analyze data (i.e., FCS Express).

Raw flow cytometry data of microbial community experiment focused on switching between r- and K- selection. The files are in the Flow Cytometry Standard (FCS) format. They are compressed and packed into a tar-archive using the tar -a option.For each sample, the bacterial density was quantified using flow cytometry (BC Accuri C6). In brief, the bacterial communities were diluted in 0.1x TE buffer, mixed with 2x SYBR Green II RNA gel stain (ThermoFisher Scientific) and incubated in the dark at room temperature for 15 minutes. Then, each sample was measured for 2.5 minutes at 35 uL/min with an FL1-H (533/30 nm) threshold of 3000. We gated the bacterial population as those events with an FL1-A > 104 and FSC-A < 105.

EdU Flow Cytometry data for this research project

FlowRepository is a database of flow cytometry experiments where you can query and download data collected and annotated according to the MIFlowCyt standard. It is primarily used as a data deposition place for experimental findings published in peer-reviewed journals in the flow cytometry field.

Phytoplankton community composition and size structure vary considerably between oligotrophic and eutrophic regions (areas of low or high macro and micronutrients (e.g., iron)) (Hill et al., 2005; Martin et al., 1989; Strom et al., 2006; 2016), between surface and subsurface depths (Hill et al., 2005), and with season (Moran et al., 2012) and climatic conditions (Batten et al., 2021). Phytoplankton represent the base of the food web providing energy for zooplankton, which in turn support the growth of juvenile and adult salmon populations. Certain phytoplankton, like many diatom species, are particularly important food items in the Gulf of Alaska (GOA, Odate 1996, Strom et al., 2007). We aim to broaden understanding of phytoplankton dynamics in the GOA/North Pacific Ocean by investigating spatial and temporal patterns in community structure and biomass and exploring environmental (physics and nutrients) drivers of taxonomic variability that may lead to variation in the quality of phytoplankton biomass available to primary consumers during winter. The spatial variations in phytoplankton biomass, and taxa and community size structure were characterized through measurements of (among others) flow cytometry data collected at all IYS stations. In zone 4 (US Ship NOAA Bell M. Shimada) flow cytometry samples were collected from 5, 25, and 50 m for assessment of microbial community cell sizes (Moran et al. 2012). Flow cytometry were also collected at 5 m depths in zone 5 (on Canadian ship CCGS Sir John Franklin) and in zones 2-3 (on Russian ship R/V TINRO), so we have samples over the entire area surveyed. The flow cytometry analysis deliverables include tabulated counts (cells/ml) and estimated carbon content (C/cell and C/population) for the following 4 phytoplankton pico- and nanoplankton: Synechococcus, Cryptophytes, picoeukaryotes, nanoeukaryotes (excluding Cryptophytes).

Datafiles are named after the used fluorophore, CD61 clone, dilution fold and either plasma or DPBS as used sample. IgG1 serves as isotype control and DPBS as buffer control. Apogee and Exometry beads are measured for calibration.

This data set includes SBI Flow Cytometry data for a subset of Primary Production cast profiles in the upper 50 m of the water column 1) 2002 data includes abundances of heterotrophic bacterial cells: total, high nucleic acid, and low nucleic acid, < approx. 5 um eukaryotic phototrophic cells, and > approx. 5 um eukaryotic phototrophic cells, which were mainly diatoms 2 )2004 data includes abundances of coccoid cyanobacteria, < approx. 5 um eukaryotic phototrophic cells, and > approx. 5 um eukaryotic phototrophic cells, which were mainly diatoms. These data are in Excel format.

流式细胞术数据（应用和细胞周期）

The deposit contains flow cytometric characterization and Treg suppression assay data for the paper "Single cell characterization of blood and expanded regulatory T cells in autoimmune polyendocrine syndrome type 1", published in iScience in 2024.

1. Flow cytometry characterization of expanded Tregs Tregs were isolated from whole blood from 17 APS-1 patients and 14 healthy controls and expanded in vitro for 14 days and then frozen. These were then characterized by flow cytometry. Please see the publication for details.
2. Tregs suppression assay Thawed PBMCs from 15 APS-1 patients and 15 healthy controls were used to obtain responder T cells (Tresp). Cells were then cultured to a concentration of 1x106 cells/ml and rested overnight. Next day, Tresp cells were stained with the CellTrace Violet Cell Proliferation Kit. To assess the suppressive capacity, recovered expanded Tregs were activated and co-cultured at different ratios for 5 days at 37℃ and 5% CO2. Cells were harvested and stained for flow cytometry.

Day: Day of the measure Treatment: Experimental treatment Other columns are the parameters for each object (bacteria)

Supplementary Information Files for Current trends in flow cytometry automated data analysis softwareAutomated flow cytometry (FC) data analysis tools for cell population identification and characterisation are increasingly being used in academic, biotechnology, pharmaceutical and clinical laboratories. Development of these computational methods are designed to overcome reproducibility and process bottleneck issues in manual gating, however the take-up of these tools remains (anecdotally) low.Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbour embedding (t-SNE) and its initial Matlab based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms.Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though amongst those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support.This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration and visualisation more popular in academia, whereas automated tools for specialised targeted analysis that apply supervised learning methods were more used in clinical settings.

PBMC sample collection and processing are described in Tsang, J. S. et al. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157, 499–513 (2014). Additional B cell subpopulations were gated for the publication "Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus". (Kotliarov Y, Sparks R et al. Nature Medicine 2020). These new gates include the CD20+CD38++ cells whose frequency evaluated prior to vaccination was predictive of antibody responses to vaccination.This item is a part of the collection: https://doi.org/10.35092/yhjc.c.4753772If you use our data (including CITE-seq data) or code for your work please cite the following publication:Kotliarov, Y., Sparks, R. et al. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nat. Med. DOI: https://doi.org/10.1038/s41591-020-0769-8 (2020)AbstractResponses to vaccination and to diseases vary widely across individuals, which may be partly due to baseline immune variations. Identifying such baseline predictors and their biological basis are of broad interest given their potential importance for cancer immunotherapy, disease outcomes, vaccination and infection responses. Here we uncover baseline blood transcriptional signatures predictive of antibody responses to both influenza and yellow fever vaccinations in healthy subjects. These same signatures evaluated at clinical quiescence are correlated with disease activity in systemic lupus erythematosus patients with plasmablast-associated flares. CITE-seq profiling of 82 surface proteins and transcriptomes of 53,201 single cells from healthy high and low influenza-vaccination responders revealed that our signatures reflect the extent of activation in a plasmacytoid dendritic cell—Type I IFN—T/B lymphocyte network. Our findings raise the prospect that modulating such immune baseline states may improve vaccine responsiveness and mitigate undesirable autoimmune disease activities.General contact: John Tsang (john.tsang@nih.gov)Questions about software/code: Yuri Kotliarov (yuri.kotliarov@nih.gov)

This Dataset includes the spatial and temporal quantification of planktothrix cyanobacteria filaments within the canal and river system of St. Mary's, OH for the year 2023.

This dataset contains flow-cytometry data from the JR20131005 AMT23 research cruise. Data was provided by the British Oceanography Data Centre (BODC).

This U.S. Geological Survey (USGS) data release contains phytoplankton classification and enumeration results from near-surface samples analyzed by imaging flow cytometry and collected as part of a harmful algae bloom (HAB) monitoring study conducted in collaboration with the New York State Department of Environmental Conservation (NYSDEC). Samples were collected biweekly from monitoring platforms in Owasco and Seneca Lakes and one bloom sample site at Emerson Park Boat Launch in Owasco Lake. The platforms were deployed from June-October in 2020. This dataset includes all routine and quality assurance/quality control samples collected at the three sampling locations. Phytoplankton were identified to the lowest possible taxonomic level, and abundance (density reported as both natural units and cells) and biovolume are reported. All data are reported as raw calculated values and are not rounded to USGS significant figures. This data release was produced in compliance with open data requirements to make scientific data associated with USGS research efforts and publications available to the public.

Sample flow cytometry dataset from T cell.

Flow cytometry data is collected with an Accuri-C6 flow cytometer as part of the regular LTER sampling protocol. Data for samples stained with SYBR-green (i.e. for cells that are not autofluorescent) were exported as fcs format files. Sample naming convention, which includes information on sample date and location, can be found in README.txt.

Funding: NSF PLR-1440435

Values derived from flow cytometry analysis for baseline B cell and T cell lineage frequencies, overall infection frequency at 3dpi and lineage-specific infection frequencies for B cells. Comments associated with column headers contain detailed definitions for each subset. (XLSX)

Flow Cytometry data