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

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.

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)

EdU Flow Cytometry data for this research project

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.

Here, the whole cultivation system was covered with sterile breathable film and acclimated to a 5-day temperature pulse cycle (25 °C for 2 days, 37 °C for 2 days and 25 °C for 1 day) in a controllable temperature incubator under a light intensity of 3000 lux with a 12 h light/12 h dark cycle. Cells reached a plateau after 5 days of growth at 25 °C (Fig. S1). At the end of the first increasing temperature cultivation, a small culture aliquot was inoculated into fresh medium to reinitiate growth for the second increasing temperature treatment. The initial cell density of each increasing temperature treatment was approximately 1 × 104 cells/mL. The control group was exposed to a constant temperature (25 °C for 5 days) for all three stages (Fig. S2). All experiments were performed in three biological replicates. Since the volume of the inoculation solution was much smaller than that of the culture system (

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.

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

These data are the flow cytometry replicates for the experiments presented in the linked manuscript.

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)

Flow cytometry analysis for cell apoptosis in LPS-induced A549 cells.

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.

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 ASCII 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.

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods.Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This data set includes flow cytometry data for planktothrix in samples collected from Gran Lake St. Marys and downstream waterways in 2025

Supplementary information files for article Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

Flow Cytometry data from "Systematic characterization of maturation time of fluorescent proteins in living cells"