This dataset contains a diverse collection of pre-processed flow cytometry data assembled to support the training and evaluation of machine learning (ML) models for the gating of cell populations. The data was curated through a citizen science initiative embedded in the EVE Online video game, known as Project Discovery. Participants contributed to scientific research by gating bivariate plots generated from flow cytometry data, creating a crowdsourced reference set. The original flow cytometry datasets were sourced from publicly available COVID-19 and immunology-related studies on FlowRepository.org and PubMed. Data were compensated, transformed, and split into bivariate plots for analysis. This datset includes: 1) CSV files containing two-channel marker combinations per plot, 2) A SQL database capturing player-generated gating polygons in normalized coordinates, 3) Scripts and containerized environments (Singularity and Docker) for reproducible evaluation of gating accuracy and consensus scoring using the flowMagic pipeline, 4) Code for filtering bot inputs, evaluating user submissions, calculating F1 scores, and generating consensus gating regions. This data is especially valuable for training and benchmarking models that aim to automate the labor-intensive gating process in immunological and clinical cytometry applications.

R script that accesses data and plots all of it; Figure plots are indicated in the script.

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.

Biological utility of AutoSpill. Downstream analyses of data compensated by either the traditional compensation algorithm or AutoSpill. (a) Plots were prepared and compensated using FlowJo v.10.6, using either the default traditional algorithm or uploading the spillover matrix generated by AutoSpill. Representative flow cytometry plots illustrating errors corrected by AutoSpill (first and second column, MM3 dataset; third and fourth column, MM2 dataset). (b-e) All plots were prepared from the same FCS files and compensated using FlowJo v.10.7, using either the traditional algorithm or the AutoSpill option. (b) Hierarchical gating for CD4+CD8+CD25+ lymphocytes, using data compensated by the traditional algorithm or AutoSpill (MM3 dataset). (c) The CD4+CD25+ population was backgated to identify the source of population loss in the traditional algorithm (MM3 dataset). (d) MHCII expression on known negative cells (CD4 T cells), known positive cells (CD11b+ splenocytes), and microglia (MM4 dataset). Percent positive was thresholded using CD4 T cells as the negative. MHCII knockout microglia were used as a “true negative" staining control. (e) Foxp3GFP expression on known bimodal cells (CD4+ splenocytes) and CD11b+ macrophages (MM5 dataset). The positive population was thresholded using the negative CD4 T cell peak. Wildtype mice, without the GFP transgene, were used as a “true negative" staining control. This dataset includes the spillover matrices (traditional and AutoSpill) used in this analysis.

Datasets corresponding to publication: Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues.

This repository contains data to reproduce each figure contained in the manuscript
"Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues".
The folders:
- 20151028_TiagoF_MaikH_Ader_Retina_Nrl-GFP-P10
- 20190114_Maik_Ahsan_retina_sorting
- 20190204_MaikH_Retina_Nrl_P04
- 20190624_Maik_Ahsan_Neutrophils_AI_Sorting
- 20191021_MaikH_Nrl_P05
- 20210304_MaikH_Retina_mGluR6-GFP_P04
- 20210305_MaikH_Nrl_P04_Sorting
- DataSet_Cones_Gated
- DataSet_Cones_Raw
- DataSet_HRO_Labelled
- DataSet_Nrl-eGFP_Gated
- DataSet_Nrl-eGFP_Raw
contain data (either original, or readily gated/labelled)

The folders:
- Figure 2...Figure S8
contain Python scripts and further resources to reproduce plots and analyses.
There are folders for each subpanel in each figure and a "HowTo.txt" in each directory describes
how to proceed.
To provide a Python environment that contains all the required Python packages of the correct version,
we designed PyBox 0.1.0. PyBox is essentially a readily installed Python environment in a zip file.
Alternatively, the Python environment can manually be installed as described here:
https://github.com/maikherbig/PyBox#which-packages-are-contained-in-pybox

This dataset is a subset of the abundance of microorganisms (smaller than 20 µm) enumerated using flow cytometry (FCM) during the Multidisciplinary drifting observatory for the study of Arctic Climate (MOSAiC) sampled from first year sea ice (FYI) core bottom 5 cm sections from leg 2 and 3 (February, March, April 2020). For sea ice derived FCM abundance data, subsamples of 15 mL were taken from pooled ice core sections that were melted in filtered sea water and correspondingly a correction factor applied (details provided in the data-file), to enumerate the abundance of microorganisms per mL of melted sea ice. Additional expedition and sampling details can be found in the ECO-overview paper (Fong et al., to be submitted to Elementa). We thank all persons involved in the expedition of the Research Vessel Polarstern during MOSAiC in 2019-2020 (AWI_PS122_00) as listed in Nixdorf et al. (2021). Flow cytometry (FCM) is a fast, high-throughput method to enumerate the abundance of microorganism (smaller than 20 µm). FCM uses the hydrodynamic focusing of a laminar flow to separate and line up microscopic particles. When particles pass a laser beam, the generated light scattering can be used to estimate their cell size, obtain information about cell granularity and surface characteristics and determine fluorescence from inherent pigments or applied stains, such as DNA binding ones. Photosynthetic microorganisms have auto-fluorescent pigments, such as chlorophylls which in combination with the light scattering properties (cell size) or surface properties, can be used to group them into clusters of similar or identical organism types. Heterotrophic microorganisms, including archaea, bacteria and heterotrophic nanoflagellates, and virus do not have fluorescent pigments and require staining, for example using SYBR Green to stain Nucleic Acids (DNA) in order to distinguish these cells from other organic and inorganic particles in the sample. Samples for flow cytometric analysis were taken in triplicates or quadruplicates of 1.8 mL of sample water and fixed with 36 μL 25 % glutaraldehyde (0.5 % final concentration) at 4 °C in the dark for approximately 2 hours, then flash frozen in liquid nitrogen and stored at -80 °C until analysis. The abundance of pico- and nano-sized phytoplankton and heterotrophic nanoflagellates (HNF) were determined using an Attune® NxT, Acoustic Focusing Cytometer (Invitrogen by Thermo Fisher Scientific) with a 20 mW 488 nm (blue) laser. Autotrophic pico-and nano-sized plankton were counted directly after thawing and the various groups discriminated based on their red fluorescence (BL3) vs. orange fluorescence (BL2), red fluorescence (BL3) vs. side scatter (SSC) and orange fluorescence (BL2) vs. side scatter (SSC). For HNF analysis, the samples were stained with SYBR Green I for 2 h in the dark and 1-2 mL were subsequently measured at a flow rate of 500 µl min-1 following the protocol of Zubkov et al. 2007. Following the Zubkov protocol, HNF are enumerated using a fixed gate and in case of sea ice samples, there is an overlap between HNA-bacteria with very high fluorescence and HNF, which is not possible to disentangle with current methodology. The abundance of virus and bacteria was determined using a FACS Calibur (Becton Dickinson) flow cytometer with a 15 mW 480 nm (blue) laser. Prior analysis of virus and bacteria, samples were first thawed, diluted x10 and x100 with 0.2 μm filtered TE buffer (Tris 10 mM, EDTA 1 mM, pH 8), stained with a green fluorescent nucleic acid dye (SYBR Green I ; Molecular Probes, Eugene, Oregon, USA) and then incubated for 10 min at 80°C in a water bath (Marie et al. 1999). Stained samples were counted at a flow rate of around 60 µL min-1 and different groups discriminated on a biparametric plot of green florescence (BL1) vs. side scatter (SSC). This allowed to distinguish virus particles of different sizes, and different bacterial groups including low nuclear acid (LNA) and high nuclear acid (HNA) bacteria. Names of size groups of photosynthetic and heterotrophic organisms are in accordance to "Standards and Best Practices For Reporting Flow Cytometry Observations: a technical manual (Version 1.1)" (Neeley et al., 2023). A short summary is listed here: RedPico = picophytoplankton (1-2 µm); RedNano = Nanophytoplankton (2-20µm), which includes subgroups RedNano_small (2-5 µm), RedNano_large (5-20 µm); OraPico = Nanophytoplankton with more orange fluorescence; OraNano = Cryptophytes; OraPicoProk = Synechococcus; HetNano = heterotrophic nanoflagellates; HetProk = bacteria (and when present archaea); HetLNA = low nucleic acid (LNA) containing bacteria; HetHNA = high nucleic acid (HNA) containing bacteria with the subgroups HetProk_medium = HNA-bacteria subgroup with less fluorescence signal, HetProk_large = HNA-bacteria subgroup with more fluorescence signal and HetProk_verylarge = HNA-bacteria subgroup with very strong fluorescence signal; Virus = virus-like particles, including size refined subgroups: LFV (low fluorescence virus or small virus); MFV (medium fluorescence virus or medium virus); HFV (high fluorescence virus or large virus) according to Larsen et al., 2008. Exemplary plots showing the gating strategies that were followed can be found in "Interoperable vocabulary for marine microbial flow cytometry" (Thyssen et al., 2022).

Yeast and bacteria in monoculture and mixed culture were used to validate the CytoFLEX instrument (flow cytometer) for cell enumeration. Other media tests are also in the excel file, but were not relevant to the study and can be ignored. The data was compared on the flow cytometer versus on agar plates - the sheets "CytoFLEX vs plates" indicate graphs for either yeast or bacteria growth.

This data contains values extracted from flow cytometry data that were generated from THP-1 knockout cells infected with HIV-1-GFP.

(G) Control and Dcp-1 RNAi-treated cells were subjected to nutrient-rich or starvation conditions and stained with NAO. Mean fluorescence was measured by flow cytometry. Graph represents ± SEM of three independent experiments (n = 3).. List of tagged entities: cardiolipin (CHEBI:28494), mitochondrion (go:GO:0005739), nutrient (CHEBI:33284), Dcp-1 (ncbigene:37729), NAO,flow cytometry (bao:BAO_0000005)

Chromosome-containing micronuclei are a hallmark of aggressive cancers. Micronuclei frequently undergo irreversible collapse exposing their enclosed chromatin to the cytosol. Micronuclear rupture catalyzes chromosomal rearrangements, epigenetic abnormalities, and inflammation, yet mechanisms governing micronuclear integrity are poorly understood. Here we show that mitochondria-derived reactive oxygen species (ROS) disrupt micronuclei by promoting a noncanonical function of the ESCRT-III nuclear membrane repair complex protein, CHMP7. ROS retain CHMP7 in micronuclei while disrupting its interaction with other ESCRT-III components. Instead, ROS-induced cysteine oxidation promotes CHMP7 oligomerization and binding to the nuclear membrane protein LEMD2 disrupting micronuclear envelopes. We show that this ROS-CHMP7 axis promotes chromosome shattering known to result from micronuclear rupture. It also mediates micronuclear rupture under hypoxic conditions, linking tumor hypoxia with downstrea..., Flow cytometry files (.fcs) represent the distribution of cells in the cell cycle (DAPI signal) after double thymidine blockade or CDK1 inhibition or a combination of both. Excel tables represent the source data for each figure and supplementary figure of the article-refer to online Materials and Methods for more information on how they have been collected. They are data underlying graphs and original immunoblot membranes. Every piece of data has a small caption near to it, to help identification., , # Data from:Micronuclear collapse from oxidative damage

https://doi.org/10.5061/dryad.ngf1vhj39

Description of the data and file structure

Flow cytometry (.fcs) files obtained by flow cytometry analysis of DAPI stained HeLa cells collected after double thymidine block, with or without 1uM CDK1 inhibition, and after 1 and 10uM CDK1 inhibition.

The following files pertain to figure S12 F of the paper:

0h; 4h; 8h; 12h; 16h; 20h; 24h are the distribution of cells collected at the indicated time points after double thymidine block, while Unstained 1, 2 and 3 and DAPI 1, 2 and 3 are their negative control.

The following files pertain to figure S12 J and K of the paper:

DMSO1, 2, 3; Ro1 1,2,3; Ro10 1,2,3 are the distribution of cells in the cell cycle (DAPI stained) collected after 24h incubation with DMSO or Ro at 1uM or 10uM. The files Unstained1Ro2 and Unstained 2Ro2 are the negative controls.

The following files pertain to figure S12 ...

Tfh cell development in the spleen (SPL), mesenteric lymph nodes (MLN) and Peyer's patches (PP) of WT:Pou2af1-/- mixed bone marrow chimeras seven days after immunization with SRBC. (C) Frequencies of Pou2af1+/+ and Pou2af1-/- Tfh cells as identified by the coexpression of CXCR5 and ICOS (CXCR5hiICOS+), CXCR5 and PD1 (CXCR5hiPD1+), or CXCR5 and BTLA (CXCR5hiBTLAhi) among CD4+ T cells. The graphs in (A-C) show combined data from two out of four independent experiments (n=11).. List of tagged entities: T follicular helper cell (cl:CL:0002038), Pou2af1 (ncbigene:18985), flow cytometry (bao:BAO_0000005)

We are submitting a manuscript and this R dataset is served as a supplementary data. In this study (PROMISSE), we longitudinally profiled the healthy and SLE blood transcriptome dynamics during and after pregnancy using microarray and flow cytometry. Matched non-pregnant healthy and SLE subjects were also recruited. To determine molecular signatures associated with healthy pregnancy, SLE pregnancy, and SLE-related pregnancy complications, linear mixed model analysis were performed on the microarray and flow cytometry and the results were combined into an R dataset that can be interactively uploaded and accessed through http://52.25.7.162/bart/. Users can access sample information, run statistics and generate transcriptional heatmaps and module maps interactively. Users can browse the mixed model and Q-gen analysis, as well as the flow cytometry results and can generate their own heatmaps and plots.

(A) Intracellular detection of NOD2 (bold line in the histogram plot) was performed by flow cytometry using freshly isolated alveolar macrophages selected from a gate set on large granular bronchoalveolar cells (R1, dot plot). One representative experiment out of three experiments is presented.. List of tagged entities: NOD2 (uniprot:Q9HC29), alveolar macrophage (cl:CL:0000583), , flow cytometry (bao:BAO_0000005)

Flow cytometry is widely used to measure gene expression and other molecular biological processes with single cell resolution via fluorescent probes. Flow cytometers output data in arbitrary units (a.u.) that vary with the probe, instrument, and settings. Arbitrary units can be converted to the calibrated unit molecules of equivalent fluorophore (MEF) using commercially available calibration particles. However, there is no convenient, nonproprietary tool available to perform this calibration. Consequently, most researchers report data in a.u., limiting interpretation. Here, we report a software tool named FlowCal to overcome current limitations. FlowCal can be run using an intuitive Microsoft Excel interface, or customizable Python scripts. The software accepts Flow Cytometry Standard (FCS) files as inputs and is compatible with different calibration particles, fluorescent probes, and cell types. Additionally, FlowCal automatically gates data, calculates common statistics, and produces publication quality plots. We validate FlowCal by calibrating a.u. measurements of E. coli expressing superfolder GFP (sfGFP) collected at 10 different detector sensitivity (gain) settings to a single MEF value. Additionally, we reduce day-to-day variability in replicate E. coli sfGFP expression measurements due to instrument drift by 33%, and calibrate S. cerevisiae Venus expression data to MEF units. Finally, we demonstrate a simple method for using FlowCal to calibrate fluorescence units across different cytometers. FlowCal should ease the quantitative analysis of flow cytometry data within and across laboratories and facilitate the adoption of standard fluorescence units in synthetic biology and beyond.

This flow cytometry dataset includes abundances of microorganisms (< 20 µm) from legs 1-5 (November 2019 – September 2020) of the Multidisciplinary drifting observatory for the study of Arctic Climate (MOSAiC) on RV Polarstern 122. Samples were collected from ship-based and on-ice CTD rosettes, preserved, and stored at -80 °C until analysis. x000D x000D Methods: “Samples for flow cytometric analysis were taken in triplicates or quadruplicates of 1.8 mL of sample water and fixed with 36 µL 25 % glutaraldehyde (0.5 % final concentration) at 4 °C in the dark for approximately 2 hours, then flash frozen in liquid nitrogen and stored at -80 °C until analysis. The abundance of pico- and nano-sized phytoplankton and heterotrophic nanoflagellates (HNF) were determined using an Attune® NxT, Acoustic Focusing Cytometer (Invitrogen by Thermo Fisher Scientific) with a 20 mW 488 nm (blue) laser. Autotrophic pico-and nano-sized plankton were counted directly after thawing and the various groups discriminated based on their red fluorescence (BL3) vs. orange fluorescence (BL2), red fluorescence (BL3) vs. side scatter (SSC) and orange fluorescence (BL2) vs. side scatter (SSC). For HNF analysis, the samples were stained with SYBR Green I for 2 h in the dark and 1-2 mL were subsequently measured at a flow rate of 500 µl min-1 following the protocol of Zubkov et al. 2007. The abundance of virus and bacteria was determined using a FACS Calibur (Becton Dickinson) ' flow cytometer with a 15 mW 480 nm (blue) laser. Prior analysis of virus and bacteria, samples were first thawed, diluted x10 and x100 with 0.2 µm filtered TE buffer (Tris 10 mM, EDTA 1 mM, pH 8), stained with a green ' fluorescent nucleic acid dye (SYBR Green I ; Molecular Probes, Eugene, Oregon, USA) and then incubated for 10 min at 80°C in a water bath (Marie et al. 1999). Stained samples were counted at a flow rate of around 60 µL min-1 and different groups discriminated on a biparametric plot of green florescence (BL1) vs. side scatter (SSC). This allowed to distinguish virus particles of different sizes, and different bacterial groups including low nuclear acid (LNA) and high nuclear acid (HNA) bacteria. Names of size groups of photosynthetic and heterotrophic organisms are in accordance to ''Standards and Best Practices For Reporting Flow Cytometry Observations: a technical manual (Version 1.1)'' (Neeley et al., 2023). A short summary is listed here: RedPico = picophytoplankton (1-2 µm); RedNano = Nanophytoplankton (2-20µm), which includes subgroups RedNano_small (2-5 µm), RedNano_large (5-20 µm); OraPico = Nanophytoplankton with more orange fluorescence; OraNano = Cryptophytes; OraPicoProk = Synechococcus; HetNano = heterotrophic nanoflagellates; HetProk = bacteria (and when present archaea); HetLNA = low nucleic acid (LNA) containing bacteria; HetHNA = high nucleic acid (HNA) containing bacteria with the subgroups HetProk_medium = HNA-bacteria subgroup with less fluorescence signal, HetProk_large = HNA-bacteria subgroup with more fluorescence signal and HetProk_verylarge = HNA-bacteria subgroup with very strong fluorescence signal; Virus = virus-like particles, including size refined subgroups: LFV (low fluorescence virus or small virus); MFV (medium fluorescence virus or medium virus); HFV (high fluorescence virus or large virus) according to Larsen et al., 2008. Exemplary plots showing the gating strategies that were followed can be found in ''Interoperable vocabulary for marine microbial flow cytometry'' (Thyssen et al., 2022).'' (This description was adapted from PANGAEA, https://doi.org/10.1594/PANGAEA.963430)_x000D_ x000D The timestamp is in UTC.

A-J Flow cytometry analysis of freshly isolated crypt cells from the small intestine of young (2-3 month) and old (12-16 month) LGR5‐GFPki, mTerc+/+mice and LGR5‐GFPki, G3 mTerc−/−mice (n = 4 mice per group). (A, B, D, E, H, I) Representative FACS plots depicting the analysis of LGR5+ cells. Note the reduction in LGR5+ cells in G3 mTerc−/−mice with the remaining cells showing almost exclusively weak expression of GFP (LGR5lo) and that within the fraction of LGR5hi cells, the cells with high LGR5‐reporter activity (LGR5hi‐high) are preferentially depleted in response to IR. (C, F) Quantification of (C) the number of LGR5+ cells and (F) the number of LGR5hi and LGR5lo cells. (G) Quantification of the percentage of LGR5hi cells and LGR5lo cells within the fraction of LGR5+ cells. (J) Quantification of the survival rate of LGR5hi‐high cells and LGR5hi‐low cells within the fraction of LGR5hi cells comparing old G3 mTerc−/−mice to mTerc+/+mice.. List of tagged entities: Lgr5 (uniprot:Q9Z1P4), intestinal crypt stem cell (cl:CL:0002250), Terc (ncbigene:21748), flow cytometry (bao:BAO_0000005)

This data contains values extracted from flow cytometry data that were generated from THP-1 cells infected with wt HIV-1-GFP compared to capsid mutants.

F Representative FACS plots showing gating of LGR5hi‐high, LGR5hi‐low, LGR5lo‐high, and LGR5lo‐low populations within the LGR5hi gating.. List of tagged entities: Lgr5 (uniprot:Q9Z1P4), intestinal crypt stem cell (cl:CL:0002250), , flow cytometry (bao:BAO_0000005)

The 1858C>T allele of the tyrosine phosphatase PTPN22 is present in 5-10% of the North American population and is strongly associated with numerous autoimmune diseases. Although research has been done to define how this allele potentiates autoimmunity, the influence PTPN22 and its pro-autoimmune allele have in anti-viral immunity remains poorly defined. Here, we use single-cell RNA-sequencing and functional studies to interrogate the impact of this pro-autoimmune allele on anti-viral immunity during Lymphocytic Choriomeningitis Virus clone 13 (LCMV-cl13) infection. Mice homozygous for this allele (PEP-619WW) clear the LCMV-cl13 virus whereas wildtype (PEP-WT) mice cannot. This is associated with enhanced anti-viral CD4 T cell responses and a more immunostimulatory CD8a- cDC phenotype. Adoptive transfer studies demonstrated that PEP-619WW enhanced anti-viral CD4 T cell function through virus-specific CD4 T cell-intrinsic and extrinsic mechanisms. Taken together, our data show that the..., , , # Autoimmunity-associated allele of tyrosine phosphatase gene PTPN22 enhances anti-viral immunity

These data demonstrate the mice expressing the autoimmunity-associated allele of PTPN22 (PEP-619WW) have improved clearance of LCMV-clone 13 and this is associated with an enhanced immune response in numerous immune cell types. This was shown through single-cell RNA sequencing, infection disease course studies, and functional analysis using adoptive transfers and flow cytometry. The Prism file contains information on all the raw data tables, statistical analysis, and the resulting graphs. There are 55 data tables.

Description of the data and file structure

The dataset contains raw data values, in Prism, for experiments listed in the publication. These datasets include weight loss values and survival for LCMV-clone13 infection studies, organ LCMV-clone13 titer data, and numerous files on quantifying different immune cell types, phenotypes, and functions derived from flow cytometry. Eac...

C Anti-BDCA2-mediated internalization in whole-blood assays. Whole blood was treated with increasing concentrations of 24F4A for 16 h. Mean fluorescence intensity (MFI) of BDCA2 was determined with a non-cross-blocking anti-BDCA2 mAb (2D6). Shown is a representative plot of 10 experiments conducted.. List of tagged entities: CLEC4C (uniprot:Q8WTT0), 24F4A, CLEC4C (uniprot:Q8WTT0), 2D6,flow cytometry (bao:BAO_0000005)