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.

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.

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.

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.

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 Escherichia coli. 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 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 constrain sequence divergence over evolutionary time scales. Methods 1. Flow cytometry data of E.coli populations evolved in galactose for 12 days.

Data: EE24.4/8/12 (evolution experiment 24, day 4/8/12) plates 1-3 (delta IS1C - medium C,B,A) + plates 7-10 (IT030 - medium C, B, A; controll plate medium E) Evolution in galactose (A-1%, B-0.1%, C-0.01%, E - 0%) + 0.1% CASAMINOACIDS. FlowJo used to export data as scale values into new folder with naming scheme: "ScaleVal_EE24_12C30" (evolution experiment 24_day12 populations_0.01%galactose_strain IT030) Autogating was used in flow jo to gate the a single concise population of cells (by eye, same within plate, and similar (~60%) for all different plates).

1. amplicon deep sequencing data of E. coli populations evolving in galactose.

Contains random P0 sequences of all evolving pooled populations and R script to generate the plots shown in the figures (Readme)

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).

Accurate analysis of S-phase fraction is crucial for the assessment of cell proliferation levels, tumor malignancy and prognostic effects of treatment. Most of the currently developed methods for S-phase cell analysis rely on flow cytometric analysis of DNA content determination. However, the lack of standardized procedures for sample analysis and interpretation of cell cycle fitting graphs poses a significant limitation in clinical practice for utilizing flow cytometry to measure the cell cycle based on DNA content. Herein, we developed an approach for analyzing S-phase cells based on telomerase activity determination. Briefly, this approach distinguishes S-phase cells in cell populations via direct fluorescence tracking of telomerase activity within individual cells. The dynamic analysis of telomerase activity in different cell cycles was made possible by the ALTMAN strategy developed in our previous studies, which has been successfully employed to distinguish S-phase cells in cultured cells. This method offers a novel avenue for the assessment of cell cycle status and the evaluation of the proliferation status of tumor cells and the prognosis effect of tumor patients via analyzing the differences in telomerase activity during different cell cycle processes.

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.

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.

(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 ...

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)

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)

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

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) 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)

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...

Bacterial conjugation plays a major role in the dissemination of antibiotic resistance and virulence traits through horizontal transfer of plasmids. Robust measurement of the conjugation frequency of plasmids between bacterial strains and species is therefore important to understand the transfer dynamics and epidemiology 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. 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. Methods Flow Cytometry Filter mating was performed in four biological replicates on four different days. Stationary phase cultures of the recipient strain J53Az + p_mCherry-stable and the donor strain UB5201Rf + pConj_blue-strong were grown in LB containing 8 µg/mL gentamicin and 50 µg/mL ampicillin respectively at 37°C with shaking. Each culture was washed in antibiotic-free LB broth and adjusted to the same OD600 in the 0.9±0.1 range. 1.6 mL of the donor and recipient strains were mixed, pelleted, and re-suspended in a 170 µL LB medium. Concentrated cultures of the single-color donor and recipient strains were prepared in an identical way. 40 µL drops of each cell suspension were transferred onto individual Whatman cellulose nitrate membranes (GE Healthcare, United States) placed on antibiotic-free 1.5% LB agar plates. At the start of the conjugation experiment, one membrane containing each cell suspension was re-suspended in 4 mL sterile-filtered PBS containing 0.2 mM EDTA and vortexed to dislodge bacterial cells. This procedure was repeated for the remaining membranes every 2 hours of incubation on LB agar plates at 37°C for 6 hours. The recovered cell suspensions were further diluted 1:250 in 1 mL PBS + 0.2 mM EDTA. A culture of the non-fluorescent J53Az strain was also diluted at 1:1000 in PBS + 0.2 mM EDTA to be used as the non-fluorescent control strain. Data Analysis The diluted bacterial suspensions were well vortexed prior to analysis on the BD FACSymphony flow cytometer (BD Biosciences, United States). The mCherry fluorophore was excited by the yellow laser and detected through a 610/20 nm bandpass filter. The ebfp2 fluorophore was excited by the violet laser and detected through a 474/25 nm bandpass filter. Fluorescent beads with 0.88 µm and 1.34 µm diameters from the Size Standard Kit (Spherotech, United States) were used to validate the performance of the flow cytometer in detecting particle sizes. For each sample, 105 events were acquired and recorded by the flow cytometer within a time limit of 10 minutes. Flow cytometry data analysis was performed using the FlowJo software (v10.7, FlowJo LLC, United States). Briefly, a universal rectangular gate was used to separate bacterial cells from background noise events in all the SSC-A vs. FSC-A plots (Panels A and B, Figure S2, Supplementary Materials). The autogating tool was used to capture approximately 90% of all events based on the contour of each bivariate plot within each rectangular gate (Panel C, Figure S2, Supplementary Materials). Doublet discrimination was performed on the SSC-H vs. SSC-A bivariate plots by drawing a narrow rectangular gate along the diagonal to retain single cells (Panel D, Figure S2, Supplementary Materials). Compensation matrices were calculated for single-color control strains for mCherry+ and ebfp2+ from the 4 h time point and were applied to all samples. A spider gate was drawn on the compensated mCherry vs. compensated BFP bivariate plots to distinguish between J53Az (double negative), UB5201Rf + pConj_blue-strong control (ebfp2+) and J53Az + p_mCherry-stable control (mCherry+) populations.

Omicron recently emerged sub-lineage BA.5, together with BA.4, caused a 5th wave of coronavirus disease (COVID-19) in South Africa and subsequently emerged as a predominant strain globally due to its high transmissibility. The lethality of BA.5 infection has not been studied in acute hACE2 transgenic (hACE2.Tg) mice models. Here, we investigated tissue-tropism and immuno-pathology induced by BA.5 infection in hACE2.Tg mice. Our data show that intranasal BA.5 infection in hACE2.Tg mice result in attenuated pulmonary infection and pathology with diminished COVID-19-induced clinical and pathological manifestations. BA.5, similar to Omicron (B.1.1.529), infection led to attenuated production of inflammatory cytokines, anti-viral response and effector T cell response as compared to the ancestral strain. Mice recovered from B.1.1.529 infection showed robust protection against BA.5 infection with reduced lung viral load and pathology. Our data provide insights as to why BA.5 infection escapes ..., Flow cytometry and intracellular cytokine staining SCS obtained from spleen and dLN were either surface stained by using fluorescent anti-mouse antibodies in FACS buffer (PBS with 1% FBS) and analysed as previously described (Rizvi et al, 2021 & 2022); or were in-vitro in the presence of RBD (2µg/ml) or its absence (with phorbol 12-myristate13-aceate (PMA; 50 ng/ml; Sigma-Aldrich) and ionomycin (1 µg/ml; Sigma-Aldrich). RBD stimulation was performed for 6 days and was used for intracellular cytokine staining. For PMA + Ionomycin cells were activated for 4 h in the presence of Monensin (#554724 Golgi-Stop, BD Biosciences). All the cells were first washed and blocked with Fc block (anti-mouse CD16/32, Biolegend) at room temperature (RT) for 20 min. Thereafter, cells were used for  surface staining with α-CD3, α-CD4, α-CD8, α-CD11b, α-NK1.1, α-Gr1, α-c-kit, α-γδTCR, α-FcÆ r1α for 20 min at RT in dark. Thereafter, cells were fixed in Cytofix and permeabilized with Perm/Wash Buffer using ..., FLowJo for FACS files. GraphPad Prism for graph files. README file