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 repository contains the data presented in our original article, "Superior Precision of Clinical Predictions after CD3-relativisation to Align Flow Cytometry Data," including raw and processed FCS files from 482 samples. This dataset captures information about T cell distributions in human healthy donors through standardised flow cytometry measurements made in four internationally collaborating laboratories over 17 months using 6 different cytometers. A subset of 329 samples split for parallel measurements on at least 2 instruments. This repository also reports donor-level clinical and demographic information, as well as QC files.

If you want to start analysing the data, we propose you download CORE.06-1_rel.asinhCD3 which are CD3-relativised and properly arcsinh transformed data of the CORE studies R1, R2 and SAN. In addition, download patientdata.zip, where all anonymized patient information necessary is reported.

See technical note below if you want to use CORE.02-COMPENSATED.7z!

Cohort overview

CORE:

Our core dataset comprises 459 samples from 358 unique donors, which are organized into three main cohorts (R1, R2 and SAN). R1 includes 254 samples from 153 unique donors, which were analysed in Regensburg. Clinical and demographic variables are recorded. Within R1, 101 repeated samples were collected from 60 donors to test the biological stability of T cell subset distributions over time. The first samples taken from each R1 donor were randomised into training (50), validation (50) and test (53) sets. R2 is a prospective dataset collected 4 months after R1 that includes 52 samples from unique donors, who are not represented in R1. R1 and R2 samples were split after staining and measured in parallel with a Navios™ (Navios) and CytoFLEX LX™ (LX) cytometer. SAN comprises 153 samples from unique donors that were measured with a DxFlex™ (DxFLEX) cytometer in Santander. As with R1, SAN samples were randomised into training (50), validation (50) and test (53) sets.

EXTENDED:

Our extended dataset incorporates two cohorts, FORT and BAD. FORT comprises 14 samples from unique donors that were split into equal parts, then measured in parallel using 3 cytometers – namely, a Navios™ and CytoFLEX LX™ from Beckman Coulter, and an LSRFortessa™ (LSR) from Becton Dickenson. The BAD cohort incorporates samples from 9 unique donors that were split after staining and measured in parallel using an LSRFortessa™ and a Cytek Aurora™ (CA) spectral cytometer.

Processing overview

For each cohort, we report (a subset of) the data in the following processing stages, denoted as e.g. CORE.01_RAW. The subsets -1 and -2 include different staining panels after gating them to "useful" cells, where -1 is the thing you probably want for analysis as these are the samples stained with all colors together gated to T cells based on CD3+.

• 01_RAW: Raw, untransformed data for full and single stained samples. Only marker renaming was performed to harmonize measurements from all cytometers.
• 02_COMPENSATED: Compensated FCS files, using by-sample manually curated spillover matrices. Only BAD cohort used untouched device-compensations.
• 03-1_gatedCD3: Singlets/CD45+ Leukocytes/CD3+ T cell gated samples. Only `...12-panel.fcs` are included.
• 03-2_gatedLympho: Lymphocyte gated samples based on forward and side scatter. All single stained and unstained samples (...01-CD3-FITC.fcs, until ...11-none.fcs)
• 04-1_asinhCD3 and 04-2_asinhLympho: Data from 03-1_gatedCD3 or 03-2_gatedLympho after arcsinh transformation, with different manually optimized arcsinh-cofactors per cytometer.
• 05-1_relativizedCD3 and 05-2_relativizedLympho: Data from 03-1_gatedCD3 or 03-2_gatedLympho after applying sample-wise relativisation.
• 06-1_rel.asinhCD3 and 06-2_rel.asinhLympho: Data from 05-1_relativizedCD3 and 05-2_relativizedLympho after applying one global arcsinh transformation

Patient information

Can be found in patientdata.zip.

Content is the patient information of

• BAD_pheno_processed.csv: BAD cohort
• FORT_pheno_processed.csv: FORT cohort
• pheno_full_processed.csv: Complete CORE cohort
• R1_pheno_first_processed.csv: R1 samples from a donor's first presentation
• R1_pheno_processed.csv: All R1 samples+patients
• R2_pheno_processed.csv: R2 cohort
• SAN_pheno_processed.csv: SAN cohort

Gatings

For CORE and FORT cohort samples the gating strategies are manually curated for each sample on compensated, untransformed files - for full, single and unstained samples. They are supplied as flowWorkspace GatingSets and we have applied them sample by sample. The gating strategy is always the same, just the gate positions have been curated.

For BAD samples, we have one gating strategy for all samples per cytometer.

These are the raw spleen flow cytometry .fcs files used in this manuscript.

The last dataset is a lung cancer dataset, which collected a total of 27 sample single-cell data from 10.5 million cells (31 features measured per cell). The condition of progression-free survival (PFS) was defined as a predicted label. The age, sex, systemic immunosuppressive treatment for adverse events, and drug-related adverse events were defined as covariates.

This dataset contains all the raw mass cytometry (CyTOF) and flow cytometry fcs files associated with Capelle et al. 'Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females', Nature Communications, 2023, In Press. The dataset contains the following information: 1, The folder " CoPImmunoPD Flow Zenodo V2.zip " contains all the raw fcs files of flow cytometry analysis and the excel table with marker information of five staining panels in the initial discovery analysis using fresh blood samples. The folder also includes the fcs files of analyzing cytotoxicity potential within CD8 T cells and of validation analyses using cryopreserved samples. Single-color/fluorochrome staining files have also been provided for the relevant experiments in the given subfolders for compensation. 2, The folder "CoPImmunoPD_CyTOF_Zenodo.zip" contains all the raw fcs files generated from the CyTOF measurements in the initial discovery analysis. To reproduce our published Figures, please be assure to first read all the accompanied readme/excel information annotation files deposited in the corresponding folders within the zip files, all the Source Data files of different main and supplementary Figure subpanels, Methods and/or any other relevant sections in our manuscript.

The first dataset is the preeclampsia dataset, in which 19 profiles of blood samples were collected from 9.7 million. The data are from 45 women throughout their pregnancies, which include 33 features measured per cell. The clinical outcome of the covariate for this dataset was cell gestational age, which ranged from 8 to 28 weeks. The dataset is applied in CytoCo-set evaluation.

Here is the file structure that fits in CytoCo-set input format (symbol "X(_)X" defined as file name describe):

data folder

• fcs_info.csv
• filenames_X(trials_number)X.json
• pree_fcs
  • all
    • X(sample_file_name)X.fcs
    • test_labels_X(trials_number)X.csv
    • train_labels_X(trials_number)X.csv
  • marker.csv
• tripletlists_X(covariate)X_X(trials_number)X
  • X(covariate)X_tripletlist_subpick_test_rffX(medianpooling_or_maxpooling)X_sameX(same_threhold_percange)X_diffX(diff_threhold_percange)X.txt
  • X(covariate)X_tripletlist_subpick_trainval_rffX(medianpooling_or_maxpooling)X_sameX(same_threhold_percange)X_diffX(diff_threhold_percange)X.txt

The dataset uploaded all the sample's raw features data (fcs file) and some file examples. fcs_info.csv records all sample's true labels and covariates.

KLRG1+ CD8 T cells persist for months after acute infections are cleared and maintain high levels of effector molecules, contributing essential protective immunity against systemic pathogens. Upon secondary infection, these long-lived effector cells (LLECs) are incapable of forming other circulating KLRG1− memory subsets such as central and effector memory T cells. Thus, KLRG1+ memory T cells are frequently referred to as a terminally differentiated population that is relatively short lived. Here, we show that during infection, effector cells derived from LLEC rapidly enter nonlymphoid tissues and reduce pathogen burden, but are largely dependent on receiving antigen cues from vascular endothelial cells. Single-cell RNA sequencing revealed that secondary memory cells in nonlymphoid tissues arising from either KLRG1+ or KLRG1− memory precursors developed a similar transcriptional signature. Thus, although KLRG1+ memory T cells cannot differentiate into other circulating memory population..., , , # Circulating KLRG1+ memory T cells retain the flexibility to become tissue-resident

https://doi.org/10.5061/dryad.dncjsxm68

The goal of this study was to determine if long-lived effector CD8 T cells (LLEC), which are usually confined to circulation will enter inflamed tissues upon infectious challenge and contribute to the resident memory T cell pool. This data set contains flow cytometry files (FCS files) and the raw tiffs from the microscopy files from each figure in the manuscript. The flow cytometry data was collected on isolated white blood cells isolated from various tissues. Briefly, purified populations of memory CD8 cell P14 or endogenous subsets were transferred into mice, and then the mice were infected with either LCMV or IAV-gp33. At differing time points tissues were harvested and processed for flow cytometry. Samples were stained with antibody cocktails that allowed for the identification of the transferred cells and the chara...

Heat shock induces a conserved transcriptional program regulated by heat shock factor 1 (Hsf1) in eukaryotic cells. Activation of this heat-shock response is triggered by heat-induced misfolding of newly synthesized polypeptides, and so has been thought to depend on ongoing protein synthesis. Here, using the budding yeast Saccharomyces cerevisiae, we report the discovery that Hsf1 can be robustly activated when protein synthesis is inhibited, so long as cells undergo cytosolic acidification. Heat shock has long been known to cause transient intracellular acidification which, for reasons which have remained unclear, is associated with increased stress resistance in eukaryotes. We demonstrate that acidification is required for heat shock response induction in translationally inhibited cells, and specifically affects Hsf1 activation. Physiological heat-triggered acidification also increases population fitness and promotes cell cycle reentry following heat shock. Our results uncover a previously unknown adaptive dimension of the well-studied eukaryotic heat shock response.

Methods Raw data: Flow cytometry .fcs files (contained in zipped directories starting with 'Triandafillou') are raw flow cytometry data; the experiment names contain the metadata. The script 'process-raw-data.R' processes raw flow cytometry data (internally normalizes fluorescent readings and performs calibration curve analysis to calculate intracellular pH where appropriate) and combines experiments into tidy datasets.

Processed data: Processed data are .tsv and .csv files and include processed flow datasets (generated by 'process-raw-data.R'). Raw qPCR data, and raw protein translation data (generated by liquid scintillation counting (LSC) of samples from radiolabeled amino acid incorporation experiments) are also included.

Zipped directory 2017-11-18_pH-TSP-bio1.zip contains SDS-PAGE gel and Western Blot data; protocol, raw images, quantification, and processed images and summaries are all included.

General information

This item containst data sets for Schlegel et al, Nano Letters, 2023.

DOI: https://doi.org/10.1021/acs.nanolett.2c04884

It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files.

Abstract

Speed is key during infectious disease outbreaks. It

is essential, for example, to identify critical host binding factors to

pathogens as fast as possible. The complexity of host plasma

membrane is often a limiting factor hindering fast and accurate

determination of host binding factors as well as high-throughput

screening for neutralizing antimicrobial drug targets. Here, we

describe a multiparametric and high-throughput platform tackling

this bottleneck and enabling fast screens for host binding factors as

well as new antiviral drug targets. The sensitivity and robustness of

our platform were validated by blocking SARS-CoV-2 particles

with nanobodies and IgGs from human serum samples.

Data usage

Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License.

Data Content

Excel files for graphs

Microscopy Images

Flow cytometry data

Software to open files:

.csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel

.xlsx: Microsoft Excel

.tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads)

.pzfx: GraphPad Prism

.svg: Inkscape (https://inkscape.org/)

.fcs: FCS Express

.pdf: AdobeAcrobat or Mozilla Firefox

.ijm: Fiji (https://imagej.net/software/fiji/downloads)

Data set of FCS files from murine tissue samples (please see xlsx file for detailed sample description). They represent the raw data files for flow cytometry data published in Figure 2, Figure 3, Figure 5, Figure 7 and Figure 8 of the publication "Pregnancy-acquired memory CD4⁺ regulatory T cells improve pregnancy outcome in mice" by Thiele, K. et. al. published in Nature Communication (DOI: 10.1038/s41467-025-61572-w).

Abstract of the publication:

Subsequent pregnancies are generally less prone to obstetric complications. A successful pregnancy outcome requires pivotal immunological adaptation to ensure immune tolerance towards the fetus. Thus, the lower risk for pregnancy complication during subsequent pregnancies may be attributable to immune memory mounted during first pregnancies. Here we identify higher frequencies of fetal-antigen-specific CD4⁺ regulatory T (Treg) cells both postpartum and in subsequent pregnancies in mice which are partly originating from trans-differentiated Th17 cells. Our functional experiments demonstrate that these CD4⁺ Treg cells have memory functions (CD4⁺ mTreg) and account for an improved fetal development and pregnancy outcome, also during adverse conditions, such as gestational sound stress. Using a high-throughput single-cell quantification method, we identify candidate markers for the detection of CD4⁺ mTreg cells, which include CXCR4 and CD274. Our findings thus contribute to the improved understanding of pregnancy-induced immune memory and foster the identification of immune targets aiming to reduce the risk for immune-mediated pregnancy complications.

The cosmopolitan coccolithophore Emiliania huxleyi is a unicellular alga that forms massive oceanic blooms covering thousands of square kilometers (Tyrrell & Merico 2004). The intricate calcite exoskeleton of E. huxleyi accounts for ~1/3 of total marine CaCO3 production (Monteiro et al. 2016). E. huxleyi blooms are an important source of DMS, which is, by far, the most abundant volatile sulfur compound in the surface ocean and the best studied aerosol precursor (Simó 2001) with a significant climate-regulating role that enhances cloud formation (Alcolombri et al. 2015; Simó 2001). Biotic interactions that regulate the fate of these blooms play a profound role in determining carbon and nutrient cycling in the ocean and feedback to the atmosphere. Annual E. huxleyi spring blooms are frequently terminated following infection by a specific large dsDNA virus (EhV) that belongs to the Coccolithovirus group (Schroeder et al. 2002). Despite the huge ecological importance of host-virus interactions, the ability to assess their ecological impact is limited to questions that focus mainly on quantification of viral abundance and diversity in a reductionist manner. The project in which this dataset was collected is a holistic approach to untangle the complexity in alga-virus-bacterium interactions during an E. huxleyi bloom, their effect on the metabolome of the phycosphere, and their possible implications to C and S cycles. The project took place for 24 days, including daily sampling for various biological and physiochemical parameters. Flow cytometry was used to monitor different populations of phytoplankton, bacteria and virus-like particles (VLP). Additionally, physiochemical properties of the water such as salinity, temperature and nutrient concentrations were acquired, as well as viral abundances estimated by qPCR. These data compose the contextual data for various scientific papers. Methods Mesocosm setup The mesocosm experiment AQUACOSM VIMS-Ehux was carried out for 24 days between 24th May (day 0) and 16th June (day 23) 2018 in Raunefjorden at the University of Bergen’s Marine Biological Station Espegrend, Norway (60°16′11N; 5°13′07E). The experiment consisted of seven enclosure bags made of transparent polyethylene (11 m3, 4 m deep and 2 m wide, 90% photosynthetically active radiation) mounted on floating frames and moored to a raft in the middle of the fjord. The bags were filled with surrounding fjord water (day -1; pumped from 5 m depth) and continuously mixed by aeration (from day 0 onwards). Each bag was supplemented with nutrients at a nitrogen to phosphorous ratio of 16:1 (1.6 µM NaNO3 and 0.1 µM KH2PO4 final concentration) on days 0-5 and 14-17, whereas on days 6, 7 and 13 only nitrogen was added. Sampling for flow cytometry analysis Samples for flow cytometric counts were collected twice a day, in the morning (07:00 AM) and evening (08:00-09:00 PM) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, pre-filtered using 40 µm cell strainers, and immediately analysed with an Eclipse iCyt (Sony Biotechology, Champaign, IL, USA) flow cytometer. A total volume of 300 µL with a flow rate of 150 µL/min was analyzed. A threshold was applied based on the forward scatter signal to reduce the background noise. Enumeration of phytoplankton cells by flow cytometry Phytoplankton populations were identified by plotting the autofluorescence of chlorophyll versus phycoerythrin and side scatter: calcified E. huxleyi (high side scatter), Synechococcus (high phycoerythrin), nano- and picophytoplankton (high and low chlorophyll, respectively).

PHYTOPLANKTON GATES

Criteria 1 Criteria 2 Group

Low Chlorophyll (Low FL4) High phycorerythrin (High FL3 Low FSC) Syn = Synechococcus

Low phycorerythrin (Low FL3) Pico-Euks = Pico-eukaryotes

High Chlorophyll (High FL4) High Side Scatter Neuks_HighSS = Calcified Ehux

Low Side Scatter Neuks_LowSS = Diatoms, Dinoflagellates, or uncalcified Ehux

Chlorophyll fluorescence was detected by FL4 (excitation (ex): 488nm and emission (em): 663-737 nm). Phycoerythrin was detected by FL3 (excitation (ex): 488 nm and emission (em): 570-620 nm). Raw .fcs files were extracted and analyzed in R using ‘flowCore’ and ‘ggcyto’ packages. Enumeration of large virus-like particles and bacteria by flow cytometry For extracellular VLP counts, 200 µL of sample were fixed with 4 µL glutaraldehyde 20% (final concentration of 0.5%) for one hour at 4°C and flash frozen. They were thawed and stained with SYBR gold (Invitrogen) that was diluted 1:10,000 in Tris-EDTA buffer, incubated for 20 min at 80°C and cooled to room temperature. Bacteria, VLP and larger VLP were counted and analysed using a Cytoflex and identified based on the Violet SSC-A versus FITC-A by comparing to reference samples containing fixed EhV201 and bacteria from lab cultures. A total volume of 60 µL with a flow rate of 10 µL/min was analyzed. A threshold was applied based on the forward scatter signal to reduce the background noise. Measurement of dissolved inorganic nutrients Unfiltered seawater aliquots (10 mL) were collected from each bag and the surrounding fjord water in 12 mL polypropylene tubes and stored frozen at −20 °C. Dissolved inorganic nutrients were measured with standard segmented flow analysis with colorimetric detection (Hansen & Grasshoff 1983), using a Bran & Luebe autoanalyser. Measurement of water temperature and salinity Water temperature and salinity were measured in each bag and the surrounding fjord water using a SD204 CTD/STD (SAIV A/S, Laksevag, Norway). Data points were averaged for 1-3 m depth (descending only). When this depth was not available, the available data points were taken. Data is missing for the fjord in days 0-1. Outliers were removed for the following samples: bag 1 at days 0, 4, 15; bag 7 at day 15. Enumeration of extracellular EhV abundance by qPCR. Each filter from the core microbiome was diluted 100 times, and 1 µL was then used for qPCR analysis. EhV abundance was determined by qPCR for the major capsid protein (mcp) gene: 5′-acgcaccctcaatgtatggaagg-3′ (mcp1F5) and 5′-rtscrgccaactcagcagtcgt -3′ (mcp94Rv). All reactions were carried out in technical triplicates. For all reactions, Platinum SYBER Green qPCR SuperMix-UDG with ROX (Invitrogen, Carlsbad, CA, USA) was used as described by the manufacturer. Reactions were performed on a QuantStudio 5 Real-Time PCR System equipped with the QuantStudio Design and Analysis Software version 1.5.1 (Applied Biosystems, Foster City, CA, USA) as follows: 50°C for 2 min, 95°C for 5 min, 40 cycles of 95°C for 15 s, and 60° C for 30 s. Results were calibrated against serial dilutions of EhV201 DNA at known concentrations, enabling exact enumeration of viruses. Samples showing multiple peaks in melting curve analysis or peaks that were not corresponding to the standard curves were omitted.