Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14−CD19− PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.

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.

Insulin and Tetanus binding B cells were collected via MACS sorting for analysis using CyTOF (cytometry by time of flight). Data was then analyzed using unsupervised clustering and manual gating to find unique phenotypes and populations associated with recent-onset individuals with type 1 diabetes (T1D) compared to age-matched healthy controls (HC).

High-parameter single-cell phenotyping has enabled in-depth classification and interrogation of immune cells, but to date has not allowed for glycan characterization. Here, we develop CyTOF-Lec as an approach to simultaneously characterize many protein and glycan features of human immune cells at the single-cell level. We implemented CyTOF-Lec to compare glycan features between different immune subsets from blood and multiple tissue compartments, and to characterize HIV-infected cell cultures. Using bioinformatics approaches to distinguish preferential infection of cellular subsets from viral-induced remodeling, we demonstrate that HIV upregulates the levels of cell surface fucose and sialic acid in a cell-intrinsic manner, and that memory CD4+ T cells co-expressing high levels of fucose and sialic acid are highly susceptible to HIV infection. Sialic acid levels were found to distinguish memory CD4+ T cell subsets expressing different amounts of viral entry receptors, pro-survival factors, homing receptors, and activation markers. The ability of sialic acid to distinguish memory CD4+ T cells with different susceptibilities to HIV infection was experimentally validated through sorting experiments. Together, these results suggest that HIV remodels not only cellular proteins but also glycans, and that glycan expression can differentiate memory CD4+ T cells with vastly different susceptibility to HV infection.

This dataset presents CyTOF（Cytometry by Time of Flight）-based single-cell proteomic analysis of PBMCs from individuals with varying health conditions, including healthy controls, Mycoplasma pneumoniae pneumonia (MPP), and refractory MPP (RMPP). Using a 39-marker CyTOF panel, this study aimed to identify and characterize immune cell subsets and their protein expression profiles, highlighting immune system alterations associated with MPP and RMPP.

This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the Integrated iMMUnoprofiling of large adaptive CANcer patient cohorts (IMMUcan) project (immucan.eu) using the Hyperion imaging system (www.fluidigm.com/products-services/instruments/hyperion). To get an overview on the technology and available analysis strategies, please visit bodenmillergroup.github.io/IMCWorkflow. The individual data files are described below:

• Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.
• compensation.zip: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
• panel.csv: This file contains metadata for each antibody/channel used in the experiment. The full column indicates which channel should be analysed. The ilastik column specifies which channels were used for ilastik pixel classification and the deepcell column indicates the channels used for deepcell segmentation.
• sample_metadata.csv: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).

This is a dataset of 1,108,853 blood and bone marrow cells collected from 3 pediatric B-cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL) patients and 3 healthy controls. Each ALL sample is made up of a mixture of cancer cells and healthy cells, whereas the healthy samples do not contain and cancer cells. This dataset can be used to evaluate models trained to classify cells as either cancerous or non-cancerous. Each BCP-ALL patient has samples collected from 3 timepoints and 2 tissues: diagnosis (bone marrow and blood), day 8 post-treatment initiation with chemotherapy (blood), and day 15 post-treatment initiation (blood). Healthy patients only have samples collected from a single timepoint (the time of donation) and one tissue (bone marrow). These different tissues and timepoints can be used to assess a classifier's ability to generalize to new contexts (i.e. from bone marrow to blood, or from the diagnostic timepoint to a timepoint later in treatment). , All cells have been analyzed for the presence of 28 proteins as previously described using mass cytometry (CyTOF), a high-dimensional cytometry platform similar to multicolor flow cytometers commonly used to analyze leukemic tissue specimens in clinical laboratories. CyTOF analysis allows a high-dimensional sample characterization, extending the capabilities beyond those of conventional multicolor flow cytometers, typically employed for the analysis of leukemic tissue specimens in clinical laboratories. The files are in the flow cytometry standard (.FCS) file format and include information about 28 proteins as read off the mass cytometer (unit: ion counts) and an additional column called ('cell_type') that encodes healthy cells with a value of 0 and cancerous cells as a value of 1. These labels were manually annotated by an expert BCP-ALL cytometrist and verified by a physician-scientist board-certified in pediatric hematology and oncology. The samples were extracted, debarcoded, and fi..., , # Healthy and B-cell precursor Acute Lymphoblastic Leukemia cells analyzed via CyTOF

https://doi.org/10.5061/dryad.8gtht76vw

This repository contains 15 .FCS (Flow Cytometry Standard) data files. Each of these files represents a sample collected from a BCP-ALL patient or a healthy control patient.

Each BCP-ALL patient has samples collected from 3 timepoints and 2 tissues: diagnosis (bone marrow and blood), day 8 post-treatment initiation with chemotherapy (blood), and day 15 post-treatment initiation (blood). Healthy patients only have samples collected from a single timepoint (the time of donation) and one tissue (bone marrow).

Description of the data and file structure

The information about the patient, timepoint, and tissue information about each sample is encoded in the .FCS filename. The .FCS filename is a string formatted as follows:

{patient_name}_{tissue}_{timepoint}.fcs.

In this string, {patient_name} is one of the fo..., All human samples and associated data in this study were collected under protocols approved by the Stanford University Institutional Review Board (IRB). Informed consent was obtained from all participants or their legal guardians, including explicit consent to publish de-identified data in public repositories.

The data shared in this submission have been fully de-identified in accordance with applicable legal and ethical standards. No personally identifiable information, including names, dates of birth, medical record numbers, or geographic identifiers, is included. Sample identifiers (e.g., ID numbers) are randomly assigned codes that cannot be traced back to individual participants.

Only single-cell protein expression data derived from mass cytometry are included. These data do not contain genetic information or any direct or indirect identifiers.

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology

Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, in vitro T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon.

• All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment)
• MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell
• Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing
• MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results

The goal of this study was to apply a standardized CyTOF workflow and analysis pipeline in conjunction with the Biolegend Legendscreen kit to comprehensively screen surface protein expression patterns across all major defined immune cell subsets in peripheral blood and to evaluate the impact of fixation on these expression patterns

All six datasets and analysis code for reproducing the findings of "Cytomulate: accurate and efficient simulation of cytof data". The analysis code is also available at https://github.com/kevin931/cytomulate/releases/tag/benchmark.rev.1. The Cytomulate Python software can be found at https://github.com/kevin931/cytomulate.

CD4 T lymphocytes belong to diverse cellular subsets whose sensitivity or resistance to HIV-associated killing remains to be defined. Working with lymphoid cells from human tonsils, we characterized the HIV-associated depletion of various CD4 T cell subsets using mass cytometry and single-cell RNA-seq. CD4 T cell subsets preferentially killed by HIV are phenotypically distinct from those resistant to HIV-associated cell death, in a manner not fully accounted for by their susceptibility to productive infection. Preferentially-killed subsets express CXCR5 and CXCR4 while preferentially-infected subsets exhibit an activated and exhausted effector memory cell phenotype. Single-cell RNA-seq analysis reveals that the subsets of preferentially-killed cells express genes favoring abortive infection and pyroptosis. These studies emphasize a complex interplay between HIV and distinct tissue-based CD4 T cell subsets, and the important contribution of abortive infection and inflammatory programmed cell death to the overall depletion of CD4 T cells that accompanies untreated HIV infection. Methods mass cytometry; single-cell RNA-seq mass cytometry data has been pre-gated on live singlets and normalized by CD8 cell number single-cell RNA-seq data are raw data

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm “pattern recognition of immune cells (PRI)” to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

The global mass cytometry market is experiencing robust growth, driven by increasing demand for high-throughput, high-dimensional analysis in various research and clinical applications. Advancements in technology, offering improved sensitivity and resolution, are fueling market expansion. The rising prevalence of chronic diseases, such as cancer and autoimmune disorders, is significantly increasing the need for precise diagnostic tools and personalized medicine strategies, further boosting demand. Furthermore, the growing adoption of mass cytometry in drug discovery and development, particularly for immune-oncology research, is contributing to market growth. We estimate the market size in 2025 to be $350 million, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This strong growth trajectory is underpinned by continuous technological innovation, expanding research activities, and increasing investments in life sciences globally. Segmentation analysis reveals a dynamic market landscape. The 130-channel and 135-channel segments dominate the market, reflecting the preference for higher dimensionality in data acquisition. The application segment is diversified across laboratories, universities, and hospitals, highlighting the widespread adoption of mass cytometry across various research and clinical settings. Geographically, North America currently holds a significant market share, driven by robust research infrastructure and early adoption of innovative technologies. However, Asia-Pacific is anticipated to experience substantial growth in the coming years due to increasing investments in healthcare infrastructure and expanding research activities. Competitive landscape features several key players, including Standard Big Tools, POWCLIN, Polaris Biology, and PLT Tech, driving technological advancements and market competition. This competitive landscape fosters innovation and drives the development of more sophisticated and accessible mass cytometry solutions.

This repository documents the raw data and analysis of the CyTOF results reported in:

Contents:

Ndufs4.zip

/Rcode/ --- notebooks used for the analysis and figure generation

/data/ndufs4_lymphoid/ --- fcs files for lymphoid gating

/data/ndufs4_myeloid/ --- fcs files for myeloid gating

/data/Ndufs4_metadata.xlsx --- metadata

/data/suspension_mass_cytometry_panel.xlsx --- CyTOF panel

small_molecule.zip

/Rcode/ --- notebooks used for the analysis and figure generation

/data/cleaned_and_gated/lymphoid/ --- fcs files for lymphoid gating (after Gd correction)

/data/cleaned_and_gated/myeloid/ --- fcs files for myeloid gating (after Gd correction)

/data/cleaned_and_gated/small_molecules_metadata_lymphoid.xlsx --- metadata

/data/cleaned_and_gated/small_molecules_metadata_myeloid.xlsx --- metadata

/data/cleaned_and_gated/suspension_mass_cytometry_panel.xlsx --- CyTOF panel

/data/raw/ --- fcs files (before Gd correction)

This dataset includes raw and processed mass cytometry (CyTOF) data from mouse gut tissues treated with bacterial extracellular vesicles (bEVs). The data provide high-dimensional immune cell profiling and phenotyping, used to quantify population shifts and activation states. File formats include analysis-ready .csv exports. These data were used for dimensionality reduction, clustering, and immune cell abundance visualization in the manuscript by Kammala et al., submitted to Microbiome.

Not all individuals who produce peanut-specific IgE will react upon consumption of peanut. Infants with detectable peanut sIgE who can eat the food without adverse consequences are referred to as peanut sensitized tolerant (PST). Understanding the immune mechanisms that govern why some individuals go on to develop clinical peanut allergy (PA), whilst others do not, despite the presence of peanut-specific IgE, is central in diagnostic, prevention and early management strategies. This study used mass cytometry-based immune profiling to define the circulating immune cell signatures associated with PST vs. PA vs. non allergic healthy controls (NA) in the first year of life. Resting PBMCs and PBMCs after stimulation with endotoxin-free pure peanut solution or PMA/ionomycin were studied. A sub group of infants from the HealthNuts cohort (total n=5000 children) were used in this study. PA infants (n=12) were defined as having a peanut skin prick test (SPT) wheal diameter of ≥2mm or a peanut-specific IgE level of ≥0.35 kUA/L, and an unequivocal objective allergic reaction during peanut OFC at age 1 year. PST infants (n=12) had a peanut SPT≥2 mm and peanut-specific IgE level of ≥0.35 kUA/L and a negative peanut OFC at age 1 year. The NA group infants (n=12) were non-sensitized and non-allergic, with a negative SPT to peanut, egg, sesame, and cow’s milk together with a negative peanut OFC outcome at age 1 year.

This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at zenodo.org/record/5949116 using the steinbock framework available at github.com/BodenmillerGroup/steinbock. Please refer to steinbock.sh for how these data were generated from the raw data.

The following files are part of this repository:

• panel.csv: contains channel information regarding the used antibodies in steinbock format
• img.zip: contains hot pixel filtered multi-channel images derived from the IMC raw data. One file per acquisition is generated
• images.csv: contains metadata per acquisition
• pixel_classifier.ilp: ilastik pixel classifier (same as the one in zenodo.org/record/6043544)
• ilastik_crops.zip: image crops on which the ilastik classifier was trained (same as the ones in zenodo.org/record/6043544)
• ilastik_img.zip: contains multi-channel images (one per acquisition) in .h5 format for ilastik pixel classification
• ilastik_probabilities.zip: 3 channel images containing the pixel probabilities after pixel classification
• masks_ilastik.zip: segmentation masks derived from the ilastik pixel probabilities using the cell_segmentation.cppipe pipeline
• masks_deepcell.zip: segmentation masks derived by deepcell segmentation
• intensities.zip: Contains one .csv file per acquisition. Each file contains single-cell measures of the mean pixel intensity per cell and channel based on the files in img.zip and masks_deepcell.zip.
• regionprops.zip: Contains one .csv file per acquisition. Each file contains single-cell measures of the morphological features and location of cells based on masks_deepcell.zip.
• neighbors.zip: Contains one .csv file per acquisition. Each file contains an edge list of cell IDs indicating cells in close proximity based on masks_deepcell.zip.
• ome.zip: contains .ome.tiff files derived from img.zip; one file per acquisition
• histocat.zip: contains single-channel .tiff files with segmentation masks derived from masks_deepcell.zip for upload to histoCAT (bodenmillergroup.github.io/histoCAT)
• cells.csv: contains intensity and regionprop measurements of all cells
• cells_csv.zip: contains intensity and regionprop measurements of all cells per acquisition
• cells.fcs: contains intensity and regionprop measurements of all cells in fcs format
• cells_fcs.zip: contains intensity and regionprop measurements of all cells per acquisition in fcs format
• cells.h5ad: contains intensity, regionprop and neighbor measurements of all cells in anndata format
• cells_h5ad: contains intensity regionprop and neighbor measurements of all cells per acquisition in anndata format
• graphs.zip: contains spatial object graphs in .graphml format; one file per acquisition

Extremely premature infants (EPI) born prior to 30 weeks gestation are highly susceptible to infection. The trajectory of peripheral immunity in EPIs is poorly understood. Longitudinal analysis of immune cells from 250mL of whole blood at 1 week (n=7), 1 month (n=7), and 2 months (n=5) from 10 EPI was compared to healthy adults (n=6) and to neonatal cord blood (n=13). Single-cell suspensions from individual samples were split to perform single-cell(sc) RNA-, T- and B-cell receptor sequencing (seq), and phosphoprotein mass cytometry. Our scRNAseq data was integrated with existing data from full-term infants at 2 months of age. The trajectory of circulating T-, B-, myeloid, and natural killer cells in EPI infants over the first two months of life is distinct from full-term infants. Peripheral T cell development rapidly progressed over the first month of EPI life with an increase in the proportion of naïve CD4, regulatory, and cycling T cells, accompanied by increased STAT5 signaling compa..., In this study, we performed multi-omic analysis including mass cytometry by time of flight (CyTOF), single-cell RNA sequencing (scRNAseq), T cell and B cell receptor sequencing (TCR and BCR-seq) on circulating immune cells that were isolated from 100 to 250 microliters of blood obtained from extremely premature infants (EPI, n=10) and compared to cord blood from premature (n=5) and full-term infants (n=5) as well as adult blood. The samples from EPI were obtained at 1 week, 1 month, and 2 months of life. Here we have included CyTOF data (.Rdata file format) that has been gated on live, DNA+, CD45+ circulating leukocytes. Data was demultiplexed using Premessa (https://github.com/ParkerICI/premessa) and automated RPhenograph clustering with k=30 in Cytofkit2 (https://github.com/JinmiaoChenLab/cytofkit2) and included here is the Rdata file that includes analysis of ~15,000 cells that have been clustered based upon surface marker expression to identify 28 distinct populati..., , # A single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants

https://doi.org/10.5061/dryad.pk0p2ngxg

Description of the data and file structure

Here we have included data from mass cytometry by time of flight (CyTOF) analysis of 15,000 leukocytes from 28 individual samples. Furthermore, there are the major scripts we used for our scRNAseq, TCR, and BCR data analysis. We uploaded 9 files of Python and R scripts. One file is a .Rdata file exported from cytofkit2 of CyTOF data analysis.

1. File: pilotk5.Rdata

   Description: Exported data from CyTOF analysis.

2. File: gene-usage.html

   Description: R script that was used to determine the BCR gene usage across samples for heavy and light-chain based upon the identified B cell populations in the B cell analysis from the scRNAseq data set.

3. File: All_samples_plots.html. Description: R script that includes all packages used to determ...

This bachelor thesis demonstrates the results of mass cytometry data re-analysis using multivariate regression. I reanalyse a dataset by Palgen et al. (2019) using two models: a Poisson log-normal mixed model and a logistic linear mixed model from the R package ‘cytoeffect’ (Seiler et al., 2019). By exposing multivariate patterns and the associated uncertainty profiles in the data, the aim of this analysis is to replicate biological conclusions and uncover new biological findings.