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Liu P, Pan Y, Chang HC, Wang W, Fang Y, Xue X, Zou J, Toothaker JM, Olaloye O, Santiago EG, McCourt B, Mitsialis V, Presicce P, Kallapur SG, Snapper SB, Liu JJ, Tseng GC, Konnikova L, Liu S. Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating. Brief Bioinform 2024; 26:bbae633. [PMID: 39656848 PMCID: PMC11630031 DOI: 10.1093/bib/bbae633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 11/13/2024] [Accepted: 11/25/2024] [Indexed: 12/17/2024] Open
Abstract
Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.
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Affiliation(s)
- Peng Liu
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Yuchen Pan
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX 77030, US
| | - Hung-Ching Chang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Wenjia Wang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Yusi Fang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Xiangning Xue
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Jian Zou
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Jessica M Toothaker
- Department of Immunology, University of Pittsburgh, 5051 Centre Avenue, Pittsburgh, PA 15213, US
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | - Oluwabunmi Olaloye
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | | | - Black McCourt
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | - Vanessa Mitsialis
- Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
- Department of Medicine, Division of Gastroenterology, Hepatology, and Endoscopy, Brigham & Women’s Hospital and Department of Medicine, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
| | - Pietro Presicce
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
| | - Suhas G Kallapur
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
| | - Scott B Snapper
- Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
- Department of Medicine, Division of Gastroenterology, Hepatology, and Endoscopy, Brigham & Women’s Hospital and Department of Medicine, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
| | - Jia-Jun Liu
- Drug Discovery Institute, School of Medicine, University of Pittsburgh, 700 Technology Dr, Pittsburgh, PA 15219, US
- Pittsburgh Liver Research Center, School of Medicine, University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA 15261, US
| | - George C Tseng
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
- Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15213, US
| | - Liza Konnikova
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University, 333 Cedar Street, New Haven, CT 06510, US
- Department of Immunobiology, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Program in Human and Translational Immunology, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Program in Translational Biomedicine, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Center for Systems and Engineering Immunology, Yale University, 100 College St., New Haven, CT 06510, US
| | - Silvia Liu
- Drug Discovery Institute, School of Medicine, University of Pittsburgh, 700 Technology Dr, Pittsburgh, PA 15219, US
- Pittsburgh Liver Research Center, School of Medicine, University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA 15261, US
- Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15213, US
- Department of Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, 200 Lothrop St., Pittsburgh, PA 15261, US
- Hillman Cancer Center, University of Pittsburgh, 5150 Centre Ave., Pittsburgh, PA 15232, US
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2
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Hunewald O, Demczuk A, Longworth J, Ollert M. CyCadas: accelerating interactive annotation and analysis of clustered cytometry data. Bioinformatics 2024; 40:btae595. [PMID: 39374546 PMCID: PMC11488975 DOI: 10.1093/bioinformatics/btae595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 06/19/2024] [Accepted: 10/03/2024] [Indexed: 10/09/2024] Open
Abstract
MOTIVATION Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. RESULTS We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. AVAILABILITY AND IMPLEMENTATION CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.
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Affiliation(s)
- Oliver Hunewald
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg
- Bioinformatics & AI, Department of Medical Informatics, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
| | - Agnieszka Demczuk
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg
| | - Joseph Longworth
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg
- Immunology & Genetics, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg
- Department of Dermatology and Allergy Centre, Odense University Hospital, 5000 Odense, Denmark
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3
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Sun J, Choy D, Sompairac N, Jamshidi S, Mishto M, Kordasti S. ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling. eLife 2024; 13:RP95494. [PMID: 39240985 PMCID: PMC11379455 DOI: 10.7554/elife.95494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2024] Open
Abstract
Mass cytometry is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level, advancing clinical research in immune monitoring. Nevertheless, the vast data generated by cytometry by time-of-flight (CyTOF) poses a significant analytical challenge. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel toolkit for CyTOF data analysis. This framework incorporates BinaryClust, an in-house developed semi-supervised clustering tool that automatically identifies main cell types. BinaryClust outperforms existing clustering tools in accuracy and speed, as shown in benchmarks with two datasets of approximately 4 million cells, matching the precision of manual gating by human experts. Furthermore, ImmCellTyper offers various visualisation and analytical tools, spanning from quality control to differential analysis, tailored to users' specific needs for a comprehensive CyTOF data analysis solution. The workflow includes five key steps: (1) batch effect evaluation and correction, (2) data quality control and pre-processing, (3) main cell lineage characterisation and quantification, (4) in-depth investigation of specific cell types; and (5) differential analysis of cell abundance and functional marker expression across study groups. Overall, ImmCellTyper combines expert biological knowledge in a semi-supervised approach to accurately deconvolute well-defined main cell lineages, while maintaining the potential of unsupervised methods to discover novel cell subsets, thus facilitating high-dimensional immune profiling.
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Affiliation(s)
- Jing Sun
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom
| | - Desmond Choy
- School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - Nicolas Sompairac
- School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - Shirin Jamshidi
- School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom
- Research Group of Molecular Immunology, Francis Crick Institute, London, United Kingdom
| | - Shahram Kordasti
- School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom
- Haematology Department, Guy's Hospital, London, United Kingdom
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, Ancona, Italy
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4
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Ask EH, Tschan-Plessl A, Hoel HJ, Kolstad A, Holte H, Malmberg KJ. MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration. PATTERNS (NEW YORK, N.Y.) 2024; 5:100989. [PMID: 39081571 PMCID: PMC11284499 DOI: 10.1016/j.patter.2024.100989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/12/2024] [Accepted: 04/15/2024] [Indexed: 08/02/2024]
Abstract
Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.
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Affiliation(s)
- Eivind Heggernes Ask
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Astrid Tschan-Plessl
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Division of Hematology, University Hospital Basel, Basel, Switzerland
| | - Hanna Julie Hoel
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Arne Kolstad
- Department of Oncology, Innlandet Hospital Trust Division Gjøvik, Lillehammer, Norway
| | - Harald Holte
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karl-Johan Malmberg
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
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5
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Yang Y, Wang K, Lu Z, Wang T, Wang X. Cytomulate: accurate and efficient simulation of CyTOF data. Genome Biol 2023; 24:262. [PMID: 37974276 PMCID: PMC10652542 DOI: 10.1186/s13059-023-03099-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/24/2023] [Indexed: 11/19/2023] Open
Abstract
Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.
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Affiliation(s)
- Yuqiu Yang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Kaiwen Wang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
| | - Zeyu Lu
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Xinlei Wang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, 75275, USA.
- Department of Mathematics, University of Texas at Arlington, Arlington, 76019, USA.
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, 76019, USA.
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6
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Hamad MA, Krauel K, Schanze N, Gauchel N, Stachon P, Nuehrenberg T, Zurek M, Duerschmied D. Platelet Subtypes in Inflammatory Settings. Front Cardiovasc Med 2022; 9:823549. [PMID: 35463762 PMCID: PMC9021412 DOI: 10.3389/fcvm.2022.823549] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 03/09/2022] [Indexed: 12/24/2022] Open
Abstract
In addition to their essential role in hemostasis and thrombosis, platelets also modulate inflammatory reactions and immune responses. This is achieved by specialized surface receptors as well as secretory products including inflammatory mediators and cytokines. Platelets can support and facilitate the recruitment of leukocytes into inflamed tissue. The various properties of platelet function make it less surprising that circulating platelets are different within one individual. Platelets have different physical properties leading to distinct subtypes of platelets based either on their function (procoagulant, aggregatory, secretory) or their age (reticulated/immature, non-reticulated/mature). To understand the significance of platelet phenotypic variation, qualitatively distinguishable platelet phenotypes should be studied in a variety of physiological and pathological circumstances. The advancement in proteomics instrumentation and tools (such as mass spectrometry-driven approaches) improved the ability to perform studies beyond that of foundational work. Despite the wealth of knowledge around molecular processes in platelets, knowledge gaps in understanding platelet phenotypes in health and disease exist. In this review, we report an overview of the role of platelet subpopulations in inflammation and a selection of tools for investigating the role of platelet subpopulations in inflammation.
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Affiliation(s)
- Muataz Ali Hamad
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Krystin Krauel
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Department of Cardiology, Angiology, Haemostaseology, and Medical Intensive Care, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nancy Schanze
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Department of Cardiology, Angiology, Haemostaseology, and Medical Intensive Care, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nadine Gauchel
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Peter Stachon
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Thomas Nuehrenberg
- Department of Cardiology and Angiology II, Heart Center, Faculty of Medicine, University of Freiburg, Bad Krozingen, Germany
| | - Mark Zurek
- Department of Cardiology and Angiology II, Heart Center, Faculty of Medicine, University of Freiburg, Bad Krozingen, Germany
| | - Daniel Duerschmied
- Department of Cardiology and Angiology I, Heart Center, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Department of Cardiology, Angiology, Haemostaseology, and Medical Intensive Care, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- European Center for AngioScience (ECAS) and German Center for Cardiovascular Research (DZHK) Partner Site Heidelberg/Mannheim, Mannheim, Germany
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7
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Klug M, Kirmes K, Han J, Lazareva O, Rosenbaum M, Viggiani G, von Scheidt M, Ruland J, Baumbach J, Condorelli G, Laugwitz KL, List M, Bernlochner I, Bongiovanni D. Mass cytometry of platelet-rich plasma: a new approach to analyze platelet surface expression and reactivity. Platelets 2021; 33:841-848. [PMID: 34957922 DOI: 10.1080/09537104.2021.2009453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Mass cytometry (CyTOF) is a new technology that allows the investigation of protein expression at single cell level with high resolution. While several protocols are available to investigate leukocyte expression, platelet staining and analysis with CyTOF have been described only from whole blood. Moreover, available protocols do not allow sample storage but require fresh samples to be obtained, processed, and measured immediately. We provide a structured and reproducible method to stain platelets from platelet-rich plasma to study thrombocyte protein expression and reactivity using mass cytometry. With our method, it is possible to acquire a large number of events allowing deep bioinformatic investigation of platelet expression heterogeneity. Integrated in our protocol is also a previously established freezing protocol that allows the storage of stained samples and to delay their measurement. Finally, we provide a structured workflow using different platelet stimulators and a freely available bioinformatic pipeline to analyze platelet expression. Our protocol unlocks the potential of CyTOF analysis for studying platelet biology in health and disease.
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Affiliation(s)
- Melissa Klug
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.,Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Kilian Kirmes
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jiaying Han
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Olga Lazareva
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Marc Rosenbaum
- School of Medicine, Institute of Clinical Chemistry and Pathobiochemistry, Technical University of Munich, Munich, Germany.,TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Giacomo Viggiani
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.,Department of Cardiology, Deutsches Herzzentrum München, Munich, Germany
| | - Jürgen Ruland
- School of Medicine, Institute of Clinical Chemistry and Pathobiochemistry, Technical University of Munich, Munich, Germany.,TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Gianluigi Condorelli
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center Irccs and Humanitas University, Rozzano, Italy
| | - Karl-Ludwig Laugwitz
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Isabell Bernlochner
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Dario Bongiovanni
- Department of Internal Medicine I, School of Medicine, University Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.,Department of Cardiovascular Medicine, Humanitas Clinical and Research Center Irccs and Humanitas University, Rozzano, Italy
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