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Eslami M, Moseley RC, Eramian H, Bryce D, Haase SB. AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses. Sci Rep 2024; 14:23581. [PMID: 39384769 DOI: 10.1038/s41598-024-66936-8] [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: 12/05/2022] [Accepted: 07/05/2024] [Indexed: 10/11/2024] Open
Abstract
Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer's fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.
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Affiliation(s)
| | - Robert C Moseley
- Department of Biology, Duke University, Durham, NC, USA
- Cymantix, LLC, Chapel Hill, NC, USA
| | | | - Daniel Bryce
- Smart Information Flow Technologies, LLC, St. Paul, USA
| | - Steven B Haase
- Departments of Biology and Medicine, Duke University, Durham, NC, USA.
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2
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Zhang M, Zhang Y, Zhang J, Zhang J, Gao S, Li Z, Tao K, Liang X, Pan J, Zhu M. An automatic analysis and quality assurance method for lymphocyte subset identification. Clin Chem Lab Med 2024; 62:1411-1420. [PMID: 38217085 DOI: 10.1515/cclm-2023-1141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/20/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVES Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets. METHODS We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results. RESULTS The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %. CONCLUSIONS Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.
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Affiliation(s)
- MinYang Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - YaLi Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JingWen Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JiaLi Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - SiYuan Gao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - ZeChao Li
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - KangPei Tao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - XiaoDan Liang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JianHua Pan
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - Min Zhu
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
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3
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Dutta S, Box AC, Li Y, Sardiu ME. Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach. Proteomics 2023; 23:e2200290. [PMID: 36852539 DOI: 10.1002/pmic.202200290] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/30/2023] [Accepted: 02/17/2023] [Indexed: 03/01/2023]
Abstract
The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a "wisdom of the crowd" approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.
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Affiliation(s)
- Sreejata Dutta
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Andrew C Box
- Stowers Institute for Medical Research, Kansas City, Missouri, USA
| | - Yanming Li
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, Kansas, USA
| | - Mihaela E Sardiu
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, Kansas, USA
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
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4
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. Bioinformatics 2023; 39:btad585. [PMID: 37756695 PMCID: PMC10563151 DOI: 10.1093/bioinformatics/btad585] [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: 02/06/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. RESULTS We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. AVAILABILITY AND IMPLEMENTATION The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).
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Affiliation(s)
- Edgar E Robles
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Ye Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Padhraic Smyth
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pathology, University of California, San Diego, CA 92093, United States
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Jack D Bui
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Huan-You Wang
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Jean Oak
- Department of Pathology, Stanford University, Stanford, CA 94305, United States
| | - Yu Qian
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
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5
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Zhang J, Li J, Lin L. Statistical and machine learning methods for immunoprofiling based on single-cell data. Hum Vaccin Immunother 2023:2234792. [PMID: 37485833 PMCID: PMC10373621 DOI: 10.1080/21645515.2023.2234792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/30/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration.
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Affiliation(s)
- Jingxuan Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Jia Li
- Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Lin Lin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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6
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Covens K, Verbinnen B, de Jong BG, Moens L, Wuyts G, Verheyen G, Nys K, Cremer J, Smulders S, Schrijvers R, Weinhäusel A, Vermeire S, Verschueren P, Langhe ED, van Dongen JJM, van Zelm MC, Bossuyt X. Plasma cells are not restricted to the CD27+ phenotype: characterization of CD27-CD43+ antibody-secreting cells. Front Immunol 2023; 14:1165936. [PMID: 37492569 PMCID: PMC10364057 DOI: 10.3389/fimmu.2023.1165936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/11/2023] [Indexed: 07/27/2023] Open
Abstract
Circulating antibody-secreting cells are present in the peripheral blood of healthy individuals reflecting the continued activity of the humoral immune system. Antibody-secreting cells typically express CD27. Here we describe and characterize a small population of antibody-secreting class switched CD19+CD43+ B cells that lack expression of CD27 in the peripheral blood of healthy subjects. In this study, we characterized CD27-CD43+ cells. We demonstrate that class-switched CD27-CD43+ B cells possess characteristics of conventional plasmablasts as they spontaneously secrete antibodies, are morphologically similar to antibody-secreting cells, show downregulation of B cell differentiation markers, and have a gene expression profile related to conventional plasmablasts. Despite these similarities, we observed differences in IgA and IgG subclass distribution, expression of homing markers, replication history, frequency of somatic hypermutation, immunoglobulin repertoire, gene expression related to Toll-like receptors, cytokines, and cytokine receptors, and antibody response to vaccination. Their frequency is altered in immune-mediated disorders. Conclusion we characterized CD27-CD43+ cells as antibody-secreting cells with differences in function and homing potential as compared to conventional CD27+ antibody-secreting cells.
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Affiliation(s)
- Kris Covens
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
- Biocartis, Research and Development, Mechelen, Belgium
| | - Bert Verbinnen
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
- Biomedical Laboratory Technology, Radius, Life Sciences and Chemistry, Thomas More Kempen, Geel, Belgium
| | - Britt G. de Jong
- Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, Netherlands
- Department of Periodontology, ACTA, University of Amsterdam and VU University, Amsterdam, Netherlands
| | - Leen Moens
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
- Department of Microbiology and Immunology, Inborn Errors of Immunity, Leuven, Belgium
| | - Greet Wuyts
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
| | - Geert Verheyen
- Biomedical Laboratory Technology, Radius, Life Sciences and Chemistry, Thomas More Kempen, Geel, Belgium
| | - Kris Nys
- Gastroenterology, University Hospitals Leuven, Leuven, Belgium
| | - Jonathan Cremer
- Department of Microbiology and Immunology, Allergy and Clinical Immunology Research Group, Leuven, Belgium
| | - Stijn Smulders
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
| | - Rik Schrijvers
- Department of Microbiology and Immunology, Allergy and Clinical Immunology Research Group, Leuven, Belgium
| | - Andreas Weinhäusel
- AIT Austrian Institute of Technology GmbH, Center for Health and Bioresources, Molecular Diagnostics, Vienna, Austria
| | | | | | - Ellen De Langhe
- Department of Rheumatology, University Hospitals Leuven, Leuven, Belgium
| | - Jacques J. M. van Dongen
- Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, Netherlands
- Department of Immunology, Leiden University Medical Center (LUMC), Leiden, Netherlands
- Centro de Investigación del Cáncer-Instituto de Biología Molecular y Celular del Cáncer (CIC-IBMCC, USAL-CSIC-FICUS), Salamanca, Spain
- Department of Medicine, University of Salamanca (USAL), Salamanca, Spain
| | - Menno C. van Zelm
- Department of Immunology, Erasmus MC, University Medical Center, Rotterdam, Netherlands
- Department of Immunology and Pathology, Central Clinical School, Monash University and Alfred Hospital, Melbourne, VIC, Australia
| | - Xavier Bossuyt
- Department of Microbiology and Immunology, Clinical and Diagnostic Immunology Research Group, Leuven, Belgium
- Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
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7
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Chen W, Hong SH, Jenks SA, Anam FA, Tipton CM, Woodruff MC, Hom JR, Cashman KS, Faliti CE, Wang X, Kyu S, Wei C, Scharer CD, Mi T, Hicks S, Hartson L, Nguyen DC, Khosroshahi A, Lee S, Wang Y, Bugrovsky R, Ishii Y, Lee FEH, Sanz I. SLE Antibody-Secreting Cells Are Characterized by Enhanced Peripheral Maturation and Survival Programs. RESEARCH SQUARE 2023:rs.3.rs-3016327. [PMID: 37461641 PMCID: PMC10350208 DOI: 10.21203/rs.3.rs-3016327/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Systemic Lupus Erythematosus (SLE) is an autoimmune disease characterized by multiple autoantibodies, some of which are present in high titers in a sustained, B cell-independent fashion consistent with their generation from long-lived plasma cells (LLPC). Active SLE displays high numbers of circulating antibody-secreting cells (ASC). Understanding the mechanisms of generation and survival of SLE ASC would contribute important insight into disease pathogenesis and novel targeted therapies. We studied the properties of SLE ASC through a systematic analysis of their phenotypic, molecular, structural, and functional features. Our results indicate that in active SLE, relative to healthy post-immunization responses, blood ASC contain a much larger fraction of newly generated mature CD19- CD138+ ASC similar to bone marrow (BM) LLPC. SLE ASC were characterized by morphological and structural features of premature maturation. Additionally, SLE ASC express high levels of CXCR4 and CD138, and molecular programs consistent with increased longevity based on pro-survival and attenuated pro-apoptotic pathways. Notably, SLE ASC demonstrate autocrine production of APRIL and IL-10 and experience prolonged in vitro survival. Combined, our findings indicate that SLE ASC are endowed with enhanced peripheral maturation, survival and BM homing potential suggesting that these features likely underlie BM expansion of autoreactive PC.
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Affiliation(s)
- Weirong Chen
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - So-Hee Hong
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Scott A. Jenks
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Fabliha A. Anam
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Christopher M. Tipton
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Matthew C. Woodruff
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Jennifer R. Hom
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Kevin S. Cashman
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Caterina Elisa Faliti
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Xiaoqian Wang
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Shuya Kyu
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Chungwen Wei
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Christopher D. Scharer
- Department of Microbiology and Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Tian Mi
- Department of Microbiology and Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Sakeenah Hicks
- Department of Microbiology and Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Louise Hartson
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Doan C. Nguyen
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Arezou Khosroshahi
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Saeyun Lee
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Youliang Wang
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Regina Bugrovsky
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Yusho Ishii
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
| | - F. Eun-Hyung Lee
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Ignacio Sanz
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, School of Medicine, Emory University, Atlanta, GA, USA
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Fuda F, Chen M, Chen W, Cox A. Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress. Semin Diagn Pathol 2023; 40:120-128. [PMID: 36894355 DOI: 10.1053/j.semdp.2023.02.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/07/2023]
Abstract
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.
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Affiliation(s)
- Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Weina Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Cox
- Lyda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, USA; Department of Cell and Molecular Biology, University of Texas, Southwestern Medical Center, Dallas, Texas, USA.
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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10
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.07.23285606. [PMID: 36798344 PMCID: PMC9934808 DOI: 10.1101/2023.02.07.23285606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Motivation Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. Results We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes the available sample-level training data and predicts both the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. Availability The source code of CSNN and datasets used in the experiments are publicly available on GitHub and FlowRepository. Contact Edgar E. Robles: roblesee@uci.edu and Yu Qian: mqian@jcvi.org. Supplementary information Supplementary data are available on GitHub and at Bioinformatics online.
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11
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Van Coillie J, Pongracz T, Rahmöller J, Chen HJ, Geyer CE, van Vught LA, Buhre JS, Šuštić T, van Osch TLJ, Steenhuis M, Hoepel W, Wang W, Lixenfeld AS, Nouta J, Keijzer S, Linty F, Visser R, Larsen MD, Martin EL, Künsting I, Lehrian S, von Kopylow V, Kern C, Lunding HB, de Winther M, van Mourik N, Rispens T, Graf T, Slim MA, Minnaar RP, Bomers MK, Sikkens JJ, Vlaar AP, van der Schoot CE, den Dunnen J, Wuhrer M, Ehlers M, Vidarsson G. The BNT162b2 mRNA SARS-CoV-2 vaccine induces transient afucosylated IgG1 in naive but not in antigen-experienced vaccinees. EBioMedicine 2022; 87:104408. [PMID: 36529104 PMCID: PMC9756879 DOI: 10.1016/j.ebiom.2022.104408] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Afucosylated IgG1 responses have only been found against membrane-embedded epitopes, including anti-S in SARS-CoV-2 infections. These responses, intrinsically protective through enhanced FcγRIIIa binding, can also trigger exacerbated pro-inflammatory responses in severe COVID-19. We investigated if the BNT162b2 SARS-CoV-2 mRNA also induced afucosylated IgG responses. METHODS Blood from vaccinees during the first vaccination wave was collected. Liquid chromatography-Mass spectrometry (LC-MS) was used to study anti-S IgG1 Fc glycoprofiles. Responsiveness of alveolar-like macrophages to produce proinflammatory cytokines in presence of sera and antigen was tested. Antigen-specific B cells were characterized and glycosyltransferase levels were investigated by Fluorescence-Activated Cell Sorting (FACS). FINDINGS Initial transient afucosylated anti-S IgG1 responses were found in naive vaccinees, but not in antigen-experienced ones. All vaccinees had increased galactosylated and sialylated anti-S IgG1. Both naive and antigen-experienced vaccinees showed relatively low macrophage activation potential, as expected, due to the low antibody levels for naive individuals with afucosylated IgG1, and low afucosylation levels for antigen-experienced individuals with high levels of anti-S. Afucosylation levels correlated with FUT8 expression in antigen-specific plasma cells in naive individuals. Interestingly, low fucosylation of anti-S IgG1 upon seroconversion correlated with high anti-S IgG levels after the second dose. INTERPRETATION Here, we show that BNT162b2 mRNA vaccination induces transient afucosylated anti-S IgG1 responses in naive individuals. This observation warrants further studies to elucidate the clinical context in which potent afucosylated responses would be preferred. FUNDING LSBR1721, 1908; ZonMW10430012010021, 09150161910033, 10430012010008; DFG398859914, 400912066, 390884018; PMI; DOI4-Nr. 3; H2020-MSCA-ITN 721815.
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Affiliation(s)
- Julie Van Coillie
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Tamas Pongracz
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Johann Rahmöller
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany,Department of Anesthesiology and Intensive Care, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Hung-Jen Chen
- Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences, Amsterdam Infection and Immunity, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Chiara Elisabeth Geyer
- Center for Experimental and Molecular Medicine, Amsterdam Infection & Immunity Institute, Amsterdam, the Netherlands
| | - Lonneke A. van Vught
- Center for Experimental and Molecular Medicine, Amsterdam Infection & Immunity Institute, Amsterdam, the Netherlands,Department of Intensive Care, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Jana Sophia Buhre
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Tonći Šuštić
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Thijs Luc Junior van Osch
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Maurice Steenhuis
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands,Department of Immunopathology, Sanquin Research, Amsterdam, the Netherlands
| | - Willianne Hoepel
- Department of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands,Department of Rheumatology and Clinical Immunology, Amsterdam UMC, Amsterdam Rheumatology and Immunology Center, Amsterdam, the Netherlands
| | - Wenjun Wang
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Anne Sophie Lixenfeld
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Jan Nouta
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Sofie Keijzer
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands,Department of Immunopathology, Sanquin Research, Amsterdam, the Netherlands
| | - Federica Linty
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Remco Visser
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Mads Delbo Larsen
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Emily Lara Martin
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Inga Künsting
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Selina Lehrian
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Vera von Kopylow
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Carsten Kern
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Hanna Bele Lunding
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany
| | - Menno de Winther
- Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences, Amsterdam Infection and Immunity, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Niels van Mourik
- Center for Experimental and Molecular Medicine, Amsterdam Infection & Immunity Institute, Amsterdam, the Netherlands,Department of Intensive Care, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Theo Rispens
- Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands,Department of Immunopathology, Sanquin Research, Amsterdam, the Netherlands
| | - Tobias Graf
- Medical Department 2, University Heart Center of Schleswig-Holstein, Lübeck, Germany
| | - Marleen Adriana Slim
- Center for Experimental and Molecular Medicine, Amsterdam Infection & Immunity Institute, Amsterdam, the Netherlands,Department of Intensive Care, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Marije Kristianne Bomers
- Department of Internal Medicine, Amsterdam Infection and Immunity Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands
| | - Jonne Jochum Sikkens
- Department of Internal Medicine, Amsterdam Infection and Immunity Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands
| | - Alexander P.J. Vlaar
- Department of Intensive Care, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - C. Ellen van der Schoot
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands
| | - Jeroen den Dunnen
- Center for Experimental and Molecular Medicine, Amsterdam Infection & Immunity Institute, Amsterdam, the Netherlands
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands,Corresponding author.
| | - Marc Ehlers
- Laboratories of Immunology and Antibody Glycan Analysis, Institute of Nutritional Medicine, University of Lübeck and University Medical Center of Schleswig-Holstein, Lübeck, Germany,Airway Research Center North, University of Lübeck, German Center for Lung Research (DZL), Lübeck, Germany,Corresponding author.
| | - Gestur Vidarsson
- Department of Experimental Immunohematology, Sanquin Research, Amsterdam, the Netherlands,Department of Biomolecular Mass Spectrometry and Proteomics, Utrecht Institute for Pharmaceutical Sciences and Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, the Netherlands,Corresponding author.
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12
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Agglomerative and divisive hierarchical Bayesian clustering. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Sztein MB, Booth JS. Controlled human infectious models, a path forward in uncovering immunological correlates of protection: Lessons from enteric fevers studies. Front Microbiol 2022; 13:983403. [PMID: 36204615 PMCID: PMC9530043 DOI: 10.3389/fmicb.2022.983403] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Enteric infectious diseases account for more than a billion disease episodes yearly worldwide resulting in approximately 2 million deaths, with children under 5 years old and the elderly being disproportionally affected. Enteric pathogens comprise viruses, parasites, and bacteria; the latter including pathogens such as Salmonella [typhoidal (TS) and non-typhoidal (nTS)], cholera, Shigella and multiple pathotypes of Escherichia coli (E. coli). In addition, multi-drug resistant and extensively drug-resistant (XDR) strains (e.g., S. Typhi H58 strain) of enteric bacteria are emerging; thus, renewed efforts to tackle enteric diseases are required. Many of these entero-pathogens could be controlled by oral or parenteral vaccines; however, development of new, effective vaccines has been hampered by lack of known immunological correlates of protection (CoP) and limited knowledge of the factors contributing to protective responses. To fully comprehend the human response to enteric infections, an invaluable tool that has recently re-emerged is the use of controlled human infection models (CHIMs) in which participants are challenged with virulent wild-type (wt) organisms. CHIMs have the potential to uncover immune mechanisms and identify CoP to enteric pathogens, as well as to evaluate the efficacy of therapeutics and vaccines in humans. CHIMs have been used to provide invaluable insights in the pathogenesis, host-pathogen interaction and evaluation of vaccines. Recently, several Oxford typhoid CHIM studies have been performed to assess the role of multiple cell types (B cells, CD8+ T, Tregs, MAIT, Monocytes and DC) during S. Typhi infection. One of the key messages that emerged from these studies is that baseline antigen-specific responses are important in that they can correlate with clinical outcomes. Additionally, volunteers who develop typhoid disease (TD) exhibit higher levels and more activated cell types (e.g., DC and monocytes) which are nevertheless defective in discrete signaling pathways. Future critical aspects of this research will involve the study of immune responses to enteric infections at the site of entry, i.e., the intestinal mucosa. This review will describe our current knowledge of immunity to enteric fevers caused byS. Typhi and S. Paratyphi A, with emphasis on the contributions of CHIMs to uncover the complex immunological responses to these organisms and provide insights into the determinants of protective immunity.
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Affiliation(s)
- Marcelo B. Sztein
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- *Correspondence: Marcelo B. Sztein,
| | - Jayaum S. Booth
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, United States
- Jayaum S. Booth,
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14
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Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nat Commun 2022; 13:5455. [PMID: 36114209 PMCID: PMC9481560 DOI: 10.1038/s41467-022-33136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 09/05/2022] [Indexed: 11/30/2022] Open
Abstract
Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks. Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. Here the authors propose a local direction centrality clustering algorithm that copes with heterogeneous density and weak connectivity issues.
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15
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Kwong HS, Nadarajah S. Finite mixtures of multivariate skew Student’s t distributions with independent logistic skewing functions. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/22-bjps542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Hok Shing Kwong
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Saralees Nadarajah
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
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16
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Wang X, Xu Z, Hu H, Zhou X, Zhang Y, Lafyatis R, Chen K, Huang H, Ding Y, Duerr RH, Chen W. SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics. PNAS NEXUS 2022; 1:pgac165. [PMID: 36157595 PMCID: PMC9491696 DOI: 10.1093/pnasnexus/pgac165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/16/2022] [Indexed: 01/29/2023]
Abstract
The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have gained enormous popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT is used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include (1) using confident cell type label identified from surface protein data as guidance for cell clustering, (2) providing general annotation of confident cell types for each cell cluster, (3) utilizing cells with uncertain or missing cell type label to increase performance, and (4) accurate prediction of confident cell types for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results through easily interpretable posterior probability, and our framework can be potentially extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house datasets from multiple tissues. We believe this new method will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single-cell multi-omics data.
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Affiliation(s)
- Xinjun Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Zhongli Xu
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA 15224, USA
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Haoran Hu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Xueping Zhou
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Yanfu Zhang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Robert Lafyatis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Kong Chen
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Wei Chen
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA 15224, USA
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17
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Simonin EM, Babasyan S, Wagner B. Peripheral CD23hi/IgE+ Plasmablasts Secrete IgE and Correlate with Allergic Disease Severity. THE JOURNAL OF IMMUNOLOGY 2022; 209:665-674. [DOI: 10.4049/jimmunol.2101081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 06/16/2022] [Indexed: 01/04/2023]
Abstract
Abstract
Production and secretion of IgE by B cells, plasmablasts, and plasma cells is a central step in the development and maintenance of allergic diseases. IgE can bind to one of its receptors, the low-affinity IgE receptor CD23, which is expressed on activated B cells. As a result, most B cells bind IgE through CD23 on their surface. This makes the identification of IgE producing cells challenging. In this study, we report an approach to clearly identify live IgE+ plasmablasts in peripheral blood for application by both flow cytometry analysis and in vitro assay. These IgE+ plasmablasts readily secrete IgE, upregulate specific mRNA transcripts (BLIMP-1 IRF4, XBP1, CD138, and TACI), and exhibit highly differentiated morphology all consistent with plasmablast differentiation. Most notably, we compared the presence of IgE+ plasmablasts in peripheral blood of allergic and healthy individuals using a horse model of naturally occurring seasonal allergy, Culicoides hypersensitivity. The model allows the comparison of immune cells both during periods of clinical allergy and when in remission and clinically healthy. Allergic horses had significantly higher percentages of IgE+ plasmablasts and IgE secretion while experiencing clinical allergy compared with healthy horses. Allergy severity and IgE secretion were both positively correlated to the frequency of IgE+ plasmablasts in peripheral blood. These results provide strong evidence for the identification and quantification of peripheral IgE-secreting plasmablasts and provide a missing cellular link in the mechanism of IgE secretion and upregulation during allergy.
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Affiliation(s)
- Elisabeth M. Simonin
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Susanna Babasyan
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Bettina Wagner
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
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Liu Y, Zhao H, Fu B, Jiang S, Wang J, Wan Y. Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:272-281. [PMID: 36939758 PMCID: PMC9590532 DOI: 10.1007/s43657-021-00031-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/28/2021] [Accepted: 10/11/2021] [Indexed: 11/26/2022]
Abstract
Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.
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Affiliation(s)
- Yang Liu
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
| | - Haichu Zhao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Boqiang Fu
- National Institute of Metrology, Beijing, 100029 China
| | - Shan Jiang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Jing Wang
- National Institute of Metrology, Beijing, 100029 China
| | - Ying Wan
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
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19
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Morales-Núñez JJ, García-Chagollán M, Muñoz-Valle JF, Díaz-Pérez SA, Torres-Hernández PC, Rodríguez-Reyes SC, Santoscoy-Ascencio G, Sierra García de Quevedo JJ, Hernández-Bello J. Differences in B-Cell Immunophenotypes and Neutralizing Antibodies Against SARS-CoV-2 After Administration of BNT162b2 (Pfizer-BioNTech) Vaccine in Individuals with and without Prior COVID-19 - A Prospective Cohort Study. J Inflamm Res 2022; 15:4449-4466. [PMID: 35958186 PMCID: PMC9361858 DOI: 10.2147/jir.s374304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/30/2022] [Indexed: 12/15/2022] Open
Abstract
Purpose Understanding the humoral immune response dynamics carried out by B cells in COVID-19 vaccination is little explored; therefore, we analyze the changes induced in the different cellular subpopulations of B cells after vaccination with BNT162b2 (Pfizer-BioNTech). Methods This prospective cohort study evaluated thirty-nine immunized health workers (22 with prior COVID-19 and 17 without prior COVID-19) and ten subjects not vaccinated against SARS-CoV-2 (control group). B cell subpopulations (transitional, mature, naïve, memory, plasmablasts, early plasmablast, and double-negative B cells) and neutralizing antibody levels were analyzed and quantified by flow cytometry and ELISA, respectively. Results The dynamics of the B cells subpopulations after vaccination showed the following pattern: the percentage of transitional B cells was higher in the prior COVID-19 group (p < 0.05), whereas virgin B cells were more prevalent in the group without prior COVID-19 (p < 0.05), mature B cells predominated in both vaccinated groups (p < 0.01), and memory B cells, plasmablasts, early plasmablasts, and double-negative B cells were higher in the not vaccinated group (p < 0.05). Conclusion BNT162b2 vaccine induces changes in B cell subpopulations, especially generating plasma cells and producing neutralizing antibodies against SARS-CoV-2. However, the previous infection with SARS-CoV-2 does not significantly alter the dynamics of these subpopulations but induces more rapid and optimal antibody production.
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Affiliation(s)
- José Javier Morales-Núñez
- Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - Mariel García-Chagollán
- Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - José Francisco Muñoz-Valle
- Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
| | - Saúl Alberto Díaz-Pérez
- Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
| | | | - Saraí Citlalic Rodríguez-Reyes
- Institute of Translational Nutrigenetics and Nutrigenomics, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
| | | | | | - Jorge Hernández-Bello
- Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, Mexico
- Correspondence: Jorge Hernández-Bello,s Institute of Research in Biomedical Sciences, University Center of Health Sciences (CUCS), University of Guadalajara, Guadalajara, Jalisco, 44340, Mexico, Tel +52 3334509355, Email
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20
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Villanueva-Hernández S, Adib Razavi M, van Dongen KA, Stadler M, de Luca K, Beyersdorf N, Saalmüller A, Gerner W, Mair KH. Co-Expression of the B-Cell Key Transcription Factors Blimp-1 and IRF4 Identifies Plasma Cells in the Pig. Front Immunol 2022; 13:854257. [PMID: 35464468 PMCID: PMC9024106 DOI: 10.3389/fimmu.2022.854257] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
Antibody-secreting plasma cells (PCs) have remained largely uncharacterized for years in the field of porcine immunology. For an in-depth study of porcine PCs, we identified cross-reactive antibodies against three key transcription factors: PR domain zinc finger protein-1 (Blimp-1), interferon regulatory factor 4 (IRF4), and paired box 5 (Pax5). A distinct Blimp-1+IRF4+ cell population was found in cells isolated from blood, spleen, lymph nodes, bone marrow, and lung of healthy pigs. These cells showed a downregulation of Pax5 compared to other B cells. Within Blimp-1+IRF4+ B cells, IgM-, IgG-, and IgA-expressing cells were identified and immunoglobulin-class distribution was clearly different between the anatomical locations, with IgA+ PCs dominating in lung tissue and IgM+ PCs dominating in the spleen. Expression patterns of Ki-67, MHC-II, CD9, and CD28 were investigated in the different organs. A high expression of Ki-67 was observed in blood, suggesting a plasmablast stage. Blimp-1+IRF4+ cells showed an overall lower expression of MHC-II compared to regular B cells, confirming a progressive loss in B-cell differentiation toward the PC stage. CD28 showed slightly elevated expression levels in Blimp-1+IRF4+ cells in most organs, a phenotype that is also described for PCs in mice and humans. This was not seen for CD9. We further developed a FACS-sorting strategy for live porcine PCs for functional assays. CD3-CD16-CD172a– sorted cells with a CD49dhighFSC-Ahigh phenotype contained Blimp-1+IRF4+ cells and were capable of spontaneous IgG production, thus confirming PC identity. These results reveal fundamental phenotypes of porcine PCs and will facilitate the study of this specific B-cell subset in the future.
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Affiliation(s)
- Sonia Villanueva-Hernández
- Christian Doppler (CD) Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Mahsa Adib Razavi
- Christian Doppler (CD) Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Katinka A. van Dongen
- Christian Doppler (CD) Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Maria Stadler
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Karelle de Luca
- Laboratory of Veterinary Immunology, Global Innovation, Boehringer Ingelheim Animal Health, Lyon, France
| | - Niklas Beyersdorf
- Institute for Virology and Immunobiology, Julius-Maximilians-University, Würzburg, Germany
| | - Armin Saalmüller
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Wilhelm Gerner
- Christian Doppler (CD) Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
| | - Kerstin H. Mair
- Christian Doppler (CD) Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria
- *Correspondence: Kerstin H. Mair,
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21
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Cheung M, Campbell JJ, Thomas RJ, Braybrook J, Petzing J. Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing. Int J Mol Sci 2022; 23:ijms23063224. [PMID: 35328645 PMCID: PMC8955358 DOI: 10.3390/ijms23063224] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022] Open
Abstract
Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.
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Affiliation(s)
- Melissa Cheung
- Centre for Biological Engineering, Loughborough University, Loughborough LE11 3TU, Leicestershire, UK; (R.J.T.); (J.P.)
- Correspondence:
| | - Jonathan J. Campbell
- National Measurement Laboratory, LGC, Queens Road, Teddington TW11 0LY, Middlesex, UK; (J.J.C.); (J.B.)
| | - Robert J. Thomas
- Centre for Biological Engineering, Loughborough University, Loughborough LE11 3TU, Leicestershire, UK; (R.J.T.); (J.P.)
| | - Julian Braybrook
- National Measurement Laboratory, LGC, Queens Road, Teddington TW11 0LY, Middlesex, UK; (J.J.C.); (J.B.)
| | - Jon Petzing
- Centre for Biological Engineering, Loughborough University, Loughborough LE11 3TU, Leicestershire, UK; (R.J.T.); (J.P.)
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22
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Yang ZK, Pan L, Zhang Y, Luo H, Gao F. Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data. Brief Bioinform 2021; 22:bbab307. [PMID: 34382087 PMCID: PMC8385964 DOI: 10.1093/bib/bbab307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/01/2021] [Accepted: 07/17/2021] [Indexed: 01/08/2023] Open
Abstract
For epidemic prevention and control, the identification of SARS-CoV-2 subpopulations sharing similar micro-epidemiological patterns and evolutionary histories is necessary for a more targeted investigation into the links among COVID-19 outbreaks caused by SARS-CoV-2 with similar genetic backgrounds. Genomic sequencing analysis has demonstrated the ability to uncover viral genetic diversity. However, an objective analysis is necessary for the identification of SARS-CoV-2 subpopulations. Herein, we detected all the mutations in 186 682 SARS-CoV-2 isolates. We found that the GC content of the SARS-CoV-2 genome had evolved to be lower, which may be conducive to viral spread, and the frameshift mutation was rare in the global population. Next, we encoded the genomic mutations in binary form and used an unsupervised learning classifier, namely PhenoGraph, to classify this information. Consequently, PhenoGraph successfully identified 303 SARS-CoV-2 subpopulations, and we found that the PhenoGraph classification was consistent with, but more detailed and precise than the known GISAID clades (S, L, V, G, GH, GR, GV and O). By the change trend analysis, we found that the growth rate of SARS-CoV-2 diversity has slowed down significantly. We also analyzed the temporal, spatial and phylogenetic relationships among the subpopulations and revealed the evolutionary trajectory of SARS-CoV-2 to a certain extent. Hence, our results provide a better understanding of the patterns and trends in the genomic evolution and epidemiology of SARS-CoV-2.
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Affiliation(s)
- Zhi-Kai Yang
- Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510700, China
| | - Lingyu Pan
- Guangzhou Nanxin Pharmaceutical Co., Ltd., Guangzhou 510700, China
| | - Yanming Zhang
- SinoGenoMax Co., Ltd./Chinese National Human Genome Center, Guangzhou 510700, China
| | - Hao Luo
- Department of Physics, School of Science, Tianjin University, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, and the Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
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23
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Bernal-Estévez DA, Ortíz Barbosa MA, Ortíz-Montero P, Cifuentes C, Sánchez R, Parra-López CA. Autologous Dendritic Cells in Combination With Chemotherapy Restore Responsiveness of T Cells in Breast Cancer Patients: A Single-Arm Phase I/II Trial. Front Immunol 2021; 12:669965. [PMID: 34489928 PMCID: PMC8417880 DOI: 10.3389/fimmu.2021.669965] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/27/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction Animal studies and preclinical studies in cancer patients suggest that the induction of immunogenic cell death (ICD) by neoadjuvant chemotherapy with doxorubicin and cyclophosphamide (NAC-AC) recovers the functional performance of the immune system. This could favor immunotherapy schemes such as the administration of antigen-free autologous dendritic cells (DCs) in combination with NAC-AC to profit as cryptic vaccine immunogenicity of treated tumors. Objective To explore the safety and immunogenicity of autologous antigen-free DCs administered to breast cancer patients (BCPs) in combination with NAC-AC. Materials and Methods A phase I/II cohort clinical trial was performed with 20 BCPs treated with NAC-AC [nine who received DCs and 11 who did not (control group)]. The occurrence of adverse effects and the functional performance of lymphocytes from BCPs before and after four cycles of NAC-AC receiving DCs or not were assessed using flow cytometry and compared with that from healthy donors (HDs). Flow cytometry analysis using manual and automated algorithms led us to examine functional performance and frequency of different lymphocyte compartments in response to a stimulus in vitro. This study was registered at clinicaltrials.gov (NCT03450044). Results No grade II or higher adverse effects were observed associated with the transfer of DCs to patients during NAC-AC. Interestingly, in response to the in vitro stimulation, deficient phosphorylation of Zap70 and AKT proteins observed before chemotherapy in most patients’ CD4 T cells significantly recovered after NAC-AC only in patients who received DCs. Conclusions The transfer of autologous DCs in combination with NAC-AC in BCPs is a safe procedure. That, in BCPs, the administration of DCs in combination with NAC-AC favors the recovery of the functional capacity of T cells suggests that this combination may potentiate the adjuvant effect of ICD induced by NAC-AC on T cells and, hence, potentiate the immunogenicity of tumors as cryptic vaccines.
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Affiliation(s)
- David A Bernal-Estévez
- Immunology and Clinical Oncology Research Group, Fundación Salud de los Andes, Bogotá, Colombia
| | - Mauren A Ortíz Barbosa
- Immunology and Clinical Oncology Research Group, Fundación Salud de los Andes, Bogotá, Colombia
| | - Paola Ortíz-Montero
- Immunology and Clinical Oncology Research Group, Fundación Salud de los Andes, Bogotá, Colombia
| | - Claudia Cifuentes
- Oncology Department, Hospital Universitario Mayor de Méderi, Bogotá, Colombia
| | - Ramiro Sánchez
- Immunology and Translational Medicine Research Group, Department of Microbiology, Medical School, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Carlos A Parra-López
- Immunology and Translational Medicine Research Group, Department of Microbiology, Medical School, Universidad Nacional de Colombia, Bogotá, Colombia
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24
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Grasseau A, Boudigou M, Michée-Cospolite M, Delaloy C, Mignen O, Jamin C, Cornec D, Pers JO, Le Pottier L, Hillion S. The diversity of the plasmablast signature across species and experimental conditions: A meta-analysis. Immunology 2021; 164:120-134. [PMID: 34041745 DOI: 10.1111/imm.13344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 03/15/2021] [Accepted: 04/12/2021] [Indexed: 12/17/2022] Open
Abstract
Antibody-secreting cells (ASC) are divided into two principal subsets, including the long-lived plasma cell (PC) subset residing in the bone marrow and the short-lived subset, also called plasmablast (PB). PB are described as a proliferating subset circulating through the blood and ending its differentiation in tissues. Due to their inherent heterogeneity, the molecular signature of PB is not fully established. The purpose of this study was to decipher a specific PB signature in humans and mice through a comprehensive meta-analysis of different data sets exploring the PB differentiation in both species and across different experimental conditions. The present study used recent analyses using whole RNA sequencing in prdm1-GFP transgenic mice to define a reliable and accurate PB signature. Next, we performed similar analysis using current data sets obtained from human PB and PC. The PB-specific signature is composed of 155 and 113 genes in mouse and human being, respectively. Although only nine genes are shared between the human and mice PB signature, the loss of B-cell identity such as the down-regulation of PAX5, MS4A1, (CD20) CD22 and IL-4R is a conserved feature across species and across the different experimental conditions. Additionally, we observed that the IRF8 and IRF4 transcription factors have a specific dynamic range of expression in human PB. We thus demonstrated that IRF4/IRF8 intranuclear staining was useful to define PB in vivo and in vitro and able to discriminate between atypical PB populations and transient states.
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Affiliation(s)
| | | | | | - Céline Delaloy
- UMR U1236, INSERM, Etablissement Français du Sang (EFS) de Bretagne, Université de Rennes 1, Rennes, France
| | | | - Christophe Jamin
- UMR1227, LBAI, INSERM, Univ Brest, Brest, France.,UMR1227, LBAI, INSERM, CHU de Brest, Univ Brest, Brest, France
| | - Divi Cornec
- UMR1227, LBAI, INSERM, Univ Brest, Brest, France.,UMR1227, LBAI, INSERM, CHU de Brest, Univ Brest, Brest, France
| | - Jacques-Olivier Pers
- UMR1227, LBAI, INSERM, Univ Brest, Brest, France.,UMR1227, LBAI, INSERM, CHU de Brest, Univ Brest, Brest, France
| | | | - Sophie Hillion
- UMR1227, LBAI, INSERM, Univ Brest, Brest, France.,UMR1227, LBAI, INSERM, CHU de Brest, Univ Brest, Brest, France
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25
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Cheung M, Campbell JJ, Whitby L, Thomas RJ, Braybrook J, Petzing J. Current trends in flow cytometry automated data analysis software. Cytometry A 2021; 99:1007-1021. [PMID: 33606354 DOI: 10.1002/cyto.a.24320] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 12/16/2022]
Abstract
Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.
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Affiliation(s)
- Melissa Cheung
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | | | - Liam Whitby
- UK NEQAS for Leucocyte Immunophenotyping, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Robert J Thomas
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Julian Braybrook
- National Measurement Laboratory, LGC, Teddington, United Kingdom
| | - Jon Petzing
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
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26
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Byazrova M, Yusubalieva G, Spiridonova A, Efimov G, Mazurov D, Baranov K, Baklaushev V, Filatov A. Pattern of circulating SARS-CoV-2-specific antibody-secreting and memory B-cell generation in patients with acute COVID-19. Clin Transl Immunology 2021; 10:e1245. [PMID: 33552508 PMCID: PMC7848539 DOI: 10.1002/cti2.1245] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/19/2022] Open
Abstract
Objectives To predict the spread of coronavirus disease (COVID‐19), information regarding the immunological memory for disease‐specific antigens is necessary. The possibility of reinfection, as well as the efficacy of vaccines for COVID‐19 that are currently under development, will largely depend on the quality and longevity of immunological memory in patients. To elucidate the process of humoral immunity development, we analysed the generation of plasmablasts and virus receptor‐binding domain (RBD)‐specific memory B (Bmem) cells in patients during the acute phase of COVID‐19. Methods The frequencies of RBD‐binding plasmablasts and RBD‐specific antibody‐secreting cells (ASCs) in the peripheral blood samples collected from patients with COVID‐19 were measured using flow cytometry and the ELISpot assay. Results The acute phase of COVID‐19 was characterised by the transient appearance of total as well as RBD‐binding plasmablasts. ELISpot analysis indicated that most patients exhibited a spontaneous secretion of RBD‐specific ASCs in the circulation with good correlation between the IgG and IgM subsets. IL‐21/CD40L stimulation of purified B cells induced the activation and proliferation of Bmem cells, which led to the generation of plasmablast phenotypic cells as well as RBD‐specific ASCs. No correlation was observed between the frequency of Bmem cell‐derived and spontaneous ASCs, suggesting that the two types of ASCs were weakly associated with each other. Conclusion Our findings reveal that SARS‐CoV‐2‐specific Bmem cells are generated during the acute phase of COVID‐19. These findings can serve as a basis for further studies on the longevity of SARS‐CoV‐2‐specific B‐cell memory.
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Affiliation(s)
- Maria Byazrova
- National Research Center Institute of Immunology of Federal Medical Biological Agency of Russia Moscow Russia.,Department of Immunology Faculty of Biology Lomonosov Moscow State University Moscow Russia
| | - Gaukhar Yusubalieva
- Federal Research and Clinical Center for Specialized Types of Medical Care and Medical Technologies of the FMBA of Russia Moscow Russia
| | - Anna Spiridonova
- National Research Center Institute of Immunology of Federal Medical Biological Agency of Russia Moscow Russia
| | | | - Dmitriy Mazurov
- Institute of Gene Biology Russian Academy of Sciences Center for Precision Genome Editing and Genetic Technologies for Biomedicine Moscow Russia
| | - Konstantin Baranov
- Institute of Molecular and Cellular Biology SB RAS Lomonosov Moscow State University Novosibirsk Russia
| | - Vladimir Baklaushev
- Federal Research and Clinical Center for Specialized Types of Medical Care and Medical Technologies of the FMBA of Russia Moscow Russia
| | - Alexander Filatov
- National Research Center Institute of Immunology of Federal Medical Biological Agency of Russia Moscow Russia.,Department of Immunology Faculty of Biology Lomonosov Moscow State University Moscow Russia
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27
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Del Barrio E, Inouzhe H, Loubes JM, Matrán C, Mayo-Íscar A. optimalFlow: optimal transport approach to flow cytometry gating and population matching. BMC Bioinformatics 2020; 21:479. [PMID: 33109072 PMCID: PMC7590740 DOI: 10.1186/s12859-020-03795-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 10/01/2020] [Indexed: 11/12/2022] Open
Abstract
Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. Results We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. Conclusions optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.
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Affiliation(s)
- Eustasio Del Barrio
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
| | - Hristo Inouzhe
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain. .,IMUVA, Calle Paseo de Belén, Valladolid, Spain.
| | - Jean-Michel Loubes
- Université Paul Sabatier, Route de Narbonne, Toulouse, France.,IMT, Route de Narbonne, Toulouse, France
| | - Carlos Matrán
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
| | - Agustín Mayo-Íscar
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
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28
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Varnaitė R, García M, Glans H, Maleki KT, Sandberg JT, Tynell J, Christ W, Lagerqvist N, Asgeirsson H, Ljunggren HG, Ahlén G, Frelin L, Sällberg M, Blom K, Klingström J, Gredmark-Russ S. Expansion of SARS-CoV-2-Specific Antibody-Secreting Cells and Generation of Neutralizing Antibodies in Hospitalized COVID-19 Patients. THE JOURNAL OF IMMUNOLOGY 2020; 205:2437-2446. [PMID: 32878912 DOI: 10.4049/jimmunol.2000717] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/20/2020] [Indexed: 01/16/2023]
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in late 2019 and has since become a global pandemic. Pathogen-specific Abs are typically a major predictor of protective immunity, yet human B cell and Ab responses during COVID-19 are not fully understood. In this study, we analyzed Ab-secreting cell and Ab responses in 20 hospitalized COVID-19 patients. The patients exhibited typical symptoms of COVID-19 and presented with reduced lymphocyte numbers and increased T cell and B cell activation. Importantly, we detected an expansion of SARS-CoV-2 nucleocapsid protein-specific Ab-secreting cells in all 20 COVID-19 patients using a multicolor FluoroSpot Assay. Out of the 20 patients, 16 had developed SARS-CoV-2-neutralizing Abs by the time of inclusion in the study. SARS-CoV-2-specific IgA, IgG, and IgM Ab levels positively correlated with SARS-CoV-2-neutralizing Ab titers, suggesting that SARS-CoV-2-specific Ab levels may reflect the titers of neutralizing Abs in COVID-19 patients during the acute phase of infection. Last, we showed that IL-6 and C-reactive protein serum concentrations were higher in patients who were hospitalized for longer, supporting the recent observations that IL-6 and C-reactive protein could be used as markers for COVID-19 severity. Altogether, this study constitutes a detailed description of clinical and immunological parameters in 20 COVID-19 patients, with a focus on B cell and Ab responses, and describes tools to study immune responses to SARS-CoV-2 infection and vaccination.
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Affiliation(s)
- Renata Varnaitė
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Marina García
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Hedvig Glans
- Department of Infectious Diseases, Karolinska University Hospital, 141 86 Stockholm, Sweden.,Department of Medicine Solna, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - Kimia T Maleki
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - John Tyler Sandberg
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Janne Tynell
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Wanda Christ
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | | | - Hilmir Asgeirsson
- Department of Infectious Diseases, Karolinska University Hospital, 141 86 Stockholm, Sweden.,Division of Infectious Diseases, Department of Medicine Huddinge, Karolinska Institutet, 141 86 Stockholm, Sweden; and
| | - Hans-Gustaf Ljunggren
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden.,Division of Infectious Diseases, Department of Medicine Huddinge, Karolinska Institutet, 141 86 Stockholm, Sweden; and
| | - Gustaf Ahlén
- Department of Laboratory Medicine, Division of Clinical Microbiology, ANA Futura, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Lars Frelin
- Department of Laboratory Medicine, Division of Clinical Microbiology, ANA Futura, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Matti Sällberg
- Department of Laboratory Medicine, Division of Clinical Microbiology, ANA Futura, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Kim Blom
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden
| | - Jonas Klingström
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden.,Public Health Agency of Sweden, 171 65 Solna, Sweden
| | - Sara Gredmark-Russ
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Stockholm, Sweden; .,Department of Infectious Diseases, Karolinska University Hospital, 141 86 Stockholm, Sweden
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29
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Abstract
Cytometry technologies are able to profile immune cells at single-cell resolution. They are widely used for both clinical diagnosis and biological research. We developed a deep learning model for analyzing cytometry data. We demonstrated that the deep learning model accurately diagnoses the latent cytomegalovirus (CMV) in healthy individuals. In addition, we developed a method for interpreting the deep learning model, allowing us to identify biomarkers associated with latent CMV infection. The deep learning model is widely applicable to other cytometry data related to human diseases. Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).
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Standardization procedure for flow cytometry data harmonization in prospective multicenter studies. Sci Rep 2020; 10:11567. [PMID: 32665668 PMCID: PMC7360585 DOI: 10.1038/s41598-020-68468-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 06/22/2020] [Indexed: 01/26/2023] Open
Abstract
One of the most challenging objective for clinical cytometry in prospective multicenter immunomonitoring trials is to compare frequencies, absolute numbers of leukocyte populations and further the mean fluorescence intensities of cell markers, especially when the data are generated from different instruments. Here, we describe an innovative standardization workflow to compare all data to carry out any large-scale, prospective multicentric flow cytometry analysis whatever the duration, the number or type of instruments required for the realization of such projects.
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31
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Woodruff M, Ramonell R, Cashman K, Nguyen D, Saini A, Haddad N, Ley A, Kyu S, Howell JC, Ozturk T, Lee S, Chen W, Estrada J, Morrison-Porter A, Derrico A, Anam F, Sharma M, Wu H, Le S, Jenks S, Tipton CM, Hu W, Lee FEH, Sanz I. Dominant extrafollicular B cell responses in severe COVID-19 disease correlate with robust viral-specific antibody production but poor clinical outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32511635 DOI: 10.1101/2020.04.29.20083717] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A wide clinical spectrum has become a hallmark of the SARS-CoV-2 (COVID-19) pandemic, although its immunologic underpinnings remain to be defined. We have performed deep characterization of B cell responses through high-dimensional flow cytometry to reveal substantial heterogeneity in both effector and immature populations. More notably, critically ill patients displayed hallmarks of extrafollicular B cell activation as previously described in autoimmune settings. Extrafollicular activation correlated strongly with large antibody secreting cell expansion and early production of high levels of SARS-CoV-2-specific antibodies. Yet, these patients fared poorly with elevated inflammatory biomarkers, multi-organ failure, and death. Combined, the findings strongly indicate a major pathogenic role for immune activation in subsets of COVID-19 patients. Our study suggests that, as in autoimmunity, targeted immunomodulatory therapy may be beneficial in specific patient subpopulations that can be identified by careful immune profiling.
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Hunter-Schlichting D, Lane J, Cole B, Flaten Z, Barcelo H, Ramasubramanian R, Cassidy E, Faul J, Crimmins E, Pankratz N, Thyagarajan B. Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study. Sci Rep 2020; 10:8759. [PMID: 32472068 PMCID: PMC7260195 DOI: 10.1038/s41598-020-65016-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 04/01/2020] [Indexed: 12/11/2022] Open
Abstract
Traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. Therefore, we developed an approach that utilizes a previously published automated algorithm (OpenCyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (FlowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). Our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies.
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Affiliation(s)
| | - John Lane
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Benjamin Cole
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Zachary Flaten
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Helene Barcelo
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Ramya Ramasubramanian
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Erin Cassidy
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Jessica Faul
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA
| | - Eileen Crimmins
- Davis School of Gerontology, University of Southern California Davis, Los Angeles, CA, USA
| | - Nathan Pankratz
- Divison of Computational Pathology, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA
| | - Bharat Thyagarajan
- Division of Molecular Pathology and Genomics, Department of Laboratory Medicine and Pathology, Minneapolis, MN, USA.
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33
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SwiftReg cluster registration automatically reduces flow cytometry data variability including batch effects. Commun Biol 2020; 3:218. [PMID: 32382076 PMCID: PMC7205614 DOI: 10.1038/s42003-020-0938-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 04/10/2020] [Indexed: 01/29/2023] Open
Abstract
Biological differences of interest in large, high-dimensional flow cytometry datasets are often obscured by undesired variations caused by differences in cytometers, reagents, or operators. Each variation type requires a different correction strategy, and their unknown contributions to overall variability hinder automated correction. We now describe swiftReg, an automated method that reduces undesired sources of variability between samples and particularly between batches. A high-resolution cluster map representing the multidimensional data is generated using the SWIFT algorithm, and shifts in cluster positions between samples are measured. Subpopulations are aligned between samples by displacing cell parameter values according to registration vectors derived from independent or locally-averaged cluster shifts. Batch variation is addressed by registering batch control or consensus samples, and applying the resulting shifts to individual samples. swiftReg selectively reduces batch variation, enhancing detection of biological differences. swiftReg outputs registered datasets as standard .FCS files to facilitate further analysis by other tools. Rebhahn et al. develop swiftReg that automatically corrects undesired sources of variability of flow cytometry data. To identify batch variation, this method registers an internal standard or consensus sample from each batch and applies the resulting registration shifts to individual samples, reducing the batch variation while preserving biological differences.
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34
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Stassen SV, Siu DMD, Lee KCM, Ho JWK, So HKH, Tsia KK. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells. Bioinformatics 2020; 36:2778-2786. [PMID: 31971583 PMCID: PMC7203756 DOI: 10.1093/bioinformatics/btaa042] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/24/2019] [Accepted: 01/16/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity. RESULTS We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis. AVAILABILITY AND IMPLEMENTATION https://github.com/ShobiStassen/PARC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | | | - Kevin K Tsia
- Department of Electrical and Electronic Engineering
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35
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Liu P, Liu S, Fang Y, Xue X, Zou J, Tseng G, Konnikova L. Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data. Front Cell Dev Biol 2020; 8:234. [PMID: 32411698 PMCID: PMC7198724 DOI: 10.3389/fcell.2020.00234] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 03/20/2020] [Indexed: 11/13/2022] Open
Abstract
The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.
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Affiliation(s)
- Peng Liu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Silvia Liu
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yusi Fang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiangning Xue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jian Zou
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - George Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Liza Konnikova
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United States
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36
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Lucchesi S, Furini S, Medaglini D, Ciabattini A. From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies. Vaccines (Basel) 2020; 8:E138. [PMID: 32244919 PMCID: PMC7157606 DOI: 10.3390/vaccines8010138] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 12/15/2022] Open
Abstract
Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.
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Affiliation(s)
- Simone Lucchesi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Simone Furini
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
| | - Donata Medaglini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Annalisa Ciabattini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
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37
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Ji D, Putzel P, Qian Y, Chang I, Mandava A, Scheuermann RH, Bui JD, Wang H, Smyth P. Machine Learning of Discriminative Gate Locations for Clinical Diagnosis. Cytometry A 2020; 97:296-307. [PMID: 31691488 PMCID: PMC7079150 DOI: 10.1002/cyto.a.23906] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 08/22/2019] [Accepted: 09/18/2019] [Indexed: 01/03/2023]
Abstract
High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally-optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Disi Ji
- Department of Computer ScienceUniversity of CaliforniaIrvineCalifornia
| | - Preston Putzel
- Department of Computer ScienceUniversity of CaliforniaIrvineCalifornia
| | - Yu Qian
- InformaticsJ. Craig Venter InstituteLa JollaCalifornia
| | - Ivan Chang
- InformaticsJ. Craig Venter InstituteLa JollaCalifornia
| | | | - Richard H. Scheuermann
- InformaticsJ. Craig Venter InstituteLa JollaCalifornia
- Department of PathologyUniversity of CaliforniaSan Diego, La JollaCalifornia
| | - Jack D. Bui
- Department of PathologyUniversity of CaliforniaSan Diego, La JollaCalifornia
| | - Huan‐You Wang
- Department of PathologyUniversity of CaliforniaSan Diego, La JollaCalifornia
| | - Padhraic Smyth
- Department of Computer ScienceUniversity of CaliforniaIrvineCalifornia
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38
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Lucchesi S, Nolfi E, Pettini E, Pastore G, Fiorino F, Pozzi G, Medaglini D, Ciabattini A. Computational Analysis of Multiparametric Flow Cytometric Data to Dissect B Cell Subsets in Vaccine Studies. Cytometry A 2019; 97:259-267. [PMID: 31710181 PMCID: PMC7079172 DOI: 10.1002/cyto.a.23922] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/11/2019] [Accepted: 10/07/2019] [Indexed: 01/03/2023]
Abstract
The generation of the B cell response upon vaccination is characterized by the induction of different functional and phenotypic subpopulations and is strongly dependent on the vaccine formulation, including the adjuvant used. Here, we have profiled the different B cell subsets elicited upon vaccination, using machine learning methods for interpreting high‐dimensional flow cytometry data sets. The B cell response elicited by an adjuvanted vaccine formulation, compared to the antigen alone, was characterized using two automated methods based on clustering (FlowSOM) and dimensional reduction (t‐SNE) approaches. The clustering method identified, based on multiple marker expression, different B cell populations, including plasmablasts, plasma cells, germinal center B cells and their subsets, while this profiling was more difficult with t‐SNE analysis. When undefined phenotypes were detected, their characterization could be improved by integrating the t‐SNE spatial visualization of cells with the FlowSOM clusters. The frequency of some cellular subsets, in particular plasma cells, was significantly higher in lymph nodes of mice primed with the adjuvanted formulation compared to antigen alone. Thanks to this automatic data analysis it was possible to identify, in an unbiased way, different B cell populations and also intermediate stages of cell differentiation elicited by immunization, thus providing a signature of B cell recall response that can be hardly obtained with the classical bidimensional gating analysis. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Simone Lucchesi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Emanuele Nolfi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Elena Pettini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Gabiria Pastore
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Fabio Fiorino
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Gianni Pozzi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Donata Medaglini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Annalisa Ciabattini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
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39
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Sanz I, Wei C, Jenks SA, Cashman KS, Tipton C, Woodruff MC, Hom J, Lee FEH. Challenges and Opportunities for Consistent Classification of Human B Cell and Plasma Cell Populations. Front Immunol 2019; 10:2458. [PMID: 31681331 PMCID: PMC6813733 DOI: 10.3389/fimmu.2019.02458] [Citation(s) in RCA: 306] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 10/01/2019] [Indexed: 12/11/2022] Open
Abstract
The increasingly recognized role of different types of B cells and plasma cells in protective and pathogenic immune responses combined with technological advances have generated a plethora of information regarding the heterogeneity of this human immune compartment. Unfortunately, the lack of a consistent classification of human B cells also creates significant imprecision on the adjudication of different phenotypes to well-defined populations. Additional confusion in the field stems from: the use of non-discriminatory, overlapping markers to define some populations, the extrapolation of mouse concepts to humans, and the assignation of functional significance to populations often defined by insufficient surface markers. In this review, we shall discuss the current understanding of human B cell heterogeneity and define major parental populations and associated subsets while discussing their functional significance. We shall also identify current challenges and opportunities. It stands to reason that a unified approach will not only permit comparison of separate studies but also improve our ability to define deviations from normative values and to create a clean framework for the identification, functional significance, and disease association with new populations.
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Affiliation(s)
- Ignacio Sanz
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Chungwen Wei
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Scott A Jenks
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Kevin S Cashman
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Christopher Tipton
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Matthew C Woodruff
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Jennifer Hom
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Rheumatology, Department of Medicine, Emory University, Atlanta, GA, United States
| | - F Eun-Hyung Lee
- Lowance Center for Human Immunology, Emory University, Atlanta, GA, United States.,Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, Emory University, Atlanta, GA, United States
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Cossarizza A, Chang HD, Radbruch A, Acs A, Adam D, Adam-Klages S, Agace WW, Aghaeepour N, Akdis M, Allez M, Almeida LN, Alvisi G, Anderson G, Andrä I, Annunziato F, Anselmo A, Bacher P, Baldari CT, Bari S, Barnaba V, Barros-Martins J, Battistini L, Bauer W, Baumgart S, Baumgarth N, Baumjohann D, Baying B, Bebawy M, Becher B, Beisker W, Benes V, Beyaert R, Blanco A, Boardman DA, Bogdan C, Borger JG, Borsellino G, Boulais PE, Bradford JA, Brenner D, Brinkman RR, Brooks AES, Busch DH, Büscher M, Bushnell TP, Calzetti F, Cameron G, Cammarata I, Cao X, Cardell SL, Casola S, Cassatella MA, Cavani A, Celada A, Chatenoud L, Chattopadhyay PK, Chow S, Christakou E, Čičin-Šain L, Clerici M, Colombo FS, Cook L, Cooke A, Cooper AM, Corbett AJ, Cosma A, Cosmi L, Coulie PG, Cumano A, Cvetkovic L, Dang VD, Dang-Heine C, Davey MS, Davies D, De Biasi S, Del Zotto G, Cruz GVD, Delacher M, Bella SD, Dellabona P, Deniz G, Dessing M, Di Santo JP, Diefenbach A, Dieli F, Dolf A, Dörner T, Dress RJ, Dudziak D, Dustin M, Dutertre CA, Ebner F, Eckle SBG, Edinger M, Eede P, Ehrhardt GR, Eich M, Engel P, Engelhardt B, Erdei A, Esser C, Everts B, Evrard M, Falk CS, Fehniger TA, Felipo-Benavent M, Ferry H, Feuerer M, Filby A, Filkor K, Fillatreau S, Follo M, Förster I, Foster J, Foulds GA, Frehse B, Frenette PS, Frischbutter S, Fritzsche W, Galbraith DW, Gangaev A, Garbi N, Gaudilliere B, Gazzinelli RT, Geginat J, Gerner W, Gherardin NA, Ghoreschi K, Gibellini L, Ginhoux F, Goda K, Godfrey DI, Goettlinger C, González-Navajas JM, Goodyear CS, Gori A, Grogan JL, Grummitt D, Grützkau A, Haftmann C, Hahn J, Hammad H, Hämmerling G, Hansmann L, Hansson G, Harpur CM, Hartmann S, Hauser A, Hauser AE, Haviland DL, Hedley D, Hernández DC, Herrera G, Herrmann M, Hess C, Höfer T, Hoffmann P, Hogquist K, Holland T, Höllt T, Holmdahl R, Hombrink P, Houston JP, Hoyer BF, Huang B, Huang FP, Huber JE, Huehn J, Hundemer M, Hunter CA, Hwang WYK, Iannone A, Ingelfinger F, Ivison SM, Jäck HM, Jani PK, Jávega B, Jonjic S, Kaiser T, Kalina T, Kamradt T, Kaufmann SHE, Keller B, Ketelaars SLC, Khalilnezhad A, Khan S, Kisielow J, Klenerman P, Knopf J, Koay HF, Kobow K, Kolls JK, Kong WT, Kopf M, Korn T, Kriegsmann K, Kristyanto H, Kroneis T, Krueger A, Kühne J, Kukat C, Kunkel D, Kunze-Schumacher H, Kurosaki T, Kurts C, Kvistborg P, Kwok I, Landry J, Lantz O, Lanuti P, LaRosa F, Lehuen A, LeibundGut-Landmann S, Leipold MD, Leung LY, Levings MK, Lino AC, Liotta F, Litwin V, Liu Y, Ljunggren HG, Lohoff M, Lombardi G, Lopez L, López-Botet M, Lovett-Racke AE, Lubberts E, Luche H, Ludewig B, Lugli E, Lunemann S, Maecker HT, Maggi L, Maguire O, Mair F, Mair KH, Mantovani A, Manz RA, Marshall AJ, Martínez-Romero A, Martrus G, Marventano I, Maslinski W, Matarese G, Mattioli AV, Maueröder C, Mazzoni A, McCluskey J, McGrath M, McGuire HM, McInnes IB, Mei HE, Melchers F, Melzer S, Mielenz D, Miller SD, Mills KH, Minderman H, Mjösberg J, Moore J, Moran B, Moretta L, Mosmann TR, Müller S, Multhoff G, Muñoz LE, Münz C, Nakayama T, Nasi M, Neumann K, Ng LG, Niedobitek A, Nourshargh S, Núñez G, O’Connor JE, Ochel A, Oja A, Ordonez D, Orfao A, Orlowski-Oliver E, Ouyang W, Oxenius A, Palankar R, Panse I, Pattanapanyasat K, Paulsen M, Pavlinic D, Penter L, Peterson P, Peth C, Petriz J, Piancone F, Pickl WF, Piconese S, Pinti M, Pockley AG, Podolska MJ, Poon Z, Pracht K, Prinz I, Pucillo CEM, Quataert SA, Quatrini L, Quinn KM, Radbruch H, Radstake TRDJ, Rahmig S, Rahn HP, Rajwa B, Ravichandran G, Raz Y, Rebhahn JA, Recktenwald D, Reimer D, e Sousa CR, Remmerswaal EB, Richter L, Rico LG, Riddell A, Rieger AM, Robinson JP, Romagnani C, Rubartelli A, Ruland J, Saalmüller A, Saeys Y, Saito T, Sakaguchi S, de-Oyanguren FS, Samstag Y, Sanderson S, Sandrock I, Santoni A, Sanz RB, Saresella M, Sautes-Fridman C, Sawitzki B, Schadt L, Scheffold A, Scherer HU, Schiemann M, Schildberg FA, Schimisky E, Schlitzer A, Schlosser J, Schmid S, Schmitt S, Schober K, Schraivogel D, Schuh W, Schüler T, Schulte R, Schulz AR, Schulz SR, Scottá C, Scott-Algara D, Sester DP, Shankey TV, Silva-Santos B, Simon AK, Sitnik KM, Sozzani S, Speiser DE, Spidlen J, Stahlberg A, Stall AM, Stanley N, Stark R, Stehle C, Steinmetz T, Stockinger H, Takahama Y, Takeda K, Tan L, Tárnok A, Tiegs G, Toldi G, Tornack J, Traggiai E, Trebak M, Tree TI, Trotter J, Trowsdale J, Tsoumakidou M, Ulrich H, Urbanczyk S, van de Veen W, van den Broek M, van der Pol E, Van Gassen S, Van Isterdael G, van Lier RA, Veldhoen M, Vento-Asturias S, Vieira P, Voehringer D, Volk HD, von Borstel A, von Volkmann K, Waisman A, Walker RV, Wallace PK, Wang SA, Wang XM, Ward MD, Ward-Hartstonge KA, Warnatz K, Warnes G, Warth S, Waskow C, Watson JV, Watzl C, Wegener L, Weisenburger T, Wiedemann A, Wienands J, Wilharm A, Wilkinson RJ, Willimsky G, Wing JB, Winkelmann R, Winkler TH, Wirz OF, Wong A, Wurst P, Yang JHM, Yang J, Yazdanbakhsh M, Yu L, Yue A, Zhang H, Zhao Y, Ziegler SM, Zielinski C, Zimmermann J, Zychlinsky A. Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). Eur J Immunol 2019; 49:1457-1973. [PMID: 31633216 PMCID: PMC7350392 DOI: 10.1002/eji.201970107] [Citation(s) in RCA: 710] [Impact Index Per Article: 142.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
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Affiliation(s)
- Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, Univ. of Modena and Reggio Emilia School of Medicine, Modena, Italy
| | - Hyun-Dong Chang
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Andreas Radbruch
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Andreas Acs
- Department of Biology, Nikolaus-Fiebiger-Center for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Dieter Adam
- Institut für Immunologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Sabine Adam-Klages
- Institut für Transfusionsmedizin, Universitätsklinik Schleswig-Holstein, Kiel, Germany
| | - William W. Agace
- Mucosal Immunology group, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
- Immunology Section, Lund University, Lund, Sweden
| | - Nima Aghaeepour
- Departments of Anesthesiology, Pain and Perioperative Medicine; Biomedical Data Sciences; and Pediatrics, Stanford University, Stanford, CA, USA
| | - Mübeccel Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Matthieu Allez
- Université de Paris, Institut de Recherche Saint-Louis, INSERM U1160, and Gastroenterology Department, Hôpital Saint-Louis – APHP, Paris, France
| | | | - Giorgia Alvisi
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center, Rozzano, Italy
| | | | - Immanuel Andrä
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | - Francesco Annunziato
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Achille Anselmo
- Flow Cytometry Core, Humanitas Clinical and Research Center, Milan, Italy
| | - Petra Bacher
- Institut für Immunologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
- Institut für Klinische Molekularbiologie, Christian-Albrechts Universität zu Kiel, Germany
| | | | - Sudipto Bari
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
- Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore
| | - Vincenzo Barnaba
- Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy
- Center for Life Nano Science@Sapienza, Istituto Italiano di Tecnologia, Rome, Italy
- Istituto Pasteur - Fondazione Cenci Bolognetti, Rome, Italy
| | | | | | - Wolfgang Bauer
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Sabine Baumgart
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Nicole Baumgarth
- Center for Comparative Medicine & Dept. Pathology, Microbiology & Immunology, University of California, Davis, CA, USA
| | - Dirk Baumjohann
- Institute for Immunology, Faculty of Medicine, Biomedical Center, LMU Munich, Planegg-Martinsried, Germany
| | - Bianka Baying
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Mary Bebawy
- Discipline of Pharmacy, Graduate School of Health, The University of Technology Sydney, Sydney, NSW, Australia
| | - Burkhard Becher
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Comprehensive Cancer Center Zurich, Switzerland
| | - Wolfgang Beisker
- Flow Cytometry Laboratory, Institute of Molecular Toxicology and Pharmacology, Helmholtz Zentrum München, German Research Center for Environmental Health, München, Germany
| | - Vladimir Benes
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Rudi Beyaert
- Department of Biomedical Molecular Biology, Center for Inflammation Research, Ghent University - VIB, Ghent, Belgium
| | - Alfonso Blanco
- Flow Cytometry Core Technologies, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Dominic A. Boardman
- Department of Surgery, The University of British Columbia, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | - Christian Bogdan
- Mikrobiologisches Institut - Klinische Mikrobiologie, Immunologie und Hygiene, Universitätsklinikum Erlangen, Erlangen, Germany
- Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg and Medical Immunology Campus Erlangen, Erlangen, Germany
| | - Jessica G. Borger
- Department of Immunology and Pathology, Monash University, Melbourne, Victoria, Australia
| | - Giovanna Borsellino
- Neuroimmunology and Flow Cytometry Units, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - Philip E. Boulais
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- The Ruth L. and David S. Gottesman Institute for Stem Cell and Regenerative Medicine Research, Bronx, New York, USA
| | | | - Dirk Brenner
- Luxembourg Institute of Health, Department of Infection and Immunity, Experimental and Molecular Immunology, Esch-sur-Alzette, Luxembourg
- Odense University Hospital, Odense Research Center for Anaphylaxis, University of Southern Denmark, Department of Dermatology and Allergy Center, Odense, Denmark
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg
| | - Ryan R. Brinkman
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada
| | - Anna E. S. Brooks
- University of Auckland, School of Biological Sciences, Maurice Wilkins Center, Auckland, New Zealand
| | - Dirk H. Busch
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
- German Center for Infection Research (DZIF), Munich, Germany
- Focus Group “Clinical Cell Processing and Purification”, Institute for Advanced Study, Technische Universität München, Munich, Germany
| | - Martin Büscher
- Biophysics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Timothy P. Bushnell
- Department of Pediatrics and Shared Resource Laboratories, University of Rochester Medical Center, Rochester, NY, USA
| | - Federica Calzetti
- University of Verona, Department of Medicine, Section of General Pathology, Verona, Italy
| | - Garth Cameron
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Ilenia Cammarata
- Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy
| | - Xuetao Cao
- National Key Laboratory of Medical Immunology, Nankai University, Tianjin, China
| | - Susanna L. Cardell
- Department of Microbiology and Immunology, University of Gothenburg, Gothenburg, Sweden
| | - Stefano Casola
- The FIRC Institute of Molecular Oncology (FOM), Milan, Italy
| | - Marco A. Cassatella
- University of Verona, Department of Medicine, Section of General Pathology, Verona, Italy
| | - Andrea Cavani
- National Institute for Health, Migration and Poverty (INMP), Rome, Italy
| | - Antonio Celada
- Macrophage Biology Group, School of Biology, University of Barcelona, Barcelona, Spain
| | - Lucienne Chatenoud
- Université Paris Descartes, Institut National de la Santé et de la Recherche Médicale, Paris, France
| | | | - Sue Chow
- Divsion of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, Ontario, Canada
| | - Eleni Christakou
- Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, UK
- National Institutes of Health Research Biomedical Research Centre at Guy’s and St. Thomas’ National Health Service, Foundation Trust and King’s College London, UK
| | - Luka Čičin-Šain
- Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Mario Clerici
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
- Department of Physiopathology and Transplants, University of Milan, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | | | - Laura Cook
- BC Children’s Hospital Research Institute, Vancouver, Canada
- Department of Medicine, The University of British Columbia, Vancouver, Canada
| | - Anne Cooke
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Andrea M. Cooper
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Alexandra J. Corbett
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Antonio Cosma
- National Cytometry Platform, Luxembourg Institute of Health, Department of Infection and Immunity, Esch-sur-Alzette, Luxembourg
| | - Lorenzo Cosmi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Pierre G. Coulie
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
| | - Ana Cumano
- Unit Lymphopoiesis, Department of Immunology, Institut Pasteur, Paris, France
| | - Ljiljana Cvetkovic
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Van Duc Dang
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Chantip Dang-Heine
- Clinical Research Unit, Berlin Institute of Health (BIH), Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Martin S. Davey
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
| | - Derek Davies
- Flow Cytometry Scientific Technology Platform, The Francis Crick Institute, London, UK
| | - Sara De Biasi
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | | | - Gelo Victoriano Dela Cruz
- Novo Nordisk Foundation Center for Stem Cell Biology – DanStem, University of Copenhagen, Copenhagen, Denmark
| | - Michael Delacher
- Regensburg Center for Interventional Immunology (RCI), Regensburg, Germany
- Chair for Immunology, University Regensburg, Germany
| | - Silvia Della Bella
- Department of Medical Biotechnologies and Translational Medicine, University of Milan, Milan, Italy
| | - Paolo Dellabona
- Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Scientific Institute, Milan, Italy
| | - Günnur Deniz
- Istanbul University, Aziz Sancar Institute of Experimental Medicine, Department of Immunology, Istanbul, Turkey
| | | | - James P. Di Santo
- Innate Immunty Unit, Department of Immunology, Institut Pasteur, Paris, France
- Institut Pasteur, Inserm U1223, Paris, France
| | - Andreas Diefenbach
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Laboratory of Innate Immunity, Department of Microbiology, Infectious Diseases and Immunology, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Francesco Dieli
- University of Palermo, Central Laboratory of Advanced Diagnosis and Biomedical Research, Department of Biomedicine, Neurosciences and Advanced Diagnostics, Palermo, Italy
| | - Andreas Dolf
- Flow Cytometry Core Facility, Institute of Experimental Immunology, University of Bonn, Bonn, Germany
| | - Thomas Dörner
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Dept. Medicine/Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Germany
| | - Regine J. Dress
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Diana Dudziak
- Department of Dermatology, Laboratory of Dendritic Cell Biology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Michael Dustin
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Charles-Antoine Dutertre
- Program in Emerging Infectious Disease, Duke-NUS Medical School, Singapore
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Friederike Ebner
- Institute of Immunology, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin, Germany
| | - Sidonia B. G. Eckle
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Matthias Edinger
- Regensburg Center for Interventional Immunology (RCI), Regensburg, Germany
- Department of Internal Medicine III, University Hospital Regensburg, Germany
| | - Pascale Eede
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neuropathology, Germany
| | | | - Marcus Eich
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), Heidelberg, Germany
| | - Pablo Engel
- University of Barcelona, Faculty of Medicine and Health Sciences, Department of Biomedical Sciences, Barcelona, Spain
| | | | - Anna Erdei
- Department of Immunology, University L. Eotvos, Budapest, Hungary
| | - Charlotte Esser
- Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Bart Everts
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Maximilien Evrard
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Christine S. Falk
- Institute of Transplant Immunology, Hannover Medical School, MHH, Hannover, Germany
| | - Todd A. Fehniger
- Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Mar Felipo-Benavent
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Principe Felipe Research Center, Valencia, Spain
| | - Helen Ferry
- Experimental Medicine Division, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Markus Feuerer
- Regensburg Center for Interventional Immunology (RCI), Regensburg, Germany
- Chair for Immunology, University Regensburg, Germany
| | - Andrew Filby
- The Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | - Simon Fillatreau
- Institut Necker-Enfants Malades, Université Paris Descartes Sorbonne Paris Cité, Faculté de Médecine, AP-HP, Hôpital Necker Enfants Malades, INSERM U1151-CNRS UMR 8253, Paris, France
| | - Marie Follo
- Department of Medicine I, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Universitaetsklinikum FreiburgLighthouse Core Facility, Zentrum für Translationale Zellforschung, Klinik für Innere Medizin I, Freiburg, Germany
| | - Irmgard Förster
- Immunology and Environment, LIMES Institute, University of Bonn, Bonn, Germany
| | | | - Gemma A. Foulds
- John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK
| | - Britta Frehse
- Institute for Systemic Inflammation Research, University of Luebeck, Luebeck, Germany
| | - Paul S. Frenette
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- The Ruth L. and David S. Gottesman Institute for Stem Cell and Regenerative Medicine Research, Bronx, New York, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Stefan Frischbutter
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Dermatology, Venereology and Allergology
| | - Wolfgang Fritzsche
- Nanobiophotonics Department, Leibniz Institute of Photonic Technology (IPHT), Jena, Germany
| | - David W. Galbraith
- School of Plant Sciences and Bio5 Institute, University of Arizona, Tucson, USA
- Honorary Dean of Life Sciences, Henan University, Kaifeng, China
| | - Anastasia Gangaev
- Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Natalio Garbi
- Institute of Experimental Immunology, University of Bonn, Germany
| | - Brice Gaudilliere
- Stanford Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, CA, USA
| | - Ricardo T. Gazzinelli
- Fundação Oswaldo Cruz - Minas, Laboratory of Immunopatology, Belo Horizonte, MG, Brazil
- Department of Mecicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jens Geginat
- INGM - Fondazione Istituto Nazionale di Genetica Molecolare “Ronmeo ed Enrica Invernizzi”, Milan, Italy
| | - Wilhelm Gerner
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Austria
- Christian Doppler Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Austria
| | - Nicholas A. Gherardin
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lara Gibellini
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | - Florent Ginhoux
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keisuke Goda
- Department of Bioengineering, University of California, Los Angeles, California, USA
- Department of Chemistry, University of Tokyo, Tokyo, Japan
- Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Dale I. Godfrey
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | | | - Jose M. González-Navajas
- Alicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain
- Networked Biomedical Research Center for Hepatic and Digestive Diseases (CIBERehd), Madrid, Spain
| | - Carl S. Goodyear
- Institute of Infection Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow Biomedical Research Centre, Glasgow, UK
| | - Andrea Gori
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, University of Milan
| | - Jane L. Grogan
- Cancer Immunology Research, Genentech, South San Francisco, CA, USA
| | | | - Andreas Grützkau
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Claudia Haftmann
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Jonas Hahn
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Medicine 3, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Hamida Hammad
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Zwijnaarde, Belgium
| | | | - Leo Hansmann
- Berlin Institute of Health (BIH), Berlin, Germany
- German Cancer Consortium (DKTK), partner site Berlin, Berlin, Germany
- Department of Hematology, Oncology, and Tumor Immunology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum, Berlin, Germany
| | - Goran Hansson
- Department of Medicine and Center for Molecular Medicine at Karolinska University Hospital, Solna, Sweden
| | | | - Susanne Hartmann
- Institute of Immunology, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin, Germany
| | - Andrea Hauser
- Department of Internal Medicine III, University Hospital Regensburg, Germany
| | - Anja E. Hauser
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin
- Department of Rheumatology and Clinical Immunology, Berlin Institute of Health, Berlin, Germany
| | - David L. Haviland
- Flow Cytometry, Houston Methodist Hospital Research Institute, Houston, TX, USA
| | - David Hedley
- Divsion of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, Ontario, Canada
| | - Daniela C. Hernández
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Medical Department I, Division of Gastroenterology, Infectiology and Rheumatology, Berlin, Germany
| | - Guadalupe Herrera
- Cytometry Service, Incliva Foundation. Clinic Hospital and Faculty of Medicine, University of Valencia, Valencia, Spain
| | - Martin Herrmann
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Medicine 3, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Christoph Hess
- Immunobiology Laboratory, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Thomas Höfer
- German Cancer Research Center (DKFZ), Division of Theoretical Systems Biology, Heidelberg, Germany
| | - Petra Hoffmann
- Regensburg Center for Interventional Immunology (RCI), Regensburg, Germany
- Department of Internal Medicine III, University Hospital Regensburg, Germany
| | - Kristin Hogquist
- Center for Immunology, University of Minnesota, Minneapolis, MN, USA
| | - Tristan Holland
- Institute of Experimental Immunology, University of Bonn, Germany
| | - Thomas Höllt
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Computer Graphics and Visualization, Department of Intelligent Systems, TU Delft, Delft, The Netherlands
| | | | - Pleun Hombrink
- Department of Experimental Immunology, Amsterdam Infection and Immunity Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jessica P. Houston
- Department of Chemical & Materials Engineering, New Mexico State University, Las Cruces, NM, USA
| | - Bimba F. Hoyer
- Rheumatologie/Klinische Immunologie, Klinik für Innere Medizin I und Exzellenzzentrum Entzündungsmedizin, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Bo Huang
- Department of Immunology & National Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China
| | - Fang-Ping Huang
- Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, China
| | - Johanna E. Huber
- Institute for Immunology, Faculty of Medicine, Biomedical Center, LMU Munich, Planegg-Martinsried, Germany
| | - Jochen Huehn
- Experimental Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Michael Hundemer
- Department of Hematology, Oncology and Rheumatology, University Heidelberg, Heidelberg, Germany
| | - Christopher A. Hunter
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - William Y. K. Hwang
- Department of Hematology, Singapore General Hospital, Singapore
- Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore
- Executive Offices, National Cancer Centre Singapore, Singapore
| | - Anna Iannone
- Department of Diagnostic Medicine, Clinical and Public Health, Univ. of Modena and Reggio Emilia, Modena, Italy
| | - Florian Ingelfinger
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Sabine M Ivison
- Department of Surgery, The University of British Columbia, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
| | - Hans-Martin Jäck
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Peter K. Jani
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Beatriz Jávega
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Department of Biochemistry and Molecular Biology, University of Valencia, Valencia, Spain
| | - Stipan Jonjic
- Department of Histology and Embryology/Center for Proteomics, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Toralf Kaiser
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Tomas Kalina
- Department of Paediatric Haematology and Oncology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Thomas Kamradt
- Jena University Hospital, Institute of Immunology, Jena, Germany
| | | | - Baerbel Keller
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Steven L. C. Ketelaars
- Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Ahad Khalilnezhad
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Srijit Khan
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Jan Kisielow
- Institute of Molecular Health Sciences, ETH Zurich, Zürich, Switzerland
| | - Paul Klenerman
- Experimental Medicine Division, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmin Knopf
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Medicine 3, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Hui-Fern Koay
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Katja Kobow
- Department of Neuropathology, Universitätsklinikum Erlangen, Germany
| | - Jay K. Kolls
- John W Deming Endowed Chair in Internal Medicine, Center for Translational Research in Infection and Inflammation Tulane School of Medicine, New Orleans, LA, USA
| | - Wan Ting Kong
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Manfred Kopf
- Institute of Molecular Health Sciences, ETH Zurich, Zürich, Switzerland
| | - Thomas Korn
- Department of Neurology, Technical University of Munich, Munich, Germany
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, University Heidelberg, Heidelberg, Germany
| | - Hendy Kristyanto
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas Kroneis
- Division of Cell Biology, Histology & Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - Andreas Krueger
- Institute for Molecular Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Jenny Kühne
- Institute of Transplant Immunology, Hannover Medical School, MHH, Hannover, Germany
| | - Christian Kukat
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Désirée Kunkel
- Flow & Mass Cytometry Core Facility, Charité - Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany
- BCRT Flow Cytometry Lab, Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin
| | - Heike Kunze-Schumacher
- Institute for Molecular Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tomohiro Kurosaki
- WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Christian Kurts
- Institute of Experimental Immunology, University of Bonn, Germany
| | - Pia Kvistborg
- Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Immanuel Kwok
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Jonathan Landry
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Olivier Lantz
- INSERM U932, PSL University, Institut Curie, Paris, France
| | - Paola Lanuti
- Department of Medicine and Aging Sciences, Centre on Aging Sciences and Translational Medicine (Ce.S.I.-Me.T.), University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Francesca LaRosa
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Agnès Lehuen
- Institut Cochin, CNRS8104, INSERM1016, Department of Endocrinology, Metabolism and Diabetes, Université de Paris, Paris, France
| | | | - Michael D. Leipold
- The Human Immune Monitoring Center (HIMC), Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, CA, USA
| | - Leslie Y.T. Leung
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Megan K. Levings
- Department of Surgery, The University of British Columbia, Vancouver, Canada
- BC Children’s Hospital Research Institute, Vancouver, Canada
- School of Biomedical Engineering, The University of British Columbia, Vancouver, Canada
| | - Andreia C. Lino
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Dept. Medicine/Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Germany
| | - Francesco Liotta
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | | | - Yanling Liu
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Hans-Gustaf Ljunggren
- Center for Infectious Medicine, Department of Medicine Huddinge, ANA Futura, Karolinska Institutet, Stockholm, Sweden
| | - Michael Lohoff
- Inst. f. Med. Mikrobiology and Hospital Hygiene, University of Marburg, Germany
| | - Giovanna Lombardi
- King’s College London, “Peter Gorer” Department of Immunobiology, London, UK
| | | | - Miguel López-Botet
- IMIM(Hospital de Mar Medical Research Institute), University Pompeu Fabra, Barcelona, Spain
| | - Amy E. Lovett-Racke
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA
| | - Erik Lubberts
- Department of Rheumatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Herve Luche
- Centre d’Immunophénomique - CIPHE (PHENOMIN), Aix Marseille Université (UMS3367), Inserm (US012), CNRS (UMS3367), Marseille, France
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Enrico Lugli
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center, Rozzano, Italy
- Flow Cytometry Core, Humanitas Clinical and Research Center, Milan, Italy
| | - Sebastian Lunemann
- Department of Virus Immunology, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Holden T. Maecker
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Maggi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Orla Maguire
- Flow and Image Cytometry Shared Resource, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Florian Mair
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
| | - Kerstin H. Mair
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Austria
- Christian Doppler Laboratory for Optimized Prediction of Vaccination Success in Pigs, Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Austria
| | - Alberto Mantovani
- Istituto Clinico Humanitas IRCCS and Humanitas University, Pieve Emanuele, Milan, Italy
- William Harvey Research Institute, Queen Mary University, London, United Kingdom
| | - Rudolf A. Manz
- Institute for Systemic Inflammation Research, University of Luebeck, Luebeck, Germany
| | - Aaron J. Marshall
- Department of Immunology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Glòria Martrus
- Department of Virus Immunology, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Ivana Marventano
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Wlodzimierz Maslinski
- National Institute of Geriatrics, Rheumatology and Rehabilitation, Department of Pathophysiology and Immunology, Warsaw, Poland
| | - Giuseppe Matarese
- Treg Cell Lab, Dipartimento di Medicina Molecolare e Biotecologie Mediche, Università di Napoli Federico II and Istituto per l’Endocrinologia e l’Oncologia Sperimentale, Consiglio Nazionale delle Ricerche (IEOS-CNR), Napoli, Italy
| | - Anna Vittoria Mattioli
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
- Lab of Clinical and Experimental Immunology, Humanitas Clinical and Research Center, Rozzano, Milan, Italy
| | - Christian Maueröder
- Cell Clearance in Health and Disease Lab, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Alessio Mazzoni
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - James McCluskey
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia
| | - Mairi McGrath
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Helen M. McGuire
- Ramaciotti Facility for Human Systems Biology, and Discipline of Pathology, The University of Sydney, Camperdown, Australia
| | - Iain B. McInnes
- Institute of Infection Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow Biomedical Research Centre, Glasgow, UK
| | - Henrik E. Mei
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Fritz Melchers
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Susanne Melzer
- Clinical Trial Center Leipzig, University Leipzig, Leipzig, Germany
| | - Dirk Mielenz
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Stephen D. Miller
- Interdepartmental Immunobiology Center, Dept. of Microbiology-Immunology, Northwestern Univ. Medical School, Chicago, IL, USA
| | - Kingston H.G. Mills
- Trinity College Dublin, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Dublin, Ireland
| | - Hans Minderman
- Flow and Image Cytometry Shared Resource, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Jenny Mjösberg
- Center for Infectious Medicine, Department of Medicine Huddinge, ANA Futura, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical and Experimental Medine, Linköping University, Linköping, Sweden
| | - Jonni Moore
- Abramson Cancer Center Flow Cytometry and Cell Sorting Shared Resource, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Barry Moran
- Trinity College Dublin, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Dublin, Ireland
| | - Lorenzo Moretta
- Department of Immunology, IRCCS Bambino Gesu Children’s Hospital, Rome, Italy
| | - Tim R. Mosmann
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | - Susann Müller
- Centre for Environmental Research - UFZ, Department Environmental Microbiology, Leipzig, Germany
| | - Gabriele Multhoff
- Institute for Innovative Radiotherapy (iRT), Experimental Immune Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Radiation Immuno-Oncology Group, Center for Translational Cancer Research Technische Universität München (TranslaTUM), Klinikum rechts der Isar, Munich, Germany
| | - Luis Enrique Muñoz
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Medicine 3, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Christian Münz
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Comprehensive Cancer Center Zurich, Switzerland
| | - Toshinori Nakayama
- Department of Immunology, Graduate School of Medicine, Chiba University, Chiba city, Chiba, Japan
| | - Milena Nasi
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | - Katrin Neumann
- Institute of Experimental Immunology and Hepatology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lai Guan Ng
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
- Discipline of Dermatology, University of Sydney, Sydney, New South Wales, Australia
- State Key Laboratory of Experimental Hematology, Institute of Hematology, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Antonia Niedobitek
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Sussan Nourshargh
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - Gabriel Núñez
- Department of Pathology and Rogel Cancer Center, the University of Michigan, Ann Arbor, Michigan, USA
| | - José-Enrique O’Connor
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Department of Biochemistry and Molecular Biology, University of Valencia, Valencia, Spain
| | - Aaron Ochel
- Institute of Experimental Immunology and Hepatology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Anna Oja
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Diana Ordonez
- Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Alberto Orfao
- Department of Medicine, Cancer Research Centre (IBMCC-CSIC/USAL), Cytometry Service, University of Salamanca, CIBERONC and Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Eva Orlowski-Oliver
- Burnet Institute, AMREP Flow Cytometry Core Facility, Melbourne, Victoria, Australia
| | - Wenjun Ouyang
- Inflammation and Oncology, Research, Amgen Inc, South San Francisco, USA
| | | | - Raghavendra Palankar
- Department of Transfusion Medicine, Institute of Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Isabel Panse
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Kovit Pattanapanyasat
- Center of Excellence for Flow Cytometry, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Malte Paulsen
- Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Dinko Pavlinic
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Livius Penter
- Department of Hematology, Oncology, and Tumor Immunology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum, Berlin, Germany
| | - Pärt Peterson
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Christian Peth
- Biophysics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Jordi Petriz
- Functional Cytomics Group, Josep Carreras Leukaemia Research Institute, Campus ICO-Germans Trias i Pujol, Universitat Autònoma de Barcelona, UAB, Badalona, Spain
| | - Federica Piancone
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Winfried F. Pickl
- Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Silvia Piconese
- Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Rome, Italy
- Istituto Pasteur - Fondazione Cenci Bolognetti, Rome, Italy
| | - Marcello Pinti
- Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - A. Graham Pockley
- John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK
- Chromocyte Limited, Electric Works, Sheffield, UK
| | - Malgorzata Justyna Podolska
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Medicine 3, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
- Department for Internal Medicine 3, Institute for Rheumatology and Immunology, AG Munoz, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Zhiyong Poon
- Department of Hematology, Singapore General Hospital, Singapore
| | - Katharina Pracht
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Immo Prinz
- Institute of Immunology, Hannover Medical School, Hannover, Germany
| | | | - Sally A. Quataert
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | - Linda Quatrini
- Department of Immunology, IRCCS Bambino Gesu Children’s Hospital, Rome, Italy
| | - Kylie M. Quinn
- School of Biomedical and Health Sciences, RMIT University, Bundoora, Victoria, Australia
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
| | - Helena Radbruch
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neuropathology, Germany
| | - Tim R. D. J. Radstake
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Susann Rahmig
- Regeneration in Hematopoiesis, Leibniz-Institute on Aging, Fritz-Lipmann-Institute (FLI), Jena, Germany
| | - Hans-Peter Rahn
- Preparative Flow Cytometry, Max-Delbrück-Centrum für Molekulare Medizin, Berlin, Germany
| | - Bartek Rajwa
- Bindley Biosciences Center, Purdue University, West Lafayette, IN, USA
| | - Gevitha Ravichandran
- Institute of Experimental Immunology and Hepatology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yotam Raz
- Department of Internal Medicine, Groene Hart Hospital, Gouda, The Netherlands
| | - Jonathan A. Rebhahn
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Dorothea Reimer
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | | | - Ester B.M. Remmerswaal
- Department of Experimental Immunology, Amsterdam Infection and Immunity Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Renal Transplant Unit, Division of Internal Medicine, Academic Medical Centre, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Lisa Richter
- Core Facility Flow Cytometry, Biomedical Center, Ludwig-Maximilians-University Munich, Germany
| | - Laura G. Rico
- Functional Cytomics Group, Josep Carreras Leukaemia Research Institute, Campus ICO-Germans Trias i Pujol, Universitat Autònoma de Barcelona, UAB, Badalona, Spain
| | - Andy Riddell
- Flow Cytometry Scientific Technology Platform, The Francis Crick Institute, London, UK
| | - Aja M. Rieger
- Department of Medical Microbiology and Immunology, University of Alberta, Alberta, Canada
| | - J. Paul Robinson
- Purdue University Cytometry Laboratories, Purdue University, West Lafayette, IN, USA
| | - Chiara Romagnani
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Medical Department I, Division of Gastroenterology, Infectiology and Rheumatology, Berlin, Germany
| | - Anna Rubartelli
- Cell Biology Unit, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Jürgen Ruland
- Institut für Klinische Chemie und Pathobiochemie, Fakultät für Medizin, Technische Universität München, München, Germany
| | - Armin Saalmüller
- Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine Vienna, Austria
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Takashi Saito
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shimon Sakaguchi
- WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Francisco Sala de-Oyanguren
- Flow Cytometry Facility, Ludwig Cancer Institute, Faculty of Medicine and Biology, University of Lausanne, Epalinges, Switzerland
| | - Yvonne Samstag
- Heidelberg University, Institute of Immunology, Section of Molecular Immunology, Heidelberg, Germany
| | - Sharon Sanderson
- Translational Immunology Laboratory, NIHR BRC, University of Oxford, Kennedy Institute of Rheumatology, Oxford, UK
| | - Inga Sandrock
- Institute of Immunology, Hannover Medical School, Hannover, Germany
| | - Angela Santoni
- Department of Molecular Medicine, Sapienza University of Rome, IRCCS, Neuromed, Pozzilli, Italy
| | - Ramon Bellmàs Sanz
- Institute of Transplant Immunology, Hannover Medical School, MHH, Hannover, Germany
| | - Marina Saresella
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | | | - Birgit Sawitzki
- Charité – Universitätsmedizin Berlin, and Berlin Institute of Health, Institute of Medical Immunology, Berlin, Germany
| | - Linda Schadt
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Comprehensive Cancer Center Zurich, Switzerland
| | - Alexander Scheffold
- Institut für Immunologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Hans U. Scherer
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthias Schiemann
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | - Frank A. Schildberg
- Clinic for Orthopedics and Trauma Surgery, University Hospital Bonn, Bonn, Germany
| | | | - Andreas Schlitzer
- Quantitative Systems Biology, Life & Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Josephine Schlosser
- Institute of Immunology, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin, Germany
| | - Stephan Schmid
- Internal Medicine I, University Hospital Regensburg, Germany
| | - Steffen Schmitt
- Flow Cytometry Core Facility, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Kilian Schober
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | - Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Wolfgang Schuh
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Thomas Schüler
- Institute of Molecular and Clinical Immunology, Otto-von-Guericke University, Magdeburg, Germany
| | - Reiner Schulte
- University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK
| | - Axel Ronald Schulz
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Sebastian R. Schulz
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Cristiano Scottá
- King’s College London, “Peter Gorer” Department of Immunobiology, London, UK
| | - Daniel Scott-Algara
- Institut Pasteur, Cellular Lymphocytes Biology, Immunology Departement, Paris, France
| | - David P. Sester
- TRI Flow Cytometry Suite (TRI.fcs), Translational Research Institute, Wooloongabba, QLD, Australia
| | | | - Bruno Silva-Santos
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | | | - Katarzyna M. Sitnik
- Department of Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Silvano Sozzani
- Dept. Molecular Translational Medicine, University of Brescia, Brescia, Italy
| | - Daniel E. Speiser
- Department of Oncology, University of Lausanne and CHUV, Epalinges, Switzerland
| | | | - Anders Stahlberg
- Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | | | - Natalie Stanley
- Departments of Anesthesiology, Pain and Perioperative Medicine; Biomedical Data Sciences; and Pediatrics, Stanford University, Stanford, CA, USA
| | - Regina Stark
- Department of Experimental Immunology, Amsterdam Infection and Immunity Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Christina Stehle
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Medical Department I, Division of Gastroenterology, Infectiology and Rheumatology, Berlin, Germany
| | - Tobit Steinmetz
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Hannes Stockinger
- Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | | | - Kiyoshi Takeda
- WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Leonard Tan
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Attila Tárnok
- Departement for Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
- Department of Precision Instruments, Tsinghua University, Beijing, China
| | - Gisa Tiegs
- Institute of Experimental Immunology and Hepatology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Julia Tornack
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- BioGenes GmbH, Berlin, Germany
| | - Elisabetta Traggiai
- Novartis Biologics Center, Mechanistic Immunology Unit, Novartis Institute for Biomedical Research, NIBR, Basel, Switzerland
| | - Mohamed Trebak
- Department of Cellular and Molecular Physiology, Penn State University College of Medicine, PA, United States
| | - Timothy I.M. Tree
- Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, UK
- National Institutes of Health Research Biomedical Research Centre at Guy’s and St. Thomas’ National Health Service, Foundation Trust and King’s College London, UK
| | | | - John Trowsdale
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | - Henning Ulrich
- Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, SP, Brazil
| | - Sophia Urbanczyk
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Willem van de Veen
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
- Christine Kühne Center for Allergy Research and Education (CK-CARE), Davos, Switzerland
| | - Maries van den Broek
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- Comprehensive Cancer Center Zurich, Switzerland
| | - Edwin van der Pol
- Vesicle Observation Center; Biomedical Engineering & Physics; Laboratory Experimental Clinical Chemistry; Amsterdam University Medical Centers, Location AMC, The Netherlands
| | - Sofie Van Gassen
- Data Mining and Modeling for Biomedicine, VIB-UGent Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | | | - René A.W. van Lier
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marc Veldhoen
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | | | - Paulo Vieira
- Unit Lymphopoiesis, Department of Immunology, Institut Pasteur, Paris, France
| | - David Voehringer
- Department of Infection Biology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Hans-Dieter Volk
- BIH Center for Regenerative Therapies (BCRT) Charité Universitätsmedizin Berlin and Berlin Institute of Health, Core Unit ImmunoCheck
| | - Anouk von Borstel
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
| | | | - Ari Waisman
- Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | | | - Paul K. Wallace
- Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, USA
| | - Sa A. Wang
- Dept of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin M. Wang
- The Scientific Platforms, the Westmead Institute for Medical Research, the Westmead Research Hub, Westmead, New South Wales, Australia
| | | | | | - Klaus Warnatz
- Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gary Warnes
- Flow Cytometry Core Facility, Blizard Institute, Queen Mary London University, London, UK
| | - Sarah Warth
- BCRT Flow Cytometry Lab, Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin
| | - Claudia Waskow
- Regeneration in Hematopoiesis, Leibniz-Institute on Aging, Fritz-Lipmann-Institute (FLI), Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany
| | | | - Carsten Watzl
- Department for Immunology, Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), Dortmund, Germany
| | - Leonie Wegener
- Biophysics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Thomas Weisenburger
- Department of Biology, Nikolaus-Fiebiger-Center for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Annika Wiedemann
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Dept. Medicine/Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Germany
| | - Jürgen Wienands
- Institute for Cellular & Molecular Immunology, University Medical Center Göttingen, Göttingen, Germany
| | - Anneke Wilharm
- Institute of Immunology, Hannover Medical School, Hannover, Germany
| | - Robert John Wilkinson
- Department of Infectious Disease, Imperial College London, UK
- Wellcome Centre for Infectious Diseases Research in Africa and Department of Medicine, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Republic of South Africa
- Tuberculosis Laboratory, The Francis Crick Institute, London, UK
| | - Gerald Willimsky
- Cooperation Unit for Experimental and Translational Cancer Immunology, Institute of Immunology (Charité - Universitätsmedizin Berlin) and German Cancer Research Center (DKFZ), Berlin, Germany
| | - James B. Wing
- WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Rieke Winkelmann
- Institut für Immunologie, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Thomas H. Winkler
- Department of Biology, Nikolaus-Fiebiger-Center for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Oliver F. Wirz
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Alicia Wong
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Peter Wurst
- University Bonn, Medical Faculty, Bonn, Germany
| | - Jennie H. M. Yang
- Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, UK
- National Institutes of Health Research Biomedical Research Centre at Guy’s and St. Thomas’ National Health Service, Foundation Trust and King’s College London, UK
| | - Juhao Yang
- Experimental Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Maria Yazdanbakhsh
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Alice Yue
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Hanlin Zhang
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Yi Zhao
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Susanne Maria Ziegler
- Department of Virus Immunology, Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Christina Zielinski
- German Center for Infection Research (DZIF), Munich, Germany
- Institute of Virology, Technical University of Munich, Munich, Germany
- TranslaTUM, Technical University of Munich, Munich, Germany
| | - Jakob Zimmermann
- Maurice Müller Laboratories (Department of Biomedical Research), Universitätsklinik für Viszerale Chirurgie und Medizin Inselspital, University of Bern, Bern, Switzerland
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Palm AKE, Henry C. Remembrance of Things Past: Long-Term B Cell Memory After Infection and Vaccination. Front Immunol 2019; 10:1787. [PMID: 31417562 PMCID: PMC6685390 DOI: 10.3389/fimmu.2019.01787] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/16/2019] [Indexed: 02/03/2023] Open
Abstract
The success of vaccines is dependent on the generation and maintenance of immunological memory. The immune system can remember previously encountered pathogens, and memory B and T cells are critical in secondary responses to infection. Studies in mice have helped to understand how different memory B cell populations are generated following antigen exposure and how affinity for the antigen is determinant to B cell fate. Additionally, such studies were fundamental in defining memory B cell niches and how B cells respond following subsequent exposure with the same antigen. On the other hand, human studies are essential to the development of better, newer vaccines but sometimes limited by the difficulty to access primary and secondary lymphoid organs. However, work using human influenza and HIV virus infection and/or immunization in particular has significantly advanced today's understanding of memory B cells. This review will focus on the generation, function, and longevity of B-cell mediated immunological memory (memory B cells and plasma cells) in response to infection and vaccination both in mice and in humans.
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Affiliation(s)
- Anna-Karin E Palm
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Carole Henry
- Section of Rheumatology, Department of Medicine, University of Chicago, Chicago, IL, United States
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Reiter M, Diem M, Schumich A, Maurer-Granofszky M, Karawajew L, Rossi JG, Ratei R, Groeneveld-Krentz S, Sajaroff EO, Suhendra S, Kampel M, Dworzak MN. Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning. Cytometry A 2019; 95:966-975. [PMID: 31282025 DOI: 10.1002/cyto.a.23852] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/30/2019] [Accepted: 05/28/2019] [Indexed: 12/22/2022]
Abstract
Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymphoblastic leukemia (B-ALL). However, reliable flow cytometric detection of MRD strongly depends on operator skills and expert knowledge. Hence, an objective, automated tool for reliable FCM-MRD quantification, able to overcome the technical diversity and analytical subjectivity, would be most helpful. We developed a supervised machine learning approach using a combination of multiple Gaussian Mixture Models (GMM) as a parametric density model. The approach was used for finding the weights of a linear combination of multiple GMMs to represent new, "unseen" samples by an interpolation of stored samples. The experimental data set contained FCM-MRD data of 337 bone marrow samples collected at day 15 of induction therapy in three different laboratories from pediatric patients with B-ALL for which accurate, expert-set gates existed. We compared MRD quantification by our proposed GMM approach to operator assessments, its performance on data from different laboratories, as well as to other state-of-the-art automated read-out methods. Our proposed GMM-combination approach proved superior over support vector machines, deep neural networks, and a single GMM approach in terms of precision and average F 1 -scores. A high correlation of expert operator-based and automated MRD assessment was achieved with reliable automated MRD quantification (F 1 -scores >0.5 in more than 95% of samples) in the clinically relevant range. Although best performance was found, if test and training samples were from the same system (i.e., flow cytometer and staining panel; lowest median F 1 -score 0.92), cross-system performance remained high with a median F 1 -score above 0.85 in all settings. In conclusion, our proposed automated approach could potentially be used to assess FCM-MRD in B-ALL in an objective and standardized manner across different laboratories. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Michael Reiter
- Immunological Diagnostics, Children's Cancer Research Institute, Vienna, Austria.,Computer Vision Lab, Faculty of Informatics, Technical University of Vienna, Vienna, Austria
| | - Markus Diem
- Immunological Diagnostics, Children's Cancer Research Institute, Vienna, Austria.,Computer Vision Lab, Faculty of Informatics, Technical University of Vienna, Vienna, Austria
| | - Angela Schumich
- Immunological Diagnostics, Children's Cancer Research Institute, Vienna, Austria
| | | | - Leonid Karawajew
- Department of Pediatric Oncology/Hematology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jorge G Rossi
- Cellular Immunology Laboratory, Hospital de Pediatria "Dr. Juan P. Garrahan", Buenos Aires, Argentina
| | - Richard Ratei
- Department of Hematology, Oncology and Tumor Immunology, HELIOS Klinikum Berlin-Buch, Berlin, Germany
| | | | - Elisa O Sajaroff
- Cellular Immunology Laboratory, Hospital de Pediatria "Dr. Juan P. Garrahan", Buenos Aires, Argentina
| | | | - Martin Kampel
- Computer Vision Lab, Faculty of Informatics, Technical University of Vienna, Vienna, Austria
| | - Michael N Dworzak
- Immunological Diagnostics, Children's Cancer Research Institute, Vienna, Austria.,Labdia Labordiagnostik GmbH, Vienna, Austria
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43
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Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry. Methods Mol Biol 2019. [PMID: 31077112 DOI: 10.1007/978-1-4939-9454-0_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Recent advances in single cell multi-omics methodologies significantly expand our understanding of cellular heterogeneity, particularly in the field of immunology. Today's state-of-the-art flow and mass cytometers can detect up to 50 parameters to comprehensively characterize the identity and function of individual cells within a heterogeneous population. As a consequence, the increasing number of parameters that can be detected simultaneously also introduces substantial complexity for the experimental setup and downstream data processing. However, this challenge in data analysis fostered the development of novel bioinformatic tools to fully exploit the high-dimensional data. These tools will eventually replace cumbersome serial, manual gating in the two-dimensional space driven by a priori knowledge, which still represents the gold standard in flow cytometric analysis, to meet the needs of the today's immunologist. To this end, we provide guidelines for a high-dimensional cytometry workflow including experimental setup, panel design, fluorescent spillover compensation, and data analysis.
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44
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Garimalla S, Nguyen DC, Halliley JL, Tipton C, Rosenberg AF, Fucile CF, Saney CL, Kyu S, Kaminski D, Qian Y, Scheuermann RH, Gibson G, Sanz I, Lee FEH. Differential transcriptome and development of human peripheral plasma cell subsets. JCI Insight 2019; 4:126732. [PMID: 31045577 DOI: 10.1172/jci.insight.126732] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/27/2019] [Indexed: 01/02/2023] Open
Abstract
Human antibody-secreting cells (ASCs) triggered by immunization are globally recognized as CD19loCD38hiCD27hi. Yet, different vaccines give rise to antibody responses of different longevity, suggesting ASC populations are heterogeneous. We define circulating-ASC heterogeneity in vaccine responses using multicolor flow cytometry, morphology, VH repertoire, and RNA transcriptome analysis. We also tested differential survival using a human cell-free system that mimics the bone marrow (BM) microniche. In peripheral blood, we identified 3 CD19+ and 2 CD19- ASC subsets. All subsets contributed to the vaccine-specific responses and were characterized by in vivo proliferation and activation. The VH repertoire demonstrated strong oligoclonality with extensive interconnectivity among the 5 subsets and switched memory B cells. Transcriptome analysis showed separation of CD19+ and CD19- subsets that included pathways such as cell cycle, hypoxia, TNF-α, and unfolded protein response. They also demonstrated similar long-term in vitro survival after 48 days. In summary, vaccine-induced ASCs with different surface markers (CD19 and CD138) are derived from shared proliferative precursors yet express distinctive transcriptomes. Equal survival indicates that all ASC compartments are endowed with long-lived potential. Accordingly, in vivo survival of peripheral long-lived plasma cells may be determined in part by their homing and residence in the BM microniche.
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Affiliation(s)
- Swetha Garimalla
- School of Biological Sciences, Georgia Institute of Technology, and
| | - Doan C Nguyen
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Department of Medicine, Atlanta, Georgia, USA
| | | | - Christopher Tipton
- Division of Rheumatology, Emory University, and.,Lowance Center for Human Immunology in the Departments of Medicine and Pediatrics at Emory University, Atlanta, Georgia, USA
| | - Alexander F Rosenberg
- Department of Microbiology and Informatics Institute, University of Alabama, Birmingham, Alabama, USA
| | | | - Celia L Saney
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Department of Medicine, Atlanta, Georgia, USA
| | - Shuya Kyu
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Department of Medicine, Atlanta, Georgia, USA
| | | | - Yu Qian
- J. Craig Venter Institute, La Jolla, California, USA
| | | | - Greg Gibson
- School of Biological Sciences, Georgia Institute of Technology, and
| | - Iñaki Sanz
- Division of Rheumatology, Emory University, and.,Lowance Center for Human Immunology in the Departments of Medicine and Pediatrics at Emory University, Atlanta, Georgia, USA
| | - F Eun-Hyung Lee
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Department of Medicine, Atlanta, Georgia, USA.,Lowance Center for Human Immunology in the Departments of Medicine and Pediatrics at Emory University, Atlanta, Georgia, USA
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45
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Serra LM, Duncan WD, Diehl AD. An ontology for representing hematologic malignancies: the cancer cell ontology. BMC Bioinformatics 2019; 20:181. [PMID: 31272372 PMCID: PMC6509834 DOI: 10.1186/s12859-019-2722-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data. RESULTS We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies. CONCLUSION We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.
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Affiliation(s)
- Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - William D Duncan
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
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Abstract
Background Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells. Results Here, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error. Conclusions FlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid.
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Affiliation(s)
- Xiaoxin Ye
- Victor Chang Cardiac Research Institute, Sydney, Australia.,University of New South Wales, Sydney, Australia
| | - Joshua W K Ho
- Victor Chang Cardiac Research Institute, Sydney, Australia. .,University of New South Wales, Sydney, Australia. .,School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.
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47
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Lee H, Sun Y, Patti-Diaz L, Hedrick M, Ehrhardt AG. High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating. Bioinform Biol Insights 2019; 13:1177932219838851. [PMID: 30983860 PMCID: PMC6448119 DOI: 10.1177/1177932219838851] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 02/13/2019] [Indexed: 11/30/2022] Open
Abstract
Advancements in flow cytometers with capability to measure 15 or more parameters have enabled us to characterize cell populations at unprecedented levels of detail. Beyond discovery research, there is now a growing demand to dive deeper into evaluating the immune response in clinical trials for immune modulating compounds. However, for high-volume, complex flow cytometry data generated in clinical trials, conventional manual gating remains the standard of practice. Traditional manual gating is resource intense and becomes a bottleneck and an impractical method to complete high volumes of flow cytometry data analysis. Current efforts to automate “manual gating” have shown that computational algorithms can facilitate the analysis of daunting multi-parameter data; however, a greater degree of precision in comparison with traditional manual gating is needed for wide-scale adoption of automated gating methods. In an effort to more closely follow the manual gating process, our automated gating pipeline was created to include negative controls (Fluorescence Minus One [FMO]) to enhance the reliability of gate placement. We demonstrate that use of an automated pipeline, heavily relying on FMO controls for population discrimination, can analyze multi-parameter, large-scale clinical datasets with comparable precision and accuracy to traditional manual gating.
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Affiliation(s)
- Hunjoong Lee
- Hunjoong Lee, Clinical Flow Cytometry, Translational Medicine, Bristol-Myers Squibb, Pennington, NJ 08534, USA.
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48
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Tipton CM, Hom JR, Fucile CF, Rosenberg AF, Sanz I. Understanding B-cell activation and autoantibody repertoire selection in systemic lupus erythematosus: A B-cell immunomics approach. Immunol Rev 2019; 284:120-131. [PMID: 29944759 DOI: 10.1111/imr.12660] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Understanding antibody repertoires and in particular, the properties and fates of B cells expressing potentially pathogenic antibodies is critical to define the mechanisms underlying multiple immunological diseases including autoimmune and allergic conditions as well as transplant rejection. Moreover, an integrated knowledge of the antibody repertoires expressed by B cells and plasma cells (PC) of different functional properties and longevity is essential to develop new therapeutic strategies, better biomarkers for disease segmentation, and new assays to measure restoration of B-cell tolerance or, at least, of normal B-cell homeostasis. Reaching these goals, however, will require a more precise phenotypic, functional and molecular definition of B-cell and PC populations, and a comprehensive analysis of the antigenic reactivity of the antibodies they express. While traditionally hampered by technical and ethical limitations in human experimentation, new technological advances currently enable investigators to address these questions in a comprehensive fashion. In this review, we shall discuss these concepts as they apply to the study of Systemic Lupus Erythematosus.
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Affiliation(s)
- Christopher M Tipton
- Lowance Center for Human Immunology, Division of Rheumatology, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | - Jennifer R Hom
- Lowance Center for Human Immunology, Division of Rheumatology, Department of Medicine, Emory University School of Medicine, Atlanta, GA
| | | | - Alexander F Rosenberg
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL.,Department of Microbiology, University of Alabama, Birmingham, AL
| | - Inaki Sanz
- Lowance Center for Human Immunology, Division of Rheumatology, Department of Medicine, Emory University School of Medicine, Atlanta, GA
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49
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Hejblum BP, Alkhassim C, Gottardo R, Caron F, Thiébaut R. Sequential Dirichlet process mixtures of multivariate skew $t$-distributions for model-based clustering of flow cytometry data. Ann Appl Stat 2019. [DOI: 10.1214/18-aoas1209] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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50
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Touzani F, Pozdzik A. New insights into immune cells cross-talk during IgG4-related disease. Clin Immunol 2018; 198:1-10. [PMID: 30419354 DOI: 10.1016/j.clim.2018.11.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/25/2018] [Accepted: 11/09/2018] [Indexed: 12/24/2022]
Abstract
Immunoglobulin G4-related disease (IgG4-RD) is a newly acknowledged entity, characterized by an immune-mediated fibro-inflammatory process affecting virtually all organs, with infiltration of IgG4+ bearing plasma cells. Until today the pathogenesis of IgG4-RD remains unknown. Treatment with anti-CD20 monoclonal antibodies efficiently induced remission and attenuated the secretory phenotype of myofibroblasts responsible of uncontrolled collagen deposition. This supports the pathogenic role of the adaptive immunity, particularly B cell compartment and B cell/T cell interaction. Latest studies have also highlighted the importance of innate immune system that has been underestimated before and the key role of a specific T cell subset, T follicular helper cells that are involved in IgG4-class-switching and plasmablast differentiation. In this review, we aim to review the most recent knowledge of innate immunity, T and B cells involvement in IgG4-RD, and introduce tertiary lymphoid organs (TLO) as a potential marker of relapse in this condition.
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Affiliation(s)
- Fahd Touzani
- Internal medicine department, Hospital Brugmann, Brussels, Belgium; Nephrology and dialysis clinic, Hospital Brugmann, Brussels, Belgium.
| | - Agnieszka Pozdzik
- Nephrology and dialysis clinic, Hospital Brugmann, Brussels, Belgium; Faculty of Medicine, Université Libre de Bruxelles (ULB), Brussels, Belgium
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