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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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2
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Garg T, Weiss CR, Sheth RA. Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology. Cancers (Basel) 2022; 14:3628. [PMID: 35892890 PMCID: PMC9332307 DOI: 10.3390/cancers14153628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/07/2022] Open
Abstract
In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.
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Affiliation(s)
- Tushar Garg
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (C.R.W.)
| | - Clifford R. Weiss
- Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (T.G.); (C.R.W.)
| | - Rahul A. Sheth
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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3
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Tinnevelt GH, Wouters K, Postma GJ, Folcarelli R, Jansen JJ. High-throughput single cell data analysis - A tutorial. Anal Chim Acta 2021; 1185:338872. [PMID: 34711307 DOI: 10.1016/j.aca.2021.338872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 06/28/2021] [Accepted: 07/21/2021] [Indexed: 11/30/2022]
Abstract
White blood cells protect the body against disease but may also cause chronic inflammation, auto-immune diseases or leukemia. There are many different white blood cell types whose identity and function can be studied by measuring their protein expression. Therefore, high-throughput analytical instruments were developed to measure multiple proteins on millions of single cells. The information-rich biochemistry information may only be fully extracted using multivariate statistics. Here we show an overview of the most essential steps for multivariate data analysis of single cell data. We used white blood cells (immunology) as a case study, but a similar approach may be used in environment or biotech research. The first step is analyzing the study design and subsequently formulating a research question. The three main designs are immunophenotyping (finding different cell types), cell activation and rare cell discovery. When preparing the data it is essential to consider the design and focus on the cell type of interest by removing all unwanted events. After pre-processing, the ten-thousands to millions of single cells per sample need to be converted into a cellular distribution. For immunophenotyping a clustering method such as Self-Organizing Maps is useful and for cell activation a model that describes the covariance such as Principal Component Analysis is useful. In rare cell discovery it is useful to first model all common cells and remove them to find the rare cells. Finally discriminant analysis based on the cellular distribution may highlight which cell (sub)types are different between groups.
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Affiliation(s)
- Gerjen H Tinnevelt
- Radboud University, Institute for Molecules and Materials, Analytical Chemistry, P.O. Box 9010, 6500, GL, Nijmegen, the Netherlands.
| | - Kristiaan Wouters
- Department of Internal Medicine, Laboratory of Metabolism and Vascular Medicine, P.O. Box 616 (UNS50/14), 6200, MD, Maastricht, the Netherlands
| | - Geert J Postma
- Radboud University, Institute for Molecules and Materials, Analytical Chemistry, P.O. Box 9010, 6500, GL, Nijmegen, the Netherlands
| | - Rita Folcarelli
- Corbion, Arkelsedijk 46, 4206, AC, Gorinchem, the Netherlands
| | - Jeroen J Jansen
- Radboud University, Institute for Molecules and Materials, Analytical Chemistry, P.O. Box 9010, 6500, GL, Nijmegen, the Netherlands
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High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies. Curr Hematol Malig Rep 2021; 16:112-116. [PMID: 33449291 DOI: 10.1007/s11899-020-00602-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/29/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW High-dimensional flow cytometry experiments have become a method of choice for high-throughput integration and characterization of cell populations. Here, we present a summary of state-of-the-art R-based pipelines used for differential analyses of cytometry data, largely based on chimeric antigen receptor (CAR) T cell therapies. These pipelines are based on publicly available R libraries, put together in a systematic and functional fashion, therefore free of cost. RECENT FINDINGS In recent years, existing tools tailored to analyze complex high-dimensional data such as single-cell RNA sequencing (scRNAseq) have been successfully ported to cytometry studies due to the similar nature of flow cytometry and scRNAseq platforms. Existing environments like Cytobank (Kotecha et al., 2010), FlowJo (FlowJo™ Software) and FCS Express (https://denovosoftware.com) already offer a variety of these ported tools, but they either come at a premium or are fairly complicated to manage by an inexperienced user. To mitigate these limitations, experienced cytometrists and bioinformaticians usually incorporate these functions into an RShiny (https://shiny.rstudio.com) application that ultimately offers a user-friendly, intuitive environment that can be used to analyze flow cytometry data. Computational tools and Shiny-based tools are the perfect answer to the ever-growing dimensionality and complexity of flow cytometry data, by offering a dynamic, yet user-friendly exploratory space, tailored to bridge the space between the lab experimental world and the computational, machine learning space.
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Rybakowska P, Alarcón-Riquelme ME, Marañón C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput Struct Biotechnol J 2020; 18:874-886. [PMID: 32322369 PMCID: PMC7163213 DOI: 10.1016/j.csbj.2020.03.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 03/18/2020] [Accepted: 03/25/2020] [Indexed: 01/05/2023] Open
Abstract
High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.
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Affiliation(s)
- Paulina Rybakowska
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
| | - Marta E. Alarcón-Riquelme
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
- Institute for Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Concepción Marañón
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
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6
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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:vaccines8010138. [PMID: 32244919 PMCID: PMC7157606 DOI: 10.3390/vaccines8010138] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [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.)
- Correspondence:
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7
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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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8
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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: 689] [Impact Index Per Article: 137.8] [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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Montante S, Brinkman RR. Flow cytometry data analysis: Recent tools and algorithms. Int J Lab Hematol 2019; 41 Suppl 1:56-62. [PMID: 31069980 DOI: 10.1111/ijlh.13016] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/25/2019] [Accepted: 02/26/2019] [Indexed: 12/21/2022]
Abstract
Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.
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Affiliation(s)
| | - Ryan R Brinkman
- Terry Fox Laboratory, BC Cancer, Vancouver, British Columbia, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
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Hartmann FJ, Simonds EF, Vivanco N, Bruce T, Borges L, Nolan GP, Spitzer MH, Bendall SC. Scalable Conjugation and Characterization of Immunoglobulins with Stable Mass Isotope Reporters for Single-Cell Mass Cytometry Analysis. Methods Mol Biol 2019; 1989:55-81. [PMID: 31077099 PMCID: PMC6687300 DOI: 10.1007/978-1-4939-9454-0_5] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The advent of mass cytometry (CyTOF®) has permitted simultaneous detection of more than 40 antibody parameters at the single-cell level, although a limited number of metal-labeled antibodies are commercially available. Here we present optimized and scalable protocols for conjugation of lanthanide as well as bismuth ions to immunoglobulin (Ig) using a maleimide-functionalized chelating polymer and for characterization of the conjugate. The maleimide functional group is reactive with cysteine sulfhydryl groups generated through partial reduction of the Ig Fc region. Incubation of Ig with polymer pre-loaded with lanthanide ions produces metal-labeled Ig without disrupting antigen specificity. Antibody recovery rates can be determined by UV spectrophotometry and frequently exceeds 60%. Each custom-conjugated antibody is validated using positive and negative cellular control populations and is titrated for optimal staining at concentrations ranging from 0.1 to 10 μg/mL. The preparation of metal-labeled antibodies can be completed in 4.5 h, and titration requires an additional 3-5 h.
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Affiliation(s)
| | - Erin F Simonds
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nora Vivanco
- Immunology Program, Stanford University, Stanford, CA, USA
| | - Trevor Bruce
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Luciene Borges
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Matthew H Spitzer
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Immunology Program, Stanford University, Stanford, CA, USA.
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11
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Abstract
The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.
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Affiliation(s)
- Sherrie Wang
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Ryan R Brinkman
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada.
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12
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Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat Neurosci 2018; 22:78-90. [DOI: 10.1038/s41593-018-0290-2] [Citation(s) in RCA: 209] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 11/13/2018] [Indexed: 11/08/2022]
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13
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CytoBinning: Immunological insights from multi-dimensional data. PLoS One 2018; 13:e0205291. [PMID: 30379838 PMCID: PMC6209166 DOI: 10.1371/journal.pone.0205291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/22/2018] [Indexed: 01/25/2023] Open
Abstract
New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses.
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14
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Yang X, Qiu P. Automatically generate two-dimensional gating hierarchy from clustered cytometry data. Cytometry A 2018; 93:1039-1050. [PMID: 30176185 DOI: 10.1002/cyto.a.23577] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 07/17/2018] [Accepted: 07/18/2018] [Indexed: 12/29/2022]
Abstract
Cytometry is an important technique widely used in medicine and biological research. Biologists traditionally analyze single-cell cytometry data by manual gating, which can be subjective and labor intensive. To address this issue, many automated and semiautomated methods have been developed. These advanced methods are designed to speed up and standardize the analysis of cytometry data, but their popularity is limited by their visualizations which are not intuitive to biologists who are accustomed to the conventional biaxial gating plots. In this article, we present a new method called Cluster-to-Gate (C2G) that can take clustering results as input, and automatically generate a nested two-dimensional gating hierarchy, which is a visualization representation that biologists are familiar with. This method can generate gating sequences for multiple target populations simultaneously and summarize them in one hierarchical tree that represents the gating hierarchy. We have tested this method on target populations defined by manual gating, automated clustering algorithms (k-means for example), and visualization-assisted methods (SPADE and tSNE). We have demonstrated that C2G is able to generate gating sequences that capture cell populations defined by the various clustering strategies, and robust to over-clustered and overlapping target populations. © 2018 International Society for Advancement of Cytometry.
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Affiliation(s)
- Xingyu Yang
- Department of Biology, Georgia Institute of Technology, Atlanta, Georgia
| | - Peng Qiu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
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15
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Rahim A, Meskas J, Drissler S, Yue A, Lorenc A, Laing A, Saran N, White J, Abeler-Dörner L, Hayday A, Brinkman RR. High throughput automated analysis of big flow cytometry data. Methods 2018; 134-135:164-176. [PMID: 29287915 PMCID: PMC5815930 DOI: 10.1016/j.ymeth.2017.12.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 12/07/2017] [Accepted: 12/15/2017] [Indexed: 11/20/2022] Open
Abstract
The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).
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Affiliation(s)
- Albina Rahim
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Justin Meskas
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Sibyl Drissler
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Alice Yue
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Anna Lorenc
- Department of Immunobiology, King's College London, United Kingdom
| | - Adam Laing
- Department of Immunobiology, King's College London, United Kingdom
| | - Namita Saran
- Department of Immunobiology, King's College London, United Kingdom
| | - Jacqui White
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | | | - Adrian Hayday
- Department of Immunobiology, King's College London, United Kingdom; The Francis Crick Institute, London, United Kingdom
| | - Ryan R Brinkman
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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16
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Cossarizza A, Chang HD, Radbruch A, Akdis M, Andrä I, Annunziato F, Bacher P, Barnaba V, Battistini L, Bauer WM, Baumgart S, Becher B, Beisker W, Berek C, Blanco A, Borsellino G, Boulais PE, Brinkman RR, Büscher M, Busch DH, Bushnell TP, Cao X, Cavani A, Chattopadhyay PK, Cheng Q, Chow S, Clerici M, Cooke A, Cosma A, Cosmi L, Cumano A, Dang VD, Davies D, De Biasi S, Del Zotto G, Della Bella S, Dellabona P, Deniz G, Dessing M, Diefenbach A, Di Santo J, Dieli F, Dolf A, Donnenberg VS, Dörner T, Ehrhardt GRA, Endl E, Engel P, Engelhardt B, Esser C, Everts B, Dreher A, Falk CS, Fehniger TA, Filby A, Fillatreau S, Follo M, Förster I, Foster J, Foulds GA, Frenette PS, Galbraith D, Garbi N, García-Godoy MD, Geginat J, Ghoreschi K, Gibellini L, Goettlinger C, Goodyear CS, Gori A, Grogan J, Gross M, Grützkau A, Grummitt D, Hahn J, Hammer Q, Hauser AE, Haviland DL, Hedley D, Herrera G, Herrmann M, Hiepe F, Holland T, Hombrink P, Houston JP, Hoyer BF, Huang B, Hunter CA, Iannone A, Jäck HM, Jávega B, Jonjic S, Juelke K, Jung S, Kaiser T, Kalina T, Keller B, Khan S, Kienhöfer D, Kroneis T, Kunkel D, Kurts C, Kvistborg P, Lannigan J, Lantz O, Larbi A, LeibundGut-Landmann S, Leipold MD, Levings MK, Litwin V, Liu Y, Lohoff M, Lombardi G, Lopez L, Lovett-Racke A, Lubberts E, Ludewig B, Lugli E, Maecker HT, Martrus G, Matarese G, Maueröder C, McGrath M, McInnes I, Mei HE, Melchers F, Melzer S, Mielenz D, Mills K, Mirrer D, Mjösberg J, Moore J, Moran B, Moretta A, Moretta L, Mosmann TR, Müller S, Müller W, Münz C, Multhoff G, Munoz LE, Murphy KM, Nakayama T, Nasi M, Neudörfl C, Nolan J, Nourshargh S, O'Connor JE, Ouyang W, Oxenius A, Palankar R, Panse I, Peterson P, Peth C, Petriz J, Philips D, Pickl W, Piconese S, Pinti M, Pockley AG, Podolska MJ, Pucillo C, Quataert SA, Radstake TRDJ, Rajwa B, Rebhahn JA, Recktenwald D, Remmerswaal EBM, Rezvani K, Rico LG, Robinson JP, Romagnani C, Rubartelli A, Ruckert B, Ruland J, Sakaguchi S, Sala-de-Oyanguren F, Samstag Y, Sanderson S, Sawitzki B, Scheffold A, Schiemann M, Schildberg F, Schimisky E, Schmid SA, Schmitt S, Schober K, Schüler T, Schulz AR, Schumacher T, Scotta C, Shankey TV, Shemer A, Simon AK, Spidlen J, Stall AM, Stark R, Stehle C, Stein M, Steinmetz T, Stockinger H, Takahama Y, Tarnok A, Tian Z, Toldi G, Tornack J, Traggiai E, Trotter J, Ulrich H, van der Braber M, van Lier RAW, Veldhoen M, Vento-Asturias S, Vieira P, Voehringer D, Volk HD, von Volkmann K, Waisman A, Walker R, Ward MD, Warnatz K, Warth S, Watson JV, Watzl C, Wegener L, Wiedemann A, Wienands J, Willimsky G, Wing J, Wurst P, Yu L, Yue A, Zhang Q, Zhao Y, Ziegler S, Zimmermann J. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur J Immunol 2017; 47:1584-1797. [PMID: 29023707 PMCID: PMC9165548 DOI: 10.1002/eji.201646632] [Citation(s) in RCA: 381] [Impact Index Per Article: 54.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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
| | - Mübeccel Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Immanuel Andrä
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | | | | | - Vincenzo Barnaba
- Dipartimento di Medicina Interna e Specialità Mediche, Sapienza Università di Roma, Via Regina Elena 324, 00161 Rome, Italy
- Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Rome, Italy
| | - Luca Battistini
- Neuroimmunology and Flow Cytometry Units, Santa Lucia Foundation, Rome, Italy
| | - Wolfgang M 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
| | - Burkhard Becher
- University of Zurich, Institute of Experimental Immunology, Zürich, Switzerland
| | - Wolfgang Beisker
- Flow Cytometry Laboratory, Institute of Molecular Toxicology and Pharmacology, Helmholtz Zentrum München, German Research Center for Environmental Health
| | - Claudia Berek
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Alfonso Blanco
- Flow Cytometry Core Technologies, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Giovanna Borsellino
- Neuroimmunology and Flow Cytometry Units, Santa Lucia Foundation, Rome, Italy
| | - Philip E Boulais
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, USA
- The Ruth L. and David S. Gottesman Institute for Stem Cell and Regenerative Medicine Research, Bronx, New York, USA
| | - Ryan R Brinkman
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Martin Büscher
- Biopyhsics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Dirk H Busch
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
- DZIF - National Centre for Infection Research, Munich, Germany
- Focus Group ''Clinical Cell Processing and Purification", Institute for Advanced Study, Technische Universität München, Munich, Germany
| | - Timothy P Bushnell
- Department of Pediatrics and Shared Resource Laboratories, University of Rochester Medical Center, Rochester NY, United States of America
| | - Xuetao Cao
- Institute of Immunology, Zhejiang University School of Medicine, Hangzhou 310058, China
- National Key Laboratory of Medical Immunology & Institute of Immunology, Second Military Medical University, Shanghai 200433, China
- Department of Immunology & Center for Immunotherapy, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China
| | | | | | - Qingyu Cheng
- Medizinische Klinik mit Schwerpunkt Rheumatologie und Medizinische Immunolologie Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Sue Chow
- Divsion of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, Ontario, Canada
| | - Mario Clerici
- University of Milano and Don C Gnocchi Foundation IRCCS, Milano, Italy
| | - Anne Cooke
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Antonio Cosma
- CEA - Université Paris Sud - INSERM U, Immunology of viral infections and autoimmune diseases, France
| | - Lorenzo Cosmi
- Department of Experimental and Clinical Medicine, University of Firenze, Firenze, Italia
| | - Ana Cumano
- Lymphopoiesis Unit, Immunology Department Pasteur Institute, Paris, France
| | - Van Duc Dang
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Derek Davies
- Flow Cytometry Facility, The Francis Crick Institute, London, United Kingdom
| | - Sara De Biasi
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | | | - Silvia Della Bella
- University of Milan, Department of Medical Biotechnologies and Translational Medicine
- Humanitas Clinical and Research Center, Lab of Clinical and Experimental Immunology, Rozzano, Milan, Italy
| | - Paolo Dellabona
- Experimental Immunology Unit, Head, Division of Immunology, Transplantation and Infectious Diseases, San Raffaele Scientific Institute, Milano, Italy
| | - Günnur Deniz
- Istanbul University, Aziz Sancar Institute of Experimental Medicine, Department of Immunology, Istanbul, Turkey
| | | | | | | | - Francesco Dieli
- University of Palermo, Department of Biopathology, Palermo, Italy
| | - Andreas Dolf
- Institute of Experimental Immunology, University Bonn, Bonn, Germany
| | - Vera S Donnenberg
- Department of Cardiothoracic Surgery, School of Medicine, University of Pittsburgh, PA
| | - Thomas Dörner
- Department of Medicine/Rheumatology and Clinical Immunology, Charite Universitätsmedizin Berlin, Germany
| | | | - Elmar Endl
- Department of Molecular Medicine and Experimental Immunology, (Core Facility Flow Cytometry) University of Bonn, Germany
| | - Pablo Engel
- Department of Biomedical Sciences, University of Barcelona, Barcelona, Spain
| | - Britta Engelhardt
- Professor for Immunobiology, Director, Theodor Kocher Institute, University of Bern, Bern, Switzerland
| | - Charlotte Esser
- IUF - Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Bart Everts
- Leiden University Medical Center, Department of Parasitology, Leiden, The Netherlands
| | - Anita Dreher
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Christine S Falk
- Institute of Transplant Immunology, IFB-Tx, MHH Hannover Medical School, Hannover, Germany
- German Center for Infectious diseases (DZIF), TTU-IICH, Hannover, Germany
| | - Todd A Fehniger
- Divisions of Hematology & Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO
| | - Andrew Filby
- The Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Simon Fillatreau
- Institut Necker-Enfants Malades (INEM), INSERM U-CNRS UMR, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Faculté de Médecine, Paris, France
- Assistance Publique - Hôpitaux de Paris (AP-HP), Hôpital Necker Enfants Malades, Paris, France
| | - Marie Follo
- Department of Medicine I, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Irmgard Förster
- Immunology and Environment, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | | | - Gemma A Foulds
- John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, UK
| | - Paul S Frenette
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
| | - David Galbraith
- University of Arizona, Bio Institute, School of Plant Sciences and Arizona Cancer Center, Tucson, Arizona, USA
| | - Natalio Garbi
- Institute of Experimental Immunology, University Bonn, Bonn, Germany
- Department of Molecular Immunology, Institute of Experimental Immunology, Bonn, Germany
| | | | - Jens Geginat
- INGM, Istituto Nazionale Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Kamran Ghoreschi
- Flow Cytometry Core Facility, Department of Dermatology, University Medical Center, Eberhard Karls University Tübingen, Germany
| | - Lara Gibellini
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | | | - Carl S Goodyear
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow
| | - Andrea Gori
- Clinic of Infectious Diseases, "San Gerardo" Hospital - ASST Monza, University Milano-Bicocca, Monza, Italy
| | - Jane Grogan
- Genentech, Department of Cancer Immunology, South San Francisco, California, USA
| | - Mor Gross
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Andreas Grützkau
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | | | - Jonas Hahn
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Quirin Hammer
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Anja E Hauser
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Immundynamics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - David Hedley
- Divsion of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, Ontario, Canada
| | - Guadalupe Herrera
- Cytometry Service, Incliva Foundation. Clinic Hospital and Faculty of Medicine, The University of Valencia. Av. Blasco Ibáñez, Valencia, Spain
| | - Martin Herrmann
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Falk Hiepe
- Medizinische Klinik mit Schwerpunkt Rheumatologie und Medizinische Immunolologie Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tristan Holland
- Department of Molecular Immunology, Institute of Experimental Immunology, Bonn, Germany
| | - Pleun Hombrink
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, The Netherlands
| | - Jessica P Houston
- Chemical and Materials Engineering, New Mexico State University, Las Cruces, NM, 88003, USA
| | - Bimba F Hoyer
- Medizinische Klinik mit Schwerpunkt Rheumatologie und Medizinische Immunolologie Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Bo Huang
- Department of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Immunology, Institute of Basic Medical Sciences & State Key Laboratory of Medical Molecular Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Clinical Immunology Center, Chinese Academy of Medical Sciences, Beijing, China
| | - Christopher A Hunter
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anna Iannone
- Department of Diagnostic Medicine, Clinical and Public Health, Univ. of Modena and Reggio Emilia, Modena, Italy
| | - Hans-Martin Jäck
- Division of Molecular Immunology, Internal Medicine III, Nikolaus-Fiebiger-Center of MolecularMedicine, University Hospital Erlangen, Erlangen, Germany
| | - Beatriz Jávega
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Department of Biochemistry and Molecular Biology, The University of Valencia. Av. Blasco Ibáñez, Valencia, Spain
| | - Stipan Jonjic
- Faculty of Medicine, Center for Proteomics, University of Rijeka, Rijeka, Croatia
- Department for Histology and Embryology, Faculty of Medicine, University of Rijeka, Rijeka, Croatia
| | - Kerstin Juelke
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Steffen Jung
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - 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 and University Hospital Motol, Prague, Czech Republic
| | - Baerbel Keller
- Center for Chronic Immunodeficiency (CCI), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Srijit Khan
- Department of Immunology, University of Toronto, Toronto, Canada
| | - Deborah Kienhöfer
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Thomas Kroneis
- Medical University of Graz, Institute of Cell Biology, Histology & Embryology, Graz, Austria
| | - Désirée Kunkel
- BCRT Flow Cytometry Lab, Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin
| | - Christian Kurts
- Institute of Experimental Immunology, University Bonn, Bonn, Germany
| | - Pia Kvistborg
- Division of immunology, the Netherlands Cancer Institute, Amsterdam
| | - Joanne Lannigan
- University of Virginia School of Medicine, Flow Cytometry Shared Resource, Charlottesville, VA, USA
| | - Olivier Lantz
- INSERM U932, Institut Curie, Paris 75005, France
- Laboratoire d'immunologie clinique, Institut Curie, Paris 75005, France
- Centre d'investigation Clinique en Biothérapie Gustave-Roussy Institut Curie (CIC-BT1428), Institut Curie, Paris 75005, France
| | - Anis Larbi
- Singapore Immunology Network (SIgN), Principal Investigator, Biology of Aging Program
- Director Flow Cytomerty Platform, Immunomonitoring Platform, Agency for Science Technology and Research (A*STAR), Singapore
- Department of Medicine, University of Sherbrooke, Qc, Canada
- Faculty of Sciences, ElManar University, Tunis, Tunisia
| | | | - Michael D Leipold
- The Human Immune Monitoring Center (HIMC), Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, CA, USA
| | - Megan K Levings
- Department of Surgery, University of British Columbia & British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada
| | | | - Yanling Liu
- Department of Immunology, University of Toronto, Toronto, Canada
| | - Michael Lohoff
- Institute for Medical Microbiology and Hospital Hygiene, University of Marburg, Marburg 35043, Germany
| | - Giovanna Lombardi
- MRC Centre for Transplantation, King's College London, Guy's Hospital, SE1 9RT London, UK
| | | | - Amy Lovett-Racke
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA
| | - Erik Lubberts
- Erasmus MC, University Medical Center, Department of Rheumatology, Rotterdam, The Netherlands
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Enrico Lugli
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center, Rozzano, Milan, Italy
- Humanitas Flow Cytometry Core, Humanitas Clinical and Research Center, Rozzano, Milan, Italy
| | - Holden T Maecker
- The Human Immune Monitoring Center (HIMC), Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, CA, USA
| | - Glòria Martrus
- Department of Virus Immunology, Heinrich-Pette-Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Giuseppe Matarese
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università di Napoli Federico II, Napoli, Italy and Istituto per l'Endocrinologia e l'Oncologia Sperimentale, Consiglio Nazionale delle Ricerche (IEOS-CNR), Napoli, Italy
| | - Christian Maueröder
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Mairi McGrath
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Iain McInnes
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow
| | - Henrik E Mei
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Fritz Melchers
- Senior Group on Lymphocyte Development, 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
| | - Kingston Mills
- Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - David Mirrer
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Jenny Mjösberg
- Center for Infectious Medicine, Department of Medicine, Karolinska Institute Stockholm, Sweden
- Department of Clinical and Experimental Medicine, Linköping University, Sweden
| | - Jonni Moore
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Barry Moran
- Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - Alessandro Moretta
- Department of Experimental Medicine, University of Genova, Genova, Italy
- Centro di Eccellenza per la Ricerca Biomedica-CEBR, Genova, Italy
| | - 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 Environemntal Microbiology, Leipzig, Germany
| | - Werner Müller
- Bill Ford Chair in Cellular Immunology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Christian Münz
- University of Zurich, Institute of Experimental Immunology, Zürich, Switzerland
| | - Gabriele Multhoff
- Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München (TUM), Munich, Germany
- Institute for Innovative Radiotherapy (iRT), Experimental Immune Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Luis Enrique Munoz
- Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Kenneth M Murphy
- Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Howard Hughes Medical Institute, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Toshinori Nakayama
- Department of Immunology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Milena Nasi
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, Univ. of Modena and Reggio Emilia, Modena, Italy
| | - Christine Neudörfl
- Institute of Transplant Immunology, IFB-Tx, MHH Hannover Medical School, Hannover, Germany
| | - John Nolan
- The Scintillon Institute, Nancy Ridge Drive, San Diego, CA, USA
| | - Sussan Nourshargh
- Centre for Microvascular Research, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - José-Enrique O'Connor
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Department of Biochemistry and Molecular Biology, The University of Valencia. Av. Blasco Ibáñez, Valencia, Spain
| | - Wenjun Ouyang
- Department of Inflammation and Oncology, Amgen Inc., South San Francisco, CA, USA
| | | | - Raghav Palankar
- Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Straße, 17489, Greifswald, Germany
| | - Isabel Panse
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
| | - Pärt Peterson
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Christian Peth
- Biopyhsics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Jordi Petriz
- Josep Carreras Leukemia Research Institute, Barcelona, Spain
| | - Daisy Philips
- Division of immunology, the Netherlands Cancer Institute, Amsterdam
| | - Winfried 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, Via Regina Elena 324, 00161 Rome, Italy
- Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Rome, Italy
| | - Marcello Pinti
- Department of Life Sciences, Univ. 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 Internal Medicine, Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen
| | - Carlo Pucillo
- Univeristy of Udine - Department of Medicine, Lab of Immunology, Udine, Italy
| | - Sally A Quataert
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | - Timothy R D J Radstake
- Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands; Laboratory of Translational Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bartek Rajwa
- Bindley Biosciences Center, Purdue University, West Lafayette, In, USA
| | - Jonathan A Rebhahn
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Ester B M Remmerswaal
- Department of Experimental Immunology and Renal Transplant Unit, Division of Internal Medicine, Academic Medical Centre, The Netherlands
| | - Katy Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Laura G Rico
- Josep Carreras Leukemia Research Institute, Barcelona, Spain
| | - J Paul Robinson
- The SVM Professor of Cytomics & Professor of Biomedical Engineering, Purdue University Cytometry Laboratories, Purdue University, West Lafayette, IN, USA
| | - Chiara Romagnani
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | | | - Beate Ruckert
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Jürgen Ruland
- Institut für Klinische Chemie und Pathobiochemie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Center for Infection Research (DZIF), partner site Munich, Munich, Germany
| | - Shimon Sakaguchi
- Laboratory of Experimental Immunology, WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita 565-0871, Japan
- Department of Experimental Pathology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Francisco Sala-de-Oyanguren
- Laboratory of Cytomics, Joint Research Unit CIPF-UVEG, Department of Biochemistry and Molecular Biology, The University of Valencia. Av. Blasco Ibáñez, Valencia, Spain
| | - Yvonne Samstag
- Institute of Immunology, Section Molecular Immunology, Ruprecht-Karls-University, D-69120, Heidelberg, Germany
| | - Sharon Sanderson
- Translational Immunology Laboratory, NIHR BRC, University of Oxford, Kennedy Institute of Rheumatology,Oxford, United Kingdom
| | - Birgit Sawitzki
- Charité-Universitaetsmedizin Berlin, Corporate Member of Freie Universitaet Berlin, Humboldt-Universitaet zu Berlin
- Berlin Institute of Health, Institute of Medical Immunology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Alexander Scheffold
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Germany
| | - Matthias Schiemann
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | - Frank Schildberg
- Harvard Medical School, Department of Microbiology and Immunobiology, Boston, MA, USA
| | | | - Stephan A Schmid
- Klinik und Poliklinik für Innere Medizin I, Universitätsklinikum Regensburg, Regensburg, Germany
| | - Steffen Schmitt
- Imaging and Cytometry Core Facility, Flow Cytometry Unit, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Kilian Schober
- Institut für Medizinische Mikrobiologie, Immunologie und Hygiene, Technische Universität München, Munich, Germany
| | - Thomas Schüler
- Institute of Molecular and Clinical Immunology, Otto-von-Guericke University, Magdeburg, Germany
| | - Axel Ronald Schulz
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Ton Schumacher
- Division of immunology, the Netherlands Cancer Institute, Amsterdam
| | - Cristiano Scotta
- MRC Centre for Transplantation, King's College London, Guy's Hospital, SE1 9RT London, UK
| | | | - Anat Shemer
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Josef Spidlen
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | | | - Regina Stark
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, The Netherlands
| | - Christina Stehle
- Deutsches Rheuma-Forschungszentrum (DRFZ), an Institute of the Leibniz Association, Berlin, Germany
| | - Merle Stein
- Division of Molecular Immunology, Nikolaus-Fiebiger-Center, Dept. of Internal Medicine III, University of Erlangen-Nuremberg, Erlangen, 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
| | - Yousuke Takahama
- Division of Experimental Immunology, Institute of Advanced Medical Sciences, University of Tokushima, Tokushima, Japan
| | - Attila Tarnok
- Departement for Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany
- Institute for Medical Informatics, IMISE, Leipzig, Germany
| | - ZhiGang Tian
- School of Life Sciences and Medical Center, Institute of Immunology, Key Laboratory of Innate Immunity and Chronic Disease of Chinese Academy of Science, University of Science and Technology of China, Hefei, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Gergely Toldi
- University of Birmingham, Institute of Immunology and Immunotherapy, Birmingham, UK
| | - Julia Tornack
- Senior Group on Lymphocyte Development, Max Planck Institute for Infection Biology, Berlin, Germany
| | | | | | - Henning Ulrich
- Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo
| | | | - René A W van Lier
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam, The Netherlands
| | | | | | - Paulo Vieira
- Unité Lymphopoiese, Institut Pasteur, Paris, France
| | - David Voehringer
- Department of Infection Biology, University Hospital Erlangen, Wasserturmstr. 3/5, 91054 Erlangen, Germany
| | | | | | - Ari Waisman
- Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg University of Mainz, Mainz, Germany
| | | | | | - Klaus Warnatz
- Center for Chronic Immunodeficiency (CCI), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sarah Warth
- BCRT Flow Cytometry Lab, Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin
| | | | - Carsten Watzl
- Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, IfADo, Department of Immunology, Dortmund, Germany
| | - Leonie Wegener
- Biopyhsics, R&D Engineering, Miltenyi Biotec GmbH, Bergisch Gladbach, Germany
| | - Annika Wiedemann
- Department of Medicine/Rheumatology and Clinical Immunology, Charite Universitätsmedizin Berlin, Germany
| | - Jürgen Wienands
- Universitätsmedizin Göttingen, Georg-August-Universität, Abt. Zelluläre und Molekulare Immunologie, Humboldtallee 34, 37073 Göttingen, Germany
| | - 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 Wing
- Laboratory of Experimental Immunology, WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita 565-0871, Japan
- Department of Experimental Pathology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto 606-8507, Japan
| | - Peter Wurst
- Institute of Experimental Immunology, University Bonn, Bonn, Germany
| | | | - Alice Yue
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | | | - Yi Zhao
- Department of Rheumatology & Immunology, West China Hospital, Sichuan University, Chengdu, China
| | - Susanne Ziegler
- Department of Virus Immunology, Heinrich-Pette-Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Jakob Zimmermann
- Maurice Müller Laboratories (DKF), Universitätsklinik für Viszerale Chirurgie und Medizin Inselspital, University of Bern, Murtenstrasse, Bern
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17
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Spear TT, Nishimura MI, Simms PE. Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior. J Leukoc Biol 2017; 102:551-561. [PMID: 28550117 DOI: 10.1189/jlb.6a0417-140r] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 05/08/2017] [Accepted: 05/08/2017] [Indexed: 11/24/2022] Open
Abstract
Advancement in flow cytometry reagents and instrumentation has allowed for simultaneous analysis of large numbers of lineage/functional immune cell markers. Highly complex datasets generated by polychromatic flow cytometry require proper analytical software to answer investigators' questions. A problem among many investigators and flow cytometry Shared Resource Laboratories (SRLs), including our own, is a lack of access to a flow cytometry-knowledgeable bioinformatics team, making it difficult to learn and choose appropriate analysis tool(s). Here, we comparatively assess various multidimensional flow cytometry software packages for their ability to answer a specific biologic question and provide graphical representation output suitable for publication, as well as their ease of use and cost. We assessed polyfunctional potential of TCR-transduced T cells, serving as a model evaluation, using multidimensional flow cytometry to analyze 6 intracellular cytokines and degranulation on a per-cell basis. Analysis of 7 parameters resulted in 128 possible combinations of positivity/negativity, far too complex for basic flow cytometry software to analyze fully. Various software packages were used, analysis methods used in each described, and representative output displayed. Of the tools investigated, automated classification of cellular expression by nonlinear stochastic embedding (ACCENSE) and coupled analysis in Pestle/simplified presentation of incredibly complex evaluations (SPICE) provided the most user-friendly manipulations and readable output, evaluating effects of altered antigen-specific stimulation on T cell polyfunctionality. This detailed approach may serve as a model for other investigators/SRLs in selecting the most appropriate software to analyze complex flow cytometry datasets. Further development and awareness of available tools will help guide proper data analysis to answer difficult biologic questions arising from incredibly complex datasets.
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Affiliation(s)
- Timothy T Spear
- Department of Surgery, Cardinal Bernardin Cancer Center, Loyola University Chicago, Maywood, Illinois, USA; and
| | - Michael I Nishimura
- Department of Surgery, Cardinal Bernardin Cancer Center, Loyola University Chicago, Maywood, Illinois, USA; and
| | - Patricia E Simms
- Flow Cytometry Core Facility, Office of Research Services, Loyola University Chicago, Maywood, Illinois, USA
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18
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Abraham Y, Gerrits B, Ludwig MG, Rebhan M, Gubser Keller C. Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations. CYTOMETRY PART B-CLINICAL CYTOMETRY 2017; 92:42-56. [DOI: 10.1002/cyto.b.21499] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 11/18/2016] [Accepted: 12/01/2016] [Indexed: 01/09/2023]
Affiliation(s)
- Yann Abraham
- Developmental and Molecular Pathway, Computational Biology; Novartis Institute for Biomedical Research; Novartis Campus CH-4056 Basel, Switzerland
| | - Bertran Gerrits
- Developmental and Molecular Pathway, Computational Biology; Novartis Institute for Biomedical Research; Novartis Campus CH-4056 Basel, Switzerland
| | - Marie-Gabrielle Ludwig
- Developmental and Molecular Pathway Immunity; Novartis Institute for Biomedical Research; Novartis Campus CH-4056 Basel, Switzerland
| | - Michael Rebhan
- Developmental and Molecular Pathway, Computational Biology; Novartis Institute for Biomedical Research; Novartis Campus CH-4056 Basel, Switzerland
| | - Caroline Gubser Keller
- Developmental and Molecular Pathway, Computational Biology; Novartis Institute for Biomedical Research; Novartis Campus CH-4056 Basel, Switzerland
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19
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Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL. Biologically Relevant Heterogeneity: Metrics and Practical Insights. SLAS DISCOVERY 2017; 22:213-237. [PMID: 28231035 DOI: 10.1177/2472555216682725] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
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Affiliation(s)
- Albert Gough
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Andrew M Stern
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - John Maier
- 3 Department of Family Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Lezon
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Tong-Ying Shun
- 2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Chakra Chennubhotla
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E Schurdak
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Steven A Haney
- 5 Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
| | - D Lansing Taylor
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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20
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Abstract
High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.
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21
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Gondois-Rey F, Granjeaud S, Rouillier P, Rioualen C, Bidaut G, Olive D. Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational-based interactive analyses. Cytometry A 2016; 89:480-90. [PMID: 27059253 DOI: 10.1002/cyto.a.22850] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 02/18/2016] [Accepted: 03/08/2016] [Indexed: 01/07/2023]
Abstract
The wide possibilities opened by the developments of multi-parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi-dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi-stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi-Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi-stained samples and allows improving multi-parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry.
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Affiliation(s)
- F Gondois-Rey
- Team Immunity and Cancer, Inserm, U1068, CRCM, Marseille, F-13009, France.,Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France
| | - S Granjeaud
- Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France.,CiBi Platform, Inserm, U1068, CRCM, Marseille, F-13009, France
| | - P Rouillier
- Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France.,CiBi Platform, Inserm, U1068, CRCM, Marseille, F-13009, France
| | - C Rioualen
- Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France.,CiBi Platform, Inserm, U1068, CRCM, Marseille, F-13009, France
| | - G Bidaut
- Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France.,CiBi Platform, Inserm, U1068, CRCM, Marseille, F-13009, France
| | - D Olive
- Team Immunity and Cancer, Inserm, U1068, CRCM, Marseille, F-13009, France.,Institut Paoli-Calmettes, Marseille, F-13009, France.,Aix-Marseille Univ, UM 105, Marseille, F-13284, France.,CNRS, UMR7258, CRCM, Marseille, F-13009, France
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22
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Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S, Kursa M, Lambrecht BN, Malek M, Qian Y, Qiu P, Saeys Y, Stanton R, Tong D, Vens C, Walkowiak S, Wang K, Finak G, Gottardo R, Mosmann T, Nolan G, Scheuermann RH, Brinkman RR. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 2016; 89:16-21. [PMID: 26447924 PMCID: PMC4874734 DOI: 10.1002/cyto.a.22732] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 05/20/2015] [Accepted: 07/16/2015] [Indexed: 11/07/2022]
Abstract
The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.
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Affiliation(s)
- Nima Aghaeepour
- British Columbia Cancer Agency, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford, CA, USA
| | - Pratip Chattopadhyay
- ImmunoTechnology Section, Vaccine Research Center, National Institutes of Health, Washington, DC, USA
| | - Maria Chikina
- Department Computational and Systems Biology, University of Pittsburgh, Pittsburg, USA
| | - Tom Dhaene
- Department of Information Technology, Ghent University - iMinds, Ghent, Belgium
| | - Sofie Van Gassen
- Department of Information Technology, Ghent University - iMinds, Ghent, Belgium
- Inflammation Research Center, VIB, Ghent, Belgium
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Miron Kursa
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland
| | - Bart N. Lambrecht
- Inflammation Research Center, VIB, Ghent, Belgium
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | | | - Yu Qian
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Peng Qiu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University
| | - Yvan Saeys
- Inflammation Research Center, VIB, Ghent, Belgium
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | | | - Dong Tong
- The John van Geest Cancer Research Centre, Nottingham Trent University, UK & Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Celine Vens
- Inflammation Research Center, VIB, Ghent, Belgium
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Public Health and Primary Care, KU Leuven Kulak, Kortrijk, Belgium
| | - Sławomir Walkowiak
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland
| | - Kui Wang
- Department of Mathematics, University of Queensland, St. Lucia, Brisbane, Australia
- School of Medicine, Shihezi University, Xinjiang 832000, China
| | - Greg Finak
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Raphael Gottardo
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Tim Mosmann
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, USA
| | - Garry Nolan
- Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford, CA, USA
| | - Richard H. Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
| | - Ryan R. Brinkman
- British Columbia Cancer Agency, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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Abstract
Multi-color flow cytometry has become a valuable and highly informative tool for diagnosis and therapeutic monitoring of patients with immune deficiencies or inflammatory disorders. However, the method complexity and error-prone conventional manual data analysis often result in a high variability between different analysts and research laboratories. Here, we provide strategies and guidelines aiming at a more standardized multi-color flow cytometric staining and unsupervised data analysis for whole blood patient samples.
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Chester C, Maecker HT. Algorithmic Tools for Mining High-Dimensional Cytometry Data. THE JOURNAL OF IMMUNOLOGY 2015; 195:773-9. [PMID: 26188071 DOI: 10.4049/jimmunol.1500633] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The advent of mass cytometry has led to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. Although this technology is ideally suited to the detailed examination of the immune system, the applicability of the different methods for analyzing such complex data is less clear. Conventional data analysis by manual gating of cells in biaxial dot plots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns, and several such tools have been applied recently to mass cytometry data. We review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists to identify suitable algorithmic tools for their particular projects.
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Affiliation(s)
- Cariad Chester
- The Human Immune Monitoring Center, Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305; and Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305
| | - Holden T Maecker
- The Human Immune Monitoring Center, Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA 94305; and
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25
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Rebhahn JA, Roumanes DR, Qi Y, Khan A, Thakar J, Rosenberg A, Lee FEH, Quataert SA, Sharma G, Mosmann TR. Competitive SWIFT cluster templates enhance detection of aging changes. Cytometry A 2015; 89:59-70. [PMID: 26441030 PMCID: PMC4737406 DOI: 10.1002/cyto.a.22740] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 04/21/2015] [Accepted: 08/05/2015] [Indexed: 12/17/2022]
Abstract
Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.
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Affiliation(s)
- Jonathan A Rebhahn
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York
| | - David R Roumanes
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York
| | - Yilin Qi
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York
| | - Atif Khan
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York
| | - Juilee Thakar
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York.,Department of Microbiology and Immunology, University of Rochester
| | | | - F Eun-Hyung Lee
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Sally A Quataert
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York
| | - Gaurav Sharma
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York.,Department of Electrical and Computer Engineering, University of Rochester
| | - Tim R Mosmann
- David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York.,Department of Microbiology and Immunology, University of Rochester
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26
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Kvistborg P, Gouttefangeas C, Aghaeepour N, Cazaly A, Chattopadhyay PK, Chan C, Eckl J, Finak G, Hadrup SR, Maecker HT, Maurer D, Mosmann T, Qiu P, Scheuermann RH, Welters MJP, Ferrari G, Brinkman RR, Britten CM. Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 2015; 42:591-2. [PMID: 25902473 PMCID: PMC4824634 DOI: 10.1016/j.immuni.2015.04.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Pia Kvistborg
- Netherlands Cancer Institute, 1006 BE Amsterdam, the Netherlands
| | - Cécile Gouttefangeas
- Institute for Cell Biology, Department of Immunology, University of Tübingen, 72076 Tuebingen, Germany
| | - Nima Aghaeepour
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | | | | | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27710, USA
| | - Judith Eckl
- Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Greg Finak
- Vaccine and Infectious Disease Division, Department of Biostatistics, Bioinformatics, and Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, WA 98155, USA
| | - Sine Reker Hadrup
- Center for Cancer Immune Therapy, Herlev Hospital, 2730 Herlev, Denmark
| | - Holden T Maecker
- Institute for Immunity, Transplantation, and Infection, Stanford School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Tim Mosmann
- David D. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Peng Qiu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA 92037, USA; Department of Pathology, University of California, San Diego, San Diego, CA 92093, USA
| | - Marij J P Welters
- Department of Clinical Oncology, Leiden University Medical Center, 2300 RA Leiden, the Netherlands
| | - Guido Ferrari
- Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Ryan R Brinkman
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
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27
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O'Neill K, Aghaeepour N, Parker J, Hogge D, Karsan A, Dalal B, Brinkman RR. Deep profiling of multitube flow cytometry data. ACTA ACUST UNITED AC 2015; 31:1623-31. [PMID: 25600947 DOI: 10.1093/bioinformatics/btv008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Accepted: 01/02/2015] [Indexed: 01/11/2023]
Abstract
MOTIVATION Deep profiling the phenotypic landscape of tissues using high-throughput flow cytometry (FCM) can provide important new insights into the interplay of cells in both healthy and diseased tissue. But often, especially in clinical settings, the cytometer cannot measure all the desired markers in a single aliquot. In these cases, tissue is separated into independently analysed samples, leaving a need to electronically recombine these to increase dimensionality. Nearest-neighbour (NN) based imputation fulfils this need but can produce artificial subpopulations. Clustering-based NNs can reduce these, but requires prior domain knowledge to be able to parameterize the clustering, so is unsuited to discovery settings. RESULTS We present flowBin, a parameterization-free method for combining multitube FCM data into a higher-dimensional form suitable for deep profiling and discovery. FlowBin allocates cells to bins defined by the common markers across tubes in a multitube experiment, then computes aggregate expression for each bin within each tube, to create a matrix of expression of all markers assayed in each tube. We show, using simulated multitube data, that flowType analysis of flowBin output reproduces the results of that same analysis on the original data for cell types of >10% abundance. We used flowBin in conjunction with classifiers to distinguish normal from cancerous cells. We used flowBin together with flowType and RchyOptimyx to profile the immunophenotypic landscape of NPM1-mutated acute myeloid leukemia, and present a series of novel cell types associated with that mutation.
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Affiliation(s)
- Kieran O'Neill
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Nima Aghaeepour
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Jeremy Parker
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Donna Hogge
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Aly Karsan
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Bakul Dalal
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Ryan R Brinkman
- Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada
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28
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Atkuri KR, Stevens JC, Neubert H. Mass cytometry: a highly multiplexed single-cell technology for advancing drug development. Drug Metab Dispos 2014; 43:227-33. [PMID: 25349123 DOI: 10.1124/dmd.114.060798] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Advanced single-cell analysis technologies (e.g., mass cytometry) that help in multiplexing cellular measurements in limited-volume primary samples are critical in bridging discovery efforts to successful drug approval. Mass cytometry is the state-of-the-art technology in multiparametric single-cell analysis. Mass cytometers (also known as cytometry by time-of-flight or CyTOF) combine the cellular analysis principles of traditional fluorescence-based flow cytometry with the selectivity and quantitative power of inductively coupled plasma-mass spectrometry. Standard flow cytometry is limited in the number of parameters that can be measured owing to the overlap in signal when detecting fluorescently labeled antibodies. Mass cytometry uses antibodies tagged to stable isotopes of rare earth metals, which requires minimal signal compensation between the different metal tags. This unique feature enables researchers to seamlessly multiplex up to 40 independent measurements on single cells. In this overview we first present an overview of mass cytometry and compare it with traditional flow cytometry. We then discuss the emerging and potential applications of CyTOF technology in the pharmaceutical industry, including quantitative and qualitative deep profiling of immune cells and their applications in assessing drug immunogenicity, extensive mapping of signaling networks in single cells, cell surface receptor quantification and multiplexed internalization kinetics, multiplexing sample analysis by barcoding, and establishing cell ontologies on the basis of phenotype and/or function. We end with a discussion of the anticipated impact of this technology on drug development lifecycle with special emphasis on the utility of mass cytometry in deciphering a drug's pharmacokinetics and pharmacodynamics relationship.
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Affiliation(s)
- Kondala R Atkuri
- Pharmacokinetics, Dynamics and Metabolism, New Biological Entities, Pfizer, Andover, Massachusetts
| | - Jeffrey C Stevens
- Pharmacokinetics, Dynamics and Metabolism, New Biological Entities, Pfizer, Andover, Massachusetts
| | - Hendrik Neubert
- Pharmacokinetics, Dynamics and Metabolism, New Biological Entities, Pfizer, Andover, Massachusetts
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29
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Beer PA, Knapp DJHF, Kannan N, Miller PH, Babovic S, Bulaeva E, Aghaeepour N, Rabu G, Rostamirad S, Shih K, Wei L, Eaves CJ. A dominant-negative isoform of IKAROS expands primitive normal human hematopoietic cells. Stem Cell Reports 2014; 3:841-57. [PMID: 25418728 PMCID: PMC4235152 DOI: 10.1016/j.stemcr.2014.09.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Revised: 09/08/2014] [Accepted: 09/08/2014] [Indexed: 12/11/2022] Open
Abstract
Disrupted IKAROS activity is a recurrent feature of some human leukemias, but effects on normal human hematopoietic cells are largely unknown. Here, we used lentivirally mediated expression of a dominant-negative isoform of IKAROS (IK6) to block normal IKAROS activity in primitive human cord blood cells and their progeny. This produced a marked (10-fold) increase in serially transplantable multipotent IK6+ cells as well as increased outputs of normally differentiating B cells and granulocytes in transplanted immunodeficient mice, without producing leukemia. Accompanying T/natural killer (NK) cell outputs were unaltered, and erythroid and platelet production was reduced. Mechanistically, IK6 specifically increased human granulopoietic progenitor sensitivity to two growth factors and activated CREB and its targets (c-FOS and Cyclin B1). In more primitive human cells, IK6 prematurely initiated a B cell transcriptional program without affecting the hematopoietic stem cell-associated gene expression profile. Some of these effects were species specific, thus identifying novel roles of IKAROS in regulating normal human hematopoietic cells. IKAROS protein is abundantly expressed in primitive human hematopoietic cells IK6 enhances human blood stem cell expansion in vivo without causing leukemia IK6 has a unique profile of lineage-specific effects on human hematopoietic cells IK6 activates B-lineage transcripts prematurely in human blood stem cells
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Affiliation(s)
- Philip A Beer
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - David J H F Knapp
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Nagarajan Kannan
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Paul H Miller
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Sonja Babovic
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Elizabeth Bulaeva
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Nima Aghaeepour
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Gabrielle Rabu
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Shabnam Rostamirad
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Kingsley Shih
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Lisa Wei
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| | - Connie J Eaves
- Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada.
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30
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Anchang B, Do MT, Zhao X, Plevritis SK. CCAST: a model-based gating strategy to isolate homogeneous subpopulations in a heterogeneous population of single cells. PLoS Comput Biol 2014; 10:e1003664. [PMID: 25078380 PMCID: PMC4117418 DOI: 10.1371/journal.pcbi.1003664] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Accepted: 04/25/2014] [Indexed: 12/12/2022] Open
Abstract
A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.
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Affiliation(s)
- Benedict Anchang
- Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America
| | - Mary T. Do
- Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America
| | - Xi Zhao
- Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America
| | - Sylvia K. Plevritis
- Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, California, United States of America
- * E-mail:
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31
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Candia J, Banavar JR, Losert W. Understanding health and disease with multidimensional single-cell methods. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2014; 26:073102. [PMID: 24451406 PMCID: PMC4020281 DOI: 10.1088/0953-8984/26/7/073102] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.
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Affiliation(s)
- Julián Candia
- Department of Physics, University of Maryland, College Park, MD 20742, USA. School of Medicine, University of Maryland, Baltimore, MD 21201, USA. IFLYSIB and CONICET, University of La Plata, 1900 La Plata, Argentina
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32
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Chattopadhyay PK, Gierahn TM, Roederer M, Love JC. Single-cell technologies for monitoring immune systems. Nat Immunol 2014; 15:128-35. [PMID: 24448570 PMCID: PMC4040085 DOI: 10.1038/ni.2796] [Citation(s) in RCA: 275] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 11/25/2013] [Indexed: 12/12/2022]
Abstract
The complex heterogeneity of cells, and their interconnectedness with each other, are major challenges to identifying clinically relevant measurements that reflect the state and capability of the immune system. Highly multiplexed, single-cell technologies may be critical for identifying correlates of disease or immunological interventions as well as for elucidating the underlying mechanisms of immunity. Here we review limitations of bulk measurements and explore advances in single-cell technologies that overcome these problems by expanding the depth and breadth of functional and phenotypic analysis in space and time. The geometric increases in complexity of data make formidable hurdles for exploring, analyzing and presenting results. We summarize recent approaches to making such computations tractable and discuss challenges for integrating heterogeneous data obtained using these single-cell technologies.
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Affiliation(s)
- Pratip K Chattopadhyay
- ImmunoTechnology Section, Vaccine Research Center, NIAID, National Institutes of Health, Bethesda, Maryland, USA
| | - Todd M Gierahn
- Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Mario Roederer
- ImmunoTechnology Section, Vaccine Research Center, NIAID, National Institutes of Health, Bethesda, Maryland, USA
| | - J Christopher Love
- Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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33
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O'Neill K, Jalali A, Aghaeepour N, Hoos H, Brinkman RR. Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics 2014; 30:1329-30. [PMID: 24407226 DOI: 10.1093/bioinformatics/btt770] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We present a significantly improved version of the flowType and RchyOptimyx BioConductor-based pipeline that is both 14 times faster and can accommodate multiple levels of biomarker expression for up to 96 markers. With these improvements, the pipeline is positioned to be an integral part of data analysis for high-throughput experiments on high-dimensional single-cell assay platforms, including flow cytometry, mass cytometry and single-cell RT-qPCR.
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Affiliation(s)
- Kieran O'Neill
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, Max-Planck-Institut fur Informatik, Saarland University, 66123, Saarbrucken, Germany, Departments of Computer Science and Departments of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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34
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Stuchlý J, Kalina T. Analyses of large flow cytometry datasets. Cytometry A 2013; 85:203-5. [PMID: 24382687 DOI: 10.1002/cyto.a.22431] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 11/29/2013] [Indexed: 01/25/2023]
Affiliation(s)
- Jan Stuchlý
- Department of Pediatric Hematology/Oncology, 2nd Faculty of Medicine, Charles University Prague, Czech Republic
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35
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Abstract
Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.
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Affiliation(s)
- Kieran O'Neill
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nima Aghaeepour
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada
| | - Josef Špidlen
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Ryan Brinkman
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
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Candia J, Maunu R, Driscoll M, Biancotto A, Dagur P, McCoy JP, Sen HN, Wei L, Maritan A, Cao K, Nussenblatt RB, Banavar JR, Losert W. From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells. PLoS Comput Biol 2013; 9:e1003215. [PMID: 24039568 PMCID: PMC3763994 DOI: 10.1371/journal.pcbi.1003215] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 07/23/2013] [Indexed: 11/25/2022] Open
Abstract
Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques. The behavior of organisms is based on the concerted action occurring on an astonishing range of scales from the molecular to the organismal level. Molecular properties control the function of a cell, while cell ensembles form tissues and organs, which work together as an organism. In order to understand and characterize the molecular nature of the emergent properties of a cell, it is essential that multiple components of the cell are measured simultaneously in the same cell. Similarly, multiple cells must be measured in order to understand health and disease in the organism. In this work, we develop an approach that is able to determine how many cells, how many measurements per cell, and which measurements are needed to reliably diagnose disease. We apply this method to two different problems: the diagnosis of a premature aging disorder using images of cell nuclei, and the distinction between two similar autoimmune eye diseases using stained cells from patients' blood samples. Our findings shed new light on the role of specific kinds of immune system cells in systemic inflammatory diseases and may lead to improved diagnosis and treatment.
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Affiliation(s)
- Julián Candia
- Department of Physics, University of Maryland, College Park, Maryland, United States of America ; School of Medicine, University of Maryland, Baltimore, Maryland, United States of America ; IFLYSIB and CONICET, University of La Plata, La Plata,
| | - Ryan Maunu
- Department of Physics, University of Maryland, College Park, Maryland, United States of America
| | - Meghan Driscoll
- Department of Physics, University of Maryland, College Park, Maryland, United States of America
| | - Angélique Biancotto
- Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, Maryland, United States of America, Hematology Branch, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Pradeep Dagur
- Hematology Branch, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - J Philip McCoy
- Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, Maryland, United States of America,; Hematology Branch, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - H Nida Sen
- Laboratory of Immunology, National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Lai Wei
- Laboratory of Immunology, National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amos Maritan
- Dipartimento di Fisica “G. Galilei,” Università di Padova, Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia and Istituto Nazionale di Fisica Nucleare, Padua, Italy
| | - Kan Cao
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
| | - Robert B Nussenblatt
- Laboratory of Immunology, National Eye Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jayanth R Banavar
- Department of Physics, University of Maryland, College Park, Maryland, United States of America
| | - Wolfgang Losert
- Department of Physics, University of Maryland, College Park, Maryland, United States of America
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Craig FE, Brinkman RR, Ten Eyck S, Aghaeepour N. Computational analysis optimizes the flow cytometric evaluation for lymphoma. CYTOMETRY PART B-CLINICAL CYTOMETRY 2013; 86:18-24. [PMID: 24002786 DOI: 10.1002/cyto.b.21115] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2013] [Revised: 05/20/2013] [Accepted: 07/01/2013] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although many clinical laboratories are adopting higher color flow cytometric assays, the approach to optimizing panel design and data analysis is often traditional and subjective. In order to address the question "What is the best flow cytometric strategy to reliably distinguish germinal center B-cell lymphoma (GC-L) from germinal center hyperplasia (GC-H)?" we applied a computational tool that identifies target populations correlated with a desired outcome, in this case diagnosis. DESIGN Cases of GC-H and GC-L evaluated by flow cytometric immunophenotyping using CD45, CD20, kappa, lambda, CD19, CD5, CD10, CD38, were analyzed with flowType and RchyOptimyx to construct cellular hierarchies that best distinguished the two diagnostic groups. RESULTS The population CD5-CD19+CD10+CD38- had the highest predictive power. Manual reanalysis confirmed significantly higher CD10+/CD38-B-cells in GC-L (median 12.44%, range 0.74-63.29, n = 52) than GC-H (median 0.24%, 0.03-4.49, n = 48, P = 0.0001), but was not entirely specific. Difficulties encountered using this computational approach included the presence of CD10+ granulocytes, continuously variable B-cell expression of CD38, more variable intensity antigen staining in GC-L and inability to assess the contribution of light chain restriction. CONCLUSION Computational analysis with construction of cellular hierarchies related to diagnosis helped guide manual analysis of high dimensional flow cytometric data. This approach highlighted the diagnostic utility of CD38 expression in the evaluation of B-cells with a CD10+ GC phenotype. In contrast to computational analysis of non-neoplastic cell populations, evaluation of neoplastic cells must be able to take into consideration increased variability in antigen expression.
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Affiliation(s)
- Fiona E Craig
- Division of Hematopathology, Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Cossarizza A, Radbruch A. Cytometry for immunology: A stable and happy marriage. Cytometry A 2013; 83:673-5. [DOI: 10.1002/cyto.a.22336] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Andrea Cossarizza
- Department of Surgery, Medicine, Dentistry and Morphological Sciences; University of Modena and Reggio Emilia; via Campi 287; Modena; 41125; Italy
| | - Andreas Radbruch
- German Rheumatism Research Center; Charité University; Hannoversche Strasse 27; Berlin; 10115; Germany
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Villanova F, Di Meglio P, Inokuma M, Aghaeepour N, Perucha E, Mollon J, Nomura L, Hernandez-Fuentes M, Cope A, Prevost AT, Heck S, Maino V, Lord G, Brinkman RR, Nestle FO. Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery. PLoS One 2013; 8:e65485. [PMID: 23843942 PMCID: PMC3701052 DOI: 10.1371/journal.pone.0065485] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Accepted: 04/23/2013] [Indexed: 12/29/2022] Open
Abstract
Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.
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Affiliation(s)
- Federica Villanova
- St. John's Institute of Dermatology, King's College London, London, United Kingdom
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
| | - Paola Di Meglio
- St. John's Institute of Dermatology, King's College London, London, United Kingdom
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
- Molecular Immunology, National Institute for Medical Research, Mill Hill, London, United Kingdom
| | - Margaret Inokuma
- Biological Research & Development, BD Biosciences, San Jose, California, United States of America
| | - Nima Aghaeepour
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | - Esperanza Perucha
- The Medical Research Council (MRC) Centre for Transplantation, King's College London, London, United Kingdom
| | - Jennifer Mollon
- Division of Genetics and Molecular Medicine, Statistical Genetics Unit, King's College London, London, United Kingdom
| | - Laurel Nomura
- Biological Research & Development, BD Biosciences, San Jose, California, United States of America
| | - Maria Hernandez-Fuentes
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
- The Medical Research Council (MRC) Centre for Transplantation, King's College London, London, United Kingdom
| | - Andrew Cope
- Academic Department of Rheumatology, King's College London, London United Kingdom
| | - A. Toby Prevost
- Department of Primary Care and Public Health Sciences, King's College London, London, United Kingdom
| | - Susanne Heck
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
| | - Vernon Maino
- Biological Research & Development, BD Biosciences, San Jose, California, United States of America
| | - Graham Lord
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
- The Medical Research Council (MRC) Centre for Transplantation, King's College London, London, United Kingdom
| | - Ryan R. Brinkman
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Frank O. Nestle
- St. John's Institute of Dermatology, King's College London, London, United Kingdom
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust, (NIHR GSTT/KCL) London, United Kingdom
- * E-mail:
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McNeil LK, Price L, Britten CM, Jaimes M, Maecker H, Odunsi K, Matsuzaki J, Staats JS, Thorpe J, Yuan J, Janetzki S. A harmonized approach to intracellular cytokine staining gating: Results from an international multiconsortia proficiency panel conducted by the Cancer Immunotherapy Consortium (CIC/CRI). Cytometry A 2013; 83:728-38. [PMID: 23788464 DOI: 10.1002/cyto.a.22319] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Revised: 04/18/2013] [Accepted: 05/17/2013] [Indexed: 11/06/2022]
Abstract
Previous results from two proficiency panels of intracellular cytokine staining (ICS) from the Cancer Immunotherapy Consortium and panels from the National Institute of Allergy and Infectious Disease and the Association for Cancer Immunotherapy highlight the variability across laboratories in reported % CD8+ or % CD4+ cytokine-positive cells. One of the main causes of interassay variability in flow cytometry-based assays is due to differences in gating strategies between laboratories, which may prohibit the generation of robust results within single centers and across institutions. To study how gating strategies affect the variation in reported results, a gating panel was organized where all participants analyzed the same set of Flow Cytometry Standard (FCS) files from a four-color ICS assay using their own gating protocol (Phase I) and a gating protocol drafted by consensus from the organizers of the panel (Phase II). Focusing on analysis removed donor, assay, and instrument variation, enabling us to quantify the variability caused by gating alone. One hundred ten participating laboratories applied 110 different gating approaches. This led to high variability in the reported percentage of cytokine-positive cells and consequently in response detection in Phase I. However, variability was dramatically reduced when all laboratories used the same gating strategy (Phase II). Proximity of the cytokine gate to the negative population most impacted true-positive and false-positive response detection. Recommendations are provided for the (1) placement of the cytokine-positive gate, (2) identification of CD4+ CD8+ double-positive T cells, (3) placement of lymphocyte gate, (4) inclusion of dim cells, (5) gate uniformity, and 6) proper adjustment of the biexponential scaling.
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Tanner SD, Baranov VI, Ornatsky OI, Bandura DR, George TC. An introduction to mass cytometry: fundamentals and applications. Cancer Immunol Immunother 2013; 62:955-65. [PMID: 23564178 PMCID: PMC11029414 DOI: 10.1007/s00262-013-1416-8] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Accepted: 03/11/2013] [Indexed: 11/26/2022]
Abstract
Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics. The many available enriched stable isotopes of the transition elements can provide up to 100 distinguishable reporting tags, which can be measured simultaneously because of the essential independence of detection provided by the mass spectrometer. We discuss the adaptation of traditional inductively coupled plasma mass spectrometry to cytometry applications. We focus on the generation of cytometry-compatible data and on approaches to unsupervised multivariate clustering analysis. Finally, we provide a high-level review of some recent benchmark reports that highlight the potential for massively multi-parameter mass cytometry.
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Affiliation(s)
- Scott D Tanner
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.
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42
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Tárnok A. New colors and lights to illuminate cell biology. Cytometry A 2013; 83:251-2. [PMID: 23426985 DOI: 10.1002/cyto.a.22268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 01/29/2013] [Indexed: 11/09/2022]
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43
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Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 2013; 10:228-38. [PMID: 23396282 PMCID: PMC3906045 DOI: 10.1038/nmeth.2365] [Citation(s) in RCA: 350] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2012] [Accepted: 01/14/2013] [Indexed: 12/14/2022]
Abstract
In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
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Affiliation(s)
- Nima Aghaeepour
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
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Studying the human immunome: the complexity of comprehensive leukocyte immunophenotyping. Curr Top Microbiol Immunol 2013; 377:23-60. [PMID: 23975032 DOI: 10.1007/82_2013_336] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A comprehensive study of the cellular components of the immune system requires both deep and broad immunophenotyping of numerous cell populations in an efficient and practical manner. In this chapter, we describe the technical aspects of studying the human immunome using high-dimensional (15 color) fluorescence-based immunophenotyping. We focus on the technical aspects of polychromatic flow cytometry and the initial stages in developing a panel for comprehensive leukocyte immunophenotyping (CLIP). We also briefly discuss how this panel is being used and the challenges of encyclopedic analysis of these rich data sets.
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45
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Aghaeepour N, Brinkman R. Computational analysis of high-dimensional flow cytometric data for diagnosis and discovery. Curr Top Microbiol Immunol 2013; 377:159-75. [PMID: 23975083 DOI: 10.1007/82_2013_337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.
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Affiliation(s)
- Nima Aghaeepour
- Terry Fox Laboratory, BC Cancer Agency, 675 West 10th Avenue, Vancouver BC, V5Z 1L3, Canada
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Robinson JP, Rajwa B, Patsekin V, Davisson VJ. Computational analysis of high-throughput flow cytometry data. Expert Opin Drug Discov 2012; 7:679-93. [PMID: 22708834 PMCID: PMC4389283 DOI: 10.1517/17460441.2012.693475] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology. AREAS COVERED Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension. EXPERT OPINION There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.
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Affiliation(s)
- J Paul Robinson
- Purdue University Cytometry Laboratories, Purdue University, West Lafayette, IN 47907, USA.
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