Author: "Sofie Van Gassen" / Topic: 03 medical and health sciences - Searchworks@Jio Institute Digital Library Search Results

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21 results on '"Sofie Van Gassen"'

1. Analyzing high-dimensional cytometry data using FlowSOM

Author: A. Emmaneel, Artuur Couckuyt, Yvan Saeys, K. Quintelier, Joachim G.J.V. Aerts, and Sofie Van Gassen
Subjects: Protocol (science), 0303 health sciences, Data processing, Computer science, Troubleshooting, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Field (computer science), Visualization, 03 medical and health sciences, Identification (information), 0302 clinical medicine, Workflow, Data mining, Cluster analysis, computer, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1–3 h to complete, though quality issues can increase this time considerably. This protocol describes FlowSOM, a clustering and visualization algorithm for unsupervised analysis of high-dimensional cytometry data. The protocol provides clearly annotated R code and an example dataset for inexperienced users.
Published: 2021

2. Computational flow cytometry as a diagnostic tool in suspected‐myelodysplastic syndromes

Author: Theresia M. Westers, Costa Bachas, Carolien Duetz, Yvan Saeys, Margot F. van Spronsen, Sofie Van Gassen, Arjan A. van de Loosdrecht, Hematology, Hematology laboratory, and CCA - Imaging and biomarkers
Subjects: 0301 basic medicine, medicine.medical_specialty, Histology, MODELS, Population, education, CLASSIFICATION, Immunophenotyping, Pathology and Forensic Medicine, Flow cytometry, Cohort Studies, Single tube, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, hematological malignancies, education.field_of_study, Learning classifier system, medicine.diagnostic_test, business.industry, Myelodysplastic syndromes, flow cytometry, Biology and Life Sciences, CLONAL HEMATOPOIESIS, Cell Biology, Original Articles, medicine.disease, Training cohort, myelodysplastic syndromes, Random forest, Mathematics and Statistics, 030104 developmental biology, Workflow, machine learning, diagnostic test, 030220 oncology & carcinogenesis, Cytogenetic Analysis, Original Article, Radiology, business
Abstract: The diagnostic work‐up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected‐MDS. The computational diagnostic workflow consists of methods for pre‐processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).
Published: 2021

3. TAO-kinase 3 governs the terminal differentiation of NOTCH2-dependent splenic conventional dendritic cells

Author: Sofie Van Gassen, Bart N. Lambrecht, Bastiaan Maes, Kim Deswarte, Lana Vandersarren, Connie M. Arthur, Manon Vanheerswynghels, Sean R. Stowell, Matthias Vanderkerken, Margaret E. Kirkling, Wendy Toussaint, Boris Reizis, Daniel H. Conrad, Philippe Pouliot, Hamida Hammad, Liesbet Martens, and Pulmonary Medicine
Subjects: CD4-Positive T-Lymphocytes, Notch signaling pathway, Spleen, Biology, 03 medical and health sciences, 0302 clinical medicine, Immune system, Protein Domains, Antigens, CD, Intestine, Small, medicine, Animals, Receptor, Notch2, Transcription factor, 030304 developmental biology, 0303 health sciences, Multidisciplinary, Kinase, Cell Differentiation, Dendritic Cells, Dendritic cell, Biological Sciences, In vitro, Cell biology, Mice, Inbred C57BL, Phenotype, Lymphatic system, medicine.anatomical_structure, Gene Expression Regulation, Protein Kinases, Signal Transduction, 030215 immunology
Abstract: Antigen-presenting conventional dendritic cells (cDCs) are broadly divided into type 1 and type 2 subsets that further adapt their phenotype and function to perform specialized tasks in the immune system. The precise signals controlling tissue-specific adaptation and differentiation of cDCs are currently poorly understood. We found that mice deficient in the Ste20 kinase Thousand and One Kinase 3 (TAOK3) lacked terminally differentiated ESAM(+) CD4(+) cDC2s in the spleen and failed to prime CD4(+) T cells in response to allogeneic red-blood-cell transfusion. These NOTCH2- and ADAM10-dependent cDC2s were absent selectively in the spleen, but not in the intestine of Taok3(−/−) and CD11c-cre Taok3(fl/fl) mice. The loss of splenic ESAM(+) cDC2s was cell-intrinsic and could be rescued by conditional overexpression of the constitutively active NOTCH intracellular domain in CD11c-expressing cells. Therefore, TAOK3 controls the terminal differentiation of NOTCH2-dependent splenic cDC2s.
Published: 2020

4. CytoNorm: A Normalization Algorithm for Cytometry Data

Author: Nima Aghaeepour, Sofie Van Gassen, Brice Gaudilliere, Yvan Saeys, and Martin S. Angst
Subjects: mass cytometry, Proteomics, 0301 basic medicine, Normalization (statistics), Histology, Computer science, computer.software_genre, Pathology and Forensic Medicine, 03 medical and health sciences, 0302 clinical medicine, computational cytometry, Medicine and Health Sciences, Cluster Analysis, Humans, Mass cytometry, Cluster analysis, FLOW-CYTOMETRY, Biology and Life Sciences, Original Articles, Genomics, Cell Biology, Flow Cytometry, barcoding, Data set, normalization, 030104 developmental biology, 030220 oncology & carcinogenesis, Test set, Data quality, Original Article, Data mining, computer, Cytometry, Algorithms, Quantile
Abstract: High‐dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high‐content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross‐sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single‐cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population‐specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset‐specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch‐specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real‐world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package “CytoNorm” and available via the following link: http://www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Published: 2019

5. Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Author: Lara Gibellini, Sussan Nourshargh, Susanna Cardell, Wlodzimierz Maslinski, Mar Felipo-Benavent, Florian Mair, Hans-Martin Jäck, Lilly Lopez, Klaus Warnatz, John Trowsdale, Diana Ordonez, Marcus Eich, William Hwang, Anne Cooke, Dirk Mielenz, Alberto Orfao, Winfried F. Pickl, Vladimir Benes, Alice Yue, T. Vincent Shankey, Maria Tsoumakidou, Virginia Litwin, Gelo Victoriano Dela Cruz, Andrea Cavani, Sara De Biasi, Larissa Nogueira Almeida, Jonathan J M Landry, Claudia Haftmann, Charlotte Esser, Ana Cumano, Anneke Wilharm, Francesco Dieli, Rudi Beyaert, Alessio Mazzoni, Burkhard Ludewig, Carlo Pucillo, Dirk H. Busch, Joe Trotter, Stipan Jonjić, Marc Veldhoen, Josef Spidlen, Aja M. Rieger, Dieter Adam, Srijit Khan, Todd A. Fehniger, Giuseppe Matarese, Maximilien Evrard, Christian Maueröder, Steffen Schmitt, Kristin A. Hogquist, Barry Moran, Raghavendra Palankar, Markus Feuerer, S Schmid, Susann Rahmig, Amy E. Lovett-Racke, James V. Watson, Megan K. Levings, Susanne Melzer, Dinko Pavlinic, Christopher M. Harpur, Christina Stehle, A. Graham Pockley, Toshinori Nakayama, Attila Tárnok, Juhao Yang, Michael Lohoff, Paulo Vieira, Francisco Sala-de-Oyanguren, Christian Kurts, Anastasia Gangaev, Alfonso Blanco, Hans Scherer, Regine J. Dress, Bruno Silva-Santos, Kiyoshi Takeda, Bimba F. Hoyer, Ilenia Cammarata, Daryl Grummitt, Isabel Panse, Günnur Deniz, Bianka Baying, Friederike Ebner, Esther Schimisky, Leo Hansmann, Thomas Kamradt, Edwin van der Pol, Daniel Scott-Algara, Anna Iannone, Giorgia Alvisi, Sebastian R. Schulz, Francesco Liotta, Irmgard Förster, Beatriz Jávega, Hans-Peter Rahn, Caetano Reis e Sousa, Livius Penter, Xuetao Cao, David P. Sester, Keisuke Goda, Peter Wurst, Iain B. McInnes, Ricardo T. Gazzinelli, Federica Piancone, Gerald Willimsky, Yotam Raz, Pärt Peterson, Wolfgang Fritzsche, Yvonne Samstag, Martin Büscher, Thomas Schüler, Susanne Hartmann, Robert J. Wilkinson, Anna E. S. Brooks, Steven L. C. Ketelaars, Catherine Sautès-Fridman, Anna Rubartelli, Petra Bacher, Katja Kobow, Marco A. Cassatella, Andrea Hauser, Henrik E. Mei, Kilian Schober, Silvia Della Bella, Graham Anderson, Michael D. Ward, Garth Cameron, Sebastian Lunemann, Katharina Kriegsmann, Katarzyna M. Sitnik, Brice Gaudilliere, Chantip Dang-Heine, Marcello Pinti, Paul Klenerman, Frank A. Schildberg, Joana Barros-Martins, Laura G. Rico, Hanlin Zhang, Christian Münz, Thomas Dörner, Jakob Zimmermann, Andrea M. Cooper, Jonni S. Moore, Andreas Diefenbach, Yanling Liu, Wolfgang Bauer, Tobit Steinmetz, Katharina Pracht, Leonard Tan, Peter K. Jani, Alan M. Stall, Petra Hoffmann, Christine S. Falk, Jasmin Knopf, Simon Fillatreau, Hans-Dieter Volk, Luis E. Muñoz, David L. Haviland, William W. Agace, Jonathan Rebhahn, Ljiljana Cvetkovic, Mohamed Trebak, Jordi Petriz, Mario Clerici, Diether J. Recktenwald, Anders Ståhlberg, Tristan Holland, Helen M. McGuire, Sa A. Wang, Christian Kukat, Thomas Kroneis, Laura Cook, Wan Ting Kong, Xin M. Wang, Britta Engelhardt, Pierre Coulie, Genny Del Zotto, Sally A. Quataert, Kata Filkor, Gabriele Multhoff, Bartek Rajwa, Federica Calzetti, Hans Minderman, Cosima T. Baldari, Jens Geginat, Hervé Luche, Gert Van Isterdael, Linda Schadt, Sophia Urbanczyk, Giovanna Borsellino, Liping Yu, Dale I. Godfrey, Achille Anselmo, Rachael C. Walker, Andreas Grützkau, David W. Hedley, Birgit Sawitzki, Silvia Piconese, Maria Yazdanbakhsh, Burkhard Becher, Ramon Bellmas Sanz, Michael Delacher, Hyun-Dong Chang, Immanuel Andrä, Hans-Gustaf Ljunggren, José-Enrique O'Connor, Ahad Khalilnezhad, Sharon Sanderson, Federico Colombo, Götz R. A. Ehrhardt, Inga Sandrock, Enrico Lugli, Christian Bogdan, James B. Wing, Susann Müller, Tomohiro Kurosaki, Derek Davies, Ester B. M. Remmerswaal, Kylie M. Quinn, Christopher A. Hunter, Andreas Radbruch, Timothy P. Bushnell, Anna Erdei, Sabine Adam-Klages, Pascale Eede, Van Duc Dang, Rieke Winkelmann, Thomas Korn, Gemma A. Foulds, Dirk Baumjohann, Matthias Schiemann, Manfred Kopf, Jan Kisielow, Lisa Richter, Jochen Huehn, Gloria Martrus, Alexander Scheffold, Jessica G. Borger, Sidonia B G Eckle, John Bellamy Foster, Anna Katharina Simon, Alicia Wong, Mübeccel Akdis, Gisa Tiegs, Toralf Kaiser, James McCluskey, Anna Vittoria Mattioli, Aaron J. Marshall, Hui-Fern Koay, Eva Orlowski-Oliver, Anja E. Hauser, J. Paul Robinson, Jay K. Kolls, Luca Battistini, Mairi McGrath, Jane L. Grogan, Natalio Garbi, Timothy Tree, Kingston H. G. Mills, Stefan H. E. Kaufmann, Wolfgang Schuh, Ryan R. Brinkman, Tim R. Mosmann, Vincenzo Barnaba, Andreas Dolf, Lorenzo Cosmi, Bo Huang, Andreia C. Lino, Baerbel Keller, René A. W. van Lier, Alexandra J. Corbett, Paul S. Frenette, Pleun Hombrink, Helena Radbruch, Sofie Van Gassen, Olivier Lantz, Lorenzo Moretta, Désirée Kunkel, Kirsten A. Ward-Hartstonge, Armin Saalmüller, Leslie Y. T. Leung, Salvador Vento-Asturias, Paola Lanuti, Alicia Martínez-Romero, Sarah Warth, Zhiyong Poon, Diana Dudziak, Andrea Cossarizza, Kovit Pattanapanyasat, Konrad von Volkmann, Jessica P. Houston, Agnès Lehuen, Andrew Filby, Pratip K. Chattopadhyay, Stefano Casola, Annika Wiedemann, Hannes Stockinger, Jürgen Ruland, Arturo Zychlinsky, Claudia Waskow, Katrin Neumann, Ari Waisman, Lucienne Chatenoud, Sudipto Bari, Kamran Ghoreschi, David W. Galbraith, Yvan Saeys, Hamida Hammad, Andrea Gori, Miguel López-Botet, Gabriel Núñez, Sabine Ivison, Michael Hundemer, Dorothea Reimer, Mark C. Dessing, Günter J. Hämmerling, Rudolf A. Manz, Tomas Kalina, Jonas Hahn, Holden T. Maecker, Hendy Kristyanto, Martin S. Davey, Henning Ulrich, Michael L. Dustin, Takashi Saito, Yousuke Takahama, Milena Nasi, Johanna Huber, Jürgen Wienands, Paolo Dellabona, Andreas Schlitzer, Michael D. Leipold, Kerstin H. Mair, Christian Peth, Immo Prinz, Chiara Romagnani, José M. González-Navajas, Josephine Schlosser, Marina Saresella, Matthias Edinger, Dirk Brenner, Nicole Baumgarth, Rikard Holmdahl, Fang-Ping Huang, Guadalupe Herrera, Malte Paulsen, Gergely Toldi, Luka Cicin-Sain, Reiner Schulte, Christina E. Zielinski, Thomas Winkler, Christoph Goettlinger, Philip E. Boulais, Jennie H M Yang, Antonio Celada, Heike Kunze-Schumacher, Julia Tornack, Florian Ingelfinger, Jenny Mjösberg, Andy Riddell, Leonie Wegener, Thomas Höfer, Christoph Hess, James P. Di Santo, Anna E. Oja, J. Kühne, Willem van de Veen, Mary Bebawy, Alberto Mantovani, Bart Everts, Giovanna Lombardi, Laura Maggi, Anouk von Borstel, Pia Kvistborg, Elisabetta Traggiai, A Ochel, Nima Aghaeepour, Charles-Antoine Dutertre, Matthieu Allez, Thomas Höllt, Wenjun Ouyang, Regina Stark, Maries van den Broek, Shimon Sakaguchi, Paul K. Wallace, Silvano Sozzani, Francesca LaRosa, Annette Oxenius, Malgorzata J. Podolska, Ivana Marventano, Wilhelm Gerner, Oliver F. Wirz, Britta Frehse, Gevitha Ravichandran, Martin Herrmann, Carl S. Goodyear, Gary Warnes, Helen Ferry, Stefan Frischbutter, Tim R. Radstake, Salomé LeibundGut-Landmann, Yi Zhao, Axel Schulz, Angela Santoni, Pablo Engel, Daniela C. Hernández, Andreas Acs, Cristiano Scottà, Francesco Annunziato, Thomas Weisenburger, Wolfgang Beisker, Sue Chow, Fritz Melchers, Daniel E. Speiser, Immanuel Kwok, Florent Ginhoux, Dominic A. Boardman, Natalie Stanley, Carsten Watzl, Marie Follo, Erik Lubberts, Andreas Krueger, Susanne Ziegler, Göran K. Hansson, David Voehringer, Antonia Niedobitek, Eleni Christakou, Lai Guan Ng, Sabine Baumgart, Nicholas A Gherardin, Antonio Cosma, Orla Maguire, Jolene Bradford, Daniel Schraivogel, Linda Quatrini, Stephen D. Miller, Rheumatology, Università degli Studi di Modena e Reggio Emilia (UNIMORE), Deutsches Rheuma-ForschungsZentrum (DRFZ), Deutsches Rheuma-ForschungsZentrum, Swiss Institute of Allergy and Asthma Research (SIAF), Universität Zürich [Zürich] = University of Zurich (UZH), Institut de Recherche Saint-Louis - Hématologie Immunologie Oncologie (Département de recherche de l’UFR de médecine, ex- Institut Universitaire Hématologie-IUH) (IRSL), Université de Paris (UP), Ecotaxie, microenvironnement et développement lymphocytaire (EMily (UMR_S_1160 / U1160)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Department of Internal Medicine, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI)-DENOTHE Center, Institute of Clinical Molecular Biology, Kiel University, Department of Life Sciences [Siena, Italy], Università degli Studi di Siena = University of Siena (UNISI), Institut Pasteur, Fondation Cenci Bolognetti - Istituto Pasteur Italia, Fondazione Cenci Bolognetti, Réseau International des Instituts Pasteur (RIIP), Dulbecco Telethon Institute/Department of Biology, Caprotec Bioanalytics GmbH, International Occultation Timing Association European Section (IOTA ES), International Occultation Timing Association European Section, European Molecular Biology Laboratory [Heidelberg] (EMBL), VIB-UGent Center for Inflammation Research [Gand, Belgique] (IRC), VIB [Belgium], Fondazione Santa Lucia (IRCCS), Department of Immunology, Chinese Academy of Medical Sciences, FIRC Institute of Molecular Oncology Foundation, IFOM, Istituto FIRC di Oncologia Molecolare (IFOM), Institut Necker Enfants-Malades (INEM - UM 111 (UMR 8253 / U1151)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Physiopatology and Transplantation, University of Milan (DEPT), University of Milan, Monash University [Clayton], Institut des Maladies Emergentes et des Thérapies Innovantes (IMETI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institute of Cellular Pathology, Université Catholique de Louvain = Catholic University of Louvain (UCL), Lymphopoïèse (Lymphopoïèse (UMR_1223 / U1223 / U-Pasteur_4)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Experimental Immunology Unit, Dept. of Oncology, DIBIT San Raffaele Scientific Institute, Immunité Innée - Innate Immunity, Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur [Paris], Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Department of Biopharmacy [Bruxelles, Belgium] (Institute for Medical Immunology IMI), Université libre de Bruxelles (ULB), Charité Hospital, Humboldt-Universität zu Berlin, Agency for science, technology and research [Singapore] (A*STAR), Laboratory of Molecular Immunology and the Howard Hughes Institute, Rockefeller University [New York], Kennedy Institute of Rheumatology [Oxford, UK], Imperial College London, Theodor Kocher Institute, University of Bern, Leibniz Research Institute for Environmental Medicine [Düsseldorf, Germany] ( IUF), Université Lumière - Lyon 2 (UL2), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of Edinburgh, Integrative Biology Program [Milano], Istituto Nazionale Genetica Molecolare [Milano] (INGM), Singapore Immunology Network (SIgN), Biomedical Sciences Institute (BMSI), Universitat de Barcelona (UB), Rheumatologie, Cell Biology, Department of medicine [Stockholm], Karolinska Institutet [Stockholm]-Karolinska University Hospital [Stockholm], Department for Internal Medicine 3, Institute for Clinical Immunology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Delft University of Technology (TU Delft), Medical Inflammation Research, Karolinska Institutet [Stockholm], Department of Photonics Engineering [Lyngby], Technical University of Denmark [Lyngby] (DTU), Dpt of Experimental Immunology [Braunschweig], Helmholtz Centre for Infection Research (HZI), Department of Internal Medicine V, Universität Heidelberg [Heidelberg], Department of Histology and Embryology, University of Rijeka, Freiburg University Medical Center, Nuffield Dept of Clinical Medicine, University of Oxford [Oxford]-NIHR Biomedical Research Centre, Institute of Integrative Biology, Molecular Biomedicine, Berlin Institute of Health (BIH), Laboratory for Lymphocyte Differentiation, RIKEN Research Center, Institutes of Molecular Medicine and Experimental Immunology, University of Bonn, Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Department of Surgery [Vancouver, BC, Canada] (Child and Family Research Institute), University of British Columbia (UBC)-Child and Family Research Institute [Vancouver, BC, Canada], College of Food Science and Technology [Shangai], Shanghai Ocean University, Institute for Medical Microbiology and Hygiene, University of Marburg, King‘s College London, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Centre d'Immunophénomique (CIPHE), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Brustzentrum Kantonsspital St. Gallen, Immunotechnology Section, Vaccine Research Center, National Institutes of Health [Bethesda] (NIH)-National Institute of Allergy and Infectious Diseases, Heinrich Pette Institute [Hamburg], Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Department of Immunology and Cell Biology, Mario Negri Institute, Laboratory of Molecular Medicine and Biotechnology, Don C. Gnocchi ONLUS Foundation, Institute of Translational Medicine, Klinik für Dermatologie, Venerologie und Allergologie, School of Biochemistry and Immunology, Department of Medicine Huddinge, Karolinska Institutet [Stockholm]-Karolinska University Hospital [Stockholm]-Lipid Laboratory, Università di Genova, Dipartimento di Medicina Sperimentale, Department of Environmental Microbiology, Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), Department of Radiation Oncology [Munich], Ludwig-Maximilians-Universität München (LMU), Centre de Recherche Publique- Santé, Université du Luxembourg (Uni.lu), William Harvey Research Institute, Barts and the London Medical School, University of Michigan [Ann Arbor], University of Michigan System, Centro de Investigacion del Cancer (CSIC), Universitario de Salamanca, Molecular Pathology [Tartu, Estonia], University of Tartu, Hannover Medical School [Hannover] (MHH), Centre d'Immunologie de Marseille - Luminy (CIML), Monash Biomedicine Discovery Institute, Cytometry Laboratories and School of Veterinary Medicine, Purdue University [West Lafayette], Data Mining and Modelling for Biomedicine [Ghent, Belgium], VIB Center for Inflammation Research [Ghent, Belgium], Laboratory for Cell Signaling, RIKEN Research Center for Allergy and Immunology, RIKEN Research Center for Allergy and Immunology, Osaka University [Osaka], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Institute of Medical Immunology [Berlin, Germany], FACS and Array Core Facility, Johannes Gutenberg - Universität Mainz (JGU), Otto-von-Guericke University [Magdeburg] (OVGU), SUPA School of Physics and Astronomy [University of St Andrews], University of St Andrews [Scotland]-Scottish Universities Physics Alliance (SUPA), Biologie Cellulaire des Lymphocytes - Lymphocyte Cell Biology, Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), General Pathology and Immunology (GPI), University of Brescia, Université de Lausanne (UNIL), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, Department of Molecular Immunology, Medizinische Universität Wien = Medical University of Vienna, Dept. Pediatric Cardiology, Universität Leipzig [Leipzig], Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Center for Cardiovascular Sciences, Albany Medical College, Dept Pathol, Div Immunol, University of Cambridge [UK] (CAM), Department of Information Technology [Gent], Universiteit Gent, Department of Plant Systems Biology, Department of Plant Biotechnology and Genetics, Universiteit Gent = Ghent University [Belgium] (UGENT), Division of Molecular Immunology, Institute for Immunology, Department of Geological Sciences, University of Oregon [Eugene], Centers for Disease Control and Prevention [Atlanta] (CDC), Centers for Disease Control and Prevention, University of Colorado [Colorado Springs] (UCCS), FACS laboratory, Cancer Research, London, Cancer Research UK, Regeneration in Hematopoiesis and Animal Models of Hematopoiesis, Faculty of Medicine, Dresden University of Technology, Barbara Davis Center for Childhood Diabetes (BDC), University of Colorado Anschutz [Aurora], School of Computer and Electronic Information [Guangxi University], Guangxi University [Nanning], School of Materials Science and Engineering, Nanyang Technological University [Singapour], Max Planck Institute for Infection Biology (MPIIB), Max-Planck-Gesellschaft, Work in the laboratory of Dieter Adam is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 125440785 – SFB 877, Project B2.Petra Hoffmann, Andrea Hauser, and Matthias Edinger thank BD Biosciences®, San José, CA, USA, and SKAN AG, Bale, Switzerland for fruitful cooperation during the development, construction, and installation of the GMP‐compliant cell sorting equipment and the Bavarian Immune Therapy Network (BayImmuNet) for financial support.Edwin van der Pol and Paola Lanuti acknowledge Aleksandra Gąsecka M.D. for excellent experimental support and Dr. Rienk Nieuwland for textual suggestions. This work was supported by the Netherlands Organisation for Scientific Research – Domain Applied and Engineering Sciences (NWO‐TTW), research program VENI 15924.Jessica G Borger, Kylie M Quinn, Mairi McGrath, and Regina Stark thank Francesco Siracusa and Patrick Maschmeyer for providing data.Larissa Nogueira Almeida was supported by DFG research grant MA 2273/14‐1. Rudolf A. Manz was supported by the Excellence Cluster 'Inflammation at Interfaces' (EXC 306/2).Susanne Hartmann and Friederike Ebner were supported by the German Research Foundation (GRK 2046).Hans Minderman was supported by NIH R50CA211108.This work was funded by the Deutsche Forschungsgemeinschaft through the grant TRR130 (project P11 and C03) to Thomas H. Winkler.Ramon Bellmàs Sanz, Jenny Kühne, and Christine S. Falk thank Jana Keil and Kerstin Daemen for excellent technical support. The work was funded by the Germany Research Foundation CRC738/B3 (CSF).The work by the Mei laboratory was supported by German Research Foundation Grant ME 3644/5‐1 and TRR130 TP24, the German Rheumatism Research Centre Berlin, European Union Innovative Medicines Initiative ‐ Joint Undertaking ‐ RTCure Grant Agreement 777357, the Else Kröner‐Fresenius‐Foundation, German Federal Ministry of Education and Research e:Med sysINFLAME Program Grant 01ZX1306B and KMU‐innovativ 'InnoCyt', and the Leibniz Science Campus for Chronic Inflammation (http://www.chronische-entzuendung.org).Axel Ronald Schulz, Antonio Cosma, Sabine Baumgart, Brice Gaudilliere, Helen M. McGuire, and Henrik E. Mei thank Michael D. Leipold for critically reading the manuscript.Christian Kukat acknowledges support from the ISAC SRL Emerging Leaders program.John Trowsdale received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant Agreement 695551)., European Project: 7728036(1978), Università degli Studi di Modena e Reggio Emilia = University of Modena and Reggio Emilia (UNIMORE), Université Paris Cité (UPCité), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Università degli Studi di Firenze = University of Florence (UniFI)-DENOTHE Center, Università degli Studi di Milano = University of Milan (UNIMI), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Humboldt University Of Berlin, Leibniz Research Institute for Environmental Medicine [Düsseldorf, Germany] (IUF), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Universität Heidelberg [Heidelberg] = Heidelberg University, Universitäts Klinikum Freiburg = University Medical Center Freiburg (Uniklinik), University of Oxford-NIHR Biomedical Research Centre, Universität Bonn = University of Bonn, Università degli Studi di Firenze = University of Florence (UniFI), Università degli studi di Genova = University of Genoa (UniGe), Universidad de Salamanca, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Otto-von-Guericke-Universität Magdeburg = Otto-von-Guericke University [Magdeburg] (OVGU), Université de Lausanne = University of Lausanne (UNIL), Universität Leipzig, Universiteit Gent = Ghent University (UGENT), HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany., Cossarizza, A., Chang, H. -D., Radbruch, A., Acs, A., Adam, D., Adam-Klages, S., Agace, W. W., Aghaeepour, N., Akdis, M., Allez, M., Almeida, L. N., Alvisi, G., Anderson, G., Andra, I., Annunziato, F., Anselmo, A., Bacher, P., Baldari, C. T., 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, D. A., Bogdan, C., Borger, J. G., Borsellino, G., Boulais, P. E., Bradford, J. A., Brenner, D., Brinkman, R. R., Brooks, A. E. S., Busch, D. H., Buscher, M., Bushnell, T. P., Calzetti, F., Cameron, G., Cammarata, I., Cao, X., Cardell, S. L., Casola, S., Cassatella, M. A., Cavani, A., Celada, A., Chatenoud, L., Chattopadhyay, P. K., Chow, S., Christakou, E., Cicin-Sain, L., Clerici, M., Colombo, F. S., Cook, L., Cooke, A., Cooper, A. M., Corbett, A. J., Cosma, A., Cosmi, L., Coulie, P. G., Cumano, A., Cvetkovic, L., Dang, V. D., Dang-Heine, C., Davey, M. S., Davies, D., De Biasi, S., Del Zotto, G., Dela Cruz, G. V., Delacher, M., Della Bella, S., Dellabona, P., Deniz, G., Dessing, M., Di Santo, J. P., Diefenbach, A., Dieli, F., Dolf, A., Dorner, T., Dress, R. J., Dudziak, D., Dustin, M., Dutertre, C. -A., Ebner, F., Eckle, S. B. G., Edinger, M., Eede, P., Ehrhardt, G. R. A., Eich, M., Engel, P., Engelhardt, B., Erdei, A., Esser, C., Everts, B., Evrard, M., Falk, C. S., Fehniger, T. A., Felipo-Benavent, M., Ferry, H., Feuerer, M., Filby, A., Filkor, K., Fillatreau, S., Follo, M., Forster, I., Foster, J., Foulds, G. A., Frehse, B., Frenette, P. S., Frischbutter, S., Fritzsche, W., Galbraith, D. W., Gangaev, A., Garbi, N., Gaudilliere, B., Gazzinelli, R. T., Geginat, J., Gerner, W., Gherardin, N. A., Ghoreschi, K., Gibellini, L., Ginhoux, F., Goda, K., Godfrey, D. I., Goettlinger, C., Gonzalez-Navajas, J. M., Goodyear, C. S., Gori, A., Grogan, J. L., Grummitt, D., Grutzkau, A., Haftmann, C., Hahn, J., Hammad, H., Hammerling, G., Hansmann, L., Hansson, G., Harpur, C. M., Hartmann, S., Hauser, A., Hauser, A. E., Haviland, D. L., Hedley, D., Hernandez, D. C., Herrera, G., Herrmann, M., Hess, C., Hofer, T., Hoffmann, P., Hogquist, K., Holland, T., Hollt, T., Holmdahl, R., Hombrink, P., Houston, J. P., Hoyer, B. F., Huang, B., Huang, F. -P., Huber, J. E., Huehn, J., Hundemer, M., Hunter, C. A., Hwang, W. Y. K., Iannone, A., Ingelfinger, F., Ivison, S. M., Jack, H. -M., Jani, P. K., Javega, B., Jonjic, S., Kaiser, T., Kalina, T., Kamradt, T., Kaufmann, S. H. E., Keller, B., Ketelaars, S. L. C., Khalilnezhad, A., Khan, S., Kisielow, J., Klenerman, P., Knopf, J., Koay, H. -F., Kobow, K., Kolls, J. K., Kong, W. T., Kopf, M., Korn, T., Kriegsmann, K., Kristyanto, H., Kroneis, T., Krueger, A., Kuhne, 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, M. D., Leung, L. Y. T., Levings, M. K., Lino, A. C., Liotta, F., Litwin, V., Liu, Y., Ljunggren, H. -G., Lohoff, M., Lombardi, G., Lopez, L., Lopez-Botet, M., Lovett-Racke, A. E., Lubberts, E., Luche, H., Ludewig, B., Lugli, E., Lunemann, S., Maecker, H. T., Maggi, L., Maguire, O., Mair, F., Mair, K. H., Mantovani, A., Manz, R. A., Marshall, A. J., Martinez-Romero, A., Martrus, G., Marventano, I., Maslinski, W., Matarese, G., Mattioli, A. V., Maueroder, C., Mazzoni, A., Mccluskey, J., Mcgrath, M., Mcguire, H. M., Mcinnes, I. B., Mei, H. E., Melchers, F., Melzer, S., Mielenz, D., Miller, S. D., Mills, K. H. G., Minderman, H., Mjosberg, J., Moore, J., Moran, B., Moretta, L., Mosmann, T. R., Muller, S., Multhoff, G., Munoz, L. E., Munz, C., Nakayama, T., Nasi, M., Neumann, K., Ng, L. G., Niedobitek, A., Nourshargh, S., Nunez, G., O'Connor, J. -E., 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, W. F., Piconese, S., Pinti, M., Pockley, A. G., Podolska, M. J., Poon, Z., Pracht, K., Prinz, I., Pucillo, C. E. M., Quataert, S. A., Quatrini, L., Quinn, K. M., Radbruch, H., Radstake, T. R. D. J., Rahmig, S., Rahn, H. -P., Rajwa, B., Ravichandran, G., Raz, Y., Rebhahn, J. A., Recktenwald, D., Reimer, D., Reis e Sousa, C., Remmerswaal, E. B. M., Richter, L., Rico, L. G., Riddell, A., Rieger, A. M., Robinson, J. P., Romagnani, C., Rubartelli, A., Ruland, J., Saalmuller, A., Saeys, Y., Saito, T., Sakaguchi, S., Sala-de-Oyanguren, F., Samstag, Y., Sanderson, S., Sandrock, I., Santoni, A., Sanz, R. B., Saresella, M., Sautes-Fridman, C., Sawitzki, B., Schadt, L., Scheffold, A., Scherer, H. U., Schiemann, M., Schildberg, F. A., Schimisky, E., Schlitzer, A., Schlosser, J., Schmid, S., Schmitt, S., Schober, K., Schraivogel, D., Schuh, W., Schuler, T., Schulte, R., Schulz, A. R., Schulz, S. R., Scotta, C., Scott-Algara, D., Sester, D. P., Shankey, T. V., Silva-Santos, B., Simon, A. K., Sitnik, K. M., Sozzani, S., Speiser, D. E., Spidlen, J., Stahlberg, A., Stall, A. M., Stanley, N., Stark, R., Stehle, C., Steinmetz, T., Stockinger, H., Takahama, Y., Takeda, K., Tan, L., Tarnok, A., Tiegs, G., Toldi, G., Tornack, J., Traggiai, E., Trebak, M., Tree, T. I. M., 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, R. A. W., Veldhoen, M., Vento-Asturias, S., Vieira, P., Voehringer, D., Volk, H. -D., von Borstel, A., von Volkmann, K., Waisman, A., Walker, R. V., Wallace, P. K., Wang, S. A., Wang, X. M., Ward, M. D., Ward-Hartstonge, K. A., Warnatz, K., Warnes, G., Warth, S., Waskow, C., Watson, J. V., Watzl, C., Wegener, L., Weisenburger, T., Wiedemann, A., Wienands, J., Wilharm, A., Wilkinson, R. J., Willimsky, G., Wing, J. B., Winkelmann, R., Winkler, T. H., Wirz, O. F., Wong, A., Wurst, P., Yang, J. H. M., Yang, J., Yazdanbakhsh, M., Yu, L., Yue, A., Zhang, H., Zhao, Y., Ziegler, S. M., Zielinski, C., Zimmermann, J., Zychlinsky, A., UCL - SSS/DDUV - Institut de Duve, UCL - SSS/DDUV/GECE - Génétique cellulaire, Netherlands Organization for Scientific Research, German Research Foundation, European Commission, European Research Council, Repositório da Universidade de Lisboa, CCA - Imaging and biomarkers, Experimental Immunology, AII - Infectious diseases, AII - Inflammatory diseases, Biomedical Engineering and Physics, ACS - Atherosclerosis & ischemic syndromes, and Landsteiner Laboratory
Subjects: 0301 basic medicine, Consensus, Immunology, Consensu, Cell Separation, Biology, Article, Flow cytometry, 03 medical and health sciences, 0302 clinical medicine, Guidelines, Allergy and Immunology, medicine, Cell separation, Immunology and Allergy, Humans, guidelines, flow cytometry, immunology, medicine.diagnostic_test, BIOMEDICINE AND HEALTHCARE. Basic Medical Sciences, Cell sorting, Flow Cytometry, Cell selection, Data science, 3. Good health, 030104 developmental biology, Phenotype, [SDV.IMM]Life Sciences [q-bio]/Immunology, BIOMEDICINA I ZDRAVSTVO. Temeljne medicinske znanosti, 030215 immunology, Human
Abstract: All authors: Andrea Cossarizza Hyun‐Dong Chang Andreas Radbruch Andreas Acs Dieter Adam Sabine Adam‐Klages William W. Agace Nima Aghaeepour Mübeccel Akdis Matthieu Allez Larissa Nogueira Almeida Giorgia Alvisi Graham Anderson Immanuel Andrä Francesco Annunziato Achille Anselmo Petra Bacher Cosima T. Baldari Sudipto Bari Vincenzo Barnaba Joana Barros‐Martins Luca Battistini Wolfgang Bauer Sabine Baumgart Nicole Baumgarth Dirk Baumjohann Bianka Baying Mary Bebawy Burkhard Becher Wolfgang Beisker Vladimir Benes Rudi Beyaert Alfonso Blanco Dominic A. Boardman Christian Bogdan Jessica G. Borger Giovanna Borsellino Philip E. Boulais Jolene A. Bradford Dirk Brenner Ryan R. Brinkman Anna E. S. Brooks Dirk H. Busch Martin Büscher Timothy P. Bushnell Federica Calzetti Garth Cameron Ilenia Cammarata Xuetao Cao Susanna L. Cardell Stefano Casola Marco A. Cassatella Andrea Cavani Antonio Celada Lucienne Chatenoud Pratip K. Chattopadhyay Sue Chow Eleni Christakou Luka Čičin‐Šain Mario Clerici Federico S. Colombo Laura Cook Anne Cooke Andrea M. Cooper Alexandra J. Corbett Antonio Cosma Lorenzo Cosmi Pierre G. Coulie Ana Cumano Ljiljana Cvetkovic Van Duc Dang Chantip Dang‐Heine Martin S. Davey Derek Davies Sara De Biasi Genny Del Zotto Gelo Victoriano Dela Cruz Michael Delacher Silvia Della Bella Paolo Dellabona Günnur Deniz Mark Dessing James P. Di Santo Andreas Diefenbach Francesco Dieli Andreas Dolf Thomas Dörner Regine J. Dress Diana Dudziak Michael Dustin Charles‐Antoine Dutertre Friederike Ebner Sidonia B. G. Eckle Matthias Edinger Pascale Eede Götz R.A. Ehrhardt Marcus Eich Pablo Engel Britta Engelhardt Anna Erdei Charlotte Esser Bart Everts Maximilien Evrard Christine S. Falk Todd A. Fehniger Mar Felipo‐Benavent Helen Ferry Markus Feuerer Andrew Filby Kata Filkor Simon Fillatreau Marie Follo Irmgard Förster John Foster Gemma A. Foulds Britta Frehse Paul S. Frenette Stefan Frischbutter Wolfgang Fritzsche David W. Galbraith Anastasia Gangaev Natalio Garbi Brice Gaudilliere Ricardo T. Gazzinelli Jens Geginat Wilhelm Gerner Nicholas A. Gherardin Kamran Ghoreschi Lara Gibellini Florent Ginhoux Keisuke Goda Dale I. Godfrey Christoph Goettlinger Jose M. González‐Navajas Carl S. Goodyear Andrea Gori Jane L. Grogan Daryl Grummitt Andreas Grützkau Claudia Haftmann Jonas Hahn Hamida Hammad Günter Hämmerling Leo Hansmann Goran Hansson Christopher M. Harpur Susanne Hartmann Andrea Hauser Anja E. Hauser David L. Haviland David Hedley Daniela C. Hernández Guadalupe Herrera Martin Herrmann Christoph Hess Thomas Höfer Petra Hoffmann Kristin Hogquist Tristan Holland Thomas Höllt Rikard Holmdahl Pleun Hombrink Jessica P. Houston Bimba F. Hoyer Bo Huang Fang‐Ping Huang Johanna E. Huber Jochen Huehn Michael Hundemer Christopher A. Hunter William Y. K. Hwang Anna Iannone Florian Ingelfinger Sabine M Ivison Hans‐Martin Jäck Peter K. Jani Beatriz Jávega Stipan Jonjic Toralf Kaiser Tomas Kalina Thomas Kamradt Stefan H. E. Kaufmann Baerbel Keller Steven L. C. Ketelaars Ahad Khalilnezhad Srijit Khan Jan Kisielow Paul Klenerman Jasmin Knopf Hui‐Fern Koay Katja Kobow Jay K. Kolls Wan Ting Kong Manfred Kopf Thomas Korn Katharina Kriegsmann Hendy Kristyanto Thomas Kroneis Andreas Krueger Jenny Kühne Christian Kukat Désirée Kunkel Heike Kunze‐Schumacher Tomohiro Kurosaki Christian Kurts Pia Kvistborg Immanuel Kwok Jonathan Landry Olivier Lantz Paola Lanuti Francesca LaRosa Agnès Lehuen Salomé LeibundGut‐Landmann Michael D. Leipold Leslie Y.T. Leung Megan K. Levings Andreia C. Lino Francesco Liotta Virginia Litwin Yanling Liu Hans‐Gustaf Ljunggren Michael Lohoff Giovanna Lombardi Lilly Lopez Miguel López‐Botet Amy E. Lovett‐Racke Erik Lubberts Herve Luche Burkhard Ludewig Enrico Lugli Sebastian Lunemann Holden T. Maecker Laura Maggi Orla Maguire Florian Mair Kerstin H. Mair Alberto Mantovani Rudolf A. Manz Aaron J. Marshall Alicia Martínez‐Romero Glòria Martrus Ivana Marventano Wlodzimierz Maslinski Giuseppe Matarese Anna Vittoria Mattioli Christian Maueröder Alessio Mazzoni James McCluskey Mairi McGrath Helen M. McGuire Iain B. McInnes Henrik E. Mei Fritz Melchers Susanne Melzer Dirk Mielenz Stephen D. Miller Kingston H.G. Mills Hans Minderman Jenny Mjösberg Jonni Moore Barry Moran Lorenzo Moretta Tim R. Mosmann Susann Müller Gabriele Multhoff Luis Enrique Muñoz Christian Münz Toshinori Nakayama Milena Nasi Katrin Neumann Lai Guan Ng Antonia Niedobitek Sussan Nourshargh Gabriel Núñez José‐Enrique O'Connor Aaron Ochel Anna Oja Diana Ordonez Alberto Orfao Eva Orlowski‐Oliver Wenjun Ouyang Annette Oxenius Raghavendra Palankar Isabel Panse Kovit Pattanapanyasat Malte Paulsen Dinko Pavlinic Livius Penter Pärt Peterson Christian Peth Jordi Petriz Federica Piancone Winfried F. Pickl Silvia Piconese Marcello Pinti A. Graham Pockley Malgorzata Justyna Podolska Zhiyong Poon Katharina Pracht Immo Prinz Carlo E. M. Pucillo Sally A. Quataert Linda Quatrini Kylie M. Quinn Helena Radbruch Tim R. D. J. Radstake Susann Rahmig Hans‐Peter Rahn Bartek Rajwa Gevitha Ravichandran Yotam Raz Jonathan A. Rebhahn Diether Recktenwald Dorothea Reimer Caetano Reis e Sousa Ester B.M. Remmerswaal Lisa Richter Laura G. Rico Andy Riddell Aja M. Rieger J. Paul Robinson Chiara Romagnani Anna Rubartelli Jürgen Ruland Armin Saalmüller Yvan Saeys Takashi Saito Shimon Sakaguchi Francisco Sala‐de‐Oyanguren Yvonne Samstag Sharon Sanderson Inga Sandrock Angela Santoni Ramon Bellmàs Sanz Marina Saresella Catherine Sautes‐Fridman Birgit Sawitzki Linda Schadt Alexander Scheffold Hans U. Scherer Matthias Schiemann Frank A. Schildberg Esther Schimisky Andreas Schlitzer Josephine Schlosser Stephan Schmid Steffen Schmitt Kilian Schober Daniel Schraivogel Wolfgang Schuh Thomas Schüler Reiner Schulte Axel Ronald Schulz Sebastian R. Schulz Cristiano Scottá Daniel Scott‐Algara David P. Sester T. Vincent Shankey Bruno Silva‐Santos Anna Katharina Simon Katarzyna M. Sitnik Silvano Sozzani Daniel E. Speiser Josef Spidlen Anders Stahlberg Alan M. Stall Natalie Stanley Regina Stark Christina Stehle Tobit Steinmetz Hannes Stockinger Yousuke Takahama Kiyoshi Takeda Leonard Tan Attila Tárnok Gisa Tiegs Gergely Toldi Julia Tornack Elisabetta Traggiai Mohamed Trebak Timothy I.M. Tree Joe Trotter John Trowsdale Maria Tsoumakidou Henning Ulrich Sophia Urbanczyk Willem van de Veen Maries van den Broek Edwin van der Pol Sofie Van Gassen Gert Van Isterdael René A.W. van Lier Marc Veldhoen Salvador Vento‐Asturias Paulo Vieira David Voehringer Hans‐Dieter Volk Anouk von Borstel Konrad von Volkmann Ari Waisman Rachael V. Walker Paul K. Wallace Sa A. Wang Xin M. Wang Michael D. Ward Kirsten A Ward‐Hartstonge Klaus Warnatz Gary Warnes Sarah Warth Claudia Waskow James V. Watson Carsten Watzl Leonie Wegener Thomas Weisenburger Annika Wiedemann Jürgen Wienands Anneke Wilharm Robert John Wilkinson Gerald Willimsky James B. Wing Rieke Winkelmann Thomas H. Winkler Oliver F. Wirz Alicia Wong Peter Wurst Jennie H. M. Yang Juhao Yang Maria Yazdanbakhsh Liping Yu Alice Yue Hanlin Zhang Yi Zhao Susanne Maria Ziegler Christina Zielinski Jakob Zimmermann Arturo Zychlinsky., 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., This work was supported by the Netherlands Organisation for Scientific Research – Domain Applied and Engineering Sciences (NWO-TTW), research program VENI 15924. This work was funded by the Deutsche Forschungsgemeinschaft. European Union Innovative Medicines Initiative - Joint Undertaking - RTCure Grant Agreement 777357 and innovation program (Grant Agreement 695551).
Published: 2019

6. Immune Monitoring in Melanoma and Urothelial Cancer Patients Treated with Anti-PD-1 Immunotherapy and SBRT Discloses Tumor Specific Immune Signatures

Author: Sylvie Rottey, Mathieu Spaas, Simon J. Tavernier, Liesbeth Ferdinande, Karel Decaestecker, Annelies Demeyer, Sofie Van Gassen, Piet Ost, Annabel Meireson, Lieve Brochez, Vibeke Kruse, Benjamin Hennart, Filomeen Haerynck, Jo Van Dorpe, Nora Sundahl, Delphine Allorge, Yvan Saeys, CHU Lille, Institut Pasteur de Lille, Université de Lille, Universiteit Gent = Ghent University [UGENT], IMPact de l'Environnement Chimique sur la Santé humaine (IMPECS) - ULR 4483, Ghent University Hospital, Vlaams Instituut voor Biotechnologie [Ghent, Belgique] [VIB], VIB-UGent Center for Inflammation Research [Gand, Belgique] [IRC], Impact de l'environnement chimique sur la santé humaine - ULR 4483 [IMPECS], Universiteit Gent = Ghent University (UGENT), Vlaams Instituut voor Biotechnologie [Ghent, Belgique] (VIB), VIB-UGent Center for Inflammation Research [Gand, Belgique] (IRC), VIB [Belgium], Impact de l'environnement chimique sur la santé humaine - ULR 4483 (IMPECS), and Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)
Subjects: EXPRESSION, PD-L1, 0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, Myeloid, immune monitoring, [SDV]Life Sciences [q-bio], medicine.medical_treatment, MONOTHERAPY, MULTICENTER, Pembrolizumab, immunotherapy, anti-PD-1, melanoma, urothelial cancer, blood biomarkers, THERAPY, Article, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Medicine and Health Sciences, medicine, Progression-free survival, Neutrophil to lymphocyte ratio, PEMBROLIZUMAB, RC254-282, biology, business.industry, Melanoma, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Biology and Life Sciences, NIVOLUMAB, Immunotherapy, medicine.disease, FDA APPROVAL, 030104 developmental biology, medicine.anatomical_structure, 030220 oncology & carcinogenesis, T-CELLS, biology.protein, Nivolumab, BURDEN, business
Abstract: Simple Summary Currently available biomarkers for response to checkpoint inhibitors are incomplete and predominantly focus on tumor tissue analysis e.g., tumor mutational burden, programmed cell death-ligand 1 (PD-L1) expression. Biomarkers in peripheral blood would allow a more dynamic monitoring and could offer a way for sequential adaptation of treatment strategy. We conducted an in-depth analysis of baseline and on-treatment systemic immune features in a cohort of stage III/IV melanoma and stage IV urothelial cancer (UC) patients treated with anti-programmed cell death-1 (anti-PD-1) therapy combined with stereotactic body radiotherapy (SBRT) in a similar regimen/schedule. Baseline immunity was clearly different between these two cohorts, indicating a less active immune landscape in UC patients. This study also detected signatures of proliferation in the CD8+ T-cell compartment pre-treatment and early after anti-PD-1 initiation that were positively correlated with clinical outcome in both tumor types. In addition our data support the biological relevance of PD-1/PD-L1 expression on circulating immune cell subsets, especially in melanoma. Abstract (1) Background: Blockade of the PD-1/PD-L1 pathway has revolutionized the oncology field in the last decade. However, the proportion of patients experiencing a durable response is still limited. In the current study, we performed an extensive immune monitoring in patients with stage III/IV melanoma and stage IV UC who received anti-PD-1 immunotherapy with SBRT. (2) Methods: In total 145 blood samples from 38 patients, collected at fixed time points before and during treatment, were phenotyped via high-parameter flow cytometry, luminex assay and UPLC-MS/MS. (3) Results: Baseline systemic immunity in melanoma and UC patients was different with a more prominent myeloid compartment and a higher neutrophil to lymphocyte ratio in UC. Proliferation (Ki67+) of CD8+ T-cells and of the PD-1+/PD-L1+ CD8+ subset at baseline correlated with progression free survival in melanoma. In contrast a higher frequency of PD-1/PD-L1 expressing non-proliferating (Ki67−) CD8+ and CD4+ T-cells before treatment was associated with worse outcome in melanoma. In UC, the expansion of Ki67+ CD8+ T-cells and of the PD-L1+ subset relative to tumor burden correlated with clinical outcome. (4) Conclusion: This study reveals a clearly different immune landscape in melanoma and UC at baseline, which may impact immunotherapy response. Signatures of proliferation in the CD8+ T-cell compartment prior to and early after anti-PD-1 initiation were positively correlated with clinical outcome in both cohorts. PD-1/PD-L1 expression on circulating immune cell subsets seems of clinical relevance in the melanoma cohort.
Published: 2021

7. Myeloid cell heterogeneity in cancer: not a single cell alike

Author: Mate Kiss, Kiavash Movahedi, Damya Laoui, Sofie Van Gassen, Yvan Saeys, Faculty of Sciences and Bioengineering Sciences, Department of Bio-engineering Sciences, and Cellular and Molecular Immunology
Subjects: 0301 basic medicine, Myeloid, Neutrophils, MDSC, Immunology, Cell, Tumor-associated macrophage, Biology, TADC, Monocytes, Metastasis, Single-cell RNA sequencing, myeloid-derived suppressor cell, tumor-associated macrophage, 03 medical and health sciences, Neoplasms, medicine, Animals, Humans, Myeloid Cells, Antigen-presenting cell, Cell Proliferation, Tumor microenvironment, Macrophages, Tumor-associated dendritic cell, Dendritic Cells, medicine.disease, 030104 developmental biology, medicine.anatomical_structure, TAM, Cancer research, Myeloid-derived Suppressor Cell, Mass cytometry, Bone marrow, Myeloid cell heterogeneity, TUMOR MICROENVIRONMENT
Abstract: Tumors of various histological origins show abundant infiltration of myeloid cells from early stages of disease progression. These cells have a profound impact on antitumor immunity and influence fundamental processes that underlie malignancy, including neoangiogenesis, sustained cancer cell proliferation, metastasis and therapy resistance. For these reasons, development of therapeutic approaches to deplete or reprogram myeloid cells in cancer is an emerging field of interest. However, knowledge about the heterogeneity of myeloid cells in tumors and their variability between patients and disease stages is still limited. In this review, we summarize the most recent advances in our understanding about how the phenotype of tumor-associated macrophages, monocytes, neutrophils, myeloid-derived suppressor cells and dendritic cells is dictated by their ontogeny, activation status and localization. We also outline major open questions that will only be resolved by applying high-dimensional single-cell technologies and systems biology approaches in the analysis of the tumor microenvironment.
Published: 2018

8. A Computational Pipeline for the Diagnosis of CVID Patients

Author: Annelies Emmaneel, Delfien J. Bogaert, Sofie Van Gassen, Simon J. Tavernier, Melissa Dullaers, Filomeen Haerynck, and Yvan Saeys
Subjects: 0301 basic medicine, Male, Pediatrics, FLOW, Pipeline (computing), Hypogammaglobulinemia, 0302 clinical medicine, Immunophenotyping, Medicine and Health Sciences, Immunology and Allergy, Child, IgM deficiency, Original Research, Aged, 80 and over, education.field_of_study, B-Lymphocytes, biology, CVID, Middle Aged, Diagnosis of exclusion, Phenotype, Child, Preschool, Female, Antibody, lcsh:Immunologic diseases. Allergy, Adult, medicine.medical_specialty, Adolescent, Population, Immunology, Immunoglobulins, computational pipeline, 03 medical and health sciences, Young Adult, medicine, Humans, education, FlowSOM, Aged, business.industry, Common variable immunodeficiency, flow cytometry, Biology and Life Sciences, medicine.disease, PAD, 030104 developmental biology, Common Variable Immunodeficiency, Case-Control Studies, biology.protein, lcsh:RC581-607, business, 030215 immunology
Abstract: Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub) classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (+/- 0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (+/- 0.23) is achieved.
Published: 2019

9. RORγt inhibition selectively targets IL-17 producing iNKT and γδ-T cells enriched in Spondyloarthritis patients

Author: Koen Venken, Peggy Jacques, H. Cypers, Céline Mortier, Mark E. Labadia, Filip Van den Bosch, Anita L. Wayne, Lieve Gyselbrecht, Kathleen Hoyt, Yves Piette, Robert Hughes, G. Varkas, Nadia Schryvers, Julie Coudenys, Gerald Nabozny, Eveline Verheugen, Yvan Saeys, Tine Decruy, Chao-ting Wang, Dirk Elewaut, Dustin Smith, Michael Robert Turner, Serge Van Calenbergh, Ruth Wittoek, Christian Harcken, Joseph Wahle, Liesbet Martens, and Sofie Van Gassen
Subjects: 0301 basic medicine, Cell, General Physics and Astronomy, 02 engineering and technology, PHENOTYPE, DOUBLE-BLIND, RAR-related orphan receptor gamma, T-Lymphocyte Subsets, Transcriptional regulation, Medicine and Health Sciences, PLZF CONTROLS, PROGRAM, lcsh:Science, Receptor, Multidisciplinary, Interleukin-17, Receptors, Antigen, T-Cell, gamma-delta, Nuclear Receptor Subfamily 1, Group F, Member 3, 021001 nanoscience & nanotechnology, medicine.anatomical_structure, DIFFERENTIATION, Interleukin 17, ARTHRITIS, 0210 nano-technology, musculoskeletal diseases, ARYL-HYDROCARBON RECEPTOR, INTESTINAL INFLAMMATION, Science, T cell, Biology, IMMUNITY, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Immune system, Antigen, Spondylarthritis, medicine, Humans, INTERLEUKIN-17, T(H)17, Biology and Life Sciences, General Chemistry, Receptors, Interleukin, stomatognathic diseases, 030104 developmental biology, Case-Control Studies, Immunology, Natural Killer T-Cells, lcsh:Q
Abstract: Dysregulated IL-23/IL-17 responses have been linked to psoriatic arthritis and other forms of spondyloarthritides (SpA). RORγt, the key Thelper17 (Th17) cell transcriptional regulator, is also expressed by subsets of innate-like T cells, including invariant natural killer T (iNKT) and γδ-T cells, but their contribution to SpA is still unclear. Here we describe the presence of particular RORγt+T-betloPLZF− iNKT and γδ-hi T cell subsets in healthy peripheral blood. RORγt+ iNKT and γδ-hi T cells show IL-23 mediated Th17-like immune responses and were clearly enriched within inflamed joints of SpA patients where they act as major IL-17 secretors. SpA derived iNKT and γδ-T cells showed unique and Th17-skewed phenotype and gene expression profiles. Strikingly, RORγt inhibition blocked γδ17 and iNKT17 cell function while selectively sparing IL-22+ subsets. Overall, our findings highlight a unique diversity of human RORγt+ T cells and underscore the potential of RORγt antagonism to modulate aberrant type 17 responses., The role of innate T cell subsets in the pathogenesis of spondyloarthritis (SpA) is not well understood. Here, the authors examine the role of invariant natural killer T (iNKT) and γδ-T cells in SpA and show that disease-derived iNKT and γδ-T cells have unique and Th17-skewed phenotype and gene expression profiles within inflamed joints.
Published: 2019

10. The ORMDL3 asthma susceptibility gene regulates systemic ceramide levels without altering key asthma features in mice

Author: Farzaneh Fayazpour, Sophie Janssens, Hamida Hammad, Assem Zhakupova, Regula Steiner, Nincy Debeuf, Thorsten Hornemann, Justine Van Moorleghem, Kim Deswarte, Bart N. Lambrecht, Kelly Lemeire, Sofie Van Gassen, Benjamin Pavie, Karl Vergote, University of Zurich, Lambrecht, Bart N, and Pulmonary Medicine
Subjects: 0301 basic medicine, AIRWAY, VARIANTS, RESPONSIVENESS, chemistry.chemical_compound, ALLERGEN, Mice, 0302 clinical medicine, 540 Chemistry, Medicine and Health Sciences, Immunology and Allergy, Eosinophilia, house dust mite, 10038 Institute of Clinical Chemistry, Mice, Knockout, ROLES, biology, ORMDL3, 3. Good health, SPHINGOLIPID BIOSYNTHESIS, 2723 Immunology and Allergy, Alternaria alternata, Cytokines, medicine.symptom, EXPRESSION, Ceramide, Immunology, 610 Medicine & health, Ceramides, Article, 03 medical and health sciences, Th2 Cells, medicine, Animals, Genetic Predisposition to Disease, Sphingosine-1-phosphate, Asthma, House dust mite, 2403 Immunology, sphingolipids, ceramides, SPHINGOSINE-1-PHOSPHATE, Serine C-palmitoyltransferase, Biology and Life Sciences, Membrane Proteins, medicine.disease, biology.organism_classification, Lipid Metabolism, Sphingolipid, SERINE PALMITOYLTRANSFERASE, respiratory tract diseases, MODEL, Ovalbumin, Disease Models, Animal, 030104 developmental biology, chemistry, biology.protein, 030217 neurology & neurosurgery, PROTEINS MEDIATE, Genome-Wide Association Study
Abstract: BACKGROUND: Genome-wide association studies in asthma have repeatedly identified single nucleotide polymorphisms in the ORM (yeast)-like protein isoform 3 (ORMDL3) gene across different populations. Although the ORM-homologues in yeast are well-known inhibitors of sphingolipid (SL) synthesis, it is still unclear whether and how mammalian ORMDL3 regulates SL metabolism and whether altered SL synthesis would be causally related to asthma risk. OBJECTIVE: To examine the in vivo role of ORMDL3 in SL metabolism and allergic asthma METHODS: Ormdl3-LacZ reporter mice, gene deficient Ormdl3(-/-) and overexpressing Ormdl3 (Tg/wt) mice were exposed to physiologically relevant aeroallergens such as house dust mite or Alternaria alternata to induce experimental asthma. Mass spectrometry based sphingolipidomics was performed, and airway eosinophilia, Th2 cytokine production, Ig synthesis, airway remodeling and bronchial hyperreactivity measured. RESULTS: HDM challenge significantly increased total sphingolipids in the lung of HDM-sensitized mice compared to controls. In Ormdl3(Tg/wt) mice, the allergen-induced increase in lung ceramides was significantly reduced, whereas total sphingolipid levels were not affected. Conversely, in liver and serum, total sphingolipids including ceramides were increased in Ormdl3(-/-) mice, while they were lowered in Ormdl3(Tg/wt) mice. This difference was independent of allergen exposure. Despite these changes, all features of asthma were identical between wildtype, Ormdl3(Tg/wt) and Ormdl3(-/-) mice, across several models of experimental asthma. CONCLUSION: ORMDL3 regulates systemic ceramide levels, but genetically interfering with Ormdl3. expression does not result in altered experimental asthma. CAPSULE SUMMARY: ORMDL3 regulates systemic ceramide levels, but genetically interfering with Ormdl3 expression does not result in altered experimental asthma.
Published: 2019
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11. Stabilization of cytokine mRNAs in iNKT cells requires the serine-threonine kinase IRE1alpha

Author: Srinath Govindarajan, Yehudi Bloch, Eveline Verheugen, Djoere Gaublomme, Savvas N. Savvides, Yvan Saeys, Takao Iwawaki, Dirk Elewaut, Simon J. Tavernier, Bart N. Lambrecht, Sofie Van Gassen, Renée Van der Cruyssen, Michael B. Drennan, Sophie Janssens, and Pulmonary Medicine
Subjects: 0301 basic medicine, medicine.medical_treatment, General Physics and Astronomy, Lymphocyte Activation, p38 Mitogen-Activated Protein Kinases, ACTIVATION, Mice, Medicine and Health Sciences, TRANSCRIPTION, lcsh:Science, Cells, Cultured, Mice, Knockout, Multidisciplinary, NKT CELLS, UNFOLDED PROTEIN RESPONSE, Chemistry, Colitis, Endoplasmic Reticulum Stress, Natural killer T cell, Cell biology, DIFFERENTIATION, Cytokine, IRE1-ALPHA, Cytokines, Signal transduction, Signal Transduction, XBP-1, Science, p38 mitogen-activated protein kinases, ENDOPLASMIC-RETICULUM, Protein Serine-Threonine Kinases, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, OXAZOLONE COLITIS, Endoribonucleases, medicine, Animals, RNA, Messenger, STRESS-RESPONSE, Serine/threonine-specific protein kinase, Endoplasmic reticulum, Oxazolone, Biology and Life Sciences, General Chemistry, 030104 developmental biology, Unfolded Protein Response, Unfolded protein response, Natural Killer T-Cells, lcsh:Q, Cytokine secretion, Gene Deletion
Abstract: Activated invariant natural killer T (iNKT) cells rapidly produce large amounts of cytokines, but how cytokine mRNAs are induced, stabilized and mobilized following iNKT activation is still unclear. Here we show that an endoplasmic reticulum stress sensor, inositol-requiring enzyme 1α (IRE1α), links key cellular processes required for iNKT cell effector functions in specific iNKT subsets, in which TCR-dependent activation of IRE1α is associated with downstream activation of p38 MAPK and the stabilization of preformed cytokine mRNAs. Importantly, genetic deletion of IRE1α in iNKT cells reduces cytokine production and protects mice from oxazolone colitis. We therefore propose that an IRE1α-dependent signaling cascade couples constitutive cytokine mRNA expression to the rapid induction of cytokine secretion and effector functions in activated iNKT cells., Invariant natural killer T (iNKT) cells rapidly enhance cytokine secretion and effector function following activation, but the underlying mechanism is still unclear. Here the authors show that an endoplasmic reticulum stress sensor, inositol-requiring enzyme 1α, activates the p38 kinase to stabilize cytokine mRNA for enhanced iNKT functions.
Published: 2018

12. Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species

Author: Bjarne Bogen, Mahesh Choolani, Dedrick Kok Hong Chan, Tony Lim Kiat Hon, Charles-Antoine Dutertre, Jerry Kok Yen Chan, Florent Ginhoux, Anis Larbi, Sandrine Henri, Svetoslav Chakarov, Citra Nurfarah Zaini Mattar, Evan W. Newell, Naomi McGovern, Sofie Van Gassen, Jinmiao Chen, Simon Tavernier, Tam John Kit Chung, Michael Poidinger, Sergio Erdal Irac, Dorine Sichien, Hervé Luche, Ivy Low, Hermi Sumatoh, Sofie De Prijck, Gillian Low, Ker-Kan Tan, Bart N. Lambrecht, Even Fossum, Martin Guilliams, Yvan Saeys, Bernard Malissen, Charlotte L. Scott, Pulmonary Medicine, McGovern, Naomi [0000-0001-5200-2698], Apollo - University of Cambridge Repository, Department of Biomedical Molecular Biology [Ghent], Universiteit Gent = Ghent University (UGENT), Centre d'Immunologie de Marseille - Luminy (CIML), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Unit of Immunoregulation and Mucosal Immunology [Ghent, Belgium], VIB Inflammation Research Center [Ghent, Belgium], Program in Emerging Infectious Disease, Duke-NUS Medical School [Singapore], Singapore Immunology Network (SIgN), Biomedical Sciences Institute (BMSI), Department of Information Technology [Gent], Data Mining and Modeling for Biomedicine [Ghent, Belgium], Department of Internal Medicine [Ghent, Belgium], Experimental Fetal Medicine Group [Singapore], National University of Singapore (NUS)-Yong Loo Lin School of Medicine [Singapore], Department of Surgery [Singapore], Department of Pathology [Singapour], National University of Singapore (NUS), K.G. Jebsen Centre for Influenza Vaccine Research [Oslo, Norway], University of Oslo (UiO)-Oslo University Hospital [Oslo], Center for Immune Regulation [Oslo] (CIR), Faculty of Medicine [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, Department of Reproductive Medicine [Singapore], KK Women's and Children's Hospital [Singapore], Cancer and Stem Cell Biology Program [Singapore], Centre d'Immunophénomique (CIPHE), Department of Pulmonary Medicine [Rotterdam, The Netherlands], Erasmus University Medical Center [Rotterdam] (Erasmus MC), Universiteit Gent = Ghent University [Belgium] (UGENT), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Universiteit Gent, and HENRI, Sandrine
Subjects: EXPRESSION, 0301 basic medicine, Resource, STEADY-STATE, HOMEOSTASIS, BLOOD, [SDV.IMM] Life Sciences [q-bio]/Immunology, BONE-MARROW, Cellular differentiation, Immunology, Inflammation, Computational biology, Biology, Macaque, Flow cytometry, 03 medical and health sciences, Mice, 0302 clinical medicine, biology.animal, medicine, Immunology and Allergy, Macrophage, Animals, Humans, Mass cytometry, MACROPHAGES, SIGNALING CONTROLS, LYMPH-NODES, medicine.diagnostic_test, Biology and Life Sciences, hemic and immune systems, Cell Differentiation, Dendritic Cells, Flow Cytometry, CLONOGENIC PROGENITOR, Mice, Inbred C57BL, Protein Transport, 030104 developmental biology, Infectious Diseases, [SDV.IMM]Life Sciences [q-bio]/Immunology, Macaca, medicine.symptom, Conventional Dendritic Cell, Function (biology), RESPONSES, 030215 immunology
Abstract: Summary Dendritic cells (DCs) are professional antigen-presenting cells that hold great therapeutic potential. Multiple DC subsets have been described, and it remains challenging to align them across tissues and species to analyze their function in the absence of macrophage contamination. Here, we provide and validate a universal toolbox for the automated identification of DCs through unsupervised analysis of conventional flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues. The use of a minimal set of lineage-imprinted markers was sufficient to subdivide DCs into conventional type 1 (cDC1s), conventional type 2 (cDC2s), and plasmacytoid DCs (pDCs) across tissues and species. This way, a large number of additional markers can still be used to further characterize the heterogeneity of DCs across tissues and during inflammation. This framework represents the way forward to a universal, high-throughput, and standardized analysis of DC populations from mutant mice and human patients., Graphical Abstract Image 1, Highlights • A conserved gating strategy aligns dendritic cells (DCs) in mouse and human tissues • Unsupervised computational analysis of flow cytometry data outperforms manual analysis • Mass cytometry reveals heterogeneity of DC subsets across mouse and human tissues • DC activation upon inflammation tracked by automated analysis of mass cytometry, Using unsupervised analysis of flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues, Guilliams et al. provide a universal toolbox for the automated identification of dendritic cells. This framework represents the way forward to high-throughput and standardized analysis of dendritic cells from mutant mice and patients.
Published: 2016

13. IRF8 Transcription Factor Controls Survival and Function of Terminally Differentiated Conventional and Plasmacytoid Dendritic Cells, Respectively

Author: Sofie Van Gassen, Yvan Saeys, Maud Plantinga, Charlotte L. Scott, William W. Agace, Leen Vanhoutte, Karl Vergote, Gert Van Isterdael, Björn E. Clausen, Hamida Hammad, Martin Guilliams, Liesbet Martens, Filipe Branco Madeira, Manon Vanheerswynghels, Marc Dalod, Matthias Vanderkerken, Bart N. Lambrecht, Thorsten Joeris, Wendy Toussaint, Sofie De Prijck, Dorine Sichien, Centre d'Immunologie de Marseille - Luminy (CIML), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Pulmonary Medicine
Subjects: 0301 basic medicine, T-Lymphocytes, Cellular differentiation, Immunology, Cre recombinase, Plasmacytoid dendritic cell, Biology, Monocytes, Mice, 03 medical and health sciences, 0302 clinical medicine, Interferon, medicine, Animals, Immunology and Allergy, Promoter Regions, Genetic, Monocyte, Cell Differentiation, Dendritic Cells, Dendritic cell, Cell biology, Mice, Inbred C57BL, 030104 developmental biology, Infectious Diseases, medicine.anatomical_structure, Interferon Regulatory Factors, Interferon Type I, Cancer research, [SDV.IMM]Life Sciences [q-bio]/Immunology, IRF8, Transcription Factors, 030215 immunology, IRF4, medicine.drug
Abstract: International audience; Interferon regulatory factor-8 (IRF8) has been proposed to be essential for development of monocytes, plasmacytoid dendritic cells (pDCs) and type 1 conventional dendritic cells (cDC1s) and remains highly expressed in differentiated DCs. Transcription factors that are required to maintain the identity of terminally differentiated cells are designated `' terminal selectors.'' Using BM chimeras, conditional Irf8(fl/fl) mice and various promotors to target Cre recombinase to different stages of monocyte and DC development, we have identified IRF8 as a terminal selector of the cDC1 lineage controlling survival. In monocytes, IRF8 was necessary during early but not late development. Complete or late deletion of IRF8 had no effect on pDC development or survival but altered their phenotype and gene-expression profile leading to increased T cell stimulatory function but decreased type 1 interferon production. Thus, IRF8 differentially controls the survival and function of terminally differentiated monocytes, cDC1s, and pDCs.
Published: 2016

14. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

Author: Greg Finak, Geoffrey J. McLachlan, Sofie Van Gassen, Raphael Gottardo, Celine Vens, Peng Qiu, Pratip K. Chattopadhyay, Dong Tong, Maria Chikina, Kui Wang, Miron B. Kursa, Garry P. Nolan, Nima Aghaeepour, Tom Dhaene, Richard H. Scheuermann, Yvan Saeys, Yu Qian, Slawomir Walkowiak, Mehrnoush Malek, Bart N. Lambrecht, Rick Stanton, Ryan R. Brinkman, and Tim R. Mosmann
Subjects: 0301 basic medicine, education.field_of_study, Histology, medicine.diagnostic_test, business.industry, Population, Human immunodeficiency virus (HIV), Cell Biology, medicine.disease_cause, Peripheral blood mononuclear cell, Pathology and Forensic Medicine, Flow cytometry, 03 medical and health sciences, Identification (information), 030104 developmental biology, Cohort, Benchmark (computing), medicine, Critical assessment, education, business, Algorithm
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.
Published: 2015

15. An immune clock of human pregnancy

Author: Maurice L. Druzin, Gary L. Darmstadt, Sofie Van Gassen, Martha Tingle, Virginia D. Winn, David K. Stevenson, Quentin Baca, Robin Okada, Nima Aghaeepour, Garry P. Nolan, Leslie McNeil, David B. Lewis, Ronald J. Wong, David Furman, Yaser Y. El-Sayed, Amy S. Tsai, Martin S. Angst, Mohammad Sajjad Ghaemi, Robert Tibshirani, Edward A. Ganio, Ronald S. Gibbs, Gary M. Shaw, Brice Gaudilliere, David R. McIlwain, Dyani Gaudilliere, and Cecele C. Quaintance
Subjects: 0301 basic medicine, Elastic net regularization, Cell signaling, T cell, Immunology, Computational biology, Biology, Bioinformatics, INFLUENZA-A VIRUS, PHENOTYPE, DENDRITIC CELLS, ACTIVATION, 03 medical and health sciences, 0302 clinical medicine, Immune system, medicine, Medicine and Health Sciences, Mass cytometry, REGULATORY T-CELLS, TOLL-LIKE RECEPTORS, Pregnancy, Term pregnancy, Biology and Life Sciences, General Medicine, medicine.disease, MASS CYTOMETRY, 3. Good health, 030104 developmental biology, medicine.anatomical_structure, Mathematics and Statistics, HEALTH, Signal transduction, NK CELLS, 030217 neurology & neurosurgery, RESPONSES
Abstract: The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.
Published: 2017

16. Computational flow cytometry: helping to make sense of high-dimensional immunology data

Author: Yvan Saeys, Bart N. Lambrecht, Sofie Van Gassen, and Pulmonary Medicine
Subjects: 0301 basic medicine, History, medicine.diagnostic_test, Intersection (set theory), Computational Biology, High dimensional, Biology, Human b cell, Flow Cytometry, Field (computer science), Computer Science Applications, Education, Flow cytometry, Visualization, Bioconductor, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Immunology, medicine, Immunologic Techniques, Animals, Humans, Mass cytometry, 030215 immunology
Abstract: Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.
Published: 2016

17. Machine Learning Challenges for Single Cell Data

Author: Tom Dhaene, Sofie Van Gassen, and Yvan Saeys
Subjects: 0301 basic medicine, 03 medical and health sciences, 030104 developmental biology, Single-cell analysis, Computer science, business.industry, Artificial intelligence, business, Machine learning, computer.software_genre, Data science, computer, Field (computer science)
Abstract: Recent technological advances in the fields of biology and medicine allow measuring single cells into unprecedented depth. This results in new types of high-throughput datasets that shed new lights on cell development, both in healthy as well as diseased tissues. However, studying these biological processes into greater detail crucially depends on novel computational techniques that efficiently mine single cell data sets. In this paper, we introduce machine learning techniques for single cell data analysis: we summarize the main developments in the field, and highlight a number of interesting new avenues that will likely stimulate the design of new types of machine learning algorithms.
Published: 2016

18. Response to Orlova et al. 'Science not art: statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets'

Author: Yvan Saeys, Sofie Van Gassen, and Bart N. Lambrecht
Subjects: 0301 basic medicine, History, geography, geography.geographical_feature_category, Computer Science Applications, Education, 03 medical and health sciences, 030104 developmental biology, Flow (mathematics), Multi dimensional, Mass cytometry, Algorithm, Sound (geography), Mathematics
Abstract: Response to Orlova et al . “Science not art: statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets”
Published: 2018

19. Unsupervised trajectory inference using graph mining

Author: Leen De Baets, Yvan Saeys, Sofie Van Gassen, and Tom Dhaene
Subjects: 0301 basic medicine, Technology and Engineering, Computer science, business.industry, Cellular differentiation, Inference, Pattern recognition, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Graph (abstract data type), IBCN, False positive rate, Artificial intelligence, Cluster analysis, business, 030217 neurology & neurosurgery
Abstract: Cell differentiation is a complex dynamic process and although the main cellular states are well studied, the intermediate stages are often still unknown. Single cell data (such as obtained by flow cytometry) is typically analysed by clustering the cells into distinct cell types, which does not model these gradual changes. Alternative approaches that explicitly model such gradual changes using seriation methods seems promising, but are only able to model a single differentiation pathway. In this paper, we introduce a new, graph-based approach that is able to model multiple branching differentiation pathways as continuous trajectories. Results on synthetic and real data show that this is a promising approach which is moreover robust to parameter changes.
Published: 2015

20. FloReMi: Flow density survival regression using minimal feature redundancy

Author: Tom Dhaene, Yvan Saeys, Celine Vens, Bart N. Lambrecht, Sofie Van Gassen, BRINKMAN, Ryan R, Aghaeepour, Nima, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, and Scheuermann, Richard H
Subjects: 0301 basic medicine, Histology, Computer science, T-Lymphocytes, Feature selection, Disease, Bioinformatics, Machine learning, computer.software_genre, Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, HIV Seropositivity, Redundancy (engineering), Feature (machine learning), Cluster Analysis, Humans, Cluster analysis, Acquired Immunodeficiency Syndrome, Staining and Labeling, business.industry, Computational Biology, Regression analysis, Cell Biology, Flow Cytometry, Regression, Benchmarking, 030104 developmental biology, Flow cytometry bioinformatics, Data Interpretation, Statistical, Disease Progression, Regression Analysis, Artificial intelligence, business, computer, Algorithms
Abstract: Advances in flow cytometry bioinformatics have resulted in a wide variety of clustering, classification and visualization techniques. To objectively evaluate the performance of such methods, common benchmarks such as the FlowCAP initiative have proven to be of great value. In this work, we report on a novel method, FloReMi, which was developed to tackle the most recent FlowCAP IV challenge. This challenge was formulated as a survival modeling problem, where participants were expected to design a model to predict the time until progression to AIDS for HIV patients. It is known that variability in progression rate cannot be fully predicted by simple CD4(+) T cell counts. However, it is hypothesized that the immunopathogenesis established early in HIV already indicates the course of future disease. Adequately estimating the progression rate of HIV patients is crucial in their treatment. Using an automated pipeline to preprocess the data, and subsequently identify and select informative cell subsets, a survival regression method based on random survival forests was built, which obtained the best results of all submitted approaches to the FlowCAP IV challenge. ispartof: Cytometry A vol:89 issue:1 pages:22-29 ispartof: location:United States status: published
Published: 2014

21. Stabilization of Human Whole Blood Samples for Multicenter and Retrospective Immunophenotyping Studies

Author: Sofie Van Gassen, Marta E. Alarcón-Riquelme, Nieves Varela, Rocio Aguilar-Quesada, Yvan Saeys, Concepción Marañón, Catalina Burbano, and Paulina Rybakowska
Subjects: Proteomics, 0301 basic medicine, Histology, Population, Biology, Peripheral blood mononuclear cell, Immunophenotyping, Pathology and Forensic Medicine, Flow cytometry, Andrology, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Mass cytometry, education, Biological Specimen Banks, Retrospective Studies, Whole blood, education.field_of_study, medicine.diagnostic_test, Cell Biology, Flow Cytometry, 3. Good health, 030104 developmental biology, 030220 oncology & carcinogenesis, biology.protein, Antibody, Cytometry
Abstract: Whole blood is often collected for large-scale immune monitoring studies to track changes in cell frequencies and responses using flow (FC) or mass cytometry (MC). In order to preserve sample composition and phenotype, blood samples should be analyzed within 24 h after bleeding, restricting the recruitment, analysis protocols, as well as biobanking. Herein, we have evaluated two whole blood preservation protocols that allow rapid sample processing and long-term stability. Two fixation buffers were used, Phosphoflow Fix and Lyse (BD) and Proteomic Stabilizer (PROT) to fix and freeze whole blood samples for up to 6 months. After analysis by an 8-plex panel by FC and a 26-plex panel by MC, manual gating of circulating leukocyte populations and cytokines was performed. Additionally, we tested the stability of a single sample over a 13-months period using 45 consecutive aliquots and a 34-plex panel by MC. We observed high correlation and low bias toward any cell population when comparing fresh and 6 months frozen blood with FC and MC. This correlation was confirmed by hierarchical clustering. Low coefficients of variation (CV) across studied time points indicate good sample preservation for up to 6 months. Cytokine detection stability was confirmed by low CVs, with some differences between fresh and fixed conditions. Thirteen months regular follow-up of PROT samples showed remarkable sample stability. Whole blood can be preserved for phenotyping and cytokine-response studies provided the careful selection of a compatible antibody panel. However, possible changes in cell morphology, differences in antibody affinity, and changes in cytokine-positive cell frequencies when compared to fresh blood should be considered. Our setting constitutes a valuable tool for multicentric and retrospective studies. (c) 2020 International Society for Advancement of Cytometry
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21 results on '"Sofie Van Gassen"'

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