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

Autor: 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
Přispěvatelé: 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, HENRI, Sandrine
Rok vydání: 2016
Předmět:
Zdroj: Immunity, 45(3), 669-684. Cell Press
Immunity
Immunity, 2016, 45, pp.669-684. ⟨10.1016/j.immuni.2016.08.015⟩
Immunity, Elsevier, 2016, 45, pp.669-684. ⟨10.1016/j.immuni.2016.08.015⟩
IMMUNITY
ISSN: 1097-4180
1074-7613
DOI: 10.1016/j.immuni.2016.08.015⟩
Popis: 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.
Databáze: OpenAIRE
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