title: High-dimensional analysis of the murine myeloid cell system.
abstract: Advances in cell-fate mapping have revealed the complexity in phenotype, ontogeny and tissue distribution of the mammalian myeloid system. To capture this phenotypic diversity, we developed a 38-antibody panel for mass cytometry and used dimensionality reduction with machine learning-aided cluster analysis to build a composite of murine (mouse) myeloid cells in the steady state across lymphoid and nonlymphoid tissues. In addition to identifying all previously described myeloid populations, higher-order analysis allowed objective delineation of otherwise ambiguous subsets, including monocyte-macrophage intermediates and an array of granulocyte variants. Using mice that cannot sense granulocyte macrophage-colony stimulating factor GM-CSF (Csf2rb(-/-)), which have discrete alterations in myeloid development, we confirmed differences in barrier tissue dendritic cells, lung macrophages and eosinophils. The methodology further identified variations in the monocyte and innate lymphoid cell compartment that were unexpected, which confirmed that this approach is a powerful tool for unambiguous and unbiased characterization of the myeloid system.
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Burkhard
authors.identifier: 0000000215417867
authors.initials: B
authors.lastname: Becher
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schlitzer
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Jinmiao
authors.initials: J
authors.lastname: Chen
authors.affiliation: Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland.
authors.forename: Florian
authors.initials: F
authors.lastname: Mair
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Hermi R
authors.initials: HR
authors.lastname: Sumatoh
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Karen Wei Weng
authors.initials: KW
authors.lastname: Teng
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Donovan
authors.initials: D
authors.lastname: Low
authors.affiliation: School of Biological Sciences, Nanyang Technological University, Singapore.
authors.forename: Christiane
authors.initials: C
authors.lastname: Ruedl
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Paola
authors.initials: P
authors.lastname: Riccardi-Castagnoli
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Michael
authors.identifier: 0000000210472277
authors.initials: M
authors.lastname: Poidinger
authors.affiliation: Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland.
authors.forename: Melanie
authors.initials: M
authors.lastname: Greter
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Florent
authors.initials: F
authors.lastname: Ginhoux
authors.affiliation: Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.
authors.forename: Evan W
authors.identifier: 000000022889243X
authors.initials: EW
authors.lastname: Newell
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D005434:Flow Cytometry / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D022423:Myeloid Cells / Q000166:cytology*