期刊
NATURE IMMUNOLOGY
卷 15, 期 12, 页码 1181-1189出版社
NATURE PORTFOLIO
DOI: 10.1038/ni.3006
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资金
- A*STAR/SIgN
- Swiss National Science Foundation [PP03P3_144781, 316030_150768, 310030_146130, CRSII3_136203]
- European Union FP7 project TargetBraIn
- University Research Priority Project 'Translational Cancer Research'
- European Union FP7 project NeuroKine
- European Union FP7 project Advanced T-cell Engineered for Cancer Therapy (ATECT)
- Swiss National Science Foundation (SNF) [310030_146130, 316030_150768, CRSII3_136203] Funding Source: Swiss National Science Foundation (SNF)
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.
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