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Dendritic cells

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Nature Reviews Immunology volume 16, page 657 (2016)Cite this article

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It was recently proposed (see Further reading) that DCs be classified into three main lineages according to their ontogeny: conventional type 1 DCs (cDC1s; which depends on the transcription factors IFR8, BATF3 and ID2), cDC2s (which depend on IRF4 and ZEB2) and plasmacytoid DCs (pDCs; which develop from distinct E2-2-dependent progenitors). However, there has been a lack of consensus on how to reliably identify these subsets based on phenotypical markers. In this study, Guilliams et al. describe a more accurate flow cytometry method for identifying mouse cDC1s and cDC2s in isolates of CD45+ leukocytes. Briefly, their protocol involves excluding macrophages as CD64+F4/80+ cells, excluding cells that express lymphocyte lineage markers (CD3, B220, CD19 and NK1.1) and selecting for MHC class II+ cells; cDCs are then identified as CD11c+CD26+ cells and can be subdivided based on expression of XCR1 and CD172a into cDC1s (XCR1hiCD172alow) and cDC2s (XCR1lowCD172ahi). pDCs express B220 and are identified among the lymphocyte lineage as 120G8+B220+CD11c+LY6C+CD11b− cells. The authors validated their approach by staining for subset-specific transcription factors and, importantly, found that this protocol could accurately identify these DC subsets in all mouse tissues. Furthermore, the inclusion of a CD24 gate allowed Langerhans cells (CD26lowCD24hi) to be distinguished from dermal cDC2s (CD26midCD24low) in both the skin and draining lymph nodes, something that had previously only been achieved using bone marrow chimaera systems.

Having determined a more accurate manual gating strategy, the same phenotypical markers were used to develop automated methods for identifying DC lineages. The authors used FlowSOM and t-SNE (which allow high-dimensional datasets to be visualized in low-dimensional data trees) to generate nodes corresponding to cDC1s and cDC2s; this automated gating strategy accurately identified these DC subsets in all mouse tissues. Furthermore, the automated method was more reliable than manual gating for identifying DC subsets in mutant mice that show altered expression of lineage-associated markers. By slightly adapting their protocol, the authors were able to identify cDC1s, cDC2s and pDCs in mice, humans and macaques using a similar gating strategy across species. In these experiments, macrophages in humans and macaques were excluded as CD14+CD16+ cells, CD1c was included to more reliably identify cDC2s, and CADM1 was substituted for XCR1 (as there is not yet a suitable antibody for detecting XCR1 in humans or macaques). These modifications allowed cDC1s and cDC2s to be identified in all tissues of each of the species as CADM1hiCD172alowCD11cmid/hiCD26hi cells and CADM1lowCD172ahiCD1chiCD11chi cells, respectively.

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References

  1. Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity http://dx.doi.org/10.1016/j.immuni.2016.08.015 (2016)

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  1. Yvonne Bordon
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Guilliams, M. et al. Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny. Nat. Rev. Immunol. 14, 571–587 (2014)

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Saeys, Y., Gassen, S.V. & Lambrecht, B.N. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16, 449–462 (2016)

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Bordon, Y. Sorting, sorted!. Nat Rev Immunol 16, 657 (2016). https://doi.org/10.1038/nri.2016.115

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