Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Alexandra-Chloé Villani, Rahul Satija, Gary Reynolds, Siranush Sarkizova, Karthik Shekhar, James Fletcher, Morgane Griesbeck, Andrew Butler, Shiwei Zheng, Suzan Lazo, Laura Jardine, David Dixon, Emily Stephenson, Emil Nilsson, Ida Grundberg, David McDonald, Andrew Filby, Weibo Li, Philip L. De Jager, Orit Rozenblatt-Rosen, Andrew A. Lane, Muzlifah Haniffa, Aviv Regev, Nir Hacohen

Science 21 April 2017 DOI: 10.1126/science.aah4573 
Contact: cvillani@broadinstitute.org

SUMMARY

Introduction

Dendritic cells (DCs) and monocytes consist of multiple specialized subtypes that play a central role in pathogen sensing, phagocytosis, and antigen presentation. However, their identities and interrelationships are not fully understood, as these populations have historically been defined by a combination of morphology, physical properties, localization, functions, developmental origins, and expression of a restricted set of surface markers.

Rationale

To overcome this inherently biased strategy for cell identification, we performed single-cell RNA sequencing (Smart-Seq2) of ~2400 cells isolated from healthy blood donors and enriched for HLA-DR+ Lineage (CD3, CD19, CD56, CD14)– cells.  This single-cell profiling strategy and unbiased genomic classification, together with follow-up profiling, functional and phenotypic characterization of prospectively isolated subsets, led us to identify and validate six DC and four monocyte subtypes, and thus revise the taxonomy of these cells.

Results

Our study reveals: (1) a new DC subset, representing 2-3% of the DC populations across all 10 donors tested, characterized by the expression of AXL, SIGLEC1, and SIGLEC6 antigens, named AS DC. The AS DC population further divides into two populations captured in the traditionally defined plasmacytoid DCs (pDC) and the CD1C+ conventional DC (cDC) gates. This split is further reflected through AS DC gene expression signature spanning a spectrum between cDC-like and pDC-like gene sets. Although AS DC shares properties with pDC, they more potently activate T cells. This discovery led us to reclassify pDCs as the originally described “natural interferon-producing cells (IPC)” with weaker T-cell proliferation induction ability; (2) a new subdivision within the CD1C+ DC subset: one defined by a major histocompatibility complex class II-like gene set and one by a CD14+ monocyte-like prominent gene sets. These CD1C+ DC subsets, which can be enriched by combining CD1C with CD32B, CD36 and CD163 antigens, can both potently induce T cell proliferation; (3) the existence of a circulating and dividing cDC progenitor giving rise to CD1C+ and CLEC9A+ DCs through in vitro differentiation assays. This blood precursor is defined by the expression of CD100hiCD34int and observed at a frequency of ~0.02% of the LIN–HLA-DR+ fraction; (4) two additional monocyte populations: one expressing classical monocyte genes and cytotoxic genes, and the other with unknown functions. (5) Evidence for a relationship between blastic plasmacytoid DC neoplasia (BPDCN) cells and healthy DCs.

Conclusion

Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease. The discovery of AS DC within the traditionally defined pDC population explains many of the cDC properties previously assigned to pDC, highlighting the need to revisit the definition of pDCs.  Furthermore, the discovery of blood cDC progenitors represent a new therapeutic target readily accessible in bloodstream for manipulation, as well as a new source for better in vitro DC generation. Although the current results focus on DCs and monocytes, a similar strategy can be applied to build a comprehensive human immune cell atlas.

EXAMPLES

Below please find a few example of discriminative gene sets, which can be used as an input in the gene search box (list of multiple genes can be used together to create a heatmap; entry of a single gene will generate a box plot). More extensive list of genes can be found in Supplementary Tables associated with the manuscript.

• DC1: CLEC9A C1ORF54 CADM1 CAMK2D IDO1 XCR1
• DC2: CD1C FCER1A CLEC10A ADAM8 CD1D FCGR2B
• DC3: S100A9 S100A8 VCAN RNASE2 CD1C FCER1A CLEC10A
• DC4: FCGR3A FTL SERPINA1 LST1 AIF1 LILRB2 TMEM176B LOC200772 CLEC4F
• DC5: AXL PPP1R14A SIGLEC6 CD22 CD5 SIGLEC1
• DC6: GZMB IGJ AK128525 SERPINF1 ITM2C TCF4 CLEC4C NRP1
• Mono1: CD14 VCAN S100A8 S100A9 FCN1 ITGAM
• Mono2: LAIR2 ASAH1 APOBEC3A TSPAN14 LIPA ITGAM
• Mono3 (also shares signature with Mono1): G0S2 CXCR1 CXCR2 NAMPT NEAT1 AL137655 CSF3R CD14 VCAN S100A8 S100A9 FCN1 ITGAM
• Mono4 (also shares signature with Mono1): PRF1 GNLY CTSW FGFBP2 IL2RB GZMA CD14 VCAN S100A8 S100A9 FCN1 ITGAM

NOTES

1. The cluster diagram below does not look identical to Fig 3B but the clusters are the same ones (generated by a newer version of Seurat).

2. To see where clusters are, you can click on the names of the clusters in the legend to show and hide them.

3. You can enter one or more genes into the search gene box to look at expression. Single gene entries give you a barplot and colored clusters, and multiple gene entries gives you heatmaps.