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Characterization of Dendritic Cell Subtypes in Human Cord Blood by Single-Cell Sequencing

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Characterization of Dendritic Cell Subtypes in Human Cord Blood by Single-Cell Sequencing Biophys Rep 2019, 5(4):199–208 https://doi.org/10.1007/s41048-019-00096-5 Biophysics Reports RESEARCH ARTICLE Characterization of dendritic cell subtypes in human cord blood by single-cell sequencing Xiaoyang Jin1,2, Lingyuan Meng3, Zhao Yin4, Haisheng Yu5, Linnan Zhang1,2, Weifeng Liang1,2, Shouli Wang4, Guanyuan Liu3&, Liguo Zhang1,2& 1 Key Laboratory of Immunity and Infection, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China 2 University of Chinese Academy of Sciences, Beijing 100080, China 3 Department of Gynecology and Obstetrics, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China 4 Department of Cardiology, 306th Hospital of PLA, Beijing 100101, China 5 Key Laboratory of Human Disease Comparative Medicine and Beijing Key Laboratory for Animal Models of Emerging and Reemerging Infectious, Institute of Laboratory Animal Science (ILAS), Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical Collage (PUMC), Beijing 100021, China Received: 25 March 2019 / Accepted: 14 April 2019 / Published online: 30 September 2019 Abstract Dendritic cells (DCs) are professional antigen-presenting cells (APCs). The key functions of DCs include engulfing, processing and presenting antigens to T cells and regulating the activation of T cells. There are two major DC subtypes in human blood: plasmacytoid DCs (pDCs) and conventional DCs. To define the differences between the adult and infant immune systems, especially in terms of DC constitution, we enriched DCs from human cord blood and generated single-cell RNA sequencing data from about 7000 cells using the 10x Genomics Single Cell 30 Solution. After incorporating the differential expression analysis method in our clustering process, we identified all the known dendritic cell subsets. Inter- estingly, we also found a group of DCs with gene expression that was a mix of megakaryocytes and pDCs. Further, we verified the expression of selected genes at both the RNA level by PCR and the protein level by flow cytometry. This study further demonstrates the power of single-cell RNA sequencing in dendritic cell research. Keywords Cord blood, Dendritic cells, Hematology, Immunology, Single-cell sequencing INTRODUCTION (Collin and Bigley 2018). Although DCs are small in number, as they constitute only *1% of human Dendritic cells (DCs) are professional antigen- peripheral blood mononuclear cells (Ginhoux et al. presenting cells and are essential regulators of adaptive 2018), their subsets are heterogeneous and have immune responses. They are derived from bone marrow diverse functions. DCs are divided into three major and exist in both lymphoid and nonlymphoid tissues subsets, include plasmacytoid DCs (pDCs) and two types of ‘‘conventional’’ or ‘‘classical’’ DCs (cDCs), called cDC1s and cDC2s and each subset is controlled in Electronic supplementary material The online version of this development by a specific repertoire of transcription article (https://doi.org/10.1007/s41048-019-00096-5) contains supplementary material, which is available to authorized users. factors (Merad et al. 2013). pDCs produce large quan- tities of type I interferon upon exposure to different & Correspondence: [email protected] (G. Liu), viruses and are also called interferon-producing cells. [email protected] (L. Zhang) Ó The Author(s) 2019 199 | August 2019 | Volume 5 | Issue 4 RESEARCH ARTICLE X. Jin et al. cDC1s are specialized for activating CD8? T cells, while mononuclear cells (CBMCs), we labeled T cells, B cells, cDC2s are more potent in activating CD4? T cells. We NK cells and monocytes with the lineages-specific recently reported that a distinct CD56? DC subpopula- markers CD3, CD19, CD16 and CD14, respectively, fol- tion exists in human blood that expresses pDC-specific lowed by magnetic bead depletion. Then, we applied surface markers (CD123 and BDCA2) but shares similar single-cell RNA sequencing on all lineage-negative cells functions with cDCs (Yu et al. 2015). We also found that and performed clustering analysis and dimensional human blood cDC2s can be further divided into two reduction. subsets (CD5high and CD5low) that differ significantly in Within the 7004 single cells which passed quality both gene expression and function (Yin et al. 2017). control, eight clusters (C1–C8) were identified (Fig. 1A). Recent advances in single-cell RNA sequencing facil- We mapped each cluster to known immune cell types itate the identification of novel cell types (Jaitin et al. according to a set of lineage-specific markers (Butler 2014; See et al. 2017; Shalek et al. 2014; Villani et al. et al. 2018) and the genes highly expressed in each 2017) and determination of cellular developmental cluster (Fig. 1B, and supplementary Fig. S1A, Table S1). trajectory (Zheng et al. 2018). Human blood DCs have C1, C2 and C3 cells are considered DCs that highly been divided into six dendritic cell types (named DC1 to express HLA-DQA1 and GPR183, and these three clus- DC6) by single-cell sequencing (Villani et al. 2017). ters make up 61.1% of all cells. C4 cells, which express These six DC populations can be mapped to previously high levels of NKG5 and NKG7 (GNLY), are natural killer identified subtypes: DC1 corresponds to cDC1 (ex- (NK) cells. C5, C6 and C7 cells are diverse types of presses high levels of CLEC9A and XCR1), DC2 corre- progenitor cells, and they express CD34 at different sponds to CD5high cDC2 (expresses high levels of levels (C5 [ C6 [ C7). These three clusters make up CD1C)(Yin et al. 2017), DC3 corresponds to CD5low 31.3% of all the cells. Interestingly, C5 cells express cDC2 (expresses high levels of S100A8 and S100A9) FLT3, which is essential for DC development (Waskow (Yin et al. 2017), DC5 corresponds to CD56? DC (ex- et al. 2008), suggesting that C5 may be the progenitor of presses high levels of AXL, SIGLEC6, and CD22; also DCs. Cells in C8 are erythrocytes according to their high- called AXL? DC) (Yu et al. 2015), and DC6 corresponds level expression of hemoglobin genes, such as HBA1, to pDC (expresses high levels of CD123 and GZMB). HBA2 and HBB (supplementary Fig. S1B). Further, DC4 is a double-negative (CD1C-CD141-) Currently, t-distributed stochastic neighbor embed- monocyte-like cell type, which clusters with monocytes ding (t-SNE) is the most widely used method to preform but not dendritic cells (Villani et al. 2017). dimensional reduction in single-cell analysis; this The frequencies and functions of DCs are different method can help to reveal local data structure in mass between neonates and adults (Schuller et al. 2013; cytometry or single-cell transcriptomic data (Amir et al. Willems et al. 2009; Zhang et al. 2013). To further 2013). A new algorithm with similar functionality called characterize neonatal DCs, we enriched dendritic cells uniform manifold approximation and projection (UMAP) from cord blood mononuclear cells by depleting T cells, was developed recently (McInnes et al. 2018) and B cells, monocytes and most NK cells and then applied applied to biological data. UMAP has outperformed single-cell RNA sequencing on the enriched cells. Com- t-SNE by generating more meaningful organization of paring the resultant data with data from peripheral cell clusters (Becht et al. 2018). To reveal whether this blood dendritic cells led us to identify all the five den- advantage applies to our data, we compared the UMAP dritic cell subtypes (Villani et al. 2017) and a potentially output to principal component analysis (PCA, which is a novel cell type that expresses characteristic genes of well-known dimensional reduction method widely used both megakaryocytes and pDCs. in microarray and bulk RNA-seq analysis) and t-SNE. In the UMAP output, cell clusters identified as dendritic cells (C1, C2, and C3) grouped together in the plot, as RESULTS did cell clusters identified as progenitor cells (C5, C6, and C7) (Fig. 1A). The aggregation of DC clusters and Overview of scRNA-seq data of enriched progenitor cell clusters was not observed in the PCA or dendritic cells from human cord blood t-SNE outputs (supplementary Fig. S2A, S2B). In the PCA output, different cell clusters were not well separated. DCs contain heterogenous populations with diverse In the t-SNE output, although cluster structure was surface markers and functions (Yin et al. 2017;Yuet al. clearly present, DC clusters scattered in the plot and 2015; Villani et al. 2017). In both adult and neonatal were interwoven with progenitor cell clusters. Thus, we blood mononuclear cells, DC frequencies are low choose UMAP for plots for the entire analysis over (*1%). To acquire enriched DCs from cord blood t-SNE. 200 | August 2019 | Volume 5 | Issue 4 Ó The Author(s) 2019 Single-cell analysis of dendritic cells in human cord blood RESEARCH ARTICLE C2 C5 C6 JCHAIN GZMB ALOX5AP NPC2 LILRA4 PPBP PF4 NRGN C4 GNG11 TUBB1 CST3 LYZ COTL1 TMSB10 S100A10 KLRB1 TRDC BTG1 CTSW CD7 SPINK2 RPS4X RPS24 RPS3 RPL7 NPM1 PTMA HMGA1 HSP90AB1 HNRNPA1 LMO4 HDC CNRIP1 GATA2 CD63 HBG1 AHSP HBA2 HBG2 0 HBA1 Fig. 1 Main clusters revealed by single-cell RNA sequencing after DC enrichment from human cord blood. A Cell clusters visualized using uniform manifold approximation and projection (UMAP). In total, eight clusters were identified and named as C1 (2804 cells), C2 (794 cells), C3 (657 cells), C4 (473 cells), C5 (1256 cells), C6 (834 cells), C7 (101 cells), and C8 (62 cells). Each dot represents an individual cell, among which the C1, C2 and C3 clusters are DCs, the C4 cluster is NK cells, the C5, C6 and C7 clusters are progenitor cells, and the C8 cluster is erythrocytes. B Heatmap showing the top five signature genes for each cluster. Clusters larger than 300 cells were randomly down sampled to 300 cells to increase the visibility of small clusters such as C7 and C8. Cells in the heatmap are ordered according to hierarchical clustering of expression profiles in each cluster.
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