德国马克斯普朗克研究所的Dominic Grün团队与法国斯特拉斯堡大学的Thomas F. Baumert团队成功地构建出完整的人类肝脏细胞精细图谱，该研究成果发表于《Nature》杂志上，论文题目为A human liver cell atlas reveals heterogeneity and epithelial progenitors。
 
研究方法：
该团队使用mCEL-Seq2方法对来自9个健康人类肝脏组织的10000多个细胞进行了深度单细胞测序分析，绘制的图谱涵盖了所有重要的肝脏细胞类型，包括肝实质细胞、血管内皮细胞、巨噬细胞以及其他免疫细胞类型，并且找到了多种新型肝脏细胞。
肝细胞悬液通过流式系统把单个细胞分选到384孔板，基于微孔板的mCEL-Seq2单细胞测序步骤由mosquito纳升级单细胞建库系统完成。 outline of the protocol used for scRNA-seq of human liver cells：Samples from liver resections were digested to prepare single-cell suspensions. Cells were sorted into 384-well plates and processed according to the mCEL-Seq2 protocol.
 
mCEL-Seq2方法在cDNA第一链合成的过程中给每个细胞都加入了barcoded primer，通过体外转录(IVT)方式扩增单细胞cDNA，这既保证了检测方法的灵敏度，又通过把所有样本pooling后再做建库的方式降低了检测成本。通过mosquito纳升级单细胞建库系统的加持，再把反应体系缩小了5倍，把成本压到最低！
 
Materials&Methods

• mosquito LV, 384-well plates
• cells sorted into 240 nL lysis buffer
• 160 nL RT mix added
• cDNA synthesis in 400 nL total
• using Vapor-Lock to block evaporation
• add cell barcodes; pooled library prep

 
研究结果：
该图谱根据marker基因的表达水平鉴定了几乎所有的肝脏细胞类型，包括肝细胞、EPCAM+胆管细胞(胆管细胞)、CLEC4G+肝血源性内皮细胞(LSECs)、CD34+ PECAM high大血管内皮细胞(MaVECs)、肝星状细胞和肌成纤维细胞、Kupffer细胞和免疫细胞。
 
研究人员同时发现了从未被报道过的新的肝脏细胞类型。这些细胞亚型虽然在形态上与普通的肝脏细胞没有什么不同，但在基因表达上却截然不同。这些发现归功于单细胞测序实验方法mCEL-seq2和分析算法FateID的重大进展，通过这些方法得以对单个细胞进行高分辨率的深度分析。 Fig. 1 | scRNA-seq reveals cell types in the adult human liver. b, t-SNE map of single-cell transcriptomes from normal liver tissue from nine donors highlighting the main liver cell compartments. ‘Other’ denotes various small populations comprising 22 red blood cells and 46 cells that cannot be unambiguously annotated. ‘Other endothelial cells’ cannot be unambiguously classified as LSECs or MaVECs. c, t-SNE map of single-cell transcriptomes highlighting RaceID3 clusters, which reveals subtype heterogeneity in all major cell populations of the human liver. Numbers denote clusters. d, Heat map showing the expression of established marker genes for each cell compartment. Colour bars indicate patient, major cell type, and RaceID3 cluster. Scale bar, log2-transformed normalized expression. b, c, n = 10,372 cells.
 
参考文献：
Aizarani N ,  Saviano A , Sagar, et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors[J]. Nature, 2019, 572(7768):199-204.
Sagar,  Herman J S ,  Pospisilik J A , et al. High-Throughput Single-Cell RNA Sequencing and Data Analysis[M].  2018.
Herman J S , Sagar,  D  Grün. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data[J]. Nature Methods, 2018.
DNA Damage Signaling Instructs Polyploid Macrophage Fate in Granulomas.[J]. Cell, 2018.
 
德国马普所的自动化CEL-Seq2流程，节省5倍成本