mosquito單細(xì)胞測(cè)序應(yīng)用：Nature 人類肝細(xì)胞圖譜

德國(guó)馬克斯普朗克研究所的Dominic Grün團(tuán)隊(duì)與法國(guó)斯特拉斯堡大學(xué)的Thomas F. Baumert團(tuán)隊(duì)成功地構(gòu)建出完整的人類肝臟細(xì)胞精細(xì)圖譜，該研究成果發(fā)表于《Nature》雜志上，論文題目為A human liver cell atlas reveals heterogeneity and epithelial progenitors。
 
研究方法：
該團(tuán)隊(duì)使用mCEL-Seq2方法對(duì)來(lái)自9個(gè)健康人類肝臟組織的10000多個(gè)細(xì)胞進(jìn)行了深度單細(xì)胞測(cè)序分析，繪制的圖譜涵蓋了所有重要的肝臟細(xì)胞類型，包括肝實(shí)質(zhì)細(xì)胞、血管內(nèi)皮細(xì)胞、巨噬細(xì)胞以及其他免疫細(xì)胞類型，并且找到了多種新型肝臟細(xì)胞。
肝細(xì)胞懸液通過(guò)流式系統(tǒng)把單個(gè)細(xì)胞分選到384孔板，基于微孔板的mCEL-Seq2單細(xì)胞測(cè)序步驟由mosquito納升級(jí)單細(xì)胞建庫(kù)系統(tǒng)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第一鏈合成的過(guò)程中給每個(gè)細(xì)胞都加入了barcoded primer，通過(guò)體外轉(zhuǎn)錄(IVT)方式擴(kuò)增單細(xì)胞cDNA，這既保證了檢測(cè)方法的靈敏度，又通過(guò)把所有樣本pooling后再做建庫(kù)的方式降低了檢測(cè)成本。通過(guò)mosquito納升級(jí)單細(xì)胞建庫(kù)系統(tǒng)的加持，再把反應(yīng)體系縮小了5倍，把成本壓到最低！
 
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

 
研究結(jié)果：
該圖譜根據(jù)marker基因的表達(dá)水平鑒定了幾乎所有的肝臟細(xì)胞類型，包括肝細(xì)胞、EPCAM+膽管細(xì)胞(膽管細(xì)胞)、CLEC4G+肝血源性內(nèi)皮細(xì)胞(LSECs)、CD34+ PECAM high大血管內(nèi)皮細(xì)胞(MaVECs)、肝星狀細(xì)胞和肌成纖維細(xì)胞、Kupffer細(xì)胞和免疫細(xì)胞。
 
研究人員同時(shí)發(fā)現(xiàn)了從未被報(bào)道過(guò)的新的肝臟細(xì)胞類型。這些細(xì)胞亞型雖然在形態(tài)上與普通的肝臟細(xì)胞沒(méi)有什么不同，但在基因表達(dá)上卻截然不同。這些發(fā)現(xiàn)歸功于單細(xì)胞測(cè)序?qū)嶒?yàn)方法mCEL-seq2和分析算法FateID的重大進(jìn)展，通過(guò)這些方法得以對(duì)單個(gè)細(xì)胞進(jìn)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.
 
參考文獻(xiàn)：
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.
 
德國(guó)馬普所的自動(dòng)化CEL-Seq2流程，節(jié)省5倍成本