mosquito單細胞測序應用：Nature 人類肝細胞圖譜

德國馬克斯普朗克研究所的Dominic Grün團隊與法國斯特拉斯堡大學的Thomas F. Baumert團隊成功地構建出完整的人類肝臟細胞精細圖譜，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：
該團隊使用mCEL-Seq2方法對來自9個健康人類肝臟組織的10000多個細胞進行了深度單細胞測序分析，繪制的圖譜涵蓋了所有重要的肝臟細胞類型，包括肝實質(zhì)細胞、血管內(nèi)皮細胞、巨噬細胞以及其他免疫細胞類型，并且找到了多種新型肝臟細胞。
肝細胞懸液通過流式系統(tǒng)把單個細胞分選到384孔板，基于微孔板的mCEL-Seq2單細胞測序步驟由mosquito納升級單細胞建庫系統(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第一鏈合成的過程中給每個細胞都加入了barcoded primer，通過體外轉(zhuǎn)錄(IVT)方式擴增單細胞cDNA，這既保證了檢測方法的靈敏度，又通過把所有樣本pooling后再做建庫的方式降低了檢測成本。通過mosquito納升級單細胞建庫系統(tǒng)的加持，再把反應體系縮小了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基因的表達水平鑒定了幾乎所有的肝臟細胞類型，包括肝細胞、EPCAM+膽管細胞(膽管細胞)、CLEC4G+肝血源性內(nèi)皮細胞(LSECs)、CD34+ PECAM high大血管內(nèi)皮細胞(MaVECs)、肝星狀細胞和肌成纖維細胞、Kupffer細胞和免疫細胞。
 
研究人員同時發(fā)現(xiàn)了從未被報道過的新的肝臟細胞類型。這些細胞亞型雖然在形態(tài)上與普通的肝臟細胞沒有什么不同，但在基因表達上卻截然不同。這些發(fā)現(xiàn)歸功于單細胞測序?qū)嶒灧椒╩CEL-seq2和分析算法FateID的重大進展，通過這些方法得以對單個細胞進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：
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.
 
德國馬普所的自動化CEL-Seq2流程，節(jié)省5倍成本