Microfluidics-Mass Spectrometry Combination Systems for Single-Cell Analysis

Author(s):  
Dan Gao ◽  
Chao Song ◽  
Jin-Ming Lin
Author(s):  
Dinesh K. Patel ◽  
Sayan Deb Dutta ◽  
Ki-Taek Lim

2017 ◽  
Vol 90 ◽  
pp. 14-26 ◽  
Author(s):  
Yunyun Yang ◽  
Yanying Huang ◽  
Junhui Wu ◽  
Ning Liu ◽  
Jiewei Deng ◽  
...  

2014 ◽  
Vol 86 (8) ◽  
pp. 3809-3816 ◽  
Author(s):  
Xiaoyun Gong ◽  
Yaoyao Zhao ◽  
Shaoqing Cai ◽  
Shujie Fu ◽  
Chengdui Yang ◽  
...  

2021 ◽  
pp. 1033-1049
Author(s):  
Dinesh K. Patel ◽  
Sayan Deb Dutta ◽  
Ki-Taek Lim

2015 ◽  
Vol 29 (7) ◽  
pp. 681-689 ◽  
Author(s):  
Jia Yu ◽  
Chunyan Li ◽  
Shuijie Shen ◽  
Xiaoqiu Liu ◽  
Ying Peng ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zishuai Li ◽  
Simin Cheng ◽  
Qiaohong Lin ◽  
Wenbo Cao ◽  
Jing Yang ◽  
...  

AbstractSingle-cell analysis is critical to revealing cell-to-cell heterogeneity that would otherwise be lost in ensemble analysis. Detailed lipidome characterization for single cells is still far from mature, especially when considering the highly complex structural diversity of lipids and the limited sample amounts available from a single cell. We report the development of a general strategy enabling single-cell lipidomic analysis with high structural specificity. Cell fixation is applied to retain lipids in the cell during batch treatments prior to single-cell analysis. In addition to tandem mass spectrometry analysis revealing the class and fatty acyl-chain for lipids, batch photochemical derivatization and single-cell droplet treatment are performed to identify the C=C locations and sn-positions of lipids, respectively. Electro-migration combined with droplet-assisted electrospray ionization enables single-cell mass spectrometry analysis with easy operation but high efficiency in sample usage. Four subtypes of human breast cancer cells are correctly classified through quantitative analysis of lipid C=C location or sn-position isomers in ~160 cells. Most importantly, the single-cell deep lipidomics strategy successfully discriminates gefitinib-resistant cells from a population of wild-type human lung cancer cells (HCC827), highlighting its unique capability to promote precision medicine.


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