Microfluidic Printing-Based Method for the Multifactorial Study of Cell-Free Protein Networks.
Microfluidic Printing-Based Method for the Multifactorial Study of Cell-Free Protein Networks.
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DOI:
10.1021/acs.analchem.2c01851
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发表时间:
2022-08-09
影响因子:
7.4
通讯作者:
Pan, Tingrui
中科院分区:
文献类型:
--
作者:
Zhou, Chuqing;Shim, Jiyoung;Fang, Zecong;Meyer, Conary;Gong, Ting;Wong, Matthew;Tan, Cheemeng;Pan, Tingrui
Protein networks can be assembled in vitro for basic biochemistry research, drug screening, and the creation of artificial cells. Two standard methodologies are used: manual pipetting; pipetting robots. Manual pipetting has limited throughput in the number of input reagents and the combination of reagents in a single sample. While pipetting robots are evident in improving pipetting efficiency and saving hands-on time, their liquid handling volume usually ranges from a few to hundreds of microliters. Microfluidic methods have been developed to minimize the reagent consumption and speed up screening, but are challenging in multifactorial protein studies due to their reliance on complex structures and labeling dyes. Here, we engineered a new impact-printing-based methodology to generate printed microdroplet arrays containing water-in-oil droplets. The printed droplet volume was linearly proportional (R2=0.9999) to the single droplet number, and each single droplet volume was around 59.2 nL (Coefficient of Variation=93.8%). Our new methodology enables the study of protein networks in both membrane-unbound and -bound states, without and with anchor lipids DGS-NTA(Ni) respectively. The methodology is demonstrated using a sub-network of mitogen-activated protein kinase (MAPK). It takes less than 10 minutes to prepare 100 different droplet-based reactions, using < 1 μL reaction volume at each reaction site. We validate the kinase (ATPase) activity of MEK1 (R4F) * and ERK2 WT individually and together under different concentrations, without and with the selective membrane attachment. Our new methodology provides a reagent-saving, efficient and flexible way for protein network research and related applications.
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影响因子:
16
作者:
通讯作者:
--
影响因子:
7.4
作者:
Sun, Shuwen;Slaney, Thomas R.;Kennedy, Robert T.
通讯作者:
Kennedy, Robert T.
影响因子:
16.8
作者:
Hui E;Vale RD
通讯作者:
Vale RD
DOI:
10.1126/science.aaz6802
发表时间:
2020-05-08
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Miller TE;Beneyton T;Schwander T;Diehl C;Girault M;McLean R;Chotel T;Claus P;Cortina NS;Baret JC;Erb TJ
通讯作者:
Erb TJ
影响因子:
7.4
作者:
Rane, Tushar D.;Zec, Helena C.;Wang, Tza-Huei
通讯作者:
Wang, Tza-Huei