| Literature DB >> 35901235 |
Chuqing Zhou1, Jiyoung Shim1, Zecong Fang2,3, Conary Meyer1, Ting Gong1, Matthew Wong1, Cheemeng Tan1, Tingrui Pan2,3,4,5.
Abstract
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 and 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 subnetwork of mitogen-activated protein kinase (MAPK). It takes less than 10 min 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.Entities:
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Year: 2022 PMID: 35901235 PMCID: PMC9558566 DOI: 10.1021/acs.analchem.2c01851
Source DB: PubMed Journal: Anal Chem ISSN: 0003-2700 Impact factor: 8.008