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Updated: May 19, 2026
Digital Microfluidics for Automated Proteomic Processing
Published on: November 6, 2009
A Miniaturized, Automated Proteomic Sample Preparation Platform for High-Throughput Target Deconvolution of Natural
Qiong Wu1, Yue Lin1, Jiayi Chen1,2
1School of Basic Medical Sciences, State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou 511436, China.
We developed μPAS, an automated system for natural product target identification. This platform streamlines sample preparation, improving efficiency and enabling automated drug discovery for bioactive molecules.
Area of Science:
- Biochemistry
- Proteomics
- Drug Discovery
Background:
- Natural products are crucial for drug discovery but face challenges in target identification.
- Proteomics sample preparation is a bottleneck for high-throughput target deconvolution.
Purpose of the Study:
- To develop an automated and miniaturized platform for efficient natural product target identification.
- To streamline proteomic sample preparation for enhanced throughput and cost-efficiency.
Main Methods:
- Developed μPAS (micro proteomics automation system), integrating reduction, alkylation, digestion, and TMTpro labeling.
- Reduced sample volumes by 3-7 fold, decreasing TMT reagent consumption by 2-7.5 fold.
- Validated platform reproducibility (Pearson correlation >0.96) and efficiency (>90% digestion, >96% labeling).
Main Results:
- μPAS enabled automated target discovery for three model compounds.
- Applied to 96 samples, μPAS facilitated target deconvolution for 18 natural products.
- Identified and validated key candidate targets (HIF1AN, FECH, TXNRD1) using thermal shift assays.
Conclusions:
- μPAS is a robust and scalable platform for high-throughput natural product target discovery.
- The system accelerates mechanistic elucidation of natural product bioactivity.
- Automated sample preparation overcomes a critical bottleneck in drug discovery pipelines.
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