AutoProteome Chip System for Fully Automated and Integrated Proteomics Sample Preparation and Peptide Fractionation
- 3 June 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Analytical Chemistry
- Vol. 92 (13), 8893-8900
- https://doi.org/10.1021/acs.analchem.0c00752
Abstract
With recent advances in LC-MS systems, current MS-based proteomics has an increasing need for automated, high-throughput sample preparation with neglectable sample loss. In this study, we developed a microfluidic system for fully automated proteomics sample preparation. All of the required proteomics sample preparation steps for both protein digestion and peptide fractionation are fully integrated into a disposable plastic chip device (named AutoProteome Chip). The AutoProteome Chip packed with mixed-mode ion exchange beads and C18 membrane in tandem could be fabricated with very low cost and high stability in organic reagents. Benefiting from its low backpressure, the AutoProteome Chip could be precisely driven by gas pressure which could be easily multiplexed. As low as 2 ng of standard protein BSA could be trapped into the AutoProteome chip and processed within 2 h. Fully automated processing of 10 μg protein extracts of HEK 293T cells achieved more than 97% of digestion efficiency with missed cleavage less than 2 and comparable performance with conventional approaches. More than 4,700 proteins could be readily identified within 80 min of LC-MS analysis with good label-free quantification performance (Pearson correlation coefficient > 0.99). Furthermore, deep proteome profiling by integrated high-pH RP fractionation in the same AutoProteome Chip resulted in more than 7500 proteins identified from only 20 μg protein extracts of HEK 293T cells and comparable reprodicibility as single-shot analysis. The AutoProteome Chip system provided a valuable prototype for developing fully automated proteome analysis workflow and for proteomic applications with high demand for processing throughput, reproducibility and sensitivity.Funding Information
- Ministry of Science and Technology of the People's Republic of China (2016YFA0501403, 2016YFA0501404)
- National Natural Science Foundation of China (31700088)
- Shenzhen Innovation of Science and Technology Commission (JCYJ20170412154126026)
- Guangdong Province (2019B151502050)
- Guangdong Provincial Natural Science Grant (2016A030312016)
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