Literature DB >> 32490667

AutoProteome Chip System for Fully Automated and Integrated Proteomics Sample Preparation and Peptide Fractionation.

Xue Lu1, Zhikun Wang1, Yan Gao2, Wendong Chen1, Lingjue Wang1, Peiwu Huang1, Weina Gao1, Mi Ke1, An He1, Ruijun Tian1.   

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 of 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 4700 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 being identified from only 20 μg of protein extracts of HEK 293T cells and comparable reprodicibility as single-shot analysis. The AutoProteome Chip system provided a valuable prototype for developing a fully automated proteome analysis workflow and for proteomic applications with high demand for processing throughput, reproducibility, and sensitivity.

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Year:  2020        PMID: 32490667     DOI: 10.1021/acs.analchem.0c00752

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  2 in total

1.  Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Authors:  Yan Chen; Nurgul Kaplan Lease; Jennifer W Gin; Tadeusz L Ogorzalek; Paul D Adams; Nathan J Hillson; Christopher J Petzold
Journal:  PLoS One       Date:  2022-02-25       Impact factor: 3.240

2.  Alterations of the Platelet Proteome in Lung Cancer: Accelerated F13A1 and ER Processing as New Actors in Hypercoagulability.

Authors:  Huriye Ercan; Lisa-Marie Mauracher; Ella Grilz; Lena Hell; Roland Hellinger; Johannes A Schmid; Florian Moik; Cihan Ay; Ingrid Pabinger; Maria Zellner
Journal:  Cancers (Basel)       Date:  2021-05-08       Impact factor: 6.639

  2 in total

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