Microchip-Based Analytical Tools for Proteomic Research

摘要

The field of proteomics has contributed a lot to comprehensive understanding of cellular and biological system in normal or disease psychological states at molecular level. One of the essential tasks of proteomics is the identification and analysis of target proteins in cells or tissues from which information such as protein molecular weight, isoelectric point, and amino acid sequence. The rapid development of mass spectrometry and associated coupled technology has provided the opportunity to perform protein and peptide separation, measurement and identification in a highly automated and rapid fashion, hence more comprehensive protein mapping in cells or tissues can be obtained. Recent advances in microfluidics techniques have the potential to meet the requirements involved in proteomics and will play an important role in future biological analysis. Their miniaturized architecture offers several advantages, such as higher sample throughput and processing rates, low manufacturing costs, advanced system integration, and reduced volumes of samples and analytes. Especially, microfluidic technology enables the implementation of miniaturized platforms containing immobilized enzymes and antibodies for proteome analysis. Herein, we would report a microchip-based immunosensors for sensitive analysis of IgG with a detection limit of 1pg/mL. A PMMA microchip incorporating end-column electrochemical detection has been developed for immunoassys in microchannels. Additionally, we would introduce a chip-based electrospray ionization technique coupled to MS measurement, and microchip reactors modified with sol-gel entrapment and nanoparticle assembly for efficient proteolysis. Combined with 2D-LC-ESI MS/MS, the reactors have been employed in the identification of the protein mixtures. Coupled with the laser induced fluorescence detection and mass spectrometry system, a novel technique based on capillary reverse phase LC-chip-CZE for two dimensional separation of proteins has been developed and successfully applied to the high throughput analysis of the real biological samples.