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Mass spectrometry-based monitoring of millisecond protein-ligand binding dynamics using an automated microfluidic platform.

ABSTRACT: Characterizing protein-ligand binding dynamics is crucial for understanding protein function and for developing new therapeutic agents. We present a novel microfluidic platform that features rapid mixing of protein and ligand solutions, variable incubation times, and an integrated electrospray ionization source for mass spectrometry-based monitoring of protein-ligand binding dynamics. This platform offers many advantages, including solution-based binding, label-free detection, automated operation, rapid mixing, and low sample consumption.

SUBMITTER: Cong Y

PROVIDER: S-EPMC4846533 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Mass spectrometry-based monitoring of millisecond protein-ligand binding dynamics using an automated microfluidic platform.

Cong Yongzheng Y Katipamula Shanta S Trader Cameron D CD Orton Daniel J DJ Geng Tao T Baker Erin S ES Kelly Ryan T RT

Lab on a chip 20160401 9

Characterizing protein-ligand binding dynamics is crucial for understanding protein function and for developing new therapeutic agents. We present a novel microfluidic platform that features rapid mixing of protein and ligand solutions, variable incubation times, and an integrated electrospray ionization source for mass spectrometry-based monitoring of protein-ligand binding dynamics. This platform offers many advantages, including solution-based binding, label-free detection, automated operatio ...[more]

PMID: 27009517

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Project description:To address the challenges of tracking the multitude of signaling molecules and metabolites that is the basis of biological complexity, we describe a strategy to expand the analytical techniques for dynamic systems biology. Using microfluidics, online desalting, and mass spectrometry technologies, we constructed and validated a platform well suited for sampling the cellular microenvironment with high temporal resolution. Our platform achieves success in: automated cellular stimulation and microenvironment control; reduced non-specific adsorption to polydimethylsiloxane due to surface passivation; real-time online sample collection; near real-time sample preparation for salt removal; and real-time online mass spectrometry. When compared against the benchmark of "in-culture" experiments combined with ultraperformance liquid chromatography-electrospray ionization-ion mobility-mass spectrometry (UPLC-ESI-IM-MS), our platform alleviates the volume challenge issues caused by dilution of autocrine and paracrine signaling and dramatically reduces sample preparation and data collection time, while reducing undesirable external influence from various manual methods of manipulating cells and media (e.g., cell centrifugation). To validate this system biologically, we focused on cellular responses of Jurkat T cells to microenvironmental stimuli. Application of these stimuli, in conjunction with the cell's metabolic processes, results in changes in consumption of nutrients and secretion of biomolecules (collectively, the exometabolome), which enable communication with other cells or tissues and elimination of waste. Naïve and experienced T-cell metabolism of cocaine is used as an exemplary system to confirm the platform's capability, highlight its potential for metabolite discovery applications, and explore immunological memory of T-cell drug exposure. Our platform proved capable of detecting metabolomic variations between naïve and experienced Jurkat T cells and highlights the dynamics of the exometabolome over time. Upregulation of the cocaine metabolite, benzoylecgonine, was noted in experienced T cells, indicating potential cellular memory of cocaine exposure. These metabolomics distinctions were absent from the analogous, traditional "in-culture" UPLC-ESI-IM-MS experiment, further demonstrating this platform's capabilities.

Project description:Over the past decades, protein O-GlcNAcylation has been found to play a fundamental role in cell cycle control, metabolism, transcriptional regulation, and cellular signaling. Nevertheless, quantitative approaches to determine in vivo GlcNAc dynamics at a large-scale are still not readily available. Here, we have developed an approach to isotopically label O-GlcNAc modifications on proteins by producing (13)C-labeled UDP-GlcNAc from (13)C6-glucose via the hexosamine biosynthetic pathway. This metabolic labeling was combined with quantitative mass spectrometry-based proteomics to determine protein O-GlcNAcylation turnover rates. First, an efficient enrichment method for O-GlcNAc peptides was developed with the use of phenylboronic acid solid-phase extraction and anhydrous DMSO. The near stoichiometry reaction between the diol of GlcNAc and boronic acid dramatically improved the enrichment efficiency. Additionally, our kinetic model for turnover rates integrates both metabolomic and proteomic data, which increase the accuracy of the turnover rate estimation. Other advantages of this metabolic labeling method include in vivo application, direct labeling of the O-GlcNAc sites and higher confidence for site identification. Concentrating only on nuclear localized GlcNAc modified proteins, we are able to identify 105 O-GlcNAc peptides on 42 proteins and determine turnover rates of 20 O-GlcNAc peptides from 14 proteins extracted from HeLa nuclei. In general, we found O-GlcNAcylation turnover rates are slower than those published for phosphorylation or acetylation. Nevertheless, the rates widely varied depending on both the protein and the residue modified. We believe this methodology can be broadly applied to reveal turnovers/dynamics of protein O-GlcNAcylation from different biological states and will provide more information on the significance of O-GlcNAcylation, enabling us to study the temporal dynamics of this critical modification for the first time.

Dataset Information

Mass spectrometry-based monitoring of millisecond protein-ligand binding dynamics using an automated microfluidic platform.

Publications

Mass spectrometry-based monitoring of millisecond protein-ligand binding dynamics using an automated microfluidic platform.

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