Name: absolute quantification of cell-free protein synthesis metabolism by reversed-phase liquid chromatography-mass spectrometry
Uploaded: 2019-10-25T14:00:00
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The work described was supported by the Center on the Physics of Cancer Metabolism through Award Number 1U54CA210184-01 from the National Cancer Institute ( https://www.cancer.gov/ ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1. Preparation of reagents for aniline tagging
<ol>
	<li>Prepare a 6 M aniline solution at pH 4.5. Working in a hood, combine 550 &#181;L of aniline with 337.5 &#181;L of LCMS grade water and 112.5 &#181;L of 12 M hydrochloric acid (HCl) in a centrifuge tube. Vortex well and store at 4 &#176;C. 
	NOTE: Aniline can be stored at 4 &#176;C for 2 months. 
	CAUTION: Aniline is highly toxic and should be worked with in a fume hood. Hydrochloric acid is highly corrosive</li>
	<li>Prepare a 6 M 13C aniline...

<ol>
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Cell-free systems have no cell wall, thus there is direct access to metabolites and the biosynthetic machinery without the need for complex sample preparation. However, very little work has been done to develop thorough and robust protocols to quantitatively interrogate cell-free reaction systems. In this study, we developed a fast, robust method to quantify metabolites in cell-free reaction mixtures and potentially in whole-cell extracts. Individual quantification of metabolites in complex mixtures, such as those found ...

Cell-free protein synthesis (CFPS) is a promising platform for manufacturing of proteins and chemicals, an application that has traditionally been reserved for living cells. Cell-free systems are derived from crude cell extracts and eliminate the complications associated with cell growth1. In addition, CFPS allows for direct access to metabolites and the biosynthetic machinery without the interference of a cell wall. However, a fundamental understanding of the performance limits of cell-free processes has been lacking. High-throughput methods for metabolite quantification are valuable for the characterization of metabolism and are critical for the construction of metabolic computational models2,3,4. Common methods used to determine metabolite concentrations include nuclear magnetic resonance (NMR), Fourier transform-infrared spectroscopy (FT-IR), enzyme-based assays, and mass spectrometry (MS)5,6,7,8. However, these methods are often limited by their inability to efficiently measure multiple compounds at once and often require a sample size greater than typical cell-free reactions. For example, enzyme-based assays can often only be used to quantify a single compound in a run, and are limited when the sample size is small, such as in cell-free protein synthesis reactions (typically run on a 10-15 &#956;L scale). Meanwhile, NMR requires a high abundance of metabolites for detection and quantification5.&#160; Toward these shortcomings, chromatography methods in tandem with mass spectrometry (LC/MS) provide several advantages, including high sensitivity and the capability of measuring multiple species simultaneously9; however, the analytical complexity increases considerably with the number and diversity of species being measured. It is important, therefore, to develop methods that fully realize the high-throughput potential of LC/MS systems. Compounds in a sample are separated by liquid chromatography and identified through mass spectrometry. The signal of the compound depends on its concentration and ionization efficiency, where the ionization can vary between compounds and may also depend on the sample matrix. &#160;
Achieving the same ionization efficiency between the sample and standards is a challenge when using LC/MS to quantify analytes. Further, quantification becomes more challenging with metabolite diversity due to signal splitting and heterogeneity in proton affinity and polarity10. Lastly, the co-eluting matrix of the sample can also affect the ionization efficiencies of the compounds. To address these issues, metabolites can be chemically derivatized, increasing the separation resolution and sensitivity by LC/MS systems, while simultaneously decreasing signal splitting in some cases10,11. Chemical derivatization works by tagging specific functional groups of metabolites to adjust their physical properties like charge or hydrophobicity to increase ionization efficiency11. Various tagging agents can be used to target different functional groups (e.g., amines, hydroxyls, phosphates, carboxylic acids, etc.). Aniline, one such derivatization agent, targets multiple functional groups at once, and adds a hydrophobic component into hydrophilic molecules, thereby increasing their separation resolution and signal12. To address the co-eluting matrix ion suppression effect, Yang and coworkers developed a technique based on Group Specific Internal Standard Technology (GSIST) labeling where standards are tagged with 13C aniline isotopes and mixed with the sample12,13. The metabolite and corresponding internal standard have the same ionization efficiency since they co-elute, and their intensity ratio can be used to quantify the concentration in the experimental sample.
In this study, we developed a protocol to detect and quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method is based on the GSIST approach, where we used 12C-aniline and 13C-aniline to tag, detect, and quantify metabolites using reversed-phase LC/MS. The linear range of all compounds spanned 2-3 orders of magnitude with an average correlation coefficient of 0.988. Thus, the method is a robust and accurate approach to interrogate cell-free metabolism, and possibly whole-cell extracts.&#160;

<table border="0" cellpadding="0" cellspacing="0" style="width:665px;" width="665">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">12C Aniline</td>
			<td style="width:121px;">Sigma-Aldrich</td>
			<td style="width:161px;">242284</td>
			<td style="width:167px;">Aniline 12C</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">13C labeled aniline</td>
			<td>Sigma-Aldrich</td>
			<td>485797</td>
			<td>Aniline 13C6</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">3-Phosphoglyceric acid</td>
			<td>Sigma-Aldrich</td>
			<td>P8877</td>
			<td>3PG</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acetic Acid</td>
			<td>FisherScientific</td>
			<td>AC222140010</td>
			<td>ACE</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acetonitrile, LCMS</td>
			<td>JT BAKER</td>
			<td>9829-03</td>
			<td>ACN</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acetyl-coenzyme A</td>
			<td>Sigma-Aldrich</td>
			<td>A2056</td>
			<td>ACA</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acquity UPLC BEH C18 1.7 &#956;M, 2.1 x 150 mm Column</td>
			<td>Waters</td>
			<td>186002353</td>
			<td>Column</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Adenosine diphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>A2754</td>
			<td>ADP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Adenosine monophosphate</td>
			<td>Sigma-Aldrich</td>
			<td>A1752</td>
			<td>AMP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Adenosine triphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>A2383</td>
			<td>ATP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Alpha-ketoglutarate</td>
			<td>Sigma-Aldrich</td>
			<td>K1128</td>
			<td>aKG</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Citrate</td>
			<td>Sigma-Aldrich</td>
			<td>251275</td>
			<td>CIT</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Cytidine diphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>C9755</td>
			<td>CDP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Cytidine monophosphate</td>
			<td>Sigma-Aldrich</td>
			<td>C1006</td>
			<td>CMP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Cytidine triphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>C9274</td>
			<td>CTP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">D-glyceraldehyde 3-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>39705</td>
			<td>GAP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Erythrose 4-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>E0377</td>
			<td>E4P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ethanol</td>
			<td>Sigma-Aldrich</td>
			<td>EX0276</td>
			<td>EtOH</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fisher Scientific accuSpin Micro 17 Centrifuge</td>
			<td>FisherScientific</td>
			<td></td>
			<td>Centrifuge</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Flavin adenine dinucleotide</td>
			<td>Sigma-Aldrich</td>
			<td>F6625</td>
			<td>FAD</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fructose 1,6-bisphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>F6803</td>
			<td>F16P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fructose 6-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>F3627</td>
			<td>F6P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fumarate</td>
			<td>Sigma-Aldrich</td>
			<td>F8509</td>
			<td>FUM</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Gluconate 6-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>P7877</td>
			<td>6PG</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Glucose</td>
			<td>Sigma-Aldrich</td>
			<td>G8270</td>
			<td>GLC</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Glucose 6-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>G7879</td>
			<td>G6P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Glycerol 3-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>G7886</td>
			<td>Gly3P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Guanosine diphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>G7127</td>
			<td>GDP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Guanosine monophosphate</td>
			<td>Sigma-Aldrich</td>
			<td>G8377</td>
			<td>GMP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Guanosine triphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>G8877</td>
			<td>GTP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Hydrochloric acid</td>
			<td>Sigma-Aldrich</td>
			<td>258148</td>
			<td>HCl</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Isocitrate</td>
			<td>Sigma-Aldrich</td>
			<td>I1252</td>
			<td>ICIT</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Lactate</td>
			<td>Sigma-Aldrich</td>
			<td>L1750</td>
			<td>LAC</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Malate</td>
			<td>Sigma-Aldrich</td>
			<td>02288</td>
			<td>MAL</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">myTXTL - Sigma 70 Master Mix Kit</td>
			<td>ArborBiosciences</td>
			<td>507024</td>
			<td>Cell-free protein synthesis</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">N-(3-dimethylaminopropyl)-N&#8242;-ethylcarbodiimide hydrochloride</td>
			<td>Sigma-Aldrich</td>
			<td>03449</td>
			<td>EDC</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nicotinamide adenine dinucleotide</td>
			<td>Sigma-Aldrich</td>
			<td>43410</td>
			<td>NAD</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nicotinamide adenine dinucleotide phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>N5755</td>
			<td>NADP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nicotinamide adenine dinucleotide phosphate reduced</td>
			<td>Sigma-Aldrich</td>
			<td>481973</td>
			<td>NADPH</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Nicotinamide adenine dinucleotide reduced</td>
			<td>Sigma-Aldrich</td>
			<td>N8129</td>
			<td>NADH</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Oxalacetate</td>
			<td>Sigma-Aldrich</td>
			<td>O4126</td>
			<td>OAA</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phosphoenolpyruvate</td>
			<td>Sigma-Aldrich</td>
			<td>P0564</td>
			<td>PEP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Pyruvate</td>
			<td>Sigma-Aldrich</td>
			<td>P5280</td>
			<td>PYR</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ribose 5-phosphate</td>
			<td>Sigma-Aldrich</td>
			<td>R7750</td>
			<td>R5P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ribulose 5-phosphate</td>
			<td>CarboSynth</td>
			<td>MR45852</td>
			<td>RL5P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Sedoheptulose 7-phosphate</td>
			<td>CarboSynth</td>
			<td>MS07457</td>
			<td>S7P</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Succinate</td>
			<td>Sigma-Aldrich</td>
			<td>S3674</td>
			<td>SUCC</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Tributylamine</td>
			<td>Sigma-Aldrich</td>
			<td>90780</td>
			<td>TBA</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Triethylamine</td>
			<td>FisherScientific</td>
			<td>O4884</td>
			<td>TEA</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ultrapure water</td>
			<td>FisherScientific</td>
			<td>10977-015</td>
			<td>water</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Uridine diphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>U4125</td>
			<td>UDP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Uridine monophosphate</td>
			<td>Sigma-Aldrich</td>
			<td>U6375</td>
			<td>UMP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Uridine triphosphate</td>
			<td>Sigma-Aldrich</td>
			<td>U6625</td>
			<td>UTP</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">VWR Heavy Duty Vortex</td>
			<td>VWR</td>
			<td></td>
			<td>Vortex</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Water, LCMS</td>
			<td>JT BAKER</td>
			<td>9831-03</td>
			<td>WATER</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Waters Acquity H UPLC Class Quaternary Solvent Manager</td>
			<td>Waters</td>
			<td></td>
			<td>LCMS</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Waters Acquity H UPLC Class Sample Manager FTN</td>
			<td>Waters</td>
			<td></td>
			<td>LCMS</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Waters Acquity Qda detector</td>
			<td>Waters</td>
			<td></td>
			<td>LCMS</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Waters Empower 3</td>
			<td>Waters</td>
			<td></td>
			<td>Software</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Waters LCMS Total Recovery Vial</td>
			<td>Waters</td>
			<td>186000384c</td>
			<td>LCMS Vial</td>
		</tr>
	</tbody>
</table>

absolute quantification of cell-free protein synthesis metabolism by reversed-phase liquid chromatography-mass spectrometry

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

As a proof-of-concept, we used the protocol to quantify metabolites in an E. coli based CFPS system expressing green fluorescent protein (GFP). &#160;The CFPS reaction (14 &#956;L) was quenched and deproteinized with ethanol. The CFPS sample was then tagged with 12C-aniline, while standards were tagged with 13C-aniline. The tagged sample and standards were then combined and injected into the LC/MS (Figure 1). The protocol detected and quantified 40 metabolites ...

Watch this Scientific Journal Video about Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry at JoVE.com

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry

Here, we present a robust protocol to quantify 40 compounds involved in central carbon and energy metabolism in cell-free protein synthesis reactions. The cell-free synthesis mixture is derivatized with aniline for effective separation using reversed-phase liquid chromatography and then quantified by mass spectrometry using isotopically labelled internal standards.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is ...

Cell-free protein synthesis is an emerging technology in systems and synthetic biology for the in vitro production of proteins. Cell-free protein synthesis systems have no cell wall, thus we have direct access to metabolites. In this protocol, we quantify the absolute measurements of 40 metabolites involved in central carbon and energy metabolism, which allows us to characterize cell-free metabolism.The protocol relies on the metastasizing the compounds with aniline, which allows for better separation and higher mass spectrometry resolution. In addition, we use internal standards with an isotopic we label aniline, for absolute quantification of the metabolites. The tagged metabolites coelute, which eliminates the effects of iron suppression.Allowing for the accurate quantification of the compounds. Working in a hood, combine 550 microliters of the aniline, with 337.5 microliters of LC-MS grade water. And 112.5 microliters of 12 molar hydrochloric acid in a centrifuge tube.Vortex well, and store the six molar aniline solution at pH 4.5 and four degrees Celsius. To prepare a six molar carbon-13 aniline solution, at pH 4.5, combine 250 milligrams of carbon-13 aniline with 132 microliters of water, and 44 microliters of 12 molar hydrochloric acid. Vortex well, and store at four degrees Celsius.To prepare 200 milligrams per milliliter EDC solution, dissolve two milligrams of EDC in 10 microliters of water for every sample to be tagged. And vortex well. To prepare samples, quench and precipitate the proteins in a cell-free protein synthesis reaction, by adding an equal volume of ice-cold 100%ethanol to the cell-free synthesis reaction.Centrifuge the sample at 12, 000 times g, for 15 minutes at four degrees Celsius. Transfer the supernatant of the sample to a new centrifuge tube. To label the sample with carbon-12 aniline solution, transfer six microliters of the sample into a new centrifuge tube, and bring the volume to 50 microliters with water.Add 5 microliters of 200 milligrams per milliliter EDC solution, mix the carbon-12 aniline solution well, and transfer 5 microliters into the tube. Vortex the reaction with gentle shaking for two hours at room temperature. After two hours, remove the tubes from the shaker and transfer them into a fume hood.Add 1.5 microliters of triethylamine to each reaction to stop the aniline tagging reaction and stabilize the compounds. Centrifuge at 13, 500 times g for three minutes. And save the supernatant.To label the internal standards with carbon-13 aniline solution, dilute the internal stock solution to 80 micromolar with a final volume of 50 microliters. Add five microliters of the 200 milligrams per milliliter EDC solution, and five microliters of the well-mixed carbon-13 aniline solution. Vortex the reaction with gentle shaking for two hours at room temperature.After two hours, remove the tubes from the shaker, and add 1.5 microliters of triethylamine to the reaction in a fume hood. Centrifuge at 13, 500 times g for three minutes and save the supernatant. To divine the tagged internal standard, and the tagged sample, mix 25 microliters of each in an autosampler vial.And analyze by the LC-MS procedure. Then, to create a standard curve for untagged metabolites, first, dilute the stock solution of untagged metabolites to different concentrations, with volume of 50 microliters. Add five microliters of the 200 milligrams per milliliter EDC solution, and five microliters of carbon-12 aniline solution to each concentration.Vortex the reaction with gentle shaking for two hours at room temperature. And proceed with the triethylamine treatment. And centrifugalization before analyzing by the LC-MS as previously.After initializing LC-MS, according to manufacturer's instructions, inject five microliters of the sample into the column. And acquire the appropriate m over z ion intensities for the carbon-12 aniline tagged sample. Then, inject five microliters of the same sample again.And acquire the m over z ion intensities for the carbon-13 aniline tagged standards. In this protocol, metabolites expressing green fluorescent protein in an E.coli-based cell-free protein synthesis were quantified. 40 metabolites involved in central carbon and energy metabolism, were detected and quantified using internal standards.While a standard curve for five of the metabolites that were not tagged with aniline was also developed. The diverse metabolites involved in these pathways were a class of phosphorylated sugars, phospho-carboxylic acids, carboxylic acids, nucleotides, and co-factors. In addition, the method enabled the separation of structural isomer pairs, such as glucose-six phosphate, and fructose-six phosphate in a single LC-MS run.The most important step is labeling the de-proteinized sample with aniline. Since the aniline solution separates, it is important to work quickly and effectively, while maintaining good safety practices. This method helps to characterize cell-free metabolism with the quantification of 40 metabolites.But, additional compounds are in the cell-free mixture, such as amino acids that can be quantified. This would help in providing a more comprehensive look into cell-free metabolism. This method revealed that cell-free systems have complex metabolisms, still operating that can be potentially exploited for better energy efficiency and carbon yield.The protocol relies on tagging the compounds with aniline with the use of EDC as a catalyst. Both these reagents are hazardous and should be used with caution.

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Research

JoVE Journal

Biochemistry

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

A Tandem Liquid Chromatography&amp;#8211;Mass Spectrometry-based Approach for Metabolite Analysis of &lt;em&gt;Staphylococcus aureus&lt;/em&gt;

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of biological membranes, energy storage and production, cellular signaling, vesicular transport of proteins, organelle biogenesis, and regulated cell death. Because the budding yeast Saccharomyces cerevisiae is a unicellular eukaryote amenable to thorough molecular analyses, its use as a model organism helped uncover mechanisms linking lipid metabolism and intracellular transport to complex biological processes within eukaryotic cells. The availability of a versatile analytical method for the robust, sensitive, and accurate quantitative assessment of major classes of lipids within a yeast cell is crucial for getting deep insights into these mechanisms. Here we present a protocol to use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of major cellular lipids of S. cerevisiae. The LC-MS/MS method described is versatile and robust. It enables the identification and quantification of numerous species (including different isobaric or isomeric forms) within each of the 10 lipid classes. This method is sensitive and allows identification and quantitation of some lipid species at concentrations as low as 0.2 pmol/&#181;L. The method has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. The use of alternative mobile phase additives for electrospray ionization mass spectrometry in this method can increase the efficiency of ionization for some lipid species and can be therefore used to improve their identification and quantitation.

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol using liquid chromatography coupled with tandem mass spectrometry to identify and quantify major cellular lipids in Saccharomyces cerevisiae. The described method for a quantitative assessment of major lipid classes within a yeast cell is versatile, robust, and sensitive.

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of ...

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism&#39;s biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

A method for the untargeted analysis of wheat grain metabolites and lipids is presented. The protocol includes an acetonitrile metabolite extraction method and reversed phase liquid chromatography-mass spectrometry methodology, with acquisition in positive and negative electrospray ionization modes.

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management ...

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan structure are fairly conserved across all bacteria, there is also considerable variation between Gram-positives/negatives and between species. In addition, there are numerous known variations, modifications, or adaptations to the peptidoglycan that can occur within a bacterial species in response to growth phase and/or environmental stimuli. These variations produce a highly dynamic structure that is known to participate in many cellular functions, including growth/division, antibiotic resistance, and host defense avoidance. To understand the variation within peptidoglycan, the overall structure must be broken down into its constitutive parts (known as muropeptides) and assessed for overall cellular composition. Peptidoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. The following protocol describes the purification of peptidoglycan from bacterial cultures, the acquisition of muropeptide intensity data through a liquid chromatograph&#8212;mass spectrometer, and the differential analysis of peptidoglycan composition using bioinformatics.

This protocol covers a detailed analysis of peptidoglycan composition using liquid chromatography mass spectrometry coupled with advanced feature extraction and bioinformatic analysis software.

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan ...

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/&#181;L. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol for the identification and quantitation of major classes of water-soluble metabolites in the yeast Saccharomyces cerevisiae. The described method is versatile, robust, and sensitive. It allows the separation of structural isomers and stereoisomeric forms of water-soluble metabolites from each other.

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within ...

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits.
Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.

A headspace solid-phase microextraction-gas-chromatography platform is described here for fast, reliable, and semi-automated volatile identification and quantification in ripe blackcurrant fruits. This technique can be used to increase knowledge about fruit aroma and to select cultivars with enhanced flavor for the purpose of breeding.

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars ...