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 solution at pH 4.5. Combine 250 mg of 13C6-aniline with 132 &#181;L of water and 44 &#181;L of 12 M HCl. Vortex well and store at 4 &#176;C.</li>
	<li>Prepare 200 mg/mL N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride (EDC) solution. Dissolve 2 mg of EDC in 10 &#181;L of water for every sample to be tagged and vortex well. 
	NOTE: EDC solution should be prepared the same day as the reaction. EDC acts as a catalyst for the derivatization of compounds with aniline12.</li>
</ol>
2. Preparation of standards
<ol>
	<li>Make separate stock solutions of all compounds dissolved in LC/MS grade water (Table 1).</li>
	<li>Preparation of internal standard stock solution
	<ol>
		<li>Combine all compounds except for nicotinamide adenine dinucleotide (NAD), nicotinamide adenine dinucleotide phosphate (NADP), flavin adenine dinucleotide (FAD), acetyl coenzyme A (ACA), and glycerol 3-phosphate (Gly3P), with the appropriate volumes to create a 2 mM stock solution of all compounds.</li>
	</ol>
	</li>
	<li>Combine NAD, NADP, FAD, ACA, and Gly3P with the appropriate volumes to create a 2 mM stock solution.</li>
</ol>
3. Preparation of sample (Figure 1)
<ol>
	<li>Quench and precipitate the proteins in a cell-free protein synthesis reaction by adding an equal volume of ice-cold 100% ethanol to the reaction. Centrifuge the sample at 12,000 x g for 15 min at 4 &#176;C. Transfer the supernatant to a new centrifuge tube. 
	NOTE: Samples can be stored at -80 &#176;C at this point and analyzed at a later time</li>
</ol>
4. Labeling reaction&#160;&#160;
<ol>
	<li>Labeling sample with 12C-aniline solution
	<ol>
		<li>Transfer 6 &#181;L of sample into a new centrifuge tube and bring the volume to 50 &#181;L with water. 
		NOTE: Volume sample size may depend on the specific CFPS reaction.</li>
		<li>Add 5 &#181;L of 200 mg/mL EDC solution.</li>
		<li>Add 5 &#181;L of 12C-aniline solution. 
		NOTE: The aniline solution separates into two phases. Mix well before adding to the reaction.</li>
		<li>Vortex the reaction with gentle shaking for 2 h at room temperature.</li>
		<li>After 2 h, remove the tubes from the shaker and add 1.5 &#181;L of triethylamine (TEA) to the reaction in a fume hood. 
		NOTE: Triethylamine raises the pH of the solution which stops the aniline tagging reaction and stabilizes the compounds. 
		CAUTION: Triethylamine is toxic and causes irritation of the eyes and respiratory tract.</li>
		<li>Centrifuge at 13,500 x g for 3 min.</li>
	</ol>
	</li>
	<li>Labeling internal standards with 13C-aniline solution
	<ol>
		<li>Dilute internal stock solution to 80 &#181;M with a final volume of 50 &#181;L. 
		NOTE: Concentration of internal standards can be adjusted to levels close to the experimental sample.</li>
		<li>Add 5 &#181;L of 200 mg/mL EDC solution.</li>
		<li>Add 5 &#181;L of 13C-aniline solution.</li>
		<li>Vortex the reaction with gentle shaking for 2 h at room temperature.</li>
		<li>After 2 h, remove the tubes from the shaker and add 1.5 &#181;L of TEA to the reaction in a fume hood.</li>
		<li>Centrifuge at 13,500 x g for 3 min.</li>
	</ol>
	</li>
	<li>Combining tagged internal standard and tagged sample
	<ol>
		<li>Mix 25 &#181;L of 12C-aniline labeled sample with 25 &#181;L of 13C-aniline labeled standard.</li>
		<li>Transfer to an auto-sampler vial and analyze by the LC/MS procedure.</li>
	</ol>
	</li>
	<li>Creating a standard curve for untagged metabolites
	<ol>
		<li>Dilute stock solution of untagged metabolites (NAD, NADP, FAD, ACA, and Gly3P) to final concentrations of 320 &#181;M, 80 &#181;M, 20 &#181;M and 5 &#181;M with a volume of 50 &#181;L.</li>
		<li>Add 5 &#181;L of 200 mg/mL EDC solution.</li>
		<li>Add 5 &#181;L of 12C-aniline solution.</li>
		<li>Vortex the reaction with gentle shaking for 2 h at room temperature.</li>
		<li>After 2 h, remove the tubes from the shaker and add 1.5 &#181;L of TEA to the reaction in a fume hood.</li>
		<li>Centrifuge at 13,500 x g for 3 min.</li>
		<li>Transfer supernatant to an auto-sampler vial and analyze by the LC/MS procedure. 
		NOTE: The untagged metabolites follow the same procedure as the sample to replicate the sample matrix in order to maintain similar ionization efficiency.</li>
	</ol>
	</li>
</ol>
5. Setup of LC/MS procedure
<ol>
	<li>Preparation of solvents
	<ol>
		<li>Prepare 5 mM tri-butylamine (TBA) aqueous solution adjusted to pH 4.75 with acetic acid. 
		NOTE: TBA in the mobile phase helps the analytes achieve good resolution and separation14.</li>
		<li>Prepare 5 mM TBA in acetonitrile (ACN).</li>
		<li>Prepare wash solvent with 5% water and 95% ACN.</li>
		<li>Prepare purge solvent with 95% water and 5% ACN.</li>
	</ol>
	</li>
	<li>Setup of MS conditions
	<ol>
		<li>Set the mass spectrometer to negative ion mode with a probe temperature of 520 &#176;C, negative capillary voltage of -0.8 kV, positive capillary voltage of 0.8 kV, and set the software to acquire data at 5 points/s.</li>
		<li>Set selected ion recordings (SIR) for each metabolite with specified cone voltages and mass over charge (m/z) values. See Table 1.</li>
	</ol>
	</li>
	<li>Initializing LC/MS according to manufacturer&#8217;s instructions
	<ol>
		<li>Prime solvent lines in the solvent manager for 3 min.</li>
		<li>Prime wash solvent (5% water, 95% ACN) and purge solvent (95% water, 5% ACN) for 15 s for 5 cycles.</li>
		<li>Set the sample manager to 10 &#176;C.</li>
		<li>Install a C18 (1.7&#181;m, 2.1mm x 150mm) column and initialize column with 100% ACN at 0.3 mL/min for 10 min.</li>
		<li>Condition the column at 95% water and 5% ACN at 0.3 mL/min for 10 min prior to introducing solvents with buffers.</li>
		<li>Condition the column at 95% solvent A (5mM TBA aqueous, pH 4.75) and 5% solvent B (5 mM TBA in ACN) at 0.3 mL/min for 10 min.</li>
		<li>Set up a gradient protocol with the elution starting at 95% solvent A and 5% solvent B, raised to 70% solvent B in 10 min, raised to 100% solvent B in 2 min and held at 100% solvent B for 3 min. Return to initial conditions (95% solvent A, 5% solvent B) over 1 min and hold for 9 min to re-equilibrate the column.</li>
		<li>Condition the column with the gradient protocol 3 times prior to any injections onto the column.</li>
	</ol>
	</li>
	<li>Injecting sample and standards
	<ol>
		<li>Inject 5 &#181;L of the sample into the column and acquire the appropriate m/z ion intensities for the 12C-aniline tagged sample.</li>
		<li>Inject 5 &#181;L of the same sample again, but this time acquire the m/z ion intensities for the 13C-aniline tagged standards. 
		NOTE: Our LC/MS system is unable to acquire both 12C and 13C m/z intensities at the specified SIR time windows, since it is too much data to acquire in the specified time window. Therefore, we inject the same sample twice.</li>
		<li>Inject untagged metabolite standards from lowest concentration to highest and record the appropriate m/z ion intensities.</li>
	</ol>
	</li>
</ol>
6. Quantification
<ol>
	<li>Creating Export method 
	<ol>
		<li>In data acquisition software, select File &#62; New Method &#62; Export Method.</li>
		<li>Specify a Filename, such as AnilineTagging_Date.</li>
		<li>Check the Export ASCII File and choose a directory to export the text file to.</li>
		<li>In Report Type, select Summary by All.</li>
		<li>In Delimiters, for Column select a ,. For Row, select [cr][if].</li>
		<li>In Table, select Export and then Edit Table to include SampleName, Area, Height, Amount and Units.</li>
		<li>Save export method.</li>
	</ol>
	</li>
	<li>Quantifying metabolites with internal standards using data acquisition software
	<ol>
		<li>Under the Sample Sets tab, right click the corresponding LC/MS run and select View as &#62; Channels.</li>
		<li>Select all SIR channels for the 13C-aniline internal standards of one injection, right click and select Review.&#160;</li>
		<li>If the LC Processing Method Layout window does not automatically appear, go to View &#62; Processing Method Layout.</li>
		<li>In Processing Method Layout, go to the Integration tab and set ApexTrack as the algorithm.</li>
		<li>Go to the Smoothing tab and set the type to Mean and the smoothing level to 13. 
		NOTE: Any smoothing level can be selected, as long as it is consistent across all samples.</li>
		<li>In the MS Channel tab, disable MS 3D Processing.</li>
		<li>In the SIR channel window, integrate each peak, one channel at a time. Once a peak is integrated, go to Options &#62; Fill from Result and the details of the peak will be filled in the Components tab. Change the peak name to the corresponding compound name.</li>
		<li>Once all the SIR channels have been evaluated, save the processing method and close window.</li>
		<li>Select all SIR channels of the 13C-aniline and 12C-aniline tagged sample, right click and select Process.</li>
		<li>Check the Process box, select Use specified processing method, and choose the processing method that is just saved. Also check the Export box, select Use specified export method and choose the saved export method created earlier. Click OK.</li>
		<li>Open the exported text file with Excel and calculate the concentration of the unknown compound using: 
		<img alt="Equation 1" src="/files/ftp_upload/60329/60329eq1.jpg" /> 
		where Cx,i is the concentration of the unknown sample for metabolite i, Ax,i is the integrated area of the unknown metabolite i, Astd,i is the integrated area of the internal standard of metabolite i, Cstd,i is the concentration of the internal standard of metabolite i, and D is the dilution factor.</li>
	</ol>
	</li>
	<li>Quantifying untagged metabolites with standard curve
	<ol>
		<li>Under the Sample Sets tab, right click the corresponding LC/MS run and select View as &#62; Channels.</li>
		<li>Select all SIR channels for the untagged standards of one injection, right click and select Review.&#160;</li>
		<li>If the LC Processing Method Layout window does not automatically appear, go to View &#62; Processing Method Layout.</li>
		<li>In Processing Method Layout, go to the Integration tab and set ApexTrack as the algorithm.</li>
		<li>Go to the Smoothing tab and set the type to Mean and the smoothing level to 13. 
		NOTE: Any smoothing level can be selected, as long as it is consistent across all samples.</li>
		<li>In the MS Channel tab, disable MS 3D Processing.</li>
		<li>In the SIR channel window, integrate each peak, one channel at a time. Once a peak is integrated, go to Options &#62; Fill from Result and the details of the peak will be filled in the Components tab. Change the peak name to the corresponding compound name.</li>
		<li>Once all the SIR channels have been evaluated, save the processing method and close window.</li>
		<li>Under the Sample Sets tab, right click on the sample set and select Alter Sample.</li>
		<li>Select Amount in the new window.</li>
		<li>Select copy from Process method and choose the process method that was just saved.</li>
		<li>Enter the concentration of each metabolite for each vial and enter the unit as &#60;&#956;M for each component (or the corresponding unit) and select OK.</li>
		<li>Select the sample set again, right click, View as &#62; Channels.</li>
		<li>Select all SIR channels of the untagged metabolites for the standards, right click and select Process.</li>
		<li>Check the Process box and choose Use specified processing method. Select the appropriate processing method and click OK.</li>
		<li>Select SIR channels for all untagged metabolites for the samples, right click and select Process.</li>
		<li>Check the Process box, select Use specified processing method, and choose the processing method that was just saved. Also check the Export box, select Use specified export method and choose the saved export method created earlier. Click OK.</li>
		<li>Quantify the untagged metabolites with the standard curve and export the results to a text file to the directory specified.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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 ...

absolute-quantification-cell-free-protein-synthesis-metabolism

Research

JoVE Journal

Biochemistry

9.0K Views.  Cornell University. 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.

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

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.

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) ...

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing approaches suffer from significant limitations, we have developed a new method for determining TF-DNA binding affinities with high sensitivity on a large scale. The assay relies on the established fluorescence anisotropy (FA) principle but introduces important technical improvements. First, we measure a full FA competitive titration curve in a single well by incorporating TF and a fluorescently labeled reference DNA in a porous agarose gel matrix. Unlabeled DNA oligomer is loaded on the top as a competitor and, through diffusion, forms a spatio-temporal gradient. The resulting FA gradient is then read out using a customized epifluorescence microscope setup. This improved setup greatly increases the sensitivity of FA signal detection, allowing both weak and strong binding to be reliably quantified, even for molecules of similar molecular weights. In this fashion, we can measure one titration curve per well of a multi-well plate, and through a fitting procedure, we can extract both the absolute dissociation constant (KD) and active protein concentration. By testing all single-point mutation variants of a given consensus binding sequence, we can survey the entire binding specificity landscape of a TF, typically on a single plate. The resulting position weight matrices (PWMs) outperform those derived from other methods in predicting in vivo TF occupancy. Here, we present a detailed guide for implementing HiP-FA on a conventional automated fluorescent microscope and the data analysis pipeline.

Here we present a novel method for determining binding affinities at equilibrium and in solution with high sensitivity on a large scale. This improves the quantitative analysis of transcription factor-DNA binding. The method is based on automated fluorescence anisotropy measurements in a controlled delivery system.

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing ...

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 ...

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the dissection and manipulation of biological pathways. Currently, only a limited number of chemically induced dimerization (CID) systems exist and engineering new ones with desired sensitivity and selectivity for specific small-molecule ligands remains a challenge in the field of protein engineering. We here describe a high throughput screening method, combinatorial binders-enabled selection of CID (COMBINES-CID), for the de novo engineering of CID systems applicable to a large variety of ligands. This method uses the two-step selection of a phage-displayed combinatorial nanobody library to obtain 1) &#34;anchor binders&#34; that first bind to a ligand of interest and then 2) &#34;dimerization binders&#34; that only bind to anchor binder-ligand complexes. To select anchor binders, a combinatorial library of over 109 complementarity-determining region (CDR)-randomized nanobodies is screened with a biotinylated ligand and hits are validated with the unlabeled ligand by bio-layer interferometry (BLI). To obtain dimerization binders, the nanobody library is screened with anchor binder-ligand complexes as targets for positive screening and the unbound anchor binders for negative screening. COMBINES-CID is broadly applicable to select CID binders with other immunoglobulin, non-immunoglobulin, or computationally designed scaffolds to create biosensors for in vitro and in vivo detection of drugs, metabolites, signaling molecules, etc.

Creating chemically induced protein dimerization systems with desired affinity and specificity for any given small molecule ligand would have many biological sensing and actuation applications. Here, we describe an efficient, generalizable method for de novo engineering of chemically induced dimerization systems via the stepwise selection of a phage-displayed combinatorial single-domain antibody library.

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the ...

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.