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 in cell-free reactions, or whole-cell extracts, is challenging for several reasons. Central amongst these reasons is chemical diversity. The array of functional groups simultaneously present in these mixtures (e.g., carboxylic acids, amines, phosphates, hydroxyls, etc.) greatly increases the analytical complexity. To circumvent this, we used an aniline derivatization method in combination with 13C internal standards to introduce hydrophobic components to the metabolite mixtures. Using this method, we robustly detected and quantified 40 metabolites in a cell-free reaction in a single LC/MS run. The protocol tagged 35 of the 40 compounds in this study, while the remaining 5 compounds were quantified with a standard curve method. Earlier work suggested the reaction conditions formed an intramolecular salt between the amine and phosphate group that inhibited derivatization12. Reaction conditions were not identified for simultaneous derivatization of all 40 compounds; however, the current alternative is quantification with a standard curve method. While we demonstrated this technique in a cell-free reaction mixture, it could also likely be applied to whole-cell extracts, thus, potentially allowing the absolute quantification of intracellular metabolites concentrations. The latter application has relevance to a variety of important questions in biotechnology and human health.
The method presented here was based on a previous technique (GSIST) that was applied to whole-cell extracts of the yeast S. cerevisiae12,13. In this study, we expanded the number of compounds which could be detected and quantified, including all 12 nucleotides (xMP, xDP, xTP, where x is A, C, G and U). Addition of these compounds could have important biological implications. For example, these nucleotides are heavily involved in transcription and translation processes, which is one of the central processes of interest in CFPS applications, and more generally the compounds are important in a variety of physiological functions. In addition, we were able to detect acetic acid which is an important metabolite when examining overflow metabolism. However, we did not include it in the study because there was a significant reduction of signal in multiple compounds, especially nicotinamide adenine dinucleotide reduced (NADH) and nicotinamide adenine dinucleotide phosphate reduced (NADPH) when acetic acid was added to the standard mixture. Acetic acid had a high limit of detection of 612 &#181;M, thus at these high levels it had a negative effect on the other metabolites&#8217; signals. Despite this, acetic acid can still be detected and quantified in samples by creating a standard curve with just acetic acid in the vial. Acetic acid had a m/z value of 134.0, retention time of 5.78 min, and a linear range from 612 &#181;M to 5000 &#181;M (R2 = 0.986) when tagged with 12C-aniline. The remaining metabolites did not alter each other&#8217;s ion signal and represent a comprehensive mixture to characterize CFPS metabolism. The protocol presented here is limited to metabolites involved in central carbon and energy metabolism. Thus, the current method is unable to measure metabolite abundance from other pathways that may be of importance, such as fatty acid and amino acid metabolism.
Taken together, we developed a fast, robust protocol for the characterization and absolute quantification of 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism, and cofactor regeneration in CFPS reactions. The method relied on internal standards tagged with 13C-aniline, while the sample was tagged with 12C-aniline. The internal standards and sample compounds co-eluted and eliminated ion-suppression effects which enabled accurate quantification of individual metabolites in complex metabolite mixtures. We identified a total of 40 compounds (41, if including acetic acid) that can be detected and quantified in a cell-free reaction mixture; however, the list of metabolites could be further expanded and adjusted towards the particular biochemical process of interest. Thus, the method provides a robust and accurate approach to characterize cell-free metabolism, which is potentially critical to improving the yield, productivity and energy efficiency of cell-free processes. &#160;

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

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

Nanyang Technological University

Research

JoVE Journal

Biochemistry

9.2K Views.  Cornell University. Celvrije eiwitsynthese is een opkomende technologie in systemen en synthetische biologie voor de in vitro productie van eiwitten. Celvrije eiwitsynthesesystemen hebben geen celwand, dus we hebben directe toegang tot metabolieten. In dit protocol kwantificeren we de absolute metingen van 40 metabolieten die betrokken zijn bij het centrale koolstof- en energiemetabolisme, waardoor we het celvrije metabolisme kunnen karakteriseren.Het protocol is gebaseerd op het uitzaaien van de verbindi...

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

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type.

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between ...

Chemistry

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase solubility and facilitate excretion. On some occasions those structural modifications lead to the formation of new toxic products. In order to reduce animal testing, chemical risk can be assessed using metabolically competent cells. The expression of metabolic enzymes, however, is not stable over time in many in vitro primary culture systems and is often partial or absent in cell lines. Therefore, the study of medicines, additives, and environmental pollutants metabolism in vitro should ideally be conducted in cell systems where metabolic activity has been characterized. We explain here an approach to measure the activity of a class of metabolic enzymes (Human Phase I) in 2D cell lines and primary 3D cultures using chemical probes and their metabolic products quantifiable by UPLC mass spectrometry and luminometry. The method can be implemented to test the metabolic activity in cell lines and primary cells derived from a variety of tissues.

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures

Metabolic competency of in vitro systems is a key requirement for the biotransformation and disposition of drugs and toxicants. In this protocol, we describe the application of reference metabolic probes to assess phase I metabolism in cell cultures.

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase ...

Cellular Lipid Extraction for Targeted Stable Isotope Dilution Liquid Chromatography-Mass Spectrometry Analysis

The metabolism of fatty acids, such as arachidonic acid (AA) and linoleic acid (LA), results in the formation of oxidized bioactive lipids, including numerous stereoisomers1,2. These metabolites can be formed from free or esterified fatty acids. Many of these oxidized metabolites have biological activity and have been implicated in various diseases including cardiovascular and neurodegenerative diseases, asthma, and cancer3-7. Oxidized bioactive lipids can be formed enzymatically or by reactive oxygen species (ROS). Enzymes that metabolize fatty acids include cyclooxygenase (COX), lipoxygenase (LO), and cytochromes P450 (CYPs)1,8. Enzymatic metabolism results in enantioselective formation whereas ROS oxidation results in the racemic formation of products.
While this protocol focuses primarily on the analysis of AA- and some LA-derived bioactive metabolites; it could be easily applied to metabolites of other fatty acids. Bioactive lipids are extracted from cell lysate or media using liquid-liquid (l-l) extraction. At the beginning of the l-l extraction process, stable isotope internal standards are added to account for errors during sample preparation. Stable isotope dilution (SID) also accounts for any differences, such as ion suppression, that metabolites may experience during the mass spectrometry (MS) analysis9. After the extraction, derivatization with an electron capture (EC) reagent, pentafluorylbenzyl bromide (PFB) is employed to increase detection sensitivity10,11. Multiple reaction monitoring (MRM) is used to increase the selectivity of the MS analysis. Before MS analysis, lipids are separated using chiral normal phase high performance liquid chromatography (HPLC). The HPLC conditions are optimized to separate the enantiomers and various stereoisomers of the monitored lipids12. This specific LC-MS method monitors prostaglandins (PGs), isoprostanes (isoPs), hydroxyeicosatetraenoic acids (HETEs), hydroxyoctadecadienoic acids (HODEs), oxoeicosatetraenoic acids (oxoETEs) and oxooctadecadienoic acids (oxoODEs); however, the HPLC and MS parameters can be optimized to include any fatty acid metabolites13.
Most of the currently available bioanalytical methods do not take into account the separate quantification of enantiomers. This is extremely important when trying to deduce whether or not the metabolites were formed enzymatically or by ROS. Additionally, the ratios of the enantiomers may provide evidence for a specific enzymatic pathway of formation. The use of SID allows for accurate quantification of metabolites and accounts for any sample loss during preparation as well as the differences experienced during ionization. Using the PFB electron capture reagent increases the sensitivity of detection by two orders of magnitude over conventional APCI methods. Overall, this method, SID-LC-EC-atmospheric pressure chemical ionization APCI-MRM/MS, is one of the most sensitive, selective, and accurate methods of quantification for bioactive lipids.

This protocol will demonstrate the extraction and analysis of free and esterified bioactive fatty acids from cells. Fatty acids are accurately quantified using stable isotope dilution, chiral liquid chromatography, electron capture atmospheric chemical ionization multiple reaction monitoring mass spectrometry (SID-LC-ECAPCI-MRM/MS).

The metabolism of fatty acids, such as arachidonic acid (AA) and linoleic acid (LA), results in the formation of oxidized bioactive lipids, including ...

Bioengineering

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) ...

An HS-MRM Assay for the Quantification of Host-cell Proteins in Protein Biopharmaceuticals by Liquid Chromatography Ion Mobility QTOF Mass Spectrometry

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring high sensitivity and a wide dynamic range. Mass spectrometry-based quantification assays for proteins typically involve protein digestion followed by the selective reaction monitoring/multiple reaction monitoring (SRM/MRM) quantification of peptides using a low-resolution (Rs ~1,000) tandem quadrupole mass spectrometer. One of the limitations of this approach is the interference phenomenon observed when the peptide of interest has the "same" precursor and fragment mass (in terms of m/z values) as other co-eluting peptides present in the sample (within a 1-Da window). To avoid this phenomenon, we propose an alternative mass spectrometric approach, a high selectivity (HS) MRM assay that combines the ion mobility separation of peptide precursors with the high-resolution (Rs ~30,000) MS detection of peptide fragments. We explored the capabilities of this approach to quantify low-abundance peptide standards spiked in a monoclonal antibody (mAb) digest and demonstrated that it has the sensitivity and dynamic range (at least 3 orders of magnitude) typically achieved in HCP analysis. All six peptide standards were detected at concentrations as low as 0.1 nM (1 femtomole loaded on a 2.1-mm ID chromatographic column) in the presence of a high-abundance peptide background (2 &#181;g of a mAb digest loaded on-column). When considering the MW of rabbit phosphorylase (97.2 kDa), from which the spiked peptides were derived, the LOQ of this assay is lower than 50 ppm. Relative standard deviations (RSD) of peak areas (n = 4 replicates) were less than 15% across the entire concentration range investigated (0.1-100 nM or 1-1,000 ppm) in this study.

Here, we describe a chromatographic assay coupled with the ion mobility separation of peptide precursors followed by the high-resolution (~30,000) MS-detection of peptide fragments for the quantification of spiked peptide standards in a monoclonal antibody digest.

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring ...

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 ...

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Mounting evidence has shown that the accumulation of senescent cells in the central nervous system contributes to neurodegenerative disorders such as Alzheimer&#39;s and Parkinson&#39;s diseases. Cellular senescence is a state of permanent cell cycle arrest that typically occurs in response to exposure to sub-lethal stresses. However, like other non-dividing cells, senescent cells remain metabolically active and carry out many functions that require unique transcriptional and translational demands and widespread changes in the intracellular and secreted proteomes. Understanding how protein synthesis and decay rates change during senescence can illuminate the underlying mechanisms of cellular senescence and find potential therapeutic avenues for diseases exacerbated by senescent cells. This paper describes a method for proteome-scale evaluation of protein half-lives in non-dividing cells using pulsed stable isotope labeling by amino acids in cell culture (pSILAC) in combination with mass spectrometry. pSILAC involves metabolic labeling of cells with stable heavy isotope-containing versions of amino acids. Coupled with modern mass spectrometry approaches, pSILAC enables the measurement of protein turnover of hundreds or thousands of proteins in complex mixtures. After metabolic labeling, the turnover dynamics of proteins can be determined based on the relative enrichment of heavy isotopes in peptides detected by mass spectrometry. In this protocol, a workflow is described for the generation of senescent fibroblast cultures and similarly arrested quiescent fibroblasts, as well as a simplified, single-time point pSILAC labeling time-course that maximizes coverage of anticipated protein turnover rates. Further, a pipeline is presented for the analysis of pSILAC mass spectrometry data and user-friendly calculation of protein degradation rates using spreadsheets. The application of this protocol can be extended beyond senescent cells to any non-dividing cultured cells such as neurons.

This protocol describes the workflow for metabolic labeling of senescent and non-dividing cells with pulsed SILAC, untargeted mass spectrometry analysis, and a streamlined calculation of protein half-lives.

Mounting evidence has shown that the accumulation of senescent cells in the central nervous system contributes to neurodegenerative disorders such as ...

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of applications. The cell surface proteome ("surfaceome") in human disease is of significant interest, as plasma membrane proteins are the primary target of most clinically approved therapeutics, as well as a key feature by which to diagnostically distinguish diseased cells from healthy tissues. However, focused characterization of membrane and surface proteins of the cell has remained challenging, primarily due to the complexity of cellular lysates, which mask proteins of interest by other high-abundance proteins. To overcome this technical barrier and accurately define the cell surface proteome of various cell types using mass spectrometry proteomics, it is necessary to enrich the cell lysate for cell surface proteins prior to analysis on the mass spectrometer. This paper presents a detailed workflow for labeling cell surface proteins from cancer cells, enriching these proteins out of the cell lysate, and subsequent sample preparation for mass spectrometry analysis.

Here, we describe a proteomics workflow for characterization of the cell surface proteome of various cell types. This workflow includes cell surface protein enrichment, subsequent sample preparation, analysis using an LC-MS/MS platform, and data processing with specialized software.

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of ...

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell&#39;s survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

The protocols describe high-performance liquid chromatography methods coupled to refractive index or mass spectrometric detection for studying metabolic reactions in complex lysate-based cell-free systems.

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the ...

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein &#945;-subunit) is described in detail.

This protocol describes how to perform absolute quantification assays of target proteins within complex biological samples using selected reaction monitoring. It was used to accurately quantify proteins of the mouse macrophage chemotaxis signaling pathway. Target peptide selection, assay development, and qualitative and quantitative assays are described in detail.

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This ...

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

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