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>

<ol>
	<li>Hodgman, C. E., Jewett, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-free+synthetic+biology:+thinking+outside+the+cell.">Cell-free synthetic biology: thinking outside the cell.</a> Metabolic Engineering. 14, 261-269 (2012).</li><li>Vilkhovoy, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sequence+specific+modeling+of+E.+coli+cell-free+protein+synthesis.">Sequence specific modeling of E. coli cell-free protein synthesis.</a> ACS Synthetic Biology. 7 (8), 1844-1857 (2018).</li><li>Vilkhovoy, M., Minot, M., Varner, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effective+dynamic+models+of+metabolic+networks.">Effective dynamic models of metabolic networks.</a> IEEE Life Sciences Letters. 2 (4), 51-54 (2016).</li><li>Horvath, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+genome+scale+sequence+specific+dynamic+model+of+cell-free+protein+synthesis+in+Escherichia+coli.">Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli.</a> bioRxiv. , 215012 (2017).</li><li>Dettmer, K., Aronov, P. A., Hammock, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry-based+metabolomics.">Mass spectrometry-based metabolomics.</a> Mass spectrometry reviews. 26 (1), 51-78 (2007).</li><li>Hajjaj, H., Blanc, P. J., Goma, G., Fran&#231;ois, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sampling+techniques+and+comparative+extraction+procedures+for+quantitative+determination+of+intra-+and+extracellular+metabolites+in+filamentous+fungi.">Sampling techniques and comparative extraction procedures for quantitative determination of intra- and extracellular metabolites in filamentous fungi.</a> FEMS Microbiology Letters. 164 (1), 195-200 (1998).</li><li>Ruijter, G. J. G., Visser, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+intermediary+metabolites+in+Aspergillus+niger.">Determination of intermediary metabolites in Aspergillus niger.</a> Journal of Microbiological Methods. 25 (3), 295-302 (1996).</li><li>Mailinger, W., Baltes, M., Theobald, U., Reuss, M., Rizzi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+analysis+of+metabolic+dynamics+in+Saccharomyces+cerevisiae:+I.+Experimental+observations.">In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations.</a> Biotechnology and Bioengineering. 55 (2), 305-316 (1997).</li><li>Dunn, W. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+appeal:+metabolite+identification+in+mass+spectrometry-focused+untargeted+metabolomics.">Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics.</a> Metabolomics. 9 (1), 44-66 (2013).</li><li>Huang, T., Toro, M., Lee, R., Hui, D. S., Edwards, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-functional+derivatization+of+amine,+hydroxyl,+and+carboxylate+groups+for+metabolomic+investigations+of+human+tissue+by+electrospray+ionization+mass+spectrometry.">Multi-functional derivatization of amine, hydroxyl, and carboxylate groups for metabolomic investigations of human tissue by electrospray ionization mass spectrometry.</a> Analyst. 143 (14), 3408-3414 (2018).</li><li>Huang, T., Armbruster, M. R., Coulton, J. B., Edwards, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemical+Tagging+in+Mass+Spectrometry+for+Systems+Biology.">Chemical Tagging in Mass Spectrometry for Systems Biology.</a> Analytical Chemistry. 91 (1), 109-125 (2019).</li><li>Yang, W. C., Sedlak, M., Regnier, F. E., Mosier, N., Ho, N., Adamec, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+quantification+of+metabolites+involved+in+central+carbon+and+energy+metabolism+using+reversed-phase+liquid+chromatography-mass+spectrometry+and+in+vitro+13C+labeling.">Simultaneous quantification of metabolites involved in central carbon and energy metabolism using reversed-phase liquid chromatography-mass spectrometry and in vitro 13C labeling.</a> Analytical Chemistry. 80 (24), 9508-9516 (2008).</li><li>Jannasch, A., Sedlak, M., Adamec, J., Metz, T. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+Pentose+Phosphate+Pathway+(PPP)+Metabolites+by+Liquid+Chromatography-Mass+Spectrometry+(LC-MS).">Quantification of Pentose Phosphate Pathway (PPP) Metabolites by Liquid Chromatography-Mass Spectrometry (LC-MS).</a> Metabolic Profiling. Methods in Molecular Biology 708 (Methods and Protocols). , 159-171 (2011).</li><li>Luo, B., Groenke, K., Takors, R., Wandrey, C., Oldiges, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+determination+of+multiple+intracellular+metabolites+in+glycolysis,+pentose+phosphate+pathway,+and+tricarboxylic+acid+cycle+by+liquid+chromatography-mass+spectrometry.">Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway, and tricarboxylic acid cycle by liquid chromatography-mass spectrometry.</a> Journal of Chromatography A. 1147 (2), 153-164 (2007).</li></ol>

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><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.

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

Universidad de Cartagena UDEC

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

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 ...

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of virion morphogenesis. Quantitative lipid droplet proteome analysis can be used to identify proteins that localize to or are displaced from lipid droplets under conditions such as virus infections. Here, we describe a protocol that has been successfully used to characterize the changes in the lipid droplet proteome following infection with HCV. We use Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and thus label the complete proteome of one population of cells with &#34;heavy&#34; amino acids to quantitate the proteins by mass spectrometry. For lipid droplet isolation, the two cell populations (i.e.&#160;HCV-infected/&#34;light&#34; amino acids and uninfected control/&#34;heavy&#34; amino acids) are mixed 1:1 and lysed mechanically in hypotonic buffer. After removing the nuclei and cell debris by low speed centrifugation, lipid droplet-associated proteins are enriched by two subsequent ultracentrifugation steps followed by three washing steps in isotonic buffer. The purity of the lipid droplet fractions is analyzed by western blotting with antibodies recognizing different subcellular compartments. Lipid droplet-associated proteins are then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie staining. After tryptic digest, the peptides are quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Using this method, we identified proteins recruited to lipid droplets upon HCV infection that might represent pro- or antiviral host factors. Our method can be applied to a variety of different cells and culture conditions, such as infection with pathogens, environmental stress, or drug treatment.

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of ...

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 ...

Chemistry

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. Herein we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

A protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM is a convenient method for multiplexed targeted proteomics analysis of small numbers of cells, including single cells. It capitalizes on using excessive exogenous protein as a carrier and high-specificity LC-SRM for targeted quantification.

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent ...

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic ...

Medicine

"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 ...

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in quantitation, are associated with a high cost, long lead time, and are fraught with drawbacks (e.g. heterophilic antibodies, autoantibody interference, 'hook-effect').1 An alternative technique is affinity enrichment of peptides coupled with quantitative mass spectrometry, commonly referred to as SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies).2 In this technique, affinity enrichment of peptides with stable isotope dilution and detection by selected/multiple reaction monitoring mass spectrometry (SRM/MRM-MS) provides quantitative measurement of peptides as surrogates for their respective proteins. SRM/MRM-MS is well established for accurate quantitation of small molecules 3, 4 and more recently has been adapted to measure the concentrations of proteins in plasma and cell lysates.5-7 To achieve quantitation of proteins, these larger molecules are digested to component peptides using an enzyme such as trypsin. One or more selected peptides whose sequence is unique to the target protein in that species (i.e. "proteotypic" peptides) are then enriched from the sample using anti-peptide antibodies and measured as quantitative stoichiometric surrogates for protein concentration in the sample. Hence, coupled to stable isotope dilution (SID) methods (i.e. a spiked-in stable isotope labeled peptide standard), SRM/MRM can be used to measure concentrations of proteotypic peptides as surrogates for quantification of proteins in complex biological matrices. The assays have several advantages compared to traditional immunoassays. The reagents are relatively less expensive to generate, the specificity for the analyte is excellent, the assays can be highly multiplexed, enrichment can be performed from neat plasma (no depletion required), and the technique is amenable to a wide array of proteins or modifications of interest.8-13 In this video we demonstrate the basic protocol as adapted to a magnetic bead platform.

Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution mass spectrometry (MRM-MS) to provide quantitative measurement of peptides as surrogates for their respective proteins. Here we describe the protocol using magnetic particles in a partially automated format.

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in ...

Biology

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 ...

Bioengineering

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

Summary

Explore More Videos

Chapters in this video

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

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

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

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

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

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

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

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

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification