We acknowledge support of NIH grants P30ES013508 and 5T32GM008076. We also thank Thermo Scientific for access to SIEVE 2.0 and Drs. Eugene Ciccimaro and Mark Sanders of Thermo Scientific for useful discussions.

1. Sample Extraction from Cells
<ol>
	<li>For a 10 cm plate of cells: collect 1.5 ml of lifted cell suspension in media into a pre-labeled 10 ml glass centrifuge tube. For adherent lines, cells should be lifted with gentle scraping in 1.5 ml of media kept on ice. Optional: If internal standards are used, add an appropriate aliquot at this step. 
	 Comment: Quenching of cellular metabolism is crucial for certain metabolites. For analysis of time-sensitive metabolites, procedures such as cold methanol extraction should be considered.17</li>
	<li>Add 6 ml of chloroform (CHCl3)/methanol (CH3OH) (2:1, v/v) to each of the 10 ml glass tubes containing the samples. Set the samples in a shaker or multi-vortexer on low speed for 30 min. After shaking, the samples are centrifuged with a low acceleration/deceleration setting at 1,935 x g for 10 min at 4 &deg;C to completely separate the phases. 
	Optional: To avoid using CHCl3, extractions can be conducted with dichloromethane (CH2Cl2).18,12</li>
	<li>Using a long stem Pasteur pipette, transfer the organic (bottom) layer to a new pre-labeled 10 ml glass centrifuge tube. Continue to 4.1.</li>
	<li>Transfer the upper aqueous later into a clean plastic 2 ml Eppendorf tube. Evaporate the sample under nitrogen gas. Continue to 2.6.2 for aqueous phase desalting or continue to 4.1 for direct analysis.</li>
</ol>
2. Sample Extraction from Serum
<ol>
	<li>Transfer 20 &mu;l of serum into a plastic 2 ml Eppendorf tube. Optional: If internal standards are used, add an appropriate aliquot at this step.</li>
	<li>Add 190 &mu;l of CH3OH and vortex for 10 sec.</li>
	<li>Add 380 &mu;l of CHCl3 and vortex for 10 sec.</li>
	<li>Add 120 &mu;l of H2O to induce phase separation. Vortex the samples for 10 sec and allow to equilibrate at RT for 10 min.</li>
	<li>Centrifuge the samples at 8,000 x g for 10 min at 4 &deg;C to separate the phases.</li>
	<li>Transfer the lower organic phase into a clean plastic 2 ml Eppendorf tube. Continue to 4.1.</li>
	<li>Transfer the upper aqueous later into a clean plastic 2 ml Eppendorf tube. Evaporate the sample under nitrogen gas.</li>
	<li>Re-suspend the sample in 200 &mu;l H2O. Acid or base reagents (0.1% Acetic acid, formic acid or 0.1% ammonium hydroxide) may be used to preferentially extract pH sensitive metabolites. Sonicate for 20 min on ice to re-suspend the sample completely.</li>
	<li>Activate the C18 spin columns with 500 &mu;l CH3OH.</li>
	<li>Equilibrate the spin column with two washes of 500 &mu;l H2O and then load the sample. If acid/base reagents are used maintain this concentration in the wash steps.</li>
	<li>Wash with 500 &mu;l H2O to desalt the sample. Again, if acid/base reagents are used maintain this concentration in the wash steps.</li>
	<li>Elute with two volumes of 200 &mu;l 80% CH3OH in H2O into a pre-labeled clean 2 ml Eppendorf tube. Again, if acid/base reagents are used maintain this concentration in the wash steps. Continue to 4.1.</li>
</ol>
3. Sample Extraction from Tissue
<ol>
	<li>Depending on the tissue, use between 10-100 mg of frozen tissue. Add the whole piece of tissue to a pre-labeled 2 ml low retention Eppendorf tube with 1 ml of PBS (1 M, pH 6.8) in an ice bath. Optional: If internal standards are used, add it into the buffer, before adding the tissue.</li>
	<li>Homogenize the tissue in ice with an electric tissue grinder for 30 sec in each Eppendorf. Between samples, clean the tissue grinder in two separate tubes containing CH3OH and H2O, respectively.</li>
	<li>Transfer the tissue homogenate with a plastic pipette into a pre-labeled 10 ml glass centrifuge tube. After the transfer, rinse each Eppendorf tube 2x with 1 ml of CH3OH and add the washes to the tissue homogenate in the glass tubes.</li>
	<li>Add 4 ml of CHCl3 to each of the 10 ml glass tubes containing the tissue homogenate and CH3OH washes. Set the samples in a shaker or multi-vortexer on low speed for 30 min.</li>
	<li>After shaking, centrifuge the samples with a low acceleration/deceleration setting at 1,935 x g for 10 min to completely separate the phases. Optional: To avoid using chloroform, extractions have been developed with CH2Cl2.</li>
	<li>Using a long stem Pasteur pipette, transfer the organic (bottom) layer to a new pre-labeled 10 ml glass centrifuge tube. Continue to 4.1.</li>
	<li>Using a long stem Pasteur pipette, transfer the aqueous layer to a new pre-labeled 10 ml glass centrifuge tube. Go to 2.6.2 for desalting or continue to 4.1.</li>
</ol>
4. Re-suspension and Filtration of Samples for UPLC
<ol>
	<li>Evaporate the samples under nitrogen gas.</li>
	<li>Reconstitute dried down samples in an appropriate volume of the starting solution for the desired UPLC method (see Table 1). 50-100 &mu;l is usually a desirable re-suspension volume. Gently vortex and/or pipette the sample up and down to aid in dissolving the analytes.</li>
	<li>Transfer the sample into a 0.22 &mu;m nylon tube filter and spin at up to 14,000 x g until the sample has completely passed through the filter (~5 min). Make sure that there are no visible precipitates remaining in the sample.</li>
	<li>Transfer the supernatant of the filtered sample into a pre-labeled UPLC vial with insert. Cap vial, then flick the vial to remove any bubbles from the bottom of the sample vial. Place the sample in the chilled autosampler (4-5 &deg;C).</li>
</ol>
5. UPLC Setup
<ol>
	<li>Using the control software for the liquid chromatograph, create a run for the organic sample based off of the conditions listed in Table 1. Then create a method for the aqueous phase off the conditions listed in Table 1. Optional: If a known set of target analytes are of high interest, optimize the UPLC conditions using the heavy labeled analog of these compounds, and generate a method using these conditions.</li>
	<li>Allow the UPLC to adequately equilibrate. This should include a full priming of solvents before attaching a column, and following the manufacturer's directions for equilibration volume of a new column (usually 3-5 column volumes). Maintain a log of starting back pressures to help diagnose future problems.</li>
</ol>
6. Mass Spectrometer Setup
<ol>
	<li>Using the control software for the high resolution mass spectrometer, create a positive mode method using the conditions listed in Table 2. Then using the same conditions, create a negative mode method. Optional: If a known set of target analytes are of high interest, optimize source and optics conditions using the heavy labeled analog of these compounds, and generate a method using these conditions.</li>
	<li>Clean and calibrate the instrument, establish a stable spray into the spectrometer, and allow time for the calibration solution to adequately dissipate from the source and optics. Acceptable stability of spray should give a 3-5% RSD of intensity over at least 100 scans with injection of calibration solution at the same flow rate as the LC method being used.</li>
	<li>Set up the sequence for all the samples, set appropriate injection volume, and run a blank before the first sample. If carryover or matrix effects between samples are suspected, run adequate blanks/washes. Samples should be setup in a randomized manner using a random number generator and a key to avoid any batch effects introduced by analysis. 
	Comment: Quality control samples including stable isotope standards or analytical standards of likely targets can be extremely valuable in troubleshooting and assuring reliable, reproducible, and valid results. A technical replicate of a standard only sample followed by a solvent blank can be used to accesses deviation of signal intensity and retention time, as well as carryover into the blank run.</li>
	<li>Begin sequence and monitor periodically for problems including pressure fluctuations, or loss of signal intensity across the run. If severe problems are detected, stop the run, and repeat from 6.2.</li>
</ol>
7. Differential Analysis
<ol>
	<li>Two workflows are presented for this protocol. The first is through SIEVE 2.0, proprietary label-free differential analysis software sold by Thermo Fisher. The second workflow is through XCMS Online, a free platform through Scripps Research Institute.11 Before beginning differential analysis, manually check the TICs or look at filtered chromatograms for any known compound in the sample to ensure reproducibility of runs and injection/UPLC/spray stability throughout all the samples.</li>
	<li>SIEVE
	<ol>
		<li>Transfer the .raw data files from the mass spectrometer to the hard drive of the workstation where SIEVE is installed. (Note: running SIEVE with data stored on a networked or USB port connected drive is not advisable as it can limit the speed of analysis.)</li>
		<li>Open SIEVE, and begin a new experiment.</li>
		<li>Load the appropriate files into SIEVE.</li>
		<li>Assign comparison groups and select a single reference file to allow SIEVE to generate the parameters for analysis.</li>
		<li>Follow the prompts and adjust any parameters per requirements of any individual experiment. It is often advisable to start with stringent parameters and then go back and loosen the parameters as more generous settings can lengthen analysis time.</li>
		<li>Load the correct adduct list for positive or negative mode in the parameters.</li>
	</ol>
	</li>
	<li>XCMS Online
	<ol>
		<li>Convert files into an acceptable format for XCMS Online. In our case, we convert to mzXML format.19</li>
		<li>Open XCMS Online, and Create Job.</li>
		<li>Upload and name datasets. Only two datasets (e.g. Control vs Treatment) can be uploaded as XCMS stand-alone is limited to head-to-head comparisons.</li>
		<li>Select desired settings from the drop-down list. Pre-populated parameters are available for different instrumentation setups, and these can be further modified for experimental needs. In our case, we use the HPLC-Orbi2 settings.</li>
		<li>Submit and confirm job.</li>
	</ol>
	</li>
	<li>Data Analysis
	<ol>
		<li>For SIEVE and XCMS a list of frames and features, respectively, will be populated once the analysis is finished. Ensure that the LC alignment overlays any landmark peaks. If any of the unaligned LC runs show large deviation in landmark peaks this may indicate a problem with the sample. 
		Optional: For SIEVE, if internal standards are used, locate the corresponding peak group and normalize to the selected frame. For XCMS On-line, normalization can be done in a spreadsheet after export.</li>
		<li>Plot the data by coefficient of variation (CV) within a like sample population (e.g. control samples). Any large CV values may indicate a problem with one or more sample. Sort the list based on desired traits (e.g. p-value&lt;0.05, Fold Change&gt;1.5, low standard deviation within a group, etc).</li>
		<li>Examine each peak for appropriate peak alignment across groups, peak integration, Gaussian peak shape, signal intensity, isotopes, and adduct assignment.</li>
		<li>Export data as desired for further analysis or to generate targeted mass lists for confirmation and MSn experiments.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Pluskal, T., Nakamura, T., Villar-Briones, A., Yanagida, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolic+profiling+of+the+fission+yeast+S.+pombe:+quantification+of+compounds+under+different+temperatures+and+genetic+perturbation.">Metabolic profiling of the fission yeast S. pombe: quantification of compounds under different temperatures and genetic perturbation.</a> Mol. Biosyst. 6 (1), 182-198 (2010).</li><li>Makarov, A., Denisov, E., 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=Performance+Evaluation+of+a+Hybrid+Linear+Ion+Trap/Orbitrap+Mass+Spectrometer.">Performance Evaluation of a Hybrid Linear Ion Trap/Orbitrap Mass Spectrometer.</a> Analytical Chemistry. 78 (7), 2113-2120 (2006).</li><li>Timischl, B., Dettmer, K., Kaspar, H., Thieme, M., Oefner, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+a+quantitative,+validated+capillary+electrophoresis-time+of+flight-mass+spectrometry+method+with+integrated+high-confidence+analyte+identification+for+metabolomics.">Development of a quantitative, validated capillary electrophoresis-time of flight-mass spectrometry method with integrated high-confidence analyte identification for metabolomics.</a> Electrophoresis. 29 (10), 2203-2214 (2008).</li><li>Katajamaa, M., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+processing+for+mass+spectrometry-based+metabolomics.">Data processing for mass spectrometry-based metabolomics.</a> J. Chromatogr. A. 1158 (1-2), 318-328 (2007).</li><li>Katajamaa, M., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Processing+methods+for+differential+analysis+of+LC/MS+profile+data.">Processing methods for differential analysis of LC/MS profile data.</a> BMC Bioinformatics. 6, 179 (2005).</li><li>Wilson, I. D., Nicholson, J. K., 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=High+resolution+ultra+performance+liquid+chromatography+coupled+to+q-TOF+mass+spectrometry+as+a+tool+for+differential+metabolic+pathway+profiling+in+functional+genomic+studies.">High resolution ultra performance liquid chromatography coupled to q-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies.</a> Journal of Proteome Research. 4 (2), 591-598 (2005).</li><li>Benton, H. P., Wong, D. M., Trauger, S. A., Siuzdak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XCMS2:+processing+tandem+mass+spectrometry+data+for+metabolite+identification+and+structural+characterization.">XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization.</a> Anal. Chem. 80 (16), 6382-6389 (2008).</li><li>Katajamaa, M., Miettinen, J., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MZmine:+toolbox+for+processing+and+visualization+of+mass+spectrometry+based+molecular+profile+data.">MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.</a> Bioinformatics. 22 (5), 634-636 (2006).</li><li>Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MZmine+2:+Modular+framework+for+processing,+visualizing,+and+analyzing+mass+spectrometry-based+molecular+profile+data.">MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data.</a> BMC Bioinformatics. 11 (1), 395 (2010).</li><li>Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., Siuzdak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XCMS:+Processing+Mass+Spectrometry+Data+for+Metabolite+Profiling+Using+Nonlinear+Peak+Alignment,+Matching,+and+Identification.">XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification.</a> Analytical Chemistry. 78 (3), 779-787 (2006).</li><li>Tautenhahn, R., Patti, G. J., Rinehart, D., Siuzdak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XCMS+Online:+A+Web-Based+Platform+to+Process+Untargeted+Metabolomic+Data.">XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data.</a> Analytical Chemistry. 84 (11), 5035-5039 (2012).</li><li>Gelhaus, S. L., Mesaros, A. C., Blair, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+Lipid+Extraction+for+Targeted+Stable+Isotope+Dilution+Liquid+Chromatography-Mass+Spectrometry+Analysis.">Cellular Lipid Extraction for Targeted Stable Isotope Dilution Liquid Chromatography-Mass Spectrometry Analysis.</a> J. Vis. Exp. (57), e3399 (2011).</li><li>Want, E. J., Wilson, I. D., 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=Global+metabolic+profiling+procedures+for+urine+using+UPLCG&#199;&#244;MS.">Global metabolic profiling procedures for urine using UPLCG&#199;&#244;MS.</a> Nature Protocols. 5 (6), 1005-1018 (2010).</li><li>Sellick, C. A., Hansen, R., Stephens, G. M., Goodacre, R., Dickson, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolite+extraction+from+suspension-cultured+mammalian+cells+for+global+metabolite+profiling.">Metabolite extraction from suspension-cultured mammalian cells for global metabolite profiling.</a> Nature Protocols. 6 (8), 1241-1249 (2011).</li><li>Dunn, W. B., Broadhurst, D., 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=Procedures+for+large-scale+metabolic+profiling+of+serum+and+plasma+using+gas+chromatography+and+liquid+chromatography+coupled+to+mass+spectrometry.">Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.</a> Nature protocols. 6 (7), 1060-1083 (2011).</li><li>Masson, P., Alves, A. C., Ebbels, T. M. D., Nicholson, J. K., Want, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+and+evaluation+of+metabolite+extraction+protocols+for+untargeted+metabolic+profiling+of+liver+samples+by+UPLC-MS.">Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS.</a> Analytical Chemistry. 82 (18), 7779-7786 (2010).</li><li>Shaham, O., Slate, N. G., 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=A+plasma+signature+of+human+mitochondrial+disease+revealed+through+metabolic+profiling+of+spent+media+from+cultured+muscle+cells.">A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells.</a> Proceedings of the National Academy of Sciences. 107 (4), 1571-1575 (2010).</li><li>Cequier-Sa&#252;nchez, E., Rodri&#252;guez, C., Ravelo, A. G., Za&#252;rate, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dichloromethane+as+a+Solvent+for+Lipid+Extraction+and+Assessment+of+Lipid+Classes+and+Fatty+Acids+from+Samples+of+Different+Natures.">Dichloromethane as a Solvent for Lipid Extraction and Assessment of Lipid Classes and Fatty Acids from Samples of Different Natures.</a> Journal of Agricultural and Food Chemistry. 56 (12), 4297-4303 (2008).</li><li>Keller, A., Eng, J., Zhang, N., Li, X. J., Aebersold, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+uniform+proteomics+MS/MS+analysis+platform+utilizing+open+XML+file+formats.">A uniform proteomics MS/MS analysis platform utilizing open XML file formats.</a> Mol. Syst. Biol. 1, (2005).</li><li>Cleveland, W. S., Devlin, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Locally+weighted+regression+-+an+approach+to+regression-analysis+by+local+fitting.">Locally weighted regression - an approach to regression-analysis by local fitting.</a> J. Am. Stat. Assoc. 83 (403), 596-610 (1988).</li><li>Lange, E., Tautenhahn, R., Neumann, S., Gropl, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Critical+assessment+of+alignment+procedures+for+LC-MS+proteomics+and+metabolomics+measurements.">Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.</a> BMC Bioinformatics. 9 (1), 375 (2008).</li><li>Tautenhahn, R., Bottcher, C., Neumann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+sensitive+feature+detection+for+high+resolution+LC/MS.">Highly sensitive feature detection for high resolution LC/MS.</a> BMC Bioinformatics. 9 (1), 504 (2008).</li><li>Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., Neumann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CAMERA:+An+Integrated+Strategy+for+Compound+Spectra+Extraction+and+Annotation+of+Liquid+Chromatography/Mass+Spectrometry+Data+Sets.">CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets.</a> Analytical Chemistry. 84 (1), 283-289 (2012).</li><li>Kanehisa, M., Goto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG:+kyoto+encyclopedia+of+genes+and+genomes.">KEGG: kyoto encyclopedia of genes and genomes.</a> Nucleic Acids Res. 28 (1), 27-30 (2000).</li><li>Smith, C. A., O'Maille, G., 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=METLIN:+a+metabolite+mass+spectral+database.">METLIN: a metabolite mass spectral database.</a> Ther. Drug Monit. 27 (6), 747-751 (2005).</li><li>Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., Bryant, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PubChem:+a+public+information+system+for+analyzing+bioactivities+of+small+molecules.">PubChem: a public information system for analyzing bioactivities of small molecules.</a> Nucleic Acids Res. 37 Web Server, W623-W633 (2009).</li><li>Wishart, D. S., Knox, C., 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=HMDB:+a+knowledgebase+for+the+human+metabolome.">HMDB: a knowledgebase for the human metabolome.</a> Nucleic Acids Res. 37 Database, D603-D610 (2009).</li><li>Bligh, E. G., Dyer, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+method+of+total+lipid+extraction+and+purification.">A rapid method of total lipid extraction and purification.</a> Canadian Journal of Biochemistry and Physiology. 37 (8), 911-917 (1959).</li><li>Folch, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+method+for+the+isolation+and+purification+of+total+lipids+from+animal+tissues.">A simple method for the isolation and purification of total lipids from animal tissues.</a> J. Biol. Chem. 226, 497-509 (1957).</li><li>Avery, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+characterization+of+differential+ion+suppression+on+liquid+chromatography/atmospheric+pressure+ionization+mass+spectrometric+bioanalytical+methods.">Quantitative characterization of differential ion suppression on liquid chromatography/atmospheric pressure ionization mass spectrometric bioanalytical methods.</a> Rapid Communications in Mass Spectrometry. 17 (3), 197-201 (2003).</li></ol>

Authors have nothing to disclose.

Untargeted metabolomics offers a powerful tool for investigating endogenous or xenobiotic biotransformations, or capturing a metabolic profile from a sample of interest. The output of the technique scales with the resolution and sensitivity of the technology used to separate and analyze the sample, the ability to deal with the large datasets generated, and the ability to mine the dataset for useful information (e.g. accurate mass database searching). Recently, this has been facilitated by advances in high resolu...

Due to recent technological advances in the field of HRMS, untargeted, hypothesis-generating metabolomics approaches have become a feasible approach to analysis of complex samples.1 Mass spectrometers capable of 100,000 resolution facilitating routine low part per million (ppm) mass accuracy have become widely available from multiple vendors.2,3 This mass accuracy allows greater specificity and confidence in a preliminary assignment of analyte identity, isotopic pattern recognition, and adduct identification.4 When coupled with an appropriate extraction procedure and high-performance LC or UPLC, complex mixtures can be analyzed with additional specificity derived from retention time data.5 UPLC possesses greater chromatographic efficiency and allows greater sensitivity, resolution and analysis time making a greater coverage of the metabolome possible.6 The resulting large datasets can be integrated into any of multiple differential analysis software and mined for useful patterns or individual analytes of interest.7,8,9,10,11 Putative hits can be initially identified using a combination of peak detection algorithms, accurate mass based chemical formula prediction, fragmentation prediction, and chemical database searching. This approach allows prioritization of targets for time-consuming complete structural identification or for development of more sensitive and more specific stable isotope dilution UPLC/selected or multiple reaction monitoring/MS studies that are the current gold standard methods for quantification.12
The varying nature of biological samples has led to optimization of extraction protocols for urine13, cells14, serum15, or tissue16. This protocol features extractions for cells, serum, and tissue. Where appropriate, comments and additional references have been included for modifications of the procedure to address inclusion of stable isotopes, or for inclusion of especially unstable metabolites.

<table border="1">
	<tbody>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Reagent</td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline</td>
			<td>Mediatech</td>
			<td>21-031-CM</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Water (H2O)</td>
			<td>Fisher Scientific</td>
			<td>W7-4</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>Acetonitrile (CH3CN)</td>
			<td>Fisher Scientific</td>
			<td>A996-4</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>Methanol (CH3OH)</td>
			<td>Fisher Scientific</td>
			<td>A454-4</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>Fisher Scientific</td>
			<td>A464-4</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>Chloroform (CH3Cl)</td>
			<td>Sigma-Aldrich</td>
			<td>366927</td>
			<td>Hazard</td>
		</tr>
		<tr>
			<td>Dichloromethane (CH2Cl2)</td>
			<td>Acros Organics</td>
			<td>61030-1000</td>
			<td>To replace chloroform</td>
		</tr>
		<tr>
			<td>Diethyl Ether</td>
			<td>Sigma-Aldrich</td>
			<td>346136</td>
			<td>To replace chloroform</td>
		</tr>
		<tr>
			<td>Formic Acid (FA)</td>
			<td>Fisher Scientific</td>
			<td>&nbsp;</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>NH4OH</td>
			<td>Fisher Scientific</td>
			<td>A470-250</td>
			<td>(optima)</td>
		</tr>
		<tr>
			<td>Ammonium formate (HCOONH4)</td>
			<td>Sigma-Aldrich</td>
			<td>78314</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>MicroSpin C18 Columns</td>
			<td>Nest Group Inc</td>
			<td>SS18V</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Pasteur Pipettes</td>
			<td>Fisher Scientific</td>
			<td>13-678-200</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>10 ml Glass Centrifuge Tubes</td>
			<td>Kimble Chase</td>
			<td>73785-10</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>10 ml Plastic Centrifuge Tubes</td>
			<td>CellTreat</td>
			<td>CLS-4301-015</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>LC Vials (glass)</td>
			<td>Waters</td>
			<td>60000751CV</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>LC Inserts (glass)</td>
			<td>Waters</td>
			<td>WAT094171</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>LC Vials (plastic)</td>
			<td>Waters</td>
			<td>186002640</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>0.22 &mu;m Filters</td>
			<td>Corning</td>
			<td>8169</td>
			<td>nylon</td>
		</tr>
		<tr>
			<td>2 ml Eppendorf Tubes</td>
			<td>BioExpress</td>
			<td>C-3229-1</td>
			<td>Low Retention</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Equipment</td>
		</tr>
		<tr>
			<td>High Resolution Mass Spectrometer</td>
			<td>Thermo Scientific</td>
			<td>LTQ XL-Orbitrap</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>HPLC/UPLC</td>
			<td>Waters</td>
			<td>nanoACQUITY UPLC</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Source</td>
			<td>Michrom</td>
			<td>Thermo Advance Source</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Differential Analysis Software</td>
			<td>Thermo Scientific</td>
			<td>SIEVE 2.0</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>nanoACQUITY C18 BEH130</td>
			<td>Waters</td>
			<td>186003546</td>
			<td>1.7 &mu;m particle size, 150 mm x 100 &mu;m</td>
		</tr>
		<tr>
			<td>Acentis Express C8</td>
			<td>Sigma-Aldrich</td>
			<td>54262</td>
			<td>2.7 &mu;m particle size, 15 cm x 200 &mu;m</td>
		</tr>
	</tbody>
</table>

untargeted metabolomics from biological sources using ultraperformance liquid chromatography-high resolution mass spectrometry (uplc-hrms)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

The results presented show selected data from a 6-hr treatment of SH-SY5Y glioblastoma cells with the pesticide and mitochondrial complex I inhibitor rotenone. For brevity, only the organic phase positive mode data is presented. The samples were processed and analyzed as described above (Figure 1, Table 1, Table 2) and loaded onto two differential analysis platforms for label-free quantification, SIEVE and XCMS online. Although a large number of hits (Figure 2, Figure 3)...

Watch this Scientific Journal Video about Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS at JoVE.com

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

untargeted-metabolomics-from-biological-sources-using

Microsoft Bing

Research

JoVE Journal

Chemistry

22.2K Views.  University of Pennsylvania. Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Video: Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Analyzing Protein Dynamics Using Hydrogen Exchange Mass Spectrometry

All cellular processes depend on the functionality of proteins. Although the functionality of a given protein is the direct consequence of its unique amino acid sequence, it is only realized by the folding of the polypeptide chain into a single defined three-dimensional arrangement or more commonly into an ensemble of interconverting conformations. Investigating the connection between protein conformation and its function is therefore essential for a complete understanding of how proteins are able to fulfill their great variety of tasks. One possibility to study conformational changes a protein undergoes while progressing through its functional cycle is hydrogen-1H/2H-exchange in combination with high-resolution mass spectrometry (HX-MS). HX-MS is a versatile and robust method that adds a new dimension to structural information obtained by e.g. crystallography. It is used to study protein folding and unfolding, binding of small molecule ligands, protein-protein interactions, conformational changes linked to enzyme catalysis, and allostery. In addition, HX-MS is often used when the amount of protein is very limited or crystallization of the protein is not feasible. Here we provide a general protocol for studying protein dynamics with HX-MS and describe as an example how to reveal the interaction interface of two proteins in a complex. &#160;&#160;

Protein conformation and dynamics are key to understanding the relationship between protein structure and function. Hydrogen exchange coupled with high-resolution mass spectrometry is a versatile method to study the conformational dynamics of proteins as well as characterizing protein-ligand and protein-protein interactions, including contact interfaces and allosteric effects.

All cellular processes depend on the functionality of proteins. Although the functionality of a given protein is the direct consequence of its unique ...

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

We demonstrate that direct analysis in real time-high resolution mass spectrometry can be used to produce mass spectral profiles of botanical material, and that these chemical fingerprints can be used for plant species identification. The mass spectral data can be acquired rapidly and in a high throughput manner without the need for sample extraction, derivatization or pH adjustment steps. The use of this technique bypasses challenges presented by more conventional techniques including lengthy chromatography analysis times and resource intensive methods. The high throughput capabilities of the direct analysis in real time-high resolution mass spectrometry protocol, coupled with multivariate statistical analysis processing of the data, provide not only class characterization of plants, but also yield species and varietal information. Here, the technique is demonstrated with two psychoactive plant products, Mitragyna speciosa (Kratom) and Datura (Jimsonweed), which were subjected to direct analysis in real time-high resolution mass spectrometry followed by statistical analysis processing of the mass spectral data. The application of these tools in tandem enabled the plant materials to be rapidly identified at the level of variety and species.

A method for species identification of botanical material by direct analysis in real time-high resolution mass spectrometry and multivariate statistical analysis is presented.

We demonstrate that direct analysis in real time-high resolution mass spectrometry can be used to produce mass spectral profiles of botanical material, ...

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Exhaled volatile organic compounds (VOCs) have aroused considerable interest, since they can serve as biomarkers for disease diagnosis and environmental exposure in a non-invasive manner. In this work, we present a protocol to characterize the exhaled VOCs in real time by using secondary nanoelectrospray ionization coupled to high resolution mass spectrometry (Sec-nanoESI-HRMS). The homemade Sec-nanoESI source was readily set up based on a commercial nanoESI source. Hundreds of peaks were observed in the background-subtracted mass spectra of exhaled breath, and the mass accuracy values are -4.0-13.5 ppm and -20.3-1.3 ppm in the positive and negative ion detection modes, respectively. The peaks were assigned with accurate elemental composition according to the accurate mass and isotopic pattern. Less than 30 s is used for one exhalation measurement, and it takes approximately 7 min for six replicated measurements.

A protocol for characterizing chemical composition of exhaled breath in real time by using secondary nanoelectrospray ionization coupled to high resolution mass spectrometry is demonstrated.

Exhaled volatile organic compounds (VOCs) have aroused considerable interest, since they can serve as biomarkers for disease diagnosis and environmental ...

Microprobe Capillary Electrophoresis Mass Spectrometry for Single-cell Metabolomics in Live Frog (Xenopus laevis) Embryos

The quantification of small molecules in single cells raises new potentials for better understanding the basic processes that underlie embryonic development. To enable single-cell investigations directly in live embryos, new analytical approaches are needed, particularly those that are sensitive, selective, quantitative, robust, and scalable to different cell sizes. Here, we present a protocol that enables the in situ analysis of metabolism in single cells in freely developing embryos of the South African clawed frog (Xenopus laevis), a powerful model in cell and developmental biology. This approach uses a capillary microprobe to aspirate a defined portion from single identified cells in the embryo, leaving neighboring cells intact for subsequent analysis. The collected cell content is analyzed by a microscale capillary electrophoresis electrospray ionization (CE-ESI) interface coupled to a high-resolution tandem mass spectrometer. This approach is scalable to various cell sizes and compatible with the complex three-dimensional structure of the developing embryo. As an example, we demonstrate that microprobe single-cell CE-ESI-MS enables the elucidation of metabolic cell heterogeneity that unfolds as a progenitor cell gives rise to descendants during development of the embryo. Besides cell and developmental biology, the single-cell analysis protocols described here are amenable to other cell sizes, cell types, or animal models.

Microprobe Capillary Electrophoresis Mass Spectrometry for Single-cell Metabolomics in Live Frog Xenopus laevis Embryos

We describe steps that enable fast in situ sampling of a small portion of an individual cell with high precision and minimal invasion using capillary-based micro-sampling, to facilitate chemical characterization of a snapshot of metabolic activity in live embryos using a custom-built single cell capillary electrophoresis and mass spectrometry platform.

The quantification of small molecules in single cells raises new potentials for better understanding the basic processes that underlie embryonic ...

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug crisis in public health. Conventional urine drug testing based on a two-tier immunoassay screen followed by a gas chromatography-tandem mass spectrometry (GC-MS/MS) or liquid chromatography-tandem mass spectrometry (LC-MS/MS) method are expensive and prone to bias while being limited to targeted panels of known drugs of abuse (DoA). Herein, we outline an improved method for drug surveillance that allows for the resolution and detection of an expanded panel of DoA and their metabolites when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). Multiplexed separations of ten urine samples with a quality control by CE (&lt; 3 min/sample) in conjunction with full-scan data acquisition using a time-of-flight mass spectrometer (TOF-MS) under positive ion mode detection allows for the identification and quantification of DoA above recommended cut-off levels. An excellent resolution of drug isomers and isobars, including background interferences, are achieved when using MSI-CE-MS with an electrokinetic spacer between sample segments, where accurate mass/molecular formula together with the comigration of a matching deuterated internal standard and the detection of one or more bio-transformed metabolites facilitate DoA identification over a wider detection window. Additionally, urine samples can be analyzed directly without enzyme deconjugation for the rapid screening without complicated sample workup. MSI-CE-MS enables the surveillance of a broad spectrum of DoA that is required for the treatment monitoring of high-risk patients, including confirming prescribed drug adherence, revealing illicit drug use/substitution, and evaluating optimal dosage regimes as required for new advances in precision medicine.

Here we describe a high-throughput method for comprehensive drug surveillance that allows for the improved resolution and detection of large panels of drugs of abuse and their metabolites, with quality control based on multisegment injection-capillary electrophoresis-mass spectrometry.

New analytical methods are urgently needed to enable high-throughput, yet comprehensive drug screening, given an alarming opioid and prescription drug ...

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Corrosion developed at the paint and aluminum (Al) metal-paint interface of an aluminum alloy is analyzed using time-of-flight secondary ion mass spectrometry (ToF-SIMS), illustrating that SIMS is a suitable technique to study the chemical distribution at a metal-paint interface. The painted Al alloy coupons are immersed in a salt solution or exposed to air only. SIMS provides chemical mapping and 2D molecular imaging of the interface, allowing direct visualization of the morphology of the corrosion products formed at the metal-paint interface and mapping of the chemical after corrosion occurs. The experimental procedure of this method is presented to provide technical details to facilitate similar research and highlight pitfalls that may be encountered during such experiments.

Time-of-flight secondary ion mass spectrometry is applied to demonstrate the chemical mapping and corrosion morphology at the metal-paint interface of an aluminum alloy after being exposed to a salt solution compared with a specimen exposed to air.

Corrosion developed at the paint and aluminum (Al) metal-paint interface of an aluminum alloy is analyzed using time-of-flight secondary ion mass ...

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

The presented protocol combines excellent detection limits (1 ppm to 1 ppb) using secondary ion mass spectrometry (SIMS) with reasonable spatial resolution (~1 &#181;m). Furthermore, it describes how to obtain realistic three-dimensional (3D) distributions of segregated impurities/dopants in solid state materials. Direct 3D depth profile reconstruction is often difficult to achieve due to SIMS-related measurement artifacts. Presented here is a method to identify and solve this challenge. Three major issues are discussed, including the i) nonuniformity of the detector being compensated by flat-field correction; ii) vacuum background contribution (parasitic oxygen counts from residual gases present in the analysis chamber) being estimated and subtracted; and iii)&#160;performance of all steps within a stable timespan of the primary ion source. Wet chemical etching is used to reveal the position and types of dislocation in a material, then the SIMS result is superimposed on images obtained via scanning electron microscopy (SEM). Thus, the position of agglomerated impurities can be related to the position of certain defects. The method is fast and does not require sophisticated sample preparation stage; however, it requires a high-quality, stable ion source, and the entire measurement must be performed quickly to avoid deterioration of the primary beam parameters.

The presented method describes how to identify and solve measurement artifacts related to secondary ion mass spectrometry as well as obtain realistic 3D distributions of impurities/dopants in solid state materials.

The presented protocol combines excellent detection limits (1 ppm to 1 ppb) using secondary ion mass spectrometry (SIMS) with reasonable spatial ...

Characterizing RNA Modifications in Single Neurons Using Mass Spectrometry

Post-transcriptional modifications (PTMs) of RNA represent an understudied mechanism involved in the regulation of translation in the central nervous system (CNS). Recent evidence has linked specific neuronal RNA modifications to learning and memory paradigms. Unfortunately, conventional methods for the detection of these epitranscriptomic features are only capable of characterizing highly abundant RNA modifications in bulk tissues, precluding the assessment of unique PTM profiles that may exist for individual neurons within the activated behavioral circuits. In this protocol, an approach is described&#8212;single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS)&#8212;to simultaneously detect and quantify numerous modified ribonucleosides in single neurons. The approach is validated using individual neurons of the marine mollusk, Aplysia californica, beginning with surgical isolation and enzymatic treatment of major CNS ganglia to expose neuron cell bodies, followed by manual single-neuron isolation using sharp needles and a micropipette. Next, mechanical and thermal treatment of the sample in a small volume of buffer is done to liberate RNA from an individual cell for subsequent RNA digestion. Modified nucleosides are then identified and quantified using an optimized liquid chromatography-mass spectrometry method. SNRMA-MS is employed to establish RNA modification patterns for single, identified neurons from A. californica that have known morphologies and functions. Examples of qualitative and quantitative SNRMA-MS are presented that highlight the heterogeneous distribution of RNA modifications across individual neurons in neuronal networks.

Post-transcriptional modifications of RNA represent an understudied layer of translation regulation that has recently been linked to central nervous system plasticity. Here, sample preparation and liquid chromatography-tandem mass spectrometry approach is described for simultaneous characterization of numerous RNA modifications in single neurons.

Post-transcriptional modifications (PTMs) of RNA represent an understudied mechanism involved in the regulation of translation in the central nervous ...

Extraction of Non-Protein Amino Acids from Cyanobacteria for Liquid Chromatography-Tandem Mass Spectrometry Analysis

Non-protein amino acids (NPAAs) are a large class of amino acids (AAs) that are not genetically encoded for translation into proteins. The analysis of NPAAs can provide crucial information about cellular uptake and/or function, metabolic pathways, and potential toxicity. &#946;-methylamino-L-alanine (BMAA) is a neurotoxic NPAA produced by various algae species and is associated with an increased risk for neurodegenerative diseases, which has led to significant research interest. There are numerous ways to extract AAs for analysis, with liquid chromatography-tandem mass spectrometry being the most common, requiring protein precipitation followed by acid hydrolysis of the protein pellet. Studies on the presence of BMAA in algal species provide contradictory results, with the use of unvalidated sample preparation/extraction and analysis a primary cause. Like most NPAAs, protein precipitation in 10% aqueous TCA and hydrolysis with fuming HCl is the most appropriate form of extraction for BMAA and its isomers aminoethylglycine (AEG) and 2,4-diaminobutyric acid (2,4-DAB). The present protocol describes the steps in a validated NPAA extraction method commonly used in research and teaching laboratories.

The present protocol describes the extraction of non-protein amino acids from biological matrices via trichloroacetic acid (TCA) protein precipitation and acid hydrolysis before analysis using liquid chromatography-tandem mass spectrometry.

Non-protein amino acids (NPAAs) are a large class of amino acids (AAs) that are not genetically encoded for translation into proteins. The analysis of ...