M.S. is supported by a full PhD scholarship from GERLS-DAAD program. We would like to thank Dr. Andrew Wiszniewski for proof reading and commenting on the manuscript. We are very grateful to all the members of the Giavalisco lab at Max-Planck Institute of Molecular Plant Physiology, Golm, Germany for their help.

Caution: Methanol (MeOH) and methyl tert-butyl ether (MTBE), used during extraction, are flammable, and can have, on prolonged exposure and/or contact, respiratory, eye or skin irritation. Please handle them carefully only in a fume hood and use the appropriate safety procedures during the extraction (lab coat, safety glasses, gloves, etc.). Liquid nitrogen and dry ice, used in several steps of this protocol, can cause severe burns by prolonged skin contact. Please handle them carefully by wearing protective gloves and glasses. Users can use different chemicals, reagents or internal standards for sample analysis, some of which may be toxic. Please examine the relevant chemical safety data sheets for all material used.
1. Collection and Harvesting of Biological Samples
<ol>
	<li>Prepare labelled harvesting tubes. 
	NOTE: Here, harvest biological samples in labelled, 2 mL, round-bottom, safe lock microcentrifuge tubes containing two 5 mm diameter, metal balls for the tissue homogenizer.</li>
	<li>Prepare a filled liquid nitrogen dewar.</li>
	<li>Harvest the biological sample and snap freeze the tissue in liquid nitrogen. Perform this step as quickly as possible within few seconds to avoid metabolic changes induced by wounding. 
	Note: For demonstration purposes, use rosette leaves from 30 day-old wild-type Arabidopsis thaliana (Col-0) grown on soil under long day conditions.</li>
	<li>Keep the harvested samples on dry ice for short-term breaks or store them at -80 &#176;C for longer periods.</li>
</ol>
2. Grinding and Tissue Disruption
<ol>
	<li>Pre-cool the tube holders of the tissue homogenizer in liquid nitrogen for at least 10 min. If a tissue homogenizer is not available, use clean and pre-cooled mortars and pestles.</li>
	<li>Take the samples from the liquid nitrogen, dry ice or -80 &#176;C freezer and place them in the pre-cooled tube holders.</li>
	<li>Quickly put the tube holders in the tissue homogenizer.</li>
	<li>Grind the biological material into a fine and homogeneous powder. Use 20 Hz for 1 min for leaves. 
	Note; Homogenization time and speed may be varied depending on the tissue, make sure that the sample is homogenized into a fine powder and that this powder is kept frozen at every step of the homogenization.</li>
	<li>Take the biological samples from the tube holders and keep them frozen until further extraction.</li>
</ol>
3. Weighing of Tissues
<ol>
	<li>Use an analytical balance with sufficient precision for the required sample amounts.</li>
	<li>Prepare a labelled 2 mL round bottom safe-lock microcentrifuge tube.</li>
	<li>Pre-cool the tubes and spatulas in liquid nitrogen.</li>
	<li>Aliquot the required amount of tissue powder into the 2 mL safe-lock microcentrifuge tube. 
	Caution: Avoid any defrosting of the plant material by minimizing the time taken to weigh the samples.</li>
	<li>Return the aliquoted samples immediately after weighing to liquid nitrogen.</li>
	<li>Record for each sample the exact weight. Use 10-50 mg &#177; 10% for most plant tissues.</li>
	<li>Store the aliquoted samples at -80 &#176;C until further extraction.</li>
</ol>
4. Reagent Setup
<ol>
	<li>Use an extraction mixture of methyl tert-butyl ether (MTBE)/ methanol (MeOH).
	<ol>
		<li>For the preparation of 100 mL extraction solvent, add 75 mL of MTBE to 25 mL of MeOH to make a mixture of MTBE: MeOH (3:1, vol/vol).</li>
		<li>Add internal standards for the post analysis normalization according to analytical needs. Typically, add 50 &#181;L of 1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (1 mg/mL in chloroform) as an internal standard for the UPLC-MS-based lipid analysis, while adding 50 &#181;L of 13C sorbitol (1 mg/mL in water) as internal standards for the GC-MS-based analysis of primary metabolites. Internal standards for UPLC-MS-based metabolite analysis are 50 &#181;L of corticosterone (1 mg/mL in methanol) and 25 &#181;L of ampicillin (1 mg/mL in methanol).</li>
		<li>Transfer the solvent to a clean glass bottle that was rinsed with MTBE: MeOH mixture.</li>
		<li>Store the extraction mixture for up to 1 week at 4 &#176;C. 
		NOTE: Do not store the extraction mixture for longer periods to maintain reproducible results.</li>
	</ol>
	</li>
	<li>To induce phase separation, use water (H2O)/ methanol (MeOH).
	<ol>
		<li>For the preparation of 100-mL H2O: MeOH, add 75 mL of H2O to 25 mL of MeOH to make H2O: MeOH (3:1, vol/vol).</li>
		<li>Transfer the solvent to a clean glass bottle that was rinsed with H2O: MeOH mixture. This solvent can be stored for several weeks at room temperature.</li>
	</ol>
	</li>
</ol>
5. Extraction of Samples
<ol>
	<li>Pre-cool the extraction mixture (MTBE: MeOH, 3:1, vol/vol) to -20 &#176;C using a liquid cooling system or a -20 &#176;C freezer.</li>
	<li>Take out the aliquoted samples one by one and add 1 mL of the pre-cooled extraction mixture to each sample tube. Caution: Perform this step quickly due to low viscosity of MTBE.</li>
	<li>Mix immediately on a vortex mixer until the tissue is well homogenized within the extraction mixture. 
	NOTE: This step is very important, since it is required to precipitate the proteins and inactivates their enzymatic activities.</li>
	<li>Incubate all the samples on an orbital shaker at 100 rpm for 45 min at 4&#176;C.</li>
	<li>Sonicate the samples for 15 min in an ice-cooled sonication bath.</li>
</ol>
6. Fractionation by Phase Separation
<ol>
	<li>Add 650 &#181;L of H2O: MeOH (3:1, vol/vol) to each sample tube.</li>
	<li>Mix well by vortexing for 1 min.</li>
	<li>Centrifuge the samples at a speed of 20,000 x g for 5 min at 4 &#176;C. 
	NOTE: After this step, there are two immiscible liquid phases with a solid pellet in the bottom of the tube. 
	Caution: Handle the tubes with care to avoid mixing of the two liquid phases and avoid disrupting the precipitated pellet.</li>
</ol>
7. Aliquoting of Polar and Hydrophobic Fractions
<ol>
	<li>Transfer 500 &#181;L of the solvent from the upper, lipid-containing, phase into a labelled 1.5 mL microcentrifuge tube. 
	NOTE: The aliquoted lipid samples can be directly concentrated for immediate UPLC-MS analysis (step 8.1) or stored for several weeks at -80 &#176;C.</li>
	<li>Once the 500 &#181;L are removed from the sample, remove the remaining lipid phase using a 200 &#181;L pipette.</li>
	<li>Transfer 400 &#181;L of the solvent from the lower phase (polar and semi-polar metabolites) into a labelled 1.5 mL microcentrifuge tube. The aliquoted polar samples can be directly concentrated for immediate UPLC-MS analysis (step 8.2) or stored for several weeks at -80 &#176;C.</li>
	<li>Take an additional aliquot of 200 &#181;L to perform additional analysis, e.g. gas chromatography-based metabolite analysis as described preciously16.</li>
	<li>Remove the remainder of the aqueous phase by pipetting off the excess volume.</li>
	<li>Wash the obtained protein, starch, cell wall pellet with 500 &#181;L methanol by thoroughly vortexing it for 1 min.</li>
	<li>Centrifuge the samples at a speed of 10,000 x g for 5 min at 4&#176;C.</li>
	<li>Perform protein extraction and digestion (step 11) or starch/cell wall analysis as described previously16. 
	NOTE: If not utilized immediately, these pellets can be stored for several weeks at -80 &#176;C.</li>
</ol>
8. Concentration and Storage of Fractions
<ol>
	<li>Evaporate the solvent from lipid samples (from step 7.1) in either a vacuum concentrator without heating (for 1-2 hours) or preferably use a nitrogen flow evaporator to avoid oxidative modifications of the lipids. 
	NOTE: Dried samples should be analyzed immediately. For storage, leave samples in MTBE solution, ideally in glass vials (step 7.1).</li>
	<li>Evaporate the solvent from the aqueous samples (from step 7.3 or 7.4) overnight in a vacuum concentrator without heating. NOTE: the dried samples can be stored for several weeks at -80 &#176;C before analysis.</li>
</ol>
9. Analysis of Lipids using UPLC-MS24
<ol>
	<li>Re-suspend the dried lipid fractions (from step 8.1) in 400 &#181;L of acetonitrile: 2-propanol (7:3, vol/vol).</li>
	<li>Transfer sufficient liquid to glass vials and cap tightly.</li>
	<li>Put the glass vials in a cooled autosampler (4 &#176;C).</li>
	<li>Inject 2 &#181;L per sample and separate the lipids on a Reversed Phase (RP) C8 column held at 60 &#176;C using a UPLC system running at a flow rate of 400 &#181;L/min.</li>
	<li>Use the mobile phases described in Table 1 for the chromatographic separation.</li>
	<li>Acquire the mass spectra in positive and negative ionization mode using a suitable MS instrument covering the mass range between 150 and 1,500 m/z.</li>
</ol>
10. Analysis of Polar and Semi-polar Metabolites using UPLC-MS25.
<ol>
	<li>Re-suspend the polar phase (from step 8.2) in 200 &#181;L UPLC-grade methanol: water (1:1, vol/vol).</li>
	<li>Transfer sufficient liquid to glass vials and cap tightly.</li>
	<li>Put the glass vials in a cooled autosampler (4 &#176;C).</li>
	<li>Inject 2 &#181;L from each sample and separate the metabolites on a RP C18 column held at 40 &#176;C using a UPLC system running at a flow rate of 400 &#181;L/min.</li>
	<li>Use the mobile phases for chromatographic separation with the parameters given in Table 2.</li>
	<li>Acquire full scan mass spectra in positive and negative ionization mode using a suitable mass spectrometer covering a mass range between 50 and 1,500 m/z.</li>
</ol>
11. Protein Extraction, Digestion and Analysis16
<ol>
	<li>Re-suspend the washed protein/starch/cell wall pellet (from step 7.8) in 200 &#181;L of the protein extraction buffer of choice. Note: We use urea/thiourea buffer (5 M urea, 2 mM thiourea, 15 mM DTT, 2% CHAPS and protease and phosphatase inhibitors)27.</li>
	<li>Sonicate the samples for 10 min in an ice-cooled sonic bath.</li>
	<li>Incubate samples for 30 min on an orbital shaker (100 rpm) at room temperature.</li>
	<li>Centrifuge the dissolved proteins at 10,000 x g for 5 min.</li>
	<li>Collect the protein supernatant in a new tube.</li>
	<li>Determine the protein concentration from the collected supernatant28.</li>
	<li>Digest 50 &#181;g of protein in-solution with a protocol of choice. Typically, use the Trypsin/Lys-C mix according to the instruction manual.</li>
	<li>After digestion, perform desalting of the peptides prior to mass spectrometry using C18 stage tips and elute the digested peptides 29.</li>
	<li>Concentrate the samples to near dryness in a vacuum concentrator without heating.</li>
	<li>Re-suspend the samples in appropriate loading buffer (e.g. 5% acetonitrile, 0.5% formic acid) and analyze the peptide mixtures by LC-MS/MS using a high-resolution mass spectrometer connected to a nano LC system. 
	NOTE: In the exemplary proteomics data set presented in this protocol, we used a gradient as described in Table 3.</li>
	<li>Set the mass spectrometer, using a top 15 strategy, where one full scan (FS) was followed by up to 15 data dependent MS/MS scans, to the following parameters: The FS was in the mass range 200 - 2,000 m/z at a resolution of 70,000 with a target value of 3x106 ions. Obtain the data-dependent MS/MS scans by higher-energy collisional dissociation (HCD). Set the target values for the MS/MS to 1e5 ions, with a maximal ion fill time of 50 ms, an isolation window of 4.0 m/z, normalized collision energy (NCE) of 30% and an underfill ratio of 1%. Measure the MS/MS ions at a resolution 17.500 and the dynamic exclusion was set to 60 s.</li>
</ol>

<ol><li>Hodgkin, A. L., Huxley, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+quantitative+description+of+membrane+current+and+its+application+to+conduction+and+excitation+in+nerve%5BTitle%5D)+AND+%22J+Physiol%22%5BJournal%5D)">A quantitative description of membrane current and its application to conduction and excitation in nerve.</a> J Physiol. 117 (4), 500-544 (1952).</li>
<li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-Seq%3A+a+revolutionary+tool+for+transcriptomics%5BTitle%5D)+AND+%22Nat+Rev+Genet%22%5BJournal%5D)">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nat Rev Genet. 10 (1), 57-63 (2009).</li>
<li>Yang, M. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+emerging+genomics+and+systems+biology+research+lead+to+systems+genomics+studies%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">The emerging genomics and systems biology research lead to systems genomics studies.</a> BMC Genomics. 15, Suppl 11 1(2014).</li>
<li>Sheth, B. P., Thaker, V. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Plant+systems+biology%3A+insights%2C+advances+and+challenges%5BTitle%5D)+AND+%22Planta%22%5BJournal%5D)">Plant systems biology: insights, advances and challenges.</a> Planta. 240 (1), 33-54 (2014).</li>
<li>Ideker, T., Galitski, T., Hood, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+new+approach+to+decoding+life%3A+Systems+biology%5BTitle%5D)+AND+%22Annu+Rev+Genom+Hum+G%22%5BJournal%5D)">A new approach to decoding life: Systems biology.</a> Annu Rev Genom Hum G. 2, 343-372 (2001).</li>
<li>Somvanshi, P. R., Venkatesh, K. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+conceptual+review+on+systems+biology+in+health+and+diseases%3A+from+biological+networks+to+modern+therapeutics%5BTitle%5D)+AND+%22Syst+Synth+Biol%22%5BJournal%5D)">A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics.</a> Syst Synth Biol. 8 (1), 99-116 (2014).</li>
<li>Oliver, S. G., Winson, M. K., Kell, D. B., Baganz, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systematic+functional+analysis+of+the+yeast+genome%5BTitle%5D)+AND+%22Trends+Biotechnol%22%5BJournal%5D)">Systematic functional analysis of the yeast genome.</a> Trends Biotechnol. 16 (9), 373-378 (1998).</li>
<li>Tweeddale, H., Notley-McRobb, L., Ferenci, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Effect+of+slow+growth+on+metabolism+of+Escherichia+coli%2C+as+revealed+by+global+metabolite+pool+%28%22metabolome%22%29+analysis%5BTitle%5D)+AND+%22J+Bacteriol%22%5BJournal%5D)">Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool ("metabolome") analysis.</a> J Bacteriol. 180 (19), 5109-5116 (1998).</li>
<li>Kim, H. K., Verpoorte, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sample+preparation+for+plant+metabolomics%5BTitle%5D)+AND+%22Phytochemical+analysis+%3A+PCA%22%5BJournal%5D)">Sample preparation for plant metabolomics.</a> Phytochemical analysis : PCA. 21 (1), 4-13 (2010).</li>
<li>Folch, J., Lees, M., Stanley, G. H. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Simple+Method+for+the+Isolation+and+Purification+of+Total+Lipides+from+Animal+Tissues%5BTitle%5D)+AND+%22J+Biol+Chem%22%5BJournal%5D)">A Simple Method for the Isolation and Purification of Total Lipides from Animal Tissues.</a> J Biol Chem. 226 (1), 497-509 (1957).</li>
<li>Weckwerth, W., Wenzel, K., Fiehn, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Process+for+the+integrated+extraction%2C+identification+and+quantification+of+metabolites%2C+proteins+and+RNA+to+reveal+their+co-regulation+in+biochemical+networks%5BTitle%5D)+AND+%22Proteomics%22%5BJournal%5D)">Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks.</a> Proteomics. 4 (1), 78-83 (2004).</li>
<li>Wienkoop, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integration+of+metabolomic+and+proteomic+phenotypes%3A+analysis+of+data+covariance+dissects+starch+and+RFO+metabolism+from+low+and+high+temperature+compensation+response+in+Arabidopsis+thaliana%5BTitle%5D)+AND+%22Mol+Cell+Proteomics%22%5BJournal%5D)">Integration of metabolomic and proteomic phenotypes: analysis of data covariance dissects starch and RFO metabolism from low and high temperature compensation response in Arabidopsis thaliana.</a> Mol Cell Proteomics. 7 (9), 1725-1736 (2008).</li>
<li>Valledor, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+universal+protocol+for+the+combined+isolation+of+metabolites%2C+DNA%2C+long+RNAs%2C+small+RNAs%2C+and+proteins+from+plants+and+microorganisms%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">A universal protocol for the combined isolation of metabolites, DNA, long RNAs, small RNAs, and proteins from plants and microorganisms.</a> Plant J. 79 (1), 173-180 (2014).</li>
<li>Nakayasu, E. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((MPLEx%3A+a+Robust+and+Universal+Protocol+for+Single-Sample+Integrative+Proteomic%2C+Metabolomic%2C+and+Lipidomic+Analyses%5BTitle%5D)+AND+%22mSystems%22%5BJournal%5D)">MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses.</a> mSystems. 1 (3), (2016).</li>
<li>Alfonsi, K., et al. <a target="_blank" href="http://pubs.rsc.org/en/Content/ArticleLanding/2008/GC/b711717e#!divAbstract">Green chemistry tools to influence a medicinal chemistry and research chemistry based organisation.</a> Green Chem. 10 (1), 31-36 (2008).</li>
<li>Salem, M. A., Juppner, J., Bajdzienko, K., Giavalisco, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protocol%3A+a+fast%2C+comprehensive+and+reproducible+one-step+extraction+method+for+the+rapid+preparation+of+polar+and+semi-polar+metabolites%2C+lipids%2C+proteins%2C+starch+and+cell+wall+polymers+from+a+single+sample%5BTitle%5D)+AND+%22Plant+Methods%22%5BJournal%5D)">Protocol: a fast, comprehensive and reproducible one-step extraction method for the rapid preparation of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers from a single sample.</a> Plant Methods. 12, 45(2016).</li>
<li>Hemme, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systems-wide+analysis+of+acclimation+responses+to+long-term+heat+stress+and+recovery+in+the+photosynthetic+model+organism+Chlamydomonas+reinhardtii%5BTitle%5D)+AND+%22Plant+Cell%22%5BJournal%5D)">Systems-wide analysis of acclimation responses to long-term heat stress and recovery in the photosynthetic model organism Chlamydomonas reinhardtii.</a> Plant Cell. 26 (11), 4270-4297 (2014).</li>
<li>Sharma, D. K., et al. <a target="_blank" href="http://link.springer.com/article/10.1007/s10811-014-0407-2">UPLC-MS analysis of Chlamydomonas reinhardtii and Scenedesmus obliquus lipid extracts and their possible metabolic roles.</a> J Appl Phycol. 27 (3), 1149-1159 (2015).</li>
<li>Delgado, R., Munoz, Y., Pena-Cortes, H., Giavalisco, P., Bacigalupo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Diacylglycerol+activates+the+light-dependent+channel+TRP+in+the+photosensitive+microvilli+of+Drosophila+melanogaster+photoreceptors%5BTitle%5D)+AND+%22J+Neurosci%22%5BJournal%5D)">Diacylglycerol activates the light-dependent channel TRP in the photosensitive microvilli of Drosophila melanogaster photoreceptors.</a> J Neurosci. 34 (19), 6679-6686 (2014).</li>
<li>Bozek, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Organization+and+evolution+of+brain+lipidome+revealed+by+large-scale+analysis+of+human%2C+chimpanzee%2C+macaque%2C+and+mouse+tissues%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues.</a> Neuron. 85 (4), 695-702 (2015).</li>
<li>Khrameeva, E. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neanderthal+ancestry+drives+evolution+of+lipid+catabolism+in+contemporary+Europeans%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Neanderthal ancestry drives evolution of lipid catabolism in contemporary Europeans.</a> Nat Commun. 5, 3584(2014).</li>
<li>Bozek, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exceptional+evolutionary+divergence+of+human+muscle+and+brain+metabolomes+parallels+human+cognitive+and+physical+uniqueness%5BTitle%5D)+AND+%22PLoS+Biol%22%5BJournal%5D)">Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness.</a> PLoS Biol. 12 (5), 1001871(2014).</li>
<li>Krueger, S., Steinhauser, D., Lisec, J., Giavalisco, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analysis+of+subcellular+metabolite+distributions+within+Arabidopsis+thaliana+leaf+tissue%3A+a+primer+for+subcellular+metabolomics%5BTitle%5D)+AND+%22Methods+Mol+Biol%22%5BJournal%5D)">Analysis of subcellular metabolite distributions within Arabidopsis thaliana leaf tissue: a primer for subcellular metabolomics.</a> Methods Mol Biol. 1062, 575-596 (2014).</li>
<li>Hummel, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultra+performance+liquid+chromatography+and+high+resolution+mass+spectrometry+for+the+analysis+of+plant+lipids%5BTitle%5D)+AND+%22FrontPlant+Sci%22%5BJournal%5D)">Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids.</a> FrontPlant Sci. 2, 54(2011).</li>
<li>Giavalisco, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Elemental+formula+annotation+of+polar+and+lipophilic+metabolites+using+%2813%29+C%2C+%2815%29+N+and+%2834%29+S+isotope+labelling%2C+in+combination+with+high-resolution+mass+spectrometry%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">Elemental formula annotation of polar and lipophilic metabolites using (13) C, (15) N and (34) S isotope labelling, in combination with high-resolution mass spectrometry.</a> Plant J. 68 (2), 364-376 (2011).</li>
<li>Caldana, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systemic+analysis+of+inducible+target+of+rapamycin+mutants+reveal+a+general+metabolic+switch+controlling+growth+in+Arabidopsis+thaliana%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">Systemic analysis of inducible target of rapamycin mutants reveal a general metabolic switch controlling growth in Arabidopsis thaliana.</a> Plant J. 73 (6), 897-909 (2013).</li>
<li>Giavalisco, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Proteome+analysis+of+Arabidopsis+thaliana+by+two-dimensional+gel+electrophoresis+and+matrix-assisted+laser+desorption%2Fionisation-time+of+flight+mass+spectrometry%5BTitle%5D)+AND+%22Proteomics%22%5BJournal%5D)">Proteome analysis of Arabidopsis thaliana by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionisation-time of flight mass spectrometry.</a> Proteomics. 5 (7), 1902-1913 (2005).</li>
<li>Bradford, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+rapid+and+sensitive+method+for+the+quantitation+of+microgram+quantities+of+protein+utilizing+the+principle+of+protein-dye+binding%5BTitle%5D)+AND+%22Anal+Biochem%22%5BJournal%5D)">A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.</a> Anal Biochem. 72, 248-254 (1976).</li>
<li>Rappsilber, J., Mann, M., Ishihama, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protocol+for+micro-purification%2C+enrichment%2C+pre-fractionation+and+storage+of+peptides+for+proteomics+using+StageTips%5BTitle%5D)+AND+%22Nature+protocols%22%5BJournal%5D)">Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.</a> Nature protocols. 2 (8), 1896-1906 (2007).</li>
<li>Spagou, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hydrophilic+interaction+chromatography+coupled+to+MS+for+metabonomic%2Fmetabolomic+studies%5BTitle%5D)+AND+%22J+Sep+Sci%22%5BJournal%5D)">Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies.</a> J Sep Sci. 33 (6-7), 716-727 (2010).</li>
<li>Hauck, O. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Uric+acid+accumulation+in+an+Arabidopsis+urate+oxidase+mutant+impairs+seedling+establishment+by+blocking+peroxisome+maintenance%5BTitle%5D)+AND+%22Plant+Cell%22%5BJournal%5D)">Uric acid accumulation in an Arabidopsis urate oxidase mutant impairs seedling establishment by blocking peroxisome maintenance.</a> Plant Cell. 26 (7), 3090-3100 (2014).</li>
<li>Avin-Wittenberg, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Global+analysis+of+the+role+of+autophagy+in+cellular+metabolism+and+energy+homeostasis+in+Arabidopsis+seedlings+under+carbon+starvation%5BTitle%5D)+AND+%22Plant+Cell%22%5BJournal%5D)">Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation.</a> Plant Cell. 27 (2), 306-322 (2015).</li>
<li>Watanabe, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comprehensive+dissection+of+spatiotemporal+metabolic+shifts+in+primary%2C+secondary%2C+and+lipid+metabolism+during+developmental+senescence+in+Arabidopsis%5BTitle%5D)+AND+%22Plant+physiol%22%5BJournal%5D)">Comprehensive dissection of spatiotemporal metabolic shifts in primary, secondary, and lipid metabolism during developmental senescence in Arabidopsis.</a> Plant physiol. 162 (3), 1290-1310 (2013).</li>
<li>Lambers, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Proteaceae+from+severely+phosphorus-impoverished+soils+extensively+replace+phospholipids+with+galactolipids+and+sulfolipids+during+leaf+development+to+achieve+a+high+photosynthetic+phosphorus-use-efficiency%5BTitle%5D)+AND+%22New+Phytol%22%5BJournal%5D)">Proteaceae from severely phosphorus-impoverished soils extensively replace phospholipids with galactolipids and sulfolipids during leaf development to achieve a high photosynthetic phosphorus-use-efficiency.</a> New Phytol. 196 (4), 1098-1108 (2012).</li>
<li>Degenkolbe, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Differential+remodeling+of+the+lipidome+during+cold+acclimation+in+natural+accessions+of+Arabidopsis+thaliana%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">Differential remodeling of the lipidome during cold acclimation in natural accessions of Arabidopsis thaliana.</a> Plant J. 70, 972-982 (2012).</li>
<li>Bromke, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Liquid+chromatography+high-resolution+mass+spectrometry+for+fatty+acid+profiling%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">Liquid chromatography high-resolution mass spectrometry for fatty acid profiling.</a> Plant J. 81 (3), 529-536 (2015).</li>
<li>Li, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((FAX1%2C+a+novel+membrane+protein+mediating+plastid+fatty+acid+export%5BTitle%5D)+AND+%22PLoS+Biol%22%5BJournal%5D)">FAX1, a novel membrane protein mediating plastid fatty acid export.</a> PLoS Biol. 13 (2), 1002053(2015).</li>
<li>Kueger, S., Steinhauser, D., Willmitzer, L., Giavalisco, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-resolution+plant+metabolomics%3A+from+mass+spectral+features+to+metabolites+and+from+whole-cell+analysis+to+subcellular+metabolite+distributions%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions.</a> Plant J. 70 (1), 39-50 (2012).</li>
</ol>

The authors have nothing to disclose

In this article, we describe and illustrate a simple and highly applicable extraction protocol for comprehensive lipidomic, metabolomic and proteomic analysis from a single 50 mg leaf sample. The method has been previously used in several studies, which have been published in diverse articles 17,18,19,20,21,22,23,24,25,26,31,32,33,34,35,36,37and proved, in addition to its straight-forward workflow and high applicability to be robust and reproducible.
The here provided applications show some routine methods for the initial screening of a complex biological sample. These illustrated large scale metabolomic and lipidomic data sets can provide comprehensive information about the broad or specific changes in the metabolism of the analyzed biological system, while the data obtained from the analysis of proteins, provides insights into the quantitative (abundance) and qualitative (modifications) changes of enzymes, structural proteins or transcription factors (TFs) controlling cellular functions and machinery. Accordingly, integrative omics data has the potential to reveal initial information about possible changes induced by genetic or biotic and/or abiotic perturbations of a biological system, by elucidating molecular changes of diverse molecules associated to specific metabolic pathways or cellular processes.
Of course, in the long term, it is quite essential, for a successful systems biology analysis, to maximize the number of analyzed and annotated molecular entities, allowing the monitoring of cellular functions and activities as completely as possible. For this purpose, the obtained fractions could be applied additionally to diverse analytical methods, targeting further compounds or compound classes (Figure 4).
Having said this, it has to be mentioned that the global analysis strategy of the obtained data can be following by two different strategies: on the one hand, we have been emphasizing the elucidation of cellular functions by quantification of known compounds. On the other hand, many of the measured metabolites and lipids are not yet known or annotated. These, yet, un-annotated compound measurements also contain plenty of meaningful information, which, can be utilized by statistical methods for the classification or discrimination between groups or treatments 20,21,22.
Still, these unknown compounds, especially the ones relevant for group classification or serving as biomarkers need to be identified. This identification process is unfortunately quite tedious and cannot be achieved without additional analytical measurements or strategies38. As can be seen from Figure 4, the number of un-annotated compounds is quite high (actually the vast majority). Nevertheless, as mentioned above, these chromatographic peaks can be handled within the data analysis and therefore significantly affected entities can be elucidated and subjected to further identification strategies.
In summary, we can conclude that the here introduced protocol provides several advantages for experimental systems biology as well as for classical statistical applications.
First, since all fractions are extracted from a single sample, the variation between the different experimental data sets (lipids, metabolites, proteins) is significantly reduced since every data set is derived from the same sample aliquot. This clearly leads to the increased comparability of the obtained results.
Second, the method is easily scalable and makes it therefore highly compatible with small to large sample amounts. We routinely use 10-100 mg of tissue samples, but successful lipidomic studies have also been performed on as little as 20 Arabidopsis seeds31. Especially the compatibility with small sample amounts makes this method applicable if limited amounts of biological tissues or samples are available. Still, even if sufficient sample material is available, the method presented here offers the advantage to utilize these samples in a larger number of experimental replicates instead of using them for different extraction procedures. This allows for a better and refined statistical data analysis.
Third, since the method is based on a liquid-liquid fractionation of polar and non-polar molecules, it provides, in contrast to simple one-phase extraction methods (e.g. methanol extractions) a significant de-complexing step in the procedure. This efficient sample de-complexing leads to a partial purification of the individual fractions due to the separation of chemically interfering molecules from each other. Accordingly, the chemical partitioning process not only provides a practical advantage for the systematic aliquoting of the extracted samples into different chemical classes but also improves the individual analytical measurements, since it removes contaminating compounds from the different fractions. Clearly, we can observe that especially the lipids, which are partitioned to the organic phase and which usually negatively affect the chromatographic analysis of polar compounds, will be almost completely absent from the polar fraction. The same is true for the analysis of the hydrophobic lipids, which will be depleted of the polar compounds. Besides the purification of polar and nonpolar compounds from each other, we deplete and collect proteins and other macro-molecules from the sample, which not only provides a separate fraction, which can be utilized for protein, starch and cell wall analysis16, but also leads to a cleaner sample within the individual fractions. This is especially relevant, since it is known that the presence of large macromolecules, leads to damage or at least shorter lifetime of the analytical columns.
Last but not least, the described MTBE extraction method, which relies on the less hazardous and more favorable chloroform replacement solvent15, has already been shown by several studies from our group, to be widely applicability for different biological samples from plants16, algae17,18, flies19 but also several mammalian tissues, organs or cells20,21,22.

Systems biology, which emerged in the middle of the last century1 and was advanced by the large scale analysis of genomic and transcriptomic data sets2,3, has developed into a new and indispensable approach for the analysis of complex biological systems4,5. The main goal of systems biology is to decipher the component interactions and dependencies in biological systems and to bridge the link between genotypes, their realization, molecular transformations and resulting phenotypes. Accordingly, the integration of comprehensive data sets, produced by the various large scale analytical approaches, namely genomics, transcriptomics, metabolomics, lipidomics and proteomics and their computational analysis has become a prerequisite for the description and understanding of complex biological systems.
Based on the huge chemical diversity and complexity of biological components in any living system, the production of large and comprehensive &#39;omics&#39; data sets strongly relies on the quality of the applied extraction method9. In addition to the quality of the extraction method, the economy of the method is important; this means that it would be desirable to obtain as much molecular information from as little sample input as possible. Often sample amounts can be limiting and therefore it is highly desirable to make use of an extraction method, which can derive as many molecular classes from a single extraction of a given sample. This means that instead of using several specialized extraction methods for the extraction of different compound classes from different sample aliquots of the same sample, a sequential extraction method is employed, which fractionates the molecular constituents of a single aliquot into different molecular fractions.
The common method employed for these fractionated extraction methods is based on the two phase lipid extraction method from Folch et al. developed in 195710. This method is based on a chloroform: methanol/water partitioning of polar and hydrophobic metabolites and was intended to clean-up and de-complex samples for high quality lipid analysis. With the evolution of multi-omics systems biology, the Folch method was further, stepwise, improved by utilizing it for the sample partitioning of proteins and polar metabolites and lipids for gas- and liquid chromatography-based metabolomics and lipidomics of polar and hydrophobic compounds, in addition to liquid chromatography-based proteomics11,12,13,14. Unfortunately, all of these methods rely on a chloroform-based extraction method, which not only leads to the undesired formation of the protein pellet as an interphase between the polar and the lipid phase, but which is also an undesirable solvent from a green chemistry perspective15,16. However, the solvent methyl tert-butyl ether (MTBE) overcomes both of these aforementioned problems and is a suitable replacement for chloroform. Based on these requirements, we decided to establish a MTBE: methanol : water-based extraction method, which fulfils all of the aforementioned specifications and therefore functions as an ideal starting point for comprehensive multi-omics analysis16.
This protocol guides the user step-by-step through the simple, fast and reproducible workflow of the sample preparation, including troubleshooting of commonly observed problems. Further, we will briefly introduce exemplary analytical data from ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based lipidomics, metabolomics and proteomics profiling from plant tissue samples. Even though the given examples are derived from 50 mg of an Arabidopsis thaliana leaf tissue sample, this protocol has been used for several other biological samples and tissues, including algae17,18, insects19 and mammalian cells, organs and tissue20,21,22. The scope of the presented extraction protocol is to provide a clear and detailed description of the pre-extraction sample handling and the extraction procedure itself. Even though we provide three brief examples of analytical application, detailed information about the pre- and post-analytical data handling can be obtained from our previous publications 16,23,24,25,26.

<table><tbody><tr><td>&lt;strong&gt;Reagents and standards&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Ampicillin&amp;nbsp;</td><td>Sigma Aldrich</td><td>A9393-5G</td><td>Internal standard for metabolites</td></tr><tr><td>Corticosterone&amp;nbsp;</td><td>Sigma Aldrich</td><td>27840-500MG</td><td>Internal standard for metabolites, HPLC grade</td></tr><tr><td>13C Sorbitol</td><td>Sigma Aldrich</td><td>605514</td><td>Internal standard for metabolites, ISOTEC&amp;reg;&amp;nbsp;Stable Isotopes</td></tr><tr><td>1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (17:0 PC)&amp;nbsp;</td><td>Avanti Polar Lipids</td><td>850360P</td><td>Internal standard for lipids</td></tr><tr><td>Methanol (MeOH)&amp;nbsp;</td><td>Biosolve Chemicals</td><td>13684102</td><td>ULC-MS grade</td></tr><tr><td>Water</td><td>Biosolve Chemicals</td><td>23214102</td><td>ULC-MS grade</td></tr><tr><td>Methyl tert-butyl ether (MTBE)&amp;nbsp;</td><td>Biosolve Chemicals</td><td>13890602</td><td>HPLC grade</td></tr><tr><td>Trypsin/Lys-C mix&amp;nbsp;</td><td>Promega</td><td>V5072</td><td>Enzymatic digestion of proteins</td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Balance&amp;nbsp;</td><td>Sartorius Corporation&amp;nbsp;</td><td>14 557 572</td><td></td></tr><tr><td>Tissue grinding mixer mill&amp;nbsp;</td><td>Retsch, Mixer Mill MM 300</td><td>20.746.0001</td><td></td></tr><tr><td>Mortar and pestle&amp;nbsp;</td><td>Sigma Aldrich</td><td>Z247464-1EA</td><td></td></tr><tr><td>Vortex mixer&amp;nbsp;</td><td>Vortex-Genie 2, Model G560</td><td>SI-0236</td><td></td></tr><tr><td>Vacuum concentrator</td><td>Scan Speed Maxi Vac Alpha Evaporators</td><td>7.008.500.002</td><td></td></tr><tr><td>2 mL Safe-lock microcentrifuge tubes&amp;nbsp;</td><td>Eppendorf</td><td>30120094</td><td>Used for sample extarction</td></tr><tr><td>1.5 mL Safe-lock microcentrifuge tubes&amp;nbsp;</td><td>Eppendorf</td><td>30120086</td><td>Used for fractions</td></tr><tr><td>Shaker</td><td>Eppendorf Thermomixer 5436</td><td>2050-100-05</td><td></td></tr><tr><td>Sonicator&amp;nbsp;</td><td>USC 300&amp;nbsp;TH</td><td>142-0084</td><td></td></tr><tr><td>Refrigerated microcentrifuge</td><td>Eppendorf, model 5427R</td><td>22620701</td><td></td></tr><tr><td>UPLC system&amp;nbsp;</td><td>Waters Acquity UPLC system (Waters, Machester, UK)</td><td></td><td></td></tr><tr><td>&amp;nbsp;MS system&amp;nbsp;</td><td>Exactive, Orbitrap-type, MS (Exactive, Thermo-Fisher, Bremen, Germany).</td><td></td><td></td></tr><tr><td>Reversed Phase (RP) Bridged Ethyl Hybrid (BEH) C8 column (100 mm &amp;times; 2.1 mm containing 1.7 &amp;mu;m diameter particles)</td><td>Waters, Machester, UK</td><td>186002878</td><td>Analysis of lipids</td></tr><tr><td>RP High Strength Silica (HSS) T3 column (100 mm &amp;times; 2.1 mm containing 1.8 &amp;mu;m diameter particles)&amp;nbsp;</td><td>Waters, Machester, UK</td><td>186003539</td><td>Analysis of metabolites</td></tr><tr><td>Q ExactivePlus high resolution mass spectrometer connected to an EASY-nLC 1000 system&amp;nbsp;</td><td>Thermo-Fisher, Bremen, Germany</td><td></td><td>Analysis of peptides</td></tr></tbody></table>

a simple fractionated extraction method for the comprehensive analysis of metabolites, lipids, and proteins from a single sample

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures.

Comprehensive multi-omics data sets are invaluable for understanding of complex biological systems. The strategy for a successful biological experiment usually starts from a meaningful experimental design, experiment set-up and performance, followed by sample-collection, extraction, analytical data acquisition, raw data processing, statistical data analysis, identification of relevant metabolite and biological data interpretation including pathway mapping and visualization (Figure 1).
In the extraction protocol introduced here, we focus on the sample-collection, -handling and -extraction steps, which are depicted in the detailed workflow overview in Figure 2. For demonstration purposes, 50 mg of Arabidopsis leaf tissue was selected. This material was harvested, ground and extracted before subjecting it to three exemplary analytical UPLC-MS platforms, providing data that can be utilized for targeted and untargeted lipidomic, metabolomic and proteomic analysis. Figures 2 and 6 additionally include representative pictures of how, under standard conditions, the extraction solvent should look like. Additionally, examples of samples containing excessive amounts of precipitated macromolecules (proteins and starch) and samples with inappropriate sample homogenization are shown (Figure 3). The troubleshooting for these two common problems is given in brief in Figure 3 but it is also discussed in more detail in our previous publication16.
Figures 4 and 5 outline examples of three analytical chromatograms derived from the lipid, the polar/semi-polar metabolites and protein analyses. Lipids, which were taken from the upper MTBE phase (Figure 2), were analyzed by reversed phase (RP) C8 ultra performance liquid chromatography coupled to high-resolution mass spectrometry. Lipids can be acquired using positive and negative MS ionization modes (Figure 4, upper pane)16,24.
Polar and semi-polar primary and secondary metabolites were analyzed from the polar (water/methanol) phase (Figure 2) by reversed phase (RP) C18UPLC-MS25. The illustrated method, using the reversed phase chromatography, is highly compatible to the analysis of semi-polar metabolites (namely metabolites from plant secondary metabolism), which can be analyzed using positive and negative ionization modes in the MS (Figure 4, lower pane)16. More hydrophilic metabolites from this fraction (sugars, polar amino acids etc.), which do not show good retention on the reversed phase material, can be analyzed by other analytical methods such as GC-MS16or hydrophilic interaction liquid chromatography30.
The proteins, which were retrieved from the solid pellet in the bottom of the extraction tube (Figure 2), were in-solution digested and analyzed using shot gun LC-MS (Figure 5), while the protocol for extraction of starch and cell wall material can be obtained from our previously published protocol16.
In summary, more than 200 lipid species, 50 annotated semi-polar metabolites and several thousand proteins can be routinely identified from samples of the type used in our example. Additionally, the method showed wide applicability using different tissues, organs and cell culture material (Figure 6).
<img alt="Metabolomics workflow diagram: sample collection, extraction, data acquisition, analysis, interpretation." class="xfigimg" src="/files/ftp_upload/55802/55802fig1.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1: General Workflow for Large Scale Untargeted Omics Analysis. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Biological sample extraction process diagram; homogenization, centrifugation; lipids, metabolites, proteins." class="xfigimg" src="/files/ftp_upload/55802/55802fig2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 2"> 
Figure 2: Sample Preparation and Extraction Workflow for the Analysis of Lipids, Metabolites and Proteins from a Single Aliquot of a Biological Sample. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sample preparation diagram; particle extraction and centrifugation for lipid phase removal process." class="xfigimg" src="/files/ftp_upload/55802/55802fig3.jpg" data-altupdatedat="1747911167000" data-alt="Figure 3"> 
Figure 3: Illustrative Examples of Commonly Observed Problems using Two-phase Partitioning Extraction Protocols. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Chromatography results, graph of time vs. relative abundance with pie charts, data analysis." class="xfigimg" src="/files/ftp_upload/55802/55802fig4.jpg" data-altupdatedat="1747911167000" data-alt="Figure 4"> 
Figure 4: Representative Chromatograms of Lipid and Semi-polar Metabolites from Arabidopsis thaliana Leaf Extracts. Base peak chromatograms of lipids (upper pane) and semi-polar metabolites (lower pane) analyzed in positive ionization mode16. The pie charts in the upper right corner of each chromatogram shows the number of identified lipids and metabolites assigned to different chemical classes. Chl, chlorophylls; DAG, diacylglyceride; DGDG, digalactosyldiacylglycerol; FA, fatty acid; LysoPC, lysophosphatidylcholine; MGDG, monogalactosyldiacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; SP, sphingolipid; SQDG, sulfoquinovosyldiacylglycerol; TAG, triacylglyceride. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Chromatography mass spectrometry graph and pie chart of metabolic processes; peak relative abundance." class="xfigimg" src="/files/ftp_upload/55802/55802fig5.jpg" data-altupdatedat="1747911167000" data-alt="Figure 5"> 
Figure 5: Representative Base Peak Chromatogram of Peptides from Arabidopsis thaliana Leaf Extracts. 
The pie chart shows in the upper right corner indicates the number of identified proteins assigned to different biological processes16. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Leaf and plant part extraction process, pigment analysis diagram, includes tubes with distinct layers." class="xfigimg" src="/files/ftp_upload/55802/55802fig6.jpg" data-altupdatedat="1747911167000" data-alt="Figure 6"> 
Figure 6: Representative Extraction Examples of Various Tissue Types from Wild-type Arabidopsis thaliana. 
All the samples were extracted using 50 mg fresh weight from the indicated tissues. <a href="http://cloudflare.jove.com/files/ftp_upload/55802/55802fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer A to Buffer B</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 1 min</td>
			<td>45% A </td>
			<td>Buffer A: 1% 1M NH4-Acetate, 0.1% acetic acid in UPLC MS grade water</td>
		</tr>
		<tr>
			<td>1 to 4 min </td>
			<td>Linear gradient from 45% A to 25% A</td>
			<td>Buffer B: 1% 1M NH4-Acetate, 0.1% acetic acid in acetonitrile/isopropanol 7:3, (v:v)</td>
		</tr>
		<tr>
			<td>4 to 12 min </td>
			<td>Linear gradient from 25% A to 11% A</td>
			<td>Flow rate 400 µL/min</td>
		</tr>
		<tr>
			<td>12 to 15 min </td>
			<td>Linear gradient from 11% A to 0% A</td>
			<td>Injection volume 2 µL</td>
		</tr>
		<tr>
			<td>15 to 19.5 min</td>
			<td>Wash the column for 4.5 min with 0% A</td>
			<td></td>
		</tr>
		<tr>
			<td>19.50 to 19.51 min</td>
			<td>Set back to 45% A </td>
			<td></td>
		</tr>
		<tr>
			<td>19.51 to 24 min</td>
			<td>Equilibrate with 45% A </td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: Gradient Parameters for RP-UPLC Separation of Lipids. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer A to Buffer B</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 1 min</td>
			<td>99% A</td>
			<td>Buffer A: 0.1% formic acid in UPLC grade water</td>
		</tr>
		<tr>
			<td>1 to 11 min</td>
			<td>Linear gradient from 99% A to 60% A</td>
			<td>Buffer B: 0.1% formic acid in UPLC grad acetonitrile</td>
		</tr>
		<tr>
			<td>11 to 13 min</td>
			<td>Linear gradient from 60% A to 30% A</td>
			<td>Flow rate 400 µL/min</td>
		</tr>
		<tr>
			<td>13 to 15 min</td>
			<td>Linear gradient from 30% A to 1% A</td>
			<td>Injection volume 2 µL</td>
		</tr>
		<tr>
			<td>15 to 16 min</td>
			<td>Wash the column for 1 min with 1% A </td>
			<td></td>
		</tr>
		<tr>
			<td>16 to 17 min</td>
			<td>Linear gradient from1% A to 99% A</td>
			<td></td>
		</tr>
		<tr>
			<td>17 to 20 min</td>
			<td>Equilibrate for 3 min at 99% A </td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2: Gradient Parameters for RP-UPLC Separation of Polar and Semi-polar Metabolites. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer B to Buffer A</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 5 min</td>
			<td>Linear gradient from 0 to10%</td>
			<td>Buffer A: 0.1% formic acid in UPLC grade water</td>
		</tr>
		<tr>
			<td>5 to 80 min</td>
			<td>Linear gradient from 10% to 40%</td>
			<td>Buffer B: 0.1% formic acid in 60% UPLC grade acetonitrile</td>
		</tr>
		<tr>
			<td>80 to 85 min</td>
			<td>Linear gradient from 40% to 60% </td>
			<td>Flow rate 300 nL/min</td>
		</tr>
		<tr>
			<td>85 to 86 min</td>
			<td>Linear gradient from 60%  to 95% </td>
			<td>Injection volume 5 µL</td>
		</tr>
		<tr>
			<td>86 to 91 min</td>
			<td>Wash column for 5 min with 95% </td>
			<td></td>
		</tr>
		<tr>
			<td>91 to 92 min</td>
			<td>Linear gradient from 95%  to 0% </td>
			<td></td>
		</tr>
		<tr>
			<td>93 to 110 min</td>
			<td>Equilibrate the column for 17 min at 0%</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3: Gradient Parameters for Nano-LC Separation of Peptides. LC, liquid chromatography.

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A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually ...

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Research

JoVE Journal

Biochemistry

35.0K Views.  Max Planck Institute of Molecular Plant Physiology, Golm, Germany.  Een protocol voor de uitgebreide extractie van lipiden, metabolieten en eiwitten uit biologische weefsels met behulp van een monster wordt gepresenteerd. 

Video: A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

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.

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.

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

Chemistry

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

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

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

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

Bioengineering

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

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

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

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

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Environment

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but also to identify biomarkers for early-stage cancer detection and prediction of chemotherapy response in cancer patients. Preparation of uniform samples for metabolomic analysis is a critical issue that remains to be addressed. Here, we present an easy and reliable protocol for extracting aqueous metabolites from cultured adherent cells for metabolomic analysis using capillary electrophoresis-mass spectrometry (CE-MS). Aqueous metabolites from cultured cells are analyzed by culturing and washing cells, treating cells with methanol, extracting metabolites, and removing proteins and macromolecules with spin columns for CE-MS analysis. Representative results using lung cancer cell lines treated with diamide, an oxidative reagent, illustrate the clearly observable metabolic shift of cells under oxidative stress. This article would be especially valuable to students and investigators involved in metabolomics research, who are new to harvesting metabolites from cell lines for analysis by CE-MS.

The purpose of this article is to describe a protocol for extraction of aqueous metabolites from cultured adherent cells for metabolomic analysis, particularly, capillary electrophoresis-mass spectrometry.

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but ...

Cancer Research

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital aspect of metabolomics is sample preparation whereby inconsistent techniques generate unreliable results. This technique encompasses protein precipitation, liquid-liquid extraction, and solid-phase extraction as a means of fractionating metabolites into four distinct classes.&#160;Improved enrichment of low abundance molecules with a resulting increase in sensitivity is obtained, and ultimately results in more confident identification of molecules. This technique has been applied to plasma, bronchoalveolar lavage fluid, and cerebrospinal fluid samples with volumes as low as 50 &#181;l. &#160;Samples can be used for multiple downstream applications; for example, the pellet resulting from protein precipitation can be stored for later analysis. The supernatant from that step undergoes liquid-liquid extraction using water and strong organic solvent to separate the hydrophilic and hydrophobic compounds. Once fractionated, the hydrophilic layer can be processed for later analysis or discarded if not needed. The hydrophobic fraction is further treated with a series of solvents during three solid-phase extraction steps to separate it into fatty acids, neutral lipids, and phospholipids. This allows the technician the flexibility to choose which class of compounds is preferred for analysis. It also aids in more reliable metabolite identification since some knowledge of chemical class exists.

The reliability of results in metabolomics experiments depends on the effectiveness and reproducibility of the sample preparation. Described is a rigorous and in-depth method that enables extraction of metabolites from biological fluids with the option of subsequently analyzing up to thousands of compounds, or just the compound classes of interest.

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital ...

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges, since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for the simultaneous extraction of metabolites, proteins, and lipids (MPLEx) from the same soil sample was created by adapting a solvent-based approach. This MPLEx protocol has proven to be both simple and robust for many sample types, even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.

A protocol is presented for simultaneously extracting metabolites, proteins, and lipids from a single soil sample, allowing reduced sample preparation times and enabling multi-omic mass spectrometry analyses of samples with limited quantities.

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic, and lipidomic (multi-omic) studies are transforming our ability to understand and ...

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

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

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

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

Shotgun Lipidomics of Rodent Tissues

Lipids play a vital role as essential components of all prokaryotic and eukaryotic cells. Constant technological improvements in mass spectrometry have made lipidomics a powerful analytical tool for monitoring tissue lipidome compositions in homeostatic as well as disease states. This paper presents a step-by-step protocol for a shotgun lipid analysis method that supports the simultaneous detection and quantification of a few hundred molecular lipid species in different tissue and biofluid samples at high throughput. This method leverages automated nano-flow direct injection of a total lipid extract spiked with labeled internal standards without chromatographic separation into a high-resolution mass spectrometry instrument. Starting from sub-microgram amounts of rodent tissue, the MS analysis takes 10 min per sample and covers up to 400 lipids from 14 lipid classes in mouse lung tissue. The method presented here is well suited for studying disease mechanisms and identifying and quantifying biomarkers that indicate early toxicity or beneficial effects within rodent tissues.

Shotgun mass spectrometry-based lipidomics delivers a sensitive quantitative snapshot of a broad spectrum of lipid classes simultaneously in a single measurement from various rodent tissues.

Lipids play a vital role as essential components of all prokaryotic and eukaryotic cells. Constant technological improvements in mass spectrometry have ...

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

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