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 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.
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 'omics' 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.
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
2. Grinding and Tissue Disruption
3. Weighing of Tissues
4. Reagent Setup
5. Extraction of Samples
6. Fractionation by Phase Separation
7. Aliquoting of Polar and Hydrophobic Fractions
8. Concentration and Storage of Fractions
9. Analysis of Lipids using UPLC-MS24
10. Analysis of Polar and Semi-polar Metabolites using UPLC-MS25.
11. Protein Extraction, Digestion and Analysis16
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).
Figure 1: General Workflow for Large Scale Untargeted Omics Analysis. Please click here to view a larger version of this figure.
Figure 2: Sample Preparation and Extraction Workflow for the Analysis of Lipids, Metabolites and Proteins from a Single Aliquot of a Biological Sample. Please click here to view a larger version of this figure.
Figure 3: Illustrative Examples of Commonly Observed Problems using Two-phase Partitioning Extraction Protocols. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
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. Please click here to view a larger version of this figure.
Time (min) | % Buffer A to Buffer B | |
0 to 1 min | 45% A | Buffer A: 1% 1M NH4-Acetate, 0.1% acetic acid in UPLC MS grade water |
1 to 4 min | Linear gradient from 45% A to 25% A | Buffer B: 1% 1M NH4-Acetate, 0.1% acetic acid in acetonitrile/isopropanol 7:3, (v:v) |
4 to 12 min | Linear gradient from 25% A to 11% A | Flow rate 400 µL/min |
12 to 15 min | Linear gradient from 11% A to 0% A | Injection volume 2 µL |
15 to 19.5 min | Wash the column for 4.5 min with 0% A | |
19.50 to 19.51 min | Set back to 45% A | |
19.51 to 24 min | Equilibrate with 45% A |
Table 1: Gradient Parameters for RP-UPLC Separation of Lipids. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
Time (min) | % Buffer A to Buffer B | |
0 to 1 min | 99% A | Buffer A: 0.1% formic acid in UPLC grade water |
1 to 11 min | Linear gradient from 99% A to 60% A | Buffer B: 0.1% formic acid in UPLC grad acetonitrile |
11 to 13 min | Linear gradient from 60% A to 30% A | Flow rate 400 µL/min |
13 to 15 min | Linear gradient from 30% A to 1% A | Injection volume 2 µL |
15 to 16 min | Wash the column for 1 min with 1% A | |
16 to 17 min | Linear gradient from1% A to 99% A | |
17 to 20 min | Equilibrate for 3 min at 99% A |
Table 2: Gradient Parameters for RP-UPLC Separation of Polar and Semi-polar Metabolites. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
Time (min) | % Buffer B to Buffer A | |
0 to 5 min | Linear gradient from 0 to10% | Buffer A: 0.1% formic acid in UPLC grade water |
5 to 80 min | Linear gradient from 10% to 40% | Buffer B: 0.1% formic acid in 60% UPLC grade acetonitrile |
80 to 85 min | Linear gradient from 40% to 60% | Flow rate 300 nL/min |
85 to 86 min | Linear gradient from 60% to 95% | Injection volume 5 µL |
86 to 91 min | Wash column for 5 min with 95% | |
91 to 92 min | Linear gradient from 95% to 0% | |
93 to 110 min | Equilibrate the column for 17 min at 0% |
Table 3: Gradient Parameters for Nano-LC Separation of Peptides. LC, liquid chromatography.
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.
The authors have nothing to disclose.
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.
Reagents and standards | |||
Ampicillin | Sigma Aldrich | A9393-5G | Internal standard for metabolites |
Corticosterone | Sigma Aldrich | 27840-500MG | Internal standard for metabolites, HPLC grade |
13C Sorbitol | Sigma Aldrich | 605514 | Internal standard for metabolites, ISOTEC® Stable Isotopes |
1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (17:0 PC) | Avanti Polar Lipids | 850360P | Internal standard for lipids |
Methanol (MeOH) | Biosolve Chemicals | 13684102 | ULC-MS grade |
Water | Biosolve Chemicals | 23214102 | ULC-MS grade |
Methyl tert-butyl ether (MTBE) | Biosolve Chemicals | 13890602 | HPLC grade |
Trypsin/Lys-C mix | Promega | V5072 | Enzymatic digestion of proteins |
Equipment | |||
Balance | Sartorius Corporation | 14 557 572 | |
Tissue grinding mixer mill | Retsch, Mixer Mill MM 300 | 20.746.0001 | |
Mortar and pestle | Sigma Aldrich | Z247464-1EA | |
Vortex mixer | Vortex-Genie 2, Model G560 | SI-0236 | |
Vacuum concentrator | Scan Speed Maxi Vac Alpha Evaporators | 7.008.500.002 | |
2 ml Safe-lock microcentrifuge tubes | Eppendorf | 30120094 | Used for sample extarction |
1.5 ml Safe-lock microcentrifuge tubes | Eppendorf | 30120086 | Used for fractions |
Shaker | Eppendorf Thermomixer 5436 | 2050-100-05 | |
Sonicator | USC 300 TH | 142-0084 | |
Refrigerated microcentrifuge | Eppendorf, model 5427R | 22620701 | |
UPLC system | Waters Acquity UPLC system (Waters, Machester, UK) | ||
MS system | Exactive, Orbitrap-type, MS (Exactive, Thermo-Fisher, Bremen, Germany). | ||
Reversed Phase (RP) Bridged Ethyl Hybrid (BEH) C8 column (100 mm×2.1 mm containing 1.7 μm diameter particles) | Waters, Machester, UK | 186002878 | Analysis of lipids |
RP High Strength Silica (HSS) T3 column (100 mm×2.1 mm containing 1.8 μm diameter particles) | Waters, Machester, UK | 186003539 | Analysis of metabolites |
Q ExactivePlus high resolution mass spectrometer connected to an EASY-nLC 1000 system | Thermo-Fisher, Bremen, Germany | Analysis of peptides |