Chapter 4 Quantification of Plant Volatiles Anthony V. Qualley and Natalia Dudareva Abstract Plant volatiles occupy diverse roles as signaling molecules, defensive compounds, hormones, and even waste products. Exponential growth in the related literature coupled with the availability of new analytical and computational technologies has inspired novel avenues of inquiry while giving researchers the tools to analyze the plant metabolome to an unprecedented level of detail. As availability of instrumentation and the need for qualitative and especially quantitative metabolic analysis grow within the scientific community so does the need for robust, adaptable, and widely disseminated protocols to enable rapid progression from experimental design to data analysis with minimal input toward method development. This protocol describes the collection and quantitative analysis of plant volatile headspace compounds. It is intended to guide those with little to no experience in analytical chemistry in the quantification of plant volatiles using gas chromatography coupled to mass spectrometry by describing procedures for calibrating and optimizing collection and analysis of these diverse compounds. Key words Plant volatiles, Quantitation, Dynamic headspace, Closed-loop stripping, Metabolic profiling, Gas chromatography, Mass spectrometry
1 Introduction The plant metabolome is remarkably complex. Inside tissues resides a vast collection of compounds integral in plant growth, development, survival, and fitness. The chemodiversity in plant metabolism promotes fitness in the face of innumerable biotic and abiotic challenges by providing plants with a means of adaptation. Plant metabolites are generally regarded in two broad categories as either primary metabolites (such as amino acids, sugars, fatty acids) or secondary (specialized) metabolites, compounds that are not essential for normal growth, development, and reproduction but greatly enhance plant fitness. With 1,700 compounds isolated from more than 90 plant families and representing more than 1 % of plant secondary metabolites [1, 2], plant volatile organic compounds (VOCs) are small molecules with low boiling points and high vapor pressures that enables their volatility at near-ambient temperatures. Ganesh Sriram (ed.), Plant Metabolism: Methods and Protocols, Methods in Molecular Biology, vol. 1083, DOI 10.1007/978-1-62703-661-0_4, © Springer Science+Business Media New York 2014
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In addition to providing delightful palettes of flavor and aroma to comestibles, plant VOCs contribute to attraction of pollinators, predators, and parasitoids [3, 4] while serving as a medium for intra- and interplant signaling [5] and providing direct chemical defense against attacking herbivores and microorganisms [3]. In some interesting cases flowers emit green-leaf volatiles, typically an indicator of tissue damage, as a deceptive show of chemical mimicry to attract predators as surrogate pollinators [6]. Emissions of plant VOCs are both constitutive and inducible [7, 8], highlighting the ecological significance of plant bioactive VOCs. Because plants emit VOCs in response to biotic and abiotic stresses, they have been proposed as a diagnostic indicator for greenhouse production, allowing for more effective integrated pest management strategies and reducing the need for chemical inputs as suppressants or preventatives [9, 10]. Plant VOCs are studied to understand their functions and biosyntheses and are also evaluated for potential uses to benefit an increasingly burdened agricultural system. As such, the quantification of VOCs has in a rather short time become a required method for many venues of plant biological research. As a recently published and detailed method describing the collection of plant volatiles and analysis by gas chromatography- mass spectrometry (GC-MS) is already published in the Methods in Molecular Biology series [11], only aspects of scent collection and analysis that are critical to accurate quantification will be discussed. Readers are encouraged to consult the above-referenced work as well as the body of literature at large for additional information regarding plant volatile trapping and analysis. 1.1 Quantification of Plant Volatiles
Measurement of plant VOCs is a process that has several pitfalls for researchers who may be less experienced in analytical chemistry. Critical for accurate measurements are variables related to volatile collection trap capacity such as duration of volatile collection and quantity of emitting tissues, methods for calibration of detectors (standard curves), and the use of a standard “spike” to normalize samples against volumetric fluctuations and account for day-to-day variations in detector response. This protocol describes the optimization of quantitative scent collections as well as how to properly calibrate a GC-MS analytical method for accurate quantification of volatiles in mixtures collected on sorbent matrices. Examples from VOC collection and analysis of petunia floral scent are used to illustrate a general approach applicable to accurate measurement of most plant VOCs. Data presented in Fig. 1 illustrate the application of the described method by employing a multiple point internal standard calibration (see Subheading 1.1.2, standard curves) and utilizing a selected ion monitoring (SIM) method for GC-MS analysis to quantify nine volatile compounds nocturnally emitted from petunia flowers over the course of 16 h, measured in 2-h segments.
43
Plant VOCs Quantitation Benzaldehyde
600
Methyl Benzoate
nmol*g-1
500 400 300
Benzyl Benzoate
1000
120
750
90
500
60
250
30
0
0
200 100 0
Benzyl Alcohol
200
Phenylethyl Benzoate
150
12
300
100
8
200
50
4
100
0
0
0
Eugenol
lsoeugenoI
Phenylacetaldehyde
12
600
160 9
120
6
6am-8am
4am-6am
2am-4am
12am-2am
8pm-10pm
6am-8am
4am-6am
2am-4am
12am-2am
8pm-10pm
10pm-12am
6pm-8pm
4pm-6pm
6am-8am
4am-6am
0
2am-4am
0 12am-2am
0 8pm-10pm
40
10pm-12am
3
6pm-8pm
150
10pm-12am
80
6pm-8pm
300
4pm-6pm
nmol*g-1
450
4pm-6pm
nmol*g-1
2-Phenylethanol 400
16
Fig. 1 Emission profile of nine volatiles from petunia flowers measured from 4 p.m. to 8 a.m. in late August. Scent columns were exchanged every 2 h to prevent saturation of the sorbent and provide a more accurate quantification. Data was captured by operation of the GC-MS in synchronous scan/SIM mode and volatiles were quantified using SIM peak areas. Data is representative of four biological replicates and the standard deviations illustrate the precision of both the methods for volatile collection and GC-MS analysis
1.1.1 The Volatile Collection Trap
Often taken for granted, the volatile collection trap (VCT) is the most important physical component of plant volatile collection and quantification. The VCT is composed of a small glass column packed with adsorbent material (see Fig. 2) specifically chosen to be complementary to the target volatiles. The type and quantity of adsorbent material packed inside the column will determine the selectivity and volatile capacity of the VCT during sampling. An excellent discussion of commercially available packing materials as well as the kinetics of absorption and adsorption can be found in Nongonierma et al. [12].
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Fig. 2 The volatile collection trap (VCT). This figure illustrates the fitting of a micropipette tip to the tapered end of the VCT in preparation for elution with dichloromethane
Because the bed volume of any sorbent column will ultimately determine its capacity, it is critical to evaluate VCT capacity relative to the duration of headspace sampling and amount of plant tissues sampled to avoid quantitative underestimation due to VCT saturation. As the sorbent nears saturation during sampling, analytes will begin to break through and leach away from the VCT while larger molecules of higher molecular weights (and boiling points) or those having polarities more closely matching those of the adsorbent displace others having less affinity, resulting in quantitative and qualitative misrepresentations of sample composition. Though easily overcome with a bit of preparative experimentation, column saturation is often overlooked as a potential source of artifact, possibly due to the invisible nature of the compounds collected! Nevertheless, a simple time course experiment for determining the saturation point of the VCT can be done by comparing the total ion chromatogram (TIC) peak area of samples collected for varied lengths of time. When plotting TIC peak area versus duration of collection a logarithmic curve will result, allowing the investigator to limit collection to durations falling within the linear phase of column capacity. If longer collection periods are required for an experiment, columns should be exchanged at specified intervals (based upon a time course experiment) to ensure that minimal loss of volatile analytes occurs due to sorbent overloading. The peak areas obtained in the time course experiment are also an important source of information for the researcher as it will help to define the concentration range needed to accurately standardize a quantitation method (see Subheading 1.1.2, standard curves).
Plant VOCs Quantitation 1.1.2 Quantitation Methods
45
There are six approaches typically used to quantify analytes using GC-MS. Here they are described as area percent, relative quantification, single-point external standard, multiple-point external standard, single-point internal standard, and multiple-point internal standard methods. In the area percent method, the quantity of a compound is determined as the percent of its peak area relative to the cumulative area of all peaks on the chromatogram. This results in data that can be used for semi-quantitatively measuring increases or decreases of compounds across a sample set. Though often employed for its ease of use and rapid implementation in comparative approaches to measure unidentified compounds or compounds for which authentic standards are unavailable it does not account for variability in sample preparation or detector response. Relative quantitative approaches are useful when analyzing blends containing compounds of the same class, especially those containing the same number of carbon atoms. This approach assumes that equal amounts of different compounds with similar structures will give near-identical detector responses, enabling the researcher to derive a “universal” response factor based upon a small set of authentic standards for a larger class of compounds. By injecting a known amount (single-point calibration) or several known, varied amounts (multiple-point calibration) of compound(s), the researcher assigns the same response factor derived from those injections to a broader range of similar, related compounds without calibrating for all of those target analytes. Though this method obviously will not have the same level of accuracy as other types of calibration, it is often sufficient for many applications where authentic standards are unavailable and costly or when a prohibitively large number of authentic standards are required. Single-point calibrations involve injecting a compound of known concentration and correlating the detector response to the amount injected. This can be done with or without the addition of an internal standard (ISTD). One variation on this approach, often called a standard addition method, involves quantifying a compound “X” in a sample with and without an added spike of the same compound. The peak area for compound X in the non-spiked sample is subtracted from the peak area of the spiked sample to determine the peak area of a known amount of compound X, thus deriving a detector response factor. (This can be repeated with varied amounts of compound X added to the sample for a multiple point method.) These approaches require less preparation but neglect the sigmoidal nature of the detector’s response curve by assuming that the response of the detector will be linear across all concentrations of analytes. (This is analogous to plotting a linear function with only one data point.) This requires caution, as detector responses to different classes of compounds are highly variable. In addition, poor chromatographic performance due to
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analyte/column mismatches often leads to peak broadening and bad peak shapes, both of which tend to decrease an instrument’s sensitivity. Two such compounds that display poor chromatographic performance at low concentrations in the more typical GC column (HP-5) are commonly analyzed in petunia, benzyl alcohol, and 2-phenylethanol. In our method these two compounds become difficult to quantify accurately when less than 500 pmol is injected onto the column, whereas compounds such as methyl benzoate and phenylethyl benzoate show linear detector responses below 5 pmol. The most accurate way to determine detector response factors for quantification is by using a multiple-point calibration method in conjunction with an ISTD. This is accomplished by analyzing a series of standard mixes containing identical amounts of ISTD with varied but known amounts of the standards. The data thus obtained is used to calculate a response factor relative to the ISTD compound (see Subheading 3.3). This approach not only allows the investigator to compensate for fluctuations in sample volume and detector response drift but also permits identification of a compound’s linear response range (greatest accuracy is obtained within this range). Thankfully, most instrumentation in use today for metabolite determination will offer sensitivity and a linear response range that encompasses the range of most relevant compounds without concentration or dilution. 1.1.3 Response Factors
In the context of chromatography, a response factor (Rf) is a ratio defining the relationship between the quantity of an analyte and the corresponding signal intensity from the detector. Rf is typically derived by analyzing a dilution series of authentic standards at known, varied concentrations to compare the detector response to a compound across a range of concentrations. Detector response can always be defined by a sigmoidal function across an infinite range of concentrations; a detector will show little or no response to an analyte below the limit of detection followed by a linear response as the amount increases and eventually a logarithmic saturation. In many experiments this is relevant only in terms of limit of detection and quantitation.
1.1.4 The Internal Standard
Quantification of volatile emissions collected from headspace presents unique challenges that are more easily avoided during metabolite pool extraction and analysis. Proper containment of volatiles during headspace sampling and elution requires specialized equipment due to their dispersive nature; even the most well-designed and engineered apparatuses are subject to immeasurable losses during sample collection and preparation. Thus a balance must be struck between cost, ease of use, throughput, and quantitative accuracy with the ultimate goal and measure of success being the degree of precision apparent across biological replicates within a sample set.
Plant VOCs Quantitation
47
When quantifying metabolite pools, the internal standard is most effective when added prior to any sample preparation steps. Spiking internal standard into frozen or fresh tissues prior to disruption provides the best assurance that any vestigial losses or losses due to sample degradation can be estimated and accounted for during data analysis, especially when stable isotope-labeled analogs of target metabolites are used as the internal standard. Because volatile headspace collection techniques preclude this method of introduction for the internal standard, an alternative strategy is required. As an optimum method is yet to be described for introducing an internal standard during volatile collection, the best alternative is to spike standards into samples immediately following VCT elution to provide control for volumetric fluctuations between samples. If proper technique is consistently observed, all incurred losses will be more or less equal between samples, thereby allowing for a high degree of accuracy and precision. Though this is not a technically correct terminology, for simplicity this compound will also be referred to as the ISTD. 1.1.5 Full Scan Versus Selected Ion Monitoring
Electron impact mass spectrometers in use with gas chromatography today (GC-EI/MS) offer flexibility and high sensitivity. These detectors are capable of operation in three different acquisition modes, full scan, SIM, or synchronous scan/SIM, to provide both qualitative analysis as well as selectivity for targeted metabolites and metabolite classes. In scan mode the quadrupole repeatedly sweeps across a predefined m/z range (typically from 50 to 550) producing a set of mass spectra that represents the total ion abundances across the range within a specified duration. For most laboratories this is the preferred method of operation as it provides a more universal detection for GC analytes, is compatible with commercially available mass spectral databases, and offers adequate sensitivity for most samples. SIM mode permits an increased sampling rate for targeted m/z fragments, increases accuracy 10–100- fold that of scan mode by drastically reducing background signals that can mask trace compounds, and simplifies deconvolution of “busy” chromatograms.
2 Materials 2.1 Relative Response Factors via Multiple-Point Internal Standard
1. 2-mL glass autosampler vials with polypropylene caps and PTFE/silicone septa (Agilent Technologies, Wilmington, Delaware, USA). 2. 500-μL glass autosampler vial insert (Agilent Technologies, Wilmington, Delaware, USA). 3. Dichloromethane, mass spectrometry grade.
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Anthony V. Qualley and Natalia Dudareva
4. Gas chromatograph (e.g., Agilent 6890N) coupled to a mass spectrometer (e.g., Agilent 5975B inert MSD). 5. Capillary column, HP-5MS (30 m × 0.25 mm, 0.25-μm film thickness; Agilent Technologies, Wilmington, Delaware, USA). 6. Ultrahigh-purity (99.998 %) helium for GC carrier gas. 7. Gastight syringes, 1 and 10 mL. 8. The following analytical grade authentic standards (petunia floral scent compounds): Benzaldehyde, benzyl alcohol, methyl benzoate, benzyl acetate, methyl salicylate, phenylmethylacetate, 2-phenylethanol, phenylethyl acetate, phenylethyl benzoate, benzyl benzoate, eugenol, isoeugenol, vanillin, naphthalene (ISTD). 2.2 Sorbent Saturation Curves
1. Flowering plants, Petunia x hybrida cv. Mitchell diploid (W115). 2. 5 % (w/v) sucrose in ddH2O. 3. Wheaton black phenolic 38-430 screw cap. 4. Volatile collection equipment including glass chambers, air delivery system, and PTFE tubing and adapters for attaching tubing to chambers (see Note 1 and Fig. 3).
Fig. 3 Petunia flowers enclosed in the volatile collection chamber in preparation for a volatile trapping. Note the placement of the VCT and the use of adapters to connect the airflow tubing to the column and chamber lid
Plant VOCs Quantitation
49
5. BTC diaphragm pumps with brushless motor (B.1F32E1. A12VDC; Hargraves Technology Corp., Mooresville, NC) and 12VDC power supply. 6. Volatile collection traps with Porapak-Q resin, 80/100 mesh (Analytical Research Systems, Inc., Gainesville, FL). 7. 2-mL glass autosampler vials with polypropylene caps and PTFE/silicone septa (Agilent Technologies, Wilmington, Delaware, USA). 8. 500-μL glass autosampler vial insert (Agilent Technologies, Wilmington, Delaware, USA). 9. Dichloromethane, mass spectrometry grade. 10. 1 mM naphthalene (ISTD) in dichloromethane. 11. Gas chromatograph (e.g., Agilent 6890N) coupled to a mass spectrometer (e.g., Agilent 5975B inert MSD). 12. Capillary column, HP-5MS (30 m × 0.25 mm, 0.25-μm film thickness; Agilent Technologies, Wilmington, Delaware, USA). 13. Ultrahigh-purity (99.998 %) helium for GC carrier gas.
3 Methods 3.1 Relative Response Factors via Multiple-Point Internal Standard
1. Prepare separately 5 mL stocks of 10 mM concentration (in dichloromethane) for each of the 13 analytical standards (see Note 2). Do not include ISTD compound. These are your standard stocks.
3.1.1 Analysis of Standard Stock Mixes
2. Mix 1 mL each of the 13 stocks and add 7 mL dichloromethane to produce a 20 mL mixture containing 10 μmol of each compound (500 μM each). This is the standard stock mix. 3. Make a 5× serial dilution series of the standard stock mix. Each dilution should total 600 μL. The series should contain at least five samples, preferably more (see Note 3). 4. Pipette 500 μL of each dilution into an autosampler vial insert. Add 20 μL of 1 mM naphthalene (ISTD) and seal inside the autosampler vial. 5. Analyze the samples by GC-MS. Analytical conditions should be identical to those used to analyze biological samples, especially regarding the mass spectrometer (see Note 4 regarding selection of fragment ions for SIM methods).
3.1.2 GC/MS Parameters
(a) Inlet temperature is set to 280 °C. (b) GC interface temperature set to 280 °C. (c) MS source set to 250 °C. (d) Quadrupole set to 150 °C.
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Anthony V. Qualley and Natalia Dudareva
(e) Helium flow rate set at 1.0 mL/min. (f) GC temperature gradient programmed as follows: Initial temperature of 40 °C held for 2 min followed by gradient of 8 °C/ min to 260 °C, and hold for 3 min. 3.2 Sorbent Saturation Curves
1. Dispense 10 mL of 5 % sucrose solution into each of the six phenolic screw caps. 2. Remove 54 flowers (2 days post anthesis) from the plants leaving at least 1 cm of pedicel (see Note 5). Place immediately into a suitable airtight container (plastic food containers work well) to prevent desiccation. 3. When all flowers are collected, remove the pedicels with a fresh blade and immediately place the cut end into the screw caps so that it is submerged to a depth of 3–5 mm. Use three flowers per cap. 4. When all caps are prepared, place each into its own volatile collection chamber and close the lid. 5. Connect the air pump exhaust tubing to one port of the collection chamber (see Fig. 3). 6. Using a PTFE adapter connect the VCT to the other port on the chamber. Connect tubing between the column and the air pump inlet using another adapter. 7. Activate the pumps. Label the pumps and columns 1–6. After the first hour, disconnect pump 1 and place the column into a 15 mL conical tube. Elute within 12 h or store at −20 °C. Repeat sequentially for samples 2–6 (every hour) until the 6-h collection is completed. 8. Elute the columns into the sample vial inserts by using a thin strip of parafilm to create an airtight seal between a 20 and 100 μL pipette tip and the VCT (see Fig. 2) and gently pushing 500 μL of dichloromethane through the VCT (see Note 6). 9. Add 20 μL of naphthalene ISTD to the eluted sample and analyze by GC-MS (see Subheading 3.1.2). 10. Using data obtained in a full-scan mode compute the total TIC peak area for each sample (excluding the ISTD peak). Normalize this value by the peak area of ISTD. 11. Visualize the data by creating a plot of collection time (x) versus normalized total peak area (y) to identify the point of VCT saturation. Collection times and tissue amounts should be adjusted in future experiments so that the collection is well within the linear range for total peak area (see Note 7).
Plant VOCs Quantitation
51
Phenylethyl Benzoate Calibration Curve 7.E+06
SIM Peak Area
6.E+06 5.E+06 4.E+06
R2 = 0.99956
3.E+06 2.E+06 1.E+06 0.E+00 0
2E-11
4E-11
6E-11
8E-11
1E-10
1.2E-10
Moles on Column
Fig. 4 Plot of peak areas versus moles of compound injected into the GC-MS for phenylethyl benzoate. The response is linear across the entire dilution series tested
3.3 Data Analysis and Application of Rf Values in Quantification
Once data collection is complete, chromatograms can be integrated using the data analysis software provided by the instrument manufacturer. The integration should be inspected to ensure that all peaks have a proper baseline assignment and have been integrated correctly. After the peak areas have been extracted for each target compound, plot them across the entire dilution series versus the molar amounts injected into the GC (see Fig. 4). Biological samples quantified must fall within the linear peak area response range covered by the calibration for the best accuracy. Next, calculate the Rf for each compound relative to the ISTD. For each concentration, utilize the peak areas from the analyte and the ISTD with formula 1 to calculate fi. The values across the working concentration range for a given compound will ideally be near identical. Average fi across the linear range of the calibration curve to obtain the final fi value: fi = ( ACpd / AISTD ) × ( M ISTD / M Cpd )
(1)
M is the number of moles of the given compound, A the measured peak area, and fi the derived response factor. Rearrangement of Eq. 1 yields the formula to calculate moles of compound in the biological sample:
M Cpd = ( ACpd / AISTD ) × ( M ISTD / fi )
(2)
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4 Notes 1. Equipment used for volatile sampling can become contaminated through normal use by adherence or absorption of the volatiles. This necessitates a few precautions to prevent analyte carryover between samples. Steps to mitigate contamination include the cleaning of glass containers with detergents or organic solvents and purging of scent pumps and tubes with clean air prior to use. Parameters for purging will depend ultimately on the equipment and materials used and should be optimized experimentally if needed. 2. Preparation of standard stocks for calibration of GC-MS should be done using an analytical balance for best accuracy. Because pipetting viscous compounds often produces inaccurate results a better strategy is to transfer the standards into suitable glass containers and determine their amounts by mass. Finally, solvent volume can be adjusted to achieve the desired final concentration. 3. Optimally a serial dilution series used for a quantitation curve will bracket the range of peak area values observed from biological samples during experimentation. 4. Control software for most modern GC-MS equipment facilitates the automated, optimized selection of SIM ions for targeted peaks in a chromatogram. Please consult user manuals for relevant information on its configuration. 5. As is true when sampling of petunia floral volatiles, it may be critical to select plant tissues of the same developmental stage, size, and general appearance (no obvious morphological deformities) to avoid introducing artifact into the quantification. 6. Elution of volatiles from the VCT can be done using a variety of approaches. The technique typically employed in our lab involves using parafilm to secure an airtight connection between the glass VCT (tapered end) and a 20–200 μL micropipette tip (see Fig. 2). If a thin strip of parafilm is wrapped around the VCT at the very top of the pipette tip then contact with the organic solvent, which will dissolve parafilm and contaminate the sample, can be avoided. Add solvent at the column top and use a suitable rubber bulb to force it through the sorbent bed. Practice this procedure beforehand to perfect the technique. 7. Because environmental conditions (especially temperatures and seasonal variations) and plant health can drastically affect volatile emissions, investigators should examine all experimental data to determine if the maximum capacity is being reached by comparing total peak areas to sorbent saturation curves and avoid longer collection times that may approach sorbent capacity.
Plant VOCs Quantitation
Acknowledgements
53
This work was supported by a grant from the National Science Foundation (Grant No. MCB-0911987).
References 1. Dudareva N, Negre F, Nagegowda DA, Orlova I (2006) Plant volatiles: recent advances and future perspectives. Crit Rev Plant Sci 25:417–440 2. Knudsen JT, Eriksson R, Gershenzon J, Stahl B (2006) Diversity and distribution of floral scent. Bot Rev 72:1–120 3. Paré PW, Tumlinson JH (1999) Plant volatiles as a defense against insect herbivores. Plant Phys 121:325–331 4. D’Alessandro MD, Turlings TCJ (2005) In situ modification of herbivore-induced plant odors: a novel approach to study the attractiveness of volatile organic compounds to parasitic wasps. Chem Senses 30:739–753 5. Muroi A, Ramadan A, Nishihara M, Yamamoto M, Ozawa R, Takabayashi J, Arimura G (2011) The composite effect of transgenic plant volatiles for acquired immunity to herbivory caused by inter-plant communications. PLoS One 6:e24594 6. Brodmann J, Twele R, Francke W, Hölzler G, Zhang Q, Ayasse M (2008) Orchids mimic green-leaf volatiles to attract prey-hunting wasps for pollination. Curr Biol 18:740–744 7. Peñaflor MFGV, Erb M, Robert CAM, Miranda LA, Werneburg AG, Dossi FCA,
Turlings TCJ, Bento JMS (2011) Oviposition by a moth suppresses constitutive and herbivore- induced plant volatiles in maize. Planta 234:207–215 8. Dicke M, Baldwin IT (2010) The evolutionary context for herbivore-induced plant volatiles: beyond the “cry for help”. Trends Plant Sci 15:167–175 9. Jansen RMC, Wildt J, Kappers IF, Bouwmeester HJ, Hofstee JW, Van Henten EJ (2011) Detection of diseased plants by analysis of volatile organic compound emission. Annu Rev Phytopathol 49:157–174 10. Miresmailli S, Gries R, Gries G, Zamar RH, Isman MB (2010) Herbivore-induced plant volatiles allow detection of Trichoplusia ni infestation on greenhouse tomato plants. Pest Manag Sci 66:916–924 11. Qualley AV, Dudareva N (2009) Metabolomics of plant volatiles. In: Belostotsky D (ed) Methods Mol Biol 553:329–343 12. Nongonierma A, Voilley A, Cayot P, Le Quéré JL, Springett M (2006) Mechanisms of extraction of aroma compounds from foods, using adsorbents. Effect of various parameters. Food Rev Int 22:51–94
1 Introduction The plant metabolome is remarkably complex. Inside tissues resides a vast collection of compounds integral in plant growth, development, survival, and fitness. The chemodiversity in plant metabolism promotes fitness in the face of innumerable biotic and abiotic challenges by providing plants with a means of adaptation. Plant metabolites are generally regarded in two broad categories as either primary metabolites (such as amino acids, sugars, fatty acids) or secondary (specialized) metabolites, compounds that are not essential for normal growth, development, and reproduction but greatly enhance plant fitness. With 1,700 compounds isolated from more than 90 plant families and representing more than 1 % of plant secondary metabolites [1, 2], plant volatile organic compounds (VOCs) are small molecules with low boiling points and high vapor pressures that enables their volatility at near-ambient temperatures. Ganesh Sriram (ed.), Plant Metabolism: Methods and Protocols, Methods in Molecular Biology, vol. 1083, DOI 10.1007/978-1-62703-661-0_4, © Springer Science+Business Media New York 2014
41
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Anthony V. Qualley and Natalia Dudareva
In addition to providing delightful palettes of flavor and aroma to comestibles, plant VOCs contribute to attraction of pollinators, predators, and parasitoids [3, 4] while serving as a medium for intra- and interplant signaling [5] and providing direct chemical defense against attacking herbivores and microorganisms [3]. In some interesting cases flowers emit green-leaf volatiles, typically an indicator of tissue damage, as a deceptive show of chemical mimicry to attract predators as surrogate pollinators [6]. Emissions of plant VOCs are both constitutive and inducible [7, 8], highlighting the ecological significance of plant bioactive VOCs. Because plants emit VOCs in response to biotic and abiotic stresses, they have been proposed as a diagnostic indicator for greenhouse production, allowing for more effective integrated pest management strategies and reducing the need for chemical inputs as suppressants or preventatives [9, 10]. Plant VOCs are studied to understand their functions and biosyntheses and are also evaluated for potential uses to benefit an increasingly burdened agricultural system. As such, the quantification of VOCs has in a rather short time become a required method for many venues of plant biological research. As a recently published and detailed method describing the collection of plant volatiles and analysis by gas chromatography- mass spectrometry (GC-MS) is already published in the Methods in Molecular Biology series [11], only aspects of scent collection and analysis that are critical to accurate quantification will be discussed. Readers are encouraged to consult the above-referenced work as well as the body of literature at large for additional information regarding plant volatile trapping and analysis. 1.1 Quantification of Plant Volatiles
Measurement of plant VOCs is a process that has several pitfalls for researchers who may be less experienced in analytical chemistry. Critical for accurate measurements are variables related to volatile collection trap capacity such as duration of volatile collection and quantity of emitting tissues, methods for calibration of detectors (standard curves), and the use of a standard “spike” to normalize samples against volumetric fluctuations and account for day-to-day variations in detector response. This protocol describes the optimization of quantitative scent collections as well as how to properly calibrate a GC-MS analytical method for accurate quantification of volatiles in mixtures collected on sorbent matrices. Examples from VOC collection and analysis of petunia floral scent are used to illustrate a general approach applicable to accurate measurement of most plant VOCs. Data presented in Fig. 1 illustrate the application of the described method by employing a multiple point internal standard calibration (see Subheading 1.1.2, standard curves) and utilizing a selected ion monitoring (SIM) method for GC-MS analysis to quantify nine volatile compounds nocturnally emitted from petunia flowers over the course of 16 h, measured in 2-h segments.
43
Plant VOCs Quantitation Benzaldehyde
600
Methyl Benzoate
nmol*g-1
500 400 300
Benzyl Benzoate
1000
120
750
90
500
60
250
30
0
0
200 100 0
Benzyl Alcohol
200
Phenylethyl Benzoate
150
12
300
100
8
200
50
4
100
0
0
0
Eugenol
lsoeugenoI
Phenylacetaldehyde
12
600
160 9
120
6
6am-8am
4am-6am
2am-4am
12am-2am
8pm-10pm
6am-8am
4am-6am
2am-4am
12am-2am
8pm-10pm
10pm-12am
6pm-8pm
4pm-6pm
6am-8am
4am-6am
0
2am-4am
0 12am-2am
0 8pm-10pm
40
10pm-12am
3
6pm-8pm
150
10pm-12am
80
6pm-8pm
300
4pm-6pm
nmol*g-1
450
4pm-6pm
nmol*g-1
2-Phenylethanol 400
16
Fig. 1 Emission profile of nine volatiles from petunia flowers measured from 4 p.m. to 8 a.m. in late August. Scent columns were exchanged every 2 h to prevent saturation of the sorbent and provide a more accurate quantification. Data was captured by operation of the GC-MS in synchronous scan/SIM mode and volatiles were quantified using SIM peak areas. Data is representative of four biological replicates and the standard deviations illustrate the precision of both the methods for volatile collection and GC-MS analysis
1.1.1 The Volatile Collection Trap
Often taken for granted, the volatile collection trap (VCT) is the most important physical component of plant volatile collection and quantification. The VCT is composed of a small glass column packed with adsorbent material (see Fig. 2) specifically chosen to be complementary to the target volatiles. The type and quantity of adsorbent material packed inside the column will determine the selectivity and volatile capacity of the VCT during sampling. An excellent discussion of commercially available packing materials as well as the kinetics of absorption and adsorption can be found in Nongonierma et al. [12].
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Fig. 2 The volatile collection trap (VCT). This figure illustrates the fitting of a micropipette tip to the tapered end of the VCT in preparation for elution with dichloromethane
Because the bed volume of any sorbent column will ultimately determine its capacity, it is critical to evaluate VCT capacity relative to the duration of headspace sampling and amount of plant tissues sampled to avoid quantitative underestimation due to VCT saturation. As the sorbent nears saturation during sampling, analytes will begin to break through and leach away from the VCT while larger molecules of higher molecular weights (and boiling points) or those having polarities more closely matching those of the adsorbent displace others having less affinity, resulting in quantitative and qualitative misrepresentations of sample composition. Though easily overcome with a bit of preparative experimentation, column saturation is often overlooked as a potential source of artifact, possibly due to the invisible nature of the compounds collected! Nevertheless, a simple time course experiment for determining the saturation point of the VCT can be done by comparing the total ion chromatogram (TIC) peak area of samples collected for varied lengths of time. When plotting TIC peak area versus duration of collection a logarithmic curve will result, allowing the investigator to limit collection to durations falling within the linear phase of column capacity. If longer collection periods are required for an experiment, columns should be exchanged at specified intervals (based upon a time course experiment) to ensure that minimal loss of volatile analytes occurs due to sorbent overloading. The peak areas obtained in the time course experiment are also an important source of information for the researcher as it will help to define the concentration range needed to accurately standardize a quantitation method (see Subheading 1.1.2, standard curves).
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There are six approaches typically used to quantify analytes using GC-MS. Here they are described as area percent, relative quantification, single-point external standard, multiple-point external standard, single-point internal standard, and multiple-point internal standard methods. In the area percent method, the quantity of a compound is determined as the percent of its peak area relative to the cumulative area of all peaks on the chromatogram. This results in data that can be used for semi-quantitatively measuring increases or decreases of compounds across a sample set. Though often employed for its ease of use and rapid implementation in comparative approaches to measure unidentified compounds or compounds for which authentic standards are unavailable it does not account for variability in sample preparation or detector response. Relative quantitative approaches are useful when analyzing blends containing compounds of the same class, especially those containing the same number of carbon atoms. This approach assumes that equal amounts of different compounds with similar structures will give near-identical detector responses, enabling the researcher to derive a “universal” response factor based upon a small set of authentic standards for a larger class of compounds. By injecting a known amount (single-point calibration) or several known, varied amounts (multiple-point calibration) of compound(s), the researcher assigns the same response factor derived from those injections to a broader range of similar, related compounds without calibrating for all of those target analytes. Though this method obviously will not have the same level of accuracy as other types of calibration, it is often sufficient for many applications where authentic standards are unavailable and costly or when a prohibitively large number of authentic standards are required. Single-point calibrations involve injecting a compound of known concentration and correlating the detector response to the amount injected. This can be done with or without the addition of an internal standard (ISTD). One variation on this approach, often called a standard addition method, involves quantifying a compound “X” in a sample with and without an added spike of the same compound. The peak area for compound X in the non-spiked sample is subtracted from the peak area of the spiked sample to determine the peak area of a known amount of compound X, thus deriving a detector response factor. (This can be repeated with varied amounts of compound X added to the sample for a multiple point method.) These approaches require less preparation but neglect the sigmoidal nature of the detector’s response curve by assuming that the response of the detector will be linear across all concentrations of analytes. (This is analogous to plotting a linear function with only one data point.) This requires caution, as detector responses to different classes of compounds are highly variable. In addition, poor chromatographic performance due to
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analyte/column mismatches often leads to peak broadening and bad peak shapes, both of which tend to decrease an instrument’s sensitivity. Two such compounds that display poor chromatographic performance at low concentrations in the more typical GC column (HP-5) are commonly analyzed in petunia, benzyl alcohol, and 2-phenylethanol. In our method these two compounds become difficult to quantify accurately when less than 500 pmol is injected onto the column, whereas compounds such as methyl benzoate and phenylethyl benzoate show linear detector responses below 5 pmol. The most accurate way to determine detector response factors for quantification is by using a multiple-point calibration method in conjunction with an ISTD. This is accomplished by analyzing a series of standard mixes containing identical amounts of ISTD with varied but known amounts of the standards. The data thus obtained is used to calculate a response factor relative to the ISTD compound (see Subheading 3.3). This approach not only allows the investigator to compensate for fluctuations in sample volume and detector response drift but also permits identification of a compound’s linear response range (greatest accuracy is obtained within this range). Thankfully, most instrumentation in use today for metabolite determination will offer sensitivity and a linear response range that encompasses the range of most relevant compounds without concentration or dilution. 1.1.3 Response Factors
In the context of chromatography, a response factor (Rf) is a ratio defining the relationship between the quantity of an analyte and the corresponding signal intensity from the detector. Rf is typically derived by analyzing a dilution series of authentic standards at known, varied concentrations to compare the detector response to a compound across a range of concentrations. Detector response can always be defined by a sigmoidal function across an infinite range of concentrations; a detector will show little or no response to an analyte below the limit of detection followed by a linear response as the amount increases and eventually a logarithmic saturation. In many experiments this is relevant only in terms of limit of detection and quantitation.
1.1.4 The Internal Standard
Quantification of volatile emissions collected from headspace presents unique challenges that are more easily avoided during metabolite pool extraction and analysis. Proper containment of volatiles during headspace sampling and elution requires specialized equipment due to their dispersive nature; even the most well-designed and engineered apparatuses are subject to immeasurable losses during sample collection and preparation. Thus a balance must be struck between cost, ease of use, throughput, and quantitative accuracy with the ultimate goal and measure of success being the degree of precision apparent across biological replicates within a sample set.
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When quantifying metabolite pools, the internal standard is most effective when added prior to any sample preparation steps. Spiking internal standard into frozen or fresh tissues prior to disruption provides the best assurance that any vestigial losses or losses due to sample degradation can be estimated and accounted for during data analysis, especially when stable isotope-labeled analogs of target metabolites are used as the internal standard. Because volatile headspace collection techniques preclude this method of introduction for the internal standard, an alternative strategy is required. As an optimum method is yet to be described for introducing an internal standard during volatile collection, the best alternative is to spike standards into samples immediately following VCT elution to provide control for volumetric fluctuations between samples. If proper technique is consistently observed, all incurred losses will be more or less equal between samples, thereby allowing for a high degree of accuracy and precision. Though this is not a technically correct terminology, for simplicity this compound will also be referred to as the ISTD. 1.1.5 Full Scan Versus Selected Ion Monitoring
Electron impact mass spectrometers in use with gas chromatography today (GC-EI/MS) offer flexibility and high sensitivity. These detectors are capable of operation in three different acquisition modes, full scan, SIM, or synchronous scan/SIM, to provide both qualitative analysis as well as selectivity for targeted metabolites and metabolite classes. In scan mode the quadrupole repeatedly sweeps across a predefined m/z range (typically from 50 to 550) producing a set of mass spectra that represents the total ion abundances across the range within a specified duration. For most laboratories this is the preferred method of operation as it provides a more universal detection for GC analytes, is compatible with commercially available mass spectral databases, and offers adequate sensitivity for most samples. SIM mode permits an increased sampling rate for targeted m/z fragments, increases accuracy 10–100- fold that of scan mode by drastically reducing background signals that can mask trace compounds, and simplifies deconvolution of “busy” chromatograms.
2 Materials 2.1 Relative Response Factors via Multiple-Point Internal Standard
1. 2-mL glass autosampler vials with polypropylene caps and PTFE/silicone septa (Agilent Technologies, Wilmington, Delaware, USA). 2. 500-μL glass autosampler vial insert (Agilent Technologies, Wilmington, Delaware, USA). 3. Dichloromethane, mass spectrometry grade.
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4. Gas chromatograph (e.g., Agilent 6890N) coupled to a mass spectrometer (e.g., Agilent 5975B inert MSD). 5. Capillary column, HP-5MS (30 m × 0.25 mm, 0.25-μm film thickness; Agilent Technologies, Wilmington, Delaware, USA). 6. Ultrahigh-purity (99.998 %) helium for GC carrier gas. 7. Gastight syringes, 1 and 10 mL. 8. The following analytical grade authentic standards (petunia floral scent compounds): Benzaldehyde, benzyl alcohol, methyl benzoate, benzyl acetate, methyl salicylate, phenylmethylacetate, 2-phenylethanol, phenylethyl acetate, phenylethyl benzoate, benzyl benzoate, eugenol, isoeugenol, vanillin, naphthalene (ISTD). 2.2 Sorbent Saturation Curves
1. Flowering plants, Petunia x hybrida cv. Mitchell diploid (W115). 2. 5 % (w/v) sucrose in ddH2O. 3. Wheaton black phenolic 38-430 screw cap. 4. Volatile collection equipment including glass chambers, air delivery system, and PTFE tubing and adapters for attaching tubing to chambers (see Note 1 and Fig. 3).
Fig. 3 Petunia flowers enclosed in the volatile collection chamber in preparation for a volatile trapping. Note the placement of the VCT and the use of adapters to connect the airflow tubing to the column and chamber lid
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5. BTC diaphragm pumps with brushless motor (B.1F32E1. A12VDC; Hargraves Technology Corp., Mooresville, NC) and 12VDC power supply. 6. Volatile collection traps with Porapak-Q resin, 80/100 mesh (Analytical Research Systems, Inc., Gainesville, FL). 7. 2-mL glass autosampler vials with polypropylene caps and PTFE/silicone septa (Agilent Technologies, Wilmington, Delaware, USA). 8. 500-μL glass autosampler vial insert (Agilent Technologies, Wilmington, Delaware, USA). 9. Dichloromethane, mass spectrometry grade. 10. 1 mM naphthalene (ISTD) in dichloromethane. 11. Gas chromatograph (e.g., Agilent 6890N) coupled to a mass spectrometer (e.g., Agilent 5975B inert MSD). 12. Capillary column, HP-5MS (30 m × 0.25 mm, 0.25-μm film thickness; Agilent Technologies, Wilmington, Delaware, USA). 13. Ultrahigh-purity (99.998 %) helium for GC carrier gas.
3 Methods 3.1 Relative Response Factors via Multiple-Point Internal Standard
1. Prepare separately 5 mL stocks of 10 mM concentration (in dichloromethane) for each of the 13 analytical standards (see Note 2). Do not include ISTD compound. These are your standard stocks.
3.1.1 Analysis of Standard Stock Mixes
2. Mix 1 mL each of the 13 stocks and add 7 mL dichloromethane to produce a 20 mL mixture containing 10 μmol of each compound (500 μM each). This is the standard stock mix. 3. Make a 5× serial dilution series of the standard stock mix. Each dilution should total 600 μL. The series should contain at least five samples, preferably more (see Note 3). 4. Pipette 500 μL of each dilution into an autosampler vial insert. Add 20 μL of 1 mM naphthalene (ISTD) and seal inside the autosampler vial. 5. Analyze the samples by GC-MS. Analytical conditions should be identical to those used to analyze biological samples, especially regarding the mass spectrometer (see Note 4 regarding selection of fragment ions for SIM methods).
3.1.2 GC/MS Parameters
(a) Inlet temperature is set to 280 °C. (b) GC interface temperature set to 280 °C. (c) MS source set to 250 °C. (d) Quadrupole set to 150 °C.
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(e) Helium flow rate set at 1.0 mL/min. (f) GC temperature gradient programmed as follows: Initial temperature of 40 °C held for 2 min followed by gradient of 8 °C/ min to 260 °C, and hold for 3 min. 3.2 Sorbent Saturation Curves
1. Dispense 10 mL of 5 % sucrose solution into each of the six phenolic screw caps. 2. Remove 54 flowers (2 days post anthesis) from the plants leaving at least 1 cm of pedicel (see Note 5). Place immediately into a suitable airtight container (plastic food containers work well) to prevent desiccation. 3. When all flowers are collected, remove the pedicels with a fresh blade and immediately place the cut end into the screw caps so that it is submerged to a depth of 3–5 mm. Use three flowers per cap. 4. When all caps are prepared, place each into its own volatile collection chamber and close the lid. 5. Connect the air pump exhaust tubing to one port of the collection chamber (see Fig. 3). 6. Using a PTFE adapter connect the VCT to the other port on the chamber. Connect tubing between the column and the air pump inlet using another adapter. 7. Activate the pumps. Label the pumps and columns 1–6. After the first hour, disconnect pump 1 and place the column into a 15 mL conical tube. Elute within 12 h or store at −20 °C. Repeat sequentially for samples 2–6 (every hour) until the 6-h collection is completed. 8. Elute the columns into the sample vial inserts by using a thin strip of parafilm to create an airtight seal between a 20 and 100 μL pipette tip and the VCT (see Fig. 2) and gently pushing 500 μL of dichloromethane through the VCT (see Note 6). 9. Add 20 μL of naphthalene ISTD to the eluted sample and analyze by GC-MS (see Subheading 3.1.2). 10. Using data obtained in a full-scan mode compute the total TIC peak area for each sample (excluding the ISTD peak). Normalize this value by the peak area of ISTD. 11. Visualize the data by creating a plot of collection time (x) versus normalized total peak area (y) to identify the point of VCT saturation. Collection times and tissue amounts should be adjusted in future experiments so that the collection is well within the linear range for total peak area (see Note 7).
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Phenylethyl Benzoate Calibration Curve 7.E+06
SIM Peak Area
6.E+06 5.E+06 4.E+06
R2 = 0.99956
3.E+06 2.E+06 1.E+06 0.E+00 0
2E-11
4E-11
6E-11
8E-11
1E-10
1.2E-10
Moles on Column
Fig. 4 Plot of peak areas versus moles of compound injected into the GC-MS for phenylethyl benzoate. The response is linear across the entire dilution series tested
3.3 Data Analysis and Application of Rf Values in Quantification
Once data collection is complete, chromatograms can be integrated using the data analysis software provided by the instrument manufacturer. The integration should be inspected to ensure that all peaks have a proper baseline assignment and have been integrated correctly. After the peak areas have been extracted for each target compound, plot them across the entire dilution series versus the molar amounts injected into the GC (see Fig. 4). Biological samples quantified must fall within the linear peak area response range covered by the calibration for the best accuracy. Next, calculate the Rf for each compound relative to the ISTD. For each concentration, utilize the peak areas from the analyte and the ISTD with formula 1 to calculate fi. The values across the working concentration range for a given compound will ideally be near identical. Average fi across the linear range of the calibration curve to obtain the final fi value: fi = ( ACpd / AISTD ) × ( M ISTD / M Cpd )
(1)
M is the number of moles of the given compound, A the measured peak area, and fi the derived response factor. Rearrangement of Eq. 1 yields the formula to calculate moles of compound in the biological sample:
M Cpd = ( ACpd / AISTD ) × ( M ISTD / fi )
(2)
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4 Notes 1. Equipment used for volatile sampling can become contaminated through normal use by adherence or absorption of the volatiles. This necessitates a few precautions to prevent analyte carryover between samples. Steps to mitigate contamination include the cleaning of glass containers with detergents or organic solvents and purging of scent pumps and tubes with clean air prior to use. Parameters for purging will depend ultimately on the equipment and materials used and should be optimized experimentally if needed. 2. Preparation of standard stocks for calibration of GC-MS should be done using an analytical balance for best accuracy. Because pipetting viscous compounds often produces inaccurate results a better strategy is to transfer the standards into suitable glass containers and determine their amounts by mass. Finally, solvent volume can be adjusted to achieve the desired final concentration. 3. Optimally a serial dilution series used for a quantitation curve will bracket the range of peak area values observed from biological samples during experimentation. 4. Control software for most modern GC-MS equipment facilitates the automated, optimized selection of SIM ions for targeted peaks in a chromatogram. Please consult user manuals for relevant information on its configuration. 5. As is true when sampling of petunia floral volatiles, it may be critical to select plant tissues of the same developmental stage, size, and general appearance (no obvious morphological deformities) to avoid introducing artifact into the quantification. 6. Elution of volatiles from the VCT can be done using a variety of approaches. The technique typically employed in our lab involves using parafilm to secure an airtight connection between the glass VCT (tapered end) and a 20–200 μL micropipette tip (see Fig. 2). If a thin strip of parafilm is wrapped around the VCT at the very top of the pipette tip then contact with the organic solvent, which will dissolve parafilm and contaminate the sample, can be avoided. Add solvent at the column top and use a suitable rubber bulb to force it through the sorbent bed. Practice this procedure beforehand to perfect the technique. 7. Because environmental conditions (especially temperatures and seasonal variations) and plant health can drastically affect volatile emissions, investigators should examine all experimental data to determine if the maximum capacity is being reached by comparing total peak areas to sorbent saturation curves and avoid longer collection times that may approach sorbent capacity.
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Acknowledgements
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This work was supported by a grant from the National Science Foundation (Grant No. MCB-0911987).
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