The authors thank the Servicios Centrales de Apoyo a la Investigaci&#243;n from University of Malaga for HS-SPME/GC-MS measurements. We acknowledge the assistance of Sara Fern&#225;ndez-Palacios Campos in volatile quantification. We also thanks GoodBerry&#180;s consortium members for providing the fruit material.

1. Fruit harvesting
<ol>
	<li>Grow between 4 to 6 plants per genotype and/or treatment to ensure sufficient fruit material and variability.</li>
	<li>If possible, harvest the samples on the same date; if there is not enough fruit material, pool together samples harvested on different dates. 
	NOTE: It is recommended that the harvest time (morning, noon, afternoon) remains approximately identical as VOC profiles are affected by daytime/circadian rhythm28,29,30,31.</li>
	<li>Assess fruit ripening stage by visual observation32. Pool fruits from the same ripening stage, as ripening status strongly impacts VOC emission. Discard any damaged or pathogen-infected fruits. 
	NOTE: To better assess fruit ripeness, texture analysis can be performed33. In addition, counting days after flowering can be used to ensure that pooled fruits belong to a similar ripening stage.</li>
	<li>Include a minimum of 10-15 fruits per biological replicate (3 to 5) for VOC analysis. 
	NOTE: Here, three separate pools of 13-20 fruits (biological replicates) of &#39;Andega&#39;, &#39;Ben Tron&#39;, &#39;Ben Gairn&#39;, &#39;Ben Tirran&#39;, and &#39;Tihope&#39; cultivars were harvested in two locations (Poland and Scotland) in summer 2018 and directly frozen in liquid nitrogen. Samples were then sent to the laboratory and processed as described below.</li>
	<li>Once harvested, freeze all fruits immediately in liquid nitrogen, and subsequently store them at -80 &#176;C until processing. 
	&#8203;NOTE: If possible, fruits can be directly processed after harvest. In this case, fresh fruits can be homogenized in a mixer, weighed, and directly analyzed (step 3.1 onwards). However, to prevent fruits from further postharvest degradative processes, the fresh material should be stored in a cooler (4 &#176;C) and processed as rapidly as possible. If not properly handled, liquid nitrogen can produce cold burns and can cause asphyxiation in poorly ventilated spaces.</li>
</ol>
2. Fruit sample and reagent preparation
<ol>
	<li>Grind the fruits into a fine powder, taking care to always keep them frozen with the help of liquid nitrogen. Use a cryogenic mill, bead mill, or a mortar and pestle for homogenization. Precool stainless grinding jars or mortar and pestle with liquid nitrogen to avoid sample thawing. 
	NOTE: It is critical to homogenize samples to a fine powder to ensure proper VOC extraction.</li>
	<li>Weigh 1 g of frozen material (from step 2.1.) in a 5 mL tube that is previously cooled in liquid nitrogen, and note the exact weight. Keep the material at -80 &#176;C until processing step 3.1.</li>
	<li>Include &#39;reference&#39; or &#39;control&#39; samples in the analysis to check technical variation, including VOC extraction and HS-SPME/GC-MS performance. For this purpose, pool together a mixture of randomly chosen fruit samples, and include at least one control sample per day for VOC analysis. In addition, use an internal standard, as described in step 2.5., to minimize the impact of intensity drift.</li>
	<li>Prepare 20% (w/v) sodium chloride solution in high-performance liquid chromatography (HPLC) grade water (hereafter, referred to as NaCl solution). Dissolve NaCl with the help of a magnetic stirrer; ensure the availability of 1 mL of the solution per sample.</li>
	<li>Prepare a 1 ppm solution in HPLC grade methanol of N-pentadecane (D32, 98%) from pure commercial standard (hereafter, referred to as the internal standard). 
	NOTE: N-pentadecane-d32 will be used as an internal standard, and 5 &#181;L per sample will be needed. Methanol should be manipulated under a fume hood.</li>
	<li>Prepare 1 ppm solutions in HPLC grade methanol of pure commercial standards for VOC identification (see Table 1 for the list of commercial standards used in this study).</li>
	<li>Prepare 10 mL screw-cap headspace vials by adding 0.5 g NaCl in each needed vial. Ensure that screw caps include a septum composed of a soft material, i.e., silicone, with a thin polytetrafluoroethylene film on the inner side, to avoid contamination.</li>
</ol>
3. Sample preparation
<ol>
	<li>Add 1 mL of NaCl solution to the 5 mL tube containing the weighed frozen sample. Shake the tube until the sample is completely thawed and homogenized.</li>
	<li>Centrifuge at 5000 &#215; g for 5 min at room temperature.</li>
	<li>Transfer the supernatant with a 1000 &#181;L pipette tip to the NaCl-containing headspace vial. Cut the end of the tip to facilitate this process.</li>
	<li>Add 5 &#181;L of internal standard to each sample-containing headspace vial.</li>
</ol>
4. HS-SPME/GC-MS data acquisition
<ol>
	<li>Place the closed headspace vial in a GC-MS autosampler at room temperature, for an automated HS-SPME/GC-MS run, which is described in section 4. Do not place biological replicates in successive positions in the autosampler; instead, randomly distribute them to minimize the impact of intensity drift. 
	NOTE: Approximately 10-12 vials can be placed at once in the autosampler, without affecting sample stability.</li>
	<li>Preincubate the headspace vials 10 min at 50 &#176;C with agitation at 17 x g.</li>
	<li>Insert an SPME device into the vial to expose the fiber to the headspace for VOC extraction for 30 min at 50 &#176;C with agitation at 17 x g.</li>
	<li>Introduce the fiber into the injection port for 1 min at 250 &#176;C in splitless mode for volatile desorption.</li>
	<li>Clean the fiber in an SPME cleaning station with nitrogen (1 bar N2, &#8805; 99.8% pure) for 5 min at 250 &#176;C. Reuse the fiber approximately 100x.</li>
	<li>Analyze VOCs with a gas chromatograph coupled to an ion trap mass spectrometer (see the Table of Materials), and perform chromatography under a constant flow of helium (He &#8805; 99.9999% purity) of 1 mL/min, with a column that has dimensions of 60 m x 0.25 mm x 1 &#956;m thickness. Use an oven temperature program that is isothermal at 40 &#176;C for 3 min, followed by an 8 &#176;C/min ramp to 250 &#176;C and holding at 250 &#176;C for 5 min. For mass spectrometry, set the transfer line and ion source temperatures to 260 &#176;C and 230 &#176;C, respectively. Set the ionization energy to 70 eV and the recorded mass range to m/z 35-220 at 6 scans per s.</li>
	<li>Extract and analyze 1 ppm solutions of commercial standards as described above. In addition, run a mixture containing all the diluted commercial standards mixed with 300 &#181;L NaCl solution and 900 &#181;L HPLC grade water before sample data acquisition to check the correct calibration of the equipment. Furthermore, include a blank sample containing NaCl solution alone in every batch.</li>
</ol>
5. Analysis of GC-MS profile chromatograms: VOC identification and semi-quantification
<ol>
	<li>Open raw GC-MS profile files with the software provided by the manufacturer. To identify compounds, compare their retention times and mass spectra and Kovats linear retention indices determined from the chromatograms of the samples with retention indices obtained from authentic standards. For each commercial standard, annotate retention time and the most abundant m/z ions. Then, select a specific m/z ion for each VOC (Table 1).</li>
	<li>Automatically integrate VOC peaks based on standard retention times and chosen m/z ions of the selected GC-MS raw files. For this, provide a list for each VOC with retention time and selected m/z ion. Although the software automatically integrates peak area corresponding to the same retention time and m/z ion as provided in the sequence setup, check the correct integration of each peak and correct it manually if necessary.</li>
	<li>Calculate the peak area of each VOC relative to that of the internal standard to minimize instrumental variation and intensity drift. 
	NOTE: When analyzing fruit from different genotypes or growth and storage conditions, it is highly recommended to determine the VOC content relative to the fruit dry weight content to rule out dilution effects due to differences in water content.</li>
	<li>For batch effect correction, normalize the VOC peak area of each sample to the corresponding peak area in the control sample analyzed in the same run. 
	NOTE: A relative VOC quantification is obtained; however, for the purpose of the experiment, VOC content can be then determined relative to any sample (e.g., untreated fruits to compare the effect of storage upon VOC levels).</li>
</ol>

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The authors declare no conflict of interest.

Breeding for fruit aroma has long been hindered by the complex genetics and biochemistry underlying the synthesis of volatile compounds and the lack of technologies for proper phenotyping. However, recent advances in metabolomic platforms, combined with genomic tools, are finally allowing the identification of the metabolites responsible for consumer preferences and to breed crops with improved flavor3. While most progress has been achieved in the model fruit, tomato9,...

Flavor is an essential quality trait for any fruit, impacting consumer acceptance and thus significantly affecting marketability. Flavor perception involves a combination of the taste and olfactory systems and depends chemically on the presence and concentration of a wide range of compounds that accumulate in edible plant parts, or in case of VOCs, are emitted by the ripe fruit1,2. While traditional breeding has focused on agronomic traits such as yield and pest resistance, fruit quality trait improvement, including flavor, has long been neglected due to the genetic complexity and the difficulty to properly phenotype these characteristics, leading to consumer discontent3,4. Recent advances in metabolomic platforms have been successful in identifying and quantifying key compounds responsible for fruit taste and aroma5,6,7,8. Furthermore, the combination of metabolite profiling with genomic or transcriptomic tools allows the elucidation of the genetics underlying fruit flavor, which in turn will help breeding programs develop new varieties with enhanced organoleptic characteristics2,4,9,10,11,12,13,14.
Blackcurrant (Ribes nigrum) berries are highly appreciated for their flavor and nutritional properties, being widely cultivated across the temperate zones of Europe, Asia, and New Zealand15. Most of the production is processed for food products and beverages, which are very popular in the Nordic countries, mainly due to the berries&#39; organoleptic properties. The intense color and flavor of the fruit are the result of a combination of anthocyanins, sugars, acids, and VOCs present in the ripe fruits16,17,18. The analysis of blackcurrant volatiles goes back to the 1960s19,20,21. More recently, several studies have focused on blackcurrant VOCs, identifying important compounds for fruit aroma perception and assessing the impact of genotype, environment, or storage and processing conditions on VOC content5,17,18,22,23.
Because of its numerous advantages, the technique of choice for high-throughput volatile profiling is HS-SPME/GC-MS24,25. A silica fiber, coated with a polymeric phase, is mounted on a syringe device, allowing the adsorption of the volatiles in the fiber until an equilibrium phase is reached. Headspace extraction protects the fiber from the nonvolatile compounds present in the matrix24. SPME can successfully isolate a high number of VOCs present at highly variable concentrations (parts per billion to parts per million)25. In addition, it is a solvent-free technique that requires limited sample processing. Other advantages of HS-SPME are the ease of automation and its relatively low cost.
However, its success can be limited, depending on the chemical nature of the VOCs, the extraction protocol (including time, temperature, and salt concentration), sample stability, and the availability of sufficient fruit tissue26,27. This paper presents a protocol for blackcurrant VOCs isolated by HS-SPME and analyzed by gas chromatography coupled with an ion trap mass spectrometer. A balance between the quantity of plant material, sample stability, and duration of extraction and chromatography was achieved to be able to process high numbers of blackcurrant samples, some of them presented in this study. In particular, VOC profiles and/or chromatograms of five cultivars (&#39;Andega&#39;, &#39;Ben Tron&#39;, &#39;Ben Gairn&#39;, &#39;Ben Tirran&#39;, and &#39;Tihope&#39;) will be presented and discussed as example data. Furthermore, the same protocol has been successfully put into practice for VOC measurement in other fruit berry species such as strawberry (Fragaria x ananassa), raspberry (Rubusidaeus), and blueberry (Vaccinium spp.).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>10 mL screw top headspace vials</td>
			<td>Thermo Scientific</td>
			<td>10-HSV</td>
			<td></td>
		</tr>
		<tr>
			<td>18 mm screw cap Silicone/PTFE</td>
			<td>Thermo Scientific</td>
			<td>18-MSC</td>
			<td></td>
		</tr>
		<tr>
			<td>5 mL Tube with HDPE screw cap</td>
			<td>VWR</td>
			<td>216-0153</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Thermo Scientific</td>
			<td>75002415</td>
			<td></td>
		</tr>
		<tr>
			<td>Methanol for HPLC</td>
			<td>Merck</td>
			<td>34860-1L-R</td>
			<td></td>
		</tr>
		<tr>
			<td>N-pentadecane (D32, 98%)</td>
			<td>Cambridge Isotope Laboratories</td>
			<td>DLM-1283-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium chloride</td>
			<td>Merck</td>
			<td>S9888</td>
			<td></td>
		</tr>
		<tr>
			<td>SPME fiber PDMS/DVB</td>
			<td>Merck</td>
			<td>57345-U</td>
			<td></td>
		</tr>
		<tr>
			<td>Stainless grinding jars for TissueLyser</td>
			<td>Qiagen</td>
			<td>69985</td>
			<td></td>
		</tr>
		<tr>
			<td>TissueLyser II</td>
			<td>Qiagen</td>
			<td>85300</td>
			<td>Can be subsituted by mortar and pestle or cryogenic mill</td>
		</tr>
		<tr>
			<td>Trace GC gas chromatograph-ITQ900 ion trap mass spectrometer</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Triplus RSH autosampler with automated SPME device</td>
			<td>Thermo Scientific</td>
			<td>1R77010-0450</td>
			<td></td>
		</tr>
		<tr>
			<td>Water for HPLC</td>
			<td>Merck</td>
			<td>270733-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Xcalibur 4.2 SP1</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td>software</td>
		</tr>
	</tbody>
</table>

profiling volatile compounds in blackcurrant fruit using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits.
Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.

High-throughput VOC profiling in a large set of fruit crops grown under different conditions or locations or belonging to distinct genotypes is necessary for accurate aroma phenotyping. Here, a fast and semi-automated HS-SPME/GC-MS platform for relative VOC quantification in blackcurrant cultivars is presented. VOC detection and identification were based on a library that was developed to profile berry fruit species (Table 1). A typical ripe blackcurrant fruit volatile profile (total ion chromatogram) ob...

Watch this Scientific Journal Video about Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry at JoVE.com

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

A headspace solid-phase microextraction-gas-chromatography platform is described here for fast, reliable, and semi-automated volatile identification and quantification in ripe blackcurrant fruits. This technique can be used to increase knowledge about fruit aroma and to select cultivars with enhanced flavor for the purpose of breeding.

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars ...

profiling-volatile-compounds-blackcurrant-fruit-using-headspace-solid

Research

JoVE Journal

Biochemistry

3.6K Views.  Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”, Universidad de Málaga-Consejo Superior de Investigaciones Científicas. A headspace solid-phase microextraction-gas-chromatography platform is described here for fast, reliable, and semi-automated volatile identification and quantification in ripe blackcurrant fruits. This technique can be used to increase knowledge about fruit aroma and to select cultivars with enhanced flavor for the purpose of breeding. 

Video: Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

Sheathless Capillary Electrophoresis&#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a protocol is presented for the analysis of anionic and cationic metabolites in biological samples by capillary electrophoresis&#8211;mass spectrometry (CE-MS). CE is well-suited for the analysis of highly polar and charged metabolites as compounds are separated on the basis of their charge-to-size ratio. A recently developed sheathless interfacing design, i.e., a porous tip interface, is used for coupling CE to electrospray ionization (ESI) MS. This interfacing approach allows the effective use of the intrinsically low-flow property of CE in combination with MS, resulting in nanomolar detection limits for a broad range of polar metabolite classes. The protocol presented here is based on employing a bare fused-silica capillary with a porous tip emitter at low-pH separation conditions for the analysis of a broad array of metabolite classes in biological samples. It is demonstrated that the same sheathless CE-MS method can be used for the profiling of cationic metabolites, including amino acids, nucleosides and small peptides, or anionic metabolites, including sugar phosphates, nucleotides and organic acids, by only switching the MS detection and separation voltage polarity. Highly information-rich metabolic profiles in various biological samples, such as urine, cerebrospinal fluid and extracts of the glioblastoma cell line, can be obtained by this protocol in less than 1 hr of CE-MS analysis.

Sheathless Capillary Electrophoresis&amp;#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

A protocol for metabolic profiling of biological samples by capillary electrophoresis&#8211;mass spectrometry using a sheathless porous tip interface design is presented.

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

A Tandem Liquid Chromatography&amp;#8211;Mass Spectrometry-based Approach for Metabolite Analysis of &lt;em&gt;Staphylococcus aureus&lt;/em&gt;

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

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) ...

Rapid Collection of Floral Fragrance Volatiles using a Headspace Volatile Collection Technique for GC-MS Thermal Desorption Sampling

Fragrances of many flower families have been sampled and the volatiles analyzed. Knowing the compounds that make up the fragrances can be an important step to conservation of flowers that are threatened or endangered. Because floral fragrance is critical for attracting pollinators, this method could be used to better understand or even enhance pollination. We present a protocol using a portable charcoal air filter and vacuum to collect floral fragrance volatiles, which are then analyzed by a GC-MS. By using this method, fragrance volatiles can be sampled using a non-destructive method with a machine that is easily transported. This methodology uses a rapid sampling procedure, cutting sampling time down from 2-3 hours to approximately 10 minutes. Using GC-MS, the fragrance compounds can be identified individually, based on authentic standards. The steps used for collecting fragrance and control data are presented, from material setup to collecting the data output.

Here, we present a protocol for collecting the floral fragrance volatiles from blooming flowers, using a non-destructive sampling procedure.

Fragrances of many flower families have been sampled and the volatiles analyzed. Knowing the compounds that make up the fragrances can be an important ...

Nitropeptide Profiling and Identification Illustrated by Angiotensin II

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in eukaryotic cells. Precise identification of nitration sites on proteins is crucial for understanding the physiological and pathological processes related to protein nitration, such as inflammation, aging, and cancer. Since the nitrated proteins are of low abundance in cells even under induced conditions, no universal and efficient methods have been developed for the profiling and identification of protein nitration sites. Here we describe a protocol for nitropeptide enrichment by using a chemical reduction reaction and biotin labeling, followed by high resolution mass spectrometry. In our method, nitropeptide derivatives can be identified with high accuracy. Our method exhibits two advantages compared to the previously reported methods. First, dimethyl labeling is used to block the primary amine on nitropeptides, which can be used to generate quantitative results. Second, a disulfide bond containing NHS-biotin reagent is used for the enrichment, which can be further reduced and alkylated to enhance the detection signal on a mass spectrometer. This protocol has been successfully applied to the model peptide Angiotensin II in the current paper.

Proteomic profiling of tyrosine-nitrated proteins has been a challenging technique due to the low abundance of the 3-nitrotyrosine modification. Here we describe a novel approach for nitropeptide enrichment and profiling by using Angiotensin II as the model. This method can be extended for other in vitro or in vivo systems.

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of ...

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Here, we present a robust protocol to quantify 40 compounds involved in central carbon and energy metabolism in cell-free protein synthesis reactions. The cell-free synthesis mixture is derivatized with aniline for effective separation using reversed-phase liquid chromatography and then quantified by mass spectrometry using isotopically labelled internal standards.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is ...

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of biological membranes, energy storage and production, cellular signaling, vesicular transport of proteins, organelle biogenesis, and regulated cell death. Because the budding yeast Saccharomyces cerevisiae is a unicellular eukaryote amenable to thorough molecular analyses, its use as a model organism helped uncover mechanisms linking lipid metabolism and intracellular transport to complex biological processes within eukaryotic cells. The availability of a versatile analytical method for the robust, sensitive, and accurate quantitative assessment of major classes of lipids within a yeast cell is crucial for getting deep insights into these mechanisms. Here we present a protocol to use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of major cellular lipids of S. cerevisiae. The LC-MS/MS method described is versatile and robust. It enables the identification and quantification of numerous species (including different isobaric or isomeric forms) within each of the 10 lipid classes. This method is sensitive and allows identification and quantitation of some lipid species at concentrations as low as 0.2 pmol/&#181;L. The method has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. The use of alternative mobile phase additives for electrospray ionization mass spectrometry in this method can increase the efficiency of ionization for some lipid species and can be therefore used to improve their identification and quantitation.

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol using liquid chromatography coupled with tandem mass spectrometry to identify and quantify major cellular lipids in Saccharomyces cerevisiae. The described method for a quantitative assessment of major lipid classes within a yeast cell is versatile, robust, and sensitive.

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of ...

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 ...