This work was funded by the National Natural Science Foundation of China&#39;s Youth Program (No. 82304670).

1. Experimental preparation
<ol>
	<li>Make sure that during plant growth, the relative humidity of the greenhouse is 75%, the day/night temperatures are 20 &#176;C/10 &#176;C, the photoperiod is made up of 12 h day and 12 h night, and the light intensity is 100 &#181;mol&#183;m-2&#183;s-1. Provide irradiance via light-emitting diode (LED) lamps, keeping a distance of 30 cm between the LED lamp and the plant canopy. 
	NOTE: The photoperiod and light intensity are according to the number of sunshine hours during the growing period in Yunnan. Irradiance is routinely measured by a quantum sensor (see Table of Materials). Make the necessary changes in environmental parameters according to the medicinal plant&#39;s characteristics. Once these environmental conditions have been carefully calibrated based on the plant&#39;s characteristics and the regional sunlight profile, it is critical to maintain consistency in these settings throughout the duration of the experiment. Arbitrarily changes to the environmental parameters after their initial establishment could compromise the integrity of the experiment and the reliability of the results.</li>
	<li>Use a hydroponic approach18 to ensure the accuracy and specificity of our experiment. Make sure that the washed 2-year-old PPY seedlings (harvested from Wenshan, Yunnan Province (104&#176;11&#8242;E, 23&#176;04&#8242;N) are immersed in a 1/4 standard concentration of Hoagland&#39;s nutrient solution for 3 days for adapting.
	<ol>
		<li>Prepare the Hoagland&#39;s nutrient (see Table of Materials) solution according to the instructions; add 1.26 g of Hoagland powder and 0.945 g of Calcium nitrate powder into 4 L of purified water. Prepare the 13C6-Glucose (see Table of Materials) solution as described in the standard manual; add 0.2 g of 13C6-Glucose powder into 100 mL of purified water or Hoagland&#39;s nutrient solution.</li>
		<li>Prepare the hydroponic tank and oxygen pump (Figure 2; see Table of Materials). 
		NOTE: In the hydroponic setup, utilizing a 13C6-Glucose solution for labeling circumvents the influence of soil microorganisms, thereby ensuring that the solution directly contributes to the rapid synthesis of secondary metabolites in medicinal plants. To optimize the conditions for the absorption of 13C6-Glucose by the plants, it is imperative to regulate the flow rate of the oxygen pump between 4 and 8 L/min. This controlled flow rate is crucial to prevent the seedlings from floating atop the water surface, which could significantly hinder their ability to absorb the 13C6-Glucose effectively. Maintaining the seedlings submerged at the correct depth allows for efficient uptake of the labeling compound, thereby ensuring the success of the metabolite synthesis process in the hydroponic system.</li>
	</ol>
	</li>
</ol>
2. Carbon labeling experiment operation
<ol>
	<li>Set up four treatments and trace the synthesis organs and pathways of PS VII and PS II in PPY to demonstrate that leaves are the primary organ for synthesizing PS.
	<ol>
		<li>In Treatment 1, the PPY seedling is non-feeding; in Treatment 2, spray the PPY seedling leaves with 0.2% 13C6-Glucose; in Treatment 3, feed the PPY seedling rhizomes with 0.2% 13C6-Glucose; in Treatment 3, feed the PPY seedling at the stem incision with the 0.2% 13C6-Glucose (Figure 2A). 
		NOTE: Treatment 1 is set as a control group. Treatment 2 is set up to demonstrate that leaves can synthesize the PS. Treatments 3 and 4 are set up to demonstrate that rhizomes can take up 13C6-Glucose.</li>
		<li>In Treatments 1 and 2, culture the PPY seedlings in normal Hoagland&#39;s nutrient solution. Spray 5 mL of 0.2% 13C6-Glucose solution once in the morning, afternoon, and evening on the leaves of Treatment 2.</li>
		<li>In Treatments 3 and 4, culture the PPY seedlings in Hoagland&#39;s nutrient solution containing 0.2% 13C6-Glucose. In Treatment 4, use a scalpel (see Table of Materials) to cut off the stems of PPY seedlings from the middle but keep the vascular bundles attached.</li>
		<li>Allow each treatment to be labeled for 3 days, and change the nutrient solution every 1.5 days. Conduct the experiment using a randomized block design and repeat each treatment 3x. 
		NOTE: When spraying the leaves, it is important to place an absorbent paper behind the sprayed leaves. This prevents water droplets from falling into the nutrient solution below, which could otherwise affect the results of the experiment. The cuts in the stems are not all removed but have conduits attached that are visible to the naked eye.</li>
	</ol>
	</li>
</ol>
3. Sampling and preparation methods
<ol>
	<li>Collect leaves, stems, rhizomes, and roots from each treatment separately at the end of the 3-day 13C6-Glucose experiment. Wash the collected plant organs thoroughly to remove surface impurities. 
	NOTE: The absence of 13C abundance can be found by LC-MS analysis of the washing solution, which proves that the washing is clean19.</li>
	<li>Use an electric constant temperature blast drying oven (see Table of Materials) to dry the washed plant organs. Start at 105 &#176;C for 30 min and then set at 60 &#176;C until constant weight. Crush the dried plant organs into powder using a fully automatic sample rapid grinder (see Table of Materials). 
	NOTE: The powder is used for quantitative analysis of PS VII, II, and 13C/12C ratios in different plant organs.</li>
	<li>Use an electronic analytical balance (see Table of Materials) to accurately weigh 30 mg of powder for each sample and place it in 2 mL of 75% v/v ethanol-water. Use a CNC ultrasonic cleaner (see Table of Materials) and perform ultrasonic extraction at 25 &#176;C for 20 min (60 kHz), repeat 2x; merge the extraction solutions and prepare three parallel samples20. 
	NOTE: Errors in sample weight should be minimized.</li>
	<li>Use nitrogen to blow dry the extraction solution, then dissolve in chromatographic methanol to a volume of 1 mL. Before injection, use a 0.22 &#181;m organic phase filter (see Table of Materials) for filtration (Figure 2B). 
	NOTE: Do not blow out the extract when blow drying with nitrogen gas; the extract must be passed through a microporous membrane to ensure that impurities interfere minimally.</li>
</ol>
4. LC-MS setup and operation
<ol>
	<li>MS setting
	<ol>
		<li>Activate the vacuum pump by flipping its switch to the on position. Open the main valve on the argon gas cylinder, then adjust the partial pressure valve to achieve a target pressure of about 0.3 MPa. Following this, ensure the nitrogen gas valve is also opened.</li>
		<li>Launch the MS control software (see Table of Materials). Click on the heated electrospray ionization source (HESI Source) in the software panel, and enter the parameters, including the capillary voltage (3.0 kV for negative mode), the capillary temperature (350 &#176;C), sheath gas (N2) flow rate (35 arbitrary units), auxiliary gas (N2) flow rate (15 arbitrary units), through full scanning based on the molecular mass of PS (operated from m/z 100-1,500). Click on the Apply button to activate the ion source. 
		NOTE: Wait for at least 8 h to ensure a sufficient vacuum degree for the experimental conditions. Check that the gas pressure of argon and nitrogen is high enough before analysis.</li>
	</ol>
	</li>
	<li>LC prerun
	<ol>
		<li>Prepare mobile phase A by mixing 0.1% formic acid with water and use acetonitrile for mobile phase B (see Table of Materials). Degas both phases in an ultrasound bath sonicator for 15 minutes to eliminate dissolved gases. Then, connect mobile phase A to its respective fluid passage and do the same with mobile phase B. Finally, mix a 1:9 v/v methanol-water solution for the cleanout fluid and fill the pump and injector bottles manually. 
		NOTE: The frequency of the ultrasound bath sonicator is 40 kHz.</li>
	</ol>
	</li>
	<li>LC method establishment
	<ol>
		<li>Select the &#34;Instrument Setup&#34; button to access the method-editing window.</li>
		<li>Press the &#34;New&#34; button to initiate the creation of a new LC-MS instrument method.</li>
		<li>Set the total run time for the LC method. Then, input the necessary values for the pressure limit, total flow rate, flow gradient, sample temperature, column temperature, and ready temperature delta within the method-editing window. 
		NOTE: LC-MS instrument method settings are tailored to the analyte of interest and the type of liquid chromatography column utilized. For optimal results, use a flow rate of 0.3 mL/min with a BEH C18 column maintained at 40 &#176;C (see Table of Materials). The gradient elution is configured as follows: begin at 10% B, increasing linearly to 30% B over 8 min, then to 45% B from 8 to 16 min, to 50% B from 16 to 24 min, and to 70% B from 24 to 30 min. Immediately following this, revert the gradient to the initial 10% B condition within 0.1 min, holding it there for an additional 2.9 min. Inject a sample volume of 5 &#181;L for each analysis.LC-MS instrument method settings depend on the specific substance to be analyzed and the type of liquid chromatography column used21.</li>
	</ol>
	</li>
	<li>MS acquisition
	<ol>
		<li>In the Settings tab, choose either the General MS or MSn experiment type for the MS method configuration. Input the necessary values to set up the acquisition time, polarity (positive or negative), mass rangedivert valve number, and divert valve duration. After entering these details, click on the &#34;Save&#34; button to finalize and save these settings as an instrument method. 
		NOTE: The default settings without a chromatography column are as follows: acquisition time, 2 min; polarity, positive or negative; mass range, 100 to 1,500.</li>
	</ol>
	</li>
	<li>Perform multiple-stage mass spectrometry according to conventional methods.
	<ol>
		<li>Always employ data-dependent acquisition (DDA) mode for data collection in multi-stage mass spectrometry. In DDA mode, the mass spectrometer identifies the five most intense ions during each scan in the first stage of tandem MS, which are subsequently fragmented and examined in the second stage of tandem mass spectrometry. 
		NOTE: The collected MS and MS/MS data can be used for subsequent analysis and database searching.</li>
	</ol>
	</li>
</ol>
5. Manual data acquisition, analysis, and calculation
<ol>
	<li>Data acquisition and analysis
	<ol>
		<li>Double-click on the raw files to open all the mass spectra from MS. Manually calculate the m/z difference values between the ion and the corresponding fragment ions.</li>
		<li>Import the raw data files of the control and sample treatments and blanks into the mass spectrometry analysis software to identify PSs by accurately calculating the mass and the fragments of PS. 
		NOTE: PS VII and PS II are identified by comparison with retention times and coelution of authentic standard solutions. LC-MS detects a series of small peaks at the positions of the molecular ion peaks. The ratio of each series of isotopic ion peaks is constant. When exogenous 13C isotopes are given, the total 13C abundance in the plant increases, and the ratios of the series of isotope ion peaks of plant secondary metabolites change. The maximum tolerance of mass error is set at 5 ppm.</li>
		<li>Scan the mass range m/z 500-1,500 to identify the basic peak. Determine the accurate molecular weight to 4 decimal places, according to the precise molecular weight and retention time of the target molecule. Search for the peak area of serial isotope ion flow and derive values for M&#8722;, (M+1) &#8722;, (M+2) &#8722;, (M+3) &#8722;, and (M+4) &#8722;. When the mobile phase contains formic acid, the negative ions also include (M+COOH) &#8722;, (M+COOH+1) &#8722;, (M+COOH+2) &#8722;, (M+COOH+3) &#8722; and (M+COOH+4) &#8722;. 
		NOTE: Selecting peaks within the mass range m/z 500-1,500 improves the accuracy of identifying molecular ion peaks for PS VII and PS II. For instance, the negative ion mass spectrum of PS VII (C51H82O21, 1030.5349) mainly ionizes with COOH&#8722; ions in formic acid-water mixture. The observed serial isotope ions include 1029.53&#8722;, 1030.54&#8722;, 1031.54&#8722;, 1032.55&#8722;, 1033.55&#8722;, and extending to 1079.56&#8722;.</li>
	</ol>
	</li>
	<li>Data calculation
	<ol>
		<li>To reduce the effect of 13C natural abundance, calculate the ratios of labeled to unlabeled ion currents for each organ under different treatments, specifically focusing on the ratios (M+1)&#8722;/ M&#8722;, (M+2)&#8722;/M&#8722;, (M+3)&#8722;/ M&#8722; and (M+4)&#8722;/ M&#8722;. To differentiate between labeled and naturally occurring isotopes for an accurate assessment of 13C incorporation, calculate these ratios for n = 1, 2, 3, 4 using equation (1). 
		Ratio = A (M+2) &#8722;/ AM&#8722;&#160; &#160;(1) 
		PS: (M+2) &#8722; include (M+2+COOH) &#8722; and (M+2) &#8722;; M&#8722; include (M+COOH) - and M&#8722;. 
		NOTE: Here, M&#8722; refers to the combined peak areas of ion currents for (M+COOH) &#8722; and M &#8722;, primarily ionized with COOH&#8722; in formic acid-water. A represents the peak area. For example, the formulas to calculate PS VII ratios are illustrated as follows: 
		Ratio+1= (A1030.54+A1076.55)/(A1029.53+A1075.55) 
		Ratio+2= (A1031.54+A1077.55)/(A1029.53+A1075.55) 
		Ratio+3= (A1032.55+A1078.56)/(A1029.53+A1075.55) 
		Ratio+4= (A1033.55+A1079.56)/(A1029.53+A1075.55)</li>
	</ol>
	</li>
</ol>

Primary Organ Identification in Medicinal Plants Using 13C6&#45;Glucose Labeling

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The authors declare no competing financial interests.

The successful implementation of this protocol hinges on comprehensive research into plant physiological properties, tissues, organs, and secondary metabolites. The experimental design approach outlined in the protocol lays a robust foundation for investigating the biosynthetic pathways of plant secondary metabolites. The critical factors in this experiment are (1) determining the age of the perennial seedlings and (2) choosing the correct isotope labeling-detection timing. The medicinal plants are categorized into peren...

The biosynthetic pathways of secondary metabolites in plants are intricate and diverse, involving highly specific and diverse accumulation organs1. At present, the specific synthesis sites and responsible organs for secondary metabolites in many medicinal plants are not well-defined. This ambiguity poses a significant obstacle to the strategic advancement and implementation of cultivation methods designed to optimize both the yield and quality of medicinal materials.
Molecular biology, biochemical, and isotope labeling techniques are extensively employed to unravel the synthesis pathways and sites of secondary metabolites in medicinal plants2,3,4,5, and each of these methodologies exhibits unique strengths and limitations, such as differences in efficiency and accuracy. Molecular biology approaches, for instance, offer high precision in pinpointing the sites within biosynthetic pathways but are notably time-intensive. Their utility is further constrained for species lacking publicly available genomic sequences, rendering these techniques less viable for such cases6. In contrast, isotope labeling techniques, employing isotopic ratios like 3C/12C, 2H/1H, and 18O/16O, provide a rapid and accessible means to investigate the synthesis, transport, and storage mechanisms of secondary metabolites7,8. They can reveal the spatial distribution of organic compounds and stable isotopes in leaves, thereby allowing the reconstruction of environmental conditions experienced by the leaves throughout their life cycle9. Furthermore, the application of external isotopic labels, such as 13C6-Glucose10 and 13C6-Phenylalanine11, enables the generation of carbon-labeled secondary metabolites, enhancing our understanding of their production and function.
Traditional carbon isotope labeling techniques encounter challenges in pinpointing the specific organs responsible for the synthesis of secondary metabolites due to the highly species-specific nature of their biosynthetic pathways and transport mechanisms. Liquid chromatography-mass spectrometry (LC-MS) has risen to prominence as a pivotal analytical instrument in this arena, offering a robust method for tracking exogenous isotopes in the chemical synthesis of drugs and investigating in vivo processes such as absorption, distribution, metabolism, and excretion12. The superior sensitivity, straightforwardness, and reliability of LC-MS make it an ideal choice for monitoring the production of secondary metabolites in plants13. In recent times, LC-MS has become increasingly favored for its application in external isotope labeling techniques, which enables the evaluation of labeling efficiency across different samples. This methodology provides critical insights into the primary organs engaged in the synthesis of secondary metabolites in medicinal plants, serving as an invaluable complement to biological methods for identifying the synthesis organs of these compounds14,15. Consequently, this approach not only facilitates the comparison of labeling efficiencies among various specimens but also sheds light on the key organs implicated in the generation of plant secondary metabolites, thereby enhancing our understanding of their biosynthesis.
We introduced a novel method that combines carbon isotope labeling with LC-MS detection to identify the primary organs responsible for synthesizing secondary metabolites in medicinal plants. Paris saponin (PS) has a variety of pharmacological activities such as anticancer, immunomodulation, and anti-inflammation16, and PPY is in increasing demand17. Therefore, we used PPY seedlings as research subjects and deciphered that leaves are the primary organ to synthesize the Paris saponin VII (PS VII) (Figure 1B) by using the 13C6-Glucose labeling associated with the LC-MS method. Our approach included four different treatments involving 13C6-Glucose feeding to leaf, rhizome, and stem-vascular bundles, as well as a non-feeding control. The choice of 13C6-Glucose is strategic, as it is swiftly metabolized into acetyl coenzyme A via respiration, which then facilitates PS synthesis. Employing the natural abundance of 13C, we utilized a Gas Chromatography-Stable Isotope Ratio Mass Spectrometer (GC-IRMS) system to assess the 13C/12C ratios across various plant organs and to analyze the isotopic ion peak ratios in PS VII and Paris saponins II (PS II) (Figure 1B) molecules. Our methodology, which leverages 13C-labeled plant secondary metabolite precursors and cutting-edge mass spectrometry techniques, offers a simpler and more accurate alternative to conventional carbon isotope labeling methods. This novel approach not only deepens our comprehension of the organs involved in secondary metabolite synthesis in medicinal plants but also lays a solid groundwork for future explorations into the biosynthetic pathways of these compounds.

<table><tbody><tr><td>0.1 % Formic acid water</td><td>Chengdu Kelong Chemical Reagent Factory</td><td>44890</td><td></td></tr><tr><td>&lt;sup&gt;13&lt;/sup&gt;C&lt;sub&gt;6&lt;/sub&gt;-Glucose powder</td><td>MERCK</td><td>110187-42-3</td><td></td></tr><tr><td>Acetonitrile</td><td>Chengdu Kelong Chemical Reagent Factory</td><td>44890</td><td></td></tr><tr><td>AUTOSAMPLER VIALS</td><td>Biosharp Biotechnology Company</td><td>44866</td><td></td></tr><tr><td>BEH C18 column</td><td>Waters,Milfor,MA</td><td>1.7&amp;mu;m,2.1*100 mm</td><td></td></tr><tr><td>CNC ultrasonic cleaner</td><td>Kunshan Ultrasound Instrument Co., Ltd</td><td>KQ-600DE</td><td></td></tr><tr><td>Compound DiscovererTM&amp;nbsp; software</td><td>Thermo Scientific, Fremont,CA</td><td>3</td><td></td></tr><tr><td>Compound DiscovererTM&amp;nbsp; software&amp;nbsp;</td><td>Thermo Scientific,Fremont,CA</td><td>3</td><td></td></tr><tr><td>Electric constant temperature blast drying oven</td><td></td><td>DHG-9146A</td><td></td></tr><tr><td>Electronic analytical balance</td><td>Sedolis Scientific Instruments Beijing Co., Ltd</td><td>SOP</td><td></td></tr><tr><td>Ethanol&amp;nbsp;</td><td>Chengdu Kelong Chemical Reagent Factory</td><td>44955</td><td></td></tr><tr><td>Fully automatic sample rapid grinder</td><td>Shanghai Jingxin Technology</td><td>Tissuelyser-48</td><td></td></tr><tr><td>Gas Chromatography-Stable Isotope Ratio Mass Spectrometer</td><td>Thermo Fisher</td><td>Delta V Advantage</td><td></td></tr><tr><td>Hoagland solution</td><td>Sigma-Aldrich</td><td>H2295-1L</td><td></td></tr><tr><td>Hydroponic tank</td><td>JRD</td><td>1020421</td><td></td></tr><tr><td>Isodat software</td><td>Thermo Fisher Scientific</td><td>3</td><td></td></tr><tr><td>Liquid chromatography high-resolution mass spectrometry</td><td>Agilent Technology&amp;nbsp;</td><td>Agilent 1260 -6120&amp;nbsp;</td><td></td></tr><tr><td>Nitrogen manufacturing instrument</td><td>PEAK SCIENTIFIC</td><td>Genius SQ 24</td><td></td></tr><tr><td>Organic phase filter</td><td>Tianjin Jinteng Experimental Equipment Co., Ltd</td><td>44890</td><td></td></tr><tr><td>Oxygen pump</td><td>Magic Dragon</td><td>MFL</td><td></td></tr><tr><td>Quantum sensor</td><td>Highpoint</td><td>UPRtek</td><td></td></tr><tr><td>Scalpel</td><td>Handskit</td><td>11-23</td><td></td></tr><tr><td>Sprinkling can</td><td>CHUSHI</td><td>WJ-001</td><td></td></tr><tr><td>Xcalibur&amp;nbsp; software</td><td>Thermo Fisher Scientific</td><td>4.2</td><td></td></tr></tbody></table>

13c6&#45;glucose labeling associated with lc&#45;ms&#58; identification of plant primary organs in secondary metabolite synthesis

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary metabolite in Parispolyphylla var. yunnanensis (Franch.) Hand. -Mzt. (PPY), Paris saponin (PS) has a variety of pharmacological activities and PPY is in increasing demand. This study established leaf, rhizome, and stem-vascular-bundle 13C6-Glucose feeding and non-feeding four treatments to precisely certify the primary organs involved in Paris saponins VII (PS VII) synthesis. By combining liquid chromatography-mass spectrometry (LC-MS), the 13C/12C ratios of leaf, rhizome, stem, and root in different treatments were quickly and accurately calculated, and four types of PS isotopic ion peak(M&#8722;) ratios were found: (M+1) &#8722;/M&#8722;, (M+2) &#8722;/M&#8722;, (M+3) &#8722;/M&#8722; and (M+4) &#8722;/M&#8722;. The results showed that the ratio of 13C/12C in the rhizomes of the stem-vascular-bundle and rhizome feeding treatments was significantly higher than that in the non-feeding treatment. Compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves increased significantly under leaf and stem-vascular-bundle feeding treatments. Simultaneously, compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves under rhizome treatment showed no significant difference. Furthermore, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the stem, root, and rhizome showed no differences among the four treatments. Compared to the non-feeding treatment, the ratio of the Paris saponin II (PS II) molecule (M+2) &#8722;/M&#8722; in leaves under leaf feeding treatment showed no significant difference, and the (M+3) &#8722;/M&#8722; ratio of PS II molecules in leaves under leaf feeding treatment were lower. The data confirmed that the primary organ for the synthesizing of PS VII is the leaves. It lays the foundation for future identification of the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants.

To confirm that 13C6-Glucose supply in rhizomes was successful, we further analyzed the 13C/12C isotope ratios in rhizomes. The 13C /12C isotope ratios of Treatments 3 and 4 were much higher than those of Treatment 2 (Figure 1A). The results indicated that 13C6-Glucose from Treatment 3 and 4 entered the rhizomes through ingestion.
The ratios of 13C isotope peaks, suc...

Read this Scientific Article Protocol about Author Spotlight: Plant Primary Organs Profiling Using 13C6-Glucose Labeling and LC-MS at JoVE.com

Author Spotlight: Plant Primary Organs Profiling Using 13C6&#45;Glucose Labeling and LC&#45;MS

The developed method of 13C6-Glucose labeling combined with liquid chromatography high-resolution mass spectrometry is versatile and lays the foundation for future studies on the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants, as well as the comprehensive utilization of these secondary metabolites.

13C6&#45;Glucose Labeling Associated with LC&#45;MS&#58; Identification of Plant Primary Organs in Secondary Metabolite Synthesis

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary ...

13c6-glucose-labeling-associated-with-lc-ms-identification-plant

Research

JoVE Journal

Chemistry

583 Views.  Chengdu University of Traditional Chinese Medicine. We wilden een universele en beschrijvende methode ontwikkelen voor het identificeren van de organen die verantwoordelijk zijn voor de productie van cyclische metabolen in geneeskrachtige planten. Deze methode integreert bepaalde koolstof-zes-glucose-etikettering met LC-MS-indices en biedt een proces en een toegankelijke manier om te bepalen waar deze belangrijke verbindingen worden gesynthetiseerd. De meest recente ontwikkelingen richten zich op de metabolietdynamiek, eiwitkwantificering en h...

Video: Auteur in de schijnwerpers: Profilering van primaire organen van planten met behulp van 13C 6-glucose-etikettering en LC-MS

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

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

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

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

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

Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Plant biomass offers a renewable resource for multiple products, including fuel, feed, food, and a variety of materials. In this paper we investigate the properties of tobacco tree (Nicotiana glauca) and poplar as suitable sources for a biorefinery pipeline.

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the ...

Environment

A Straightforward Method for Glucosinolate Extraction and Analysis with High-pressure Liquid Chromatography (HPLC)

Glucosinolates are a well-studied and highly diverse class of natural plant compounds. They play important roles in plant resistance, rapeseed oil quality, food flavoring, and human health. The biological activity of glucosinolates is released upon tissue damage, when they are mixed with the enzyme myrosinase. This results in the formation of pungent and toxic breakdown products, such as isothiocyanates and nitriles. Currently, more than 130 structurally different glucosinolates have been identified. The chemical structure of the glucosinolate is an important determinant of the product that is formed, which in turn determines its biological activity. The latter may range from detrimental (e.g., progoitrin) to beneficial (e.g., glucoraphanin). Each glucosinolate-containing plant species has its own specific glucosinolate profile. For this reason, it is important to correctly identify and reliably quantify the different glucosinolates present in brassicaceous leaf, seed, and root crops or, for ecological studies, in their wild relatives. Here, we present a well-validated, targeted, and robust method to analyze glucosinolate profiles in a wide range of plant species and plant organs. Intact glucosinolates are extracted from ground plant materials with a methanol-water mixture at high temperatures to disable myrosinase activity. Thereafter, the resulting extract is brought onto an ion-exchange column for purification. After sulfatase treatment, the desulfoglucosinolates are eluted with water and the eluate is freeze-dried. The residue is taken up in an exact volume of water, which is analyzed by high-pressure liquid chromatography (HPLC) with a photodiode array (PDA) or ultraviolet (UV) detector. Detection and quantification are achieved by conducting comparisons of the retention times and UV spectra of commercial reference standards. The concentrations are calculated based on a sinigrin reference curve and well-established response factors. The advantages and disadvantages of this straightforward method, when compared to faster and more technologically advanced methods, are discussed here.

A Straightforward Method for Glucosinolate Extraction and Analysis with High-pressure Liquid Chromatography HPLC

Here, we describe in great detail an established and robust protocol for the extraction of glucosinolates from ground plant materials. After an on-column sulfatase treatment of the extracts, the desulfoglucosinolates are eluted and analyzed by high-pressure liquid chromatography.

Glucosinolates are a well-studied and highly diverse class of natural plant compounds. They play important roles in plant resistance, rapeseed oil ...

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 ...

Biochemistry

Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not only provides quantitative data but also permits structural identification of the metabolites present in complex mixtures.
Metabolic profiling by 1H NMR has proven useful in the study of various types of plant scenarios, which include the evaluation of crop&#160;conditions, harvest and post- harvest treatments, metabolic phenotyping, metabolic pathways, gene regulation, identification of biomarkers, chemotaxonomy, quality control, denomination of origin, among others. However, signal overlapping of the large number of resonances with expanded J-coupling multiplicities complicates the spectra analysis and its interpretation, and represents a limitation for classical 1H NMR profiling.
In the last decade, novel NMR broadband homonuclear decoupling techniques through which multiplet signals collapse into single resonance lines - commonly called Pure Shift methods - have been developed to overcome the spectra resolution problem inherent to 1H NMR classical spectra.
Here a step-by-step protocol of the plant extract preparation and the procedure to record optimal Pure Shift PSYCHE and SAPPHIRE-PSYCHE spectra in three different plant matrices - Vanilla plant&#160;leaves, potato tubers (S. tuberosum), and Cape gooseberries (P. peruviana) - is presented. The effect of the gain in resolution in metabolic identification, correlation analysis and multivariate analyses, as compared against classical spectra, is discussed.

This paper presents the use of PSYCHE and SAPPHIRE-PSYCHE in the metabolic profiling of plants and includes detailed procedures for sample preparation and optimal Pure Shift NMR spectra recording. Examples through which the gain in resolution achieved by homonuclear decoupling allows a more comprehensive understanding of the system are discussed.

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not ...

Label-free in situ Imaging of Lignification in Plant Cell Walls

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find cost-effective and sustainable solutions has become a topical and critical task. Lignocellulosic biomass is poised to become the primary source of biomass for the conversion to liquid biofuels1-6. However, the recalcitrance of these plant cell wall materials to cost-effective and efficient degradation presents a major impediment for their use in the production of biofuels and chemicals4. In particular, lignin, a complex and irregular poly-phenylpropanoid heteropolymer, becomes problematic to the postharvest deconstruction of lignocellulosic biomass. For example in biomass conversion for biofuels, it inhibits saccharification in processes aimed at producing simple sugars for fermentation7. The effective use of plant biomass for industrial purposes is in fact largely dependent on the extent to which the plant cell wall is lignified. The removal of lignin is a costly and limiting factor8 and lignin has therefore become a key plant breeding and genetic engineering target in order to improve cell wall conversion.
Analytical tools that permit the accurate rapid characterization of lignification of plant cell walls become increasingly important for evaluating a large number of breeding populations. Extractive procedures for the isolation of native components such as lignin are inevitably destructive, bringing about significant chemical and structural modifications9-11. Analytical chemical in situ methods are thus invaluable tools for the compositional and structural characterization of lignocellulosic materials. Raman microscopy is a technique that relies on inelastic or Raman scattering of monochromatic light, like that from a laser, where the shift in energy of the laser photons is related to molecular vibrations and presents an intrinsic label-free molecular "fingerprint" of the sample. Raman microscopy can afford non-destructive and comparatively inexpensive measurements with minimal sample preparation, giving insights into chemical composition and molecular structure in a close to native state. Chemical imaging by confocal Raman microscopy has been previously used for the visualization of the spatial distribution of cellulose and lignin in wood cell walls12-14. Based on these earlier results, we have recently adopted this method to compare lignification in wild type and lignin-deficient transgenic Populus trichocarpa (black cottonwood) stem wood15. Analyzing the lignin Raman bands16,17 in the spectral region between 1,600 and 1,700 cm-1, lignin signal intensity and localization were mapped in situ. Our approach visualized differences in lignin content, localization, and chemical composition. Most recently, we demonstrated Raman imaging of cell wall polymers in Arabidopsis thaliana with lateral resolution that is sub-&mu;m18. Here, this method is presented affording visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species without staining or labeling of the tissues.

Label-free in situ Imaging of Lignification in Plant Cell Walls

A method based on confocal Raman microscopy is presented that affords label-free visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species.

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find ...

Biology

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Bioengineering

Screening Lipid Structures Using Stable Isotope Labeling

Tracing de novo Lipids using Stable Isotope Labeling LC-TIMS-TOF MS/MS

Lipids are highly diverse, and small changes in lipid structures and composition can have profound effects on critical biological functions. Stable isotope labeling (SIL) offers several advantages for the study of lipid distribution, mobilization, and metabolism, as well as de novo lipid synthesis. The successful implementation of the SIL technique requires the removal of interferences from endogenous molecules. In the present work, we describe a high-throughput analytical protocol for the screening of SIL lipids from biological samples; examples will be shown of lipid de novo identification during mosquito ovary development. The use of complementary liquid chromatography trapped ion mobility spectrometry and mass spectrometry allows for the separation and lipids assignment from a single sample in a single scan (&lt;1 h). The described approach takes advantage of recent developments in data-dependent acquisition and data-independent acquisition, using parallel accumulation in the mobility trap followed by sequential fragmentation and collision-induced dissociation. The measurement of SIL at the fatty acid chain level reveals changes in lipid dynamics during the ovary development of mosquitoes. The lipids de novo structures are confidently assigned based on their retention time, mobility, and fragmentation pattern.

Author Spotlight: Quantification of Complex Lipidomic Samples Using Stable Isotope Labeling

Presented here is a method for screening lipid structures through stable isotope labeling by using their retention time, mobility, and fragmentation.

Lipids are highly diverse, and small changes in lipid structures and composition can have profound effects on critical biological functions. Stable ...

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.

Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as ...

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine cell central metabolism and discover new enzymes is 13C-assisted metabolism analysis 1. This technique is based on isotopic labeling, whereby microbes are fed with a 13C labeled substrates. By tracing the atom transition paths between metabolites in the biochemical network, we can determine functional pathways and discover new enzymes. 
As a complementary method to transcriptomics and proteomics, approaches for isotopomer-assisted analysis of metabolic pathways contain three major steps 2. First, we grow cells with 13C labeled substrates. In this step, the composition of the medium and the selection of labeled substrates are two key factors. To avoid measurement noises from non-labeled carbon in nutrient supplements, a minimal medium with a sole carbon source is required. Further, the choice of a labeled substrate is based on how effectively it will elucidate the pathway being analyzed. Because novel enzymes often involve different reaction stereochemistry or intermediate products, in general, singly labeled carbon substrates are more informative for detection of novel pathways than uniformly labeled ones for detection of novel pathways3, 4. Second, we analyze amino acid labeling patterns using GC-MS. Amino acids are abundant in protein and thus can be obtained from biomass hydrolysis. Amino acids can be derivatized by N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS) before GC separation. TBDMS derivatized amino acids can be fragmented by MS and result in different arrays of fragments. Based on the mass to charge (m/z) ratio of fragmented and unfragmented amino acids, we can deduce the possible labeled patterns of the central metabolites that are precursors of the amino acids. Third, we trace 13C carbon transitions in the proposed pathways and, based on the isotopomer data, confirm whether these pathways are active 2. Measurement of amino acids provides isotopic labeling information about eight crucial precursor metabolites in the central metabolism. These metabolic key nodes can reflect the functions of associated central pathways.
13C-assisted metabolism analysis via proteinogenic amino acids can be widely used for functional characterization of poorly-characterized microbial metabolism1. In this protocol, we will use Cyanothece 51142 as the model strain to demonstrate the use of labeled carbon substrates for discovering new enzymatic functions.

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

13C-isotope labeling is a useful technique for determining the cell central metabolism for various types of microorganisms. After cells have been cultured with a specific labeled substrate, GC-MS measurement can reveal functional metabolic pathways based on unique labeling patterns in proteinogenic amino acids.

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine ...

¹³C₆-Glucose Labeling Associated with LC-MS: Identification of Plant Primary Organs in Secondary Metabolite Synthesis

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