An Adjustable Protocol to Analyze Chemical Profiles of Non-sterile Rhizosphere Soil

Joëlle Schläpfer Joëlle Schläpfer
Yang Bai Yang Bai
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The Plant Journal
Oct 2017



The analysis of chemical diversity in non-sterile rhizosphere soil has been a pressing methodological challenge for years. Rhizosphere-enriched chemicals (i.e., rhizochemicals) include root exudation chemicals, (microbial) breakdown products thereof, and de novo produced metabolites by rhizosphere-inhabiting microbes, all of which can play an important role in plant-soil interactions. The power and resolution of analytical methods and statistical analysis pipelines allow for better acquisition, separation and identification of rhizochemicals, thus providing unprecedented insight into the biochemistry underpinning plant-soil interactions. The current protocol describes a recently developed method to characterize rhizochemical profiles from plants, including crops, and is modular and customizable, allowing for application across a range of different plant-soil combinations. The protocol provides in-depth details about the experimental system for sample collection, data acquisition by liquid chromatography coupled to mass spectrometry, and analytical pipeline, which statistically selects for rhizochemicals by statistical comparison between metabolite profiles from plant-containing soil and plant-free soil. Moreover, the optional addition of chemical standards permits a semi-targeted approach, which improves the annotation of chemical signatures and identification of single rhizochemicals.

Keywords: Metabolomics (代谢组学), Rhizosphere (根际), Soil chemistry (土壤化学), Mass spectrometry (质谱), Exudate collection (分泌物收集), Soil leachate (土壤淋溶液)


Previous approaches to studying rhizosphere have often focused on sterile and hydroponic growth conditions (van Dam and Bouwmeester, 2016). These approaches limit our understanding of the multi-trophic nature of the rhizosphere, as they fail to provide information about (microbial) breakdown products of root exudation chemicals and de novo produced chemicals by rhizosphere-inhabiting microbes, even though these may drive plant-soil interactions in response to environmental change, such as pathogen attack or abiotic stress. The challenge arises from understanding and simplifying the complex, and often overwhelming, level of chemical diversity that originates from untargeted analyses of non-sterile soil (Figure 1). Nevertheless, untangling this diversity by identifying chemical networks, their origin and function, is critical for obtaining new insight into the ecology of a much under-explored area of plant and soil science. Although not without its caveats, recent advances in mass spectrometry (MS) technology and statistical-analytical techniques permit powerful exploratory methods to uncover the chemistry of natural soils (Pétriacq et al., 2017; Swenson et al., 2015 and 2018). Such techniques are becoming increasingly accessible.

Figure 1. Rhizochemicals that shape below-ground interactions. Rhizochemicals are compounds that are enriched in the rhizosphere and include a) root exudation chemicals, b) microbial breakdown products of root exudation chemicals, c) de novo synthesized metabolites by rhizosphere-inhabiting microbes. Rhizochemicals can have important signaling activities thereby shaping below-ground interactions between plants and soil microbes.

The protocol presented here describes an adjustable experimental system for sample collection and sample preparation, followed by data acquisition using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF). Subsequent statistical analysis allows separating chemical signatures by retention time and mass to charge (m/z) ratios between plant-free soil and plant-containing soil, which ultimately identifies profiles of rhizosphere-enriched chemical (i.e., rhizochemicals). These profiles can be further annotated using chemical databases, such as METLIN (Smith et al., 2005). Moreover, during mass spectrometry examination, running a library of relevant standards can increase the accuracy of this approach and allow focus of the analysis on specific metabolite classes.

The protocol outlined here, and originally used in Pétriacq et al. (2017), was initially developed for the analysis of rhizosphere chemistry in the model plant species Arabidopsis thaliana (Arabidopsis; Figures 2 and 3) and has been adapted for maize. For other plant species, a similar experimental design can be adapted and implemented, but method optimization is advised. Particularly, we recommend further optimization when insufficient separation is obtained between rhizochemical signatures from the plant-containing soil samples and the plant-free soil samples (see Figure 4). These adjustments involve i) testing different soils, ii) using different extraction solutions (e.g., with varying MeOH concentrations), iii) increasing root biomass in collection tubes, iv) adjusting soil moisture, v) increasing the time of sample extraction from the collection tubes, and vi) varying MS settings. Further attention should be paid to the fact that extraction solutions with high MeOH concentrations can cause damage to microbial and/or root cells, in which case specific assays can be used to assess the extent of cell damage, as detailed in Petriacq et al. (2017). If cell damage occurs, we recommend using a lower MeOH concentration in the extraction solution and/or shorter extraction times. If insufficient sample yield is obtained with shorter extraction times, a pressurized collection system can be used, as is illustrated in Figure 2D.

Materials and Reagents

Note: All chemicals and solvents used for metabolomics were of mass spectrometry grade (Sigma-Aldrich, Germany). Other solvents were of analytical grade.

  1. Tips
  2. Millipore miracloth, pore size of 22-25 µm (VWR, catalog number: 475855-1)
  3. Petri dish (lids) NunclonTM Delta, 8.8 cm2 (Thermo Fisher Scientific, catalog number: 150318)
  4. 15 ml FalconTM conical centrifuge tubes (Thermo Fisher Scientific, catalog number: 10468502)
  5. 50 ml FalconTM conical centrifuge tubes (Thermo Fisher Scientific, catalog number: 10788561)
  6. 50 ml syringe, without needle (Thermo Fisher Scientific, catalog number: 11901563)
  7. 5 ml conical preparation tube (Starlab, catalog number: E1450-1100)
  8. 2 ml microcentrifuge tube (Starlab, catalog number: I1420-2600)
  9. 30 ml SterilinTM universal container (Thermo Fisher Scientific, catalog number: 11309143)
  10. Aluminum foil
  11. Filter paper
  12. 0.2 µm disposable Merck cartridge (Thermo Fisher Scientific, catalog number: 10740365) 
  13. 1.5 ml Glass screw neck vials, with screw caps and micro inserts (VWR, catalog numbers: 548-0018, -0020, -0106)
  14. Levington M3 dry compost (Spunhill, catalog number: YEV05K) 
  15. Non staining silica sand (Sweepfast, catalog number: CH52) 
  16. Arabidopsis seeds
  17. Maize seeds (Zea mays)
  18. Acetonitrile (Hypergrade, Sigma-Aldrich, catalog number: 1000291000)
  19. Methanol (Hypergrade, Sigma-Aldrich, catalog number: 1060351000)
  20. Formic acid (FA, Sigma-Aldrich, catalog number: F0507-500ML)
  21. Liquid Nitrogen (BOC)
  22. Bleach Mexcel (5-10%) (SLS, catalog number: CLE0300)
  23. Hydrochloric acid (37%) (Sigma-Aldrich, catalog number: H1758)
  24. Sodium formate (0.01 M NaOH/1% FA (1/1, v/v) ten-fold diluted with acetonitrile/water (80/20, v/v) (Sigma-Aldrich, catalog number: 71539-500G)
  25. Leucine enkephalin (2 ppm in acetonitrile/water (50/50, v/v) with 0.1% of formic acid) (Sigma-Aldrich, catalog number: L9133-25MG)
  26. Ice-cold extraction solution MeOH 50% + FA 0.05% (see Recipes)


  1. Pipettes (P-5000, P-1000) (Starlab)
  2. Growth tubes (make these bespoke, see below)
  3. Pressure lids (make these bespoke, see below)
  4. Speedvac vacuum concentrator (Thermo Fisher Scientific, catalog number: SPD120)
  5. Freeze drier (Modulyo benchtop freeze dryer) (Edwards, catalog number: D-230) 
  6. BEH C18 column for UPLC (2.1 x 50 mm, 1.7 μm, Waters) with a guard column (VanGuard, 2.1 x 5 mm, 1.7 µm, Waters) 
  7. SYNAPT G2si Q-TOF (Waters)
  8. Centrifuge (that fits 5 ml tubes)
  9. Microcentrifuge
  10. Ultrasonic cleaning bath
  11. Soldering iron
  12. Drill (7 mm multipurpose drill bit)
  13. Growth cabinets (Fitotron, SANYO)
  14. Tweezers
  15. 4 °C refrigerator
  16. -80 °C freezer
Note: This equipment is described for Arabidopsis, but for bigger plants, such as maize, equipment use will need adjustment.


  1. MassLynx, version 4.1 with Data bridge,
  2. R with XCMS package, version 3.2,
  3. Multiple experiment viewer–MeV, version 4.9.0,
  4. MetaboAnalyst version 3.0,
  5. METLIN,


  1. Equipment preparation
    1. Growth tubes for plant development and subsequent chemical sampling (Figure 2A).
      To create growth tubes, in a fume-hood use a pre-heated soldering iron to melt 7 mm holes in the base of 30 ml plastic tubes. This step will need to be repeated for as many tubes are required. Growth tubes can be thoroughly washed with pure distilled water and reused several times. Fit tubes with 40 mm2 miracloth at the bottom to hold soil. Fill tube with desired soil matrix (~40 ml), for instance 9:1 (v/v) mixture of sand and dry compost (see Materials and Reagents for more information).
      Note: For larger plant species, such as 4-week-old maize seedlings, growing in clay agricultural soil, 50 ml Falcons were suitable with similar sized holes (Figure 2C).
    2. Pressurized lid (for larger Falcons) to aide in pushing the liquid through the growth tube (if necessary; Figure 2D).
      Create these lids by drilling a hole, smaller than the diameter of the syringe, and forcing the syringe through, securing with strong and watertight epoxy. Applying silicon to the edges and thread of the lid will ensure a tight seal to allow sufficient pressure. This set-up may be required for soil that retains more water, in order to shorten the time of flushing the soil with extraction solution.
      Note: Simultaneous assays can be performed using the same experimental set-up. For instance, non-destructive rhizosphere or bulk soil can be sampled prior to extraction solution application to allow elemental analysis, or DNA/RNA extractions for determination of microbial community compositions and soil functional analysis.

  2. Experimental set-up of growth system (Arabidopsis; Figures 2A and 2B)
    1. Stratify Arabidopsis seeds for 2 days in the dark in autoclaved water at 4 °C.
      Note: Seeds can be surface sterilized if appropriate for the experimental design. Such techniques are effective and straightforward (Lindsey et al., 2017).
    2. Wrap soil-filled growth tubes in tin foil to limit algal growth and stand growth tubes in individual Petri dishes to prevent cross-contamination between bio-replicates. 
    3. Seal tubes to the Petri dish with masking tape to provide stability. Stand tubes in a tray.
    4. Apply 15 ml of water to individual Petri dishes to saturate the soil.

    Figure 2. Experimental set-up for sampling rhizochemicals. A-C. Photos show collection tubes containing plant-free and plant-containing soils (1. Arabidopsis and 2. Maize). Tubes were covered with foil to prevent algal growth and were placed onto small Petri dishes to prevent cross-contamination. D. Example of the pressurized lid that connects to an epoxy-secured syringe. This allows the application of pressure during sample collection, in order to increase (this word was pluralized) the speed and yield of sample collection while minimizing exposure of living cells to potentially damaging organic solvents. When using larger plants such as maize, the shoot is gently removed with scissors just before applying the pressure.

    1. Pipette four seeds onto the soil surface of individual tubes. If desirable, prepare unseeded soil-only samples to act as controls. 
    2. Cover trays with a lid and place into a growth cabinet with the appropriate growth conditions. 
    3. Petri dishes should be supplied with 5-10 ml of water bi-weekly.
    4. After seedling emergence, carefully thin seedlings using tweezers to leave one seedling per pot.
    Note: In short day growth conditions (8.5:15.5 light:dark; 20 °C light, 18 °C dark; 65-70% relative humidity) final watering, prior to metabolite extraction, should be no later than 3 days.

  3. Experimental set-up of growth system (Maize; Figure 2C)
    1. Imbibe seeds overnight in autoclaved, sterile water before placing on Petri dishes containing sterile, damp filter paper in the dark at 23 °C for two days. 
    2. Plant germinated seeds in soil-filled growth tubes, 1.5 cm from the soil surface.
    3. Wrap growth tubes in foil and cover the surface with black plastic beads to limit algal growth.
    4. Place the tubes in a growth chamber with the desired environmental conditions.
    Note: Soil can be adapted depending on the experimental design, but more organically rich soil may produce harder to interpret results. Furthermore, depending on the soil used, 25% of perlite will aide drainage during the collection with extraction buffer (see below). For the example demonstrated here, compost was used, but agricultural soil can also be used (see Pétriacq et al., 2017 for more details).

  4. Metabolite extraction from control and Arabidopsis/maize soil (Figure 3)
    1. Collect plant soil samples from tubes containing one 5-week-old Arabidopsis plant, or one 17-day-old maize plant (timings can depend on experimental design).
      Note: The timings used in this protocol assume similar growth conditions to those mentioned above.
    2. For Arabidopsis, with the seedling intact, apply ice-cold extraction solution (5 ml), to the top of the tubes and avoid any disturbance of the soil surface.
      Note: With larger plant, you may want to sever, or simultaneously sample the seedling. If doing so, be careful to avoid contaminating the extraction solution with damaged plant material. Furthermore, when using larger plants such as maize, the shoot can be gently removed with scissors just before applying the pressure.
    3. After 1 min, 4-4.5 ml can be collected from the drainage hole in 5 ml centrifuge tubes.
    4. For use with maize, gently slit the seedling with a blade around 0.5 cm of the soil surface, and subsequently apply 15 ml of the extraction solution to the soil and induce pressure to the top of the pot, using a modified lid containing a syringe.
    5. After 1 min, 5-10 ml are collected in centrifuge tubes. 
    6. Centrifuge to pellet soil residues (5 min, 3,500 x g), aliquot 4 ml of supernatant into a new centrifuge tube.
    7. Flash-freeze in liquid nitrogen and freeze-dry until complete dryness.
    1. Samples can now be stored at -80 °C until chemical analysis. 
    2. If the soil is not saturated, less flow-through can be collected. The longer the buffer is in contact with the plant the more chance there is of tissue damage and bias through cellular metabolites. Limit time for which samples are exposed to MeOH is less than 1 min, to prevent the risk of root damage. Irrelevant for sampling with sterile distilled water.

    Figure 3. Sampling workflow. See figure for notes.

  5. Preparing samples for UPLC-qTOF analysis
    1. Resuspend dried aliquots into 100 µl of methanol:water:formic acid (50:49.9:0.1, v/v).
    2. Sonicate at 4 °C for 20 min, vortex and centrifuge (15 min, 14,000 x g, 4 °C) to remove potential particles that could block the UPLC column. Alternatively, filter the samples through 0.2 µm disposable cartridge obtained from analytical suppliers.
    3. Transfer final supernatants (80 µl) into glass vials containing a glass insert before injection through the UPLC system. 
    4. During this process, generate a quality control (QC) sample by mixing 5 µl of each sample into one single vial. This will act as a control for analytical reliability of the LCMS system, and for optional normalization of the data in case of MS intensity drift when running a high number of samples (Broadhurst et al., 2018).

  6. UPLC-qTOF Set-up
    1. LC and MS characteristics are fully detailed in Pétriacq et al. (2017). Using a SYNAPT G2si HDMS Q-TOF mass spectrometer (Waters), coupled to a UPLC BEH C18 column (2.1 x 50 mm, 1.7 μm, Waters) with a guard column (VanGuard, 2.1 x 5 mm, 1.7 µm, Waters) for separation of compounds at a flow rate of 400 μl min−1, prepare the mobile phases as A; water with 0.05% formic acid, and B; acetonitrile with 0.05% formic acid with the following gradient: 0-3 min 5-35% B, 3-6 min 35-100% B, holding at 100% B for 2 min, 8-10 min, 100-5% B. 
    2. Set the column temperature to 45 °C and use an injection volume of 10 μl.
    3. Use a mass range of 50-1,200 Da and a scan time of 0.2 s (ESI- and ESI+) with the instrument operating in sensitivity mode for the MS full scan (i.e., without collision energy). 
    4. Ramp collision energy in the transfer cell from 5 to 45 eV (MSE), using appropriate voltage conditions for ESI- mode (e.g., Capillary, -3 kV; Sampling cone, -25 V; Extraction cone, 4.5 V) and ESI+ mode (e.g., Capillary, +3 kV; Sampling cone, +25 V; Extraction cone, 10 V). 
    5. Source temperature for each mode should be 120 °C, desolvation temperature at 350 °C, desolvation gas flow at 800 L h-1 and cone gas flow at 60 L h-1.

  7. Calibration of the qTOF mass spectrometer
    1. Prior to analysis, calibrate the Q-TOF detector with a solution of sodium formate. 
    2. During each run, accurate mass measurements can be ensured by infusing leucine enkephalin peptide as an internal reference (i.e., lock mass) with 10 s scan frequency, cone voltage of 40 V and a capillary voltage of 3 kV.
    3. Inject 6 QCs samples at the beginning of the analytical run, then every 10 injections, and finish with QC sample at the end of the analytical run.
    4. Inject blank samples (50% methanol, v/v) between each treatment and between ESI- and ESI+ ionization modes for stabilization of the electrospray ionization source.

  8. Processing of MS data for statistical analysis (see Figure 4 for a schematic of the subsequent analytical steps) and deconstruction of rhizochemicals
    1. After the run has finished, convert raw files, here obtained from MassLynx, into CDF or mwXML format, using the Databridge function in MassLynx, or ProteoWizard (MsConvert).
    2. For subsequent alignment and integration of metabolic peaks, use R with the XCMS package installed (Smith et al., 2006) and the standard script described in Supplemental Data S1
    3. Peaks can be retained for analysis when present in all bio-replicates (n = 3, in an example where there are 3 bio-replicates), at a threshold intensity of 10 (I = 10) and at maximum resolution range of 20 ppm. XCMS parameters should be adjusted according to the mass spectrometer used in the study (here this is q-TOF), and nuances of the particular dataset being investigated.
    4. Normalize peak values from each run against total ion current (TIC). For each sample, normalized peak values will generate separate datasets for ESI+ and ESI- ionization modes.

    Figure 4. Analytical workflow for the identification of rhizosphere metabolites. Mass spectrometry data are processed using XCMS (Smith et al., 2006) for peak identification (RT-m/z features) and alignment, grouping, and retention time (RT) correction, then checked for quality control and normalized (Median-centered, cube-root transformation and Pareto scaling). Metabolomics overview is given by principal component analysis (PCA), which is crucial to verify sufficient metabolomics separation between extracts from plant-containing and plant-free tubes. If the global differences are not convincing by PCA, the rhizosphere chemistry in the plant-containing soil might be too much diluted by bulk soil chemistry, and further adjustments of collection systems are necessary (e.g., bigger plants, other soil, different extracting solutions, and longer sample extraction). In the specific case of binary comparison (plant-free versus plant-containing tubes), biomarker detection can be performed with S Plot obtained from (Orthogonal) Partial Least Square-Discriminant Analysis (O)PLS-DA (Worley and Powers, 2013). This supervised technique provides Variable Important for the Projection (VIP) scores that reflect the discriminant statistical power of each variable for the PLS discriminant model. Ultimately, univariate statistical comparison via volcano plots (fold change vs. statistical significance) allows for straightforward identification of metabolic markers that are specific to the rhizosphere (i.e., rhizochemicals).

  9. Statistical analysis of MS data
    For this experimental set-up, the strength in identifying ‘rhizosphere’ chemical signatures comes from a combination of the experimental design, i.e., free soil vs. planted soil, and the subsequent binary statistical comparison, obtained through a volcano plot showing statistical significance against biological significance. Further statistical tests (e.g., ANOVA, t-tests with consideration for multiple comparisons, such as false discovery rate–FDR or Bonferroni corrections, where appropriate) can be used for the quantification and annotation of markers that are affected by the treatment. Such pipelines depend on the conditions used in the initial structure of the experiment and question being asked.
      Prior to analysis, an appropriate normalization is required. Median-normalization, cube-root transformation and Pareto scaling of the data seem to perform best on metabolomics datasets (van den Berg et al., 2006): the original structure of the dataset is conserved while the influence of peaks with high intensity is softened compared to peaks with low intensity.
    1. Global differences in metabolic signals between treatment/point combinations can be visualized for anions (ESI-) and cations (ESI+) separately, or jointly where both datasets are concatenated, by principal component analysis (PCA), using MetaboAnalyst online (v. 3.0;; (Xia et al., 2015) on median-normalized, cube-root-transformed and Pareto-scaled data.
    2. For quantification of the number of ions showing quantitative differences between binary treatments (specifically the comparison between plant-free soil and plant-containing soil), volcano plots can be created on median normalized, Pareto-scaled and cube-root transformed data, with a cut-off value of > 2 fold-change (Log2 > 1) and a statistically significant threshold of P < 0.05 (Welch’s t-test; MetaboAnalyst). These markers can then be selected for identification. When a condition of ‘rhizosphere’ chemicals is screened against the corresponding soil, then volcano plots can deconstruct the markers that are specific to semiochemicals of the rhizosphere. When soil chemistry is the target, conventional heatmap or cluster analysis could be performed and the relevant markers further retained for subsequent analysis (such as ANOVA with subsequent Hierarchal clustering through Pearson’s correlation (Williams et al., 2018). It is highly recommended that you adhere to the guidelines laid out by the Metabolomics Standards Initiative (Fiehn et al., 2008; Goodacre et al., 2007; Sumner et al., 2007).

  10. Annotation of putative metabolic markers
    Putative annotation of the selected ion markers can be assigned, based on accurately detected m/z values at a mass accuracy < 30 ppm), using METLIN chemical database (Smith et al., 2005), or other chemical/metabolite database available online, or in house. PubChem can then be used to check the predicted pathway or class annotation (


When applying this method to larger species with greater soil volumes, it is important to note that the water content of the soil will dilute the extraction solution, which needs to be taken into account in the design of the extraction solution and the amount of flush-through collected from the sample.
  In addition, the extraction solution suggested here (50% MeOH) limits tissue damage to the sample, but the actual design of this solution can be adapted depending on the chemical profile of interest. It is worth noting that both lower and higher concentrations of methanol can be appropriate. Thus, it is possible that other extraction buffers may be preferable (such as water, 70% MeOH, 10% MeOH etc.). It is highly recommended that method optimization is performed regardless, but especially in the instance of applying a different extraction solution as to those discussed.


  1. Extraction solution
    50% methanol (hypergrade):49.95% ultra-purified water: 0.05% Formic acid
    1. Use clean equipment to produce this solution to the desired volume. Store in the fridge–shelf life is less than a week.
    2. Extraction solution can contain different levels of MeOH, depending on the type of chemistry that is desired for collection.


We would like to thank Anne Cotton and Roland Schwarzenbacher for collecting images to illustrate the collection system. JT acknowledges support by a consolidator grant from the European Research Council (ERC; no. 309944 “Prime-A-Plant”), a Research Leadership Award from the Leverhulme Trust (no. RL-2012-042) and a BBSRC-IPA grant (BB/P006698/1). PP is grateful to the MetaboHUB (ANR-11-INBS-0010) project, University of Bordeaux and P3 Research Institute for financial support.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


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  15. Xia, J., Sinelnikov, I. V., Han, B. and Wishart, D. S. (2015). MetaboAnalyst 3.0--making metabolomics more meaningful. Nucleic Acids Res 43(W1): W251-257.


摘要:多年来,非无菌根际土壤的化学多样性分析一直是一个迫切的方法论挑战。富含根际的化学物质(即,根茎化学物质)包括根分泌物化学物质,(微生物)分解产物,以及 de novo 通过根际栖息的微生物产生的代谢物,所有这些都可以在植物 - 土壤相互作用中发挥重要作用。分析方法和统计分析管道的功效和分辨率可以更好地获取,分离和鉴定根际化学物质,从而提供前所未有的洞察植物 - 土壤相互作用的生物化学。目前的方案描述了最近开发的方法来表征来自植物(包括作物)的根茎化学特征,并且是模块化和可定制的,允许应用于一系列不同的植物 - 土壤组合。该协议提供了关于样品采集实验系统,通过液相色谱与质谱联用的数据采集以及分析管道的深入细节,通过统计比较来自含植物土壤和无植物土壤的代谢物剖面,分析管道。此外,任选添加化学标准允许半靶向方法,其改进化学特征的标注和单一根茎化学物质的鉴定。

背景:以前研究根际的方法通常集中在无菌和水培生长条件上(van Dam和Bouwmeester,2016)。这些方法限制了我们对根际多营养性质的理解,因为它们无法提供有关根系分泌物化学物质(微生物)分解产物和 de novo 通过根际栖息的微生物产生的化学物质的信息,甚至尽管这些可能会驱动植物 - 土壤相互作用以应对环境变化,例如病原体攻击或非生物胁迫。挑战来自于理解和简化复杂的,通常是压倒性的化学多样性水平,这些化学多样性来源于非无菌土壤的非靶向分析(图1)。然而,通过识别化学网络,它们的起源和功能来解开这种多样性,对于获得对未充分研究的植物和土壤科学领域的生态学的新见解至关重要。虽然并非没有注意事项,但质谱(MS)技术和统计分析技术的最新进展允许使用强大的探索方法来揭示天然土壤的化学成分(Pétriacq et al。,2017; Swenson 等人,,2015年和2018年)。这些技术变得越来越容易获得。

图1.形成地下相互作用的根化学物质。根茎化学物质是在根际富集的化合物,包括a)根分泌物化学物质,b)根分泌物化学物质的微生物分解产物,c) de novo 通过根际栖息的微生物合成代谢物。根际化学物质可具有重要的信号传导活性,从而塑造植物与土壤微生物之间的地下相互作用。

这里介绍的协议描述了一个可调节的实验系统,用于样品采集和样品制备,然后使用超高效液相色谱与四极杆飞行时间质谱(UPLC-Q-TOF)相结合进行数据采集。随后的统计分析允许通过保留时间和质量与无植物土壤和含植物土壤之间的电荷( m / z )之间的化学特征分离,最终确定富含根际的化学物质的分布(即,rhizochemicals)。这些概况可以使用化学数据库进一步注释,例如METLIN(Smith et al。,2005)。此外,在质谱检查中,运行相关标准库可以提高该方法的准确性,并使分析的重点放在特定的代谢物类别上。

此处概述的方案,最初用于Pétriacq et al。(2017),最初是为模拟植物物种拟南芥中的根际化学分析而开发的( >拟南芥;图2和3)并已适应玉米。对于其他植物物种,可以调整和实施类似的实验设计,但建议进行方法优化。特别是,当含有植物的土壤样品的根际化学特征与无植物土壤样品之间的分离不充分时,我们建议进一步优化(见图4)。这些调整涉及 i )测试不同的土壤, ii )使用不同的提取溶液(例如,具有不同的MeOH浓度), iii )增加收集管中的根生物量, iv )调整土壤水分, v )增加从收集管中提取样品的时间, vi )改变MS设置。应该进一步关注具有高MeOH浓度的提取溶液可以对微生物和/或根细胞造成损害的事实,在这种情况下,可以使用特定的测定来评估细胞损伤的程度,如Petriacq et al。(2017)。如果发生细胞损伤,我们建议在提取溶液中使用较低的MeOH浓度和/或缩短提取时间。如果在较短的提取时间下获得的样品产量不足,则可以使用加压收集系统,如图2D所示。

关键字:代谢组学, 根际, 土壤化学, 质谱, 分泌物收集, 土壤淋溶液



  1. 提示
  2. Millipore miracloth,孔径22-25μm(VWR,目录号:475855-1)
  3. 培养皿(盖子)Nunclon TM Delta,8.8 cm 2 (Thermo Fisher Scientific,目录号:150318)
  4. 15 ml Falcon TM 锥形离心管(Thermo Fisher Scientific,目录号:10468502)
  5. 50 ml Falcon TM 锥形离心管(Thermo Fisher Scientific,目录号:10788561)
  6. 50 ml注射器,无针头(Thermo Fisher Scientific,目录号:11901563)
  7. 5毫升锥形制备管(Starlab,目录号:E1450-1100)
  8. 2 ml微量离心管(Starlab,目录号:I1420-2600)
  9. 30 ml Sterilin TM 通用容器(Thermo Fisher Scientific,目录号:11309143)
  10. 铝箔
  11. 过滤纸
  12. 0.2μm一次性Merck滤芯(Thermo Fisher Scientific,目录号:10740365)&nbsp;
  13. 1.5 ml玻璃螺口瓶,带螺帽和微型插件(VWR,目录号:548-0018,-0020,-0106)
  14. Levington M3干堆肥(Spunhill,目录号:YEV05K)&nbsp;
  15. 非染色硅砂(Sweepfast,目录号:CH52)&nbsp;
  16. 拟南芥种子
  17. 玉米种子( Zea mays )
  18. 乙腈(Hypergrade,Sigma-Aldrich,目录号:1000291000)
  19. 甲醇(Hypergrade,Sigma-Aldrich,目录号:1060351000)
  20. 甲酸(FA,Sigma-Aldrich,目录号:F0507-500ML)
  21. 液氮(BOC)
  22. Bleach Mexcel(5-10%)(SLS,目录号:CLE0300)
  23. 盐酸(37%)(Sigma-Aldrich,目录号:H1758)
  24. 甲酸钠(0.01 M NaOH / 1%FA(1/1,v / v)用乙腈/水(80/20,v / v)稀释10倍(Sigma-Aldrich,目录号:71539-500G)
  25. 亮氨酸脑啡肽(2 ppm在乙腈/水(50/50,v / v)中含0.1%甲酸)(Sigma-Aldrich,目录号:L9133-25MG)
  26. 冰冷萃取溶液MeOH 50%+ FA 0.05%(见食谱)


  1. 移液器(P-5000,P-1000)(Starlab)
  2. 生长管(使这些定制,见下文)
  3. 压力盖(使这些定制,见下文)
  4. Speedvac真空浓缩器(赛默飞世尔科技,目录号:SPD120)
  5. 冷冻干燥机(Modulyo台式冷冻干燥机)(爱德华兹,产品目录号:D-230)&nbsp;
  6. 用于UPLC(2.1 x 50 mm,1.7μm,Waters)的BEH C18色谱柱,带保护柱(VanGuard,2.1 x 5 mm,1.7μm,Waters)&nbsp;
  7. SYNAPT G2si Q-TOF(沃特世)
  8. 离心机(适合5毫升管)
  9. 微量
  10. 超声波清洗浴
  11. 烙铁
  12. 钻(7毫米多用途钻头)
  13. 生长柜(Fitotron,SANYO)
  14. 镊子
  15. 4°C冰箱
  16. -80°C冰柜
注意:此设备是针对 拟南芥 进行描述的,但对于较大的植物,例如玉米,需要调整设备的使用。


  1. MassLynx,带有数据桥的4.1版,
  2. R使用XCMS软件包,版本3.2,
  3. 多个实验查看器-MeV,版本4.9.0,
  4. MetaboAnalyst 3.0版,
  5. METLIN,


  1. 设备准备
    1. 用于植物发育和随后化学取样的生长管(图2A)。
      为了制造生长管,在通风橱中使用预热的烙铁来熔化30毫升塑料管底座中的7毫米孔。需要重复该步骤,因为需要许多管。生长管可以用纯蒸馏水彻底洗涤并重复使用几次。在底部安装具有40 mm 2 miracloth的管以保持土壤。用所需的土壤基质(~40 ml)填充管,例如9:1(v / v)砂和干燥堆肥的混合物(有关详细信息,请参阅材料和试剂)。
    2. 加压盖(适用于较大的Falcons)帮助推动液体通过生长管(如果需要,图2D)。
      注意:可以使用相同的实验装置进行同时测定。例如,可以在施用提取溶液之前对非破坏性根际土壤或大块土壤进行取样,以进行元素分析,或DNA / RNA提取,以确定微生物群落组成和土壤功能分析。

  2. 生长系统的实验装置(拟南芥;图2A和2B)
    1. 在4℃高压灭菌的水中,在黑暗中将拟南芥种子分层2天。
    2. 将土壤填充的生长管包裹在锡箔中以限制藻类生长并在各个培养皿中放置生长管以防止生物重复之间的交叉污染。&nbsp;
    3. 用胶带将管密封到培养皿上以提供稳定性。托管在托盘中。
    4. 在个别培养皿中加入15毫升水,使土壤饱和。

    图2.采集根际化学物质的实验装置。 A-C。照片显示收集管含有无植物和植物的土壤(1. 拟南芥和2.玉米)。用箔覆盖管以防止藻类生长,并将其置于小的培养皿上以防止交叉污染。 D.连接到环氧树脂固定注射器的加压盖的示例。这允许在样品收集期间施加压力,以增加样品收集的速度和产量(这个词被多元化),同时最小化活细胞暴露于潜在破坏性有机溶剂。当使用较大的植物如玉米时,在施加压力之前用剪刀轻轻地去除枝条。

    1. 将四粒种子吸移到各个管的土壤表面上。如果需要,准备未播种的仅土壤样品作为对照。&nbsp;
    2. 用盖子盖住托盘并放入具有适当生长条件的生长箱中。&nbsp;
    3. 培养皿应每两周提供5-10毫升水。
    4. 幼苗出苗后,用镊子仔细地将幼苗留在每盆一苗。
    注意:在短日照生长条件下(8.5:15.5光照:黑暗; 20°C光照,18°C黑暗; 65-70%相对湿度)最后浇水,在代谢物提取之前,应不迟于3天。

  3. 生长系统的实验装置(玉米;图2C)
    1. 将种子在高压灭菌的无菌水中浸泡过夜,然后在含有无菌湿滤纸的培养皿中于23℃在黑暗中放置两天。&nbsp;
    2. 植物发芽种子在土壤填充的生长管中,离土壤表面1.5厘米。
    3. 将生长管包裹在铝箔中,用黑色塑料珠覆盖表面以限制藻类生长。
    4. 将管置于具有所需环境条件的生长室中。
    注意:土壤可以根据实验设计进行调整,但更多有机质的土壤可能更难以解释结果。此外,根据所使用的土壤,25%的珍珠岩将在采集缓冲液的过程中帮助排水(见下文)。对于此处演示的示例,使用了堆肥,但也可以使用农业土壤(更多细节见Pétriacq et al。,2017)。

  4. 从对照和拟南芥 /玉米土壤中提取代谢物(图3)
    1. 从含有一个5周龄拟南芥植物或一个17日龄玉米植物的管中收集植物土壤样品(时间可能取决于实验设计)。
    2. 对于拟南芥,在幼苗完整的情况下,将冰冷的提取液(5 ml)涂抹在管的顶部,避免对土壤表面造成任何干扰。
    3. 1分钟后,可从5ml离心管中的排水孔收集4-4.5ml。
    4. 为了与玉米一起使用,用约0.5cm土壤表面的刀片轻轻切开幼苗,然后将15ml提取溶液施加到土壤中并使用含有注射器的改进的盖子向罐顶部施加压力。
    5. 1分钟后,在离心管中收集5-10ml。&nbsp;
    6. 离心沉淀土壤残留物(5分钟,3,500 x g ),将4ml上清液等分到新的离心管中。
    7. 在液氮中快速冷冻并冷冻干燥直至完全干燥。
    1. 样品现在可以储存在-80°C直到化学分析。
    2. 如果土壤未饱和,则可以收集较少的流量。缓冲液与植物接触的时间越长,组织损伤和通过细胞代谢物产生偏倚的可能性就越大。限制样品暴露于MeOH的时间少于1分钟,以防止根部受损的风险。与无菌蒸馏水取样无关。


  5. 为UPLC-qTOF分析准备样品
    1. 将干燥的等分试样重悬于100μl甲醇:水:甲酸(50:49.9:0.1,v / v)中。
    2. 在4℃下超声处理20分钟,涡旋并离心(15分钟,14,000 x g ,4℃)以除去可能阻塞UPLC柱的潜在颗粒。或者,通过从分析供应商处获得的0.2μm一次性滤芯过滤样品。
    3. 在通过UPLC系统注射之前,将最终上清液(80μl)转移到装有玻璃插入物的玻璃瓶中。&nbsp;
    4. 在此过程中,通过将5μl每种样品混合到一个单独的小瓶中来生成质量控制(QC)样品。这将作为LCMS系统的分析可靠性的控制,并且在运行大量样品时MS强度漂移的情况下可选择标准化数据(Broadhurst et al。,2018)。

  6. UPLC-qTOF设置
    1. LC和MS特征在Pétriacq等人(2017)中有详细说明。使用SYNAPT G2si HDMS Q-TOF质谱仪(Waters),偶联UPLC BEH C18色谱柱(2.1 x 50 mm,1.7μm,Waters),带保护柱(VanGuard,2.1 x 5 mm,1.7μm,Waters)以400μlmin -1 的流速分离化合物,将流动相制备为A;含0.05%甲酸的水和B;具有0.05%甲酸的乙腈,具有以下梯度:0-3分钟5-35%B,3-6分钟35-100%B,在100%B下保持2分钟,8-10分钟,100-5%B &NBSP;
    2. 将色谱柱温度设置为45°C,使用10μl的进样体积。
    3. 使用质量范围50-1,200 Da,扫描时间0.2 s(ESI - 和ESI + ),仪器在灵敏度模式下进行MS全扫描( 即,没有碰撞能量)。&nbsp;
    4. 使用适当的电压条件,在ESI - 模式下,转移电池中的碰撞能量从5到45 eV(MS E )(例如,毛细管) ,-3 kV;采样锥,-25 V;萃取锥,4.5 V)和ESI + 模式(例如,毛细管,+ 3 kV;采样锥,+ 25 V;萃取锥,10 V)。&nbsp;
    5. 每种模式的源温度应为120°C,350°C时的去溶剂化温度,800 L h -1 的去溶剂化气流和60 L h时的锥形气流 -1

  7. 校准qTOF质谱仪
    1. 在分析之前,用甲酸钠溶液校准Q-TOF检测器。&nbsp;
    2. 在每次运行期间,通过输入亮氨酸脑啡肽作为内部参考(即,锁定质量),扫描频率为10 s,锥电压为40 V,毛细管电压为3 kV,可以确保准确的质量测量。
    3. 在分析运行开始时注入6个QC样品,然后每10次注射,并在分析运行结束时用QC样品完成。
    4. 在每次处理之间和ESI - 和ESI + 电离模式之间注入空白样品(50%甲醇,v / v)以稳定电喷雾电离源。

  8. 处理MS数据用于统计分析(参见图4,用于随后的分析步骤的示意图)和解构根茎化学物质
    1. 运行完成后,使用MassLynx中的Databridge函数或ProteoWizard(MsConvert)将原始文件(此处从MassLynx获取)转换为CDF或mwXML格式。
    2. 对于代谢峰的后续比对和整合,使用安装了XCMS软件包的R(Smith et al。,2006)和补充数据S1 。&nbsp;
    3. 当存在于所有生物重复中时(n = 3,在存在3个生物重复的实例中),阈值强度为10(I = 10)且最大分辨率范围为20ppm时,可以保留峰用于分析。应根据研究中使用的质谱仪(此处为q-TOF)调整XCMS参数,并调查所研究的特定数据集的细微差别。
    4. 将每次运行的峰值归一化为总离子电流(TIC)。对于每个样品,归一化峰值将为ESI + 和ESI - 电离模式生成单独的数据集。

    图4.用于鉴定根际代谢物的分析工作流程。使用XCMS(Smith et al。,2006)处理质谱数据以进行峰识别(RT- m / z 特征)和对齐,分组和保留时间(RT)校正,然后检查质量控制和标准化(中位数,立方根变换和Pareto缩放)。代谢组学概述由主成分分析(PCA)给出,这对于验证来自含植物和无植物管的提取物之间的足够代谢组学分离是至关重要的。如果PCA无法证明全球差异,含有植物的土壤中的根际化学可能被大量土壤化学过度稀释,并且需要进一步调整收集系统(例如,更大的植物,其他土壤,不同的提取溶液和更长的样品提取)。在二元比较的特定情况下(无植物与含植物的管),生物标志物检测可以用从(正交)偏最小二乘 - 判别分析(O)PLS-DA(Worley and Powers,2013)获得的S Plot进行。 。该监督技术为投影(VIP)分数提供变量重要性,其反映了PLS判别模型的每个变量的判别统计功效。最终,通过火山图进行的单变量统计比较(倍数变化与统计显着性)允许直接识别对根际特异的代谢标记(即,根茎化学物质)。

  9. MS数据的统计分析
    对于这个实验装置,识别“根际”化学特征的强度来自实验设计,即,自由土壤与种植土壤的组合,以及随后通过以下方式获得的二元统计比较火山图显示出具有生物学意义的统计学意义。进一步的统计测试(例如,ANOVA, t - 考虑多重比较的测试,例如错误发现率-FDR或Bonferroni校正,如果适用)可用于受治疗影响的标志物的量化和注释。这些管道取决于实验初始结构中使用的条件和问题。
    &NBSP;在分析之前,需要进行适当的标准化。数据集的中位数归一化,立方根变换和Pareto缩放似乎在代谢组学数据集上表现最佳(van den Berg et al。,2006):数据集的原始结构在保留的同时受到影响与具有低强度的峰相比,具有高强度的峰的软化。
    1. 治疗/点组合之间代谢信号的全局差异可以分别显示阴离子(ESI - )和阳离子(ESI + ),或联合两个数据集连接在一起,主成分分析(PCA),在线使用MetaboAnalyst(v.3.0; ) ;(Xia et al。,2015)关于中值归一化,立方根变换和Pareto缩放数据。
    2. 为了量化二元处理之间存在数量差异的离子数量(特别是无植物土壤和含植物土壤之间的比较),可以在中值归一化,Pareto-scale和立方根变换数据上创建火山图,截止值> 2倍变化(Log 2 > 1)和统计上显着的阈值 P <1。 0.05(Welch的 t -test; MetaboAnalyst)。然后可以选择这些标记用于识别。当“根际”化学物质的条件针对相应的土壤进行筛选时,火山图可以解构对根际化学信息素特异的标记。当土壤化学成为目标时,可以进行常规热图或聚类分析,并进一步保留相关标记用于后续分析(例如通过Pearson相关性进行随后的分子聚类的方差分析(Williams et al。,2018)) 。强烈建议您遵守代谢组学标准计划制定的指南(Fiehn et al。,2008; Goodacre et al。,2007; Sumner 等人,2007)。

  10. 假定代谢标志物的注释
    基于精确检测的 m / z 值,可以分配所选离子标记的推定注释,质量精度<1。 30 ppm),使用METLIN化学数据库(Smith et al。,2005),或在线或内部提供的其他化学/代谢物数据库。然后可以使用PubChem检查预测的途径或类别注释( https://pubchem.ncbi.nlm。 )。


当将这种方法应用于土壤体积较大的较大物种时,重要的是要注意土壤的含水量会稀释提取溶液,这在提取溶液的设计和冲洗量时需要考虑到 - 通过从样本中收集。
&NBSP;此外,此处建议的提取溶液(50%MeOH)限制了样品的组织损伤,但是该溶液的实际设计可以根据感兴趣的化学特征进行调整。值得注意的是,较低和较高浓度的甲醇都是合适的。因此,可能优选其他提取缓冲液(例如水,70%MeOH,10%MeOH 等)。强烈建议无论如何都要执行方法优化,尤其是在应用与所讨论的那些不同的提取解决方案的情况下。


  1. 提取解决方案
    1. 使用清洁设备将此解决方案生成所需的体积。存放在冰箱中的保质期不到一周。
    2. 萃取溶液可含有不同含量的MeOH,具体取决于收集所需的化学类型。


我们要感谢Anne Cotton和Roland Schwarzenbacher收集图像以说明收集系统。 JT承认欧洲研究理事会(ERC;第309944号“Prime-A-Plant”)的巩固者资助,Leverhulme信托研究领导奖(编号RL-2012-042)和BBSRC-IPA资助(BB / P006698 / 1)。 PP感谢MetaboHUB(ANR-11-INBS-0010)项目,波尔多大学和P3研究所的财务支持。




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引用:Williams, A., Ton, J. and Pétriacq, P. (2019). An Adjustable Protocol to Analyze Chemical Profiles of Non-sterile Rhizosphere Soil. Bio-protocol 9(10): e3245. DOI: 10.21769/BioProtoc.3245.