Extraction and 16S rRNA Sequence Analysis of Microbiomes Associated with Rice Roots
水稻根系相关微生物群落的分离及其16S rRNA序列分析    

Joëlle Schlapfer Joëlle Schlapfer
Yang Bai Yang Bai
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Feb 2015



Plant roots associate with a wide diversity of bacteria and archaea across the root-soil spectrum. The rhizosphere microbiota, the communities of microbes in the soil adjacent to the root, can contain up to 10 billion bacterial cells per gram of soil (Raynaud and Nunan, 2014) and can play important roles for the fitness of the host plant. Subsets of the rhizospheric microbiota can colonize the root surface (rhizoplane) and the root interior (endosphere), forming an intimate relationship with the host plant. Compositional analysis of these communities is important to develop tools in order to manipulate root-associated microbiota for increased crop productivity. Due to the reduced cost and increasing throughput of next-generation sequencing, major advances in deciphering these communities have recently been achieved, mainly through the use of amplicon sequencing of the 16S rRNA gene. Here we first present a protocol for dissecting the microbiota from various root compartments, developed using rice as a model. We next present a method for amplifying fragments of the 16S rRNA gene using a dual index approach. Finally, we present a simple workflow for analyzing the resulting sequencing data to make ecological inferences.

Keywords: Root microbiome (根系微生物群落), Amplicon sequencing (扩增子测序), Rhizosphere (根际), Rhizoplane (根面), Endosphere (内部层)


Various plant root niches host different microbial communities (microbiota) originating from the soil (Bulgarelli et al., 2012; Lundberg et al., 2012; Edwards et al., 2015; Zarraonaindia et al., 2015; Wagner et al., 2016). Distinct microbiota acquired by each root niche likely have varying metabolic potential and may therefore impact the health of the host plant in different ways (Finkel et al., 2017). Bacterial and archaeal community composition in root-associated microbiota can be inferred through the use of 16S rRNA gene sequencing (Caporaso et al., 2012). The relatively low cost of sequencing now allows for comparative studies across plant species using datasets gathered by different research groups; however, small aberrations in specimen collection, sequencing, and analysis protocols may lead to large differences in the inferred microbial communities (Duvallet et al., 2017). We present this protocol detailing how to collect and analyze root microbiota from rice in an attempt to promote reproducibility across the plant microbiome field.

Materials and Reagents

  1. Falcon 50 ml conical centrifuge tubes (Corning, Falcon®, catalog number: 352070 )
  2. 1.5 ml microfuge tubes (E&K Scientific Products, catalog number: 280150 )
  3. 1.5 ml non-stick microfuge tubes (Thermo Fisher Scientific, AmbionTM, catalog number: AM12450 )
  4. 0.2 ml PCR tubes (GeneMate, catalog number: 3235-00-210IS
  5. Filtered pipette tips (10, 200, 1,000 µl) (VWR, catalog numbers: 89168-750, 89140-936, 89168-754)
    Manufacturer: Biotix, catalog number: BT10XLS3 , BT200 , BT1250 .
  6. Gloves (Medline Industries, catalog numbers: large, MDS192086 ; medium, MDS192085 ; small, MDS192084 )
  7. Qubit 0.5 ml assay tubes (Thermo Fisher Scientific, catalog number: Q32856 )
  8. Single edge razor blade (Personna, catalog number: 94-115-71 )
  9. Nuclease-free water (Thermo Fisher Scientific, AmbionTM, catalog number: AM9939
  10. DNeasy PowerSoil kit (QIAGEN, catalog number: 12888-100 )
  13. HotStar High Fidelity DNA polymerase kit (QIAGEN, catalog number: 202602 )
  14. Agencourt Ampure XP beads (Beckman Coulter, catalog number: A63880 )
  15. Qubit dsDNA HS assay kit (Thermo Fisher Scientific, catalog number: Q32851 )
  16. Ethanol 200 proof (Sigma-Aldrich, catalog number: E7023-500ML
  17. Agarose (Biotech Sources, catalog number: G01PD-500 )
  18. DNA gel loading dye
  19. NucleoSpin gel and PCR clean-up kit (MACHEREY-NAGEL, catalog number: 740609.250 )
  20. NaCl (Fisher Scientific, catalog number: S271-1 )
  21. KCl (Fisher Scientific, catalog number: P217-500 )
  22. Na2HPO4 (Fisher Scientific, catalog number: S374-500 )
  23. KH2PO4 (Fisher Scientific, catalog number: P285-500 )
  24. Autoclaved phosphate buffered saline (PBS) solution (~100 ml/plant) (see Recipes)


  1. 96 well magnetic plate (Alpaqua Engineering, catalog number: A001219R )
  2. 1.5 ml tube magnetic rack (Thermo Fisher Scientific, catalog number: MR01 )
  3. Pipettes (2.5, 10, 200, 1,000 µl) (Thermo Fisher Scientific, FinnpipetteTM, catalog numbers: 4641010N , 4641030N , 4641080N , 4641100N )
  4. Ultrasonic cleaning bath, 40 kHz (Branson, model: Branson 1800, catalog number: CPX-952-116R
  5. Dissection tools (scissors and forceps) (scissors: Bioseal, catalog number: KI011/50 ; forceps: Integra LifeSciences, Miltex, catalog number: 6-184 )
  6. -80 °C freezer
  7. Microcentrifuge (Eppendorf, model: 5417C )
  8. Mini-Beadbeater-96 high-throughput cell disrupter (Bio Spec Products, catalog number: 1001 )
  9. Electrophoresis gel unit (Bio-Rad Laboratories, catalog number: 1704468 )
  10. Qubit fluorometer (Thermo Fisher Scientific, catalog number: Q33226
  11. PCR thermal cycler (Bio-Rad Laboratories, model: T100TM Thermal Cycler , catalog number: 1861096)


  1. Python2 version 2.7.12
  2. R version 3.4.3


The procedure outlined below is generally applicable across a wide array of conditions and has been successfully used to survey bacterial and archaeal community composition in the greenhouse and the field (Edwards et al., 2015), as well as across environmental perturbations (Santos-Medellín et al., 2017) and plant developmental stages (Edwards et al., 2018).

  1. Contamination control
    Because introduced contaminants can obscure the compositional data generated from this protocol, it is important to observe the following measures to minimize contamination:
    1. Always wear gloves.
    2. Before starting each main step, wipe your hands and work surface with 70% ethanol.
    3. Avoid leaving bottles and tubes open to the environment.
    4. Use filtered pipette tips for Procedure A (compartment separation), B (DNA extraction), and C (PCR amplification). After PCR, you can switch to sterile non-filtered tips.
  2. For sample storage
    Preprocessed roots should be stored for no longer than 24 h at 4 °C before compartment separation. Additionally, individual compartments should be separated before storing them for longer periods of time at -80 °C. Freezing and thawing preprocessed roots will cause microbial cells to lyse and the DNA to possibly diffuse across compartments, therefore reducing resolution.
    1. For the rhizosphere compartment, pipette 500 µl of the soil suspension generated in Step A4 to a 1.5 ml microfuge tube, spin down (10,000 x g for 1 min), remove the supernatant, and store at -80 °C. When ready to perform DNA extractions, thaw samples at room temperature (~23 °C) and resuspend the rhizosphere in 500 µl of PBS.
    2. For the rhizoplane compartment, store the 500 µl of concentrated microbial suspension generated in Step A6 at -80 °C. When ready to perform DNA extractions, thaw samples at room temperature (~23 °C).
    3. For the endosphere compartment, use fire-sterilized forceps to transfer 0.25 g of the thrice-sonicated roots from Step A7 to a 1.5 ml microfuge tube and store at -80 °C. When ready to perform DNA extractions, thaw samples at room temperature (~23 °C).

  1. Compartment separation of root-associated microbiota
    The following protocol uses a combination of washing and sonicating steps to separate the rhizosphere, rhizoplane, and endosphere fractions of the root-associated microbiota. This approach has been successfully employed to harvest compositionally distinct communities that harbor microorganisms enriched in each of these spatial compartments (Edwards et al., 2015). The protocol for separation of the rhizoplane is based on the method for endospheric bacteria isolation developed by Lundberg et al. (2012). The method utilizes a bath sonicator to remove the microbiota in the rhizoplane, and avoids hypochlorite treatment for the reasons detailed in Lundberg et al. (2012). Because the DNA yields from rhizoplane samples are low, the sequences can exhibit higher variability following PCR amplification, and may require additional replicates to draw statistically significant conclusions. It should be noted that the compartment dissection protocol cannot ensure complete purity of samples free of contamination from adjacent compartments, especially where they overlap spatially, but despite these limitations the protocol has been proven to be efficient and reproducible for studies of overall compositional profiles (Edwards et al., 2015; Santos-Medellin et al., 2017; Edwards et al., 2018). All steps are visually detailed in Video 1.

      Video 1. Collection of rice roots and separation of compartments

    1. Using gloves, harvest the rice plant by firmly holding the shoot and slowly pulling the root system out of the ground (Figure 1A). In the case of seedlings, carefully scoop the roots to avoid ripping the tissue.
    2. Vigorously shake the roots to remove loose soil, leaving only the soil layer firmly attached to the root. This layer constitutes the rhizosphere compartment (Figure 1B).
    3. Using flame-sterilized scissors, cut ~5 cm of root immediately below the root-shoot junction (red box Figures 1B and 1C) and place the tissue in a sterile 50 ml Falcon tube with 15 ml of autoclaved PBS solution. For potted plants, avoid collecting roots immediately adjacent to the inner walls.
    4. Vortex the roots for 15 sec to mix the rhizosphere fraction in the PBS solution (Figure S1). Save the resulting soil suspension for DNA extraction (Step B1).
    5. Using flame-sterilized forceps, transfer the roots to a new 50 ml Falcon tube. Wash the roots thoroughly by adding 20 ml of fresh PBS, vortexing for 15 sec at maximum speed, and discarding the PBS. Repeat these steps for a total of three washes (Figure 1D). If any soil remains in the bottom of the tube, perform additional washing steps until no soil is visible.

      Figure 1. Root harvesting and processing for microbiome studies. A. Rice roots pulled out of the soil. B. Rice roots after being vigorously shaken to remove loose soil. The red box indicates the ~5 cm of root cut with flame-sterilized scissors. C. Rice root section collected into 50 ml Falcon tubes for compartment separation. The soil layer firmly attached to the roots constitutes the rhizosphere. D. Rice root section after being thoroughly washed with sterile PBS solution.

    6. Separate the rhizoplane compartment by sonicating the roots for 30 sec at 50-60 Hz and transferring the 10 ml of PBS with the sonicated microbes to a new tube (Figure S1). Add 1.5 ml of the microbial suspension to a 1.5 ml microfuge tube, spin down at 10,000 x g for 1 min and discard 1 ml of supernatant. Add 1 ml of microbial suspension, spin down (10,000 x g for 1 min), and discard 1 ml. Repeat these steps once more for a total of three centrifugations. Resuspend the pellet by vortexing (15 sec) and save the concentrated microbial suspension for DNA extraction.
      Note: Depending on how much root material was sampled, a pellet may or may not be visible.
    7. Add enough fresh PBS to fully cover the roots and sonicate for 30 sec at 50-60 Hz. Discard the PBS and repeat this step once more. The thrice-sonicated roots constitute the endosphere compartment (Figure S1).

  2. DNA Extraction
    Use the DNeasy PowerSoil kit to isolate the genomic DNA from the root-associated communities. The input for each of the compartments is as follows:
    1. For the rhizosphere compartment, add 500 µl of the soil suspension generated in Step A4 to a PowerBead tube.
      Note: Large particles which inhibit the uptake of the soil suspension into the pipette tip should be avoided. If the pipette tip is too clogged to continue, retrieve a new pipette tip. Repeat the process until successful.
    2. For the rhizoplane compartment, transfer the 500 µl of concentrated microbial suspension generated in Step A6 to a PowerBead tube.
    3. For the endosphere compartment, use fire-sterilized forceps to transfer 0.25 g of the thrice-sonicated roots from Step A7 to a PowerBead tube. Pre-homogenize the endosphere by bead-beating the roots in the PowerBead tube with the included garnet particles for 1 min.
    After adding the samples to the PowerBead tubes, follow the PowerSoil kit protocol with these adjustments:
    1. After adding Solution C1, the PowerBead tubes can be homogenized using a beadbeater for 2 min instead of vortexing them for 10 min.
    2. Elute the final product in 30 µl of Solution C6 instead of 100 µl.

  3. 16S rRNA amplification
    For library construction, this protocol uses primer 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT) to amplify the V4 region of the 16S rRNA gene. Primer design for Illumina sequencing follows the one described in Caporaso et al. (2012), except both forward and reverse primers are barcoded (Figure 2). By using a unique combination of barcodes for each sample, this dual-indexing strategy allows us to multiplex a large number of libraries with a limited amount of primers. Full sequences for the forward primers can be found on this GitHub page and the reverse primers can be found on this GitHub page. We recommend working with sets of 24 samples, in which all reactions share the same barcoded 515F primer but have a unique 806R barcode. Additionally, it is important to run a negative control for each individual reaction to detect any potential contamination.

    Figure 2. Schematic of 16S V4 region amplicon. A. Genomic regions before amplification. The primer binding sites are blue and the number corresponds to the position within the 16S rRNA gene where the primers bind. B. Amplicon after PCR amplification. FBC stands for forward barcode and RBC stands for reverse barcode. C. The sequencing strategy for the amplicons. Note that three custom primers are used in the sequencing: a primer for the forward read starting at position 515, a primer for the reverse sequencing read starting at position 806, and a primer for the reverse barcode. A list of forward primer sequences and reverse primer sequences can be found on GitHub.

    1. For a set of 24 reactions and 24 negative controls, prepare a master mix using the following recipe:

    2. Aliquot 20.5 µl of the master mix into each PCR tube.
    3. Add 2.5 µl of the corresponding 10 µM barcoded 806R primer into each tube and mix well by pipetting.
    4. Aliquot 11.5 µl of each reaction into new PCR tubes to run as negative controls. 
    5. Add 1 µl of the corresponding template to the remaining 11.5 µl.
    6. Cap the tubes and spin down.
    7. Run the following touchdown PCR program:
      Initial denaturation:
      95 °C, 5 min
      7 cycles, decreasing the annealing temperature 2 °C each cycle:
      95 °C, 45 sec
      65 °C, 1 min (-2 °C/cycle)
      72 °C, 1:30
      30 cycles:
      95 °C, 45 sec
      50 °C, 30 sec
      72 °C, 1:30
      Final extension:
      72 °C, 10 min
      4 °C, ∞
      Note: If the user is experiencing a high sample to sample variation, there may be noise generated during the PCR step. The Earth Microbiome Project has established a protocol where 3 separate PCRs are performed between samples and subsequently pooled in order to minimize variation.

  4. Gel
    1. Add 1 µl of PCR product to 5 µl of gel loading dye (1x).
    2. Run the samples on a 1% agarose gel at 120 V for 20 min.
    3. Verify proper amplification (expected band size is ~400 bp long) and absence of contamination in the negative controls.

  5. PCR Cleanup
    1. Remove beads from 4 °C and allow them to reach room temperature (~23 ºC).
    2. Prepare a fresh batch of 70% ethanol solution (500 µl/reaction).
    3. Aliquot 9 µl of the PCR product into a new 0.2 ml tube.
    4. Add 5.4 µl (0.6 volume) of Ampure XP beads, mix by pipetting, and let incubate at room temperature (~23 °C) for 5 min. We have found that 0.6x volume of AMPure beads to PCR product is the correct ratio to remove primer dimers and unused primers while leaving PCR product intact. Users of this protocol may need to experiment to ensure that this ratio also works for their experiments.
    5. Transfer the tubes to a magnet plate and let stand for 2 min.
    6. Carefully remove the cleared solution without disturbing the beads.
    7. Keeping the tubes on the magnet plate, add 200 µl of 70% ethanol, incubate for 30 sec, and remove with a pipette. Repeat this step once more for a total of two ethanol washes. For the final wash, remove all ethanol from the bottom.
    8. With the tubes still on the magnet plate, air-dry the beads for 2 min. Take care not to over-dry the beads as this will prevent the beads from being resuspended in Step E9.
    9. Take the plates off the magnet plate, add 50 µl of nuclease-free water, and resuspend the beads by pipetting up and down.
    10. Place the tube back to the magnet plate and incubate for 1 min.
    11. Transfer 35 µl of the eluant to a new tube.

  6. Quantification of amplicon libraries
    Use the Qubit dsDNA HS assay to measure the concentrations of the purified libraries:
    1. Set up two Assay Tubes for the standards and one for each sample to be quantified. Make sure not to label the side of the tube as this could interfere with the sample read.
    2. For each tube, prepare 200 µl of Qubit Working Solution by mixing 199 µl of Qubit buffer and 1 µl of Qubit Working Solution. It is important not to mix the working solution in a glass container.
    3. For the standards, aliquot 190 µl of working solution to 0.5 ml Assay Tubes, add 10 µl of the corresponding standard, and mix by vortexing.
    4. For the samples, aliquot 195 µl of working solution to 0.5 ml Assay Tubes, add 5 µl of the corresponding sample, and mix by vortexing.
    5. Incubate tubes for 2 min at room temperature.
    6. Select the dsDNA High Sensitivity Assay on the Qubit Fluorometer, read the standards, and run each sample. Typical DNA concentrations range from 0.5 to 2.0 ng/µl.

  7. Pooling of amplicon libraries
    1. Multiplex ~150 libraries per MiSeq sequencing run. If multiple sequencing runs are needed, randomize the libraries across sequencing runs to avoid batch effects.
    2. Based on the range of concentrations obtained, determine a target amount of DNA to be pooled. Aim for at least 5 ng of DNA per sample.
    3. For each library, calculate the volume need by dividing the target amount of DNA by the sample concentration.
    4. Pool the libraries in a non-stick RNase-free 1.5 ml microfuge tube.
    5. Avoid pipetting volumes lower than 1 µl. If some samples are too concentrated, predilute them before pooling.

  8. Library concentration
    1. Remove AmpureXP beads from 4 °C and allow them to reach room temperature.
    2. Prepare a fresh batch of 70% ethanol solution.
    3. Add 1.8 volumes of Ampure XP beads to the pooled libraries, mix by pipetting, and let incubate at room temperature for 5 min. 1.8x volume ensures that there is no loss of product due to saturation of beads.
    4. Transfer the tube to a magnet rack and let stand for 2 min.
    5. Carefully remove the cleared solution without disturbing the beads.
    6. Keeping the tubes on the magnet, add 1.5 ml of 70% ethanol, incubate for 30 sec, and remove with a pipette. Repeat this step once more for a total of two ethanol washes. For the final wash, remove all ethanol from the bottom.
    7. With the tube still on the magnet, air-dry the beads for 2 min.
    8. Take the tube off the magnet, add 50 µl of nuclease-free water, and resuspend the beads by pipetting.
    9. Place the tube back to the magnet and incubate for 1 min.
    10. Transfer the cleared eluant to a new tube.

  9. Gel cleanup
    1. Run the concentrated pool of libraries on a 1.8% agarose gel at 120 V for 40 min.
    2. Using a new blade, excise the ~400 bp band.
    3. Purify the libraries using the NucleoSpin Gel and PCR Clean-up kit.

  10. Sequencing
    1. Submit the pooled libraries for 2 x 250 MiSeq sequencing. Use the following custom sequencing primers:
    2. Include a PhiX control for low diversity samples.

  11. Sequence analysis
    A more detailed version of the sequence analysis pipeline including code for running the analysis can be found on GitHub.
    1. Compile metadata in a spreadsheet
      1. Assign each sample a unique identifier. This identifier should be unique not just across a single experiment, but across all previous experiments as well. The identifier should only contain alphanumeric and period (“.”) characters.
      2. Create column(s) for barcodes. Each sample should have a unique barcode for each run.
      3. Create additional columns for the experimental variables associated with each sample. Some typical examples of experimental variables are root compartment, plant developmental stage, plant genotype, plot location, date, year, and collector.
        Note: A detailed protocol for designing a metadata table can be found at the Earth Microbiome Project's website.
    2. Download sequencing files from sequencing facility
      Downloading of individual files can be accomplished either through an FTP client or by using command line tools (such as Wget).
    3. Demultiplex sequences
      There should be 4 fastq files provided by the sequencing facility: two read files (R1 and R2) and two index files (I1 and I2).
    4. Construct full-length contiguous sequences
      Full-length sequences can be assembled using PANDAseq (Masella et al., 2012). Note that full-length contigs are not necessary for clustering OTUs using DADA2 (see below).
    5. Cluster Sequences and build OTU table
      1. If clustering with QIIME (Caporaso et al., 2010), several options and algorithms are available. The user may want to conduct reference based clustering against a database of 16S rRNA genes (closed reference clustering) or a user may want to perform de novo OTU clustering. This method is known as closed reference clustering. Alternatively, a user may prefer to perform a hybrid between these two methods where sequences are first referenced against a database. Reads that do not have a match within the database are then clustered de novo. This method is known as open reference clustering. The user can define the similarity threshold for one read to be considered a match with an entry in the database. Historically, > 97% sequence identity has been used as the standard for clustering sequences into operational taxonomic units (OTUs). One relatively new and extremely fast method for closed reference clustering of sequences into OTUs is through using the NINJA-OPS pipeline (Al-Ghalith et al., 2016). NINJA-OPS leverages the speed and memory efficiency of Bowtie (Langmead and Salzberg, 2012), mapping reads back to a synthetic genome of concatenated 16S genes. This method can be performed on a laptop computer.
      2. Alternatively, users may prefer to bin sequences based upon exact matches using DADA2 (Callahan et al., 2016a). If using this method, there is no need to construct full-length contiguous sequences before clustering.
    6. Assign taxonomies to OTUs
      Multiple algorithms exist to assign taxonomies to the OTU sequences. If using closed-reference OTU clustering, there is no need to perform this step because the database sequences have already been classified (DeSantis et al., 2006). QIIME defaults to using the UCLUST (Edgar, 2010) algorithm for taxonomic assignment, while DADA2 uses the RDP naive Bayes method (Wang et al., 2007) for assigning taxonomies to sequences.

Data analysis

A detailed description of how to perform the data analysis including R code can be found on GitHub. In this tutorial we use data from Santos-Medellín et al. (2017) to illustrate analytical techniques.

  1. Remove plastidial and mitochondrial sequences from the dataset
    1. Mitochondria and plastids are a result of an ancient endosymbiosis event. The mitochondria and plastids have retained their own ribosomal machinery, therefore a fraction of the resulting sequences will belong to these organelles. These reads are not part of the root microbiota and should be removed from the dataset before further analysis. This is not to say that organellar reads are not useful–these reads can be used for quantification purposes (Edwards et al., 2015), but they should not be considered part of the microbiota.
    2. Mitochondrial and plastidial OTUs can be identified via their associated taxonomies by searching for ‘mitochondria’ in the Family column and ‘Chloroplast’ under the Class column.
  2. Normalize the sequencing depth for each library
    Although the libraries were pooled in an equimolar concentration, sequencing depth can vary a few orders of magnitude between each library. It is therefore necessary to normalize data to ensure that each sample is equally represented in the analysis.
    A few methods exist for normalization. Rarefaction is the process of randomly sampling from the pool of OTUs until a desired depth is achieved. It is of note that this method removes much of the data the user has acquired. For example, if the user has two libraries a and b and the depth, d, of each library is da = 100,000 and db = 5,000, the user may choose to rarefy to 5,000 sequences. This does not discard any sequences from db, but it removes 95% of the data from da. OTUs with low representation may be discarded using this method. Relative abundance is a method which divides the count of each OTU by the sequencing depth such that the user is left with proportional representation of each OTU in each library. This method makes full use of all the data the user has acquired. Depending on the particular analysis, the user may prefer to use alternative methods implemented in high throughput sequencing statistical libraries such as edgeR (Robinson et al., 2010) or DESeq (Anders and Huber, 2010).
  3. Ensure that the order of samples in the metadata file (also known as a mapping file) matches the order of samples in the OTU count table.
  4. Remove low prevalence OTUs from the data
    Low prevalence or non-reproducible OTUs may add unnecessary noise to the dataset. There is no specific rule of thumb for removing low abundance OTUs, but one metric that has been previously used is to remove OTUs that are not present in at least 5% of the samples (Callahan et al., 2016b; Edwards et al., 2018).
  5. Beta diversity plots (Figure 3A)
    Beta diversity measures the differences in microbiota composition between the samples.
    1. Calculate pairwise dissimilarities between each sample. There are ecologically appropriate metrics for this task such as Bray-Curtis, Jaccard, and UniFrac (Lozupone and Knight, 2005) dissimilarity metrics.
    2. Using the calculated dissimilarities, perform principal coordinate analysis (PCoA). 
    3. Plot the resulting axes and color the points based upon the factor of interest.
  6. Alpha diversity plots (Figure 3B)
    Alpha diversity measures the diversity within each sample.
    1. Calculate alpha diversity metric for each sample. Popular metrics are the Shannon index, the Simpson Index, species richness, and Faith’s phylogenetic distance.
    2. Plot resulting calculations, comparing the factors of interest.
  7. Phylum level analysis (Figure 3C)
    1. Summarize the mean representation of each phylum in each sample type.
    2. Plot the results. There are many ways to plot these results. Here we have chosen to display the data using a stacked bar plot. We have also only retained the 10 most highly represented phyla.
  8. OTU differential abundance (Figure 3D)
    Note: The statistical distributions of bacterial and archaeal OTU abundances do not follow a Gaussian distribution and typically cannot be log-transformed to fit a normal distribution. Therefore, methods assuming normal distributions are not recommended for performing differential abundance tests. Available statistical packages (such as edgeR or DESeq) are recommended in order to properly model OTU distributions (McMurdie and Holmes, 2014).
    1. Load non-normalized count data into the statistical package of choice.
    2. Normalize for sequencing depth.
    3. Model sample level and OTU level dispersions.
    4. Fit the model using a design matrix.
    5. Perform differential abundance tests.
    6. Plot results with the average abundance on the x-axis and the fold change between the sample types on the y-axis.

      Figure 3. Example analysis of amplicon microbiome data. A. Principal coordinates analysis showing microbial community structure between root compartments using Bray-Curtis dissimilarities. Each point represents the microbial community in one particular sample. B. Alpha diversity within each community using two commonly used metrics. Richness measures how many unique OTUs were detected in each sample while Shannon-entropy measures the randomness or uncertainty in a community. C. The distribution of the 10 most abundant phyla in the dataset. We show the similar compartments between sites host similar distributions of microbes when analyzing at the phylum level. D. Differentially abundant microbes in the rhizosphere and endosphere compartments compared to bulk soil. Each point represents a single microbial OTU. The colored points represent OTUs that were significantly differentially abundant in one of the comparisons. The color of the point represents the direction of enrichment. Differential abundance analyses were carried out using DESeq2.


It should be noted that this protocol has been optimized for rice roots and there is no guarantee that the above sonication procedure will sufficiently remove rhizoplane microbes for other plant species. Similarly, this sonication procedure may be overly invasive for other plant species. Care should be taken to optimize the procedure for specific plant taxa.


  1. Phosphate buffered saline solution (1 L)
    8 g NaCl (Fisher Scientific)
    0.2 g KCl (Fisher Scientific)
    1.44 g Na2HPO4 (Fisher Scientific)
    0.24 g KH2PO4 (Fisher Scientific)
    Adjust pH to 7.4


This protocol was adapted from Edwards et al. (2015). We thank several members of our laboratory over the years who have contributed to the development of this protocol, directly as well as indirectly by helpful suggestions to streamline the various steps: Kelsey Galimba, Cassandra Ramos, Paul Tisher, John Jaeger, Eugene Lurie, Bao Nguyen, Natraj Podishetty and Zach Liechty. We thank Derek Lundberg and Jeff Dangl (University of North Carolina, Chapel Hill) for kindly providing before publication (Lundberg et al. 2012), their sonication method for separating the rhizoplane from endosphere fractions. We thank Srijak Bhatnagar and Jonathan Eisen for their help in establishing the analysis protocol. V.S. acknowledge the support of National Science Foundation Awards DBI-0923806 and IOS-1444974 and USDA Agricultural Experiment Station grant number CAD-XXX-6973-H. JE and CSM acknowledge support from the Elsie Taylor Stocking Memorial Research Fellowship and the Henry Jastro Research Fellowship. CSM acknowledges support from the University of California Institute for Mexico (UCMEXUS)/Consejo Nacional de Ciencia y Tecnología (CONACYT) and Secretaría de Educación Pública (Mexico). The authors declare no competing interests.


  1. Al-Ghalith, G. A., Montassier, E., Ward, H. N. and Knights, D. (2016). NINJA-OPS: fast accurate marker gene alignment using concatenated ribosomes. PLoS Comput Biol 12(1): e1004658.
  2. Anders, S. and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol 11(10): R106.
  3. Bulgarelli, D., Rott, M., Schlaeppi, K., Ver Loren van Themaat, E., Ahmadinejad, N., Assenza, F., Rauf, P., Huettel, B., Reinhardt, R., Schmelzer, E., Peplies, J., Gloeckner, F. O., Amann, R., Eickhorst, T. and Schulze-Lefert, P. (2012). Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488(7409): 91-95.
  4. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. and Holmes, S. P. (2016a). DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13(7): 581-583.
  5. Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. and Holmes, S. P. (2016b). Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Res 5: 1492.
  6. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Pena, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J. and Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5): 335-336.
  7. Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S. M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J. A., Smith, G. and Knight, R. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6(8): 1621-1624.
  8. DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P. and Andersen, G. L. (2006). Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72(7): 5069-5072.
  9. Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A. and Alm, E. J. (2017). Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat Commun 8(1): 1784.
  10. Edwards, J. A., Santos-Medellin, C. M., Liechty, Z. S., Nguyen, B., Lurie, E., Eason, S., Phillips, G. and Sundaresan, V. (2018). Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol 16(2): e2003862.
  11. Edwards, J., Johnson, C., Santos-Medellin, C., Lurie, E., Podishetty, N. K., Bhatnagar, S., Eisen, J. A. and Sundaresan, V. (2015). Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci U S A 112(8): E911-920.
  12. Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19): 2460-2461.
  13. Finkel, O. M., Castrillo, G., Herrera Paredes, S., Salas Gonzalez, I. and Dangl, J. L. (2017). Understanding and exploiting plant beneficial microbes. Curr Opin Plant Biol 38: 155-163.
  14. Langmead, B. and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4): 357-359.
  15. Lozupone, C. and Knight, R. (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71(12): 8228-8235.
  16. Lundberg, D. S., Lebeis, S. L., Paredes, S. H., Yourstone, S., Gehring, J., Malfatti, S., Tremblay, J., Engelbrektson, A., Kunin, V., Del Rio, T. G., Edgar, R. C., Eickhorst, T., Ley, R. E., Hugenholtz, P., Tringe, S. G. and Dangl, J. L. (2012). Defining the core Arabidopsis thaliana root microbiome. Nature 488(7409): 86-90.
  17. Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. and Neufeld, J. D. (2012). PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13: 31.
  18. McMurdie, P. J. and Holmes, S. (2014). Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput Biol 10: e1003531.
  19. Raynaud, X. and Nunan, N. (2014). Spatial ecology of bacteria at the microscale in soil. PLoS One 9(1): e87217.
  20. Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1): 139-140.
  21. Santos-Medellín, C., Edwards, J., Liechty, Z., Nguyen, B. and Sundaresan, V. (2017). Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. MBio 8(4).
  22. Wagner, M. R., Lundberg, D. S., Del Rio, T. G., Tringe, S. G., Dangl, J. L. and Mitchell-Olds, T. (2016). Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun 7: 12151.
  23. Wang, Q., Garrity, G. M., Tiedje, J. M. and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16): 5261-5267.
  24. Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N. A., Mills, D. A., Martin, G., Taghavi, S., van der Lelie, D. and Gilbert, J. A. (2015). The soil microbiome influences grapevine-associated microbiota. MBio 6(2).


植物根系与根 - 土壤谱中的各种细菌和古细菌相关联。根际微生物群落,即与根系相邻的土壤中的微生物群落,每克土壤可含有高达100亿个细菌细胞(Raynaud和Nunan,2014),并且可以在宿主植物的适应性方面发挥重要作用。根际微生物群的亚群可以在根表面(根毛菌)和根内部(内生孢子)定居,与寄主植物形成密切关系。这些群落的成分分析对于开发工具以操纵根系相关微生物群以提高作物生产力非常重要。由于降低了成本并提高了下一代测序的通量,因此主要通过使用16S rRNA基因的扩增子测序来最终破解这些群落的主要进展。在这里,我们首先提出一个协议,用于解剖来自各种根室的微生物群,这些根室是以水稻为模型开发的。接下来我们介绍一种使用双指数方法扩增16S rRNA基因片段的方法。最后,我们提供了一个简单的工作流程来分析生成的测序数据以进行生态推理。

【背景】各种植物根生态位寄主于源自土壤的不同微生物群落(微生物群落)(Bulgarelli et al。,2012; Lundberg等人,2012; Edwards等人。2015年; Zarraonaindia等人,2015年; Wagner等人,2016年)。由每个根生态位获得的不同微生物群可能具有不同的代谢潜力,因此可能以不同方式影响宿主植物的健康(Finkel等人,2017)。可以通过使用16S rRNA基因测序来推断与根相关的微生物群中的细菌和古细菌群落组成(Caporaso等人,2012年)。测序成本相对较低,现在允许使用由不同研究组收集的数据集对植物物种进行比较研究;然而,样本收集,测序和分析方案中的小畸变可能导致推断的微生物群落的巨大差异(Duvallet等人,2017)。我们提出了这个协议,详细说明如何收集和分析水稻根系微生物群,试图促进整个植物微生物群的重现性。

关键字:根系微生物群落, 扩增子测序, 根际, 根面, 内部层


  1. Falcon 50ml锥形离心管(Corning,Falcon ,目录号:352070)

  2. 1.5ml微量离心管(E& K Scientific Products,目录号:280150)
  3. 1.5ml不粘微量离心管(Thermo Fisher Scientific,Ambion TM,目录号:AM12450)
  4. 0.2ml PCR管(GeneMate,目录号:3235-00-210IS) 
  5. 过滤移液管吸头(10,200,1,000μl)(VWR,目录号:89168-750,89140-936,89168-754)
  6. 手套(Medline Industries,目录号:大号,MDS192086;中号,MDS192085;小号,MDS192084)
  7. Qubit 0.5ml测定管(Thermo Fisher Scientific,目录号:Q32856)
  8. 单边刀片(Personna,产品目录号:94-115-71)
  9. 无核酸酶的水(Thermo Fisher Scientific,Ambion TM,目录号:AM9939) 
  10. DNeasy PowerSoil套件(QIAGEN,目录号:12888-100)
  13. HotStar High Fidelity DNA聚合酶试剂盒(QIAGEN,目录号:202602)
  14. Agencourt Ampure XP珠(Beckman Coulter,目录号:A63880)
  15. Qubit dsDNA HS分析试剂盒(Thermo Fisher Scientific,目录号:Q32851)
  16. 乙醇200标准(Sigma-Aldrich,目录号:E7023-500ML) 
  17. 琼脂糖(生物技术来源,目录号:G01PD-500)
  18. DNA凝胶加载染料
  19. NucleoSpin凝胶和PCR清理试剂盒(MACHEREY-NAGEL,目录号:740609.250)
  20. NaCl(Fisher Scientific,目录号:S271-1)
  21. KCl(Fisher Scientific,目录号:P217-500)
  22. Na 2 HPO 4(Fisher Scientific,目录号:S374-500)
  23. KH 2 PO 4(Fisher Scientific,目录号:P285-500)
  24. 高压灭菌的磷酸盐缓冲盐水(PBS)溶液(〜100毫升/植物)(见食谱)


  1. 96孔磁板(Alpaqua Engineering,目录号:A001219R)
  2. 1.5 ml管式磁力架(赛默飞世尔科技,产品目录号:MR01)
  3. 移液管(2.5,10,200,1,000μl)(Thermo Fisher Scientific,Finnpipette TM,产品目录号:4641010N,4641030N,4641080N,4641100N)
  4. 超声波清洗浴,40kHz(Branson,型号:Branson 1800,目录号:CPX-952-116R) 
  5. 解剖工具(剪刀和镊子)(剪刀:Bioseal,目录号:KI011 / 50;镊子:Integra LifeSciences,Miltex,目录号:6-184)
  6. -80°C冷冻机
  7. 微量离心机(Eppendorf,型号:5417C)
  8. Mini-Beadbeater-96高通量细胞破碎仪(Bio Spec Products,目录号:1001)
  9. 电泳凝胶单元(Bio-Rad Laboratories,目录号:1704468)
  10. Qubit荧光计(Thermo Fisher Scientific,目录号:Q33226) 
  11. PCR热循环仪(Bio-Rad Laboratories,型号:T100 TM Thermal Cycler,目录号:1861096)。


  1. Python2版本2.7.12
  2. R版本3.4.3


下面列出的程序通常适用于各种条件,并已成功用于调查温室和田间的细菌和古菌群落组成(Edwards等,2015),以及跨环境扰动(桑托斯 - 麦德林2017)和植物发育阶段(Edwards等,2018)。

  1. 污染控制
    1. 总是戴手套。
    2. 在开始每个主要步骤之前,用70%乙醇擦拭手和工作表面。
    3. 避免将瓶子和管子留在环境中。
    4. 使用过滤移液器吸头进行程序A(隔室分离),B(DNA提取)和C(PCR扩增)。 PCR后,您可以切换到无菌的未经过滤的提示。
  2. 用于存储示例
    1. 对于根际隔室,吸取步骤A4中产生的500μl土壤悬浮液到1.5ml微量离心管中,旋转(10,000×g,1分钟),除去上清液,并储存在-80℃。准备进行DNA提取时,在室温(〜23°C)下解冻样品,并在500μlPBS中重悬根际。
    2. 对于根毛菌室,将在步骤A6中产生的500μl浓缩微生物悬浮液储存在-80℃下。准备进行DNA提取时,在室温(〜23°C)下解冻样品。
    3. 对于内层隔室,使用灭菌的镊子将0.25g三次超声处理的步骤A7中的根转移到1.5ml微量离心管中并储存在-80℃。准备进行DNA提取时,在室温(〜23°C)下解冻样品。

  1. 根部相关微生物群的隔室分离
    以下方案使用洗涤和超声处理步骤的组合来分离根系相关微生物群的根际,根际和内部层部分。这种方法已成功地用于收集组成不同的群落,这些群落含有在每个这些空间区域中富集的微生物(Edwards等人,2015年)。用于分离根毛菌的方案基于由Lundberg等人开发的用于内部球菌细菌分离的方法(2012)。由于Lundberg等人(2012)详述的原因,该方法利用浴超声波发生器去除rhizoplane中的微生物群,并避免次氯酸盐处理。由于来自根毛菌样品的DNA产量较低,所以这些序列在PCR扩增后会表现出更高的变异性,并且可能需要额外的重复才能得出统计学上显着的结论。应该指出的是,隔室解剖方案不能确保完全纯化来自相邻隔室的污染物,尤其是在它们在空间上重叠的情况下,但是尽管存在这些限制,该方案已经被证明对于总体组成特征的研究是有效和可再现的(Edwards ,2015; Santos-Medellin ,2017; Edwards 等,2018)。所有步骤在视频1中都有详细的说明。

    1. 使用手套,通过牢固抓住枝条并将根系慢慢地拉出地面来收获水稻(图1A)。在幼苗的情况下,小心铲根,以避免撕裂组织。
    2. 大力摇动根部以除去松散的土壤,只留下土壤层牢固地附着在根部。这层构成了根际隔室(图1B)。
    3. 使用火焰灭菌剪刀,在根 - 芽接头正下方切下约5厘米的根(红色方框图1B和1C),并将组织置于灭菌的50毫升Falcon管中,用15毫升高压灭菌的PBS溶液。对于盆栽植物,避免直接在内壁附近收集根部。
    4. 将根部涡旋15秒以混合PBS溶液中的根际部分(图S1 )。保存得到的土壤悬浮液进行DNA提取(步骤B1)。
    5. 使用火焰消毒镊子,将根转移到新的50毫升猎鹰管。通过加入20ml新鲜PBS彻底清洗根部,以最大速度涡旋15秒并丢弃PBS。重复这些步骤共进行三次洗涤(图1D)。如果有土壤残留在管子底部,请执行额外的清洗步骤,直到看不见土壤。

      图1.微生物组学研究的根收获和加工A.水稻根从土壤中拔出。 B.大力摇动稻谷以除去松散的土壤。红色框表示用火焰消毒剪刀约5厘米的根部切割。 C.将水稻根部分收集到50ml Falcon管中用于室分离。牢固附着在根上的土壤层构成了根际。 D.用无菌PBS溶液彻底清洗后的水稻根部。

    6. 通过在50-60Hz下超声处理根部30秒来分离根茎分隔室,并将超声处理的微生物的10ml PBS转移至新管中(图S1 )。将1.5ml微生物悬浮液加入到1.5ml微量离心管中,以10,000g×g离心1分钟并弃去1ml上清液。加入1ml微生物悬浮液,旋转(10,000×g,1分钟),并丢弃1ml。重复这些步骤再进行三次离心。通过涡旋(15秒)重新悬浮沉淀,并保存浓缩的微生物悬浮液进行DNA提取。
    7. 加入足够的新鲜PBS以50-60Hz完全覆盖根部并超声处理30秒。丢弃PBS并再次重复此步骤。三次超声波处理的根构成了内部空间隔间(图S1) ;)。

  2. DNA提取
    使用DNeasy PowerSoil试剂盒从基因相关群落中分离基因组DNA。每个隔室的输入如下:
    1. 对于根际隔室,将500μl步骤A4中产生的土壤悬浮液加入PowerBead试管中。
    2. 对于rhizoplane隔间,将500μl步骤A6中产生的浓缩微生物悬液转移到PowerBead试管中。
    3. 对于内层隔室,使用灭菌镊子将0.25g三次超声处理的步骤A7中的根转移到PowerBead管中。通过在PowerBead管中用包含石榴石颗粒的根部打碎珠粒1分钟来预均质化内部球体。
    1. 加入Solution C1后,PowerBead试管可以使用珠磨机搅拌2分钟而不是涡旋10分钟。
    2. 用30μlSolution C6洗脱最终产物,而不是100μl。

  3. 16S rRNA扩增
    对于文库构建,该方案使用引物515F(GTGCCAGCMGCCGCGGTAA)和806R(GGACTACHVGGGTWTCTAAT)扩增16S rRNA基因的V4区域。 Illumina测序的引物设计遵循Caporaso等人(2012)中描述的方法,不同之处在于正向和反向引物均被条形码化(图2)。通过对每个样品使用独特的条形码组合,这种双重索引策略使我们能够将大量文库与有限数量的引物进行多重复合。正向引物的完整序列可以在此 GitHub页面和可以在此 GitHub页面上找到反向引物。我们建议使用24个样品组,其中所有反应共用相同的条形码515F引物,但具有独特的806R条形码。此外,为每个单独的反应运行阴性对照来检测任何潜在的污染都很重要。

    图2. 16S V4区域扩增子的示意图A.扩增前的基因组区域。引物结合位点是蓝色的,数字对应于引物结合的16S rRNA基因内的位置。 B. PCR扩增后的扩增子。 FBC代表向前条形码,RBC代表反向条形码。 C.扩增子的测序策略。注意,在测序中使用三种定制引物:从位置515开始的正向读数的引物,从位置806开始的反向测序读数的引物和用于反向条形码的引物。 正向引物序列和反向引物序列可以在GitHub上找到。

    1. 对于一组24个反应和24个阴性对照,使用以下配方制备主混合物:

    2. 将20.5μl主混合物分装到每个PCR管中。

    3. 添加2.5μL相应的10μM条形码806R引物到每个管中并通过移液充分混合。
    4. 将11.5μl的每个反应分装到新的PCR管中作为阴性对照。 

    5. 加入1μl相应的模板至剩余的11.5μl
    6. 盖住管子并旋下。
    7. 运行下面的触地PCR程序:
      65°C,1分钟(-2°C /循环)
      注意:如果用户正在经历较高的样本变异,则PCR步骤中可能会产生噪音。地球微生物项目已经建立了 协议 其中3个独立的PCR在样品之间进行并随后汇集以最小化变异。
  4. 凝胶

    1. 加入1μlPCR产物至5μl凝胶上样染料(1x)。

    2. 在120V的1%琼脂糖凝胶上运行样品20分钟。
    3. 确认正确的扩增(预期的条带大小约400bp长)和阴性对照中没有污染。

  5. PCR清理

    1. 从4°C取出珠子并使其达到室温(〜23°C)。
    2. 准备一批70%的乙醇溶液(500μl/反应)。
    3. 将9μlPCR产物等分至新的0.2 ml试管中。
    4. 加入5.4μl(0.6体积)的Ampure XP珠粒,通过移液混合,并且在室温(〜23℃)下孵育5分钟。我们发现0.6倍体积的AMPure珠与PCR产物是正确的比率,以除去引物二聚体和未使用的引物,同时使PCR产物保持完整。此协议的用户可能需要进行实验以确保此比例也适用于他们的实验。
    5. 将试管转移到磁板上静置2分钟。

    6. 。小心地取出已清除的溶液而不会干扰珠子。
    7. 将管保持在磁板上,加入200μl70%乙醇,孵育30秒,然后用移液管移除。重复此步骤再进行两次乙醇洗涤。为了最后的洗涤,从底部去除所有乙醇。
    8. 将试管保持在磁板上,风干2分钟。注意不要过度干燥珠子,因为这会阻止珠子在步骤E9中重新悬浮。
    9. 取下磁板上的平板,加入50μl无核酸酶的水,并通过上下移液重新悬浮珠子。
    10. 将管放回磁板并孵育1分钟。
    11. 将35μl洗脱液转移至新管中。

  6. 扩增子文库的量化
    使用Qubit dsDNA HS分析来测量纯化文库的浓度:
    1. 为标准设置两个测定管,每个样品量化一个。确保不要标记管的一侧,因为这可能会干扰样品读取。
    2. 对于每个管,通过混合199μl的Qubit缓冲液和1μl的Qubit Working Solution来制备200μl的Qubit Working Solution。不要将工作溶液混入玻璃容器中,这一点很重要。
    3. 对于标准品,将190μl工作溶液分装到0.5 ml分析试管中,加入10μl相应标准品,并通过涡旋混合。
    4. 对于样品,将195μl工作溶液等分至0.5ml测定管,加入5μl相应样品,并通过涡旋混合。
    5. 在室温下孵育2分钟。
    6. 在Qubit荧光计上选择dsDNA高灵敏度分析,阅读标准,并运行每个样品。典型的DNA浓度范围从0.5到2.0 ng /μl。

  7. 扩增子文库的汇集
    1. 每个MiSeq测序运行多个〜150个文库。如果需要进行多个测序运行,请在测序运行中随机化文库以避免批处理效应。
    2. 基于获得的浓度范围,确定要汇集的DNA的目标量。旨在每个样本至少5 ng的DNA。
    3. 对于每个文库,通过将目标DNA量除以样品浓度来计算体积需求。
    4. 将这些文库合并到一个不粘核糖核酸酶1.5毫升微量离心管中。
    5. 避免吸液量低于1μl。如果某些样品过于集中,请在合并之前对其进行预冷。

  8. 图书馆集中

    1. 从4°C取出AmpureXP微珠并使其达到室温。
    2. 准备一批新的70%乙醇溶液。
    3. 将1.8体积的Ampure XP珠粒加入到合并的文库中,通过移液混合,并且在室温下孵育5分钟。 1.8倍的体积确保了珠子饱和后产品不会有任何损失。
    4. 将试管转移到磁铁架上,静置2分钟。

    5. 。小心地取出已清除的溶液而不会干扰珠子。
    6. 将管保持在磁铁上,加入1.5毫升70%乙醇,孵育30秒,然后用移液管移出。重复此步骤再进行两次乙醇洗涤。为了最后的洗涤,从底部去除所有乙醇。
    7. 当管子仍然在磁铁上,风干2分钟。
    8. 从磁铁上取下试管,加入50μl无核酸酶的水,并通过移液重新悬浮珠子。
    9. 将管放回磁铁并孵育1分钟。
    10. 将清除过的洗脱液转移到新管中。

  9. 凝胶清理

    1. 在120V的1.8%琼脂糖凝胶上运行浓缩的文库池40分钟。
    2. 使用新的刀片,消除〜400 bp的带。
    3. 使用NucleoSpin Gel和PCR Clean-up试剂盒纯化文库。

  10. 测序
    1. 提交2 x 250 MiSeq测序的混合库。使用以下自定义测序引物:
    2. 为低多样性样本包含一个PhiX控件。

  11. 序列分析
    可以在 GitHub的。
    1. 在电子表格中编译元数据
      1. 为每个样本指定一个唯一的标识符。这个标识符应该是唯一的,不仅仅是一个实验,而且也是所有以前的实验。标识符应该只包含字母数字和句点(“。”)字符。
      2. 创建条形码栏。
      3. 为与每个样品相关的实验变量创建额外的列。一些典型的实验变量是根室,植物发育阶段,植物基因型,地块位置,日期,年份和收集器。
    2. 从测序设备下载测序文件
    3. 解复用序列
    4. 构建完整的连续序列
    5. 集群序列并构建OTU表
      1. 如果使用QIIME进行聚类(Caporaso et al。,2010),则可以使用多种选项和算法。用户可能想要针对16S rRNA基因的数据库(闭合参考聚类)进行基于参考的聚类,或者用户可能想要执行初始OTU聚类。这种方法被称为闭合参考聚类。或者,用户可能更喜欢在这两种方法之间执行混合,其中序列首先是针对数据库引用的。在数据库中没有匹配的读取然后聚集在 de novo 中。这种方法被称为开放参考聚类。用户可以为一次读取定义相似性阈值,将其视为与数据库中的条目匹配。从历史上看,> 97%的序列同一性已被用作聚类序列到操作分类单位(OTU)的标准。通过使用NINJA-OPS流水线(Al-Ghalith等人,2016年),一种用于将序列封闭参考聚类成OTU的相对较新且极其快速的方法。 NINJA-OPS利用Bowtie的速度和记忆效率(Langmead and Salzberg,2012),将回读映射回连接的16S基因的合成基因组。这种方法可以在笔记本电脑上执行。
      2. 或者,用户可能更喜欢使用DADA2基于精确匹配来筛选序列(Callahan等人,2016a)。如果使用这种方法,则不需要在聚类之前构建全长连续序列。
    6. 将分类分配给OTU
      存在多种算法来将分类法分配给OTU序列。如果使用闭基准OTU聚类,则不需要执行该步骤,因为数据库序列已经被分类(DeSantis等人,2006)。 QIIME默认使用UCLUST(Edgar,2010)算法进行分类分配,而DADA2使用RDP朴素贝叶斯方法(Wang等人,2007)将分类分配给序列。


有关如何执行数据分析(包括R代码)的详细说明可以在 GitHub的。在本教程中,我们使用来自Santos-Medellin等人的数据(2017)来说明分析技术。

  1. 从数据集中去除质体和线粒体序列
    1. 线粒体和质体是古代endosymbiosis事件的结果。线粒体和质体保留了它们自己的核糖体机制,因此一部分得到的序列将属于这些细胞器。这些读数不是根菌群的一部分,应在进一步分析前从数据集中删除。这并不是说细胞器读数没有用 - 这些读数可以用于定量目的(Edwards等人,2015年),但它们不应该被认为是微生物群的一部分。 >
    2. 线粒体和质体OTUs可以通过其相关分类法在Family列和'Classoplast'列下搜索'线粒体'进行鉴定。
  2. 规范每个库的排序深度
    存在一些用于标准化的方法。稀释是从OTU池中随机抽样直至达到所需深度的过程。值得注意的是,这种方法删除了用户获取的大部分数据。例如,如果用户有两个库 a 和 b ,并且每个库的深度 d 都是 d a = 100,000和 d b = 5,000时,用户可以选择稀有到5000个序列。这不会放弃来自 d b 的任何序列,但会从 d 中删除95%的数据> 一 。使用这种方法可以丢弃具有低表示的OTU。相对丰度是一种将每个OTU的计数除以排序深度的方法,使得用户在每个库中的每个OTU的比例表示。这种方法充分利用了用户获得的所有数据。取决于具体分析,用户可能倾向于使用在高通量测序统计库例如edgeR(Robinson等人,2010)或DESeq(Anders和Huber,2010)中实施的替代方法。
  3. 确保元数据文件(也称为映射文件)中的样本顺序与OTU计数表中的样本顺序匹配。
  4. 从数据中删除低流行的OTUs
    流行率低或不可重现的OTU可能会给数据集增加不必要的噪音。对于去除低丰度OTU没有特定的经验法则,但是之前使用的一个度量标准是去除至少5%样本中不存在的OTU(Callahan等人, 2016b; Edwards等人,2018年)。
  5. Beta多样性图(图3A)
    1. 计算每个样本之间的成对不相似度。 Bray-Curtis,Jaccard和UniFrac(Lozupone and Knight,2005)的不相似性度量标准有适合这一任务的生态适宜度量标准。
    2. 使用计算出的不相似度,执行主坐标分析(PCoA)。 
    3. 绘制结果轴并根据感兴趣的因素对点进行着色。
  6. Alpha多样性图(图3B)
    1. 计算每个样本的alpha多样性度量。流行的指标是香农指数,辛普森指数,物种丰富度和Faith的系统发育距离。
    2. 绘制计算结果,比较感兴趣的因素。
  7. 门体水平分析(图3C)
    1. 总结每个样本类型中每个门的平均表示。
    2. 绘制结果。有很多方法可以绘制这些结果。在这里,我们选择使用堆积条形图显示数据。我们也只保留了10个代表性最高的门。
  8. OTU差异丰度(图3D)
    1. 将非标准化计数数据加载到选择的统计数据包中。
    2. 对测序深度进行标准化。
    3. 模型样本水平和OTU水平分散。

    4. 使用设计矩阵来安装模型
    5. 进行差异丰度测试。
    6. 绘制x轴平均丰度结果和y轴样品类型倍数变化的结果。

      图3.扩增子微生物组数据的实例分析A.主坐标分析显示使用Bray-Curtis差异的根室之间的微生物群落结构。每个点代表一个特定样品中的微生物群落。 B.使用两个常用度量标准,每个社区内的Alpha多样性。丰富度衡量每个样本中检测到多少独特的OTU,而香农熵则衡量社区中的随机性或不确定性。 C.数据集中10个最丰富的门的分布。我们展示了在门户层面进行分析时,站点之间存在类似的微生物分布间隔。 D.与根部土壤相比,根际和内部层间的差异丰富的微生物。每个点代表一个单一的微生物OTU。有色点代表其中一个比较中显着差异丰富的OTU。点的颜色代表浓缩的方向。差异丰度分析使用DESeq2进行。




  1. 磷酸盐缓冲盐溶液(1L)
    8克NaCl(Fisher Scientific)
    0.2克KCl(Fisher Scientific)
    1.44克Na 2 HPO 4(Fisher Scientific)
    0.24克KH 2 PO 4(Fisher Scientific)


该协议改编自Edwards等人(2015年)。我们感谢多年来我们实验室的几位成员为本协议的制定做出了贡献,直接和间接地提供了有助于简化各个步骤的有益建议:Kelsey Galimba,Cassandra Ramos,Paul Tisher,John Jaeger,Eugene Lurie,Bao Nguyen,Natraj Podishetty和Zach Liechty。我们感谢Derek Lundberg和Jeff Dangl(北卡罗来纳大学教堂山分校)在发表之前(Lundberg et al。<2012>)善意提供了将它们从根际内分离出来的超声处理方法。我们感谢Srijak Bhatnagar和Jonathan Eisen在建立分析协议方面的帮助。 V.S.承认国家科学基金奖DBI-0923806和IOS-1444974以及美国农业部农业试验站授予编号CAD-XXX-6973-H的支持。 JE和CSM承认Elsie Taylor Stocking纪念研究奖学金和Henry Jastro研究奖学金的支持。 CSM承认加利福尼亚大学墨西哥研究所(UCMEXUS)/国立科技与技术研究院(CONACYT)和墨西哥教育协会(墨西哥)的支持。作者声明没有竞争利益。


  1. Al-Ghalith,G.A.,Montassier,E.,Ward,H.N。和Knights,D。(2016)。 NINJA-OPS:使用连接核糖体的快速准确的标记基因比对 PLoS Comput Biol 12(1):e1004658。
  2. Anders,S.和Huber,W。(2010)。 序列计数数据的差异表达分析 Genome Biol 11(10):R106。
  3. Bulgarelli,D.,Rott,M.,Schlaeppi,K.,Ver Loren van Themaat,E.,Ahmadinejad,N.,Assenza,F.,Rauf,P.,Huettel,B.,Reinhardt,R.,Schmelzer, E.,Peplies,J.,Gloeckner,FO,Amann,R.,Eickhorst,T。和Schulze-Lefert,P.(2012)。 揭示拟南芥根系居住的细菌微生物群的结构和组装线索 Nature 488(7409):91-95。
  4. Callahan,B.J.,McMurdie,P.J.,Rosen,M.J.,Han,A.W.,Johnson,A.J。和Holmes,S.P。(2016a)。 DADA2:Illumina扩增子数据的高分辨率样本推断 Nat Methods 13(7):581-583。
  5. Callahan,B. J.,Sankaran,K.,Fukuyama,J. A.,McMurdie,P. J.和Holmes,S. P.(2016b)。 Bioconductor微生物组数据分析工作流程:从原始阅读到社区分析 F1000Res 5:1492。
  6. Caporaso,JG,Kuczynski,J.,Stombaugh,J.,Bittinger,K.,Bushman,FD,Costello,EK,Fierer,N.,Pena,AG,Goodrich,JK,Gordon,JI,Huttley,GA,Kelley, ST,Knights,D.,Koenig,JE,Ley,RE,Lozupone,CA,McDonald,D.,Muegge,BD,Pirrung,M.,Reeder,J.,Sevinsky,JR,Turnbaugh,PJ,Walters,WA, Widmann,J.,Yatsunenko,T.,Zaneveld,J.和Knight,R。(2010)。 QIIME允许分析高通量的社区测序数据。 Nat Methods < 7(5):335-336。
  7. Caporaso,JG,Lauber,CL,Walters,WA,Berg-Lyons,D.,Huntley,J.,Fierer,N.,Owens,SM,Betley,J.,Fraser,L.,Bauer,M.,Gormley, N.,Gilbert,JA,Smith,G.和Knight,R。(2012)。 Illumina HiSeq和MiSeq平台的超高通量微生物群落分析 ISME J 6(8):1621-1624。
  8. DeSantis,T.Z.,Hugenholtz,P.,Larsen,N.,Rojas,M.,Brodie,E.L.,Keller,K.,Huber,T.,Dalevi,D.,Hu,P.and Andersen,G.L。(2006)。 Greengenes,一种嵌合检查的16S rRNA基因数据库和兼容ARB的工作台 Appl Environ Microbiol 72(7):5069-5072。
  9. Duvallet,C.,Gibbons,S.M。,Gurry,T.,Irizarry,R.A。和Alm,E.J。(2017)。 肠道微生物组学研究的荟萃分析可确定疾病特异性和共享的反应。 Nat Commun 8(1):1784.
  10. Edwards,J.A.,Santos-Medellin,C.M.,Liechty,Z.S。,Nguyen,B.,Lurie,E.,Eason,S.,Phillips,G。和Sundaresan,V.(2018)。 与根系相关的细菌和古菌微生物的组成变化可以追踪田间种植水稻的植物生命周期。 PLoS Biol 16(2):e2003862。
  11. Edwards,J.,Johnson,C.,Santos-Medellin,C.,Lurie,E.,Podishetty,N.K。,Bhatnagar,S.,Eisen,J.A。和Sundaresan,V.(2015)。 水稻根系相关微生物组的结构,变异和组装。 Proc Natl Acad Sci USA 112(8):E911-920。
  12. Edgar,R.C。(2010)。 搜索和聚类的速度比BLAST快得多。 Bioinformatics 26(19):2460-2461。
  13. Finkel,O. M.,Castrillo,G.,Herrera Paredes,S.,Salas Gonzalez,I.和Dangl,J. L.(2017)。 了解和利用植物有益微生物 Curr Opin Plant Biol 38:155-163。
  14. Langmead,B。和Salzberg,S.L。(2012)。 快速阅读与Bowtie 2对齐。 Nat Methods 9(4):357-359。
  15. Lozupone,C.和Knight,R.(2005)。 UniFrac:一种新的用于比较微生物群落的系统发育方法 Appl Environ Microbiol 71(12):8228-8235。
  16. Lundberg,DS,Lebeis,SL,Paredes,SH,Yourstone,S.,Gehring,J.,Malfatti,S.,Tremblay,J.,Engelbrektson,A.,Kunin,V.,Del Rio,TG,Edgar,RC ,Eickhorst,T.,Ley,RE,Hugenholtz,P.,Tringe,SG和Dangl,JL(2012)。 定义核心拟南芥根微生物。 自然 488(7409):86-90。
  17. Masella,A.P.,Bartram,A.K.,Truszkowski,J.M.,Brown,D.G.和Neufeld,J.D。(2012)。 PANDAseq:用于Illumina序列的配对端汇编器 BMC Bioinformatics 13:31.
  18. McMurdie,P.J。和Holmes,S。(2014)。 不浪费,不要:为什么稀有微生物数据不可接受。
  19. Raynaud,X.和Nunan,N。(2014)。 土壤微尺度细菌的空间生态学
  20. Robinson,M.D。,McCarthy,D.J。和Smyth,G.K。(2010)。 edgeR:Bioconductor软件包,用于数字基因表达数据的差异表达分析。 生物信息学 26(1):139-140。
  21. Santos-Medellín,C.,Edwards,J.,Liechty,Z.,Nguyen,B。和Sundaresan,V.(2017)。 干旱胁迫导致水稻根系相关微生物组的特定区室重组。 MBio 8(4)。
  22. Wagner,M.R.,Lundberg,D.S.,Del Rio,T.G.,Tringe,S.G.,Dangl,J.L。和Mitchell-Olds,T.(2016)。 宿主基因型和年龄形成了野生多年生植物的叶和根微生物群。 Nat Commun 7:12151.
  23. Wang,Q.,Garrity,G.M.,Tiedje,J.M。和Cole,J.R。(2007)。 朴素贝叶斯分类器,用于将rRNA序列快速分配到新的细菌分类中。 Appl Environ Microbiol 73(16):5261-5267。
  24. Zarraonaindia,I.,Owens,SM,Weisenhorn,P.,West,K.,Hampton-Marcell,J.,Lax,S.,Bokulich,NA,Mills,DA,Martin,G.,Taghavi,S.,van der Lelie,D.和Gilbert,JA(2015)。 土壤微生物对葡萄藤相关微生物群的影响。 MBio 6(2)。
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引用:Edwards, J., Santos-Medellín, C. and Sundaresan, V. (2018). Extraction and 16S rRNA Sequence Analysis of Microbiomes Associated with Rice Roots. Bio-protocol 8(12): e2884. DOI: 10.21769/BioProtoc.2884.