The PhyloChip is a high-precision microbial community assessment tool that can simultaneously track both high-abundance and low-abundance bacterial and archaeal taxa for an accurate measure of microbial community dynamics.  The platform uses a proprietary bioinformatics pipelines combined with a high-density, photolithographic oligonucleotide microarray manufactured by Affymetrix (Figure 1).  Built in controls for all steps of the microarray hybridization process and normalization of the probe intensity values result in the lowest technical variation observed by any microbial community analysis platform.  The high chip-to-chip reproducibility allows the user to identify key microbes that respond to specific environmental variables.

Phylochip Platform

Figure 1. (a) The PhyloChip is able to categorize all known bacteria and archaeal OTUs into over 50,000 taxa using 1,100,000 25-mer probes that target variations in the 16S rRNA gene. (b) Image of PhyloChip showing hybridization intensity across all 1.1 million probes. (c) Close up of highlighted region. False color imagery displays hybridization intensity from highest (white) to lowest (blue/black).

Gary Andersen with the Philochip


How it works

Multiple DNA oligonucleotide probes target taxa specific regions of the 16S rRNA gene to identify virtually all known bacteria and archaea.  This gene is used to create RNA copies of itself that are one of the two main building blocks of the ribosome complex, which is essential for making proteins in the cell (Figure 2).  The advantage of using this gene is that it is universally present in all microbes and small sequence variations within the gene can be used as a “bar-code” for bacteria and archaea identification.  Multiple DNA probes on the PhyloChip are used to identify sequence variation in the 16S rRNA gene and each probe is paired with a single-base mismatch control probe to minimize the effect of non-specific hybridization.  The current Generation 3 (G3) PhyloChip contains a total of 1,100,000 different DNA probes targeting.Philochip, Bacterial Types

Figure 2. Bacterial types (a) contain ribosomes (b) that are composed of a specific sequence of DNA/RNA bases (c), allowing them to be differentiated. The PhyloChip detects these differences, and thus is able to classify all types of DNA/RNA sequences in complex mixtures.


Each probe “spot” is precisely positioned within an 8X8 micron area on glass surface.  This spot contains several million copies of a specific 25 nucleotide sequence that is designed to specifically interact with a unique region of a 16S rRNA gene of a specific taxa or operational taxonomic unit (OTU).  Over 1.1 million different spots are used on the PhyloChip to categorize bacterial and archaeal organisms.  Amplified DNA or RNA from the 16S rRNA gene of a sample (clinical, water, soil, dust, etc.) binds (hybridizes) to exact DNA sequence matches from specific 25 nucleotide probes.  Fluorescent molecules at the end of the sequence match are used to indicate a positive interaction. Each probe with a specific DNA or RNA interaction is interrogated by a laser scan (Figure 3).  The brighter the intensity of the spot, the greater the abundance of the taxa or OTU.  All steps of the PhyloChip hybridization, stringency washes and fluorescence scanning are automated to increase the reproducibility of each test.

Phylochip Operation

Figure 3. PhyloChip operation: (a) Multiple tests conducted on a single glass surface; (b) DNA from a sample (blood, soil, water, etc.) adheres where a match is found (“hybridization”); (c) laser scanning reveals which tests were positive (i.e., which microbes are present). In this way, PhyloChip quickly and accurately identifies microbes in complex samples. (Images provided by Affymetrix, Inc., Santa Clara, CA.)


Reproducibility was tested during the G3 PhyloChip verification process using a mock community of 26 organisms applied to a microarray in varying concentrations (and then the concentration were rotated through the full set).  Each combination of concentrations was run in triplicate.  The variation between replicates was much lower than the variation across ‘samples’ (Figure 4). In collaborations with others, we have also run pooled, mixed soil samples that had been amplified and run on separate chips (PCR products were kept separate, yielding triplicates for two soil pools).  The OTU probe intensity values had a mean coefficient of variation of 8.6% (n=3, Median: 6.1%) for the first sediment ‘pool’ and of 8.2% (n=3, Median: 7.2) for the second sediment ‘pool’. The mean coefficient of variation for the technical replicates noted above was 2.9% (n=26, Median: 2.3%).

Phylochip ReproducibilityPhylochip Reproducibility

Figure 4.  nMDS plot based on Bray-Curtis similarity matrix for triplicate samples analyzed by PhyloChip.  Triplicates for each sample (n=26) are connected by green lines.

Figure 5. Cluster analysis (Bray-Curtis) of the rhizosphere microbiomes of sugar beet seedlings grown in soils with different levels of disease suppressiveness.  Suppressive soil (S), suppressive soil amended with R. solani (Sr), suppressive soil heat-treated at 50oC (S50), conducive soil amended with 10% (w/w) of suppressive soil (CS), suppressive soil heat-treated at 80oC (S80).  Numbers 1 to 4 refer to the replicates of each treatment. (Adapted from Mendes, et al. (2011) Science 332, 1097).

In a study of disease-suppressive soils, Mendes, et al. (2011) Science 332, 1097-1100, the PhyloChip was able to identify key bacterial taxa involved in suppression of a fungal root pathogen.  As seen in Figure 5, an advantage of the PhyloChip over other methods for measuring microbial community structure and dynamics is its high level of reproducibility.  In this study, all biological replicates of a treatment clustered together and the overall differences between treatments fit the proposed model for a microbial consortia protecting against pathogen infection.

Advantages of the PhyloChip

  • Analysis of entire pool of community 16S rRNA allows detection of very low abundance taxa.
  • Reproducible for multiple replicates and comparable with other experiments.
  • Controls built into each microarray to check performance characteristics of each sample.
  • No artificial inflation of low abundance taxa (rare biosphere) due to sequencing errors.
  • Responsive probes can be down-selected for follow up diagnostic tests.