Earth Sciences Division (ESD) Department of Energy (DOE) Lawrence Berkeley National Laboratory (LBNL)


PhylochipThe PhyloChip is a high-density DNA microarray capable of rapid characterization of microbial communities from complex samples in a single and reproducible test. The array consists of 1.1 million features which includes probes that target the 16S rRNA gene ubiquitous to bacteria and archaea. Isolated DNA from any source can be analyzed using the PhyloChip. Additionally, the sensitivity of the array enables the identification of low-abundance organisms that are typically missed by conventional sequencing methods. The strength of the PhyloChip lies in its ability to quickly and accurately assess changes in relative abundance across different groups of samples. This enables researchers to identify microbial signatures associated with various diseases, treatments, or environmental conditions. To date, the PhyloChip has been used to study a broad range of research topics including the Deepwater Horizon Oil Spill, fecal source tracking, and the air we breathe.

Projects that Highlight the Use of the PhyloChip:

  • The Deep Water Horizion Oil Spill: The PhyloChip enabled researchers to observe a decrease in microbial richness along with an increase in abundance of deep-ocean psychrophilic Gammaproteobacteria within plume samples collected from the Gulf of Mexico. These findings suggest that, in the aftermath of the oil spill, microbes played a significant role in the in situ bioremediation of the oil plume.
  • Fecal Source Tracking: Researchers used the PhyloChip to characterize the bacterial communities of fecal waste collected from populations of humans, birds, cows, horses, elk, and pinnipeds. The PhyloChip revealed strong differences among the different populations including a number of bacterial taxa that were unique to a given source, providing potential for fecal source tracking efforts.
    • Dubinsky et al. Application of phylogenetic microarray analysis to discriminate sources of fecal pollution. Environ Sci Technol (2012) vol. 46 (8) pp. 4340-7.
    • Wu et al. Characterization of coastal urban watershed bacterial communities leads to alternative community-based indicators. PLoS ONE (2010) vol. 5 (6) pp. e11285.
  • Air Quality: Researchers used the PhyloChip to catalog the microbial diversity found in air samples collected from two U.S. cities over the course of 17 weeks.
    • Brodie et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc Natl Acad Sci USA (2007) vol. 104 (1) pp. 299-304.
  • Coral Reef Disease: The PhyloChip was used to assess the microbial communities associated with diseased corals.
    • Sunagawa, S., T.Z. DeSantis, Y.M. Piceno, E.L. Brodie, M.K. DeSalvo, C.R. Voolstra, E. Weil, G.L. Andersen, and M. Medina (2008). "Bacterial diversity and white plague disease-associated community changes in the Caribbean Coral Montastraea
    • Kellogg et al. PhyloChip™ microarray comparison of sampling methods used for coral microbial ecology. Journal of Microbiological Methods (2012) vol. 88 (1) pp. 103-9. Link to publication: 
  • Spacecraft Cleanrooms: Researchers used the PhyloChip to assess the cleanliness of spacecraft cleanrooms.
    • La Duc et al. Comprehensive census of bacteria in clean rooms by using DNA microarray and cloning methods. Applied and Environmental Microbiology (2009) vol. 75 (20) pp. 6559-67.
    • Cooper et al. Comparison of innovative molecular approaches and standard spore assays for assessment of surface cleanliness. Applied and Environmental Microbiology (2011) vol. 77 (15) pp. 5438-44.

PhyloChip Links:

The Berkeley Lab PhyloChip

lab chip

Figure 1. The PhyloChip is able to categorize all known bacterial and archaeal OTUs into over 50,000 taxa using 1,100,000 25-mer probes that target variations in the 16S rRNA gene. The image of the PhyloChip shows hybridization intensity across all 1.1 million probes with successive close ups of each highlighted region. False color imagery displays hybridization intensity from highest (white) to lowest (blue/black).

The Platform

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 pipeline 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.

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.

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.

chip 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.)


Each probe “spot” is precisely positioned within an 8X8 micron area on a 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.


Figure 4.  Non-metric multidimensional scaling (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.


Reproducibility was tested during the G3 PhyloChip verification process using a mock community of 26 organisms applied to the microarray in varying concentrations (where concentrations 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%).


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 (C), conductive 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, 1097-1100).

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.

Peer-Reviewed Publications Describing or Utilizing the PhyloChip

  1. Dubinsky et al. Application of phylogenetic microarray analysis to discriminate sources of fecal pollution. Environ Sci Technol (2012) vol. 46 (8) pp. 4340-7.
  2. Wakelin, S. A., Anand, R. R., Reith, F., Gregg, A. L., Noble, R. R,, Goldfarb, K. C., Andersen, G. L., DeSantis, T. Z., Piceno, Y. M., Brodie, E. L. (2011) Bacterial communities associated with a mineral weathering profile at a sulphidic mine tailings dump in arid Western Australia. FEMS Microbiol Ecol. 2011 Oct 3. [Epub ahead of print] PMID:22092956
  3. Kellogg, C. A., Piceno, Y. M., Tom, L. M., DeSantis, T. Z., Zawada, D. G., Andersen, G. L. (2011) PhyloChip™ microarray comparison of sampling methods used for coral microbial ecology. J Microbiol Methods. 88:103-109. PMID:22085912
  4. García-Amado, M. A., Godoy-Vitorino, F., Piceno, Y. M., Tom, L. M., Andersen, G. L., Herrera, E. A., Domínguez-Bello, M. G. (2011) Bacterial Diversity in the Cecum of the World's Largest Living Rodent (Hydrochoerus hydrochaeris). Microb Ecol. 2011 Nov 15. [Epub ahead of print] PMID:22083250
  5. DeAngelis, K. M., Wu, C. H., Beller, H. R., Brodie, E. L., Chakraborty, R., DeSantis, T. Z., Fortney, J. L., Hazen, T. C., Osman, S. R., Singer, M. E., Tom, L. M., Andersen, G. L. (2011) PCR amplification-independent methods for detection of microbial communities by the high-density microarray PhyloChip. Appl. Environ. Microbiol. 77:6313-6322 PMID: 21764955
  6. Cooper, M., La Duc, M. T., Probst, A., Vaishampayan, P., Stam, C., Benardini, J.,N., Piceno, Y.,M., Andersen, G. L.,  Venkateswaran, K. (2011) Assessing the Cleanliness of Surfaces: Innovative Molecular Approaches vs. Standard Spore Assays. Appl. Environ. Microbiol. 77:5438-5444. PMID: 21652744
  7. Mendes, R., Kruijt, M., de Bruijn, I., Dekkers, E., van der Voort, M., Schneider, J. H., Piceno, Y. M., DeSantis, T. Z., Andersen, G. L., Bakker, P. A, and Raaijmakers, J. M. (2011) Deciphering the rhizosphere microbiome for disease-suppressive bacteria Science 332:1097-1100. PMID: 21551032
  8. Kelly, L. C., Cockell, C. S., Herrera-Belaroussi, A., Piceno, Y., Andersen, G., Desantis, T., Brodie, E., Thorsteinsson, T., Marteinsson, V., Poly, F., Leroux, X. (2011) Bacterial diversity of terrestrial crystalline volcanic rocks, Iceland. Microb. Ecol. 62:69-79 2011. PMID 21584756
  9. Weinert, N., Piceno, Y., Ding, G. C., Meincke, R., Heuer, H., Berg, G., Schloter, M., Andersen, G., and Smalla, K. (2011) PhyloChip hybridization uncovered an enormous bacterial diversity in the rhizosphere of different potato cultivars: many common and few cultivar-dependent taxa. FEMS Microbiol Ecol. 75:497-506. PMID: 21204872
  10.  Nelson, T. A., Holmes, S., Alekseyenko, A. V., Shenoy, M., DeSantis, T., Wu, C. H., Andersen, G. L., Winston, J., Sonnenburg, J., Pasricha, P.J., and Spormann, A. (2011) PhyloChip microarray analysis reveals altered gastrointestinal microbial communities in a rat model of colonic hypersensitivity. Neurogastroenterol. Motil. 23:169-177 PMID: 21129126
  11. Hazen, T. C., Dubinsky, E. A., Desantis, T. Z., Andersen, G. L., Piceno, Y. M., Singh. N. et. al. (2010) Deep-Sea Oil Plume Enriches Indigenous Oil-Degrading Bacteria. Science. 330:204-208 PMID: 20736401
  12. Godoy-Vitorino et al. Developmental microbial ecology of the crop of the folivorous hoatzin. ISME J (2010) vol. 4 (5) pp. 611-20.
  13. Vaishampayan, P., Osman, S., Andersen, G., and Venkateswaran, K. (2010) High-density 16S microarray and clone library-based microbial community composition of the phoenix spacecraft assembly clean room. Astrobiology 10:499-508. PMID: 20624058
  14. Wu, C. H., Sercu, B., Van de Werfhorst, L. C., Wong, J., DeSantis, T. Z., Brodie, E. L., Hazen, T. C., Holden, P. A., and Andersen, G. L. (2010) Characterization of coastal urban watershed bacterial communities leads to alternative community-based indicators. PLoS One. 5:e11285 PMID: 20585654
  15. Rastogi, G., Osman, S., Kukkadapu, R., Engelhard, M., Vaishampayan, P. A., Andersen, G. L., Sani RK. (2010) Microbial and Mineralogical Characterizations of Soils Collected from the Deep Biosphere of the Former Homestake Gold Mine, South Dakota. Microb Ecol. 59:94-108. PMID: 20386898
  16. Probst, A., Vaishampayan, P., Osman, S., Moissl-Eichinger, C., Andersen, G. L., Venkateswaran K. (2010) Diversity of anaerobic microbes in spacecraft assembly clean rooms. Appl Environ Microbiol. 76:2837-45. PMID: 20228115
  17. Kuramae, E. E., Gamper, H. A., Yergeau, E., Piceno, Y. M., Brodie, E. L., DeSantis, T. Z., Andersen, G. L., van Veen, J. A., and Kowalchuk, G. A. (2010) Microbial secondary succession in a chronosequence of chalk grasslands. ISME J. 4:711-5. PMID: 20164861
  18. Rastogi, G., Osman, S., Vaishampayan, P. A., Andersen, G. L., Stetler, L.D., and Sani, R.K. (2009) Microbial Diversity in Uranium Mining-Impacted Soils as Revealed by High-Density 16S Microarray and Clone Library. Microb Ecol. 59:94-108. PMID: 19888627
  19. La Duc, M. T., Osman, S., Vaishampayan, P., Piceno, Y., Andersen, G., Spry. J.A., and Venkateswaran, K. (2009) Comprehensive census of bacteria in clean rooms by using DNA microarray and cloning methods. Appl Environ Microbiol. 75:6559-6567. PMID: 19700540
  20. Sagaram, U. S., DeAngelis, K. M., Trivedi, P., Andersen, G. L., Lu, S. E., Wang, N. (2009) Bacterial diversity analysis of Huanglongbing pathogen-infected citrus using PhyloChips and 16S rDNA clone library sequencing.  Appl. Env. Micro. 75:1566-1574. PMID: 19151177
  21. Sunagawa, S., DeSantis, T. Z., Piceno, Y. M., Brodie, E. L., DeSalvo, M. K., Voolstra, C. R., Weil, E., Andersen, G. L., Medina, M. (2009) Bacterial diversity and White Plague Disease-associated community changes in the Caribbean coral Montastraea faveolata. ISME J. 3:512-521. PMID: 19129866
  22. Yergeau, E., Schoondermark-Stolk, S. A., Brodie, E. L., Déjean, S., DeSantis, T. Z., Gonçalves, O., Piceno, Y. M., Andersen, G. L., Kowalchuk, G.A. (2008) Environmental microarray analyses of Antarctic soil microbial communities. ISME J. 3:340-351. PMID: 19020556
  23. DeAngelis, K. M., Brodie, E. L., DeSantis, T. Z., Andersen, G. L., Lindow, S. E., Firestone, M. K. (2008) Selective progressive response of soil microbial community to wild oat roots. ISME J. 3:168-178 PMID: 19005498
  24. DeSantis, T. Z., Brodie, E. L., Moberg, J. P., Zubieta, I. X., Piceno, Y. M. and Andersen, G. L. (2007) High-density universal 16S rRNA microarray analysis reveals broader diversity than typical clone library when sampling the environment. Microb. Ecol. 53:371-383.
  25. Brodie, E. L., Desantis, T. Z., Parker, J. P., Zubietta, I. X., Piceno, Y. M., and Andersen, G. L. (2007) Urban aerosols harbor diverse and dynamic bacterial populations. Proc Natl Acad Sci. 104:299-304.
  26. Brodie, E. L., DeSantis, T. Z., Joyner, D. C., Baek, S. M., Larsen, J. T., Andersen, G. L., Hazen, T.C., Richardson, P. M., Herman, D. J., Tokunaga, T. K., Wan, J. M., and Firestone, M. K., (2006) Application of a high-density oligonucleotide microarray approach to study bacterial population dynamics during uranium reduction and reoxidation. Appl. Env. Micro. 72:6288-6298.
  27. DeSantis, T. Z., Stone, C. E., Murray, S. R., Moberg, J. P., and Andersen, G. L. (2005) Rapid quantification and taxonomic classification of environmental DNA from both prokaryotic and eukaryotic origins using a microarray. FEMS Micro. Lett. 245: 271-278.
  28. Wilson, K. H., Wilson, W. J., Radosevich, J. L., DeSantis, T. Z., Viswanathan, V. S., Kuczmarski, T. A., and Andersen, G. L. (2002) High density microarray of small subunit ribosomal DNA probes. Appl. Env. Micro. 68: (5) 2535-2541.