How it works
Random Peptide Array
Early immunosignature tests used glass microscope slides, with spots of 10,000 random peptides. Newer immunosignature work is run on wafers made of silicon dioxide, with each wafer cut into standard slide size and spotted with 330,034 peptides; however, further description will focus on the glass slide experiments. These random-sequence peptides, 20 amino acids long, are chemically attached to the slides. Of the 20 amino acid length, 3 amino acids (at the C-terminus side of the peptide) are common to each peptide spot. This 3 amino acid segment is used as the linker by which the 17 amino acid chain ("17-mer") is attached to the slide. The 17-mer is the "random peptide", with a random sequence selected by the use of a random number generator. This randomness makes the immunosignature technology different from existing technology to identify disease states via biomarkers, because the 10,000 unique, random peptides per slide are not specifically selected for containing particular sequences. The random sequences are not selected for containing known epitopes, or antibody binding sites, of pathogens. When a sample of diluted blood serum (containing antibodies) is applied to the surface of the peptide microarray, the 17-mers are long enough that there are multiple potential epitopes on each individual peptide.
Antibodies are present in the diluted serum sample, and are considered significant to the health of the patient, because if antibodies remain present even in the diluted serum sample, they must reasonably have been present at relatively high amounts in the blood of the patient. This collection of antibodies will bind to regions of some of the random sequence peptides. The antibodies in the serum sample will vary among patients, depending on their health or disease state. Once antibodies have been allowed to bind to the peptides on the microarray, the array is washed (to remove any unbound serum particles or antibodies). After washing, the array now has the 10,000 random peptides, and an unknown number of antibodies bound to some of those peptides.
To detect those human antibodies, the array is covered with a solution of a fluorescently labeled secondary antibody. This secondary antibody binds to the patient antibody already on the array from the diluted serum sample, and since this secondary antibody is fluorescently labeled, it is detectable using fluorescence microscopy. After the microarray is washed to remove unbound secondary antibody, and dried via centrifugation, it is scanned using fluroescence microscopy, and the pattern of peptide spots with bound antibodies versus those without antibodies becomes visible. This pattern is called the immunosignature.
Immunosignaturing has many applications in current medical research and testing, such as diagnosing valley fever infections and determining if a vaccine will be effective at protecting patients from disease.
Valley Fever Diagnosis
In the American southwest, where fungal infections of valley fever are a problem, the immunosignature platform has been tested as a way to detect infection in patients. Valley fever infections, when symptomatic, appear similar to the common flu, progressing to pneumonia-like symptoms. Current valley fever testing is unreliable, especially in patients with other infections (such as HIV), and a confident diagnosis can take weeks. Further complicating this testing, in a slim percent of cases patients do not develop any detectable antibody against the fungus. Current testing can also be invasive and more demanding than an immunosignature array, ranging from a sputum test or blood test, to bronchoscopy (the latter is more invasive in addition to taking longer to get a result). Confounding the issue of valley fever, of the 40% of patients showing symptoms, many will be mis-diagnosed with other conditions or not recognized as infected with valley fever. Using the peptide array, scientists were able to determine a distinct immunosignature for valley fever infections, even when the patients had other respiratory infections as well. The immunosignature was also clearly differentiated from that of healthy patients.
Immunosignatures were used to test if the efficacy of a vaccine could be predicted (in mice), using different strains of the influenza virus. Mice were given a seasonal flu vaccine, or a vaccine against the specific flu virus tested in the study (PR8). The mice were then infected with the PR8 flu strain. Those groups of mice which were given the PR8-specific vaccine not only survived, but did not display any symptoms of the flu. The mice which received either of the two seasonal flu vaccines all developed flu symptoms, and some (20-40%, depending on which seasonal vaccine received) were killed by the PR8 infection.
The group of mice which received sub-lethal infection doses of PR8, and the group of mice which received vaccines of killed PR8, had different immunosignatures. The two groups of mice immunized with the seasonal flu vaccines also had immunosignatures which were distinct from each other. This demonstrates that the immunosignature platform can be used to distinguish between very similar vaccines. The immunosignature of known protection (here, the signature of mice immunized with the killed virus), was compared to the immunosignatures of the mice groups given less protective vaccines. The more protective a mouse's vaccine was, the closer that mouse's immunosignature was to the protected signature.
As the immunosignature platform is useful for any disease which engages the immune system, it was tested to determine if distinct immunosignatures could be determined for various cancers. Using the 10,000 peptide array, comparing against healthy control samples was used to establish immunosignatures for the five different cancers tested. Healthy versus cancer state samples were distinguishable, but there was a slight overlap of the signatures among the cancers. This resulted in a loss of specificity in distinguishing between cancers using immunosignatures. To resolve this, peptides were determined to be statistically significant in the cancer signatures using more stringent selection processes. This eliminated the peptides in common between various cancers, and this selection of peptides was used to distinguish between the cancers, with 95% specificity.
Patent and Commercialization
Patent: "Compound Arrays for Sample Profiling", WO 2,010,148,365, inventor(s): Johnston, Stephen A; Stafford, Phillip.
- Stafford, Phillip; Halperin, Rebecca; Legutki, Joseph Bart; Magee, Dewey Mitchell; Galgiani, John; Johnston, Stephen Albert (2012-04-01). "Physical Characterization of the "Immunosignaturing Effect"". Molecular & Cellular Proteomics. 11 (4): M111.011593. ISSN 1535-9476. PMC . PMID 22261726. doi:10.1074/mcp.M111.011593.
- O’Donnell, Brian; Maurer, Alexander; Papandreou-Suppappola, Antonia; Stafford, Phillip (2015-06-18). "Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures". Cancer Informatics. 14 (Suppl 2): 219–233. ISSN 1176-9351. PMC . PMID 26157331. doi:10.4137/CIn.s17285.
- Stafford, Phillip; Cichacz, Zbigniew; Woodbury, Neal W.; Johnston, Stephen Albert (2014-07-29). "Immunosignature system for diagnosis of cancer". Proceedings of the National Academy of Sciences of the United States of America. 111 (30): E3072–E3080. ISSN 0027-8424. PMC . PMID 25024171. doi:10.1073/pnas.1409432111.
- Navalkar, Krupa Arun; Johnston, Stephen Albert; Woodbury, Neal; Galgiani, John N.; Magee, D. Mitchell; Chicacz, Zbigniew; Stafford, Phillip (2014-08-01). "Application of Immunosignatures for Diagnosis of Valley Fever". Clinical and Vaccine Immunology. 21 (8): 1169–1177. ISSN 1556-6811. PMC . PMID 24964807. doi:10.1128/CVI.00228-14.
- "Bronchoscopic diagnosis of pulmonary coccidioidomycosis comparison of cytology, culture, and transbronchial biopsy". Diagnostic Microbiology and Infectious Disease. 18: 83–87. doi:10.1016/0732-8893(94)90070-1. Retrieved 2015-11-12.
- "Valley fever Tests and diagnosis - Mayo Clinic". www.mayoclinic.org. Retrieved 2015-11-12.
- "Valley Fever". Banner Health - www.BannerHealth.com. Retrieved 2015-11-12.
- Legutki, Joseph Barten; Johnston, Stephen Albert (2013-11-12). "Immunosignatures can predict vaccine efficacy". Proceedings of the National Academy of Sciences of the United States of America. 110 (46): 18614–18619. ISSN 0027-8424. PMC . PMID 24167296. doi:10.1073/pnas.1309390110.
- "Product Platform | Immunosignature | HealthTell". HealthTell. Retrieved 2015-11-14.
For further information on immunosignatures, also review these sources
- "2007 GELSS Symposium Abstracts" (PDF). Arizona State University. 2007. p. 5. Retrieved 8 April 2010.
- Legutki JB, Magee DM, Stafford P, Johnston SA. "A general method for characterization of humoral immunity induced by a vaccine or infection". doi: 10.1016/j.vaccine.2010.04.061 Vaccine, 2010.
- Brown P, Stafford P, Johnston SA, Dinu Valentin: "Statistical Methods for Analyzing Immunosignatures" BMC Bioinformatics 12:349, doi:10.1186/1471-2105-12-349, 2011.
- Restrepo L, Stafford P, Magee DM, Johnston SA. "Application of immunosignatures to the assessment of Alzheimer's disease". Annals of Neurology doi:10.1002/ana.22405. 2011.
- Halperin RF, Stafford P, Johnston SA. "Exploring antibody recognition of sequence space through random-sequence microarrays". Molecular and Cellular Proteomics doi:10.1074/mcp.M110.000786. 2011.
- Stafford P, Johnston SA. "Microarray Technology Displays the Complexities of the Humoral Immune Response". Expert Reviews in Molecular Diagnostics 11(1):5-8. 2011
- Halperin RF, Stafford P, Emery JS, Navalkar KA, Johnston SA: "GuiTope: An Application for Mapping Random-Sequence Peptides to Protein Sequences", BMC Bioinformaticsˆ, 13:1, doi:10.1186/1471-2105-13-1. 2012.
- Stafford P, Halperin R, Legutki JB, Magee DM, Galgiani J, Johnston SA: "Physical Characterization of the ‘Immunosignaturing Effect", doi:10.1074/mcp.M111.011593, Molecular and Cellular Proteomics. 2012.
- Kukreja M, Johnston SA, Stafford P: "Comparative Study of Classification Algorithms for Immunosignaturing Data", doi:10.1186/1471-2105-13-139, BMC Bioinformatics. 2012.
- Hughes A, Cichacz Z, Scheck A, Coons SW, Johnston SA, Stafford P: "Immunosiganturing Can Detect Products from Molecular Markers in Brain Cancer", doi:10.1371/journal.pone.0040201, PLoS ONE, 2012.
- Chase BA, Johnston SA and Legutki JB. "Evaluation of Biological Sample Preparation for Immunosignature-Based Diagnostics", doi:10.1128/CVI.05667-11, Clinical and Vaccine Immunology, 2012.
- Kroening K, Johnston SA, Legutki JB. "Autoreactive antibodies raised by self derived de novo peptides can identify unrelated antigens on protein microarrays", doi:10.1016/j.yexmp.2012.03.002, Experimental and Molecular Pathology, 2012.
- Restrepo L, Stafford P, Johnston SA. "Feasibility of an early Alzheimer's disease immunosignature diagnostic test", doi:10.1016/j.jneuroim.2012.09.014, Journal of Neuroimmunology, 2012.
- Sykes K., Legutki JB, Stratford P. "Immunosignaturing: a critical review", doi: 10.1016/j.tibtech.2012.10.012, Cell Press, 2012.
- Johnston SA "Healthcare by Mail", http://www.zocalopublicsquare.org/2011/09/12/dear-john-you-have-psoriasis/ideas/nexus/
- Legutki JB, Johnston SA: "Immunosignatures can predict vaccine efficacy", Proceedings of the National Academy of Sciences doi:10.1073/pnas.130939011, 2013.
- Navalkar KA, Johnston SA, Woodbury N, Galgiani J, Magee DM, Chicacz Z, Stafford P. "Application of Immunosignatures to Diagnosis of Valley Fever", doi:10.1128/CVI.00228-14, Clinical and Vaccine Immunology, 2014.
- Williams S, Stafford P, Hoffman SA. "Diagnosis and Early Detection of CNS-SLE in MRL/lpr Mice Using Peptide Microarrays", doi:10.1186/1471-2172-15-23, BMC Immunology, 2014.
- Stafford P, Cichacz Z, Woodbury N, Johnston SA. "Immunosignature System for Diagnosis of Cancer", doi: 10.1073/pnas.1409432111, PNAS, 2014.
- Legutki JB, Zhao ZG, Greving M, Woodbury N, Johnston SA, Stafford P. "Scalable High-Density Peptide Arrays for Comprehensive Health Monitoring", doi:10.1038/ncomms5785, Nature Communications, 2014.