Simultaneous malicious introduction of genetically altered or otherwise normal pathogens at multiple locations across the country in food or water supply, or other means is a well defined bioterrorism threat. The ability to rapidly determine the presence of such an agent requires integration of three key processes: early identification & analysis system that can be replicated in a robust way & an ability to transmit a specific serotype classification to multiple sites very quickly. We propose a novel technology to testing laboratories that integrates a system wide, fully expandable, absolutely label-free identification technology for all 7 category B bacterial pathogens. The technology is based on the use of laser light scatter that interrogates bacterial colonies collecting unique scatter fingerprint patterns that instantly identify each and every colony on a plate. It is label free since it requires no antibody or nucleic acid probe, no chemistry or any kinetic reaction; non-destructive since it is based on the interrogation of the colony by a low power laser for a few milliseconds; & it provides instantaneous output for a recognizable pathogen pattern immediately via interrogating of a local or central database of classification patterns. This proposal provides a unique technology for biothreat agent detection because a clinical laboratory in city A can be linked to city B and to food production factory C to rapidly signal the presence of a common serotype bioterrorism agent with identical scatter signatures that may be intentionally distributed. The existing BioWatch and BioShield programs depend on a deployed suite of electronic detectors, coupled protection- & counter-measures within the Strategic National Stockpile for a limited number of known threat biological agents. Much more insidious, however, would be a dynamic & diverse array of threat biological agents for which the nation has no time to develop label-based detection methods. Our proposed network addresses precisely that scenario. An electronic database & automated classification technology, & the ability to recognize a common threat at the earliest time is now realized. We have established the capability of this technology to provide as high as 99% classification accuracy on a single colony in an objective &reproducible manner. The distributed system we propose could be transformational in its ability to link multiple sites very early in the infection-recognition process. For example, a genetically altered pathogen may be distributed simultaneously in food production sites or water systems around the country. Using our proposed technology each site that receives a specimen from a food production unit or water, the clinical specimen from a sick patient or a community based testing laboratory, will be instantaneously linked & even an unknown will have a specific identification pattern that will signal its presence. This unique classifier will create an alarm based on multi-site recognition of the identical unknown potential pathogen. Any site that then provides exact identification closes the link at all sites as to pathogen identity. Physical transfer of culture is not required for an exact match.
This project develops a nation-wide biothreat detection system aimed at the rapid detection of pathogens from individuals who present at hospitals or clinics with serious symptoms and from who samples are taken for microbiology evaluation. The unique technology creates signatures (patterns) that classify pathogens and if previously seen, provide the identity of the organism from the database. If not recognized, a pattern is immediately distributed over the national network via a central database that monitors all collected signatures. The alarm sequence generated will create an early warning system particularly for genetically engineered organisms for which traditional identification tools fail.
| Robinson, J Paul; Patsekin, Valery; Holdman, Cheryl et al. (2013) High-throughput secondary screening at the single-cell level. J Lab Autom 18:85-98 |
| Robinson, J Paul; Rajwa, Bartek; Patsekin, Valery et al. (2012) Computational analysis of high-throughput flow cytometry data. Expert Opin Drug Discov 7:679-93 |
| Bae, Euiwon; Patsekin, Valery; Rajwa, Bartek et al. (2012) Development of a microbial high-throughput screening instrument based on elastic light scatter patterns. Rev Sci Instrum 83:044304 |
