Adaptive virus detection using filament-coupled antibodies

Viral detection using DNA functionalized gold filaments

Early detection of pediatric viruses is critical to effective intervention. A successful clinical tool must have a low detection limit, be simple to use and report results quickly. No current method meets all three of these criteria. In this report, we describe an approach that combines simple, rapid processing and label free detection. The method detects viral RNA using DNA hairpin structures covalently attached to a gold filament. In this design, the gold filament serves both to simplify processing and enable fluorescence detection. The approach was evaluated by assaying for the presence of respiratory syncytial virus (RSV) using the DNA hairpin probe 5' [C6Thiol]TTTTTTTTTTCGACGAAAAATGGGGCAAATACGTCG[CAL] 3' covalently attached to a 5 cm length of a 100 microm diameter gold-clad filament. This sequence was designed to target a portion of the gene end-intergenic gene start signals which is repeated multiple times within the negative-sense genome giving multiple targets for each strand of genomic viral RNA present. The filament functionalized with probes was immersed in a 200 microm capillary tube containing viral RNA, moved to subsequent capillary tubes for rinsing and then scanned for fluorescence. The response curve had a typical sigmoidal shape and plateaued at about 300 plaque forming units (PFU) of viral RNA in 20 microL. The lower limit of detection was determined to be 11.9 PFU. This lower limit of detection was approximately 200 times better than a standard comparison ELISA. The simplicity of the core assay makes this approach attractive for further development as a viral detection platform in a clinical setting.

Light-guided surface engineering for biomedical applications

Free radical species generated through fluorescence photobleaching have been reported to effectively couple a water-soluble species to surfaces containing electron-rich sites . In this report, we expand upon this strategy to control the patterned attachment of antibodies and peptides to surfaces for biosensing and tissue engineering applications. In the first application, we compare hydrophobic attachment and photobleaching methods to immobilize FITC-labeled anti-M13K07 bacteriophage antibodies to the SiO2 layer of a differential capacitive biosensor and to the polyester filament of a feedback-controlled filament array. On both surfaces, antibody attachment and function were superior to the previously employed hydrophobic attachment. Furthermore, a laser scanning confocal microscope could be used for automated, software-guided photoattachment chemistry. In a second application, the cell-adhesion peptide RGDS was site-specifically photocoupled to glass coated with fluorescein-conjugated poly(ethylene glycol). RGDS attachment and bioactivity were characterized by a fibroblast adhesion assay. Cell adhesion was limited to sites of RGDS photocoupling. These examples illustrate that fluorophore-based photopatterning can be achieved by both solution-phase fluorophores or surface-adhered fluorophores. The coupling preserves the bioactivity of the patterned species, is amenable to a variety of surfaces, and is readily accessible to laboratories with fluorescence imaging equipment. The flexibility offered by visible light patterning will likely have many useful applications in bioscreening and tissue engineering where the controlled placement of biomolecules and cells is critical, and should be considered as an alternative to chemical coupling methods.

Autonomous reovirus strain classification using filament-coupled antibodies

We previously described a filament-based antibody recognition assay (FARA) that generates ELISA-like sandwich structures immobilized on a filament. FARA allows the coupling of antibodies to precise locations along a filament, on-line fluorescence detection of captured pathogen, and feedback-directed filament motion. These properties suggest that this approach might be useful as an automated means to rapidly classify unknown pathogens. In this report, we describe validation of the novel decision tree aspect of this technology using mammalian reovirus. Based on available antibodies, we developed a decision tree algorithm to detect virus with increasing specificity at each level of the tree. Using three strains of reovirus and a bacteriophage control, our system correctly classified the reovirus strains at a concentration of 2 × 10¹² virions ml⁻¹ and M13K07 phage at 3 × 10¹¹ virions ml⁻¹. Classification of reovirus strain type 3 Dearing (T3D) required three levels of testing: general reovirus classification in level 1, serotype 3 classification in level 2, and final T3D strain classification in level 3. Strain T3SA + also required three levels of testing before a final classification was returned in level 3. Classification of strain type 1 Lang (T1L) required two levels of testing. M13K07 phage detection required only one level of testing for classification. These results indicate that automated pathogen classification using FARA is feasible. Furthermore, the simplicity of the design could be exploited for development of more complex sub-classification networks with additional levels and branches.