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AssigneesUNM Rainforest Innovations, National Technology & Engineering Solutions of Sandia, LLC
InventorsRichard S. Larson, Brian Hjelle, David C. Brown, Marco Bisoffi, Susan M. Brozik, Darren W. Branch, Thayne L. Edwards, David Wheeler
Estimating the term…
Viruses and other bioagents are of high medical and biodefense concern and their detection at concentrations well below the threshold necessary to cause health hazards continues to be a challenge with respect to sensitivity, specificity, and selectivity. Ideally, assays for accurate and real time detection of viral agents and other bioagents would not necessitate any pre-processing of the analyte, which would make them applicable for example to bodily fluids (blood, sputum) and man-made as well as naturally occurring bodies of water (pools, rivers). We describe herein a robust biosensor that combines the sensitivity of surface acoustic waves (SAW) generated at a frequency of 325 MHz with the specificity provided by antibodies and other ligands for the detection of viral agents. In preferred embodiments, a lithium tantalate based SAW transducer with silicon dioxide waveguide sensor platform featuring three test and one reference delay lines was used to adsorb antibodies directed against Coxsackie virus B4 or the negative-stranded category A bioagent Sin Nombre virus (SNV), a member of the genus Hantavirus, family Bunyaviridae, negative-stranded RNA viruses. Rapid detection (within seconds) of increasing concentrations of viral particles was linear over a range of order of magnitude for both viruses, although the sensor was approximately 50×104-fold more sensitive for the detection of SNV. For both pathogens, the sensor's selectivity for its target was not compromised by the presence of confounding Herpes Simplex virus type 1. The biosensor was able to detect SNV at doses lower than the load of virus typically found in a human patient suffering from hantavirus cardiopulmonary syndrome (HCPS). Further, in a proof-of-principle real world application, the SAW biosensor was capable of selectively detecting SNV agents in complex solutions, such as naturally occurring bodies of water (river, sewage effluent) without analyte pre-processing.
A method of identifying the presence of a bioagent or analyte in a sample with a ligand based biosensor comprising a biological ligand complexed, directly or indirectly to a surface of a lithium tantalite (LiTaO3) piezoelectric material which is coated with a thin layer of silicon dioxide, said ligand being capable of binding said bioagent or analyte, said piezoelectric material being operably connected to an electric circuit and being capable of producing high frequency shear-horizontal surface acoustic waves on the surface of the piezoelectric material to detect the binding of said bioagent or analyte to said ligand on said biosensor, said method comprising establishing high frequency shear-horizontal surface acoustic waves on the surface of said piezoelectric material comprising said ligand which is capable of binding to said bioagent or analyte, exposing said surface to a sample suspected of containing said bioagent or analyte and then determining whether the sample contains a suspect bioagent or analyte if the shear horizontal surface acoustic waves at the surface of said piezoelectric material evidence a change consistent with the binding of said bioactive or analyte agent to said ligand, wherein said bioagent or analyte is a virus selected from the group consisting of coronavirus, variola (small pox) virus, hantavirus, Ebola virus, Marburg virus, Lassa virus, West Nile virus and Dengue virus, said ligand is an antibody or an antibody fragment, and wherein said high frequency shear-horizontal surface acoustic waves have an input frequency ranging from 275 to 400 Mhz.
The method according to 1 wherein a hydrophobic material is added to said silicone dioxide layer wherein said ligand is physisorbed to said hydrophobic material.
The method according to 8 wherein the hydrophobic material is hexamethyldisilazane, petrolatum, or mineral oil.
Dependencies are inferred from the claim text.