Nanoplasmonic Quantitative Detection of Intact Viruses from Unprocessed Whole Blood

Abstract

Infectious diseases such as HIV and hepatitis B pose an omnipresent threat to global health. Reliable, fast, accurate, and sensitive platforms that can be deployed at the point-of-care (POC) in multiple settings, such as airports and offices, for detection of infectious pathogens are essential for the management of epidemics and possible biological attacks. To the best of our knowledge, no viral load technology adaptable to the POC settings exists today due to critical technical and biological challenges. Here, we present for the first time a broadly applicable technology for quantitative, nanoplasmonic-based intact virus detection at clinically relevant concentrations. The sensing platform is based on unique nanoplasmonic properties of nanoparticles utilizing immobilized antibodies to selectively capture rapidly evolving viral subtypes. We demonstrate the capture, detection, and quantification of multiple HIV subtypes (A, B, C, D, E, G, and subtype panel) with high repeatability, sensitivity, and specificity down to 98 ± 39 copies/mL (i.e., HIV subtype D) using spiked whole blood samples and clinical discarded HIV-infected patient whole blood samples validated by the gold standard, i.e., RT-qPCR. This platform technology offers an assay time of 1 h and 10 min (1 h for capture, 10 min for detection and data analysis). The presented platform is also able to capture intact viruses at high efficiency using immuno-surface chemistry approaches directly from whole blood samples without any sample preprocessing steps such as spin-down or sorting. Evidence is presented showing the system to be accurate, repeatable, and reliable. Additionally, the presented platform technology can be broadly adapted to detect other pathogens having reasonably well-described biomarkers by adapting the surface chemistry. Thus, this broadly applicable detection platform holds great promise to be implemented at POC settings, hospitals, and primary care settings.