Enhancing the lateral-flow immunoassay for viral detection using an aqueous two-phase micellar system

Availability of a rapid, accurate, and reliable point-of-care (POC) device for detection of infectious agents and pandemic pathogens, such as swine-origin influenza A (H1N1) virus, is crucial for effective patient management and outbreak prevention. Due to its ease of use, rapid processing, and minimal power and laboratory equipment requirements, the lateral-flow (immuno)assay (LFA) has gained much attention in recent years as a possible solution. However, since the sensitivity of LFA has been shown to be inferior to that of the gold standards of pathogen detection, namely cell culture and real-time PCR, LFA remains an ineffective POC assay for preventing pandemic outbreaks. A practical solution for increasing the sensitivity of LFA is to concentrate the target agent in a solution prior to the detection step. In this study, an aqueous two-phase micellar system comprised of the nonionic surfactant Triton X-114 was investigated for concentrating a model virus, namely bacteriophage M13 (M13), prior to LFA. The volume ratio of the two coexisting micellar phases was manipulated to concentrate M13 in the top, micelle-poor phase. The concentration step effectively improved the M13 detection limit of the assay by tenfold from 5 × 108 plaque forming units (pfu)/mL to 5 × 107 pfu/mL. In the future, the volume ratio can be further manipulated to yield a greater concentration of a target virus and further decrease the detection limits of the LFA. Figure A schematic representation of concentrating viruses with an aqueous two-phase micellar system containing Triton X-114 surfactant prior to the detection of the virus through the lateral-flow immunoassa

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

An Aqueous Two-Phase System for the Concentration and Extraction of Proteins from the Interface for Detection Using the Lateral-Flow Immunoassay

<div><p>The paper-based immunoassay for point-of-care diagnostics is widely used due to its low cost and portability over traditional lab-based assays. Lateral-flow immunoassay (LFA) is the most well-established paper-based assay since it is rapid and easy to use. However, the disadvantage of LFA is its lack of sensitivity in some cases where a large sample volume is required, limiting its use as a diagnostic tool. To improve the sensitivity of LFA, we previously reported on the concentration of analytes into one of the two bulk phases of an aqueous two-phase system (ATPS) prior to detection. In this study, we preserved the advantages of LFA while significantly improving upon our previous proof-of-concept studies by employing a novel approach of concentrating gold nanoparticles, a common LFA colorimetric indicator. By conjugating specific antibodies and polymers to the surfaces of the particles, these gold nanoprobes (GNPs) were able to capture target proteins in the sample and subsequently be concentrated within 10 min at the interface of an ATPS solution comprised of polyethylene glycol, potassium phosphate, and phosphate-buffered saline. These GNPs were then extracted and applied directly to LFA. By combining this prior ATPS interface extraction with LFA, the detection limit of LFA for a model protein was improved by 100-fold from 1 ng/μL to 0.01 ng/μL. Additionally, we examined the behavior of the ATPS system in fetal bovine serum and synthetic urine to more closely approach real-world applications. Despite using more complex matrices, ATPS interface extraction still improved the detection limit by 100-fold within 15 to 25 min, demonstrating the system’s potential to be applied to patient samples.</p></div

Results of LFA for detecting Tf in FBS.

<p>(A) Images of test strips without (top panel) and with (bottom panel) the prior concentration step using the ATPS interface extraction step. 50 μL sample solutions were applied to each LFA test strip. (B) MATLAB quantification of test signal intensity where a value above a threshold of 0.25 corresponded to a negative test.</p

Surface modification of GNP to influence partitioning behavior in ATPS.

<p>Schematic of GNP and the functionality of each component.</p

Transition electron microscopy (TEM) image of naked gold nanoparticles.

<p>Nanoparticles were suspended in filtered ultrapure water. Length of the scale bar corresponds to 40 nm. ImageJ analysis indicated the particle diameter to be 20.0 ± 3.0 nm (n = 275).</p

Results of LFA for detecting Tf in PBS.

<p>(A) Images of test strips without (top panel) and with (bottom panel) the prior concentration step using the ATPS interface extraction step. 50 μL sample solutions were applied to each LFA test strip. (B) MATLAB quantification of test signal intensity where a value above a threshold of 0.25 corresponded to a negative test.</p

Schematic representation of the integration of ATPS interface extraction with competition-based LFA and the interpretations of the positive and negative results.

<p>An ATPS solution was constructed and allowed to phase separate for 10 min in PBS and 25 min in FBS and synthetic urine in a glass tube prior to the extraction of 30 μL of the interface containing GNPs. The extracted sample was then applied to an LFA test strip and results were read after 10 min for the PBS system and after 25 min for the FBS and synthetic urine systems. The appearance of only the control line indicated a positive result while the appearance of both the control and test lines indicated a negative result.</p

Results of LFA for detecting Tf in synthetic urine.

<p>(A) Images of test strips without (top panel) and with (bottom panel) the prior concentration step using the ATPS interface extraction step. 50 μL sample solutions were applied to each LFA test strip. (B) MATLAB quantification of test signal intensity where a value above a threshold of 0.25 corresponded to a negative test.</p