By Joseph V. Puthussery, Rajan Chakrabarty, John Cirrito, and Carla Yuede
The challenges of airborne virus detection
It has been more than two years since the start of the COVID-19 pandemic, and there is now a plethora of studies and scientific literature available on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus transmission, detection, treatment, and prevention. In the midst of the pandemic, the global community at large realized the virus to be airborne. This led to countries worldwide introducing several disease transmission controls to prevent airborne transmissions, such as social distancing, mandatory masking in public indoor settings, and quarantining individuals who tested positive for SARS-CoV-2. While conventional virus detection approaches such as swab samples and contact tracing significantly helped in managing the spread of the disease, they are limited by their ability to provide real-time information on airborne viruses. Therefore, most infection management strategies involve retrospective analysis, meaning the virus is detected after the spread has occurred. Examples of such approaches include contact tracing, serological surveys, and wastewater virus surveillance, among others. Such approaches cannot be employed to predict if a community is at risk of getting infected. A real-time noninvasive surveillance device that can detect SARS-CoV-2 aerosols directly in the air is a potential solution for proactive infection management. Recognizing this gap, our research group set out to develop a device capable of detecting the virus directly in the air in under five minutes.
The challenges in detecting viruses in the air primarily stem from two technological gaps:
Efficient sample collection: While human nasal swabs and saliva samples often have high concentrations of virus (102 - 109 copies/ml)1,2, the virus concentration in the air can be less than 100 RNA copies/m3 of air3-6, which makes direct airborne virus detection challenging. Therefore, it is crucial to have a highly efficient high-flow virus aerosol sampler that can be smoothly incorporated into a virus detector that operates in real-time.
Ultrasensitive virus detection: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), which is today considered the gold standard for virus detection, is costly, laborious, and not sensitive enough for high-time resolution real-time detection of environmental virus samples collected from the air. There is currently an urgent need for a virus detection protocol that is rapid, accurate, and sensitive enough to measure the typically low concentrations of viruses found in ambient air.
Pathogen Air Quality (pAQ) Monitor: A breakthrough solution
To address these challenges, we developed a device called the pathogen Air Quality (pAQ) monitor. This innovative solution combines a custom high-flow batch-type wet wall cyclone particle-into-liquid sampler (PILS) with a llama-derived nanobody-based a micro-immunoelectrode (MIE) biosensor specifically designed to target the SARS-CoV-2 spike protein. The wet cyclone samples air at 1000 liters per minute (lpm) and collects airborne viruses directly into a liquid collection media. The high flow rate of the wet cyclone sampler allows us to collect 5 m3 of air in just 5 minutes of sampling. Aerosols enter the wet cyclone at high velocities and impact the inner wetted walls of the cyclone, and are collected in a liquid media. The collected virus-liquid mixture is then sent to the MIE biosensor using an automated liquid transfer pump, enabling near-real-time detection of SARS-CoV-2 in the air with a five-minute time resolution.
Our proof-of-concept device uses MIE technology originally designed for detecting amyloid-β in Alzheimer's disease through square wave voltammetry by attaching an antibody specific to amyloid-β to the surface of a carbon fiber microelectrode7,8. We have adapted it to detect SARS-CoV-2 by switching the attached amyloid-β specific antibody to a nanobody (an antibody that comes from llamas) that is specific to SARS-CoV-2 to the surface of a screen-printed carbon electrode. The sensor uses a method called square wave voltammetry to oxidize a specific amino acid in the virus particle. This reaction releases electrons that the sensor can detect as a current. The concentration of SARS-CoV-2 virions attached to the electrode surface is directly proportional to the height of the oxidation peak current. The biosensor can detect the virus and report it within 30 seconds.
Assessing the effectiveness of the pAQ monitor: Implications for real-time virus detection and public health
We experimentally compared the effectiveness of the wet cyclone with other commercially available low-flow PILS, and our results showed that the wet cyclone performed either better or exhibited comparable performance. To further validate its virus sampling capability, we collected air samples from the self-isolating bedrooms of two COVID-positive individuals. All seven air samples collected using the wet cyclone in the two apartments occupied by SARS-CoV-2 patients tested positive based on RT-qPCR. Additionally, we conducted laboratory tests on the pAQ monitor by aerosolizing various strains of inactivated SARS-CoV-2. These tests revealed a sensitivity range of 77-83% and a limit of detection of 7-35 viral RNA copies/m3 of air.
Through the utilization of cutting-edge technologies and integrating them with efficient sampling and ultrasensitive and rapid detection techniques, we developed a potent instrument capable of supplying near real-time data and aiding in the formulation of proactive strategies for public health. In addition to monitoring SARS-CoV-2, the real-time monitor has broader implications for public health, as the same device can be utilized for the detection of other airborne pathogens. Current efforts are underway for the simultaneous detection of other airborne respiratory viruses (e.g., influenza, rhinovirus, respiratory syncytial virus, etc.) using the pAQ monitor via multiplexing of MIE biosensors with different target-specific nanobodies. In our ongoing fight against infectious airborne respiratory diseases, advancements such as this will play a crucial role in safeguarding global health. By enabling the early detection of disease outbreaks, this technology will not only save lives but also help in preventing the rapid spread of the disease.
1 Malik, M., Kunze, A. C., Bahmer, T., Herget-Rosenthal, S. & Kunze, T. SARS-CoV-2: Viral Loads of Exhaled Breath and Oronasopharyngeal Specimens in Hospitalized Patients with COVID-19. Int J Infect Dis 110, 105-110 (2021). https://doi.org:10.1016/j.ijid.2021.07.012
2 To, K. K. et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis 20, 565-574 (2020). https://doi.org:10.1016/S1473-3099(20)30196-1
3 Stern, R. A., Al-Hemoud, A., Alahmad, B. & Koutrakis, P. Levels and particle size distribution of airborne SARS-CoV-2 at a healthcare facility in Kuwait. Sci Total Environ 782 (2021). https://doi.org:ARTN 14679910.1016/j.scitotenv.2021.146799
4 Moore, G. et al. Detection of SARS-CoV-2 within the healthcare environment: a multi-centre study conducted during the first wave of the COVID-19 outbreak in England. Journal of Hospital Infection 108, 189-196 (2021). https://doi.org:https://doi.org/10.1016/j.jhin.2020.11.024
5 Dumont-Leblond, N. et al. Low incidence of airborne SARS-CoV-2 in acute care hospital rooms with optimized ventilation. Emerg Microbes Infect 9, 2597-2605 (2020). https://doi.org:10.1080/22221751.2020.1850184
6 Zhou, L. et al. Breath-, air- and surface-borne SARS-CoV-2 in hospitals. J Aerosol Sci 152 (2021). https://doi.org:ARTN 10569310.1016/j.jaerosci.2020.105693
7 Prabhulkar, S., Piatyszek, R., Cirrito, J. R., Wu, Z. Z. & Li, C. Z. Microbiosensor for Alzheimer's disease diagnostics: detection of amyloid beta biomarkers. J Neurochem 122, 374-381 (2012). https://doi.org:10.1111/j.1471-4159.2012.07709.x
8 Yuede, C. M. et al. Rapid in vivo measurement of beta-amyloid reveals biphasic clearance kinetics in an Alzheimer's mouse model. J Exp Med 213, 677-685 (2016). https://doi.org:10.1084/jem.20151428