Detecting viruses in the blink of an eye

Devendra Pal, Marc Amyot, Chen Liang, and Parisa A. Ariya
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Aerosol and droplet transmission is central to the spread of respiratory pathogens, which have profound impacts on human life, animals, and the global economy. The devastating COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the urgent need for robust and rapid methods to detect, monitor, and diagnose airborne viruses. However, until now, there has been a lack of real-time and in-situ technology for observing airborne viral aerosol droplets [Arnaout et al., 2020; Morawska et al., 2022].

In response to the urgent need for effective detection and diagnosis methods, significant advancements have been made in the field of SARS-CoV-2 detection [Pal et al., 2023]. These advancements encompass a range of approaches, including molecular techniques, biosensors, and novel sampling methods [Pal et al., 2023; Pan et al., 2019]. Different types of polymerase chain reaction (PCR) have been used for diagnosis and saving lives during COVID-19 pandemic. However, these methods are non-in-situ technologies and require minimal 100 copies of viral RNA per milliliter of transport media [Arnaout et al., 2020]. Yet, accurately determining and investigating the real-time 4D physicochemical characteristics (viability, viral load/size distribution, shape, phase, and surface properties) of airborne viruses remains a challenge.

To address these challenges and contribute to the advancement of rapid airborne virus detection methods and diagnostics, we have developed Nano-Digital in Line Holography Microscopy (Nano-DIHM) [Pal et al., 2023; Pal et al., 2021]. Nano-DIHM enables the detection, diagnosis, and investigation of viruses' sizes, shapes, and surface properties within seconds, without the need for sample treatment like RT-PCR. Nano-DIHM utilizes direct imaging of airborne viral particles (Fig. 1), followed by particle-by-particle measurement, estimation of settling velocities using image analysis and a particle tracking algorithm. These analyses can be performed in real-time at the observation location or in offline mode using collected samples. Nano-DIHM operates as a two-stage process: 1) recording holograms, which are images of viral-loaded samples (Fig. 1a), and 2) numerically reconstructing these viral-loaded images to extract information about the viruses (Fig. 1b-c). The configuration of the Nano-DIHM technology is straightforward, consisting of a laser source (Pinhole) and a camera, as illustrated in Figure 1b. A pinhole laser (L) emits a wave at λ = 405 nm, which illuminates the objects and generates a highly magnified diffraction pattern (hologram) on a screen (CMOS).

Figure 1:	Schematic and workflow of Nano-DIHM setup. (a) SARS-CoV-2 transmission by an infected human via airborne transmission. The airborne viral droplets were passed through the flow tube cuvette to the Nano-DIHM sample volume and Scanning Mobility Particle Sizer (SMPS). The Nano-DIHM was used to record the airborne viral droplets, and artificial intelligence was used to detect and characterize viral particles. (b) Working principle of holography microscopy, where laser/pinhole emits the light and holograms are recorded on the screen. (c) An example of Deep learning for SARS-CoV-2 analysis, where raw and background holograms are input images and Stingray software determines the SARS-CoV-2 physicochemical properties. This schematic used from our published paper [Pal et al., 2023].

Figure 1:           Schematic and workflow of Nano-DIHM setup. (a) SARS-CoV-2 transmission by an infected human via airborne transmission. The airborne viral droplets were passed through the flow tube cuvette to the Nano-DIHM sample volume and Scanning Mobility Particle Sizer (SMPS). The Nano-DIHM was used to record the airborne viral droplets, and artificial intelligence was used to detect and characterize viral particles. (b) Working principle of holography microscopy, where laser/pinhole emits the light and holograms are recorded on the screen. (c) An example of Deep learning for SARS-CoV-2 analysis, where raw and background holograms are input images and Stingray software determines the SARS-CoV-2 physicochemical properties. This schematic used from our published paper [Pal et al., 2023].

Health risks associated with exposure to airborne virus particles are influenced by the shape and size distribution of aerosols containing infectious viruses [Comber et al., 2021; Morawska et al., 2022]. The novel Nano-DIHM technology enables the in-situ, real-time characterization of the physicochemical properties of viral-loaded aerosol particles (Fig. 2). To demonstrate the feasibility and reliability of Nano-DIHM, we conducted benchmark experiments focused on determining the 4D physicochemical properties of viral-loaded aerosols and droplets in both air and water samples. We selectively detected active MS2 bacteriophages (MS2), inactivated SARS-CoV-2 and RNA fragments, as well as an MS2 mixture containing metallic and organic materials [Pal et al., 2023]. Our findings revealed that the aerosolized MS2 viral-laden particles exhibited a bimodal distribution, with peaks at approximately 60-200 nm and 2-3 microns, respectively (Fig. 2). These experiments suggested an evaporation-condensation process of viral-laden particles in the air. Additionally, the introduction of a surfactant aerosol such as titanium oxides caused a shift towards larger sizes in the distribution of airborne viral-laden MS2 particles, leading to altered settling velocities and impacting transmission processes (Fig. 2).

Figure 2:          MS2 viral laden aerosols size, shape and phase determination by Nano-DIHM. (a–c) is intensity reconstruction, and (d–f) presents phase reconstruction. (a) Intensity reconstruction of airborne MS2 viruses at Z = 627 µm, (b) Z = 687 µm, and (c) Z = 1570 µm. (d–f) Phase results of the same particles as (a–c). Size distribution of measurement of airborne MS2 bacteriophages. obtained by (g-h) Airborne MS2 particle size distribution obtained by the Scanning Mobility Particle Sizer (SMPS) and Optical Particle Sizer (OPS), respectively. (i-j) size distribution of mixed samples of MS2 and TiO2. The two-colored line corresponds to two repetitions of an experiment. The 4D dynamic trajectories of the MS2 viral particles are provided in our published paper in the Supplementary Movie 1 and Supplementary Movie 2 [Pal et al., 2023].

In another example, we have shown that the physicochemical characterisation (size, shape and phase) of the heat inactivated SARS-CoV-2 viral laden aerosols in air in real time (Fig. 3). By utilizing artificial intelligence in combination with Nano-DIHM, we were able to detect and distinguish SARS-CoV-2 viral-laden particles from mixed samples of SARS-CoV-2 and MS2 bacteriophage (Table 1). The output results indicated "YES" for SARS-CoV-2 and "NO" for MS2 [Pal et al., 2023]. This suggests that our imaging-based method holds promise as an attractive approach for rapid testing and investigating real-time 4D physicochemical characterization of viruses. However, it is important to note that the current prototype does have certain limitations [Pal et al., 2023] including the accuracy of the output which may varies or decrease depending on the complexity of the sample matrix. Nevertheless, these challenges can be overcome by developing an extensive library or databank encompassing multiple sample matrices.

Figure 3:           Inactivated SARS-CoV-2 viral laden droplet detection by Nano-DIHM. (a) SARS-CoV-2 viral droplet particles at Z = 2109 µm. (b) Zoomed-in area of (a) revealing the precise recovery of SARS-CoV-2 viral droplets and their shape. (c) A more focused zoomed-in image of (b) clearly demonstrates the SARS-CoV-2 droplet structure. (d-f) Phase images of identical SARS-CoV-2 particles in (a-c)[Pal et al., 2023].

Table 1:            Yes/No detection of SARS-CoV-2 from mixed samples using AI. A mixed sample of SARS-CoV-2 and MS2 particles was analyzed. “YES” indicates SARS-CoV-2, and “NO” indicates MS2 particles[Pal et al., 2023].

The currently developed Nano-DIHM offers rapid and comprehensive detection, classification, and determination of the physicochemical properties of SARS-CoV-2 in both air and water. Nano-DIHM can operate in static or dynamic mode on-site or in the laboratory, providing results in less than a minute with an accuracy exceeding +90%. In contrast, conventional COVID-19 testing methods are costly, time-consuming, and lack in-situ or real-time capabilities. A promising aspect of Nano-DIHM is its potential for simultaneous measurements of diverse particle types, enabling the identification of both active and past infections from multiple viruses. Real-time tracking of SARS-CoV-2 or any future viruses empowers policymakers with valuable knowledge for more informed responses in future epidemic management. Ultimately, these advancements will enable timely interventions, enhance outbreak management strategies, and protect public health in the face of emerging viral threats, and reducing substantial economic losses.

References

Arnaout, R., R. A. Lee, G. R. Lee, C. Callahan, C. F. Yen, K. P. Smith, R. Arora, and J. E. Kirby (2020), SARS-CoV2 Testing: The Limit of Detection Matters, bioRxiv : the preprint server for biology, doi:10.1101/2020.06.02.131144.

Comber, L., et al. (2021), Airborne transmission of SARS-CoV-2 via aerosols, Reviews in Medical Virology, 31(3), e2184, doi:https://doi.org/10.1002/rmv.2184.

Morawska, L., G. Buonanno, A. Mikszewski, and L. Stabile (2022), The physics of respiratory particle generation, fate in the air, and inhalation, Nat. Rev. Phys., 4(11), 723-734, doi:10.1038/s42254-022-00506-7.

Pal, D., M. Amyot, C. Liang, and P. A. Ariya (2023), Real-time 4D tracking of airborne virus-laden droplets and aerosols, Communications Engineering, 2(1), 41, doi:10.1038/s44172-023-00088-x.

Pal, D., Y. Nazarenko, T. C. Preston, and P. A. Ariya (2021), Advancing the science of dynamic airborne nanosized particles using Nano-DIHM, Commun. Chem., 4(1), 170, doi:10.1038/s42004-021-00609-9.

Pan, M., J. A. Lednicky, and C. Y. Wu (2019), Collection, particle sizing and detection of airborne viruses, J Appl Microbiol, 127(6), 1596-1611, doi:10.1111/jam.14278.

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