Beginning in May of 2020 and coming to fruition amid a global crisis, the project’s timeline runs parallel to the pandemic that prompted it. The researchers observed that while many techniques currently exist to test for SARS-CoV-2, none use a label-free optical approach.
The miniscule size of a single particle makes relying on sight alone a near impossibility, even with a microscope. Electron microscopy is useful for imaging a particle’s structure, but extensive preparation is required to ensure a sample’s visibility. Though necessary, this process can obscure the desired image.
Popescu’s team turned to a technique developed at Beckman typically reserved for visualizing cells: spatial light image microscopy, which facilitates chemical-free (or label-free) imaging.
"Applying SLIM for virus imaging is like looking at something without your glasses on. The image is blurry due to the viruses being smaller than the diffraction limit. However, owing to the high sensitivity of SLIM, we can not only detect the viruses, but also differentiate between different types", said Neha Goswami, a graduate student in bioengineering and a 2021 recipient of Beckman Institute’s Nadine Barrie Smith Memorial Fellowship.
The researchers identified a creative way to identify the viruses based on SLIM data: artificial intelligence.They introduced the AI program to a pair of images: a stained SARS-CoV-2 particle producing fluorescence, and a phase image captured with a fluorescence-SLIM multimodal microscope. The AI is trained to recognize these images as one and the same. Easily recognizable, the fluorescence-stained image functions like training wheels; with enough repetition, the machine learns to detect the viruses directly from the SLIM, label-free images without the added support.
"Afterwards we made life tough on the machine," Goswami said. "We gave it dust, beads, and other viruses to train and learn to pick the virus out of a crowd as opposed to identifying when it is by itself."The AI learned to discern between SARS-CoV-2 and other viral pathogens such as H1N1, or influenza A; HAdV, or adenovirus; and ZIKV, or Zika virus. The preclinical trial was highly successful, resulting in a 96% success rate for SARS-CoV-2 detection and classification.
The project’s goal is a sensitive and specific viral breath test detection system that aids in viral diagnostics and in transmission prevention strategies. With clinical validation pending, researchers speculate that a COVID-19 test conducted with this method would look something like this: the subject would wear a face shield, onto which a clear glass slide would be attached; they would then complete an activity wherein their breath becomes fixed to the slide. The slide, and any particles attached to it, would be imaged and analyzed to detect any viruses present.
"There are two key advantages to this kind of COVID test," Goswami said. "The first is speed: the duration can be of the order of one minute. The second is that we are not adding any chemicals or modifications to the samples provided. All we’d be paying for is the cost of the face shield and the slide itself."
MEDICA-tradefair.com; Source: Beckman Institute for Advanced Science and Technology
