Intro Sentence:
UCLA researchers in the Department of Electrical and Computer Engineering have developed a rapid and stain-free quantitative assay using lens-free holography and deep learning to efficiently and cost-effectively determine the presence of viral plaque-forming units (PFUs) in samples.
Background:
A broad range of viruses have caused global health crises throughout the world, including HIV, HPV and SARS-CoV-2. Because of their ability to spread and quickly replicate, it is essential to develop methods to rapidly, accurately, and cost-effectively quantify presence of viruses. The plaque assay was developed to quantify PFUs and thus determine the infectivity of viral samples. This method is tedious and time-consuming (2-14 days) as it requires manual counting of PFUs. Despite this technique often leading to human error, it remains the most widely-utilized method for viral infectivity quantification. Recent developments in quantitative phase imaging (QPI), deep learning and holography provide an opportunity to address the challenges and shortcomings of PFU quantification versus standard plaque assays. There is a clear and pressing need to implement these advances in imaging to improve the state of the art in PFU quantification and remove the burden of manual PFU assays.
Innovation: UCLA researchers in the Department of Electrical and Computer Engineering have developed a rapid and stain-free quantitative viral plaque assay using lens-free holographic imaging and deep learning. This cost-effective, compact, and automated device significantly reduces the incubation time needed for traditional plaque assays while preserving their advantages over other virus quantification methods. This device captures ~0.32 Giga-pixel/hour phase information of the objects per test well, covering an area of ~30 × 30 mm2, in a label-free manner, eliminating staining entirely. This computational method was successfully demonstrated using Vero E6 cells and vesicular stomatitis virus.
Using a neural network, this stain-free device automatically detected the first cell lysing events due to the viral replication as early as 5 hours after the incubation, and achieved >90% detection rate for the plaque-forming units (PFUs) with 100% specificity in <20 hours, providing major time savings compared to the traditional plaque assays that take >48 hours. This data-driven plaque assay also offers the capability of quantifying the infected area of the cell monolayer, performing automated counting and quantification of PFUs and virus-infected areas over a 10-fold larger dynamic range of virus concentration than standard viral plaque assays. This compact, low-cost, automated PFU quantification device can be broadly used in virology research, vaccine development, and clinical applications.
Image courtesy of the Ozcan Lab @ UCLA
Potential Applications:
- Virology research
- Vaccine development
- Clinical Applications
- Development of recombinant proteins or antiviral agents
Advantages:
- Lens-free
- >90% detection rate and 100% specificity
- Rapid (<20 hours) vs standard plaque assay incubation time
- Cost-effective imaging system (<$880)
- Automated counting and quantification of PFU’s
- Resilient to potential artifacts during sample preparation
- Can automatically quantify a larger dynamic range of virus concentrations per well
Development to Date:
The invention has been successfully demonstrated and validated.
Publication:
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
Reference:
UCLA Case No. 2022-326