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The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 10 3 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.
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3D printed imaging platform for portable cell counting
Despite having widespread application in the biomedical sciences, flow cytometers have several limitations that prevent their application to point-of-care (POC) diagnostics in resource-limited environments. 3D printing provides a cost-effective approach to improve the accessibility of POC devices in resource-limited environments. Towards this goal, we introduce a 3D-printed imaging platform (3DPIP) capable of accurately counting particles and perform fluorescence microscopy. In our 3DPIP, captured microscopic images of particle flow are processed on a custom developed particle counter code to provide a particle count. This prototype uses a machine vision-based algorithm to identify particles from captured flow images and is flexible enough to allow for labeled and label-free particle counting. Additionally, the particle counter code returns particle coordinates with respect to time which can further be used to perform particle image velocimetry. These results can help estimate forces acting on particles, and identify and sort different types of cells/particles. We evaluated the performance of this prototype by counting 10 μm polystyrene particles diluted in deionized water at different concentrations and comparing the results with a commercial Beckman-Coulter Z2 particle counter. The 3DPIP can count particle concentrations down to ∼100 particles per mL with a standard deviation of ±20 particles, which is comparable to the results obtained on a commercial particle counter. Our platform produces accurate results at flow rates up to 9 mL h −1 for concentrations below 1000 particle per mL, while 5 mL h −1 produces accurate results above this concentration limit. Aside from performing flow-through experiments, our instrument is capable of performing static experiments that are comparable to a plate reader. In this configuration, our instrument is able to count between 10 and 250 cells per image, depending on the prepared concentration of bacteria samples ( Citrobacter freundii ; ATCC 8090). Overall, this platform represents a first step towards the development of an affordable fully 3D printable imaging flow cytometry instrument for use in resource-limited clinical environments.
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- PAR ID:
- 10301163
- Date Published:
- Journal Name:
- The Analyst
- Volume:
- 146
- Issue:
- 12
- ISSN:
- 0003-2654
- Page Range / eLocation ID:
- 4033 to 4041
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d1an00778e
