Issue 16, 2019

Mobile imaging platform for digital influenza virus counting


Droplet-based digital bioassays enable highly sensitive and quantitative analysis of biomolecules, and are thought to be suitable for point-of-care diagnosis. However, digital bioassays generally require fluorescence microscopy for detection, which is too large for point-of-care testing. Here, we developed a simple smartphone-based mobile imaging platform for digital bioassays. The size of the mobile imaging platform was 23 × 10 × 7 cm (length × width × height). With this platform, a digital enzyme assay of bovine alkaline phosphatase was successfully completed. Digital influenza virus counting—based on a fluorogenic assay for neuraminidase activity of the virus—was also demonstrated. Distinct fluorescence spots derived from single virus particles were observed with the mobile imaging platform. The number of detected fluorescence spots showed good linearity against the virus titer, suggesting that high sensitivity and quantification were achieved, although the imaging with the mobile platform detected 60% of influenza virus particles that were identified with conventional fluorescence microscopy. The lower detection efficiency is due to its relatively lower signal-to-noise ratio than that found with conventional microscopes, and unavoidable intrinsic heterogeneity of neuraminidase activity among virus particles. Digital influenza virus counting with the mobile imaging platform still showed 100 times greater sensitivity than that with a commercial rapid influenza test kit. Virus detection of clinical samples was also successfully demonstrated, suggesting the potential to realize a highly sensitive point-of-care system for influenza virus detection with smartphones.

Graphical abstract: Mobile imaging platform for digital influenza virus counting

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Article information

Article type
16 Apr 2019
01 Jun 2019
First published
17 Jul 2019
This article is Open Access
Creative Commons BY license

Lab Chip, 2019,19, 2678-2687

Mobile imaging platform for digital influenza virus counting

Y. Minagawa, H. Ueno, K. V. Tabata and H. Noji, Lab Chip, 2019, 19, 2678 DOI: 10.1039/C9LC00370C

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