Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning
Online Publication Date2018-03-09
Print Publication Date2018-03-27
Permanent link to this recordhttp://hdl.handle.net/10754/629753
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AbstractWe present a cost-effective and portable platform based on contact lenses for noninvasively detecting Staphylococcus aureus, which is part of the human ocular microbiome and resides on the cornea and conjunctiva. Using S. aureus-specific antibodies and a surface chemistry protocol that is compatible with human tears, contact lenses are designed to specifically capture S. aureus. After the bacteria capture on the lens and right before its imaging, the captured bacteria are tagged with surface-functionalized polystyrene microparticles. These microbeads provide sufficient signal-to-noise ratio for the quantification of the captured bacteria on the contact lens, without any fluorescent labels, by 3D imaging of the curved surface of each lens using only one hologram taken with a lens-free on-chip microscope. After the 3D surface of the contact lens is computationally reconstructed using rotational field transformations and holographic digital focusing, a machine learning algorithm is employed to automatically count the number of beads on the lens surface, revealing the count of the captured bacteria. To demonstrate its proof-of-concept, we created a field-portable and cost-effective holographic microscope, which weighs 77 g, controlled by a laptop. Using daily contact lenses that are spiked with bacteria, we demonstrated that this computational sensing platform provides a detection limit of ∼16 bacteria/μL. This contact-lens-based wearable sensor can be broadly applicable to detect various bacteria, viruses, and analytes in tears using a cost-effective and portable computational imager that might be used even at home by consumers.
CitationVeli M, Ozcan A (2018) Computational Sensing of Staphylococcus aureus on Contact Lenses Using 3D Imaging of Curved Surfaces and Machine Learning. ACS Nano 12: 2554–2559. Available: http://dx.doi.org/10.1021/acsnano.7b08375.
SponsorsThe authors acknowledge the National Institutes of Health (NIH, R21EB023115). The Ozcan Research Group at UCLA acknowledges the support of NSF Engineering Research Center (ERC, PATHS-UP), the Army Research Office (ARO; W911NF-13-1-0419 and W911NF-13-1-0197), the ARO Life Sciences Division, the National Science Foundation (NSF) CBET Division Biophotonics Program, the NSF Emerging Frontiers in Research and Innovation (EFRI) Award, the NSF EAGER Award, NSF INSPIRE Award, NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) Program, Office of Naval Research (ONR), the Howard Hughes Medical Institute (HHMI), Vodafone Americas Foundation, the Mary Kay Foundation, Steven & Alexandra Cohen Foundation, and KAUST. This work is based upon research performed in a laboratory renovated by the National Science Foundation under Grant No. 0963183, which is an award funded under the American Recovery and Reinvestment Act of 2009 (ARRA). The authors also acknowledge Drs. Hatice Ceylan Koydemir and Hyou-Arm Joung of UCLA for helpful discussions regarding the bead-based immunoassay and Yichen Wu of UCLA for help in the assembly of the light source and data processing.
PublisherAmerican Chemical Society (ACS)