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Liu L, Du K. A perspective on computer vision in biosensing. BIOMICROFLUIDICS 2024; 18:011301. [PMID: 38223547 PMCID: PMC10787640 DOI: 10.1063/5.0185732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/26/2023] [Indexed: 01/16/2024]
Abstract
Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.
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Affiliation(s)
- Li Liu
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
| | - Ke Du
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
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Liu L, Xu Z, Awayda K, Dollery SJ, Bao M, Fan J, Cormier D, O'Connell M, Tobin GJ, Du K. Gold Nanoparticle-Labeled CRISPR-Cas13a Assay for the Sensitive Solid-State Nanopore Molecular Counting. ADVANCED MATERIALS TECHNOLOGIES 2022; 7:2101550. [PMID: 36338309 PMCID: PMC9632472 DOI: 10.1002/admt.202101550] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A gold nanoparticle (AuNP) labeled CRISPR-Cas13a nucleic acid assay has been developed for sensitive solid-state nanopore sensing. Instead of directly detecting the translocation of RNA through a nanopore, our system utilizes non-covalent conjugates of AuNPs and RNA targets. Upon CRISPR activation, the AuNPs are liberated from the RNA, isolated, and passed through a nanopore sensor. Detection of the AuNPs can be observed as increasing ionic current in the chip. Each AuNP that is detected is enumerated as an event, leading to quantitative of molecular targets. Leveraging the high signal-to-noise ratio enabled by the AuNPs, a detection limit of 50 fM before front-end target amplification is achieved using SARS-CoV-2 RNA segments as a Cas13 target. Furthermore, a dynamic range of six orders of magnitude is demonstrated for quantitative RNA sensing. This simplified AuNP-based CRISPR assay is performed at the physiological temperature without relying on thermal cyclers. In addition, the nanopore reader is similar in size to a smartphone, making the assay system suitable for rapid and portable nucleic acid biomarker detection in either low-resource settings or hospitals.
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Affiliation(s)
- Li Liu
- Department of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
- Department of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Zhiheng Xu
- Department of Industrial & Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Kamel Awayda
- Department of Biochemistry and Biophysics, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | | | - Mengdi Bao
- Department of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Jianlin Fan
- Institute of Semiconductors, Guangdong Academy of Sciences, Guangzhou, 510651, China
| | - Denis Cormier
- Department of Industrial & Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Mitchell O'Connell
- Department of Biochemistry and Biophysics, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Gregory J. Tobin
- Biological Mimetics, Inc., 124 Byte Drive, Frederick, MD 21702, USA
| | - Ke Du
- Department of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
- Department of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
- College of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY 14623, USA
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
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