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Lin Z, Sui J, Javanmard M. A two-minute assay for electronic quantification of antibodies in saliva enabled through a reusable microfluidic multi-frequency impedance cytometer and machine learning analysis. Biomed Microdevices 2023; 25:13. [PMID: 36933063 PMCID: PMC10024011 DOI: 10.1007/s10544-023-00647-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2023] [Indexed: 03/19/2023]
Abstract
The use of saliva as a diagnostic fluid has always been appealing due to the ability for rapid and non-invasive sampling for monitoring health status and the onset and progression of disease and treatment progress. Saliva is rich in protein biomarkers and provides a wealth of information for diagnosis and prognosis of various disease conditions. Portable electronic tools which rapidly monitor protein biomarkers would facilitate point-of-care diagnosis and monitoring of various health conditions. For example, the detection of antibodies in saliva can enable rapid diagnosis and tracking disease pathogenesis of various auto-immune diseases like sepsis. Here, we present a novel method involving immuno-capture of proteins on antibody coated beads and electrical detection of dielectric properties of the beads. The changes in electrical properties of a bead when capturing proteins are extremely complex and difficult to model physically in an accurate manner. The ability to measure impedance of thousands of beads at multiple frequencies, however, allows for a data-driven approach for protein quantification. By moving from a physics driven approach to a data driven approach, we have developed, for the first time ever to the best of our knowledge, an electronic assay using a reusable microfluidic impedance cytometer chip in conjunction with supervised machine learning to quantifying immunoglobulins G (IgG) and immunoglobulins A (IgA) in saliva within two minutes.
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Affiliation(s)
- Zhongtian Lin
- Department of Electrical and Computer Engineering, Rutgers University New Brunswick, 94 Brett Rd, NJ, 08854, New Brunswick, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University New Brunswick, 94 Brett Rd, NJ, 08854, New Brunswick, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University New Brunswick, 94 Brett Rd, NJ, 08854, New Brunswick, USA.
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2
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Chen YS, Huang CH, Pai PC, Seo J, Lei KF. A Review on Microfluidics-Based Impedance Biosensors. BIOSENSORS 2023; 13:bios13010083. [PMID: 36671918 PMCID: PMC9855525 DOI: 10.3390/bios13010083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/20/2022] [Accepted: 12/28/2022] [Indexed: 05/30/2023]
Abstract
Electrical impedance biosensors are powerful and continuously being developed for various biological sensing applications. In this line, the sensitivity of impedance biosensors embedded with microfluidic technologies, such as sheath flow focusing, dielectrophoretic focusing, and interdigitated electrode arrays, can still be greatly improved. In particular, reagent consumption reduction and analysis time-shortening features can highly increase the analytical capabilities of such biosensors. Moreover, the reliability and efficiency of analyses are benefited by microfluidics-enabled automation. Through the use of mature microfluidic technology, complicated biological processes can be shrunk and integrated into a single microfluidic system (e.g., lab-on-a-chip or micro-total analysis systems). By incorporating electrical impedance biosensors, hand-held and bench-top microfluidic systems can be easily developed and operated by personnel without professional training. Furthermore, the impedance spectrum provides broad information regarding cell size, membrane capacitance, cytoplasmic conductivity, and cytoplasmic permittivity without the need for fluorescent labeling, magnetic modifications, or other cellular treatments. In this review article, a comprehensive summary of microfluidics-based impedance biosensors is presented. The structure of this article is based on the different substrate material categorizations. Moreover, the development trend of microfluidics-based impedance biosensors is discussed, along with difficulties and challenges that may be encountered in the future.
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Affiliation(s)
- Yu-Shih Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Chun-Hao Huang
- Department of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ping-Ching Pai
- Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - Jungmok Seo
- Department of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea
| | - Kin Fong Lei
- Department of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
- Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea
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3
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Tayyab M, Xie P, Sami MA, Raji H, Lin Z, Meng Z, Mahmoodi SR, Javanmard M. A portable analog front-end system for label-free sensing of proteins using nanowell array impedance sensors. Sci Rep 2022; 12:20119. [PMID: 36418852 PMCID: PMC9684124 DOI: 10.1038/s41598-022-23286-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.
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Affiliation(s)
- Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Pengfei Xie
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Muhammad Ahsan Sami
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Zhongtian Lin
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Zhuolun Meng
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, 08901, USA.
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Multi-frequency impedance sensing for detection and sizing of DNA fragments. Sci Rep 2021; 11:6490. [PMID: 33753781 PMCID: PMC7985362 DOI: 10.1038/s41598-021-85755-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 01/11/2021] [Indexed: 01/31/2023] Open
Abstract
Electronic biosensors for DNA detection typically utilize immobilized oligonucleotide probes on a signal transducer, which outputs an electronic signal when target molecules bind to probes. However, limitation in probe selectivity and variable levels of non-target material in complex biological samples can lead to nonspecific binding and reduced sensitivity. Here we introduce the integration of 2.8 μm paramagnetic beads with DNA fragments. We apply a custom-made microfluidic chip to detect DNA molecules bound to beads by measuring Impedance Peak Response (IPR) at multiple frequencies. Technical and analytical performance was evaluated using beads containing purified Polymerase Chain Reaction (PCR) products of different lengths (157, 300, 613 bp) with DNA concentration ranging from 0.039 amol to 7.8 fmol. Multi-frequency IPR correlated positively with DNA amounts and was used to calculate a DNA quantification score. The minimum DNA amount of a 300 bp fragment coupled on beads that could be robustly detected was 0.0039 fmol (1.54 fg or 4750 copies/bead). Additionally, our approach allowed distinguishing beads with similar molar concentration DNA fragments of different lengths. Using this impedance sensor, purified PCR products could be analyzed within ten minutes to determine DNA fragment length and quantity based on comparison to a known DNA standard.
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Tayyab M, Sami MA, Raji H, Mushnoori S, Javanmard M. Potential Microfluidic Devices for COVID-19 Antibody Detection at Point-of-Care (POC): A Review. IEEE SENSORS JOURNAL 2021; 21:4007-4017. [PMID: 37974932 PMCID: PMC8768978 DOI: 10.1109/jsen.2020.3034892] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 11/19/2023]
Abstract
COVID-19 has been declared a global pandemic which has brought the world economy and the society to a standstill. The current emphasis of testing is on detection of genetic material of SARS-CoV-2. Such tests are useful for assessing the current state of a subject: Infected or not infected. In addition to such tests, antibody testing is necessary to stratify the population into three groups: never exposed, infected, and immune. Such a stratification is necessary for safely reopening the society and remobilizing the economy. The aim of this review article is to inform the audience of the current diagnostic and surveillance technologies that are being employed for the detection of SARS-CoV-2 antibodies along with their shortcomings, and to highlight microfluidic sensors and devices that show promise of being commercialized for detection and quantification of SARS-CoV-2 antibodies in low-resource and Point-of-Care (POC) settings.
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Affiliation(s)
- Muhammad Tayyab
- Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayNJ08854USA
| | - Muhammad Ahsan Sami
- Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayNJ08854USA
| | - Hassan Raji
- Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayNJ08854USA
| | - Srinivas Mushnoori
- Department of Chemical and Biochemical EngineeringRutgers UniversityPiscatawayNJ08854USA
| | - Mehdi Javanmard
- Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayNJ08854USA
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Sui J, Xie P, Lin Z, Javanmard M. Electronic classification of barcoded particles for multiplexed detection using supervised machine learning analysis. Talanta 2020; 215:120791. [PMID: 32312428 DOI: 10.1016/j.talanta.2020.120791] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/08/2020] [Accepted: 01/28/2020] [Indexed: 01/29/2023]
Abstract
Wearable biosensors are of great interest in recent years due to their potential in health related applications. Multiplex biomarker analysis is needed in wearable devices to improve the sensitivity and reliability. Electronic barcoding of micro-particles has the possibility to enable multiplexed biomarker analysis. Compared with traditional optical and plasmonic methods for barcoding, electronically barcoded particles can be classified using ultra-compact electronic readout platforms. Nano-electronic barcoding works by depositing a thin layer of oxide on the top half of a micro-particle. The thickness and dielectric property of the oxide layer can be tuned to modulate the frequency dependent impedance signature of the particles. A one to one correspondence between a target biomarker and each barcoded particle can potentially be established using this technique. The barcoded particles could be tested with wearable devices to enable multiplex analysis for portable point-of-care diagnostics and real-time monitoring. In this work, we fabricated nine barcoded particles by forming oxide layers of different thicknesses and different dielectric materials using atomic layer deposition and assessed the ability to accurately classify particle barcodes using multi-frequency impedance cytometry in conjunction with supervised machine learning.
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Affiliation(s)
- Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Pengfei Xie
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Zhongtian Lin
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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Ahuja K, Rather GM, Lin Z, Sui J, Xie P, Le T, Bertino JR, Javanmard M. Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning. MICROSYSTEMS & NANOENGINEERING 2019; 5:34. [PMID: 31645995 PMCID: PMC6799891 DOI: 10.1038/s41378-019-0073-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 01/31/2019] [Accepted: 03/25/2019] [Indexed: 05/07/2023]
Abstract
We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 μL of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.
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Affiliation(s)
- Karan Ahuja
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
| | - Gulam M. Rather
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ USA
| | - Zhongtian Lin
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
| | - Pengfei Xie
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
| | - Tuan Le
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
| | - Joseph R. Bertino
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ USA
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Herrada CA, Kabir MA, Altamirano R, Asghar W. Advances in Diagnostic Methods for Zika Virus Infection. J Med Device 2018; 12:0408021-4080211. [PMID: 30662580 DOI: 10.1115/1.4041086] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 07/31/2018] [Indexed: 12/11/2022] Open
Abstract
The Zika virus (ZIKV) is one of the most infamous mosquito-borne flavivirus on recent memory due to its potential association with high mortality rates in fetuses, microcephaly and neurological impairments in neonates, and autoimmune disorders. The severity of the disease, as well as its fast spread over several continents, has urged the World Health Organization (WHO) to declare ZIKV a global health concern. In consequence, over the past couple of years, there has been a significant effort for the development of ZIKV diagnostic methods, vaccine development, and prevention strategies. This review focuses on the most recent aspects of ZIKV research which includes the outbreaks, genome structure, multiplication and propagation of the virus, and more importantly, the development of serological and molecular detection tools such as Zika IgM antibody capture enzyme-linked immunosorbent assay (Zika MAC-ELISA), plaque reduction neutralization test (PRNT), reverse transcription quantitative real-time polymerase chain reaction (qRT-PCR), reverse transcription-loop mediated isothermal amplification (RT-LAMP), localized surface plasmon resonance (LSPR) biosensors, nucleic acid sequence-based amplification (NASBA), and recombinase polymerase amplification (RPA). Additionally, we discuss the limitations of currently available diagnostic methods, the potential of newly developed sensing technologies, and also provide insight into future areas of research.
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Affiliation(s)
- Carlos A Herrada
- Department of Computer Engineering and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
| | - Md Alamgir Kabir
- Department of Computer Engineering and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
| | - Rommel Altamirano
- Department of Computer Engineering and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
| | - Waseem Asghar
- Department of Computer Engineering and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431
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Zhao X, Sadhu V, Le T, Pompili D, Javanmard M. Toward Wireless Health Monitoring via an Analog Signal Compression-Based Biosensing Platform. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:461-470. [PMID: 29877811 DOI: 10.1109/tbcas.2018.2829512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Wireless all-analog biosensor design for the concurrent microfluidic and physiological signal monitoring is presented in this paper. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by the analog joint source-channel coding (AJSCC). Two circuit designs are discussed, including the stacked-voltage-controlled voltage source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of printed circuit board realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.
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Valera E, Berger J, Hassan U, Ghonge T, Liu J, Rappleye M, Winter J, Abboud D, Haidry Z, Healey R, Hung NT, Leung N, Mansury N, Hasnain A, Lannon C, Price Z, White K, Bashir R. A microfluidic biochip platform for electrical quantification of proteins. LAB ON A CHIP 2018; 18:1461-1470. [PMID: 29664086 DOI: 10.1039/c8lc00033f] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Sepsis, an adverse auto-immune response to an infection often causing life-threatening complications, results in the highest mortality and treatment cost of any illness in US hospitals. Several immune biomarker levels, including Interleukin 6 (IL-6), have shown a high correlation to the onset and progression of sepsis. Currently, no technology diagnoses and stratifies sepsis progression using biomarker levels. This paper reports a microfluidic biochip platform to detect proteins in undiluted human plasma samples. The device uses a differential enumeration platform that integrates Coulter counting principles, antigen specific capture chambers, and micro size bead based immunodetection to quantify cytokines. This microfluidic biochip was validated as a potential point of care technology by quantifying IL-6 from plasma samples (n = 29) with good correlation (R2 = 0.81) and agreement (Bland-Altman) compared to controls. In combination with previous applications, this point of care platform can potentially detect cell and protein biomarkers simultaneously for sepsis stratification.
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Affiliation(s)
- Enrique Valera
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 1270 Digital Computer Laboratory, 1304 W. Springfield Ave., Urbana, Illinois 61801, USA.
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