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Wu G, Zhang Z, Du M, Wu D, Zhou J, Hao T, Xie X. Optimizing Microfluidic Impedance Cytometry by Bypass Electrode Layout Design. BIOSENSORS 2024; 14:204. [PMID: 38667197 PMCID: PMC11048680 DOI: 10.3390/bios14040204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Microfluidic impedance cytometry (MIC) has emerged as a popular technique for single-cell analysis. Traditional MIC electrode designs consist of a pair of (or three) working electrodes, and their detection performance needs further improvements for microorganisms. In this study, we designed an 8-electrode MIC device in which the center pair was defined as the working electrode, and the connection status of bypass electrodes could be changed. This allowed us to compare the performance of layouts with no bypasses and those with floating or grounding electrodes by simulation and experiment. The results of detecting Φ 5 μm beads revealed that both the grounding and the floating electrode outperformed the no bypass electrode, and the grounding electrode demonstrated the best signal-to-noise ratio (SNR), coefficient of variation (CV), and detection sensitivity. Furthermore, the effects of different bypass grounding areas (numbers of grounding electrodes) were investigated. Finally, particles passing at high horizontal positions can be detected, and Φ 1 μm beads can be measured in a wide channel (150 μm) using a fully grounding electrode, with the sensitivity of bead volume detection reaching 0.00097%. This provides a general MIC electrode optimization technology for detecting smaller particles, even macromolecular proteins, viruses, and exosomes in the future.
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Affiliation(s)
- Guangzu Wu
- Systems Engineering Institute, Academy of Military Sciences, People’s Liberation Army, Tianjin 300161, China; (G.W.); (Z.Z.)
- National Bio-Protection Engineering Center, Tianjin 300161, China
| | - Zhiwei Zhang
- Systems Engineering Institute, Academy of Military Sciences, People’s Liberation Army, Tianjin 300161, China; (G.W.); (Z.Z.)
- National Bio-Protection Engineering Center, Tianjin 300161, China
| | - Manman Du
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China;
| | - Dan Wu
- School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China; (D.W.); (J.Z.); (T.H.)
| | - Junting Zhou
- School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China; (D.W.); (J.Z.); (T.H.)
| | - Tianteng Hao
- School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China; (D.W.); (J.Z.); (T.H.)
| | - Xinwu Xie
- Systems Engineering Institute, Academy of Military Sciences, People’s Liberation Army, Tianjin 300161, China; (G.W.); (Z.Z.)
- National Bio-Protection Engineering Center, Tianjin 300161, China
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Ashley BK, Sui J, Javanmard M, Hassan U. Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles. Biosens Bioelectron 2023; 241:115661. [PMID: 37690356 DOI: 10.1016/j.bios.2023.115661] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/12/2023]
Abstract
The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential.
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Affiliation(s)
- Brandon K Ashley
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Umer Hassan
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Global Health Institute, Rutgers, the State University of New Jersey, New Brunswick, NJ, 08901, USA.
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3
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Dang Z, Jiang Y, Su X, Wang Z, Wang Y, Sun Z, Zhao Z, Zhang C, Hong Y, Liu Z. Particle Counting Methods Based on Microfluidic Devices. MICROMACHINES 2023; 14:1722. [PMID: 37763885 PMCID: PMC10534595 DOI: 10.3390/mi14091722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023]
Abstract
Particle counting serves as a pivotal constituent in diverse analytical domains, encompassing a broad spectrum of entities, ranging from blood cells and bacteria to viruses, droplets, bubbles, wear debris, and magnetic beads. Recent epochs have witnessed remarkable progressions in microfluidic chip technology, culminating in the proliferation and maturation of microfluidic chip-based particle counting methodologies. This paper undertakes a taxonomical elucidation of microfluidic chip-based particle counters based on the physical parameters they detect. These particle counters are classified into three categories: optical-based counters, electrical-based particle counters, and other counters. Within each category, subcategories are established to consider structural differences. Each type of counter is described not only in terms of its working principle but also the methods employed to enhance sensitivity and throughput. Additionally, an analysis of future trends related to each counter type is provided.
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Affiliation(s)
- Zenglin Dang
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Yuning Jiang
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Xin Su
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Zhihao Wang
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China;
| | - Yucheng Wang
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Zhe Sun
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Zheng Zhao
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Chi Zhang
- College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China;
| | - Yuming Hong
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
| | - Zhijian Liu
- College of Marine Engineering, Dalian Maritime University, Dalian 116026, China; (Z.D.); (Y.J.); (X.S.); (Y.W.); (Z.S.); (Z.Z.); (Y.H.)
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Ashley BK, Hassan U. Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy. Biomed Microdevices 2022; 24:36. [PMID: 36305954 PMCID: PMC9635870 DOI: 10.1007/s10544-022-00636-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2022] [Indexed: 11/29/2022]
Abstract
Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.
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Affiliation(s)
- Brandon K Ashley
- Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Umer Hassan
- Department of Electrical Engineering, Department of Biomedical Engineering, and Global Health Institute Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA.
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Ashley BK, Sui J, Javanmard M, Hassan U. MACHINE LEARNING ENABLES QUANTIFYING CELL-JANUS PARTICLE CONJUGATES THROUGH MICROFLOWING IMPEDANCE SIGNALS. MICRO TOTAL ANALYSIS SYSTEMS : PROCEEDINGS OF THE ... [MU] TAS INTERNATIONAL CONFERENCE ON MINIATURIZED CHEMICAL AND BIOCHEMICAL ANALYSIS SYSTEMS. [MU] TAS (CONFERENCE) 2022; 26:669-670. [PMID: 38162094 PMCID: PMC10756496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
In this work, we demonstrate the differentiation of demodulated multifrequency signals from impedance sensitive microparticles when targeting surface receptors on neutrophils in a microfluidic impedance cytometer. These scheme uses a single signal input and detection configuration, and machine learning can differentiate particle types with up to 82% accuracy.
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Affiliation(s)
- Brandon K Ashley
- Department of Biomedical Engineering, Rutgers University, Piscataway, USA
| | - Jianye Sui
- Department of Electrical Engineering, Rutgers University, Piscataway, USA
| | - Mehdi Javanmard
- Department of Biomedical Engineering, Rutgers University, Piscataway, USA
- Department of Electrical Engineering, Rutgers University, Piscataway, USA
| | - Umer Hassan
- Department of Biomedical Engineering, Rutgers University, Piscataway, USA
- Department of Electrical Engineering, Rutgers University, Piscataway, USA
- Global Health Institute, Rutgers University, New Brunswick, USA
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Shen B, Dawes J, Johnston ML. A 10 M Ω, 50 kHz-40 MHz Impedance Measurement Architecture for Source-Differential Flow Cytometry. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:766-778. [PMID: 35727776 DOI: 10.1109/tbcas.2022.3182905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A low-power, impedance-based integrated circuit (IC) readout architecture is presented for cell analysis and cytometry applications. A three-electrode layout and source-differential excitation cancels baseline current prior to the sensor front-end, which enables the use of a high-gain readout circuit for the difference current. A lock-in architecture is employed with down-conversion and up-conversion in the feedback loop, enabling high closed-loop gain (up to 10 M Ω) and high bandwidth (up to 40 MHz). A hybrid-RC feedback network mitigates the SNR degradation seen over a wide operating frequency range when using purely capacitive feedback. The effect of phase shift on the closed-loop system gain and noise performance are analyzed in detail, along with optimization strategies, and the design includes fine-grained phase adjustment to minimize phase error. The impedance sensor was fabricated in a 0.18 μ m CMOS process and consumes 9.7 mW with an operating frequency from 50 kHz to 40 MHz and provides adjustable bandwidth. Measurements demonstrate that the impedance sensor achieves 6 pA [Formula: see text] input-referred noise over 200 Hz bandwidth at 0.5 MHz modulation frequency. Combined with a microfluidic flow cell, measured results using this source-differential measurement approach are presented using both monodisperse and polydisperse sample solutions and demonstrate single-cell resolution, detecting 3 μ m diameter particles in solution with 22 dB SNR.
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Ashley BK, Sui J, Javanmard M, Hassan U. Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer. LAB ON A CHIP 2022; 22:3055-3066. [PMID: 35851596 PMCID: PMC9378602 DOI: 10.1039/d2lc00563h] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity. For a recently conceived multiplexing biomarker modality, this work demonstrates the use of electrically sensitive microparticles targeting and identifying membrane receptors on leukocytes using a single detection source, with a high potential for multiplexing greater than any existing impedance-based single-detection scheme. Here, polystyrene microparticles are coated with varying thicknesses of metal oxides, which generate quantifiable impedance shifts when exposed to multifrequency electric fields depending on the metal oxide thickness. Using multifrequency impedance cytometry, these particles can be measured and differentiated rapidly across one coplanar electrode scheme. After surface-functionalizing particles with antibodies targeting CD11b and CD66b receptors, the particles are combined with isolated neutrophils to measure receptor expression. A combination of data analysis techniques including multivariate analysis, supervised machine learning, and unsupervised machine learning was able to accurately differentiate samples with up to 91% accuracy. This proof-of-concept study demonstrates the potential for these oxide-coated particles for enumerating specific leukocytes enabling multiplexing. Further, additional coating thicknesses or different metal oxide materials can enable a compendium of multiplexing targeting resource to be used to develop a high-multiplexing sensor for targeting membrane receptor expression.
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Affiliation(s)
- Brandon K Ashley
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Umer Hassan
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Global Health Institute, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08901, USA
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Ashley BK, Sui J, Javanmard M, Hassan U. Aluminum Oxide-Coated Particle Differentiation Employing Supervised Machine Learning and Impedance Cytometry. IEEE INTERNATIONAL CONFERENCE ON NANO/MICRO ENGINEERED AND MOLECULAR SYSTEMS. IEEE INTERNATIONAL CONFERENCE ON NANO/MICRO ENGINEERED AND MOLECULAR SYSTEMS 2022; 2022:10.1109/nems54180.2022.9791160. [PMID: 35782306 PMCID: PMC9245459 DOI: 10.1109/nems54180.2022.9791160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article uses a supervised machine learning (ML) system for identifying groups of nanoparticles coated with metal oxides of varying thicknesses using a microfluidic impedance cytometer. These particles generate unique impedance signatures when probed with a multifrequency electric field and finds applications in enabling many multiplexed biosensing technologies. However, current experimental and data processing techniques are unable to sensitively differentiate different metal oxide coated particle types. Here, we employ various machine learning models and collect multiple particle metrics measured. In reported experiments, a 75% accuracy was determined to separate aluminum oxide coated (10nm and 30nm), which is significantly greater than observing only univariate data between different microparticle types. This approach will enable ML models to differentiate such particles with greater accuracies.
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Affiliation(s)
- Brandon K Ashley
- Department of Biomedical Engineering Rutgers, New Jersey State University, Piscataway, United States
| | - Jianye Sui
- Department of Electrical Engineering Rutgers, New Jersey State University, Piscataway, United States
| | - Mehdi Javanmard
- Department of Electrical Engineering Rutgers, New Jersey State University, Piscataway, United States
| | - Umer Hassan
- Department of Electrical Engineering Rutgers, New Jersey State University, Piscataway, United States
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