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Zhang S, Han Z, Qi H, Liu S, Liu B, Sun C, Feng Z, Sun M, Duan X. Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation. Anal Chem 2024; 96:4419-4429. [PMID: 38448396 DOI: 10.1021/acs.analchem.3c04421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.
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Affiliation(s)
- Shuaihua Zhang
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Ziyu Han
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hang Qi
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Siyuan Liu
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Bohua Liu
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Chongling Sun
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Zhe Feng
- Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China
| | - Meiqing Sun
- Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China
| | - Xuexin Duan
- State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
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Rane A, Jarmoshti J, Siddique AB, Adair S, Torres-Castro K, Honrado C, Bauer TW, Swami NS. Dielectrophoretic enrichment of live chemo-resistant circulating-like pancreatic cancer cells from media of drug-treated adherent cultures of solid tumors. LAB ON A CHIP 2024; 24:561-571. [PMID: 38174422 PMCID: PMC10826460 DOI: 10.1039/d3lc00804e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
Due to low numbers of circulating tumor cells (CTCs) in liquid biopsies, there is much interest in enrichment of alternative circulating-like mesenchymal cancer cell subpopulations from in vitro tumor cultures for utilization within molecular profiling and drug screening. Viable cancer cells that are released into the media of drug-treated adherent cancer cell cultures exhibit anoikis resistance or anchorage-independent survival away from their extracellular matrix with nutrient sources and waste sinks, which serves as a pre-requisite for metastasis. The enrichment of these cell subpopulations from tumor cultures can potentially serve as an in vitro source of circulating-like cancer cells with greater potential for scale-up in comparison with CTCs. However, these live circulating-like cancer cell subpopulations exhibit size overlaps with necrotic and apoptotic cells in the culture media, which makes it challenging to selectively enrich them, while maintaining them in their suspended state. We present optimization of a flowthrough high frequency (1 MHz) positive dielectrophoresis (pDEP) device with sequential 3D field non-uniformities that enables enrichment of the live chemo-resistant circulating cancer cell subpopulation from an in vitro culture of metastatic patient-derived pancreatic tumor cells. Central to this strategy is the utilization of single-cell impedance cytometry with gates set by supervised machine learning, to optimize the frequency for pDEP, so that live circulating cells are selected based on multiple biophysical metrics, including membrane physiology, cytoplasmic conductivity and cell size, which is not possible using deterministic lateral displacement that is solely based on cell size. Using typical drug-treated samples with low levels of live circulating cells (<3%), we present pDEP enrichment of the target subpopulation to ∼44% levels within 20 minutes, while rejecting >90% of dead cells. This strategy of utilizing single-cell impedance cytometry to guide the optimization of dielectrophoresis has implications for other complex biological samples.
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Affiliation(s)
- Aditya Rane
- Chemistry, University of Virginia, Charlottesville, USA.
| | - Javad Jarmoshti
- Electrical & Computer Engineering, University of Virginia, Charlottesville, USA
| | | | - Sara Adair
- Surgery, School of Medicine, University of Virginia, Charlottesville, USA
| | | | - Carlos Honrado
- International Iberian Nanotechnology Laboratory, Braga, Portugal
| | - Todd W Bauer
- Surgery, School of Medicine, University of Virginia, Charlottesville, USA
| | - Nathan S Swami
- Chemistry, University of Virginia, Charlottesville, USA.
- Electrical & Computer Engineering, University of Virginia, Charlottesville, USA
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Hua H, Zou S, Ma Z, Guo W, Fong CY, Khoo BL. A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning. MICROSYSTEMS & NANOENGINEERING 2023; 9:120. [PMID: 37780810 PMCID: PMC10539402 DOI: 10.1038/s41378-023-00577-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/08/2023] [Accepted: 07/07/2023] [Indexed: 10/03/2023]
Abstract
Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.
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Affiliation(s)
- Haojun Hua
- City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China
| | - Shangjie Zou
- City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, 999077 China
| | - Zhiqiang Ma
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, 999077 China
| | - Wang Guo
- City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, 999077 China
| | - Ching Yin Fong
- City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China
| | - Bee Luan Khoo
- City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, 999077 China
- City University of Hong Kong Futian-Shenzhen Research Institute, Shenzhen, 518057 China
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Kokabi M, Tahir MN, Singh D, Javanmard M. Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis. BIOSENSORS 2023; 13:884. [PMID: 37754118 PMCID: PMC10526782 DOI: 10.3390/bios13090884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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Affiliation(s)
| | | | | | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA; (M.K.); (M.N.T.); (D.S.)
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Tang T, Julian T, Ma D, Yang Y, Li M, Hosokawa Y, Yalikun Y. A review on intelligent impedance cytometry systems: Development, applications and advances. Anal Chim Acta 2023; 1269:341424. [PMID: 37290859 DOI: 10.1016/j.aca.2023.341424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023]
Abstract
Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.
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Affiliation(s)
- Tao Tang
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Doudou Ma
- Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yang Yang
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan, 572000, PR China
| | - Ming Li
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan
| | - Yaxiaer Yalikun
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara, 630-0192, Japan; Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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Ferguson C, Zhang Y, Palego C, Cheng X. Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5990. [PMID: 37447838 DOI: 10.3390/s23135990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/17/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.
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Affiliation(s)
- Caroline Ferguson
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Cristiano Palego
- Department of Computer Science and Electronic Engineering, Bangor University, Bangor LL57 2DG, UK
| | - Xuanhong Cheng
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
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