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van Wijk XMR, Sanchez Oviol Z, Winter WE, Harris NS, Marin MJ. An Introduction to the Complete Blood Count for Clinical Chemists: Platelets. J Appl Lab Med 2024; 9:833-847. [PMID: 38549553 DOI: 10.1093/jalm/jfae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/12/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND The most ordered laboratory test worldwide is the complete blood count (CBC). CONTENT In this primer, an introduction to platelet testing in the context of the CBC is provided with a discussion of the laboratory evaluation of platelet abnormalities including thrombocytopenia and thrombocytosis. SUMMARY As clinical chemists continue to be tasked to direct laboratories outside of the traditional clinical chemistry sections such as hematology, expertise must be developed. This primer is dedicated to that effort.
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Affiliation(s)
| | - Zaraith Sanchez Oviol
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - William E Winter
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - Neil S Harris
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - Maximo J Marin
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
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2
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Goldstein Y, Cohen OT, Wald O, Bavli D, Kaplan T, Benny O. Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning. SCIENCE ADVANCES 2024; 10:eadj4370. [PMID: 38809990 DOI: 10.1126/sciadv.adj4370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.
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Affiliation(s)
- Yoel Goldstein
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ora T Cohen
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ori Wald
- Department of Cardiothoracic Surgery, Hadassah Medical Center, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Danny Bavli
- Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
| | - Tommy Kaplan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Department of Developmental Biology and Cancer Research, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
| | - Ofra Benny
- Institute for Drug Research, The School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel
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3
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Chen X, Huang X, Zhang J, Wang M, Chen D, Li Y, Qin X, Wang J, Chen J. Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks. Cytometry A 2024; 105:315-322. [PMID: 38115230 DOI: 10.1002/cyto.a.24823] [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] [Received: 10/22/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/21/2023]
Abstract
The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.
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Affiliation(s)
- Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xukun Huang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Zhang
- Beijing Institute of Genomics, China National Centre for Bioinformation, Chinese Academy of Sciences, Beijing, China
| | - Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Yueying Li
- Beijing Institute of Genomics, China National Centre for Bioinformation, Chinese Academy of Sciences, Beijing, China
| | - Xuzhen Qin
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
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4
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Hussain M, He X, Wang C, Wang Y, Wang J, Chen M, Kang H, Yang N, Ni X, Li J, Zhou X, Liu B. Recent advances in microfluidic-based spectroscopic approaches for pathogen detection. BIOMICROFLUIDICS 2024; 18:031505. [PMID: 38855476 PMCID: PMC11162289 DOI: 10.1063/5.0204987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/30/2024] [Indexed: 06/11/2024]
Abstract
Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.
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Affiliation(s)
| | - Xu He
- Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Chao Wang
- Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Yichuan Wang
- Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Jingjing Wang
- Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Mingyue Chen
- Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Haiquan Kang
- Department of Laboratory Medicine, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China
| | | | - Xinye Ni
- The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou 213161, China
| | | | - Xiuping Zhou
- Department of Laboratory Medicine, The Peoples Hospital of Rugao, Rugao Hospital Affiliated to Nantong University, Nantong 226500, China
| | - Bin Liu
- Author to whom correspondence should be addressed:
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5
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Marin MJ, van Wijk XMR, Boothe PD, Harris NS, Winter WE. An Introduction to the Complete Blood Count for Clinical Chemists: Red Blood Cells. J Appl Lab Med 2024:jfae031. [PMID: 38646908 DOI: 10.1093/jalm/jfae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 03/06/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND The most frequently ordered laboratory test worldwide is the complete blood count (CBC). CONTENT In this primer, the red blood cell test components of the CBC are introduced, followed by a discussion of the laboratory evaluation of anemia and polycythemia. SUMMARY As clinical chemists are increasingly tasked to direct laboratories outside of the traditional clinical chemistry sections such as hematology, expertise must be developed. This review article is a dedication to that effort.
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Affiliation(s)
- Maximo J Marin
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | | | - Paul D Boothe
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - Neil S Harris
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
| | - William E Winter
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States
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Chen Y, Chen X, Zhang B, Zhang Y, Li S, Liu Z, Gao Y, Zhao Y, Yan L, Li Y, Tian T, Lin Y. DNA framework signal amplification platform-based high-throughput systemic immune monitoring. Signal Transduct Target Ther 2024; 9:28. [PMID: 38320992 PMCID: PMC10847453 DOI: 10.1038/s41392-024-01736-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/14/2023] [Accepted: 01/01/2024] [Indexed: 02/08/2024] Open
Abstract
Systemic immune monitoring is a crucial clinical tool for disease early diagnosis, prognosis and treatment planning by quantitative analysis of immune cells. However, conventional immune monitoring using flow cytometry faces huge challenges in large-scale sample testing, especially in mass health screenings, because of time-consuming, technical-sensitive and high-cost features. However, the lack of high-performance detection platforms hinders the development of high-throughput immune monitoring technology. To address this bottleneck, we constructed a generally applicable DNA framework signal amplification platform (DSAP) based on post-systematic evolution of ligands by exponential enrichment and DNA tetrahedral framework-structured probe design to achieve high-sensitive detection for diverse immune cells, including CD4+, CD8+ T-lymphocytes, and monocytes (down to 1/100 μl). Based on this advanced detection platform, we present a novel high-throughput immune-cell phenotyping system, DSAP, achieving 30-min one-step immune-cell phenotyping without cell washing and subset analysis and showing comparable accuracy with flow cytometry while significantly reducing detection time and cost. As a proof-of-concept, DSAP demonstrates excellent diagnostic accuracy in immunodeficiency staging for 107 HIV patients (AUC > 0.97) within 30 min, which can be applied in HIV infection monitoring and screening. Therefore, we initially introduced promising DSAP to achieve high-throughput immune monitoring and open robust routes for point-of-care device development.
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Affiliation(s)
- Ye Chen
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Xingyu Chen
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Bowen Zhang
- Department of Prosthodontics, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, 300041, PR China
| | - Yuxin Zhang
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Songhang Li
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Zhiqiang Liu
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Yang Gao
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Yuxuan Zhao
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Lin Yan
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | - Yi Li
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, PR China.
| | - Taoran Tian
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China.
| | - Yunfeng Lin
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, PR China.
- Sichuan Provincial Engineering Research Center of Oral Biomaterials, Chengdu, 610041, Sichuan, China.
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7
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Huang X, Chen X, Tan H, Wang M, Li Y, Wei Y, Zhang J, Chen D, Wang J, Li Y, Chen J. Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties. Cytometry A 2024; 105:139-145. [PMID: 37814588 DOI: 10.1002/cyto.a.24806] [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: 03/06/2023] [Revised: 07/11/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023]
Abstract
This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.
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Affiliation(s)
- Xukun Huang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Huiwen Tan
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yimin Li
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yuanchen Wei
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jie Zhang
- Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yueying Li
- Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
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8
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Torres-Castro K, Acuña-Umaña K, Lesser-Rojas L, Reyes DR. Microfluidic Blood Separation: Key Technologies and Critical Figures of Merit. MICROMACHINES 2023; 14:2117. [PMID: 38004974 PMCID: PMC10672873 DOI: 10.3390/mi14112117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/01/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Blood is a complex sample comprised mostly of plasma, red blood cells (RBCs), and other cells whose concentrations correlate to physiological or pathological health conditions. There are also many blood-circulating biomarkers, such as circulating tumor cells (CTCs) and various pathogens, that can be used as measurands to diagnose certain diseases. Microfluidic devices are attractive analytical tools for separating blood components in point-of-care (POC) applications. These platforms have the potential advantage of, among other features, being compact and portable. These features can eventually be exploited in clinics and rapid tests performed in households and low-income scenarios. Microfluidic systems have the added benefit of only needing small volumes of blood drawn from patients (from nanoliters to milliliters) while integrating (within the devices) the steps required before detecting analytes. Hence, these systems will reduce the associated costs of purifying blood components of interest (e.g., specific groups of cells or blood biomarkers) for studying and quantifying collected blood fractions. The microfluidic blood separation field has grown since the 2000s, and important advances have been reported in the last few years. Nonetheless, real POC microfluidic blood separation platforms are still elusive. A widespread consensus on what key figures of merit should be reported to assess the quality and yield of these platforms has not been achieved. Knowing what parameters should be reported for microfluidic blood separations will help achieve that consensus and establish a clear road map to promote further commercialization of these devices and attain real POC applications. This review provides an overview of the separation techniques currently used to separate blood components for higher throughput separations (number of cells or particles per minute). We present a summary of the critical parameters that should be considered when designing such devices and the figures of merit that should be explicitly reported when presenting a device's separation capabilities. Ultimately, reporting the relevant figures of merit will benefit this growing community and help pave the road toward commercialization of these microfluidic systems.
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Affiliation(s)
- Karina Torres-Castro
- Biophysical and Biomedical Measurements Group, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, MD 20899, USA;
- Theiss Research, La Jolla, CA 92037, USA
| | - Katherine Acuña-Umaña
- Medical Devices Master’s Program, Instituto Tecnológico de Costa Rica (ITCR), Cartago 30101, Costa Rica
| | - Leonardo Lesser-Rojas
- Research Center in Atomic, Nuclear and Molecular Sciences (CICANUM), San José 11501, Costa Rica;
- School of Physics, Universidad de Costa Rica (UCR), San José 11501, Costa Rica
| | - Darwin R. Reyes
- Biophysical and Biomedical Measurements Group, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, MD 20899, USA;
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Suzuki T, Kalyan S, Berlinicke C, Yoseph S, Zack DJ, Hur SC. Deciphering viscoelastic cell manipulation in rectangular microchannels. PHYSICS OF FLUIDS (WOODBURY, N.Y. : 1994) 2023; 35:103117. [PMID: 37849975 PMCID: PMC10577600 DOI: 10.1063/5.0167285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/25/2023] [Indexed: 10/19/2023]
Abstract
Viscoelastic focusing has emerged as a promising method for label-free and passive manipulation of micro and nanoscale bioparticles. However, the design of microfluidic devices for viscoelastic particle focusing requires a thorough comprehensive understanding of the flow condition and operational parameters that lead to the desired behavior of microparticles. While recent advancements have been made, viscoelastic focusing is not fully understood, particularly in straight microchannels with rectangular cross sections. In this work, we delve into inertial, elastic, and viscoelastic focusing of biological cells in rectangular cross-section microchannels. By systematically varying degrees of fluid elasticity and inertia, we investigate the underlying mechanisms behind cell focusing. Our approach involves injecting cells into devices with a fixed, non-unity aspect ratio and capturing their images from two orientations, enabling the extrapolation of cross-sectional equilibrium positions from two dimensional (2D) projections. We characterized the changes in hydrodynamic focusing behaviors of cells based on factors, such as cell size, flow rate, and fluid characteristics. These findings provide insights into the flow characteristics driving changes in equilibrium positions. Furthermore, they indicate that viscoelastic focusing can enhance the detection accuracy in flow cytometry and the sorting resolution for size-based particle sorting applications. By contributing to the advancement of understanding viscoelastic focusing in rectangular microchannels, this work provides valuable insight and design guidelines for the development of devices that harness viscoelastic focusing. The knowledge gained from this study can aid in the advancement of viscoelastic particle manipulation technique and their application in various fields.
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Affiliation(s)
- Takayuki Suzuki
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Srivathsan Kalyan
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Cynthia Berlinicke
- Department of Ophthalmology, Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
| | - Samantha Yoseph
- Department of Architecture, University of Maryland, College Park, Maryland 20742, USA
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10
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Zhou S, Chen B, Fu ES, Yan H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. MICROSYSTEMS & NANOENGINEERING 2023; 9:116. [PMID: 37744264 PMCID: PMC10511704 DOI: 10.1038/s41378-023-00562-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 09/26/2023]
Abstract
In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.
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Affiliation(s)
- Shizheng Zhou
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Bingbing Chen
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Edgar S. Fu
- Graduate School of Computing and Information Science, University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - Hong Yan
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
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11
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Yang B, Wang C, Liang X, Li J, Li S, Wu JJ, Su T, Li J. Label-Free Sensing of Cell Viability Using a Low-Cost Impedance Cytometry Device. MICROMACHINES 2023; 14:mi14020407. [PMID: 36838107 PMCID: PMC9963508 DOI: 10.3390/mi14020407] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 05/20/2023]
Abstract
Cell viability is an essential physiological status for drug screening. While cell staining is a conventional cell viability analysis method, dye staining is usually cytotoxic. Alternatively, impedance cytometry provides a straightforward and label-free sensing approach for the assessment of cell viability. A key element of impedance cytometry is its sensing electrodes. Most state-of-the-art electrodes are made of expensive metals, microfabricated by lithography, with a typical size of ten microns. In this work, we proposed a low-cost microfluidic impedance cytometry device with 100-micron wide indium tin oxide (ITO) electrodes to achieve a comparable performance to the 10-micron wide Au electrodes. The effectiveness was experimentally verified as 7 μm beads can be distinguished from 10 μm beads. To the best of our knowledge, this is the lowest geometry ratio of the target to the sensing unit in the impedance cytometry technology. Furthermore, a cell viability test was performed on MCF-7 cells. The proposed double differential impedance cytometry device has successfully differentiated the living and dead MCF-7 cells with a throughput of ~1000 cells/s. The label-free and low-cost, high-throughput impedance cytometry could benefit drug screening, fundamental biological research and other biomedical applications.
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Affiliation(s)
- Bowen Yang
- Hebei Key Laboratory of Smart Sensing and Human-Robot Interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Chao Wang
- Hebei Key Laboratory of Smart Sensing and Human-Robot Interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Xinyi Liang
- Institute of Biophysics, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Jinchao Li
- Hebei Key Laboratory of Smart Sensing and Human-Robot Interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Shanshan Li
- Hebei Key Laboratory of Smart Sensing and Human-Robot Interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China
- Correspondence: (S.L.); (J.J.W.); (J.L.)
| | - Jie Jayne Wu
- Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37919, USA
- Correspondence: (S.L.); (J.J.W.); (J.L.)
| | - Tanbin Su
- Hebei Key Laboratory of Smart Sensing and Human-Robot Interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Junwei Li
- Institute of Biophysics, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300401, China
- Correspondence: (S.L.); (J.J.W.); (J.L.)
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12
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Xiang N, Ni Z. Microfluidics for Biomedical Applications. BIOSENSORS 2023; 13:bios13020161. [PMID: 36831927 PMCID: PMC9953641 DOI: 10.3390/bios13020161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 06/12/2023]
Abstract
Microfluidics refers to a technique for controlling and analyzing the fluids or micro-/nano-bioparticles in microscale channels or structures [...].
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Affiliation(s)
- Nan Xiang
- School of Mechanical Engineering, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
| | - Zhonghua Ni
- School of Mechanical Engineering, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
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13
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Kim H, Zhbanov A, Yang S. Microfluidic Systems for Blood and Blood Cell Characterization. BIOSENSORS 2022; 13:13. [PMID: 36671848 PMCID: PMC9856090 DOI: 10.3390/bios13010013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future.
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Affiliation(s)
- Hojin Kim
- Department of Mechatronics Engineering, Dongseo University, Busan 47011, Republic of Korea
| | - Alexander Zhbanov
- School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
| | - Sung Yang
- School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
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Wang M, Tan H, Li Y, Chen X, Chen D, Wang J, Chen J. Toward five-part differential of leukocytes based on electrical impedances of single cells and neural network. Cytometry A 2022; 103:439-446. [PMID: 36271498 DOI: 10.1002/cyto.a.24697] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/22/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022]
Abstract
The five-part differential of leukocytes plays key roles in the diagnosis of a variety of diseases and is realized by optical examinations of single cells, which is prone to various artifacts due to chemical treatments. The classification of leukocytes based on electrical impedances without cell treatments has not been demonstrated because of limitations in approaches of impedance acquisition and data processing. In this study, based on treatment-free single-cell impedance profiles collected from impedance flow cytometry leveraging constriction microchannels, two types of neural pattern recognition were conducted for comparisons with the purpose of realizing the five-part differential of leukocytes. In the first approach, 30 features from impedance profiles were defined manually and extracted automatically, and then a feedforward neural network was conducted, producing a classification accuracy of 84.9% in the five-part leukocyte differential. In the second approach, a customized recurrent neural network was developed to process impedance profiles directly and based on deep learning, a classification accuracy of 97.5% in the five-part leukocyte differential was reported. These results validated the feasibility of the five-part leukocyte differential based on label-free impedance profiles of single cells and thus provide a new perspective of differentiating white blood cells based on impedance flow cytometry.
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Affiliation(s)
- Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Huiwen Tan
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Yimin Li
- School of Advanced Engineers, University of Science and Technology Beijing, Beijing, People's Republic of China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China
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