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Sun H, Xie C, Kaw HY, Cai T, Liu L, Liu H, Shang HB, Li D. Gravity-assisted gradient size exclusion separation of microparticles by gap-modifiable silicon nanowire arrays. Talanta 2024; 280:126728. [PMID: 39191107 DOI: 10.1016/j.talanta.2024.126728] [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: 06/13/2024] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 08/29/2024]
Abstract
The separation and detection of microparticles within complex samples pose substantial challenges due to the intricate variations in size and concentration. A strategy employing gravity-assisted gradient size exclusion principle based on controllable gap sizes on the surface of silicon nanowire arrays (SiNWAs) has been established to achieve the separation of microparticles with diverse sizes. The formation of gradient gap sizes was accomplished by meticulously investigating the impact of oxidation-reduction reactions through metal-assisted chemical etching. Particles of different sizes were initially aggregated at the accumulation base, followed by a sequential size exclusion process within the finely regulated 0.9-12.5 μm gradient-gap-sized separation region facilitated with gravity-assisted, leading to a comprehensive separation of microparticles based on their respective size differences, progressing from small to large. The effective separation of four model-sized microparticles demonstrated a separation degree of ≥2.7, purity of ≥96.1 %, RSDs of ≤4.6 %, and a separation capacity of up to 107 particles. The separation efficacy of this gradient-sized chip was verified by evaluating the more complex atmospheric particulates with varying sizes, which exhibited separation degree ranging between 2 and 10. This method offers a precise separation range, easily adjustable separation sizes, and simple operation, rendering it a versatile tool for separating complex samples.
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Affiliation(s)
- Huaze Sun
- Department of Chemistry, Yanbian University, Yanji, 133002, Jilin Province, China
| | - Chenchen Xie
- Interdisciplinary of Biological Functional Molecules, College of Integration Science, Yanbian University, Yanji, 133002, Jilin Province, China
| | - Han Yeong Kaw
- NUIST Reading Academy, Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, 210044, China
| | - Tianpei Cai
- Department of Chemistry, Yanbian University, Yanji, 133002, Jilin Province, China
| | - Lu Liu
- Pathology and Pathophysiology, Medical College, Yanbian University, Park Road 977, Yanji City, 133002, Jilin Province, China
| | - Huwei Liu
- College of Life Sciences, Wuchang University of Technology, Wuhan, China
| | - Hai-Bo Shang
- Department of Chemistry, Yanbian University, Yanji, 133002, Jilin Province, China.
| | - Donghao Li
- Department of Chemistry, Yanbian University, Yanji, 133002, Jilin Province, China; Interdisciplinary of Biological Functional Molecules, College of Integration Science, Yanbian University, Yanji, 133002, Jilin Province, China.
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2
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Tsilikas I, Tsirigotis A, Sarantoglou G, Deligiannidis S, Bogris A, Posch C, Van den Branden G, Mesaritakis C. Photonic neuromorphic accelerators for event-based imaging flow cytometry. Sci Rep 2024; 14:24179. [PMID: 39406898 PMCID: PMC11480101 DOI: 10.1038/s41598-024-75667-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 10/05/2024] [Indexed: 10/19/2024] Open
Abstract
In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The event-based camera is capable of capturing 1 Gevents/sec, where events correspond to pixel contrast changes, similar to the retina's ganglion cell function. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains using a purely analogue version of the convolutional operation. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.1 m/sec. Classification accuracy, using only lightweight digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer at a rate of 3 × 106 images per second, we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network. These results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications.
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Affiliation(s)
- I Tsilikas
- Department of Information and Communication Systems Engineering, University of the Aegean, Palama 2, 83100, Samos, Greece
- Department of Physics, School of Applied Mathematical and Physical Sciences, Zografou Campus, 157 80, Athens, Greece
| | - A Tsirigotis
- Department of Information and Communication Systems Engineering, University of the Aegean, Palama 2, 83100, Samos, Greece
| | - G Sarantoglou
- Department of Information and Communication Systems Engineering, University of the Aegean, Palama 2, 83100, Samos, Greece
| | - S Deligiannidis
- Department of Informatics and Computer Engineering, University of West Attica, Ag. Spyridonos, Egaleo, Greece
| | - A Bogris
- Department of Informatics and Computer Engineering, University of West Attica, Ag. Spyridonos, Egaleo, Greece
| | - C Posch
- Prophesee Metavision, Rue du Faubourg Saint-Antoine 74, 75012, Paris, France
| | - G Van den Branden
- Prophesee Metavision, Rue du Faubourg Saint-Antoine 74, 75012, Paris, France
| | - C Mesaritakis
- Department of Information and Communication Systems Engineering, University of the Aegean, Palama 2, 83100, Samos, Greece.
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3
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Zhang K, Xia Z, Wang Y, Zheng L, Li B, Chu J. Label-free high-throughput impedance-activated cell sorting. LAB ON A CHIP 2024; 24:4918-4929. [PMID: 39315634 DOI: 10.1039/d4lc00487f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 104 cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting.
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Affiliation(s)
- Kui Zhang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Ziyang Xia
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
- Biomedical Robotics Laboratory, School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Lisheng Zheng
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui, 230027, China
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Lo MCK, Siu DMD, Lee KCM, Wong JSJ, Yeung MCF, Hsin MKY, Ho JCM, Tsia KK. Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307591. [PMID: 38864546 PMCID: PMC11304271 DOI: 10.1002/advs.202307591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/17/2024] [Indexed: 06/13/2024]
Abstract
Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.
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Affiliation(s)
- Michelle C. K. Lo
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Dickson M. D. Siu
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Kelvin C. M. Lee
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Justin S. J. Wong
- Conzeb LimitedHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Maximus C. F. Yeung
- Department of Pathology, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Michael K. Y. Hsin
- Department of Surgery, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - James C. M. Ho
- Department of Medicine, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Kevin K. Tsia
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
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5
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Kuhn TM, Paulsen M, Cuylen-Haering S. Accessible high-speed image-activated cell sorting. Trends Cell Biol 2024; 34:657-670. [PMID: 38789300 DOI: 10.1016/j.tcb.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024]
Abstract
Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.
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Affiliation(s)
- Terra M Kuhn
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Malte Paulsen
- Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Sara Cuylen-Haering
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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6
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Liu J, Du H, Huang L, Xie W, Liu K, Zhang X, Chen S, Zhang Y, Li D, Pan H. AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:38832-38851. [PMID: 39016521 DOI: 10.1021/acsami.4c07665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
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Affiliation(s)
- Junchi Liu
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Hanze Du
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Lei Huang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Wangni Xie
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Kexuan Liu
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Xue Zhang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Yuan Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Daowei Li
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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7
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Ghosh R, Arnheim A, van Zee M, Shang L, Soemardy C, Tang RC, Mellody M, Baghdasarian S, Sanchez Ochoa E, Ye S, Chen S, Williamson C, Karunaratne A, Di Carlo D. Lab on a Particle Technologies. Anal Chem 2024; 96:7817-7839. [PMID: 38650433 PMCID: PMC11112544 DOI: 10.1021/acs.analchem.4c01510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Affiliation(s)
- Rajesh Ghosh
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Alyssa Arnheim
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Mark van Zee
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Lily Shang
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Citradewi Soemardy
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Rui-Chian Tang
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Michael Mellody
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Sevana Baghdasarian
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Edwin Sanchez Ochoa
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Shun Ye
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Siyu Chen
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Cayden Williamson
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Amrith Karunaratne
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Dino Di Carlo
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
- Jonsson
Comprehensive Cancer Center, University
of California, Los Angeles, Los Angeles, California 90095, United States
- Department
of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
- California
NanoSystems Institute, Los Angeles, California 90095, United States
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8
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Zhang T, Di Carlo D, Lim CT, Zhou T, Tian G, Tang T, Shen AQ, Li W, Li M, Yang Y, Goda K, Yan R, Lei C, Hosokawa Y, Yalikun Y. Passive microfluidic devices for cell separation. Biotechnol Adv 2024; 71:108317. [PMID: 38220118 DOI: 10.1016/j.biotechadv.2024.108317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/27/2023] [Accepted: 01/06/2024] [Indexed: 01/16/2024]
Abstract
The separation of specific cell populations is instrumental in gaining insights into cellular processes, elucidating disease mechanisms, and advancing applications in tissue engineering, regenerative medicine, diagnostics, and cell therapies. Microfluidic methods for cell separation have propelled the field forward, benefitting from miniaturization, advanced fabrication technologies, a profound understanding of fluid dynamics governing particle separation mechanisms, and a surge in interdisciplinary investigations focused on diverse applications. Cell separation methodologies can be categorized according to their underlying separation mechanisms. Passive microfluidic separation systems rely on channel structures and fluidic rheology, obviating the necessity for external force fields to facilitate label-free cell separation. These passive approaches offer a compelling combination of cost-effectiveness and scalability when compared to active methods that depend on external fields to manipulate cells. This review delves into the extensive utilization of passive microfluidic techniques for cell separation, encompassing various strategies such as filtration, sedimentation, adhesion-based techniques, pinched flow fractionation (PFF), deterministic lateral displacement (DLD), inertial microfluidics, hydrophoresis, viscoelastic microfluidics, and hybrid microfluidics. Besides, the review provides an in-depth discussion concerning cell types, separation markers, and the commercialization of these technologies. Subsequently, it outlines the current challenges faced in the field and presents a forward-looking perspective on potential future developments. This work hopes to aid in facilitating the dissemination of knowledge in cell separation, guiding future research, and informing practical applications across diverse scientific disciplines.
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Affiliation(s)
- Tianlong Zhang
- College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Tianyuan Zhou
- College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Guizhong Tian
- College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
| | - Tao Tang
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Amy Q Shen
- Micro/Bio/Nanofluidics Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan
| | - Weihua Li
- School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
| | - Ming Li
- School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Yang Yang
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan 572000, China
| | - Keisuke Goda
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA; Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan; The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
| | - Ruopeng Yan
- The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, Nara 630-0192, Japan
| | - Yaxiaer Yalikun
- Division of Materials Science, Nara Institute of Science and Technology, Nara 630-0192, Japan.
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9
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Jyoti TP, Chandel S, Singh R. Flow cytometry: Aspects and application in plant and biological science. JOURNAL OF BIOPHOTONICS 2024; 17:e202300423. [PMID: 38010848 DOI: 10.1002/jbio.202300423] [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: 10/10/2023] [Accepted: 10/28/2023] [Indexed: 11/29/2023]
Abstract
Flow cytometry is a potent method that enables the quick and concurrent investigation of several characteristics of single cells in solution. Photodiodes or photomultiplier tubes are employed to detect the dispersed and fluorescent light signals that are produced by the laser beam as it passes through the cells. Photodetectors transform the light signals produced by the laser into electrical impulses. A computer then analyses these electrical impulses to identify and measure the various cell populations depending on their fluorescence or light scattering characteristics. Based on their fluorescence or light scattering properties, cell populations can be examined and/or isolated. This review covers the basic principle, components, working and specific biological applications of flow cytometry, including studies on plant, cell and molecular biology and methods employed for data processing and interpretation as well as the potential future relevance of this methodology in light of retrospective analysis and recent advancements in flow cytometry.
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Affiliation(s)
- Thakur Prava Jyoti
- Department of Pharmacognosy, ISF College of Pharmacy, Moga, Punjab, India
| | - Shivani Chandel
- Department of Pharmacognosy, ISF College of Pharmacy, Moga, Punjab, India
| | - Rajveer Singh
- Department of Pharmacognosy, ISF College of Pharmacy, Moga, Punjab, India
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10
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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11
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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12
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He Y, Qiao Y, Ding L, Cheng T, Tu J. Recent advances in droplet sequential monitoring methods for droplet sorting. BIOMICROFLUIDICS 2023; 17:061501. [PMID: 37969470 PMCID: PMC10645479 DOI: 10.1063/5.0173340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023]
Abstract
Droplet microfluidics is an attractive technology to run parallel experiments with high throughput and scalability while maintaining the heterogeneous features of individual samples or reactions. Droplet sorting is utilized to collect the desired droplets based on droplet characterization and in-droplet content evaluation. A proper monitoring method is critical in this process, which governs the accuracy and maximum frequency of droplet handling. Until now, numerous monitoring methods have been integrated in the microfluidic devices for identifying droplets, such as optical spectroscopy, mass spectroscopy, electrochemical monitoring, and nuclear magnetic resonance spectroscopy. In this review, we summarize the features of various monitoring methods integrated into droplet sorting workflow and discuss their suitable condition and potential obstacles in use. We aim to provide a systematic introduction and an application guide for choosing and building a droplet monitoring platform.
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Affiliation(s)
- Yukun He
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Lu Ding
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
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13
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Hayashi M, Ohnuki S, Tsai Y, Kondo N, Zhou Y, Zhang H, Ishii NT, Ding T, Herbig M, Isozaki A, Ohya Y, Goda K. Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae. LAB ON A CHIP 2023; 23:4232-4244. [PMID: 37650583 DOI: 10.1039/d3lc00556a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.
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Affiliation(s)
- Mika Hayashi
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yating Tsai
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yuqi Zhou
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Hongqian Zhang
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Natsumi Tiffany Ishii
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Tianben Ding
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Maik Herbig
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Akihiro Isozaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Mechanical Engineering, College of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8654, Japan
| | - Keisuke Goda
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
- CYBO, Tokyo 135-0064, Japan
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14
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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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15
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Gao Z, Li Y. Enhancing single-cell biology through advanced AI-powered microfluidics. BIOMICROFLUIDICS 2023; 17:051301. [PMID: 37799809 PMCID: PMC10550334 DOI: 10.1063/5.0170050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/23/2023] [Indexed: 10/07/2023]
Abstract
Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.
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Affiliation(s)
- Zhaolong Gao
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yiwei Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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16
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Goda K, Lu H, Fei P, Guck J. Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies. LAB ON A CHIP 2023; 23:3737-3740. [PMID: 37503818 DOI: 10.1039/d3lc90061d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Keisuke Goda, Hang Lu, Peng Fei, and Jochen Guck introduce the AI in Microfluidics themed collection, on revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies.
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Affiliation(s)
- Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
| | - Hang Lu
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Peng Fei
- School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jochen Guck
- Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
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17
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Wang Y, Huang Z, Wang X, Yang F, Yao X, Pan T, Li B, Chu J. Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms. LAB ON A CHIP 2023; 23:3615-3627. [PMID: 37458395 DOI: 10.1039/d3lc00194f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.
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Affiliation(s)
- Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Ziwei Huang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Xiaojie Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Fengrui Yang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Xuebiao Yao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Tingrui Pan
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
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18
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An L, Ji F, Zhao E, Liu Y, Liu Y. Measuring cell deformation by microfluidics. Front Bioeng Biotechnol 2023; 11:1214544. [PMID: 37434754 PMCID: PMC10331473 DOI: 10.3389/fbioe.2023.1214544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/14/2023] [Indexed: 07/13/2023] Open
Abstract
Microfluidics is an increasingly popular method for studying cell deformation, with various applications in fields such as cell biology, biophysics, and medical research. Characterizing cell deformation offers insights into fundamental cell processes, such as migration, division, and signaling. This review summarizes recent advances in microfluidic techniques for measuring cellular deformation, including the different types of microfluidic devices and methods used to induce cell deformation. Recent applications of microfluidics-based approaches for studying cell deformation are highlighted. Compared to traditional methods, microfluidic chips can control the direction and velocity of cell flow by establishing microfluidic channels and microcolumn arrays, enabling the measurement of cell shape changes. Overall, microfluidics-based approaches provide a powerful platform for studying cell deformation. It is expected that future developments will lead to more intelligent and diverse microfluidic chips, further promoting the application of microfluidics-based methods in biomedical research, providing more effective tools for disease diagnosis, drug screening, and treatment.
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Affiliation(s)
- Ling An
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Fenglong Ji
- School of Textile Materials and Engineering, Wuyi University, Jiangmen, Guangdong, China
| | - Enming Zhao
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Yi Liu
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, United States
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19
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Pfisterer F, Godino N, Gerling T, Kirschbaum M. Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells. RSC Adv 2023; 13:19379-19387. [PMID: 37383687 PMCID: PMC10294288 DOI: 10.1039/d3ra03100d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/31/2023] [Indexed: 06/30/2023] Open
Abstract
Electroporation of cells is a widely-used tool to transport molecules such as proteins or nucleic acids into cells or to extract cellular material. However, bulk methods for electroporation do not offer the possibility to selectively porate subpopulations or single cells in heterogeneous cell samples. To achieve this, either presorting or complex single-cell technologies are required currently. In this work, we present a microfluidic flow protocol for selective electroporation of predefined target cells identified in real-time by high-quality microscopic image analysis of fluorescence and transmitted light. While traveling through the microchannel, the cells are focused by dielectrophoretic forces into the microscopic detection area, where they are classified based on image analysis techniques. Finally, the cells are forwarded to a poration electrode and only the target cells are pulsed. By processing a heterogenically stained cell sample, we were able to selectively porate only target cells (green-fluorescent) while non-target cells (blue-fluorescent) remained unaffected. We achieved highly selective poration with >90% specificity at average poration rates of >50% and throughputs of up to 7200 cells per hour.
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Affiliation(s)
- Felix Pfisterer
- Fraunhofer Institute for Cell Therapy and Immunology IZI, Branch Bioanalytics and Bioprocesses IZI-BB Am Muehlenberg 13 14476 Potsdam Germany
| | - Neus Godino
- Fraunhofer Institute for Cell Therapy and Immunology IZI, Branch Bioanalytics and Bioprocesses IZI-BB Am Muehlenberg 13 14476 Potsdam Germany
| | - Tobias Gerling
- Fraunhofer Institute for Cell Therapy and Immunology IZI, Branch Bioanalytics and Bioprocesses IZI-BB Am Muehlenberg 13 14476 Potsdam Germany
| | - Michael Kirschbaum
- Fraunhofer Institute for Cell Therapy and Immunology IZI, Branch Bioanalytics and Bioprocesses IZI-BB Am Muehlenberg 13 14476 Potsdam Germany
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20
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Gerling T, Godino N, Pfisterer F, Hupf N, Kirschbaum M. High-precision, low-complexity, high-resolution microscopy-based cell sorting. LAB ON A CHIP 2023. [PMID: 37314345 DOI: 10.1039/d3lc00242j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of μl min-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.
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Affiliation(s)
- Tobias Gerling
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
- Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
| | - Neus Godino
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Felix Pfisterer
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Nina Hupf
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
| | - Michael Kirschbaum
- Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses IZI-BB, Am Muehlenberg 13, 14476 Potsdam, Germany.
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21
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Luan X, Liu P, Huang D, Zhao H, Li Y, Sun S, Zhang W, Zhang L, Li M, Zhi T, Zhao Y, Huang C. piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties. MICROSYSTEMS & NANOENGINEERING 2023; 9:77. [PMID: 37303829 PMCID: PMC10250341 DOI: 10.1038/s41378-023-00545-9] [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: 01/19/2023] [Revised: 03/04/2023] [Accepted: 03/19/2023] [Indexed: 06/13/2023]
Abstract
Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance Csm and cytoplasm conductivity σcyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' Csm and σcyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' Csm and σcyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's Csm and σcyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.
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Affiliation(s)
- Xiaofeng Luan
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Pengbin Liu
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Di Huang
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Haiping Zhao
- Cerebrovascular Diseases Research Institute, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yuang Li
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Sheng Sun
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Wenchang Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
| | - Lingqian Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
| | - Mingxiao Li
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
| | - Tian Zhi
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yang Zhao
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
| | - Chengjun Huang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
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22
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 PMCID: PMC10141994 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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23
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García-Hernández LA, Martínez-Martínez E, Pazos-Solís D, Aguado-Preciado J, Dutt A, Chávez-Ramírez AU, Korgel B, Sharma A, Oza G. Optical Detection of Cancer Cells Using Lab-on-a-Chip. BIOSENSORS 2023; 13:bios13040439. [PMID: 37185514 PMCID: PMC10136345 DOI: 10.3390/bios13040439] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 05/17/2023]
Abstract
The global need for accurate and efficient cancer cell detection in biomedicine and clinical diagnosis has driven extensive research and technological development in the field. Precision, high-throughput, non-invasive separation, detection, and classification of individual cells are critical requirements for successful technology. Lab-on-a-chip devices offer enormous potential for solving biological and medical problems and have become a priority research area for microanalysis and manipulating cells. This paper reviews recent developments in the detection of cancer cells using the microfluidics-based lab-on-a-chip method, focusing on describing and explaining techniques that use optical phenomena and a plethora of probes for sensing, amplification, and immobilization. The paper describes how optics are applied in each experimental method, highlighting their advantages and disadvantages. The discussion includes a summary of current challenges and prospects for cancer diagnosis.
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Affiliation(s)
- Luis Abraham García-Hernández
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Parque Tecnológico Querétaro, Pedro Escobedo, Querétaro C.P. 76703, Mexico
| | | | - Denni Pazos-Solís
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Campus Queretaro, Querétaro C.P. 76130, Mexico
| | - Javier Aguado-Preciado
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Campus Queretaro, Querétaro C.P. 76130, Mexico
| | - Ateet Dutt
- Instituto de Investigaciones en Materiales, Circuito Exterior S/N Ciudad Universitaria, Mexico City C.P. 04510, Mexico
| | - Abraham Ulises Chávez-Ramírez
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Parque Tecnológico Querétaro, Pedro Escobedo, Querétaro C.P. 76703, Mexico
| | - Brian Korgel
- McKetta Department of Chemical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712-1062, USA
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Campus Queretaro, Querétaro C.P. 76130, Mexico
| | - Goldie Oza
- Centro de Investigación y Desarrollo Tecnológico en Electroquímica, Parque Tecnológico Querétaro, Pedro Escobedo, Querétaro C.P. 76703, Mexico
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24
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Guo Z, Lin T, Jing D, Wang W, Sui Y. A method for real-time mechanical characterisation of microcapsules. Biomech Model Mechanobiol 2023:10.1007/s10237-023-01712-7. [PMID: 36964429 PMCID: PMC10366294 DOI: 10.1007/s10237-023-01712-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023]
Abstract
Characterising the mechanical properties of flowing microcapsules is important from both fundamental and applied points of view. In the present study, we develop a novel multilayer perceptron (MLP)-based machine learning (ML) approach, for real-time simultaneous predictions of the membrane mechanical law type, shear and area-dilatation moduli of microcapsules, from their camera-recorded steady profiles in tube flow. By MLP, we mean a neural network where many perceptrons are organised into layers. A perceptron is a basic element that conducts input-output mapping operation. We test the performance of the present approach using both simulation and experimental data. We find that with a reasonably high prediction accuracy, our method can reach an unprecedented low prediction latency of less than 1 millisecond on a personal computer. That is the overall computational time, without using parallel computing, from a single experimental image to multiple capsule mechanical parameters. It is faster than a recently proposed convolutional neural network-based approach by two orders of magnitude, for it only deals with the one-dimensional capsule boundary instead of the entire two-dimensional capsule image. Our new approach may serve as the foundation of a promising tool for real-time mechanical characterisation and online active sorting of deformable microcapsules and biological cells in microfluidic devices.
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Affiliation(s)
- Ziyu Guo
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Tao Lin
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Dalei Jing
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Wen Wang
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Yi Sui
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom.
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25
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Matsumura H, Shen LTW, Isozaki A, Mikami H, Yuan D, Miura T, Kondo Y, Mori T, Kusumoto Y, Nishikawa M, Yasumoto A, Ueda A, Bando H, Hara H, Liu Y, Deng Y, Sonoshita M, Yatomi Y, Goda K, Matsusaka S. Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells. LAB ON A CHIP 2023; 23:1561-1575. [PMID: 36648503 DOI: 10.1039/d2lc00856d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.
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Affiliation(s)
- Hiroki Matsumura
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Larina Tzu-Wei Shen
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki 305-8575, Japan.
| | - Akihiro Isozaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Dan Yuan
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Taichi Miura
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuto Kondo
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Tomoko Mori
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki 305-8575, Japan.
| | - Yoshika Kusumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Masako Nishikawa
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Atsushi Yasumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Aya Ueda
- Department of Breast and Endocrine Surgery, University of Tsukuba Hospital, 605-8576, Japan
| | - Hiroko Bando
- Department of Breast and Endocrine Surgery, Faculty of Medicine, University of Tsukuba, 305-8575, Japan
| | - Hisato Hara
- Department of Breast and Endocrine Surgery, Faculty of Medicine, University of Tsukuba, 305-8575, Japan
| | - Yuhong Liu
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Yunjie Deng
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Masahiro Sonoshita
- Division of Biomedical Oncology, Institute for Genetic Medicine, Hokkaido University, Hokkaido 060-0815, Japan
- Global Station for Biosurfaces and Drug Discovery, Hokkaido University, Hokkaido 060-0812, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Keisuke Goda
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Institute of Technological Sciences, Wuhan University, Hubei 430072, China
- CYBO, Tokyo 101-0022, Japan
| | - Satoshi Matsusaka
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki 305-8575, Japan.
- Tsukuba Clinical Research and Development Organization, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan
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26
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Schraivogel D, Steinmetz LM. Cell sorters see things more clearly now. Mol Syst Biol 2023; 19:e11254. [PMID: 36779527 PMCID: PMC9996229 DOI: 10.15252/msb.202211254] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 01/13/2023] [Indexed: 02/14/2023] Open
Abstract
Microscopy and fluorescence-activated cell sorting (FACS) are two of the most important tools for single-cell phenotyping in basic and biomedical research. Microscopy provides high-resolution snapshots of cell morphology and the inner workings of cells, while FACS isolates thousands of cells per second using simple parameters, such as the intensity of fluorescent protein labels. Recent technologies are now combining both methods to enable the fast isolation of cells with microscopic phenotypes of interest, thereby bridging a long-standing gap in the life sciences. In this Commentary, we discuss the technical advancements made by image-enabled cell sorting and highlight novel experimental strategies in functional genomics and single-cell research.
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Affiliation(s)
- Daniel Schraivogel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Lars M Steinmetz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.,Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.,Stanford Genome Technology Center, Palo Alto, CA, USA
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27
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Siu DMD, Lee KCM, Chung BMF, Wong JSJ, Zheng G, Tsia KK. Optofluidic imaging meets deep learning: from merging to emerging. LAB ON A CHIP 2023; 23:1011-1033. [PMID: 36601812 DOI: 10.1039/d2lc00813k] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Bob M F Chung
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
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28
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Kinegawa R, Gala de Pablo J, Wang Y, Hiramatsu K, Goda K. Label-free multiphoton imaging flow cytometry. Cytometry A 2023. [PMID: 36799568 DOI: 10.1002/cyto.a.24723] [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: 08/11/2022] [Revised: 01/31/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023]
Abstract
Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.
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Affiliation(s)
- Ryo Kinegawa
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | | | - Yi Wang
- Department of Chemistry, Renmin University of China, Beijing, China
| | - Kotaro Hiramatsu
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Research Centre for Spectrochemistry, The University of Tokyo, Tokyo, Japan.,PRESTO, Japan Science and Technology Agency, Saitama, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Institute of Technological Sciences, Wuhan University, Hubei, China.,Department of Bioengineering, University of California, Los Angeles, California, USA.,CYBO, Inc., Tokyo, Japan
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29
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Zhao Y, Isozaki A, Herbig M, Hayashi M, Hiramatsu K, Yamazaki S, Kondo N, Ohnuki S, Ohya Y, Nitta N, Goda K. Intelligent sort-timing prediction for image-activated cell sorting. Cytometry A 2023; 103:88-97. [PMID: 35766305 DOI: 10.1002/cyto.a.24664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/20/2022] [Accepted: 06/11/2022] [Indexed: 02/07/2023]
Abstract
Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.
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Affiliation(s)
- Yaqi Zhao
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Akihiro Isozaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Maik Herbig
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Mika Hayashi
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Kotaro Hiramatsu
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Sota Yamazaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, Japan
| | | | - Keisuke Goda
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,CYBO, Tokyo, Japan.,Department of Bioengineering, University of California, California, Los Angeles, USA.,Institute of Technological Sciences, Wuhan University, Hubei, China
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30
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Cai J, Wang Q, Wang C, Deng Y. Research on cell detection method for microfluidic single cell dispensing. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3970-3982. [PMID: 36899612 DOI: 10.3934/mbe.2023185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single cell dispensing techniques mainly include limiting dilution, fluorescent-activated cell sorting (FACS) and microfluidic approaches. Limiting dilution process is complicated by statistical analysis of clonally derived cell lines. Flow cytometry and conventional microfluidic chip methods utilize excitation fluorescence signals for detection, potentially causing a non-negligible effect on cell activity. In this paper, we implement a nearly non-destructive single-cell dispensing method based on object detection algorithm. To realize single cell detection, we have built automated image acquisition system and then employed PP-YOLO neural network model as detection framework. Through architecture comparison and parameter optimization, we select ResNet-18vd as backbone for feature extraction. We train and evaluate the flow cell detection model on train and test set consisting of 4076 and 453 annotated images respectively. Experiments show that the model inference an image of 320 × 320 pixels at least 0.9 ms with the precision of 98.6% on a NVidia A100 GPU, achieving a good balance of detection speed and accuracy.
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Affiliation(s)
- Junjing Cai
- Jihua Institute of Biomedical Engineering Technology, Jihua Laboratory, Foshan 528200, China
- School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Qiwei Wang
- Jihua Institute of Biomedical Engineering Technology, Jihua Laboratory, Foshan 528200, China
| | - Ce Wang
- Jihua Institute of Biomedical Engineering Technology, Jihua Laboratory, Foshan 528200, China
| | - Yu Deng
- School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
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31
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Xu T, Li Y, Han X, Kan L, Ren J, Sun L, Diao Z, Ji Y, Zhu P, Xu J, Ma B. Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening. LAB ON A CHIP 2022; 23:125-135. [PMID: 36477690 DOI: 10.1039/d2lc00888b] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Real-time image-based sorting of target cells in a precisely indexed manner is desirable for sequencing or cultivating individual human or microbial cells directly from clinical or environmental samples; however, the versatility of existing methods is limited as they are usually not broadly applicable to all cell sizes. Here, an optical tweezer-assisted pool-screening and single-cell isolation (OPSI) system is established for precise, indexed isolation of individual bacterial, yeast or human-cancer cells. A controllable static flow field that acts as a cell pool is achieved in a microfluidics chip, to enable precise and ready screening of cells of 1 to 40 μm in size by bright-field, fluorescence, or Raman imaging. The target cell is then captured by a 1064 nm optical tweezer and deposited as one-cell-harboring nanoliter microdroplets in a one-cell-one-tube manner. For bacterial, yeast and human cells, OPSI achieves a >99.7% target-cell sorting purity and a 10-fold elevated speed of 10-20 cells per min. Moreover, OPSI-based one-cell RNA-seq of human cancer cells yields high quality and reproducible single-cell transcriptome profiles. The versatility, facileness, flexibility, modularized design, and low cost of OPSI suggest its broad applications for image-based sorting of target cells.
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Affiliation(s)
- Teng Xu
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Yuandong Li
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Xiao Han
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- School of Chemistry and Chemical Engineering, University of Jinan, Jinan, China
| | - Lingyan Kan
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Jing Ren
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Luyang Sun
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Zhidian Diao
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Yuetong Ji
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Single-Cell Biotechnology Ltd., Qingdao, Shandong, China
| | - Pengfei Zhu
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Single-Cell Biotechnology Ltd., Qingdao, Shandong, China
| | - Jian Xu
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- Laboratory of Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Bo Ma
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China.
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
- Laboratory of Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
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32
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Harmon J, Findinier J, Ishii NT, Herbig M, Isozaki A, Grossman A, Goda K. Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization. Cytometry A 2022; 101:1027-1034. [PMID: 35643943 DOI: 10.1002/cyto.a.24661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/22/2022] [Accepted: 05/24/2022] [Indexed: 01/27/2023]
Abstract
Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.
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Affiliation(s)
- Jeffrey Harmon
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Justin Findinier
- Department of Plant Biology, Carnegie Institution for Science, Stanford, California, USA
| | | | - Maik Herbig
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Akihiro Isozaki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Arthur Grossman
- Department of Plant Biology, Carnegie Institution for Science, Stanford, California, USA.,Department of Biology, Stanford University, Stanford, California, USA
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Department of Bioengineering, University of California, California, Los Angeles, USA.,Institute of Technological Sciences, Wuhan University, Hubei, China
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Lee K, Kim SE, Nam S, Doh J, Chung WK. Upgraded User-Friendly Image-Activated Microfluidic Cell Sorter Using an Optimized and Fast Deep Learning Algorithm. MICROMACHINES 2022; 13:2105. [PMID: 36557404 PMCID: PMC9783339 DOI: 10.3390/mi13122105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/23/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Image-based cell sorting is essential in biological and biomedical research. The sorted cells can be used for downstream analysis to expand our knowledge of cell-to-cell differences. We previously demonstrated a user-friendly image-activated microfluidic cell sorting technique using an optimized and fast deep learning algorithm. Real-time isolation of cells was carried out using this technique with an inverted microscope. In this study, we devised a recently upgraded sorting system. The cell sorting techniques shown on the microscope were implemented as a real system. Several new features were added to make it easier for the users to conduct the real-time sorting of cells or particles. The newly added features are as follows: (1) a high-resolution linear piezo-stage is used to obtain in-focus images of the fast-flowing cells; (2) an LED strobe light was incorporated to minimize the motion blur of fast-flowing cells; and (3) a vertical syringe pump setup was used to prevent the cell sedimentation. The sorting performance of the upgraded system was demonstrated through the real-time sorting of fluorescent polystyrene beads. The sorter achieved a 99.4% sorting purity for 15 μm and 10 μm beads with an average throughput of 22.1 events per second (eps).
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Affiliation(s)
- Keondo Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Seong-Eun Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Seokho Nam
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Junsang Doh
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Wan Kyun Chung
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
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34
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Measurement and Characterization of Yeast Cell Size Using a Digital Optical Microscope. Processes (Basel) 2022. [DOI: 10.3390/pr10112396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Yeast produces flavor components as by-products of fermentation. Such components, which account for considerable yeast quality value, may influence cell membrane fluidity and regulate cell size. In this study, yeast cell size was measured using various methods, and cell sizes were characterized for each strain of yeast. Herein, a method for measuring the cell size and automatically acquiring the cell area was proposed using inexpensive methylene blue staining and a versatile digital microscope to automatically acquire the cell area. To evaluate the efficiency, data on cell length and area were compared using the conventional and proposed methods. Additionally, the advantages of automated measurement and the ability to count cell numbers in the future are discussed through the acquisition of cell size data, which would be required for microfabrication and cell fabrication techniques. Although there are various candidate methods for evaluating the properties of each yeast strain, the present technique may be useful for evaluating the productivity of flavor components and selecting a yeast strain for producing food and drink products with high commodity values.
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Hirotsu A, Kikuchi H, Yamada H, Ozaki Y, Haneda R, Kawata S, Murakami T, Matsumoto T, Hiramatsu Y, Kamiya K, Yamashita D, Fujimori Y, Ueda Y, Okazaki S, Kitagawa M, Konno H, Takeuchi H. Artificial intelligence-based classification of peripheral blood nucleated cells using label-free imaging flow cytometry. LAB ON A CHIP 2022; 22:3464-3474. [PMID: 35942978 DOI: 10.1039/d2lc00166g] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Label-free image identification of circulating rare cells, such as circulating tumor cells within peripheral blood nucleated cells (PBNCs), the vast majority of which are white blood cells (WBCs), remains challenging. We previously described developing label-free image cytometry for classifying live cells using computer vision technology for pattern recognition, based on the subcellular structure of the quantitative phase microscopy images. We applied our image recognition methods to cells flowing in a flow cytometer microfluidic channel, and differentiated WBCs from cancer cell lines (area under receiver operating characteristic curve = 0.957). We then applied this method to healthy volunteers' and advanced cancer patients' blood samples and found that the non-WBC fraction rates (NWBC-FRs), defined as the percentage of cells classified as non-WBCs of the total PBNCs, were significantly higher in cancer patients than in healthy volunteers. Furthermore, we monitored NWBC-FRs over the therapeutic courses in cancer patients, which revealed the potential ability in monitoring the clinical status during therapy. Our image recognition system has the potential to provide a morphological diagnostic tool for circulating rare cells as non-WBC fractions.
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Affiliation(s)
- Amane Hirotsu
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Hirotoshi Kikuchi
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Hidenao Yamada
- Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, Shizuoka, Japan
| | - Yusuke Ozaki
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Ryoma Haneda
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Sanshiro Kawata
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Tomohiro Murakami
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Tomohiro Matsumoto
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Yoshihiro Hiramatsu
- Department Perioperative Functioning Care and Support, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan
| | - Kinji Kamiya
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
| | - Daisuke Yamashita
- Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, Shizuoka, Japan
| | - Yuki Fujimori
- Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, Shizuoka, Japan
| | - Yukio Ueda
- Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, Shizuoka, Japan
| | - Shigetoshi Okazaki
- HAMAMATSU BioPhotonics Innovation Chair, Institute for Medical Photonics Research, Preeminent Medical Photonics Education and Research Centre, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan
| | - Masatoshi Kitagawa
- Department of Molecular Biology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan
- Laboratory Animal Facilities and Services, Preeminent Medical Photonics Education and Research Centre, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan
| | - Hiroyuki Konno
- Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan
| | - Hiroya Takeuchi
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
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Ahmad A, Sala F, Paiè P, Candeo A, D'Annunzio S, Zippo A, Frindel C, Osellame R, Bragheri F, Bassi A, Rousseau D. On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. LAB ON A CHIP 2022; 22:3453-3463. [PMID: 35946995 DOI: 10.1039/d2lc00482h] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).
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Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Federico Sala
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Petra Paiè
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | | | | | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Roberto Osellame
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Francesca Bragheri
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Wang S, Liu Q, Cheng L, Wang L, Xu F, Yao C. Targeting biophysical cues to address platelet storage lesions. Acta Biomater 2022; 151:118-133. [PMID: 36028196 DOI: 10.1016/j.actbio.2022.08.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/06/2022] [Accepted: 08/17/2022] [Indexed: 11/30/2022]
Abstract
Platelets play vital roles in vascular repair, especially in primary hemostasis, and have been widely used in transfusion to prevent bleeding or manage active bleeding. Recently, platelets have been used in tissue repair (e.g., bone, skin, and dental alveolar tissue) and cell engineering as drug delivery carriers. However, the biomedical applications of platelets have been associated with platelet storage lesions (PSLs), resulting in poor clinical outcomes with reduced recovery, survival, and hemostatic function after transfusion. Accumulating evidence has shown that biophysical cues play important roles in platelet lesions, such as granule secretion caused by shear stress, adhesion affected by substrate stiffness, and apoptosis caused by low temperature. This review summarizes four major biophysical cues (i.e., shear stress, substrate stiffness, hydrostatic pressure, and thermal microenvironment) involved in the platelet preparation and storage processes, and discusses how they may synergistically induce PSLs such as platelet shape change, activation, apoptosis and clearance. We also review emerging methods for studying these biophysical cues in vitro and existing strategies targeting biophysical cues for mitigating PSLs. We conclude with a perspective on the future direction of biophysics-based strategies for inhibiting PSLs. STATEMENT OF SIGNIFICANCE: Platelet storage lesions (PSLs) involve a series of structural and functional changes. It has long been accepted that PSLs are initiated by biochemical cues. Our manuscript is the first to propose four major biophysical cues (shear stress, substrate stiffness, hydrostatic pressure, and thermal microenvironment) that platelets experience in each operation step during platelet preparation and storage processes in vitro, which may synergistically contribute to PSLs. We first clarify these biophysical cues and how they induce PSLs. Strategies targeting each biophysical cue to improve PSLs are also summarized. Our review is designed to draw the attention from a broad range of audience, including clinical doctors, biologists, physical scientists, engineers and materials scientists, and immunologist, who study on platelets physiology and pathology.
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Affiliation(s)
- Shichun Wang
- Department of Blood Transfusion, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, PR China
| | - Qi Liu
- Department of Blood Transfusion, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, PR China
| | - Lihan Cheng
- Department of Blood Transfusion, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, PR China
| | - Lu Wang
- Department of Blood Transfusion, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, PR China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, PR China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China.
| | - Chunyan Yao
- Department of Blood Transfusion, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, PR China; State Key Laboratory of Trauma, Burn and Combined Injury, Third Military Medical University, Chongqing 400038, PR China.
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38
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DiSalvo M, Patrone PN, Kearsley AJ, Cooksey GA. Serial flow cytometry in an inertial focusing optofluidic microchip for direct assessment of measurement variations. LAB ON A CHIP 2022; 22:3217-3228. [PMID: 35856829 DOI: 10.1039/d1lc01169c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Flow cytometry is an invaluable technology in biomedical research, but confidence in single-cell measurements remains limited due to a lack of appropriate techniques for uncertainty quantification (UQ). It is particularly challenging to evaluate the potential for different instrumentation designs or operating parameters to influence the measurement physics in ways that change measurement repeatability. Here, we report a direct experimental approach to UQ using a serial flow cytometer that measured each particle more than once along a flow path. The instrument was automated for real-time characterization of measurement precision and operated with particle velocities exceeding 1 m s-1, throughputs above 100 s-1, and analysis yields better than 99.9%. These achievements were enabled by a novel hybrid inertial and hydrodynamic particle focuser to tightly control particle positions and velocities. The cytometer identified ideal flow conditions with fluorescence area measurement precision on the order of 1% and characterized tradeoffs between precision, throughput, and analysis yield. The serial cytometer is anticipated to improve single-cell measurements through estimation (and subsequent control) of uncertainty contributions from various other instrument parameters leading to overall improvements in the ability to better classify sample composition and to find rare events.
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Affiliation(s)
- Matthew DiSalvo
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21287, USA
- Microsystems and Nanotechnology Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
| | - Paul N Patrone
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Anthony J Kearsley
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
| | - Gregory A Cooksey
- Microsystems and Nanotechnology Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
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39
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McIntyre D, Lashkaripour A, Fordyce P, Densmore D. Machine learning for microfluidic design and control. LAB ON A CHIP 2022; 22:2925-2937. [PMID: 35904162 PMCID: PMC9361804 DOI: 10.1039/d2lc00254j] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/28/2022] [Indexed: 05/24/2023]
Abstract
Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.
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Affiliation(s)
- David McIntyre
- Biomedical Engineering Department, Boston University, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA.
| | - Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Polly Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA.
- Electrical & Computer Engineering Department, Boston University, Boston, MA, USA
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40
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Jin G, Hong S, Rich J, Xia J, Kim K, You L, Zhao C, Huang TJ. Intelligent nanoscope for rapid nanomaterial identification and classification. LAB ON A CHIP 2022; 22:2978-2985. [PMID: 35647808 PMCID: PMC9378457 DOI: 10.1039/d2lc00206j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1000 images of training sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50 000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles.
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Affiliation(s)
- Geonsoo Jin
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA.
| | - Seongwoo Hong
- Office of Biomedical Graduate Education, Duke University School of Medicine, Durham, NC 27710, USA
| | - Joseph Rich
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
| | - Jianping Xia
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA.
| | - Kyeri Kim
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27708, USA
| | - Chenglong Zhao
- Department of Physics, University of Dayton, 300 College Park, Dayton, Ohio 45469, USA.
- Department of Electro-Optics and Photonics, University of Dayton, 300 College Park, Dayton, Ohio 45469, USA
| | - Tony Jun Huang
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA.
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41
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Zhang Y, Zhao Y, Cole T, Zheng J, Bayinqiaoge, Guo J, Tang SY. Microfluidic flow cytometry for blood-based biomarker analysis. Analyst 2022; 147:2895-2917. [PMID: 35611964 DOI: 10.1039/d2an00283c] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.
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Affiliation(s)
- Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Ying Zhao
- National Chengdu Centre of Safety Evaluation of Drugs, West China Hospital of Sichuan University, Chengdu, China
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Bayinqiaoge
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jinhong Guo
- The M.O.E. Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, #1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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Vora N, Shekhar P, Esmail M, Patra A, Georgakoudi I. Label-free flow cytometry of rare circulating tumor cell clusters in whole blood. Sci Rep 2022; 12:10721. [PMID: 35750889 PMCID: PMC9232518 DOI: 10.1038/s41598-022-14003-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/31/2022] [Indexed: 11/09/2022] Open
Abstract
Circulating tumor cell clusters (CTCCs) are rare cellular events found in the blood stream of metastatic tumor patients. Despite their scarcity, they represent an increased risk for metastasis. Label-free detection methods of these events remain primarily limited to in vitro microfluidic platforms. Here, we expand on the use of confocal backscatter and fluorescence flow cytometry (BSFC) for label-free detection of CTCCs in whole blood using machine learning for peak detection/classification. BSFC uses a custom-built flow cytometer with three excitation wavelengths (405 nm, 488 nm, and 633 nm) and five detectors to detect CTCCs in whole blood based on corresponding scattering and fluorescence signals. In this study, detection of CTCC-associated GFP fluorescence is used as the ground truth to assess the accuracy of endogenous back-scattered light-based CTCC detection in whole blood. Using a machine learning model for peak detection/classification, we demonstrated that the combined use of backscattered signals at the three wavelengths enable detection of ~ 93% of all CTCCs larger than two cells with a purity of > 82% and an overall accuracy of > 95%. The high level of performance established through BSFC and machine learning demonstrates the potential for label-free detection and monitoring of CTCCs in whole blood. Further developments of label-free BSFC to enhance throughput could lead to important applications in the isolation of CTCCs in whole blood with minimal disruption and ultimately their detection in vivo.
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Affiliation(s)
- Nilay Vora
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA
| | - Prashant Shekhar
- Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA
| | - Michael Esmail
- Tufts Comparative Medicine Services, Tufts University, Medford, MA, 02155, USA
| | - Abani Patra
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Irene Georgakoudi
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.
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43
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Frey N, Sönmez UM, Minden J, LeDuc P. Microfluidics for understanding model organisms. Nat Commun 2022; 13:3195. [PMID: 35680898 PMCID: PMC9184607 DOI: 10.1038/s41467-022-30814-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/20/2022] [Indexed: 11/29/2022] Open
Abstract
New microfluidic systems for whole organism analysis and experimentation are catalyzing biological breakthroughs across many fields, from human health to fundamental biology principles. This perspective discusses recent microfluidic tools to study intact model organisms to demonstrate the tremendous potential for these integrated approaches now and into the future. We describe these microsystems' technical features and highlight the unique advantages for precise manipulation in areas including immobilization, automated alignment, sorting, sensory, mechanical and chemical stimulation, and genetic and thermal perturbation. Our aim is to familiarize technologically focused researchers with microfluidics applications in biology research, while providing biologists an entrée to advanced microengineering techniques for model organisms.
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Affiliation(s)
- Nolan Frey
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Utku M Sönmez
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jonathan Minden
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Philip LeDuc
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Computation Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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44
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Affiliation(s)
- Andrew Filby
- From the Innovation, Methodology, and Application Research Theme, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom (A.F.); and the Broad Institute of MIT and Harvard, Cambridge, MA (A.E.C.)
| | - Anne E Carpenter
- From the Innovation, Methodology, and Application Research Theme, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom (A.F.); and the Broad Institute of MIT and Harvard, Cambridge, MA (A.E.C.)
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45
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Caselli F, Reale R, De Ninno A, Spencer D, Morgan H, Bisegna P. Deciphering impedance cytometry signals with neural networks. LAB ON A CHIP 2022; 22:1714-1722. [PMID: 35353108 DOI: 10.1039/d2lc00028h] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.
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Affiliation(s)
- Federica Caselli
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy.
| | - Riccardo Reale
- Center for Life Nano Science@Sapienza, Italian Institute of Technology (IIT), Rome, Italy
| | - Adele De Ninno
- Italian National Research Council - Institute for Photonics and Nanotechnologies (CNR - IFN), Rome, Italy
| | - Daniel Spencer
- School of Electronics and Computing Science, and, Institute for Life Sciences, University of Southampton, Highfield, Southampton, UK
| | - Hywel Morgan
- School of Electronics and Computing Science, and, Institute for Life Sciences, University of Southampton, Highfield, Southampton, UK
| | - Paolo Bisegna
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy.
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46
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Radisch S, Poltorak MP, Wagner M, Cletiu V, Radisch C, Treise I, Pann S, Weigt A, Artner S, Dreher S, Fechner F, Borjan B, Fraessle SP, Effenberger M, Benke E, Navratil G, Hentschel N, Busch DH, Schmidt T, Stemberger C, Germeroth L. Next generation automated traceless cell chromatography platform for GMP-compliant cell isolation and activation. Sci Rep 2022; 12:6572. [PMID: 35449227 PMCID: PMC9023455 DOI: 10.1038/s41598-022-10320-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 04/06/2022] [Indexed: 11/21/2022] Open
Abstract
Large-scale target cell isolation from patient blood preparations is one of the critical operations during drug product manufacturing for personalized cell therapy in immuno-oncology. Use of high-affinity murine antibody coated magnetic nanoparticles that remain on isolated cells is the current standard applied for this purpose. Here, we present the transformation of previously described technology - non-magnetic immunoaffinity column chromatography-based cell selection with reversible reagents into a new clinical-grade cell isolation platform called Automated Traceless Cell affinity chromatography (ATC). ATC is a fully closed and GMP-compliant cell selection and manufacturing system. Reversibility of reagents enables (sequential) positive cell selection, optionally in combination with depletion columns, enabling capture of highly specific cell subsets. Moreover, synergy with other Streptamer-based technologies allows novel uses beyond cell isolation including integrated and automated on-column target cell activation. In conclusion, ATC technology is an innovative as well as versatile platform to select, stimulate and modify cells for clinical manufacturing and downstream therapies.
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Affiliation(s)
- Sabine Radisch
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Mateusz P Poltorak
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany.
| | - Michaela Wagner
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Vlad Cletiu
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Christian Radisch
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Irina Treise
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Steffi Pann
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Alexis Weigt
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Sophie Artner
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Stefan Dreher
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Fabian Fechner
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Bojana Borjan
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Simon P Fraessle
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Manuel Effenberger
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Eileen Benke
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Gottfried Navratil
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Norbert Hentschel
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Dirk H Busch
- Institute for Medical Microbiology Immunology and Hygiene, Technical University of Munich, Munich, Germany
| | - Thomas Schmidt
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Christian Stemberger
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
| | - Lothar Germeroth
- Juno Therapeutics GmbH, Bristol-Myers Squibb Company, Grillparzerstr. 10, 81675, Munich, Germany
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47
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Vasina M, Velecký J, Planas-Iglesias J, Marques SM, Skarupova J, Damborsky J, Bednar D, Mazurenko S, Prokop Z. Tools for computational design and high-throughput screening of therapeutic enzymes. Adv Drug Deliv Rev 2022; 183:114143. [PMID: 35167900 DOI: 10.1016/j.addr.2022.114143] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022]
Abstract
Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jan Velecký
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Sergio M Marques
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jana Skarupova
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic; Enantis, INBIT, Kamenice 34, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
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48
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de Rutte J, Dimatteo R, Zhu S, Archang MM, Di Carlo D. Sorting single-cell microcarriers using commercial flow cytometers. SLAS Technol 2022; 27:150-159. [PMID: 35058209 PMCID: PMC9018595 DOI: 10.1016/j.slast.2021.10.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The scale of biological discovery is driven by the vessels in which we can perform assays and analyze results, from multi-well plates to microfluidic compartments. We report on the compatibility of sub-nanoliter single-cell containers or "nanovials" with commercial fluorescence activated cell sorters (FACS). This recent lab on a particle approach utilizes 3D structured microparticles to isolate cells and perform single-cell assays at scale with existing lab equipment. Use of flow cytometry led to detection of fluorescently labeled protein with dynamic ranges spanning 2-3 log and detection limits down to ∼10,000 molecules per nanovial, which was the lowest amount tested. Detection limits were improved compared to fluorescence microscopy measurements using a 20X objective and a cooled CMOS camera. Nanovials with diameters between 35-85 µm could also be sorted with purity from 99-93% on different commercial instruments at throughputs up to 800 events/second. Cell-loaded nanovials were found to have unique forward and side (or back) scatter signatures that enabled gating of cell-containing nanovials using scatter metrics alone. The compatibility of nanovials with widely-available commercial FACS instruments promises to democratize single-cell assays used in discovery of antibodies and cell therapies, by enabling analysis of single cells based on secreted products and leveraging the unmatched analytical capabilities of flow cytometers to sort important clones.
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Affiliation(s)
- Joseph de Rutte
- Department of Bioengineering, University of California, Los Angeles, United States; Partillion Bioscience Corporation, Los Angeles, CA, United States.
| | - Robert Dimatteo
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, United States
| | - Sheldon Zhu
- Partillion Bioscience Corporation, Los Angeles, CA, United States
| | - Maani M Archang
- Department of Bioengineering, University of California, Los Angeles, United States
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, United States; Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, United States; California NanoSystems Institute, University of California, Los Angeles, United States; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, United States
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49
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Li W, Zhou Y, Deng Y, Khoo BL. Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare. Cancers (Basel) 2022. [PMID: 35159085 DOI: 10.3390/cancfers14030818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023] Open
Abstract
Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.
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Affiliation(s)
- Wei Li
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong 999077, China
| | - Yunlan Zhou
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200092, China
| | - Yanlin Deng
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China
| | - Bee Luan Khoo
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong 999077, China
- Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen-Futian Research Institute, Shenzhen 518057, China
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50
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Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare. Cancers (Basel) 2022; 14:cancers14030818. [PMID: 35159085 PMCID: PMC8834418 DOI: 10.3390/cancers14030818] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary We proposed a comprehensive early prediction tool based on liquid biopsy for the label-free phenotypic analysis of cell clusters from clinical samples (n = 31). Our custom algorithm analysis, combined with a microfluidic-based tumor model, was designed to assess and stratify cancer patients in a label-free, cost-effective, and user-friendly way. Multiple quantitative phenotypic parameters (cluster size, thickness, roughness, and thickness per area) were derived from the profiling of the patient-derived cell clusters. Our platform could distinguish healthy donors from pretreatment cancer patients with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). In addition, the ratio of normalized gray value to cluster size (RGVS) parameter was significantly correlated to treatment duration and cancer stage. In conclusion, our patient-centric, early prediction tool will allow the prognosis of patients in a relatively less invasive manner, which can help clinicians identify diseases or indicate the need for new treatment strategies. Abstract Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.
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