1
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Wei Y, Forelli RF, Hansen C, Levesque JP, Tran N, Agar JC, Di Guglielmo G, Mauel ME, Navratil GA. Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:073509. [PMID: 38980128 DOI: 10.1063/5.0190354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 06/18/2024] [Indexed: 07/10/2024]
Abstract
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
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Affiliation(s)
- Y Wei
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - R F Forelli
- Real-time Processing Systems Division, Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA
| | - C Hansen
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - J P Levesque
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - N Tran
- Real-time Processing Systems Division, Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - J C Agar
- Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, Pennsylvania 19104, USA
| | - G Di Guglielmo
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois 60208, USA
- Microelectronics Division, Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - M E Mauel
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - G A Navratil
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
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2
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Greatbatch CJ, Lu Q, Hung S, Tran SN, Wing K, Liang H, Han X, Zhou T, Siggs OM, Mackey DA, Liu GS, Cook AL, Powell JE, Craig JE, MacGregor S, Hewitt AW. Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology. OPHTHALMOLOGY SCIENCE 2024; 4:100504. [PMID: 38682030 PMCID: PMC11046128 DOI: 10.1016/j.xops.2024.100504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 05/01/2024]
Abstract
Purpose Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation. Design Experimental study. Subjects Primary TMCs collected from human donors. Methods Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. Main Outcome Measures Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls. Results Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC (P < 0.05) between genes, allowing for pathological gene prioritization. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). Conclusions We demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Connor J. Greatbatch
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Qinyi Lu
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Sandy Hung
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
| | - Son N. Tran
- Department of Information and Communication Technology, University of Tasmania, Hobart, Tasmania, Australia
| | - Kristof Wing
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Helena Liang
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
| | - Xikun Han
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Tiger Zhou
- Department of Ophthalmology, Flinders Medical Centre, Flinders University, Bedford Park, Australia
| | - Owen M. Siggs
- Cellular Genomics Group, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia
| | - David A. Mackey
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Western Australia, Australia
| | - Guei-Sheung Liu
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
| | - Anthony L. Cook
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia
| | - Joseph E. Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- UNSW Cellular Genomics Futures Institute, UNSW, Sydney, New South Wales, Australia
| | - Jamie E. Craig
- Department of Ophthalmology, Flinders Medical Centre, Flinders University, Bedford Park, Australia
| | - Stuart MacGregor
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Alex W. Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
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3
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Sonmez ME, Gumus NE, Eczacioglu N, Develi EE, Yücel K, Yildiz HB. Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines. MARINE POLLUTION BULLETIN 2024; 205:116616. [PMID: 38936001 DOI: 10.1016/j.marpolbul.2024.116616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/13/2024] [Accepted: 06/16/2024] [Indexed: 06/29/2024]
Abstract
Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.
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Affiliation(s)
- Mesut Ersin Sonmez
- Department of Bioengineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye
| | - Numan Emre Gumus
- Department of Environmental Protection Technology, Kazım Karabekir Vocational School, Karamanoglu Mehmetbey University, Karaman, Türkiye.
| | - Numan Eczacioglu
- Department of Bioengineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Türkiye
| | | | - Kamile Yücel
- Department of Medical Biochemistry, Faculty of Medicine, KTO, Karatay University, Konya, Türkiye
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4
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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:e2307591. [PMID: 38864546 DOI: 10.1002/advs.202307591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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 Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Maximus C F Yeung
- Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - Michael K Y Hsin
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - James C M Ho
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
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5
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Zhu J, Pan S, Chai H, Zhao P, Feng Y, Cheng Z, Zhang S, Wang W. Microfluidic Impedance Cytometry Enabled One-Step Sample Preparation for Efficient Single-Cell Mass Spectrometry. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2310700. [PMID: 38483007 DOI: 10.1002/smll.202310700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/05/2024] [Indexed: 06/27/2024]
Abstract
Single-cell mass spectrometry (MS) is significant in biochemical analysis and holds great potential in biomedical applications. Efficient sample preparation like sorting (i.e., separating target cells from the mixed population) and desalting (i.e., moving the cells off non-volatile salt solution) is urgently required in single-cell MS. However, traditional sample preparation methods suffer from complicated operation with various apparatus, or insufficient performance. Herein, a one-step sample preparation strategy by leveraging label-free impedance flow cytometry (IFC) based microfluidics is proposed. Specifically, the IFC framework to characterize and sort single-cells is adopted. Simultaneously with sorting, the target cell is transferred from the local high-salinity buffer to the MS-compatible solution. In this way, one-step sorting and desalting are achieved and the collected cells can be directly fed for MS analysis. A high sorting efficiency (>99%), cancer cell purity (≈87%), and desalting efficiency (>99%), and the whole workflow of impedance-based separation and MS analysis of normal cells (MCF-10A) and cancer cells (MDA-MB-468) are verified. As a standalone sample preparation module, the microfluidic chip is compatible with a variety of MS analysis methods, and envisioned to provide a new paradigm in efficient MS sample preparation, and further in multi-modal (i.e., electrical and metabolic) characterization of single-cells.
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Affiliation(s)
- Junwen Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Siyuan Pan
- Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Huichao Chai
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Peng Zhao
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yongxiang Feng
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Zhen Cheng
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sichun Zhang
- Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Wenhui Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
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6
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Mandracchia B, Zheng C, Rajendran S, Liu W, Forghani P, Xu C, Jia S. High-speed optical imaging with sCMOS pixel reassignment. Nat Commun 2024; 15:4598. [PMID: 38816394 PMCID: PMC11139943 DOI: 10.1038/s41467-024-48987-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Fluorescence microscopy has undergone rapid advancements, offering unprecedented visualization of biological events and shedding light on the intricate mechanisms governing living organisms. However, the exploration of rapid biological dynamics still poses a significant challenge due to the limitations of current digital camera architectures and the inherent compromise between imaging speed and other capabilities. Here, we introduce sHAPR, a high-speed acquisition technique that leverages the operating principles of sCMOS cameras to capture fast cellular and subcellular processes. sHAPR harnesses custom fiber optics to convert microscopy images into one-dimensional recordings, enabling acquisition at the maximum camera readout rate, typically between 25 and 250 kHz. We have demonstrated the utility of sHAPR with a variety of phantom and dynamic systems, including high-throughput flow cytometry, cardiomyocyte contraction, and neuronal calcium waves, using a standard epi-fluorescence microscope. sHAPR is highly adaptable and can be integrated into existing microscopy systems without requiring extensive platform modifications. This method pushes the boundaries of current fluorescence imaging capabilities, opening up new avenues for investigating high-speed biological phenomena.
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Affiliation(s)
- Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- E.T.S.I. Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Corey Zheng
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Suraj Rajendran
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Parvin Forghani
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Chunhui Xu
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
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7
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Kuhn TM, Paulsen M, Cuylen-Haering S. Accessible high-speed image-activated cell sorting. Trends Cell Biol 2024:S0962-8924(24)00094-1. [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] [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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8
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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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9
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Ferreira EKGD, Silveira GF. Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks. Sci Rep 2024; 14:9031. [PMID: 38641688 PMCID: PMC11031575 DOI: 10.1038/s41598-024-59625-z] [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: 01/05/2024] [Accepted: 04/12/2024] [Indexed: 04/21/2024] Open
Abstract
Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification-something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.
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Affiliation(s)
| | - G F Silveira
- Carlos Chagas Institute, Curitiba, PR, CEP 81310-020, Brazil.
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10
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Findinier J, Joubert LM, Schmid MF, Malkovskiy A, Chiu W, Burlacot A, Grossman AR. Dramatic Changes in Mitochondrial Subcellular Location and Morphology Accompany Activation of the CO 2 Concentrating Mechanism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586705. [PMID: 38585955 PMCID: PMC10996633 DOI: 10.1101/2024.03.25.586705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Dynamic changes in intracellular ultrastructure can be critical for the ability of organisms to acclimate to environmental conditions. Microalgae, which are responsible for ~50% of global photosynthesis, compartmentalize their Rubisco into a specialized structure known as the pyrenoid when the cells experience limiting CO2 conditions; this compartmentalization appears to be a component of the CO2 Concentrating Mechanism (CCM), which facilitates photosynthetic CO2 fixation as environmental levels of inorganic carbon (Ci) decline. Changes in the spatial distribution of mitochondria in green algae have also been observed under CO2 limiting conditions, although a role for this reorganization in CCM function remains unclear. We used the green microalgae Chlamydomonas reinhardtii to monitor changes in the position and ultrastructure of mitochondrial membranes as cells transition between high CO2 (HC) and Low/Very Low CO2 (LC/VLC). Upon transferring cells to VLC, the mitochondria move from a central to a peripheral location, become wedged between the plasma membrane and chloroplast envelope, and mitochondrial membranes orient in parallel tubular arrays that extend from the cell's apex to its base. We show that these ultrastructural changes require protein and RNA synthesis, occur within 90 min of shifting cells to VLC conditions, correlate with CCM induction and are regulated by the CCM master regulator CIA5. The apico-basal orientation of the mitochondrial membrane, but not the movement of the mitochondrion to the cell periphery, is dependent on microtubules and the MIRO1 protein, which is involved in membrane-microtubule interactions. Furthermore, blocking mitochondrial electron transport in VLC acclimated cells reduces the cell's affinity for inorganic carbon. Overall, our results suggest that CIA5-dependent mitochondrial repositioning/reorientation functions in integrating cellular architecture and energetics with CCM activities and invite further exploration of how intracellular architecture can impact fitness under dynamic environmental conditions.
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Affiliation(s)
- Justin Findinier
- The Carnegie Institution for Science, Biosphere Sciences & Engineering, Stanford, CA 94305, USA
| | - Lydia-Marie Joubert
- SLAC National Accelerator Laboratory, Division of CryoEM and Bioimaging, Menlo Park, CA 94025, USA
| | - Michael F. Schmid
- SLAC National Accelerator Laboratory, Division of CryoEM and Bioimaging, Menlo Park, CA 94025, USA
| | - Andrey Malkovskiy
- The Carnegie Institution for Science, Biosphere Sciences & Engineering, Stanford, CA 94305, USA
| | - Wah Chiu
- SLAC National Accelerator Laboratory, Division of CryoEM and Bioimaging, Menlo Park, CA 94025, USA
- Stanford University, Department of Bioengineering, Stanford, CA 94305, USA
| | - Adrien Burlacot
- The Carnegie Institution for Science, Biosphere Sciences & Engineering, Stanford, CA 94305, USA
- Stanford University, Biology Department, Stanford, CA 94305, USA
| | - Arthur R. Grossman
- The Carnegie Institution for Science, Biosphere Sciences & Engineering, Stanford, CA 94305, USA
- Stanford University, Biology Department, Stanford, CA 94305, USA
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11
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Dimitriadis S, Dova L, Kotsianidis I, Hatzimichael E, Kapsali E, Markopoulos GS. Imaging Flow Cytometry: Development, Present Applications, and Future Challenges. Methods Protoc 2024; 7:28. [PMID: 38668136 PMCID: PMC11054958 DOI: 10.3390/mps7020028] [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: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/29/2024] Open
Abstract
Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.
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Affiliation(s)
- Savvas Dimitriadis
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Lefkothea Dova
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Ioannis Kotsianidis
- Department of Hematology, University Hospital of Alexandroupolis, Democritus University of Thrace, 69100 Alexandroupolis, Greece;
| | - Eleftheria Hatzimichael
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Eleni Kapsali
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Georgios S. Markopoulos
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
- Department of Surgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
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12
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Tabata K, Kawagoe H, Taylor JN, Mochizuki K, Kubo T, Clement JE, Kumamoto Y, Harada Y, Nakamura A, Fujita K, Komatsuzaki T. On-the-fly Raman microscopy guaranteeing the accuracy of discrimination. Proc Natl Acad Sci U S A 2024; 121:e2304866121. [PMID: 38483992 PMCID: PMC10962959 DOI: 10.1073/pnas.2304866121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/15/2023] [Indexed: 03/19/2024] Open
Abstract
Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.
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Affiliation(s)
- Koji Tabata
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
| | - Hiroyuki Kawagoe
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
| | - J. Nicholas Taylor
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
| | - Kentaro Mochizuki
- Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto602–8566, Kyoto, Japan
| | - Toshiki Kubo
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
| | - Jean-Emmanuel Clement
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
| | - Yasuaki Kumamoto
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
| | - Yoshinori Harada
- Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto602–8566, Kyoto, Japan
| | - Atsuyoshi Nakamura
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo060–0814, Hokkaido, Japan
| | - Katsumasa Fujita
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, AIST-Osaka University, Suita565–0871, Osaka, Japan
| | - Tamiki Komatsuzaki
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
- Graduate School of Chemical Sciences and Engineering Materials Chemistry, and Engineering Course, Hokkaido University, Sapporo060–0812, Hokkaido, Japan
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki567-0047, Osaka, Japan
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13
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Lugagne JB, Blassick CM, Dunlop MJ. Deep model predictive control of gene expression in thousands of single cells. Nat Commun 2024; 15:2148. [PMID: 38459057 PMCID: PMC10923782 DOI: 10.1038/s41467-024-46361-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.
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Affiliation(s)
- Jean-Baptiste Lugagne
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA.
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA.
| | - Caroline M Blassick
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA
| | - Mary J Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA.
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA.
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14
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Hua X, Han K, Mandracchia B, Radmand A, Liu W, Kim H, Yuan Z, Ehrlich SM, Li K, Zheng C, Son J, Silva Trenkle AD, Kwong GA, Zhu C, Dahlman JE, Jia S. Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis. Nat Commun 2024; 15:1975. [PMID: 38438356 PMCID: PMC10912605 DOI: 10.1038/s41467-024-46250-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 02/15/2024] [Indexed: 03/06/2024] Open
Abstract
Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.
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Affiliation(s)
- Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Keyi Han
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Afsane Radmand
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Chemical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hyejin Kim
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Zhou Yuan
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Samuel M Ehrlich
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Kaitao Li
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Corey Zheng
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jeonghwan Son
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Aaron D Silva Trenkle
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Cheng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - James E Dahlman
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
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15
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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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16
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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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17
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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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18
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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19
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Xu Z, Chen Z, Yang S, Chen S, Guo T, Chen H. Passive Focusing of Single Cells Using Microwell Arrays for High-Accuracy Image-Activated Sorting. Anal Chem 2024; 96:347-354. [PMID: 38153415 DOI: 10.1021/acs.analchem.3c04195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Sorting single cells from a population was of critical importance in areas such as cell line development and cell therapy. Image-based sorting is becoming a promising technique for the nonlabeling isolation of cells due to the capability of providing the details of cell morphology. This study reported the focusing of cells using microwell arrays and the following automatic size sorting based on the real-time recognition of cells. The simulation first demonstrated the converged streamlines to the symmetrical plane contributed to the focusing effect. Then, the influence of connecting microchannel, flowing length, particle size, and the sample flow rate on the focusing effect was experimentally analyzed. Both microspheres and cells could be aligned in a straight line at the Reynolds number (Re) of 0.027-0.187 and 0.027-0.08, respectively. The connecting channel was proved to drastically improve the focusing performance. Afterward, a tapered microwell array was utilized to focus sphere/cell spreading in a wide channel to a straight line. Finally, a custom algorithm was employed to identify and sort the size of microspheres/K562 cells with a throughput of 1 event/s and an accuracy of 97.8/97.1%. The proposed technique aligned cells to a straight line at low Reynolds numbers and greatly facilitated the image-activated sorting without the need for a high-speed camera or flow control components with high frequency. Therefore, it is of enormous application potential in the field of nonlabeled separation of single cells.
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Affiliation(s)
- Zheng Xu
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Zhenlin Chen
- Department of Biomedical Engineering, College of Engineering, Kowloon, City University of Hong Kong, Hong Kong SAR, China
| | - Shiming Yang
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Siyuan Chen
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Tianruo Guo
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Huaying Chen
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
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20
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Bai Y, Camargo CM, Glasauer SMK, Gifford R, Tian X, Longhini AP, Kosik KS. Single-cell mapping of lipid metabolites using an infrared probe in human-derived model systems. Nat Commun 2024; 15:350. [PMID: 38191490 PMCID: PMC10774263 DOI: 10.1038/s41467-023-44675-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/20/2023] [Indexed: 01/10/2024] Open
Abstract
Understanding metabolic heterogeneity is the key to uncovering the underlying mechanisms of metabolic-related diseases. Current metabolic imaging studies suffer from limitations including low resolution and specificity, and the model systems utilized often lack human relevance. Here, we present a single-cell metabolic imaging platform to enable direct imaging of lipid metabolism with high specificity in various human-derived 2D and 3D culture systems. Through the incorporation of an azide-tagged infrared probe, selective detection of newly synthesized lipids in cells and tissue became possible, while simultaneous fluorescence imaging enabled cell-type identification in complex tissues. In proof-of-concept experiments, newly synthesized lipids were directly visualized in human-relevant model systems among different cell types, mutation status, differentiation stages, and over time. We identified upregulated lipid metabolism in progranulin-knockdown human induced pluripotent stem cells and in their differentiated microglia cells. Furthermore, we observed that neurons in brain organoids exhibited a significantly lower lipid metabolism compared to astrocytes.
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Affiliation(s)
- Yeran Bai
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA.
- Photothermal Spectroscopy Corp., Santa Barbara, CA, USA.
| | - Carolina M Camargo
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Stella M K Glasauer
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Raymond Gifford
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Xinran Tian
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Andrew P Longhini
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA
| | - Kenneth S Kosik
- Neuroscience Research Institute, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA.
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21
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Zheng D, Zhang J, Jiang W, Xu Y, Meng H, Poh CL, Chen CH. Graphene oxide aptasensor droplet assay for detection of metabolites secreted by single cells applied to synthetic biology. LAB ON A CHIP 2023; 24:137-147. [PMID: 38054213 DOI: 10.1039/d3lc00959a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π-π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction.
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Affiliation(s)
- Dan Zheng
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore.
| | - Jingyun Zhang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore.
| | - Wenxin Jiang
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Ying Xu
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Haixu Meng
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Chueh Loo Poh
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore.
| | - Chia-Hung Chen
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
- City University of Hong Kong Shenzhen Research Institute, Shenzhen Virtual University Park, Shenzhen, China
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22
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Cui J, Chen R, Sun H, Xue Y, Diao Z, Song J, Wang X, Zhang J, Wang C, Ma B, Xu J, Luan G, Lu X. Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing. Synth Syst Biotechnol 2023; 8:708-715. [PMID: 38053584 PMCID: PMC10693988 DOI: 10.1016/j.synbio.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/03/2023] [Accepted: 11/05/2023] [Indexed: 12/07/2023] Open
Abstract
By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.
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Affiliation(s)
- Jinyu Cui
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Rongze Chen
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
| | - Huili Sun
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
| | - Yingyi Xue
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Zhidian Diao
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
| | - Jingyun Song
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Xiaohang Wang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Jia Zhang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Chen Wang
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Bo Ma
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
| | - Jian Xu
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Guodong Luan
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
- Dalian National Laboratory for Clean Energy, Dalian, 116023, China
| | - Xuefeng Lu
- Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, No. 189 Songling Road, Qingdao, 266101, China
- Shandong Energy Institute, Qingdao, 266101, China
- Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China
- College of Life Science, University of Chinese Academy of Sciences, Beijing, China
- Dalian National Laboratory for Clean Energy, Dalian, 116023, China
- Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
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23
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Chai B, Efstathiou C, Yue H, Draviam VM. Opportunities and challenges for deep learning in cell dynamics research. Trends Cell Biol 2023:S0962-8924(23)00228-3. [PMID: 38030542 DOI: 10.1016/j.tcb.2023.10.010] [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: 07/31/2023] [Revised: 09/30/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023]
Abstract
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.
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Affiliation(s)
- Binghao Chai
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Christoforos Efstathiou
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Haoran Yue
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK
| | - Viji M Draviam
- School of Biological and Behavioural Sciences, Queen Mary University of London (QMUL), London E1 4NS, UK; The Alan Turing Institute, London NW1 2DB, UK.
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24
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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25
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Sun J, Huang X, Chen J, Xiang R, Ke X, Lin S, Xuan W, Liu S, Cao Z, Sun L. Recent advances in deformation-assisted microfluidic cell sorting technologies. Analyst 2023; 148:4922-4938. [PMID: 37743834 DOI: 10.1039/d3an01150j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cell sorting is an essential prerequisite for cell research and has great value in life science and clinical studies. Among the many microfluidic cell sorting technologies, label-free methods based on the size of different cell types have been widely studied. However, the heterogeneity in size for cells of the same type and the inevitable size overlap between different types of cells would result in performance degradation in size-based sorting. To tackle such challenges, deformation-assisted technologies are receiving more attention recently. Cell deformability is an inherent biophysical marker of cells that reflects the changes in their internal structures and physiological states. It provides additional dimensional information for cell sorting besides size. Therefore, in this review, we summarize the recent advances in deformation-assisted microfluidic cell sorting technologies. According to how the deformability is characterized and the form in which the force acts, the technologies can be divided into two categories: (1) the indirect category including transit-time-based and image-based methods, and (2) the direct category including microstructure-based and hydrodynamics-based methods. Finally, the separation performance and the application scenarios of each method, the existing challenges and future outlook are discussed. Deformation-assisted microfluidic cell sorting technologies are expected to realize greater potential in the label-free analysis of cells.
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Affiliation(s)
- Jingjing Sun
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Xiwei Huang
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Jin Chen
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Rikui Xiang
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Xiang Ke
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Siru Lin
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Weipeng Xuan
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
| | - Shan Liu
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, China
| | - Zhen Cao
- College of Information Science and Electronic Engineering, Zhejiang University, China
| | - Lingling Sun
- Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, China.
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26
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Zheng H, Huang S, Zhang J, Zhang R, Wang J, Yuan J, Li A, Yang X, Zhang Z. C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells. SCIENCE CHINA. LIFE SCIENCES 2023; 66:2415-2428. [PMID: 37243949 DOI: 10.1007/s11427-022-2327-y] [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: 01/08/2023] [Accepted: 03/17/2023] [Indexed: 05/29/2023]
Abstract
Cell instance segmentation is a fundamental task for many biological applications, especially for packed cells in three-dimensional (3D) microscope images that can fully display cellular morphology. Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional (2D) instance segmentation. However, current methods cannot achieve high segmentation accuracy for irregular cells in 3D images. In this study, we introduce a universal, morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice (C1M2), which can segment cells from a wide range of image types and does not require nucleus images. C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells. Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.
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Affiliation(s)
- Hao Zheng
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Songlin Huang
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China
| | - Jing Zhang
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ren Zhang
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jialu Wang
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jing Yuan
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Anan Li
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xin Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Zhihong Zhang
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
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27
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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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28
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Salek M, Li N, Chou HP, Saini K, Jovic A, Jacobs KB, Johnson C, Lu V, Lee EJ, Chang C, Nguyen P, Mei J, Pant KP, Wong-Thai AY, Smith QF, Huang S, Chow R, Cruz J, Walker J, Chan B, Musci TJ, Ashley EA, Masaeli MM. COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning. Commun Biol 2023; 6:971. [PMID: 37740030 PMCID: PMC10516940 DOI: 10.1038/s42003-023-05325-9] [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: 01/30/2023] [Accepted: 09/06/2023] [Indexed: 09/24/2023] Open
Abstract
Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
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Affiliation(s)
- Mahyar Salek
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA.
| | - Nianzhen Li
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Hou-Pu Chou
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Kiran Saini
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Andreja Jovic
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Kevin B Jacobs
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | - Vivian Lu
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Esther J Lee
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | - Phuc Nguyen
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Jeanette Mei
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Krishna P Pant
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | | | | | - Ryan Chow
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Janifer Cruz
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Jeff Walker
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Bryan Chan
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Thomas J Musci
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Euan A Ashley
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
- Department of Medicine, Genetics, & Biomedical Data Science, Stanford University, Stanford, CA, USA
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29
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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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30
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Ebata H, Shima T, Iizuka R, Uemura S. Accumulation of TERT in mitochondria exerts two opposing effects on apoptosis. FEBS Open Bio 2023; 13:1667-1682. [PMID: 37525387 PMCID: PMC10476567 DOI: 10.1002/2211-5463.13682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023] Open
Abstract
Telomerase reverse transcriptase (TERT) is a protein that catalyzes the reverse transcription of telomere elongation. TERT is also expected to play a non-canonical role beyond telomere lengthening since it localizes not only in the nucleus but also in mitochondria, where telomeres do not exist. Several studies have reported that mitochondrial TERT regulates apoptosis induced by oxidative stress. However, there is still some controversy as to whether mitochondrial TERT promotes or inhibits apoptosis, mainly due to the lack of information on changes in TERT distribution in individual cells over time. Here, we simultaneously detected apoptosis and TERT localization after oxidative stress in individual HeLa cells by live-cell tracking. Single-cell tracking revealed that the stress-induced accumulation of TERT in mitochondria caused apoptosis, but that accumulation increased over time until cell death. The results suggest a new model in which mitochondrial TERT has two opposing effects at different stages of apoptosis: it predetermines apoptosis at the first stage of cell-fate determination, but also delays apoptosis at the second stage. As such, our data support a model that integrates the two opposing hypotheses on mitochondrial TERT's effect on apoptosis. Furthermore, detailed statistical analysis of TERT mutations, which have been predicted to inhibit TERT transport to mitochondria, revealed that these mutations suppress apoptosis independent of mitochondrial localization of TERT. Together, these results imply that the non-canonical functions of TERT affect a wide range of mitochondria-dependent and mitochondria-independent apoptosis pathways.
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Affiliation(s)
- Hiroshi Ebata
- Department of Biological Sciences, Graduate School of ScienceThe University of TokyoJapan
- Present address:
Buck Institute for Research on AgingNovatoCAUSA
| | - Tomohiro Shima
- Department of Biological Sciences, Graduate School of ScienceThe University of TokyoJapan
| | - Ryo Iizuka
- Department of Biological Sciences, Graduate School of ScienceThe University of TokyoJapan
| | - Sotaro Uemura
- Department of Biological Sciences, Graduate School of ScienceThe University of TokyoJapan
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31
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Haddad M, Frickenstein A, Wilhelm S. High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions. Trends Analyt Chem 2023; 166:117172. [PMID: 37520860 PMCID: PMC10373476 DOI: 10.1016/j.trac.2023.117172] [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] [Indexed: 08/01/2023]
Abstract
Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.
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Affiliation(s)
- Majood Haddad
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Alex Frickenstein
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Stefan Wilhelm
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73104, USA
- Institute for Biomedical Engineering, Science, and Technology (IBEST), University of Oklahoma, Norman, Oklahoma, 73019, USA
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32
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Julian T, Tang T, Hosokawa Y, Yalikun Y. Machine learning implementation strategy in imaging and impedance flow cytometry. BIOMICROFLUIDICS 2023; 17:051506. [PMID: 37900052 PMCID: PMC10613093 DOI: 10.1063/5.0166595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.
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Affiliation(s)
- Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | - Tao Tang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
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33
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Pang K, Dong S, Zhu Y, Zhu X, Zhou Q, Gu B, Jin W, Zhang R, Fu Y, Yu B, Sun D, Duanmu Z, Wei X. Advanced flow cytometry for biomedical applications. JOURNAL OF BIOPHOTONICS 2023; 16:e202300135. [PMID: 37263969 DOI: 10.1002/jbio.202300135] [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: 04/24/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/03/2023]
Abstract
Flow cytometry (FC) is a versatile tool with excellent capabilities to detect and measure multiple characteristics of a population of cells or particles. Notable advancements in in vivo photoacoustic FC, coherent Raman FC, microfluidic FC, and so on, have been achieved in the last two decades, which endows FC with new functions and expands its applications in basic research and clinical practice. Advanced FC broadens the tools available to researchers to conduct research involving cancer detection, microbiology (COVID-19, HIV, bacteria, etc.), and nucleic acid analysis. This review presents an overall picture of advanced flow cytometers and provides not only a clear understanding of their mechanisms but also new insights into their practical applications. We identify the latest trends in this area and aim to raise awareness of advanced techniques of FC. We hope this review expands the applications of FC and accelerates its clinical translation.
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Affiliation(s)
- Kai Pang
- School of Instrument Science and Opto-Electronics Engineering of Beijing Information Science & Technology University, Beijing, China
| | - Sihan Dong
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yuxi Zhu
- School of Instrument Science and Opto-Electronics Engineering of Beijing Information Science & Technology University, Beijing, China
| | - Xi Zhu
- Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Quanyu Zhou
- Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bobo Gu
- Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Jin
- International Cancer Institute, Peking University, Beijing, China
| | - Rui Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yuting Fu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Bingchen Yu
- School of Instrument Science and Opto-Electronics Engineering of Beijing Information Science & Technology University, Beijing, China
| | - Da Sun
- School of Instrument Science and Opto-Electronics Engineering of Beijing Information Science & Technology University, Beijing, China
| | - Zheng Duanmu
- School of Instrument Science and Opto-Electronics Engineering of Beijing Information Science & Technology University, Beijing, China
| | - Xunbin Wei
- International Cancer Institute, Peking University, Beijing, China
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34
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Nishiyama R, Furuya K, McCann P, Kacenauskaite L, Laursen BW, Flood AH, Hiramatsu K, Goda K. Boosting the Brightness of Raman Tags Using Cyanostar Macrocycles. Anal Chem 2023; 95:12835-12841. [PMID: 37589955 DOI: 10.1021/acs.analchem.3c01958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Raman probes have received growing attention for their potential use in super-multiplex biological imaging and flow cytometry applications that cannot be achieved using fluorescent probes. However, obtaining strong Raman scattering signals from small Raman probes has posed a challenge that holds back their practical implementation. Here, we present new types of Raman-active nanoparticles (Rdots) that incorporate ionophore macrocycles, known as cyanostars, to act as ion-driven and structure-directing spacers to address this problem. These macrocycle-enhanced Rdots (MERdots) exhibit sharper and higher electronic absorption peaks than Rdots. When combined with resonant broadband time-domain Raman spectroscopy, these MERdots show a ∼3-fold increase in Raman intensity compared to conventional Rdots under the same particle concentration. Additionally, the detection limit on the concentration of MERdots is improved by a factor of 2.5 compared to that of Rdots and a factor of 430 compared to that of Raman dye molecules in solution. The compact size of MERdots (26 nm in diameter) and their increased Raman signal intensity, along with the broadband capabilities of time-domain resonant Raman spectroscopy, make them promising candidates for a wide range of biological applications.
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Affiliation(s)
- Ryo Nishiyama
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
| | - Kei Furuya
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
| | - Phillip McCann
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
| | | | - Bo W Laursen
- Nano-Science Center and Department of Chemistry, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Amar H Flood
- Department of Chemistry, Indiana University Bloomington, Bloomington, Indiana 47405, United States
| | - Kotaro Hiramatsu
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
- Research Center for Spectrochemistry, The University of Tokyo, Tokyo 113-0033, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- Institute of Technological Sciences, Wuhan University, Wuhan, Hubei 430072, P. R. China
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35
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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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36
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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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37
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Schwartz M, Israel U, Wang XJ, Laubscher E, Yu C, Dilip R, Li Q, Mari J, Soro J, Yu K, Pradhan E, Ates A, Gallandt D, Barnowski R, Pao E, Van Valen D. Scaling biological discovery at the interface of deep learning and cellular imaging. Nat Methods 2023; 20:956-957. [PMID: 37434003 DOI: 10.1038/s41592-023-01931-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Affiliation(s)
- Morgan Schwartz
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Uriah Israel
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Xuefei Julie Wang
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Emily Laubscher
- Department of Chemistry, California Institute of Technology, Pasadena, CA, USA
| | - Changhua Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Rohit Dilip
- Department of Computer Science, California Institute of Technology, Pasadena, CA, USA
| | - Qilin Li
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Joud Mari
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Johnathon Soro
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Kevin Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Elora Pradhan
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ada Ates
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Danielle Gallandt
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ross Barnowski
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Edward Pao
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
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38
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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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39
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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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40
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Shrivastava A, Mandal S, Pattanayek SK, Rathore AS. Rapid Estimation of Size-Based Heterogeneity in Monoclonal Antibodies by Machine Learning-Enhanced Dynamic Light Scattering. Anal Chem 2023; 95:8299-8309. [PMID: 37200383 DOI: 10.1021/acs.analchem.3c00650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Aggregation of monoclonal antibody therapeutics is a serious concern that is believed to impact product safety and efficacy. There is a need for analytical approaches that enable rapid estimation of mAb aggregates. Dynamic light scattering (DLS) is a well-established technique for estimating the average size of protein aggregates or for evaluating sample stability. It is usually used to measure the size and size distribution over a wide range of nano- to micro-sized particles using time-dependent fluctuations in the intensity of scattered light arising from the Brownian motion of particles. In this study, we present a novel DLS-based approach that allows us to quantify the relative percentage of multimers (monomer, dimer, trimer, and tetramer) in a monoclonal antibody (mAb) therapeutic product. The proposed approach uses a machine learning (ML) algorithm and regression to model the system and predict the amount of relevant species such as monomer, dimer, trimer, and tetramer of a mAb in the size range of 10-100 nm. The proposed DLS-ML technique compares favorably to all potential alternatives with respect to the key method attributes, including per sample cost of analysis, per sample time of data acquisition along with ML-based aggregate prediction (<2 min), sample requirements (<3 μg), and user-friendliness of analysis. The proposed rapid method can serve as an orthogonal tool to size exclusion chromatography, which is the current industry workhorse for aggregate assessment.
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Affiliation(s)
- Anuj Shrivastava
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Shyamapada Mandal
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Sudip K Pattanayek
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016, India
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41
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Luo Y, Huang Y, Li Y, Duan X, Jiang Y, Wang C, Fang J, Xi L, Nguyen NT, Song C. Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip. LAB ON A CHIP 2023. [PMID: 37194324 DOI: 10.1039/d3lc00127j] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Biomolecular imaging of intracellular structures of a single cell and subsequent screening of the cells are of high demand in metabolic engineering to develop strains with the desired phenotype. However, the capability of current methods is limited to population-scale identification of cell phenotyping. To address this challenge, we propose to utilize dispersive phase microscopy incorporated with a droplet-based microfluidic system that combines droplet volume-on-demand generation, biomolecular imaging, and droplet-on-demand sorting, to achieve high-throughput screening of cells with an identified phenotype. Particularly, cells are encapsulated in homogeneous environments with microfluidic droplet formation, and the biomolecule-induced dispersive phase can be investigated to indicate the biomass of a specific metabolite in a single cell. The retrieved biomass information consequently guides the on-chip droplet sorting unit to screen cells with the desired phenotype. To demonstrate the proof of concept, we showcase the method by promoting the evolution of the Haematococcus lacustris strain toward a high production of natural antioxidant astaxanthin. The validation of the proposed system with on-chip single-cell imaging and droplet manipulation functionalities reveals the high-throughput single-cell phenotyping and selection potential that applies to many other biofactory scenarios, such as biofuel production, critical quality attribute control in cell therapy, etc.
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Affiliation(s)
- Yingdong Luo
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
| | - Yuanyuan Huang
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
| | - Yani Li
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
| | - Xiudong Duan
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
| | - Yongguang Jiang
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Cong Wang
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
| | - Jiakun Fang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Lei Xi
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
| | - Nam-Trung Nguyen
- Queensland Micro, and Nanotechnology Centre, Griffith University, 170 Kessels Road, QLD 4111, Nathan, Australia
| | - Chaolong Song
- A School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.
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42
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 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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43
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Wang Y, Zhai WD, Wu C. Algal cell viability assessment: The role of environmental factors in phytoplankton population dynamics. MARINE POLLUTION BULLETIN 2023; 189:114743. [PMID: 36898274 DOI: 10.1016/j.marpolbul.2023.114743] [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: 08/11/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
The viability of algal cells is one of the most fundamental issues in marine ecological research. In this work, a method was designed to identify algal cell viability based on digital holography and deep learning, which divided algal cells into three categories: active, weak, and dead cells. This method was applied to measure algal cells in surface waters of the East China Sea in spring, revealing about 4.34 %-23.29 % weak cells and 3.98 %-19.47 % dead cells. Levels of nitrate and chlorophyll a were the main factors affecting the viability of algal cells. Furthermore, algal viability changes during the heating and cooling were observed in laboratory experiments: high temperatures led to an increase in weak algal cells. This may provide an explanation for why most harmful algal blooms occur in warming months. This study provided a novel insight into how to identify the viability of algal cells and understand their significance in the ocean.
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Affiliation(s)
- Yanyan Wang
- Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
| | - Wei-Dong Zhai
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China.
| | - Chi Wu
- Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China; Laboratory for Marine Mineral Resources, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China.
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44
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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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45
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Zhao X, Ding L, Yan J, Xu J, He H. Constructing an In Vitro and In Vivo Flow Cytometry by Fast Line Scanning of Confocal Microscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:3305. [PMID: 36992015 PMCID: PMC10059927 DOI: 10.3390/s23063305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/15/2023] [Accepted: 03/19/2023] [Indexed: 06/19/2023]
Abstract
Composed of a fluidic and an optical system, flow cytometry has been widely used for biosensing. The fluidic flow enables its automatic high-throughput sample loading and sorting while the optical system works for molecular detection by fluorescence for micron-level cells and particles. This technology is quite powerful and highly developed; however, it requires a sample in the form of a suspension and thus only works in vitro. In this study, we report a simple scheme to construct a flow cytometry based on a confocal microscope without any modifications. We demonstrate that line scanning of microscopy can effectively excite fluorescence of flowing microbeads or cells in a capillary tube in vitro and in blood vessels of live mice in vivo. This method can resolve microbeads at several microns and the results are comparable to a classic flow cytometer. The absolute diameter of flowing samples can be indicated directly. The sampling limitations and variations of this method is carefully analyzed. This scheme can be easily accomplished by any commercial confocal microscope systems, expands the function of them, and is of promising potential for simultaneous confocal microscopy and in vivo detection of cells in blood vessels of live animals by a single system.
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Affiliation(s)
- Xiaohui Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
| | - Leqi Ding
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; (L.D.)
| | - Jingsheng Yan
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; (L.D.)
| | - Jin Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
| | - Hao He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (X.Z.)
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46
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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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47
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Weng Y, Shen H, Mei L, Liu L, Yao Y, Li R, Wei S, Yan R, Ruan X, Wang D, Wei Y, Deng Y, Zhou Y, Xiao T, Goda K, Liu S, Zhou F, Lei C. Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip. LAB ON A CHIP 2023; 23:1703-1712. [PMID: 36799214 DOI: 10.1039/d2lc01048h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.
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Affiliation(s)
- Yueyun Weng
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Liye Mei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Li Liu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Yifan Yao
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Rubing Li
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Shubin Wei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Ruopeng Yan
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Xiaolan Ruan
- Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Yongchang Wei
- Department of Radiation & Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yunjie Deng
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Tinghui Xiao
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Keisuke Goda
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
- Department of bioengineering, University of California, Los Angeles, USA
| | - Sheng Liu
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
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48
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Kim T, Rao J. "SMART" cytology: The next generation cytology for precision diagnosis. Semin Diagn Pathol 2023; 40:95-99. [PMID: 36639316 DOI: 10.1053/j.semdp.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/22/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.
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Affiliation(s)
- Teresa Kim
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, United States of America
| | - Jianyu Rao
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, United States of America.
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49
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Běhal J, Pirone D, Sirico D, Bianco V, Mugnano M, Del Giudice D, Cavina B, Kurelac I, Memmolo P, Miccio L, Ferraro P. On monocytes and lymphocytes biolens clustering by in flow holographic microscopy. Cytometry A 2023; 103:251-259. [PMID: 36028475 DOI: 10.1002/cyto.a.24685] [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: 05/24/2022] [Revised: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Live cells act as biological lenses and can be employed as real-world optical components in bio-hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra-body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio-lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio-lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow-cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere-like bio-lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: I z < I th z ) in a fixed plane, is a feasible parameter for cell clustering, while achieving robustness against experimental misalignments and allowing to adjust the measurement sensitivity in post-processing. 2D scatterplots of the identified parameters (d-S) show better differentiation respect to the 1D case. The results show that the optical focusing properties of WBCs allow the clustering of the two populations by means of a mere morphological analysis, thus leading to the new concept of cell-optical-fingerprint avoiding fluorescent dyes. This perspective can open new routes in biomedical sciences, such as the chance to find optical-biomarkers at single cell level for label-free diagnosis.
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Affiliation(s)
- Jaromír Běhal
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Optics, Palacký University, Olomouc, Czech Republic
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Daniele Sirico
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Chemical, Materials and Production Engineering of the University of Naples Federico II, Naples, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Martina Mugnano
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Danila Del Giudice
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Beatrice Cavina
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Ivana Kurelac
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
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50
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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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