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Lo MCK, Siu DMD, Lee KCM, Wong JSJ, Yeung MCF, Hsin MKY, Ho JCM, Tsia KK. Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307591. [PMID: 38864546 PMCID: PMC11304271 DOI: 10.1002/advs.202307591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/17/2024] [Indexed: 06/13/2024]
Abstract
Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.
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Affiliation(s)
- Michelle C. K. Lo
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Dickson M. D. Siu
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Kelvin C. M. Lee
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Justin S. J. Wong
- Conzeb LimitedHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Maximus C. F. Yeung
- Department of Pathology, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Michael K. Y. Hsin
- Department of Surgery, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - James C. M. Ho
- Department of Medicine, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Kevin K. Tsia
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
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2
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Hou D, Zhou J, Xiao R, Yang K, Ding Y, Wang D, Wu G, Lei C. Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s. Cytometry A 2024. [PMID: 38842356 DOI: 10.1002/cyto.a.24854] [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: 02/20/2024] [Revised: 04/09/2024] [Accepted: 05/10/2024] [Indexed: 06/07/2024]
Abstract
Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.
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Affiliation(s)
- Dan Hou
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Jiehua Zhou
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Ruidong Xiao
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Kaining Yang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Yan Ding
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Guoqiang Wu
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
- Shenzhen Institute of Wuhan University, Shenzhen, China
- Suzhou Institute of Wuhan University, Suzhou, China
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3
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Sun A, Li Y, Zhu P, He X, Jiang Z, Kong Y, Liu C, Wang S. Dual-view transport of intensity phase imaging flow cytometry. BIOMEDICAL OPTICS EXPRESS 2023; 14:5199-5207. [PMID: 37854577 PMCID: PMC10581798 DOI: 10.1364/boe.504863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 10/20/2023]
Abstract
In this work, we design multi-parameter phase imaging flow cytometry based on dual-view transport of intensity (MPFC), which integrates phase imaging and microfluidics to a microscope, to obtain single-shot quantitative phase imaging on cells flowing in the microfluidic channel. The MPFC system has been proven with simple configuration, accurate phase retrieval, high imaging contrast, and real-time imaging and has been successfully employed not only in imaging, recognizing, and analyzing the flowing cells even with high-flowing velocities but also in tracking cell motilities, including rotation and binary rotation. Current results suggest that our proposed MPFC provides an effective tool for imaging and analyzing cells in microfluidics and can be potentially used in both fundamental and clinical studies.
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Affiliation(s)
- Aihui Sun
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yaxi Li
- Radiology Department, Jiangnan University Medical Center, Wuxi, Jiangsu, 214122, China
| | - Pengfei Zhu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Xiaoliang He
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zhilong Jiang
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yan Kong
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Cheng Liu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Shouyu Wang
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System & School of Electronics and Information Engineering, OptiX+ Laboratory, Wuxi University, Wuxi, Jiangsu 214105, China
- Single Molecule Nanometry Laboratory, China
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4
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Zhang Z, Lee KCM, Siu DMD, Lo MCK, Lai QTK, Lam EY, Tsia KK. Morphological profiling by high-throughput single-cell biophysical fractometry. Commun Biol 2023; 6:449. [PMID: 37095203 PMCID: PMC10126163 DOI: 10.1038/s42003-023-04839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.
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Affiliation(s)
- Ziqi Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Michelle C K Lo
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Queenie T K Lai
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong.
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5
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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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6
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Kinegawa R, Gala de Pablo J, Wang Y, Hiramatsu K, Goda K. Label-free multiphoton imaging flow cytometry. Cytometry A 2023. [PMID: 36799568 DOI: 10.1002/cyto.a.24723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/31/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023]
Abstract
Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.
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Affiliation(s)
- Ryo Kinegawa
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | | | - Yi Wang
- Department of Chemistry, Renmin University of China, Beijing, China
| | - Kotaro Hiramatsu
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Research Centre for Spectrochemistry, The University of Tokyo, Tokyo, Japan.,PRESTO, Japan Science and Technology Agency, Saitama, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Institute of Technological Sciences, Wuhan University, Hubei, China.,Department of Bioengineering, University of California, Los Angeles, California, USA.,CYBO, Inc., Tokyo, Japan
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7
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MacHugh E, Antony G, Mallik AK, Kaworek A, McCormack D, Duffy B, Oubaha M. Development and Characterisation of a Whole Hybrid Sol-Gel Optofluidic Platform for Biosensing Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:4192. [PMID: 36500816 PMCID: PMC9740286 DOI: 10.3390/nano12234192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
This work outlines, for the first time, the fabrication of a whole hybrid sol-gel optofluidic platform by integrating a microfluidic biosensor platform with optical waveguides employing a standard photolithography process. To demonstrate the suitability of this new hybrid sol-gel optofluidic platform, optical and bio-sensing proof-of-concepts are proposed. A photoreactive hybrid sol-gel material composed of a photopolymerisable organically modified silicon alkoxide and a transition metal complex was prepared and used as the fabrication material for the entire optofluidic platform, including the optical waveguides, the sensing areas, and the microfluidic device. The most suitable sol-gel materials chosen for the fabrication of the cladding and core of the waveguides showed a RIC of 3.5 × 10-3 and gave thicknesses between 5.5 and 7 μm. The material was optimised to simultaneously meet the photoreactive properties required for the photolithography fabrication process and the optical properties needed for the effective optical operability of the microstructured waveguides at 532 and 633 nm with an integrated microfluidic device. The optical proof-of-concept was performed using a fluorescent dye (Atto 633) and recording its optical responses while irradiated with a suitable optical excitation. The biosensing capability of the platform was assessed using a polyclonal primary IgG mouse antibody and a fluorescent labelled secondary IgG anti-mouse antibody. A limit of detection (LOD) of 50 ug/mL was achieved. A correlation between the concentration of the dye and the emission fluorescence was evidenced, thus clearly demonstrating the feasibility of the proposed hybrid sol-gel optofluidic platform concept. The successful integration and operability of optical and microfluidic components in the same optofluidic platform is a novel concept, particularly where the sol-gel fabrication material is concerned.
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Affiliation(s)
- Emma MacHugh
- School of Chemical and Pharmaceutical Sciences, Technological University Dublin, City Campus Grangegorman, D07 H6K8 Dublin, Ireland
- Centre for Research in Engineering Surface Technology (CREST), FOCAS Institute, Technological University Dublin, 13 Camden Row, D02 HW71 Dublin, Ireland
| | - Graceson Antony
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus Grangegorman, D07 H6K8 Dublin, Ireland
- Centre for Industrial and Engineering Optics (IEO), FOCAS Institute, Technological University Dublin, Camden Row, D07 H6K8 Dublin, Ireland
| | - Arun Kumar Mallik
- Photonics Research Centre, Technological University Dublin, City Campus Grangegorman, D07 H6K8 Dublin, Ireland
| | - Alicja Kaworek
- Centre for Research in Engineering Surface Technology (CREST), FOCAS Institute, Technological University Dublin, 13 Camden Row, D02 HW71 Dublin, Ireland
| | - Declan McCormack
- School of Chemical and Pharmaceutical Sciences, Technological University Dublin, City Campus Grangegorman, D07 H6K8 Dublin, Ireland
- Centre for Research in Engineering Surface Technology (CREST), FOCAS Institute, Technological University Dublin, 13 Camden Row, D02 HW71 Dublin, Ireland
| | - Brendan Duffy
- Centre for Research in Engineering Surface Technology (CREST), FOCAS Institute, Technological University Dublin, 13 Camden Row, D02 HW71 Dublin, Ireland
| | - Mohamed Oubaha
- Centre for Research in Engineering Surface Technology (CREST), FOCAS Institute, Technological University Dublin, 13 Camden Row, D02 HW71 Dublin, Ireland
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Algorri JF, Roldán-Varona P, Fernández-Manteca MG, López-Higuera JM, Rodriguez-Cobo L, Cobo-García A. Photonic Microfluidic Technologies for Phytoplankton Research. BIOSENSORS 2022; 12:1024. [PMID: 36421145 PMCID: PMC9688872 DOI: 10.3390/bios12111024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/10/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
Phytoplankton is a crucial component for the correct functioning of different ecosystems, climate regulation and carbon reduction. Being at least a quarter of the biomass of the world's vegetation, they produce approximately 50% of atmospheric O2 and remove nearly a third of the anthropogenic carbon released into the atmosphere through photosynthesis. In addition, they support directly or indirectly all the animals of the ocean and freshwater ecosystems, being the base of the food web. The importance of their measurement and identification has increased in the last years, becoming an essential consideration for marine management. The gold standard process used to identify and quantify phytoplankton is manual sample collection and microscopy-based identification, which is a tedious and time-consuming task and requires highly trained professionals. Microfluidic Lab-on-a-Chip technology represents a potential technical solution for environmental monitoring, for example, in situ quantifying toxic phytoplankton. Its main advantages are miniaturisation, portability, reduced reagent/sample consumption and cost reduction. In particular, photonic microfluidic chips that rely on optical sensing have emerged as powerful tools that can be used to identify and analyse phytoplankton with high specificity, sensitivity and throughput. In this review, we focus on recent advances in photonic microfluidic technologies for phytoplankton research. Different optical properties of phytoplankton, fabrication and sensing technologies will be reviewed. To conclude, current challenges and possible future directions will be discussed.
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Affiliation(s)
- José Francisco Algorri
- Photonics Engineering Group, Universidad de Cantabria, 39005 Santander, Spain
- CIBER de Bioingeniera, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain
| | - Pablo Roldán-Varona
- Photonics Engineering Group, Universidad de Cantabria, 39005 Santander, Spain
- CIBER de Bioingeniera, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain
| | | | - José Miguel López-Higuera
- Photonics Engineering Group, Universidad de Cantabria, 39005 Santander, Spain
- CIBER de Bioingeniera, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain
| | - Luis Rodriguez-Cobo
- Photonics Engineering Group, Universidad de Cantabria, 39005 Santander, Spain
- CIBER de Bioingeniera, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain
| | - Adolfo Cobo-García
- Photonics Engineering Group, Universidad de Cantabria, 39005 Santander, Spain
- CIBER de Bioingeniera, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain
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Běhal J, Borrelli F, Mugnano M, Bianco V, Capozzoli A, Curcio C, Liseno A, Miccio L, Memmolo P, Ferraro P. Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing. Cells 2022; 11:cells11162591. [PMID: 36010667 PMCID: PMC9406712 DOI: 10.3390/cells11162591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.
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Affiliation(s)
- Jaromír Běhal
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Francesca Borrelli
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Martina Mugnano
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Vittorio Bianco
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Amedeo Capozzoli
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Claudio Curcio
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Angelo Liseno
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Lisa Miccio
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Pasquale Memmolo
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
- Correspondence:
| | - Pietro Ferraro
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
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10
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Zhang Y, Zhao Y, Cole T, Zheng J, Bayinqiaoge, Guo J, Tang SY. Microfluidic flow cytometry for blood-based biomarker analysis. Analyst 2022; 147:2895-2917. [PMID: 35611964 DOI: 10.1039/d2an00283c] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.
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Affiliation(s)
- Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Ying Zhao
- National Chengdu Centre of Safety Evaluation of Drugs, West China Hospital of Sichuan University, Chengdu, China
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Bayinqiaoge
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jinhong Guo
- The M.O.E. Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, #1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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11
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Vaughan L, Zamyadi A, Ajjampur S, Almutaram H, Freguia S. A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration. ANAL SCI 2022; 38:261-279. [PMID: 35286640 PMCID: PMC8938360 DOI: 10.1007/s44211-021-00013-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/07/2021] [Indexed: 11/30/2022]
Abstract
AbstractReal-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring.
Graphical abstract
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Affiliation(s)
- Liam Vaughan
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia.
| | - Arash Zamyadi
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Suraj Ajjampur
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
| | - Husein Almutaram
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada
| | - Stefano Freguia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
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12
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Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go. Sci Rep 2022; 12:78. [PMID: 34997009 PMCID: PMC8741756 DOI: 10.1038/s41598-021-03902-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Optical imaging is paramount for disease diagnosis and to access its progression over time. The proposed optical flow imaging (VFC/iLIFE) is a powerful technique that adds new capabilities (3D volume visualization, organelle-level resolution, and multi-organelle screening) to the existing system. Unlike state-of-the-art point-illumination-based biomedical imaging techniques, the sheet-based VFC technique is capable of single-shot sectional visualization, high throughput interrogation, real-time parameter estimation, and instant volume reconstruction with organelle-level resolution of live specimens. The specimen flow system was realized on a multichannel (Y-type) microfluidic chip that enables visualization of organelle distribution in several cells in-parallel at a relatively high flow-rate (2000 nl/min). The calibration of VFC system requires the study of point emitters (fluorescent beads) at physiologically relevant flow-rates (500-2000 nl/min) for determining flow-induced optical aberration in the system point spread function (PSF). Subsequently, the recorded raw images and volumes were computationally deconvolved with flow-variant PSF to reconstruct the cell volume. High throughput investigation of the mitochondrial network in HeLa cancer cell was carried out at sub-cellular resolution in real-time and critical parameters (mitochondria count and size distribution, morphology, entropy, and cell strain statistics) were determined on-the-go. These parameters determine the physiological state of cells, and the changes over-time, revealing the metastatic progression of diseases. Overall, the developed VFC system enables real-time monitoring of sub-cellular organelle organization at a high-throughput with high-content capacity.
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13
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Kleiber A, Kraus D, Henkel T, Fritzsche W. Review: tomographic imaging flow cytometry. LAB ON A CHIP 2021; 21:3655-3666. [PMID: 34514484 DOI: 10.1039/d1lc00533b] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.
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Affiliation(s)
- Andreas Kleiber
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Daniel Kraus
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Thomas Henkel
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
| | - Wolfgang Fritzsche
- Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany
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14
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Lai QTK, Yip GGK, Wu J, Wong JSJ, Lo MCK, Lee KCM, Le TTHD, So HKH, Ji N, Tsia KK. High-speed laser-scanning biological microscopy using FACED. Nat Protoc 2021; 16:4227-4264. [PMID: 34341580 DOI: 10.1038/s41596-021-00576-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/25/2021] [Indexed: 12/28/2022]
Abstract
Laser scanning is used in advanced biological microscopy to deliver superior imaging contrast, resolution and sensitivity. However, it is challenging to scale up the scanning speed required for interrogating a large and heterogeneous population of biological specimens or capturing highly dynamic biological processes at high spatiotemporal resolution. Bypassing the speed limitation of traditional mechanical methods, free-space angular-chirp-enhanced delay (FACED) is an all-optical, passive and reconfigurable laser-scanning approach that has been successfully applied in different microscopy modalities at an ultrafast line-scan rate of 1-80 MHz. Optimal FACED imaging performance requires optimized experimental design and implementation to enable specific high-speed applications. In this protocol, we aim to disseminate information allowing FACED to be applied to a broader range of imaging modalities. We provide (i) a comprehensive guide and design specifications for the FACED hardware; (ii) step-by-step optical implementations of the FACED module including the key custom components; and (iii) the overall image acquisition and reconstruction pipeline. We illustrate two practical imaging configurations: multimodal FACED imaging flow cytometry (bright-field, fluorescence and second-harmonic generation) and kHz 2D two-photon fluorescence microscopy. Users with basic experience in optical microscope operation and software engineering should be able to complete the setup of the FACED imaging hardware and software in ~2-3 months.
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Affiliation(s)
- Queenie T K Lai
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Gwinky G K Yip
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Jianglai Wu
- Department of Physics, University of California, Berkeley, Berkeley, CA, USA.,Chinese Institute for Brain Research, Beijing, China
| | - Justin S J Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Michelle C K Lo
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Tony T H D Le
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Hayden K H So
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Na Ji
- Department of Physics, University of California, Berkeley, Berkeley, CA, USA. .,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA. .,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA. .,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. .,Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin New Town, Hong Kong.
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15
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Xu M, Harmon J, Yuan D, Yan S, Lei C, Hiramatsu K, Zhou Y, Loo MH, Hasunuma T, Isozaki A, Goda K. Morphological Indicator for Directed Evolution of Euglena gracilis with a High Heavy Metal Removal Efficiency. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:7880-7889. [PMID: 33913704 DOI: 10.1021/acs.est.0c05278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = -0.82, P = 2.1 × 10-5) between a morphological meta-feature recognized via our machine learning algorithms and the Cu2+ removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.
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Affiliation(s)
- Muzhen Xu
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Jeffrey Harmon
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Dan Yuan
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sheng Yan
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Cheng Lei
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Institute of Technological Sciences, Wuhan University, Wuhan, Hubei 430072, China
| | - Kotaro Hiramatsu
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Kanagawa Institute of Industrial Science and Technology, Ebina, Kanagawa 243-0435, Japan
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan
| | - Yuqi Zhou
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Mun Hong Loo
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohisa Hasunuma
- Graduate School of Science, Technology and Innovation, Kobe University, Hyogo, Kobe 657-8501, Japan
- Engineering Biology Research Center, Kobe University, Hyogo, Kobe 657-8501, Japan
| | - Akihiro Isozaki
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Kanagawa Institute of Industrial Science and Technology, Ebina, Kanagawa 243-0435, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Institute of Technological Sciences, Wuhan University, Wuhan, Hubei 430072, China
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
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16
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Lee KCM, Guck J, Goda K, Tsia KK. Toward deep biophysical cytometry: prospects and challenges. Trends Biotechnol 2021; 39:1249-1262. [PMID: 33895013 DOI: 10.1016/j.tibtech.2021.03.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/15/2021] [Accepted: 03/15/2021] [Indexed: 12/13/2022]
Abstract
The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.
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Affiliation(s)
- Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Jochen Guck
- Max Planck Institute for the Science of Light, and Max-Planck-Zentrum für Physik und Medizin, 91058 Erlangen, Germany; Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan; Institute of Technological Sciences, Wuhan University, Hubei 430072, China; Department of Bioengineering, University of California, Los Angeles, California 90095, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong.
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17
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Siu DMD, Lee KCM, Lo MCK, Stassen SV, Wang M, Zhang IZQ, So HKH, Chan GCF, Cheah KSE, Wong KKY, Hsin MKY, Ho JCM, Tsia KK. Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity. LAB ON A CHIP 2020; 20:3696-3708. [PMID: 32935707 DOI: 10.1039/d0lc00542h] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10-5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, Choi Yei Ching Building, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong.
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18
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Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. LAB ON A CHIP 2020; 20:3074-3090. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Jeffrey Harmon
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Shuai Li
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and The Cambridge Centre for Data-Driven Discovery, Cambridge University, Cambridge CB3 0WA, UK
| | - Yuta Nakagawa
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Mika Hayashi
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China and Department of Bioengineering, University of California, Los Angeles, California 90095, USA
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19
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Two-dimensional oscillatory motion of inertially focused particles in microfluidic flows. ADV POWDER TECHNOL 2020. [DOI: 10.1016/j.apt.2020.06.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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20
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Abstract
With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.
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Affiliation(s)
- Gurpur Rakesh D Prabhu
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Department of Applied Chemistry, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan.,Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Road, Hsinchu, 30013, Taiwan
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21
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Zhou Y, Yasumoto A, Lei C, Huang CJ, Kobayashi H, Wu Y, Yan S, Sun CW, Yatomi Y, Goda K. Intelligent classification of platelet aggregates by agonist type. eLife 2020; 9:52938. [PMID: 32393438 PMCID: PMC7217700 DOI: 10.7554/elife.52938] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 04/15/2020] [Indexed: 12/18/2022] Open
Abstract
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics. Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the “intelligent platelet aggregate classifier” or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and “learned” to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method’s accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient’s heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.
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Affiliation(s)
- Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Atsushi Yasumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo, Japan.,Institute of Technological Sciences, Wuhan University, Hubei, China
| | - Chun-Jung Huang
- Department of Photonics, National Chiao Tung University, Hsinchu, Taiwan
| | | | - Yunzhao Wu
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Sheng Yan
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Chia-Wei Sun
- Department of Photonics, National Chiao Tung University, Hsinchu, Taiwan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo, Japan.,Institute of Technological Sciences, Wuhan University, Hubei, China.,Department of Bioengineering, University of California, Los Angeles, United States
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22
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Oldenhof S, Mytnyk S, Arranja A, de Puit M, van Esch JH. Imaging-assisted hydrogel formation for single cell isolation. Sci Rep 2020; 10:6595. [PMID: 32313146 PMCID: PMC7171092 DOI: 10.1038/s41598-020-62623-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 03/17/2020] [Indexed: 12/23/2022] Open
Abstract
We report a flexible single-cell isolation method by imaging-assisted hydrogel formation. Our approach consists of imaging-aided selective capture of cells of interest by encasing them into a polymeric hydrogel, followed by removal of unwanted cells and subsequent release of isolated cells by enzymatic hydrogel degradation, thus offering an opportunity for further analysis or cultivation of selected cells. We achieved high sorting efficiency and observed excellent viability rates (>98%) for NIH/3T3 fibroblasts and A549 carcinoma cells isolated using this procedure. The method presented here offers a mask-free, cost-efficient and easy-to-use alternative to many currently existing surface-based cell-sorting techniques, and has the potential to impact the field of cell culturing and isolation, e.g. single cell genomics and proteomics, investigation of cellular heterogeneity and isolation of best performing mutants for developing new cell lines.
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Affiliation(s)
- Sander Oldenhof
- The Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, the Netherlands
- Department of Chemical Engineering, Delft University of Technology, van der Maasweg, 2629, HZ Delft, the Netherlands
| | - Serhii Mytnyk
- Department of Chemical Engineering, Delft University of Technology, van der Maasweg, 2629, HZ Delft, the Netherlands
| | - Alexandra Arranja
- Department of Chemical Engineering, Delft University of Technology, van der Maasweg, 2629, HZ Delft, the Netherlands
| | - Marcel de Puit
- The Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, the Netherlands.
- Department of Chemical Engineering, Delft University of Technology, van der Maasweg, 2629, HZ Delft, the Netherlands.
| | - Jan H van Esch
- Department of Chemical Engineering, Delft University of Technology, van der Maasweg, 2629, HZ Delft, the Netherlands.
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23
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Wu Y, Zhou Y, Huang CJ, Kobayashi H, Yan S, Ozeki Y, Wu Y, Sun CW, Yasumoto A, Yatomi Y, Lei C, Goda K. Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging. OPTICS EXPRESS 2020; 28:519-532. [PMID: 32118978 DOI: 10.1364/oe.380679] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 12/13/2019] [Indexed: 05/24/2023]
Abstract
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.
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24
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Agarwal R, Sarkar A, Paul S, Chakraborty S. A portable rotating disc as blood rheometer. BIOMICROFLUIDICS 2019; 13:064120. [PMID: 31803338 PMCID: PMC6887659 DOI: 10.1063/1.5128937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/19/2019] [Indexed: 06/10/2023]
Abstract
Abnormalities in biophysical properties of blood are often strong indicators of life threatening infections. However, there is no existing device that integrates the sensing of blood hematocrit (or equivalently, packed cell volume), viscosity, and erythrocyte sedimentation rate (ESR) in a unified paradigm for point-of-care diagnostics. In an effort to develop a rapid, integrated, accurate, portable, and inexpensive sensing platform to diagnose the corresponding pathophysical parameters, we develop a simple and portable spinning disk capable of yielding these results in a few minutes instead of the traditional duration of hours. The device requires only 40 μl of unprocessed freshly drawn blood treated with an anticoagulant ethylenediaminetetraacetic acid, instead of the traditional requirement of 2 ml of blood for just the ESR measurement and still more for hematocrit determination. In contrast to the sophisticated instrumentation required to determine these parameters by the previously proposed microfluidic devices, our device requires minimal infrastructure. The measurement of hematocrit is accomplished by means of a simple 15 cm ruler. Additionally, a simple measurement of the blood flow rate enables the determination of the ESR value. The rapidity, ease, accuracy, portability, frugality, and possible automation of the overall measurement process of some of the most important parameters of blood under infection pinpoint its utility in extreme point-of-care settings.
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Affiliation(s)
- Rahul Agarwal
- Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | | | - Subhechchha Paul
- Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur 711103, India
| | - Suman Chakraborty
- Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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25
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Duan Y, Dong X, Yang N, Zhang C, Wong KKY, Zhang X. Temporally structured illumination for ultrafast time-stretch microscopy. OPTICS LETTERS 2019; 44:4634-4637. [PMID: 31568404 DOI: 10.1364/ol.44.004634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 08/25/2019] [Indexed: 06/10/2023]
Abstract
This work developed a temporally structured illumination scheme to conquer the detector bandwidth limitation that is increasingly becoming a stumbling block of ultrafast single-pixel measurement. Inspired by structured illumination microscopy and space-time duality, an electro-optic modulator resembling the temporal counterpart of a spatial grating is used to impart a sinusoidal pattern onto the time-stretch signal before detection. Consequently, the detector bandwidth is equivalently doubled based on three measurements and a subsequent reconstruction, thereby capturing the high-frequency components originally beyond the detector bandwidth. As a proof of concept, this method is applied to an ultrafast single-pixel imaging modality, the time-stretch microscopy, to verify its capability to surpass the resolution limit imposed by the detector bandwidth. High-quality images with ∼4.0 μm spatial resolution are acquired at ∼30 MHz frame rates by merely half of the detector bandwidth, compared to the traditional system. This Letter provides a simple and economical solution for high-speed signal acquisition, which is demanded in a variety of applications, ranging from ultrafast imaging to single-shot spectroscopy.
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26
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Shi R, Wong JSJ, Lam EY, Tsia KK, So HKH. A Real-Time Coprime Line Scan Super-Resolution System for Ultra-Fast Microscopy. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:781-792. [PMID: 31059454 DOI: 10.1109/tbcas.2019.2914946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
A fundamental technical challenge for ultra-fast cell microscopy is the tradeoff between imaging throughput and resolution. In addition to throughput, real-time applications such as image-based cell sorting further requires ultra-low imaging latency to facilitate rapid decision making on a single-cell level. Using a novel coprime line scan sampling scheme, a real-time low-latency hardware super-resolution system for ultra-fast time-stretch microscopy is presented. The proposed scheme utilizes analog-to-digital converter with a carefully tuned sampling pattern (shifted sampling grid) to enable super-resolution image reconstruction using line scan input from an optical front-end. A fully pipelined FPGA-based system is built to efficiently handle the real-time high-resolution image reconstruction process with the input subpixel samples while achieving minimal output latency. The proposed super-resolution sampling and reconstruction scheme is parametrizable and is readily applicable to different line scan imaging systems. In our experiments, an imaging latency of 0.29 μs has been achieved based on a pixel-stream throughput of 4.123 giga pixels per second, which translates into imaging throughput of approximately 120000 cells per second.
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27
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Isozaki A, Mikami H, Hiramatsu K, Sakuma S, Kasai Y, Iino T, Yamano T, Yasumoto A, Oguchi Y, Suzuki N, Shirasaki Y, Endo T, Ito T, Hiraki K, Yamada M, Matsusaka S, Hayakawa T, Fukuzawa H, Yatomi Y, Arai F, Di Carlo D, Nakagawa A, Hoshino Y, Hosokawa Y, Uemura S, Sugimura T, Ozeki Y, Nitta N, Goda K. A practical guide to intelligent image-activated cell sorting. Nat Protoc 2019; 14:2370-2415. [PMID: 31278398 DOI: 10.1038/s41596-019-0183-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 04/18/2019] [Indexed: 02/08/2023]
Abstract
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Hideharu Mikami
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | | | - Shinya Sakuma
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Yusuke Kasai
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Takanori Iino
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan
| | - Takashi Yamano
- Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan
| | - Atsushi Yasumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yusuke Oguchi
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | - Nobutake Suzuki
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | | | | | - Takuro Ito
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Kei Hiraki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Makoto Yamada
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Satoshi Matsusaka
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Takeshi Hayakawa
- Department of Precision Mechanics, Chuo University, Tokyo, Japan
| | - Hideya Fukuzawa
- Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Fumihito Arai
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Dino Di Carlo
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.,California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Atsuhiro Nakagawa
- Department of Neurosurgery, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yu Hoshino
- Department of Chemical Engineering, Kyushu University, Fukuoka, Japan
| | - Yoichiroh Hosokawa
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Sotaro Uemura
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | - Takeaki Sugimura
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan
| | - Nao Nitta
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan. .,Japan Science and Technology Agency, Saitama, Japan. .,Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
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28
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Arandian A, Bagheri Z, Ehtesabi H, Najafi Nobar S, Aminoroaya N, Samimi A, Latifi H. Optical Imaging Approaches to Monitor Static and Dynamic Cell-on-Chip Platforms: A Tutorial Review. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2019; 15:e1900737. [PMID: 31087503 DOI: 10.1002/smll.201900737] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 04/14/2019] [Indexed: 06/09/2023]
Abstract
Miniaturized laboratories on chip platforms play an important role in handling life sciences studies. The platforms may contain static or dynamic biological cells. Examples are a fixed medium of an organ-on-a-chip and individual cells moving in a microfluidic channel, respectively. Due to feasibility of control or investigation and ethical implications of live targets, both static and dynamic cell-on-chip platforms promise various applications in biology. To extract necessary information from the experiments, the demand for direct monitoring is rapidly increasing. Among different microscopy methods, optical imaging is a straightforward choice. Considering light interaction with biological agents, imaging signals may be generated as a result of scattering or emission effects from a sample. Thus, optical imaging techniques could be categorized into scattering-based and emission-based techniques. In this review, various optical imaging approaches used in monitoring static and dynamic platforms are introduced along with their optical systems, advantages, challenges, and applications. This review may help biologists to find a suitable imaging technique for different cell-on-chip studies and might also be useful for the people who are going to develop optical imaging systems in life sciences studies.
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Affiliation(s)
- Alireza Arandian
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Zeinab Bagheri
- Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Hamide Ehtesabi
- Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Shima Najafi Nobar
- Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 1969764499, Iran
| | - Neda Aminoroaya
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Ashkan Samimi
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
| | - Hamid Latifi
- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran
- Department of Physics, Shahid Beheshti University, Tehran, 1983969411, Iran
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29
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Lee KCM, Lau AKS, Tang AHL, Wang M, Mok ATY, Chung BMF, Yan W, Shum HC, Cheah KSE, Chan GCF, So HKH, Wong KKY, Tsia KK. Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution. JOURNAL OF BIOPHOTONICS 2019; 12:e201800479. [PMID: 30719868 PMCID: PMC7065649 DOI: 10.1002/jbio.201800479] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/22/2019] [Accepted: 02/01/2019] [Indexed: 05/10/2023]
Abstract
A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost-effective and label-free cellular assays, which provides useful insights into understanding the biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single-cell precision to define cell identities in a large and heterogeneous population of cells-hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM) that captures and processes quantitative label-free single-cell images at ultrahigh throughput without compromising subcellular resolution. We show that multi-ATOM, based upon ultrafast phase-gradient encoding, outperforms state-of-the-art QPI in permitting robust phase retrieval at a QPI throughput of >10 000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi-ATOM for large-scale, label-free, multivariate, cell-type classification (e.g. breast cancer subtypes, and leukemic cells vs peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi-ATOM could empower new strategies in large-scale biophysical single-cell analysis with applications in biology and enriching disease diagnostics.
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Affiliation(s)
- Kelvin C. M. Lee
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Andy K. S. Lau
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Anson H. L. Tang
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Maolin Wang
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Aaron T. Y. Mok
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Bob M. F. Chung
- Department of Mechanical Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Wenwei Yan
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Ho C. Shum
- Department of Mechanical Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Kathryn S. E. Cheah
- School of Biomedical Sciences, Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong
| | - Godfrey C. F. Chan
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong
| | - Hayden K. H. So
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Kenneth K. Y. Wong
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
| | - Kevin K. Tsia
- Department of Electrical and Electronic Engineering, Faculty of EngineeringThe University of Hong KongHong Kong
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30
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Lee KCM, Wang M, Cheah KSE, Chan GCF, So HKH, Wong KKY, Tsia KK. Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping. Cytometry A 2019; 95:510-520. [PMID: 31012276 DOI: 10.1002/cyto.a.23765] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/19/2019] [Accepted: 04/01/2019] [Indexed: 12/21/2022]
Abstract
Cellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s-a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92-97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Kelvin C M Lee
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Maolin Wang
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kathryn S E Cheah
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Godfrey C F Chan
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Hayden K H So
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
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31
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Tovar M, Hengoju S, Weber T, Mahler L, Choudhary M, Becker T, Roth M. One Sensor for Multiple Colors: Fluorescence Analysis of Microdroplets in Microbiological Screenings by Frequency-Division Multiplexing. Anal Chem 2019; 91:3055-3061. [PMID: 30689354 DOI: 10.1021/acs.analchem.8b05451] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-speed multiwavelength fluorescence measurements are of paramount importance in microfluidic analytics. However, multicolor detection requires an intricate arrangement of multiple detectors and meticulously aligned filters and dichroic beamsplitters that counteract the simplicity, versatility, and low cost of microfluidic approaches. To break free from the restrictions of optical setup complexity, we introduce a simpler single-sensor setup based on laser-frequency modulation and frequency-division multiplexing (FDM). We modulate lasers to excite the sample with four non-overlapping frequency signals. A single photomultiplier tube detects all the modulated emitted light collected by an optical fiber in the microfluidic chip. Signal demodulation is performed with a lock-in amplifier separating the emitted light into four color channels in real time. This approach not only reduces complexity and provides setup flexibility but also results in improved signal quality and, thus, higher signal-to-noise ratios that translate into increased sensitivity. To validate the setup for high-throughput biological applications, we measured multiple signals from different microorganisms and fluorescently encoded droplet populations for exploring beneficial or antagonistic roles in microbial cocultivation systems, as is the case for antibiotic screening assays.
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Affiliation(s)
- Miguel Tovar
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany.,Faculty of Biology and Pharmacy , Friedrich Schiller University , 07743 Jena , Germany
| | - Sundar Hengoju
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany.,Faculty of Biology and Pharmacy , Friedrich Schiller University , 07743 Jena , Germany
| | - Thomas Weber
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany.,Ilmenau University of Technology , 98693 Ilmenau , Germany
| | - Lisa Mahler
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany.,Faculty of Biology and Pharmacy , Friedrich Schiller University , 07743 Jena , Germany
| | - Mahipal Choudhary
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany
| | | | - Martin Roth
- Bio Pilot Plant , Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute , 07745 Jena , Germany
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32
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Song L, Feng Y, Guo X, Shen Y, Wu D, Wu Z, Zhou C, Zhu L, Gao S, Liu W, Zhang X, Li Z. Ultrafast polarization bio-imaging based on coherent detection and time-stretch techniques. BIOMEDICAL OPTICS EXPRESS 2018; 9:6556-6568. [PMID: 31065449 PMCID: PMC6490988 DOI: 10.1364/boe.9.006556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/22/2018] [Accepted: 11/23/2018] [Indexed: 06/09/2023]
Abstract
Optical polarization imaging has played an important role in many biological and biomedical applications, as it provides a label-free and non-invasive detection scheme to reveal the polarization information of optical rotation, birefringence, and photoelasticity distribution inherent in biological samples. However, the imaging speeds of the previously demonstrated polarization imaging techniques were often limited by the slow frame rates of the arrayed imaging detectors, which usually run at frame rates of several hundred hertz. By combining the optical coherent detection of orthogonal polarizations and the optical time-stretch imaging technique, we achieved ultrafast polarization bio-imaging at an extremely fast record line scanning rate up to 100 MHz without averaging. We experimentally demonstrated the superior performance of our method by imaging three slices of different kinds of biological samples with the retrieved Jones matrix and polarization-sensitive information including birefringence and diattenuation. The proposed system in this paper may find potential applications for ultrafast polarization dynamics in living samples or some other advanced biomedical research.
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Affiliation(s)
- Lu Song
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Yuanhua Feng
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Xiaojie Guo
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Yuecheng Shen
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Daixuan Wu
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Zhenhua Wu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Congran Zhou
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Linyan Zhu
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Shecheng Gao
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Weiping Liu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Xuming Zhang
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhaohui Li
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
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33
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Park HS, Ceballos S, Eldridge WJ, Wax A. Invited Article: Digital refocusing in quantitative phase imaging for flowing red blood cells. APL PHOTONICS 2018; 3:110802. [PMID: 31192306 PMCID: PMC6561492 DOI: 10.1063/1.5043536] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Accepted: 09/07/2018] [Indexed: 05/19/2023]
Abstract
Quantitative phase imaging (QPI) offers high optical path length sensitivity, probing nanoscale features of live cells, but it is typically limited to imaging just few static cells at a time. To enable utility as a biomedical diagnostic modality, higher throughput is needed. To meet this need, methods for imaging cells in flow using QPI are in development. An important need for this application is to enable accurate quantitative analysis. However, this can be complicated when cells shift focal planes during flow. QPI permits digital refocusing since the complex optical field is measured. Here we analyze QPI images of moving red blood cells with an emphasis on choosing a quantitative criterion for digitally refocusing cell images. Of particular interest is the influence of optical absorption which can skew refocusing algorithms. Examples of refocusing of holographic images of flowing red blood cells using different approaches are presented and analyzed.
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Affiliation(s)
- Han Sang Park
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Silvia Ceballos
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Will J Eldridge
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Adam Wax
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
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34
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Paiè P, Martínez Vázquez R, Osellame R, Bragheri F, Bassi A. Microfluidic Based Optical Microscopes on Chip. Cytometry A 2018; 93:987-996. [PMID: 30211977 PMCID: PMC6220811 DOI: 10.1002/cyto.a.23589] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 07/23/2018] [Accepted: 07/25/2018] [Indexed: 12/21/2022]
Abstract
Last decade's advancements in optofluidics allowed obtaining an ever increasing integration of different functionalities in lab on chip devices to culture, analyze, and manipulate single cells and entire biological specimens. Despite the importance of optical imaging for biological sample monitoring in microfluidics, imaging is traditionally achieved by placing microfluidics channels in standard bench-top optical microscopes. Recently, the development of either integrated optical elements or lensless imaging methods allowed optical imaging techniques to be implemented in lab on chip systems, thus increasing their automation, compactness, and portability. In this review, we discuss known solutions to implement microscopes on chip that exploit different optical methods such as bright-field, phase contrast, holographic, and fluorescence microscopy.
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Affiliation(s)
- Petra Paiè
- Istituto di Fotonica e NanotecnologieConsiglio Nazionale dell RicerchePiazza Leonardo da Vinci 3220133 MilanItaly
| | - Rebeca Martínez Vázquez
- Istituto di Fotonica e NanotecnologieConsiglio Nazionale dell RicerchePiazza Leonardo da Vinci 3220133 MilanItaly
| | - Roberto Osellame
- Istituto di Fotonica e NanotecnologieConsiglio Nazionale dell RicerchePiazza Leonardo da Vinci 3220133 MilanItaly
- Dipartimento di FisicaPolitecnico di MilanoPiazza Leonardo da Vinci 3220133 MilanItaly
| | - Francesca Bragheri
- Istituto di Fotonica e NanotecnologieConsiglio Nazionale dell RicerchePiazza Leonardo da Vinci 3220133 MilanItaly
| | - Andrea Bassi
- Istituto di Fotonica e NanotecnologieConsiglio Nazionale dell RicerchePiazza Leonardo da Vinci 3220133 MilanItaly
- Dipartimento di FisicaPolitecnico di MilanoPiazza Leonardo da Vinci 3220133 MilanItaly
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35
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36
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Tan S, Wei X, Li B, Lai QTK, Tsia KK, Wong KKY. Ultrafast optical imaging at 2.0 μm through second-harmonic-generation-based time-stretch at 1.0 μm. OPTICS LETTERS 2018; 43:3822-3825. [PMID: 30106892 DOI: 10.1364/ol.43.003822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 07/13/2018] [Indexed: 06/08/2023]
Abstract
The performance of ultrafast time-stretch imaging at long wavelengths (beyond 1.5 μm) has suffered from low detection sensitivity due to the increasing loss of optical dispersive fibers. Here, we report an ultrafast optical imaging system with a line scan rate of ∼19 MHz at the 2.0-μm wavelength window by combining second-harmonic generation (SHG) with the highly sensitive time-stretch detection at 1.0 μm. In this imaging system, the sample is illuminated by the pulsed laser source at 2.0 μm in the spectrally encoding manner. After SHG, the encoded spectral signal at 2.0 μm is converted to 1.0 μm and then mapped to the time domain through a highly dispersive fiber at 1.0 μm, which provides a superior dispersion-to-loss ratio of ∼53 ps/nm/dB, ∼50 times larger than that of the standard fibers at 2.0 μm (typically ∼1.1 ps/nm/dB). These efforts make it possible for time-stretch technology not only being translated to longer wavelengths, where unique optical absorption contrast exists, but also benefitting from the high detection sensitivity at shorter wavelengths.
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37
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Lei C, Kobayashi H, Wu Y, Li M, Isozaki A, Yasumoto A, Mikami H, Ito T, Nitta N, Sugimura T, Yamada M, Yatomi Y, Di Carlo D, Ozeki Y, Goda K. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat Protoc 2018; 13:1603-1631. [DOI: 10.1038/s41596-018-0008-7] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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38
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Rosendahl P, Plak K, Jacobi A, Kraeter M, Toepfner N, Otto O, Herold C, Winzi M, Herbig M, Ge Y, Girardo S, Wagner K, Baum B, Guck J. Real-time fluorescence and deformability cytometry. Nat Methods 2018; 15:355-358. [PMID: 29608556 DOI: 10.1038/nmeth.4639] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 02/27/2018] [Indexed: 12/22/2022]
Abstract
The throughput of cell mechanical characterization has recently approached that of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we developed an approach that combines real-time 1D-imaging fluorescence and deformability cytometry in one instrument (RT-FDC), thus opening many new research avenues. We demonstrated its utility by using subcellular fluorescence localization to identify mitotic cells and test for mechanical changes in those cells in an RNA interference screen.
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Affiliation(s)
- Philipp Rosendahl
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Katarzyna Plak
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
- MRC Laboratory for Molecular and Cellular Biology, University College London, London, UK
| | - Angela Jacobi
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Martin Kraeter
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Medical Clinic I, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Nicole Toepfner
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Oliver Otto
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Christoph Herold
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Maria Winzi
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Maik Herbig
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Yan Ge
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Salvatore Girardo
- Microstructure Facility, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Katrin Wagner
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Buzz Baum
- MRC Laboratory for Molecular and Cellular Biology, University College London, London, UK
| | - Jochen Guck
- Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
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39
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Hayat Z, El Abed AI. High-Throughput Optofluidic Acquisition of Microdroplets in Microfluidic Systems. MICROMACHINES 2018; 9:E183. [PMID: 30424116 PMCID: PMC6187520 DOI: 10.3390/mi9040183] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 03/26/2018] [Accepted: 04/04/2018] [Indexed: 12/24/2022]
Abstract
Droplet optofluidics technology aims at manipulating the tiny volume of fluids confined in micro-droplets with light, while exploiting their interaction to create "digital" micro-systems with highly significant scientific and technological interests. Manipulating droplets with light is particularly attractive since the latter provides wavelength and intensity tunability, as well as high temporal and spatial resolution. In this review study, we focus mainly on recent methods developed in order to monitor real-time analysis of droplet size and size distribution, active merging of microdroplets using light, or to use microdroplets as optical probes.
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Affiliation(s)
- Zain Hayat
- Laboratoire de Photonique Quantique et Moléculaire, UMR 8537, Ecole Normale Supérieure Paris Saclay, CentraleSupélec, CNRS, Université Paris-Saclay, 61 avenue du Président Wilson, 94235 Cachan, France.
| | - Abdel I El Abed
- Laboratoire de Photonique Quantique et Moléculaire, UMR 8537, Ecole Normale Supérieure Paris Saclay, CentraleSupélec, CNRS, Université Paris-Saclay, 61 avenue du Président Wilson, 94235 Cachan, France.
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40
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Yan W, Wu J, Wong KKY, Tsia KK. A high-throughput all-optical laser-scanning imaging flow cytometer with biomolecular specificity and subcellular resolution. JOURNAL OF BIOPHOTONICS 2018; 11:e201700178. [PMID: 29072813 DOI: 10.1002/jbio.201700178] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 09/13/2017] [Accepted: 10/23/2017] [Indexed: 05/26/2023]
Abstract
Image-based cellular assay advances approaches to dissect complex cellular characteristics through direct visualization of cellular functional structures. However, available technologies face a common challenge, especially when it comes to the unmet need for unraveling population heterogeneity at single-cell precision: higher imaging resolution (and thus content) comes at the expense of lower throughput, or vice versa. To overcome this challenge, a new type of imaging flow cytometer based upon an all-optical ultrafast laser-scanning imaging technique, called free-space angular-chirp-enhanced delay (FACED) is reported. It enables an imaging throughput (>20 000 cells s-1 ) 1 to 2 orders of magnitude higher than the camera-based imaging flow cytometers. It also has 2 critical advantages over optical time-stretch imaging flow cytometry, which achieves a similar throughput: (1) it is widely compatible to the repertoire of biochemical contrast agents, favoring biomolecular-specific cellular assay and (2) it enables high-throughput visualization of functional morphology of individual cells with subcellular resolution. These capabilities enable multiparametric single-cell image analysis which reveals cellular heterogeneity, for example, in the cell-death processes demonstrated in this work-the information generally masked in non-imaging flow cytometry. Therefore, this platform empowers not only efficient large-scale single-cell measurements, but also detailed mechanistic analysis of complex cellular processes.
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Affiliation(s)
- Wenwei Yan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jianglai Wu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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41
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Villone MM, Memmolo P, Merola F, Mugnano M, Miccio L, Maffettone PL, Ferraro P. Full-angle tomographic phase microscopy of flowing quasi-spherical cells. LAB ON A CHIP 2017; 18:126-131. [PMID: 29168877 DOI: 10.1039/c7lc00943g] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
We report a reliable full-angle tomographic phase microscopy (FA-TPM) method for flowing quasi-spherical cells along microfluidic channels. This method lies in a completely passive optical system, i.e. mechanical scanning or multi-direction probing of the sample is avoided. It exploits the engineered rolling of cells while they are flowing along a microfluidic channel. Here we demonstrate significant progress with respect to the state of the art of in-flow TPM by showing a general extension to cells having almost spherical shapes while they are flowing in suspension. In fact, the adopted strategy allows the accurate retrieval of rotation angles through a theoretical model of the cells' rotation in a dynamic microfluidic flow by matching it with phase-contrast images resulting from holographic reconstructions. So far, the proposed method is the first and the only one that permits to get in-flow TPM by probing the cells with full-angle, achieving accurate 3D refractive index mapping and the simplest optical setup, simultaneously. Proof of concept experiments were performed successfully on human breast adenocarcinoma MCF-7 cells, opening the way for the full characterization of circulating tumor cells (CTCs) in the new paradigm of liquid biopsy.
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Affiliation(s)
- Massimiliano M Villone
- Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Napoli, Italy
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42
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Liu L, Yang G, Liu S, Wang L, Yang X, Qu H, Liu X, Cao L, Pan W, Li H. High-throughput imaging of zebrafish embryos using a linear-CCD-based flow imaging system. BIOMEDICAL OPTICS EXPRESS 2017; 8:5651-5662. [PMID: 29296494 PMCID: PMC5745109 DOI: 10.1364/boe.8.005651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 10/28/2017] [Accepted: 11/02/2017] [Indexed: 05/08/2023]
Abstract
High-throughput imaging and screening is essential for biomedical research and drug discovery using miniature model organisms such as zebrafish. This study introduces a high-speed imaging system which illuminates zebrafish embryos flowing through a capillary tube with a sheet of light and captures them using a linear charge-coupled device (CCD). This system can image dozens of zebrafish embryos per second. An image algorithm was developed to recognize each embryo and to perform automatic analysis. We distinguished dead and living embryos according to the gray level distribution and conducted statistics of morphological characteristics of embryos at different growing stages.
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Affiliation(s)
- Lifeng Liu
- School of Electronic Engineering and Optoelectronics Technology, Nanjing University of Science and Technology, Nanjing 210094, China
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Guang Yang
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shoupeng Liu
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Linbo Wang
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Xibin Yang
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Huiming Qu
- School of Electronic Engineering and Optoelectronics Technology, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xiaofen Liu
- Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Le Cao
- Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Weijun Pan
- Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Hui Li
- Jiangsu Key Laboratory of Medical Optics, CAS Center for Excellence in Molecular Cell Science, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
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43
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Holzner G, Stavrakis S, deMello A. Elasto-Inertial Focusing of Mammalian Cells and Bacteria Using Low Molecular, Low Viscosity PEO Solutions. Anal Chem 2017; 89:11653-11663. [DOI: 10.1021/acs.analchem.7b03093] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Gregor Holzner
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir Prelog Weg 1, 8093 Zürich, Switzerland
| | - Stavros Stavrakis
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir Prelog Weg 1, 8093 Zürich, Switzerland
| | - Andrew deMello
- Institute for Chemical and
Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir Prelog Weg 1, 8093 Zürich, Switzerland
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44
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Kobayashi H, Lei C, Wu Y, Mao A, Jiang Y, Guo B, Ozeki Y, Goda K. Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci Rep 2017. [PMID: 28963483 DOI: 10.1038/s41598‐017‐12378‐4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
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Affiliation(s)
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
| | - Yi Wu
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA
| | - Ailin Mao
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Yiyue Jiang
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Baoshan Guo
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, University of Tokyo, Tokyo, 113-8656, Japan
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan. .,Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan. .,Department of Electrical Engineering, University of California, Los Angeles, California, 90095, USA.
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45
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Kobayashi H, Lei C, Wu Y, Mao A, Jiang Y, Guo B, Ozeki Y, Goda K. Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning. Sci Rep 2017; 7:12454. [PMID: 28963483 PMCID: PMC5622112 DOI: 10.1038/s41598-017-12378-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 09/07/2017] [Indexed: 12/25/2022] Open
Abstract
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
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Affiliation(s)
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
| | - Yi Wu
- Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA
| | - Ailin Mao
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Yiyue Jiang
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Baoshan Guo
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, University of Tokyo, Tokyo, 113-8656, Japan
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan. .,Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan. .,Department of Electrical Engineering, University of California, Los Angeles, California, 90095, USA.
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46
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Wu J, Tang AHL, Mok ATY, Yan W, Chan GCF, Wong KKY, Tsia KK. Multi-MHz laser-scanning single-cell fluorescence microscopy by spatiotemporally encoded virtual source array. BIOMEDICAL OPTICS EXPRESS 2017; 8:4160-4171. [PMID: 28966855 PMCID: PMC5611931 DOI: 10.1364/boe.8.004160] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 08/10/2017] [Accepted: 08/15/2017] [Indexed: 05/15/2023]
Abstract
Apart from the spatial resolution enhancement, scaling of temporal resolution, equivalently the imaging throughput, of fluorescence microscopy is of equal importance in advancing cell biology and clinical diagnostics. Yet, this attribute has mostly been overlooked because of the inherent speed limitation of existing imaging strategies. To address the challenge, we employ an all-optical laser-scanning mechanism, enabled by an array of reconfigurable spatiotemporally-encoded virtual sources, to demonstrate ultrafast fluorescence microscopy at line-scan rate as high as 8 MHz. We show that this technique enables high-throughput single-cell microfluidic fluorescence imaging at 75,000 cells/second and high-speed cellular 2D dynamical imaging at 3,000 frames per second, outperforming the state-of-the-art high-speed cameras and the gold-standard laser scanning strategies. Together with its wide compatibility to the existing imaging modalities, this technology could empower new forms of high-throughput and high-speed biological fluorescence microscopy that was once challenged.
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Affiliation(s)
- Jianglai Wu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Anson H. L. Tang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Aaron T. Y. Mok
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Wenwei Yan
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Godfrey C. F. Chan
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong Pokfulam Road, Hong Kong, China
| | - Kenneth K. Y. Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Kevin K. Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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47
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Parratt K, Jeong J, Qiu P, Roy K. 3D material cytometry (3DMaC): a very high-replicate, high-throughput analytical method using microfabricated, shape-specific, cell-material niches. LAB ON A CHIP 2017; 17:2861-2872. [PMID: 28726912 PMCID: PMC5577978 DOI: 10.1039/c7lc00451f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Studying cell behavior within 3D material niches is key to understanding cell biology in health and diseases, and developing biomaterials for regenerative medicine applications. Current approaches to studying these cell-material niches have low throughput and can only analyze a few replicates per experiment resulting in reduced measurement assurance and analytical power. Here, we report 3D material cytometry (3DMaC), a novel high-throughput method based on microfabricated, shape-specific 3D cell-material niches and imaging cytometry. 3DMaC achieves rapid and highly multiplexed analyses of very high replicate numbers ("n" of 104-106) of 3D biomaterial constructs. 3DMaC overcomes current limitations of low "n", low-throughput, and "noisy" assays, to provide rapid and simultaneous analyses of potentially hundreds of parameters in 3D biomaterial cultures. The method is demonstrated here for a set of 85 000 events containing twelve distinct cell-biomaterial micro-niches along with robust, customized computational methods for high-throughput analytics with potentially unprecedented statistical power.
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Affiliation(s)
- Kirsten Parratt
- School of Materials Science and Engineering, Georgia Institute of Technology, 30332, USA
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48
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Tang AHL, Lai QTK, Chung BMF, Lee KCM, Mok ATY, Yip GK, Shum AHC, Wong KKY, Tsia KK. Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM). J Vis Exp 2017. [PMID: 28715367 DOI: 10.3791/55840] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the main trend in the advanced development of flow cytometry. Notably, adding high-resolution imaging capabilities allows for the complex morphological analysis of cellular/sub-cellular structures. This is not possible with standard flow cytometers. However, it is valuable for advancing our knowledge of cellular functions and can benefit life science research, clinical diagnostics, and environmental monitoring. Incorporating imaging capabilities into flow cytometry compromises the assay throughput, primarily due to the limitations on speed and sensitivity in the camera technologies. To overcome this speed or throughput challenge facing imaging flow cytometry while preserving the image quality, asymmetric-detection time-stretch optical microscopy (ATOM) has been demonstrated to enable high-contrast, single-cell imaging with sub-cellular resolution, at an imaging throughput as high as 100,000 cells/s. Based on the imaging concept of conventional time-stretch imaging, which relies on all-optical image encoding and retrieval through the use of ultrafast broadband laser pulses, ATOM further advances imaging performance by enhancing the image contrast of unlabeled/unstained cells. This is achieved by accessing the phase-gradient information of the cells, which is spectrally encoded into single-shot broadband pulses. Hence, ATOM is particularly advantageous in high-throughput measurements of single-cell morphology and texture - information indicative of cell types, states, and even functions. Ultimately, this could become a powerful imaging flow cytometry platform for the biophysical phenotyping of cells, complementing the current state-of-the-art biochemical-marker-based cellular assay. This work describes a protocol to establish the key modules of an ATOM system (from optical frontend to data processing and visualization backend), as well as the workflow of imaging flow cytometry based on ATOM, using human cells and micro-algae as the examples.
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Affiliation(s)
- Anson H L Tang
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | - Queenie T K Lai
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | - Bob M F Chung
- Department of Mechanical Engineering, The University of Hong Kong
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | - Aaron T Y Mok
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | - G K Yip
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | | | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong;
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49
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Guo B, Lei C, Kobayashi H, Ito T, Yalikun Y, Jiang Y, Tanaka Y, Ozeki Y, Goda K. High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy. Cytometry A 2017; 91:494-502. [PMID: 28399328 DOI: 10.1002/cyto.a.23084] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 01/19/2017] [Accepted: 01/23/2017] [Indexed: 12/16/2022]
Abstract
The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- Baoshan Guo
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.,Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | | | - Takuro Ito
- Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan
| | - Yaxiaer Yalikun
- Laboratory for Integrated Biodevices, Quantitative Biology Center, RIKEN, Osaka, 565-0871, Japan
| | - Yiyue Jiang
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan
| | - Yo Tanaka
- Laboratory for Integrated Biodevices, Quantitative Biology Center, RIKEN, Osaka, 565-0871, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, University of Tokyo, Tokyo, 113-8656, Japan
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.,Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan.,Department of Electrical Engineering, University of California, Los Angeles, California, 90095
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50
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All-passive pixel super-resolution of time-stretch imaging. Sci Rep 2017; 7:44608. [PMID: 28303936 PMCID: PMC5356014 DOI: 10.1038/srep44608] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 02/09/2017] [Indexed: 12/23/2022] Open
Abstract
Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate - hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (≈2-5 GSa/s)-more than four times lower than the originally required readout rate (20 GSa/s) - is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time-stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing.
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