1
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Wang H, Zhang L, Huang J, Yang Z, Fan C, Yuan L, Zhao H, Zhang Z, Liu X. Imaging the intracellular refractive index distribution (IRID) for dynamic label-free living colon cancer cells via circularly depolarization decay model (CDDM). BIOMEDICAL OPTICS EXPRESS 2024; 15:2451-2465. [PMID: 38633098 PMCID: PMC11019712 DOI: 10.1364/boe.518957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/19/2024]
Abstract
Label-free detection of intracellular substances for living cancer cells remains a significant hurdle in cancer pathogenesis research. Although the sensitivity of light polarization to intracellular substances has been validated, current studies are predominantly focused on tissue lesions, thus label-free detection of substances within individual living cancer cells is still a challenge. The main difficulty is to find specific detection methods along with corresponding characteristic parameters. With refractive index as an endogenous marker of substances, this study proposes a detection method of intracellular refractive index distribution (IRID) for label-free living colon cancer (LoVo) cells. Utilizing the circular depolarization decay model (CDDM) to calculate the degree of circular polarization (DOCP) modulated by the cell allows for the derivation of the IRID on the focal plane. Experiments on LoVo cells demonstrated the refractive index of single cell can be accurately and precisely measured, with precision of 10-3 refractive index units (RIU). Additionally, chromatin content during the interphases (G1, S, G2) of cell cycle was recorded at 56.5%, 64.4%, and 71.5%, respectively. A significantly finer IRID can be obtained compared to the phase measurement method. This method is promising in providing a dynamic label-free intracellular substances detection method in cancer pathogenesis studies.
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Affiliation(s)
- Huijun Wang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Lu Zhang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- School of Instrument Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Jie Huang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Zewen Yang
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Chen Fan
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- School of Instrument Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Li Yuan
- First Affiliated Hospital, Xi’an Jiaotong University, Xi’an 710049, China
| | - Hong Zhao
- State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- School of Instrument Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Zhenxi Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Xiaolong Liu
- Mengchao Hepatobiliary Hospital of Fujian Medical University, The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Provincey, Fuzhou 350025, China
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2
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Moetazedian A, Candeo A, Liu S, Hughes A, Nasrollahi V, Saadat M, Bassi A, Grover LM, Cox LR, Poologasundarampillai G. Versatile Microfluidics for Biofabrication Platforms Enabled by an Agile and Inexpensive Fabrication Pipeline. Adv Healthc Mater 2023; 12:e2300636. [PMID: 37186512 DOI: 10.1002/adhm.202300636] [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: 03/21/2023] [Indexed: 05/17/2023]
Abstract
Microfluidics have transformed diagnosis and screening in regenerative medicine. Recently, they are showing much promise in biofabrication. However, their adoption is inhibited by costly and drawn-out lithographic processes thus limiting progress. Here, multi-material fibers with complex core-shell geometries with sizes matching those of human arteries and arterioles are fabricated employing versatile microfluidic devices produced using an agile and inexpensive manufacturing pipeline. The pipeline consists of material extrusion additive manufacturing with an innovative continuously varied extrusion (CONVEX) approach to produce microfluidics with complex seamless geometries including, novel variable-width zigzag (V-zigzag) mixers with channel widths ranging from 100-400 µm and hydrodynamic flow-focusing components. The microfluidic systems facilitated rapid mixing of fluids by decelerating the fluids at specific zones to allow for increased diffusion across the interfaces. Better mixing even at high flow rates (100-1000 µL min-1 ) whilst avoiding turbulence led to high cell cytocompatibility (>86%) even when 100 µm nozzles are used. The presented 3D-printed microfluidic system is versatile, simple and efficient, offering a great potential to significantly advance the microfluidic platform in regenerative medicine.
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Affiliation(s)
- Amirpasha Moetazedian
- School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Edgbaston, Birmingham, B5 7EG, UK
- EPSRC Future Metrology Hub, School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3D, UK
| | - Alessia Candeo
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy
| | - Siyun Liu
- School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Edgbaston, Birmingham, B5 7EG, UK
| | - Arran Hughes
- Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Vahid Nasrollahi
- Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Mozafar Saadat
- Department of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Andrea Bassi
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy
| | - Liam M Grover
- School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Liam R Cox
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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3
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Liu S, Chu R, Xie J, Song K, Su X. Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry. Cytometry A 2023; 103:240-250. [PMID: 36028474 DOI: 10.1002/cyto.a.24684] [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: 02/10/2022] [Revised: 08/07/2022] [Accepted: 08/23/2022] [Indexed: 11/10/2022]
Abstract
Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light-sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two-dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi-modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label-free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi-modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label-free, automatic classification of early cervical cancer with deep learning-based 2D light scattering.
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Affiliation(s)
- Shanshan Liu
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Ran Chu
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Jinmei Xie
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Kun Song
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
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4
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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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5
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Hedde PN, Le BT, Gomez EL, Duong L, Steele RE, Ahrar S. SPIM-Flow: An Integrated Light Sheet and Microfluidics Platform for Hydrodynamic Studies of Hydra. BIOLOGY 2023; 12:biology12010116. [PMID: 36671808 PMCID: PMC9856110 DOI: 10.3390/biology12010116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/08/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
Selective plane illumination microscopy (SPIM), or light sheet microscopy, is a powerful imaging approach. However, access to and interfacing microscopes with microfluidics have remained challenging. Complex interfacing with microfluidics has limited the SPIM's utility for studying the hydrodynamics of freely moving multicellular organisms. We developed SPIM-Flow, an inexpensive light sheet platform that enables easy integration with microfluidics. We used SPIM-Flow to investigate the hydrodynamics of a freely moving Hydra polyp via particle tracking in millimeter-sized chambers. Initial experiments across multiple animals, feeding on a chip (Artemia franciscana nauplii used as food), and baseline behaviors (tentacle swaying, elongation, and bending) indicated the organisms' health inside the system. Fluidics were used to investigate Hydra's response to flow. The results suggested that the animals responded to an established flow by bending and swaying their tentacles in the flow direction. Finally, using SPIM-Flow in a proof-of-concept experiment, the shear stress required to detach an animal from a surface was demonstrated. Our results demonstrated SPIM-Flow's utility for investigating the hydrodynamics of freely moving animals.
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Affiliation(s)
- Per Niklas Hedde
- Beckman Laser Institute and Medical Clinic, University of California Irvine, Irvine, CA 92612, USA
- Correspondence: (P.N.H.); (S.A.)
| | - Brian T. Le
- Department of Biomedical Engineering, CSU Long Beach, Long Beach, CA 90840, USA
| | - Erika L. Gomez
- Department of Biomedical Engineering, CSU Long Beach, Long Beach, CA 90840, USA
| | - Leora Duong
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA 92697, USA
| | - Robert E. Steele
- Department of Biological Chemistry, University of California Irvine, Irvine, CA 92697, USA
| | - Siavash Ahrar
- Department of Biomedical Engineering, CSU Long Beach, Long Beach, CA 90840, USA
- Department of Physics and Astronomy, University of California Irvine, Irvine, CA 92697, USA
- Correspondence: (P.N.H.); (S.A.)
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6
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Zhang L, Wang H, Liu J, Chen S, Yang H, Yang Z, Zhang Z, Zhao H, Yuan L, Tian L, Zhong B, Liu X. Scattering Inversion Study for Suspended Label-Free Lymphocytes with Complex Fine Structures. BME FRONTIERS 2022; 2022:9867373. [PMID: 37850176 PMCID: PMC10521707 DOI: 10.34133/2022/9867373] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/28/2022] [Indexed: 10/19/2023] Open
Abstract
Objective and Impact Statement. Distinguishing malignant lymphocytes from normal ones is vital in pathological examination. We proposed an inverse light scattering (ILS) method for label-free suspended lymphocytes with complex fine structures to identify their volumes for pathological state. Introduction. Light scattering as cell's "fingerprint" provides valuable morphology information closely related to its biophysical states. However, the detail relationships between the morphology with complex fine structures and its scattering characters are not fully understood. Methods. To quantitatively inverse the volumes of membrane and nucleus as the main scatterers, clinical lymphocyte morphologies were modeled combining the Gaussian random sphere geometry algorithm by 750 reconstructed results after confocal scanning, which allowed the accurate simulation to solve ILS problem. For complex fine structures, the specificity for ILS study was firstly discussed (to our knowledge) considering the differences of not only surface roughness, posture, but also the ratio of nucleus to the cytoplasm and refractive index. Results. The volumes of membrane and nucleus were proved theoretically to have good linear relationship with the effective area and entropy of forward scattering images. Their specificity deviations were less than 3.5%. Then, our experimental results for microsphere and clinical leukocytes showed the Pearson product-moment correlation coefficients (PPMCC) of this linear relationship were up to 0.9830~0.9926. Conclusion. Our scattering inversion method could be effectively applied to identify suspended label-free lymphocytes without destructive sample pretreatments and complex experimental systems.
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Affiliation(s)
- Lu Zhang
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - Huijun Wang
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - Jianyi Liu
- Xi’an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi’an 710049, China
| | - Shuang Chen
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - He Yang
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - Zewen Yang
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - Zhenxi Zhang
- Xi’an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an 710049, China
| | - Hong Zhao
- Xi’an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an 710049, China
| | - Li Yuan
- Xi’an Jiaotong University, First Affiliated Hospital, Xi’an 710049, China
| | - Lifang Tian
- Xi’an Jiaotong University, Second Affiliated Hospital, Xi’an 710049, China
| | - Bo Zhong
- Xi’an Jiaotong University, Second Affiliated Hospital, Xi’an 710049, China
| | - Xiaolong Liu
- Mengchao Hepatobiliary Hospital of Fujian Medical University, The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Fuzhou 350025, China
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7
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Raji H, Tayyab M, Sui J, Mahmoodi SR, Javanmard M. Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review. Biomed Microdevices 2022; 24:26. [PMID: 35953679 DOI: 10.1007/s10544-022-00627-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 12/16/2022]
Abstract
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
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Affiliation(s)
- Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
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8
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Rafiei N, Moghadam MG, Au A, Regeenes R, Chidambaram S, Liang T, Wang Y, Yip CM, Gaisano H, Rocheleau JV. Design of a versatile microfluidic device for imaging precision-cut-tissue slices. Biofabrication 2022; 14. [PMID: 35793653 DOI: 10.1088/1758-5090/ac7eea] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/06/2022] [Indexed: 11/12/2022]
Abstract
Precision-cut-tissues (PCTs), which preserve many aspects of a tissue's microenvironment, are typically imaged using conventional sample dishes and chambers. These can require large amounts of reagent and, when used for flow-through experiments, the shear forces applied on the tissues are often ill-defined. Their physical design also makes it difficult to image large volumes and repetitively image smaller regions of interest in the living slice. We report here on the design of a versatile microfluidic device capable of holding mouse or human pancreas PCTs for 3D fluorescence imaging using confocal and selective plane illumination microscopy (SPIM). Our design positions PCTs within a 5 × 5 mm × 140µm deep chamber fitted with 150µm tall channels to facilitate media exchange. Shear stress in the device is localized to small regions on the surface of the tissue and can be easily controlled. This design allows for media exchange at flowrates ∼10-fold lower than those required for conventional chambers. Finally, this design allows for imaging the same immunofluorescently labeled PCT with high resolution on a confocal and with large field of view on a SPIM, without adversely affecting image quality.
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Affiliation(s)
- Nafiseh Rafiei
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Mohammadamir G Moghadam
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Aaron Au
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Romario Regeenes
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | | | - Tao Liang
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yufeng Wang
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Christopher M Yip
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.,Department of Biochemistry, University of Toronto, Toronto, ON, Canada
| | - Herbert Gaisano
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Jonathan V Rocheleau
- Advanced Diagnostics, Toronto General Hospital Research Institute, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada
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9
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Lv N, Zhang L, Yang Z, Wang H, Yang N, Li H. Label-free biological sample detection and non-contact separation system based on microfluidic chip. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2022; 93:063104. [PMID: 35778042 DOI: 10.1063/5.0086109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
The detection and separation of biological samples are of great significance for achieving accurate diagnoses and state assessments. Currently, the detection and separation of cells mostly adopt labeling methods, which will undoubtedly affect the original physiological state and functions of cells. Therefore, in this study, a label-free cell detection method based on microfluidic chips is proposed. By measuring the scattering of cells to identify cells and then using optical tweezers to separate the target cells, the whole process without any labeling and physical contact could realize automatic cell identification and separation. Different concentrations of 15 µm polystyrene microspheres and yeast mixed solution are used as samples for detection and separation. The detection accuracy is over 90%, and the separation accuracy is over 73%.
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Affiliation(s)
- Ning Lv
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
| | - Lu Zhang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
| | - Zewen Yang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
| | - Huijun Wang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
| | - Nan Yang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
| | - Hao Li
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi 710049, China
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10
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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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11
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Poologasundarampillai G, Haweet A, Jayash SN, Morgan G, Moore JE, Candeo A. Real-time imaging and analysis of cell-hydrogel interplay within an extrusion-bioprinting capillary. BIOPRINTING 2021; 23:e00144. [DOI: 10.1016/j.bprint.2021.e00144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
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12
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Liu S, Yuan Z, Qiao X, Liu Q, Song K, Kong B, Su X. Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells. Cytometry A 2021; 99:610-621. [PMID: 33840152 DOI: 10.1002/cyto.a.24349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/11/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.
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Affiliation(s)
- Shanshan Liu
- School of Microelectronics, Shandong University, Jinan, China.,Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zeng Yuan
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xu Qiao
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Kun Song
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Beihua Kong
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
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13
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Sun J, Wang L, Liu Q, Tárnok A, Su X. Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification. BIOMEDICAL OPTICS EXPRESS 2020; 11:6674-6686. [PMID: 33282516 PMCID: PMC7687967 DOI: 10.1364/boe.405557] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/14/2020] [Accepted: 10/14/2020] [Indexed: 05/27/2023]
Abstract
The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.
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Affiliation(s)
- Jing Sun
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lan Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Key Laboratory of Experimental Teratology (Ministry of Education); Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Attila Tárnok
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
- Advanced Medical Research Institute, Shandong University, Jinan, China
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14
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Nissim N, Dudaie M, Barnea I, Shaked NT. Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning. Cytometry A 2020; 99:511-523. [PMID: 32910546 DOI: 10.1002/cyto.a.24227] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/29/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022]
Abstract
We present a method for real-time visualization and automatic processing for detection and classification of untreated cancer cells in blood during stain-free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in the blood, following an initial label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. We automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells. We used low-coherence off-axis interferometric phase microscopy and a microfluidic channel to image cells during flow quantitatively. The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow. We achieved high accuracy of 92.56% for distinguishing between the cells, enabling further automatic enrichment and cancer-cell grading from blood. © 2020 International Society for Advancement of Cytometry.
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Affiliation(s)
- Noga Nissim
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Ramat Aviv, Israel
| | - Matan Dudaie
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Ramat Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Ramat Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Ramat Aviv, Israel
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15
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Tárnok A. COVID-19 Initiatives and a New Associate Editor. Cytometry A 2020; 97:660-661. [PMID: 32608163 DOI: 10.1002/cyto.a.24179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Attila Tárnok
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.,Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.,Department of Precision Instrument, Tsinghua University, Beijing, China
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16
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Sunny Koyagura S, Takehara H, Ichiki T. Chemical Etching Treatment of Polydimethylsiloxane for Smoothing Microchannel Surface. J PHOTOPOLYM SCI TEC 2020. [DOI: 10.2494/photopolymer.33.485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Hiroaki Takehara
- Department of Materials Engineering, School of Engineering, The University of Tokyo
- Innovation Center of NanoMedicine (iCONM)
| | - Takanori Ichiki
- Department of Materials Engineering, School of Engineering, The University of Tokyo
- Innovation Center of NanoMedicine (iCONM)
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17
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Lv N, Zhang L, Jiang L, Muhammad A, Wang H, Yuan L. A Design of Microfluidic Chip with Quasi-Bessel Beam Waveguide for Scattering Detection of Label-Free Cancer Cells. Cytometry A 2019; 97:78-90. [PMID: 31876079 DOI: 10.1002/cyto.a.23954] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 12/03/2019] [Accepted: 12/05/2019] [Indexed: 12/12/2022]
Abstract
Light scattering detection in microfluidic chips provides an important tool to identify cancer cells without any label processes. However, forward small-angle scattering signals of cells, which are related to their sizes and morphologies, are hard to be detected accurately when scattering angle is less than 11° in microfluidic chips by traditional lighting design due to the influence of incident beam. Therefore, cell's size and morphology being the golden standard for clinical detection may lose their efficacy in recognizing cancer cells from healthy ones. In this article, a novel lighting design in microfluidic chips is put forward in which traditional incident Gaussian beam can be modulated into quasi-Bessel beam by a microprism and waveguide. The quasi-Bessel beam's advantages of nondiffraction theoretically make forward scattering (FS) detection less than 11° possibly. Our experimental results for peripheral blood lymphocytes of human beings and cultured HeLa cells show that the detection rates increase by 47.87% and 46.79%, respectively, by the novel designed microfluidic chip compared to traditional Gaussian lighting method in microfluidic chips. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Ning Lv
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi, 710049, China
| | - Lu Zhang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi, 710049, China
| | - Lili Jiang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi, 710049, China
| | - Amir Muhammad
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi, 710049, China
| | - Huijun Wang
- School of Mechanical Engineering, Xian Jiaotong University, Xian, Shannxi, 710049, China
| | - Li Yuan
- First Affiliated Hospital, Xian Jiaotong University, Xian, Shannxi, 710049, China
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18
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Su X, Yuan T, Wang Z, Song K, Li R, Yuan C, Kong B. Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning. Cytometry A 2019; 97:24-30. [PMID: 31313517 DOI: 10.1002/cyto.a.23865] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 06/14/2019] [Accepted: 07/01/2019] [Indexed: 12/24/2022]
Abstract
We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label-free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label-free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Xuantao Su
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Tao Yuan
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Zhiwen Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Kun Song
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.,Gynecology Oncology Key Laboratory, Qilu Hospital, Shandong University, Jinan, 250012, China
| | - Rongrong Li
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China
| | - Cunzhong Yuan
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China
| | - Beihua Kong
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China
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19
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Zhai W, Yong D, El-Jawhari JJ, Cuthbert R, McGonagle D, Win Naing M, Jones E. Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions. Cytotherapy 2019; 21:803-819. [PMID: 31138507 DOI: 10.1016/j.jcyt.2019.05.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/30/2019] [Accepted: 05/06/2019] [Indexed: 12/11/2022]
Abstract
Regardless of their tissue of origin, multipotent mesenchymal stromal cells (MSCs) are commonly expanded in vitro for several population doublings to achieve a sufficient number of cells for therapy. Prolonged MSC expansion has been shown to result in phenotypical, morphological and gene expression changes in MSCs, which ultimately lead to the state of senescence. The presence of senescent cells in therapeutic MSC batches is undesirable because it reduces their viability, differentiation potential and trophic capabilities. Additionally, senescent cells acquire senescence-activated secretory phenotype, which may not only induce apoptosis in the neighboring host cells following MSC transplantation, but also trigger local inflammatory reactions. This review outlines the current and promising new methodologies for the identification of senescent cells in MSC cultures, with a particular emphasis on non-destructive and label-free methodologies. Technologies allowing identification of individual senescent cells, based on new surface markers, offer potential advantage for targeted senescent cell removal using new-generation senolytic agents, and subsequent production of therapeutic MSC batches fully devoid of senescent cells. Methods or a combination of methods that are non-destructive and label-free, for example, involving cell size and spectroscopic measurements, could be the best way forward because they do not modify the cells of interest, thus maximizing the final output of therapeutic-grade MSC cultures. The further incorporation of machine learning methods has also recently shown promise in facilitating, automating and enhancing the analysis of these measured data.
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Affiliation(s)
- Weichao Zhai
- Leeds Institute of Rheumatic and musculoskeletal Medicine, Leeds, UK; Singapore Institute of Manufacturing Technology, A*STAR, Innovis, Singapore
| | - Derrick Yong
- Singapore Institute of Manufacturing Technology, A*STAR, Innovis, Singapore
| | - Jehan Jomaa El-Jawhari
- Leeds Institute of Rheumatic and musculoskeletal Medicine, Leeds, UK; Department of Clinical Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Richard Cuthbert
- Leeds Institute of Rheumatic and musculoskeletal Medicine, Leeds, UK
| | - Dennis McGonagle
- Leeds Institute of Rheumatic and musculoskeletal Medicine, Leeds, UK
| | - May Win Naing
- Singapore Institute of Manufacturing Technology, A*STAR, Innovis, Singapore
| | - Elena Jones
- Leeds Institute of Rheumatic and musculoskeletal Medicine, Leeds, UK.
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20
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Albert-Smet I, Marcos-Vidal A, Vaquero JJ, Desco M, Muñoz-Barrutia A, Ripoll J. Applications of Light-Sheet Microscopy in Microdevices. Front Neuroanat 2019; 13:1. [PMID: 30760983 PMCID: PMC6362405 DOI: 10.3389/fnana.2019.00001] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 01/09/2019] [Indexed: 11/23/2022] Open
Abstract
Light-sheet fluorescence microscopy (LSFM) has been present in cell biology laboratories for quite some time, mainly as custom-made systems, with imaging applications ranging from single cells (in the micrometer scale) to small organisms (in the millimeter scale). Such microscopes distinguish themselves for having very low phototoxicity levels and high spatial and temporal resolution, properties that make them ideal for a large range of applications. These include the study of cellular dynamics, in particular cellular motion which is essential to processes such as tumor metastasis and tissue development. Experimental setups make extensive use of microdevices (bioMEMS) that provide better control over the substrate environment than traditional cell culture experiments. For example, to mimic in vivo conditions, experiment biochemical dynamics, and trap, move or count cells. Microdevices provide a higher degree of empirical complexity but, so far, most have been designed to be imaged through wide-field or confocal microscopes. Nonetheless, the properties of LSFM render it ideal for 3D characterization of active cells. When working with microdevices, confocal microscopy is more widespread than LSFM even though it suffers from higher phototoxicity and slower acquisition speeds. It is sometimes possible to illuminate with a light-sheet microdevices designed for confocal microscopes. However, these bioMEMS must be redesigned to exploit the full potential of LSFM and image more frequently on a wider scale phenomena such as motion, traction, differentiation, and diffusion of molecules. The use of microdevices for LSFM has extended beyond cell tracking studies into experiments regarding cytometry, spheroid cultures and lab-on-a-chip automation. Due to light-sheet microscopy being in its early stages, a setup of these characteristics demands some degree of optical expertise; and designing three-dimensional microdevices requires facilities, ingenuity, and experience in microfabrication. In this paper, we explore different approaches where light-sheet microscopy can achieve single-cell and subcellular resolution within microdevices, and provide a few pointers on how these experiments may be improved.
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Affiliation(s)
- Ignacio Albert-Smet
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
| | - Asier Marcos-Vidal
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
| | - Juan José Vaquero
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
- Experimental Medicine and Surgery Unit, Instituto de Investigación Sanitaria del Hospital Gregorio Marañón, Madrid, Spain
| | - Manuel Desco
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
- Experimental Medicine and Surgery Unit, Instituto de Investigación Sanitaria del Hospital Gregorio Marañón, Madrid, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Arrate Muñoz-Barrutia
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
- Experimental Medicine and Surgery Unit, Instituto de Investigación Sanitaria del Hospital Gregorio Marañón, Madrid, Spain
| | - Jorge Ripoll
- Department of Bioengineering and Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
- Experimental Medicine and Surgery Unit, Instituto de Investigación Sanitaria del Hospital Gregorio Marañón, Madrid, Spain
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21
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Bhalla N, Chiang HJ, Shen AQ. Cell biology at the interface of nanobiosensors and microfluidics. Methods Cell Biol 2018; 148:203-227. [DOI: 10.1016/bs.mcb.2018.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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