1
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Bu X, Yang L, Han X, Liu S, Lu X, Wan J, Zhang X, Tang P, Zhang W, Zhong L. DHM/SERS reveals cellular morphology and molecular changes during iPSCs-derived activation of astrocytes. BIOMEDICAL OPTICS EXPRESS 2024; 15:4010-4023. [PMID: 38867782 PMCID: PMC11166415 DOI: 10.1364/boe.524356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/14/2024]
Abstract
The activation of astrocytes derived from induced pluripotent stem cells (iPSCs) is of great significance in neuroscience research, and it is crucial to obtain both cellular morphology and biomolecular information non-destructively in situ, which is still complicated by the traditional optical microscopy and biochemical methods such as immunofluorescence and western blot. In this study, we combined digital holographic microscopy (DHM) and surface-enhanced Raman scattering (SERS) to investigate the activation characteristics of iPSCs-derived astrocytes. It was found that the projected area of activated astrocytes decreased by 67%, while the cell dry mass increased by 23%, and the cells changed from a flat polygonal shape to an elongated star-shaped morphology. SERS analysis further revealed an increase in the intensities of protein spectral peaks (phenylalanine 1001 cm-1, proline 1043 cm-1, etc.) and lipid-related peaks (phosphatidylserine 524 cm-1, triglycerides 1264 cm-1, etc.) decreased in intensity. Principal component analysis-linear discriminant analysis (PCA-LDA) modeling based on spectral data distinguished resting and reactive astrocytes with a high accuracy of 96.5%. The increase in dry mass correlated with the increase in protein content, while the decrease in projected area indicated the adjustment of lipid composition and cell membrane remodeling. Importantly, the results not only reveal the cellular morphology and molecular changes during iPSCs-derived astrocytes activation but also reflect their mapping relationship, thereby providing new insights into diagnosing and treating neurodegenerative diseases.
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Affiliation(s)
- Xiaoya Bu
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
| | - Liwei Yang
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
| | - Xianxin Han
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
| | - Shengde Liu
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
| | - Xiaoxu Lu
- Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
| | - Jianhui Wan
- Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
| | - Xiao Zhang
- Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
| | - Ping Tang
- Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
| | - Weina Zhang
- Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
| | - Liyun Zhong
- Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
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2
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Wang K, Song L, Wang C, Ren Z, Zhao G, Dou J, Di J, Barbastathis G, Zhou R, Zhao J, Lam EY. On the use of deep learning for phase recovery. LIGHT, SCIENCE & APPLICATIONS 2024; 13:4. [PMID: 38161203 PMCID: PMC10758000 DOI: 10.1038/s41377-023-01340-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 01/03/2024]
Abstract
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
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Affiliation(s)
- Kaiqiang Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Li Song
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chutian Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Zhenbo Ren
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
| | - Guangyuan Zhao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jiazhen Dou
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jianglei Di
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - George Barbastathis
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Renjie Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jianlin Zhao
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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3
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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4
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Tanwar S, Wu L, Zahn N, Raj P, Ghaemi B, Chatterjee A, Bulte JWM, Barman I. Targeted Enzyme Activity Imaging with Quantitative Phase Microscopy. NANO LETTERS 2023; 23:4602-4608. [PMID: 37154678 PMCID: PMC10798004 DOI: 10.1021/acs.nanolett.3c01090] [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] [Indexed: 05/10/2023]
Abstract
Quantitative phase imaging (QPI) is a powerful optical imaging modality for label-free, rapid, and three-dimensional (3D) monitoring of cells and tissues. However, molecular imaging of important intracellular biomolecules such as enzymes remains a largely unexplored area for QPI. Herein, we introduce a fundamentally new approach by designing QPI contrast agents that allow sensitive detection of intracellular biomolecules. We report a new class of bio-orthogonal QPI-nanoprobes for in situ high-contrast refractive index (RI) imaging of enzyme activity. The nanoprobes feature silica nanoparticles (SiO2 NPs) having higher RI than endogenous cellular components and surface-anchored cyanobenzothiazole-cysteine (CBT-Cys) conjugated enzyme-responsive peptide sequences. The nanoprobes specifically aggregated in cells with target enzyme activity, increasing intracellular RI and enabling precise visualization of intracellular enzyme activity. We envision that this general design of QPI-nanoprobes could open doors for spatial-temporal mapping of enzyme activity with direct implications for disease diagnosis and evaluating the therapeutic efficacy.
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Affiliation(s)
- Swati Tanwar
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Lintong Wu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Noah Zahn
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Piyush Raj
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Behnaz Ghaemi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, Maryland 21205, USA
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Arnab Chatterjee
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Jeff W M Bulte
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, Maryland 21205, USA
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Inc., Baltimore, Maryland 21205, USA
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, USA
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, School of Medicine, Baltimore, Maryland 21205, USA
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, USA
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5
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Gillette AA, Pham DL, Skala MC. Touch-free optical technologies to streamline the production of T cell therapies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 25:100434. [PMID: 36642996 PMCID: PMC9837746 DOI: 10.1016/j.cobme.2022.100434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Currently approved adoptive T cell therapy relies on autologous (obtained from the same patient) T cells, which often suffer from poor quality that diminishes treatment efficacy. Due to the heterogeneous nature of T cell quality between and within patients, significant efforts are aimed at optimizing cell manipulation and growth conditions for potent T cell products. We believe that touch-free imaging and sensing technologies are critical to monitor single-cell features during T cell manufacturing to ensure consistent and optimally timed methods for cell manipulation and growth. Here, we discuss emerging label-free optical imaging and sensing methods, along with machine learning techniques that could enable in-line feedback to optimize T cell quality at multiple stages during manufacturing. These methods have the potential to streamline current workflow, accelerate the manufacture of safe high-quality T cell therapies, and improve our understanding of the dynamic, heterogeneous processes of T cell manufacturing.
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Affiliation(s)
| | - Dan L Pham
- Department of Biomedical Engineering, University of Wisconsin-Madison
| | - Melissa C Skala
- Morgridge Institute for Research, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison
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6
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Chen C, Gu Y, Xiao Z, Wang H, He X, Jiang Z, Kong Y, Liu C, Xue L, Vargas J, Wang S. Automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks. Anal Chim Acta 2022; 1229:340401. [PMID: 36156229 DOI: 10.1016/j.aca.2022.340401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/27/2022] [Accepted: 09/11/2022] [Indexed: 11/01/2022]
Abstract
Whole blood cell analysis is widely used in medical applications since its results are indicators for diagnosing a series of diseases. In this work, we report automatic whole blood cell analysis from blood smear using label-free multi-modal imaging with deep neural networks. First, a commercial microscope equipped with our developed Phase Real-time Microscope Camera (PhaseRMiC) obtains both bright-field and quantitative phase images. Then, these images are automatically processed by our designed blood smear recognition networks (BSRNet) that recognize erythrocytes, leukocytes and platelets. Finally, blood cell parameters such as counts, shapes and volumes can be extracted according to both quantitative phase images and automatic recognition results. The proposed whole blood cell analysis technique provides high-quality blood cell images and supports accurate blood cell recognition and analysis. Moreover, this approach requires rather simple and cost-effective setups as well as easy and rapid sample preparations. Therefore, this proposed method has great potential application in blood testing aiming at disease diagnostics.
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Affiliation(s)
- Chao Chen
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yuanjie Gu
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zhibo Xiao
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Hailun Wang
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Xiaoliang He
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zhilong Jiang
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yan Kong
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Cheng Liu
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Liang Xue
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, 200090, China.
| | - Javier Vargas
- Applied Optics Complutense Group, Optics Department, Universidad Complutense de Madrid, Facultad de CC. Físicas, Plaza de Ciencias, 1, 28040, Madrid, Spain
| | - Shouyu Wang
- Computational Optics Laboratory, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China; OptiX+ Laboratory, Wuxi, Jiangsu, China.
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7
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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8
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Liu Z, Parida S, Wu S, Sears CL, Sharma D, Barman I. Label-Free Vibrational and Quantitative Phase Microscopy Reveals Remarkable Pathogen-Induced Morphomolecular Divergence in Tumor-Derived Cells. ACS Sens 2022; 7:1495-1505. [PMID: 35583030 DOI: 10.1021/acssensors.2c00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to Bacteroides fragilis toxin (BFT), a toxin secreted by enterotoxigenicB. fragilis. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after in vivo passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.
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Affiliation(s)
- Zhenhui Liu
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Sheetal Parida
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
| | - Shaoguang Wu
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Cynthia L. Sears
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Dipali Sharma
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, United States
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
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9
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Zhao L, Tang L, Greene MS, Sa Y, Wang W, Jin J, Hong H, Lu JQ, Hu XH. Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry. Anal Chem 2022; 94:1567-1574. [DOI: 10.1021/acs.analchem.1c03337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Lin Zhao
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States
- School of Information Science & Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Liwen Tang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Information Science & Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Marion S. Greene
- Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States
| | - Yu Sa
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Wenjin Wang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Jiahong Jin
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
| | - Heng Hong
- Department of Pathology and Comparative Medicine, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina 27109, United States
| | - Jun Q. Lu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States
| | - Xin-Hua Hu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China
- Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States
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10
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Paidi SK, Raj P, Bordett R, Zhang C, Karandikar SH, Pandey R, Barman I. Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia. Biosens Bioelectron 2021; 190:113403. [PMID: 34130086 PMCID: PMC8492164 DOI: 10.1016/j.bios.2021.113403] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 01/15/2023]
Abstract
Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.
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Affiliation(s)
- Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Piyush Raj
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rosalie Bordett
- Connecticut Children's Innovation Center, University of Connecticut School of Medicine, Farmington, CT, 06032, USA
| | - Chi Zhang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sukrut H Karandikar
- Department of Immunology, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Rishikesh Pandey
- Connecticut Children's Innovation Center, University of Connecticut School of Medicine, Farmington, CT, 06032, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Department of Oncology, Johns Hopkins University, Baltimore, MD, 21287, USA.
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11
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Kim DNH, Lim AA, Teitell MA. Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning. Sci Rep 2021; 11:19448. [PMID: 34593878 PMCID: PMC8484462 DOI: 10.1038/s41598-021-98567-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022] Open
Abstract
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.
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Affiliation(s)
- Diane N H Kim
- Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA
| | - Alexander A Lim
- Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA
| | - Michael A Teitell
- Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA.
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, 90095, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, 90095, USA.
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
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12
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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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13
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Doan M, Barnes C, McQuin C, Caicedo JC, Goodman A, Carpenter AE, Rees P. Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry. Nat Protoc 2021; 16:3572-3595. [PMID: 34145434 PMCID: PMC8506936 DOI: 10.1038/s41596-021-00549-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/29/2021] [Indexed: 11/08/2022]
Abstract
Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.
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Affiliation(s)
- Minh Doan
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Bioimaging Analytics, GlaxoSmithKline, Collegeville, PA, USA.
| | - Claire Barnes
- College of Engineering, Swansea University, Bay Campus, Swansea, UK
| | - Claire McQuin
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Juan C Caicedo
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Allen Goodman
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Paul Rees
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- College of Engineering, Swansea University, Bay Campus, Swansea, UK.
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14
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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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15
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Paidi SK, Shah V, Raj P, Glunde K, Pandey R, Barman I. Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential. Biosens Bioelectron 2021; 175:112863. [PMID: 33272866 PMCID: PMC7847362 DOI: 10.1016/j.bios.2020.112863] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/16/2020] [Accepted: 11/24/2020] [Indexed: 12/16/2022]
Abstract
Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.
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Affiliation(s)
- Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Vaani Shah
- Fischell Department of Bioengineering, University of Maryland, College Park, MD, 20742, USA
| | - Piyush Raj
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristine Glunde
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Rishikesh Pandey
- CytoVeris Inc, Farmington, CT, 06032, USA; Department of Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA; The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Department of Oncology, Johns Hopkins University, Baltimore, MD, 21287, USA.
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16
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Ayyappan V, Chang A, Zhang C, Paidi SK, Bordett R, Liang T, Barman I, Pandey R. Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning. ACS Sens 2020; 5:3281-3289. [PMID: 33092347 DOI: 10.1021/acssensors.0c01811] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays introduced by the specimen, has been demonstrated to discern cellular phenotypes based on differential morphological attributes. Rapid acquisition capability and the availability of label-free images with high information content have enabled researchers to use machine learning (ML) to reveal latent features. We developed a set of ML classifiers, including convolutional neural networks, to discern healthy B cells from lymphoblasts and classify stages of B cell acute lymphoblastic leukemia. Here, we show that the average dry mass and volume of normal B cells are lower than those of cancerous cells and that these morphologic parameters increase further alongside disease progression. We find that the relaxed training requirements of a ML approach are conducive to the classification of cell type, with minimal space, training time, and memory requirements. Our findings pave the way for a larger study on clinical samples of acute lymphoblastic leukemia, with the overarching goal of its broader use in hematopathology, where the prospect of objective diagnoses with minimal sample preparation remains highly desirable.
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Affiliation(s)
- Vinay Ayyappan
- sDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Alex Chang
- sDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Chi Zhang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Santosh Kumar Paidi
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Rosalie Bordett
- Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States
| | - Tiffany Liang
- Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, United States
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, United States
| | - Rishikesh Pandey
- Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
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17
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Li Y, Di J, Wang K, Wang S, Zhao J. Classification of cell morphology with quantitative phase microscopy and machine learning. OPTICS EXPRESS 2020; 28:23916-23927. [PMID: 32752380 DOI: 10.1364/oe.397029] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/21/2020] [Indexed: 06/11/2023]
Abstract
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.
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18
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Lin H, Luo Y, Sun Q, Deng K, Chen Y, Wang Z, Huang P. Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm. JOURNAL OF BIOPHOTONICS 2020; 13:e201960144. [PMID: 31957147 DOI: 10.1002/jbio.201960144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 12/22/2019] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocol-based datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death.
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Affiliation(s)
- Hancheng Lin
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Yiwen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Kaifei Deng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Yijiu Chen
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
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19
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Wang ZJ, Walsh AJ, Skala MC, Gitter A. Classifying T cell activity in autofluorescence intensity images with convolutional neural networks. JOURNAL OF BIOPHOTONICS 2020; 13:e201960050. [PMID: 31661592 PMCID: PMC7065628 DOI: 10.1002/jbio.201960050] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/28/2019] [Accepted: 10/16/2019] [Indexed: 05/13/2023]
Abstract
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.
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Affiliation(s)
- Zijie J. Wang
- Department of Computer SciencesUniversity of Wisconsin‐MadisonMadisonWisconsin
- Morgridge Institute for ResearchMadisonWisconsin
| | | | - Melissa C. Skala
- Morgridge Institute for ResearchMadisonWisconsin
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsin
| | - Anthony Gitter
- Department of Computer SciencesUniversity of Wisconsin‐MadisonMadisonWisconsin
- Morgridge Institute for ResearchMadisonWisconsin
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsin
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