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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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2
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Liu J, Xu Y, Wang W, Wen Y, Hong H, Lu JQ, Tian P, Hu XH. Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes. JOURNAL OF BIOPHOTONICS 2020; 13:e202000036. [PMID: 32506803 DOI: 10.1002/jbio.202000036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/17/2020] [Accepted: 05/31/2020] [Indexed: 05/25/2023]
Abstract
Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCMS , OCMO and OCML based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCMS vs OCMO and OCMO vs OCML for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.
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Affiliation(s)
- Jing Liu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Yaohui Xu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Wenjin Wang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Yuhua Wen
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Heng Hong
- Department of Pathology and Comparative Medicine, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Jun Q Lu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina, USA
| | - Peng Tian
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Xin-Hua Hu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina, USA
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3
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Jin J, Lu JQ, Wen Y, Tian P, Hu XH. Deep learning of diffraction image patterns for accurate classification of five cell types. JOURNAL OF BIOPHOTONICS 2020; 13:e201900242. [PMID: 31804752 DOI: 10.1002/jbio.201900242] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 12/01/2019] [Accepted: 12/04/2019] [Indexed: 06/10/2023]
Abstract
Development of label-free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross-polarized diffraction image (p-DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p-DIFC) method followed by cell classification with DINet on p-DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5-fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p-DI data set. To study feature-based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p-DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high-order correlations extracted at the deep layers for accurate cell classification.
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Affiliation(s)
- Jiahong Jin
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Jun Q Lu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina
| | - Yuhua Wen
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Peng Tian
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Xin-Hua Hu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina
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4
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Vazquez J, Ong IM, Stanic AK. Single-cell technologies in reproductive immunology. Am J Reprod Immunol 2019; 82:e13157. [PMID: 31206899 PMCID: PMC6697222 DOI: 10.1111/aji.13157] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/06/2019] [Accepted: 06/07/2019] [Indexed: 11/29/2022] Open
Abstract
The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.
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Affiliation(s)
- Jessica Vazquez
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
| | - Irene M Ong
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
- Division of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI
| | - Aleksandar K. Stanic
- Division of Reproductive Sciences, University of Wisconsin-Madison, Madison, WI
- Division of Reproductive Endocrinology and Infertility, Departments of Obstetrics and Gynecology, University of Wisconsin-Madison, Madison, WI
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5
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Wang S, Liu J, Lu JQ, Wang W, Al-Qaysi SA, Xu Y, Jiang W, Hu XH. Development and evaluation of realistic optical cell models for rapid and label-free cell assay by diffraction imaging. JOURNAL OF BIOPHOTONICS 2019; 12:e201800287. [PMID: 30447049 DOI: 10.1002/jbio.201800287] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/12/2018] [Accepted: 11/14/2018] [Indexed: 06/09/2023]
Abstract
Methods for rapid and label-free cell assay are highly desired in life science. Single-shot diffraction imaging presents strong potentials to achieve this goal as evidenced by past experimental results using methods such as polarization diffraction imaging flow cytometry. We present here a platform of methods toward solving these problems and results of optical cell model (OCM) evaluations by calculations and analysis of cross-polarized diffraction image (p-DI) pairs. Four types of realistic OCMs have been developed with two prostate cell structures and adjustable refractive index (RI) parameters to investigate the effects of cell morphology and index distribution on calculated p-DI pairs. Image patterns have been characterized by a gray-level co-occurrence matrix (GLCM) algorithm and four GLCM parameters and linear depolarization ratio δL have been selected to compare calculated against measured data of prostate cells. Our results show that the irregular shapes of and heterogeneity in RI distributions for organelles play significant roles in the spatial distribution of scattered light by cells in comparison to the average RI values and their differences among the organelles. Discrepancies in GLCM and δL parameters between calculated and measured p-DI data provide useful insight for understanding light scattering by single cells and improving OCM.
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Affiliation(s)
- Shuting Wang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Jing Liu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Jun Q Lu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina
| | - Wenjin Wang
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Physics and Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Safaa A Al-Qaysi
- Department of Physics, East Carolina University, Greenville, North Carolina
- College of Pharmacy, Al-Mustansiriya University, Baghdad, Iraq
| | - Yaohui Xu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China
| | - Wenhuan Jiang
- Department of Physics, East Carolina University, Greenville, North Carolina
| | - Xin-Hua Hu
- Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan, China
- Department of Physics, East Carolina University, Greenville, North Carolina
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6
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Feng J, Feng T, Yang C, Wang W, Sa Y, Feng Y. Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques. Apoptosis 2018; 23:290-298. [DOI: 10.1007/s10495-018-1454-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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7
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Zhang W, Zhu L, Zhang F, Lou X, Liu C, Meng X. Evaluating the liquid path stability of a flow cytometer. Cytometry A 2016; 89:941-948. [PMID: 27632708 DOI: 10.1002/cyto.a.22978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 07/31/2016] [Accepted: 08/25/2016] [Indexed: 11/11/2022]
Abstract
Precision in flow cytometry depends on many factors, the first of which is accurate and stable positioning of the hydrodynamically focused cells. However, no method exists to evaluate the stability of laminar flow and single-cell flow in the flow chamber of the flow cytometer directly because of the small size of the rectangular channel of the flow chamber. In this paper, a method of high-speed particle image velocimetry is proposed to solve this problem. The velocity stability of the particles in the flow chamber is used to evaluate the flow stability of the fluid path of the flow cytometer. The side scattering images of particles are obtained by a high-speed camera. Upon exposure, cells were imaged at random positions in the flow cell, resulting in four different types of the images: blank, inadequate, normal, or overlapped. Normal images were identified utilizing a grey cluster analysis algorithm based on trapezoid whitenization weight functions. A mid-point method is applied to determine the length of the particle track at a fixed exposure time. The variation of the trajectory lengths of the normal images are used to evaluate the stability of the liquid path. Experiments are carried out to verify the feasibility of our method in which different diameter microspheres at different flow rates. The results indicate that the standard deviation and relative standard deviation of the trajectory lengths can be used as the evaluation indices of the liquid path stability of the flow cytometer. © 2016 International Society for Advancement of Cytometry.
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Affiliation(s)
- Wenchang Zhang
- School of Instrumentation Science & Opto-Electronics Engineering, Hefei University of Technology, Hefei, 230009, China.,Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China.,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Lianqing Zhu
- Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China. .,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China.
| | - Fan Zhang
- Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China.,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Xiaoping Lou
- Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China.,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Chao Liu
- Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China.,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Xiaochen Meng
- Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing, 100192, China.,Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China
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8
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Jiang W, Lu JQ, Yang LV, Sa Y, Feng Y, Ding J, Hu XH. Comparison study of distinguishing cancerous and normal prostate epithelial cells by confocal and polarization diffraction imaging. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:71102. [PMID: 26616011 DOI: 10.1117/1.jbo.21.7.071102] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 10/26/2015] [Indexed: 06/05/2023]
Abstract
Accurate classification of malignant cells from benign ones can significantly enhance cancer diagnosis and prognosis by detection of circulating tumor cells (CTCs). We have investigated two approaches of quantitative morphology and polarization diffraction imaging on two prostate cell types to evaluate their feasibility as single-cell assay methods toward CTC detection after cell enrichment. The two cell types have been measured by a confocal imaging method to obtain their three-dimensional morphology parameters and by a polarization diffraction imaging flow cytometry (p-DIFC) method to obtain image texture parameters. The support vector machine algorithm was applied to examine the accuracy of cell classification with the morphology and diffraction image parameters. Despite larger mean values of cell and nuclear sizes of the cancerous prostate cells than the normal ones, it has been shown that the morphologic parameters cannot serve as effective classifiers. In contrast, accurate classification of the two prostate cell types can be achieved with high classification accuracies on measured data acquired separately in three measurements. These results provide strong evidence that the p-DIFC method has the potential to yield morphology-related “fingerprints” for accurate and label-free classification of the two prostate cell types.
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Affiliation(s)
- Wenhuan Jiang
- East Carolina University, Department of Physics, Greenville, North Carolina 27858, United States
| | - Jun Qing Lu
- East Carolina University, Department of Physics, Greenville, North Carolina 27858, United States
| | - Li V Yang
- East Carolina University, Department of Internal Medicine, Brody School of Medicine, Greenville, North Carolina 27834, United States
| | - Yu Sa
- Tianjin University, Department of Biomedical Engineering, 92 Weijin Road, Tianjin 300072, China
| | - Yuanming Feng
- Tianjin University, Department of Biomedical Engineering, 92 Weijin Road, Tianjin 300072, China
| | - Junhua Ding
- East Carolina University, Department of Computer Science, Greenville, North Carolina 27858, United States
| | - Xin-Hua Hu
- East Carolina University, Department of Physics, Greenville, North Carolina 27858, United States
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9
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Zhang J, Feng Y, Jiang W, Lu JQ, Sa Y, Ding J, Hu XH. Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus. OPTICS EXPRESS 2016; 24:366-377. [PMID: 26832267 DOI: 10.1364/oe.24.000366] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.
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10
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Wang H, Feng Y, Sa Y, Ma Y, Lu JQ, Hu XH. Acquisition of cross-polarized diffraction images and study of blurring effect by one time-delay-integration camera. APPLIED OPTICS 2015; 54:5223-5228. [PMID: 26192687 DOI: 10.1364/ao.54.005223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Blurred diffraction images acquired from flowing particles affect the measurement of fringe patterns and subsequent analysis. An imaging unit with one time-delay-integration (TDI) camera has been developed to acquire two cross-polarized diffraction images. It was shown that selected elements of Mueller matrix of single scatters can be imaged with pixel matching precision in this configuration. With the TDI camera, the effect of blurring on imaging of scattered light propagating along the side directions was found to be much more significant for biological cells than microspheres. Despite blurring, classification of MCF-7 and K562 cells is feasible since the effect has similar influence on extracted image parameters. Furthermore, image blurring can be useful for analysis of the correlations among texture parameters for characterization of diffraction images from single cells. The results demonstrate that with one TDI camera the polarization diffraction imaging flow cytometry can be significantly improved and angular distribution of selected Mueller matrix elements can be accurately measured for rapid and morphology-based assay of particles and cells without fluorescent labeling.
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11
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Pan R, Feng Y, Sa Y, Lu JQ, Jacobs KM, Hu XH. Analysis of diffraction imaging in non-conjugate configurations. OPTICS EXPRESS 2014; 22:31568-31574. [PMID: 25607106 DOI: 10.1364/oe.22.031568] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Diffraction imaging of scattered light allows extraction of information on scatterer's morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.
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12
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Feng Y, Zhang N, Jacobs KM, Jiang W, Yang LV, Li Z, Zhang J, Lu JQ, Hu XH. Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer. Cytometry A 2014; 85:817-26. [PMID: 25044756 DOI: 10.1002/cyto.a.22504] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2013] [Revised: 03/21/2014] [Accepted: 06/18/2014] [Indexed: 12/23/2022]
Abstract
Label-free and rapid classification of cells can have awide range of applications in biology. We report a robust method of polarization diffraction imaging flow cytometry (p-DIFC) for achieving this goal. Coherently scattered light signals are acquired from single cells excited by a polarized laser beam in the form of two cross-polarized diffraction images. Image texture and intensity parameters are extracted with a gray level co-occurrence matrix (GLCM) algorithm to obtain an optimized set of feature parameters as the morphological "fingerprints" for automated cell classification. We selected the Jurkat T cells and Ramos B cells to test the p-DIFC method's capacity for cell classification. After detailed statistical analysis, we found that the optimized feature vectors yield accuracies of classification between the Jurkat and Ramos ranging from 97.8% to 100% among different cell data sets. Confocal imaging and three-dimensional reconstruction were applied to gain insights on the ability of p-DIFC method for classifying the two cell lines of highly similar morphology. Based on these results we conclude that the p-DIFC method has the capacity to discriminate cells of high similarity in their morphology with "fingerprints" features extracted from the diffraction images, which may be attributed to subtle but statistically significant differences in the nucleus-to-cell volume ratio in the case of Jurkat and Ramos cells.
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Affiliation(s)
- Yuanming Feng
- Department of Biomedical Engineering, Tianjin University, Tianjin, 300072, China
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13
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Yang X, Feng Y, Liu Y, Zhang N, Lin W, Sa Y, Hu XH. A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique. BIOMEDICAL OPTICS EXPRESS 2014; 5:2172-83. [PMID: 25071957 PMCID: PMC4102357 DOI: 10.1364/boe.5.002172] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Revised: 05/13/2014] [Accepted: 06/08/2014] [Indexed: 05/04/2023]
Abstract
A quantitative method for measurement of apoptosis in HL-60 cells based on polarization diffraction imaging flow cytometry technique is presented in this paper. Through comparative study with existing methods and the analysis of diffraction images by a gray level co-occurrence matrix algorithm (GLCM), we found 4 GLCM parameters of contrast (CON), cluster shade (CLS), correlation (COR) and dissimilarity (DIS) exhibit high sensitivities as the apoptotic rates. It was further demonstrated that the CLS parameter correlates significantly (R(2) = 0.899) with the degree of nuclear fragmentation and other three parameters showed a very good correlations (R(2) ranges from 0.69 to 0.90). These results demonstrated that the new method has the capability for rapid and accurate extraction of morphological features to quantify cellular apoptosis without the need for cell staining.
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Affiliation(s)
- Xu Yang
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Yuanming Feng
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
- Department of Radiation Oncology, East Carolina University, Greenville, NC 27834, USA
| | - Yahui Liu
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Ning Zhang
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Wang Lin
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Yu Sa
- Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
| | - Xin-Hua Hu
- Department of Physics, East Carolina University, Greenville, NC 27858, USA
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