Analysis of cellular objects through diffraction images acquired by flow cytometry

Zhang, Jun; Feng, Yuanming; Moran, Marina S.; Lu, Jun Q.; Yang, Li V.; Yang, Sa; Zhang, Ning; Dong, Lixue; Hu, Xinhua

doi:10.1364/oe.21.024819

Cited by 32 publications

(37 citation statements)

References 26 publications

(41 reference statements)

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“…5 show a trend with the speckles appearing larger for cells with increasing t p . The visual differences are consistent with the facts that apoptotic cells tend to have fragmented nuclei of reduced volumes which lead to larger speckles [25]. Despite the fact that the mean values of s-CLS increases with t p as shown in Fig.…”

Section: Discussionsupporting

confidence: 88%

“…In fact, it can be readily observed that apoptotic cells distribute differentially from the viable cells in a scatter plot of the angularly integrated side scatters (SSC) versus the forward scatters (FSC) obtained with the FCM assay [18,19]. These results suggest some strong correlations between the morphological variations in apoptotic cells relative to the viable cells and spatial distribution of scattered light, which are consistent with the experimental and modeling studies on light scattering by biological cells of different morphology in general [15][16][17][20][21][22][23][24][25].…”

Section: Introductionsupporting

confidence: 83%

“…After injection, cells moved in single file through the focus of an incident laser beam of 532nm in wavelength and about 40mW in power [17,25]. The coherent light scatters along the side directions were collected by an infinity-corrected objective of 50x (378-805-3, Mitutoyo) and divided into two cross-polarized (s-polarized and p-polarized) components by a polarizing beam splitter.…”

Section: P-difc Measurement and Image Analysismentioning

confidence: 99%

“…We have recently developed a polarization diffraction imaging flow cytometry (p-DIFC) method and shown its capacity for cell characterization and classification without fluorescent staining [17,[23][24][25]. Different from the FCM assay, the p-DIFC measurement records the spatial distribution of coherent light scatters from single cells excited by a laser beam with cameras and extracts the diffraction pattern or texture features from the acquired images.…”

Section: Introductionmentioning

confidence: 99%

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A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique

Feng

Liu

et al. 2014

Biomed. Opt. Express

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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. 273-310 (1984).

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Section: Discussionsupporting

confidence: 88%

Section: Introductionsupporting

confidence: 83%

Section: P-difc Measurement and Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique

Feng

Liu

et al. 2014

Biomed. Opt. Express

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“…This is especially true in the case of diffraction imaging to record spatial distribution of coherent light scattered by single cells with a flow cytometer. Previous studies by different groups, including ours, have developed simplified OCMs of constant or varying RI within a cell and/or its individual organelles built by spheres or spheroids [4,37,38], mathematical surfaces [39], reconstruction from confocal image stack data for nucleus only [10,33,40] or Gaussian random sphere models for nucleus and mitochondria [41,42]. These models may be sufficiently accurate for investigation of certain aspects of scattered light distributions such as angularly (1D) or spectroscopically resolved measurements [43].…”

Section: Discussionmentioning

confidence: 99%

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Zhang¹,

Feng²,

Jiang³

et al. 2016

Opt. Express

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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. References and links 1.D. Zink, A. H. Fischer, and J. A. Nickerson, "Nuclear structure in cancer cells," Nat. Rev. Cancer 4, 677-687 (2004 IntroductionNucleus is one of the largest organelles inside eukaryotic cells, provides the site for DNA and RNA synthesis, plays critical roles in cell development. Hence it serves as one of major targets for cell assay by morphology and is especially important for detection of abnormal conditions and cancer diagnosis [1]. Optical detection through coherent light scattering offers a much valued platform for its label-free nature and capacities to extract both morphology and molecular information. Characterization of nucleus by scattered light signals thus attracts active research efforts [2][3][4][5][6][7]. Determination of cellular and nuclear morphology is fundamentally a challenging inverse problem for their complex 3D structures. For example, structural reconstruction requires large amount of measured data per cell and often expensive computation that is too long for rapid assay [8,9]. If one aims at only to distinguish cell types such as cancer from normal or apoptotic from viable cells, however, the goal may be achieved empirically with moderate amount of measured data per cell and powerful algorithms of pattern recognition. In either case, it is very useful to develop realistic optical cell models (OCMs) and accurate simulation tools for forward calculations of measured signals of scattered light. They can be employed, for example, to generate training data for algorithm development in search of the correlations between morphological features of cells and diffraction patterns of coherent light scatter.In this report, we present a numerical approach based on previous studies for establishing realistic OCMs for generating virtual cells and accurate simulation of polarized diffraction image (p-DI) data [9][10][11][12][13]. The new approach takes the advantage of 3D cell morphology and molecular information acquired from the fluorescent confocal images to produce simulated p-DI data that are comparable to the measured ones acquired with a polarization diffraction imaging flow cytometry (p-DIFC) system [14][15][16][17][18][19][20]. To demonstrate the utility of the realistic OCMs, we have investigated the effects of nuclear morphology and refractive index (RI) on di...

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Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer

et al. 2014

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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 cooccurrence 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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Analysis of cellular objects through diffraction images acquired by flow cytometry

Cited by 32 publications

References 26 publications

A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique

A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Polarization imaging and classification of Jurkat T and Ramos B cells using a flow cytometer

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