Polarization imaging and classification of <scp>J</scp>urkat T and <scp>R</scp>amos B cells using a flow cytometer

Feng, Yuanming; Zhang, Ning; Jacobs, Kenneth M.; Jiang, Wen; Yang, Li V.; Li, Zhigang; Zhang, Jun; Lu, Jun Q.; Hu, Xinhua

doi:10.1002/cyto.a.22504

Cited by 31 publications

(34 citation statements)

References 35 publications

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“…In comparison, imaging of coherent light scatter has been much less explored for the challenges to acquire and assess high-contrast images [5][6][7][8]. In recent years, we have developed a diffraction imaging flow cytometry (DIFC) method for measurement of high-contrast images from micrometer-sized particles carried by a laminar flow through the focus of an incident laser beam [9][10][11][12][13]. The essential design of DIFC imaging unit contains an infinity-corrected microscope objective of 0.55 in numerical aperture (NA), a tube lens and an imaging sensor placed at its focal plane Γ im as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Resolving power of diffraction imaging with an objective: a numerical study

Wang¹,

Liu²,

Lu³

et al. 2017

Opt. Express

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Diffraction imaging in far-field can detect 3D morphological features of an object for its coherent nature. We describe methods for accurate calculation and analysis of diffraction images of scatterers of single and double spheres by an imaging unit based on microscope objective at non-conjugate positions. Quantitative study of the calculated diffraction imaging in spectral domain has been performed to assess the resolving power of diffraction imaging. It has been shown numerically that with coherent illumination of 532nm in wavelength the imaging unit can resolve single spheres of 2μm or larger in diameters and double spheres separated by less than 300nm between their centers.

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Section: Introductionmentioning

confidence: 99%

Resolving power of diffraction imaging with an objective: a numerical study

Wang¹,

Liu²,

Lu³

et al. 2017

Opt. Express

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“…Finally, a 16-bit identifier is assigned to each voxel at r for labeling its region type and associated fluorescence intensity in output data files for establishing an OCM. In addition, 3D morphology parameters of the imaged cell can be determined from the output [9,18,20].…”

Section: Reconstruction Of Cell Morphology and Fluorescence Distributionmentioning

confidence: 99%

“…2(A) in which the orientation of an OCM is labelled as C(θ 0 , φ 0 ) that is defined as the line connecting mass-centers of the cell and its nucleus. A linear combination of S ij (θ s , φ s ) was first projected on an "input" plane Γ in at x = -0.15 mm inside the water-filled flow chamber to obtain a p-DI denoted as I kl (y, z) to be measured by a microscope objective based imaging unit with (y, z) as the discrete pixel coordinates [12,14,[18][19][20]33]. The image I kl (y, z) can be expressed as a linear combination of Mueller matrix elements to represent the spatial distribution of the coherent light of a polarization k scattered by the cell excited by an incident beam of polarization l [34].…”

Section: Establishment Of Ocm and Simulation Of Light Scatteringmentioning

confidence: 99%

“…2(A) based on an infinity-corrected objective to acquire the coherent light scattered by a cell through Γ in and record on its focal or image plane Γ im with one or two camera sensors [18][19][20]. It has been previously shown that a unique relation exists between the p-DI pixels on Γ in and Γ im with the imaging unit translated off-focus towards the flow chamber (Δx > 0) [12,13].…”

Section: Ray-tracing Based Diffraction Image Transform From γ In To γ Immentioning

confidence: 99%

“…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 diffraction patterns against the orientation changes of OCMs derived from prostate cells.…”

Section: Introductionmentioning

confidence: 99%

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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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Development and evaluation of realistic optical cell models for rapid and label‐free cell assay by diffraction imaging

Wang

Liu

et al. 2018

Journal of Biophotonics

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

Cited by 31 publications

References 35 publications

Resolving power of diffraction imaging with an objective: a numerical study

Resolving power of diffraction imaging with an objective: a numerical study

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

Development and evaluation of realistic optical cell models for rapid and label‐free cell assay by diffraction imaging

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