Quantification of nanoscale nuclear refractive index changes during the cell cycle

Bista, Rajan K.; Uttam, Shikhar; Pin, Wang; Staton, Kevin D.; Choi, Serah; Bakkenist, Christopher J.; Hartman, Douglas J.; Brand, Randall E.; Liu, Yang

doi:10.1117/1.3597723

Cited by 54 publications

(61 citation statements)

References 14 publications

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“…Mean optical density was considered to be a measure of the mean nuclear refractive index denoted by n. In order to establish an absolute scale between mean optical density and nuclear refractive index, the minimum value in the entire image set was assumed to correspond to 1.38 and the maximum value was assumed to correspond to 1.44, which together represent a reasonable refractive index range consistent with prior reports [19][20][21][22][23][24][25]. A given mean optical density was then converted to a refractive index value using a linear fit in accordance with a well-substantiated empirical relationship described in [26][27][28]. The extent of nuclear refractive index variations σ n was computed in a similar manner based on a given optical density variation.…”

Section: Feature Extractionmentioning

confidence: 99%

Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

Arifler

MacAulay²,

Follen

et al. 2014

Biomed. Opt. Express

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Abstract:We use an extensive set of quantitative histopathology data to construct realistic three-dimensional models of normal and dysplastic cervical cell nuclei at different epithelial depths. We then employ the finitedifference time-domain method to numerically simulate the light scattering response of these representative models as a function of the polar and azimuthal scattering angles. The results indicate that intensity and shape metrics computed from two-dimensional scattering patterns can be used to distinguish between different diagnostic categories. Our numerical study also suggests that different epithelial layers and angular ranges need to be considered separately to fully exploit the diagnostic potential of twodimensional light scattering measurements.

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Section: Feature Extractionmentioning

confidence: 99%

Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

Arifler

MacAulay²,

Follen

et al. 2014

Biomed. Opt. Express

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“…The reflectance R is plotted following Equation (18) having n1 = 1.5, n2 = 1.4, n3 = 1. The amplitude oscillates between the extremes described in Equation (19).…”

Section: Also In This Casementioning

confidence: 99%

“…We can use the procedure described in the Equation (18) and the solutions defined in Equations (16), (19) and (20). In a future paper, extending the procedures to include full numerical approach, it will be possible to fit the whole curve and compute also the imaginary part of the local RI.…”

Section: Rbc Datamentioning

confidence: 99%

“…However, in this case a wide-field microscope is used, instead of a HSCM confocal arrangement, and only one wavelength can be selected at one time, so no hyperspectral images are available: a full hyperspectral recording could be useful for future applications like serial diagnoses and unattended identifications of unknown samples, without the need of a-priori knowledge of the right spectral region. Another result using QPM technique [19] underlines very well the importance to have a method to quantify the RI on the very local scale. In this case, the observed increase in nuclear refractive index is related to cells becoming malignant.…”

Section: Introductionmentioning

confidence: 99%

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Phase and Index of Refraction Imaging by Hyperspectral Reflectance Confocal Microscopy

Selci

2016

Molecules

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A hyperspectral reflectance confocal microscope (HSCM) was realized by CNR-ISC (Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi) a few years ago. The instrument and data have been already presented and discussed. The main activity of this HSCM has been within biology, and reflectance data have shown good matching between spectral signatures and the nature or evolution on many types of cells. Such a relationship has been demonstrated mainly with statistical tools like Principal Component Analysis (PCA), or similar concepts, which represent a very common approach for hyperspectral imaging. However, the point is that reflectance data contains much more useful information and, moreover, there is an obvious interest to go from reflectance, bound to the single experiment, to reflectivity, or other physical quantities, related to the sample alone. To accomplish this aim, we can follow well-established analyses and methods used in reflectance spectroscopy. Therefore, we show methods of calculations for index of refraction n, extinction coefficient k and local thicknesses of frequency starting from phase images by fast Kramers-Kronig (KK) algorithms and the Abeles matrix formalism. Details, limitations and problems of the presented calculations as well as alternative procedures are given for an example of HSCM images of red blood cells (RBC).

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“…Cell protein content measurement can be used in many biomedical applications such as blood doping detection [1], infection monitoring [2], drug development and screening [3], studies of necrosis and apoptosis [4,5], cell cycle progression and differentiation [6][7][8], and in cancer diagnostics [9][10][11]. Current methods for cell protein concentration measurement include electrical methods based on dielectrophoresis [12], mechanical methods based on microchannel cantilevers [1], and optical methods based on scattering patterns [13], emission spectra of external cavity lasers [14], and holographic and phase microscopy [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Label-free high-throughput cell screening in flow

Mahjoubfar

Chen

Niazi

et al. 2013

Biomed. Opt. Express

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Flow cytometry is a powerful tool for cell counting and biomarker detection in biotechnology and medicine especially with regards to blood analysis. Standard flow cytometers perform cell type classification both by estimating size and granularity of cells using forward-and sidescattered light signals and through the collection of emission spectra of fluorescently-labeled cells. However, cell surface labeling as a means of marking cells is often undesirable as many reagents negatively impact cellular viability or provide activating/inhibitory signals, which can alter the behavior of the desired cellular subtypes for downstream applications or analysis. To eliminate the need for labeling, we introduce a label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously either as a stand-alone instrument or as an add-on to conventional flow cytometers. Cell protein concentration adds a parameter to cell classification, which improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. This system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second. ©2013 Optical Society of America

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Quantification of nanoscale nuclear refractive index changes during the cell cycle

Cited by 54 publications

References 14 publications

Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

Phase and Index of Refraction Imaging by Hyperspectral Reflectance Confocal Microscopy

Label-free high-throughput cell screening in flow

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