2D light scattering static cytometry for label‐free single cell analysis with submicron resolution

Xie, Lin-Yan; Yang, Yan; Sun, Xuming; Xu, Qiao; Qiao, Liang; Song, Kun; Kong, Beihua; Su, Xuantao

doi:10.1002/cyto.a.22713

Cited by 34 publications

(22 citation statements)

References 28 publications

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“…In this case, the scatter angle θ is obtained from approximately 72.5° to 107.5° considering the optical layout of our cytometer. The microscope objective can work in focusing mode and defocusing mode for the obtaining of images and 2D patterns of the scatterers, respectively [25]. The 2D light scattering patterns are obtained in the positive defocusing mode with z 200 Δ = μm in this work.…”

Section: Methodsmentioning

confidence: 97%

“…3(c) and 3(d), the intensity of the light sheet drops to zero at the medium-glass interface (the thickness of the solution layer is about 170 µm) for both of the two light sheets. In this case the light scattering that could occur at the medium-glass interface will be greatly reduced as compared with the illumination with an inserted optical fiber where the light will diverge quickly in the sample medium [25]. The light sheet has a large extent in the x and y directions, which may cause background scattering that can be detected by the microscope objective with an NA of 0.4 (although the field of view of the microscope objective is with a radius of about 500 μm).…”

Section: Measurements Of the Light Sheetsmentioning

confidence: 99%

“…Compared with the laser spot illumination with an ellipsoidal structure in conventional flow cytometer, a focused laser beam has also been used for the illumination of cells in flow with a spherical structure [24]. Prism coupling or fiber coupling technique has been adopted in flow cytometry developments [18,25]. These methods however have no effective control on the shape of the illumination beam as in conventional cytometry.…”

Section: Introductionmentioning

confidence: 99%

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Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Lin

et al. 2016

Biomed. Opt. Express

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A light-sheet-based 2D light scattering cytometer is developed for label-free characterization of senescent cells. The light-sheet provides an illumination beam with controlled thickness for single cell excitation, and 2D light scattering patterns are obtained by using a defocused imaging method. The principle of this cytometer is validated by distinguishing microspheres with submicron resolution. Automatic classification of senescent and normal cells is achieved at single cell level by using the support vector machine (SVM) algorithm, where a sensitivity of 89.1% and a specificity of 96.4% are obtained. Our results suggest that the light-sheet-based 2D light scattering label-free cytometry has the capability to perform size differentiation of beads with submicron resolution and to classify different groups of cells without fluorescent labeling, showing the potential for clinical diagnosis of senescence-related diseases.

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Section: Methodsmentioning

confidence: 97%

Section: Measurements Of the Light Sheetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Lin

et al. 2016

Biomed. Opt. Express

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“…The first technique is optical flow cytometry (OFC) which is the most common non-intrusive cell interrogation method. Multiple implementations have been proposed such as light-scattering imaging [3], reflectance confocal microscopy [4], holographic microscopy [5,6] and 2D scattering pattern imaging [7], diffraction phase microscopy [8], common-path interferometry [9] and single-shot color quantitative phase imaging [10] that have been demonstrated to operate label-free single-cell characterization.…”

Section: Introductionmentioning

confidence: 99%

Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

et al. 2020

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In this paper, we present a novel optical microfluidic cytometry scheme for label-free detection of cells that is based on the self-mixing interferometry (SMI) technique. This device enables simple, fast and accurate detection of the individual cell characteristics and efficient cell type classification. We also propose a novel parameter to classify the cell or particle size. Artificial polystyrene beads and human living cells were measured using this system, and the SMI signal properties were statistically evaluated. The capability of the proposed cytometer for cell type discrimination and size classification has been validated by the measurement results. Our study can provide a very simple technique for cell enumeration and classification without any extra devices such as high-speed camera, photomultiplier and spectrometer. Moreover, the fluorescence staining operation which is necessary in traditional flow cytometry methods is not required either in our system.

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“…These two studies highlight the tradeoff between acquisition throughput versus phase and spatial resolution/information density, which is balanced in the study by Roitshtain, Shaked and coauthors (2). Still, the labelfree nature of quantitative phase signatures is attractive for non-destructive cell sorting, and may be added to the list of list of label-free techniques useful for such purposes, including 2D light scattering (6), Raman spectroscopy, stimulated Raman scattering, dielectrophoresis, and atomic force microscopy, to name a few (7). While these techniques are sensitive to different properties of cells-phase signatures, Mie scattering, biochemistry, lipid droplets, capacitance, and elasticity, in order, for the techniques listed above-they each allow cells to retain viability for future therapeutic or functional testing.…”

mentioning

confidence: 99%

Holography, machine learning, and cancer cells

Raub

Nehmetallah

2017

Cytometry Pt A

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COMMENTARY LIQUID biopsy to detect circulating solid tumor cells, cellfree DNA or tumor exosomes in peripheral blood holds great promise as a minimally invasive method to detect cancer at an early stage, plan treatments, and monitor disease response to therapy (1). Two great challenges to detecting cancer cells in whole blood are the low relative abundance of tumor cells in proportion to erythrocytes and leukocytes, and the need to introduce highly-specific exogenous labels to identify tumor biomarkers. A recently published article in Cytometry Part A (2) detects cancer cells using a rapid, label-free technique based on machine learning from quantitative phase/digital holographic microscopy datasets. The results demonstrate automated discrimination of normal and cancer cell types flowing in a microfluidic channel, pointing to the potential for high-throughput processing of circulating tumor cells in an imaging flow cytometer. The method is compatible with microfluidic approaches to sequester or enrich circulating tumor cells from normal blood cells and whole blood, respectively.Without staining, biological cells are transparent under bright field microscopy resulting in low contrast intensity images. In that article, Shaked and coauthors (2) utilize an internal contrast mechanism based on the index of refraction. Contrary to traditional qualitative phase contrast (PC) techniques, such as Zernike's PC and differential interference contrast microscopy, this off-axis interferometric label-free approach can distinguish normal cells from cancer cell lines of different disease stages using quantitative textural and morphological parameters derived from the segmented optical thickness or optical path delay (OPD) of the cell on all spatial points. This OPD is the integral of the index of refraction at each pixel of a projection across the cells' thickness. To use OPD parameters for sensitive cell screening, the phase signal must possess high fidelity and consistency across images and during the imaging session.The optical setup used in this study is a low-coherence off-axis interferometer with a Mach-Zehnder configuration. The source used is a supercontinuum laser coupled to an acousto-optical tunable filter with a 7 nm bandwidth. The low-coherence source is used to reduce speckle noise. Retroreflectors are used to adjust the sample and reference beam paths to enhance fringe visibility. Stationary aberrations and field curvature aberrations are compensated for using empty sample measurements. Phase unwrapping was performed using a standard unweighted least square algorithm to obtain the OPD maps of the cells. A normalized cut-edge detector algorithm, which is based on graph formulation, was used to separate cells from their background. This algorithm was followed by morphological image operations (opening, dilation, and thresholding) to connect the gradient lines and to remove background pixels erroneously detected. The cells from cultured cell lines, flowed through a microfluidic channel and imaged at high frame rates, ...

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2D light scattering static cytometry for label‐free single cell analysis with submicron resolution

Cited by 34 publications

References 28 publications

Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

Holography, machine learning, and cancer cells

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